case study of renewable energy

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US Army Space and Missile Defense Command Technical Center Implements Renewable Energy

• September 19, 2024

Customer Profile : The U.S. Army Space and Missile Defense Command Technical Center , home to the Ronald Reagan Ballistic Missile Defense Test Site (RTS), is located at the U.S. Army Garrison-Kwajalein Atoll in the Republic of the Marshall Islands, Central Pacific Ocean. This facility plays a critical role in supporting U.S. Central Command, U.S. Pacific Command, and U.S. Northern Command through 24/7 space operations.

The site is equipped with radar, optical, and telemetry sensors to conduct missile testing, space reconnaissance, surveillance operations, and scientific experiments for the Department of Defense and other government agencies. Strategically positioned, RTS is essential for monitoring foreign missile launches and tracking low-inclination satellite orbits, contributing to the nation’s defense and space situational awareness.

Customer Challenge : The U.S. Army faced a significant logistical and operational challenge at RTS. The site’s reliance on diesel generators meant that 100% of its power was dependent on fuel shipments delivered across the vast expanse of the Pacific Ocean. This dependency posed a substantial risk: any disruption in fuel delivery due to weather, logistical issues, or geopolitical factors could severely impact the site’s operations, compromising mission-critical activities. The Army sought a solution that would enhance energy resilience, reduce dependence on diesel fuel, and ensure continuous operations regardless of external circumstances.

Stark Tech Solution : Stark Tech was tasked with finding a sustainable and resilient energy solution for RTS. After comprehensive site mapping and an in-depth analysis of the island’s energy needs, Stark Tech identified the optimal location for implementing a solar array. Collaborating closely with U.S. Army, the team proposed a microgrid integrated with on-site solar generation as the ideal solution to improve the site’s energy security.

Project Result : The implementation of the microgrid and solar array at RTS was successfully executed through an Energy Savings Performance Contract (ESPC) ensuring cost-effectiveness and efficiency in the deployment. The 2.3 MW solar PV system and the 3 MWh energy storage system have significantly decreased the cost of energy at the site. The microgrid not only bolsters the site’s energy resilience but also aligns with the U.S. Army’s broader goals of enhancing operational sustainability and reducing the environmental impact of its facilities.

By leveraging renewable energy and advanced microgrid technology, the customer has transformed the RTS into a self-sufficient and secure installation. This ensures its critical missions can continue uninterrupted, regardless of external factors.

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Valley Children’s Healthcare Constructs Renewable Energy Microgrid for Energy Resilience

In 2023, Valley Children’s Healthcare joined the U.S. Department of Energy’s Better Climate Challenge with a goal to reduce Scope 1 and 2 greenhouse gas emissions 50% by 2030. The renewable energy microgrid is pivotal to meeting this ambitious goal and Valley Children’s Healthcare’s overall sustainability strategy that is built on three pillars:

• Operational Resilience: Mitigating planned and unplanned power interruptions with scalable solutions.
• Financial Efficiency: Optimizing nonprofit capital and operating expenditures through cost-effective renewable energy solutions.
• Environmental Sustainability: Reducing energy consumption and greenhouse gas emissions through enhanced efficiency and renewable sources.

Valley Children’s Healthcare is making a $30 million capital investment to install the renewable energy microgrid. Prior to the Inflation Reduction Act (IRA), nonprofits like Valley Children’s could not directly benefit from such tax credits. The IRA has fundamentally altered the feasibility of this project, providing up to $13 million in tax credits, covering approximately 40% of the initial capital outlay. Additional operational income savings over 25 years are projected to recoup a minimum of $15 million of upfront costs. The IRA funding includes:

• Investment Tax Credit (ITC): Base ITC (~30%) for meeting prevailing wage and apprenticeship requirements
• Low-Income Communities Bonus Credit: ~10% for Solar PV and Battery Storage
• Domestic Content Bonus Credit: ~10%

The journey to establish the renewable energy microgrid at Valley Children’s Healthcare began with identifying the need for energy resilience. This was achieved by linking existing challenges, such as power outages, with opportunities presented by the Inflation Reduction Act (IRA). The development of the microgrid concept was meticulously aligned with the hospital's long-term organizational goals, ensuring that the project would not only address immediate energy needs but also contribute to the overall mission to provide high-quality, comprehensive healthcare services to children regardless of ability to pay.

Currently reliant on a single aging substation transformer for power, the hospital uses diesel generators for backup power. Once operational, the microgrid will power both normal and emergency operations, ensuring triple resiliency for Valley Children’s through access to the substation, microgrid, and diesel generators. Securing full approval for the project took approximately 1.5 years. This process required patience and a clear vision as the team navigated the complexities of integrating a large-scale renewable energy solution into the hospital's infrastructure. The feasibility and financial analysis phase involved conducting a comprehensive financial analysis, which included evaluating the savings from IRA tax credits. Collaboration with the Facilities team was essential to assess the technical feasibility and long-term benefits of the microgrid. Through detailed planning, the team was able to demonstrate the long-term savings and return on investment (ROI) that the project would bring to the hospital.

Stakeholder engagement was a critical component of the project's success. Early engagement was crucial for gaining support and aligning the project with the hospital's strategic priorities. The following represent key stakeholders and their roles: 

• Board of Trustees: Provided strategic oversight, ensuring that the project remained aligned with the organization's mission and goals. 
• Chief Executive Officer: Championed the project from its inception, providing the leadership and support to drive it forward.
• Chief Financial Officer: Played a crucial role in the financial modeling, securing funding, and ensuring the project's cost-effectiveness. This involved a comprehensive analysis of the financial implications, including the benefits of the Inflation Reduction Act (IRA) tax credits.
• Facilities Team: Indispensable in assessing the technical feasibility of the microgrid and planning its implementation. Their expertise ensured that the project was not only viable but also aligned with the hospital's operational needs.
• Strategy Lead: Responsible for leading the strategic vision, coordinating internal/external and cross-departmental efforts, and ensuring that the project is aligned with the broader organizational goals.

Measuring Success

Upon completion in 2025, the renewable energy microgrid will significantly reduce Valley Children’s reliance on the traditional power grid, ensuring continued hospital operations during regional power outages. In addition to the energy and cost savings benefits, the project will improve air quality, enhance operational resilience, provide continuous care to patients during power outages, and establish a sense of safety and well-being amongst staff and the greater community.

Post-implementation, Valley Children’s Healthcare will safeguard against market fluctuations and mitigate long-term financial risks, redirecting energy savings toward enhanced patient care and community support. The microgrid is poised to meet 80% of the hospital’s energy needs for current services, save approximately $15 million in operating costs over 25 years, and reduce the hospital’s greenhouse gas emissions by 50.5% (around 7,970 metric tons of CO₂). It will also ensure the hospital remains operational during regional power outages.

In line with the hospital’s strategy to increase both environmental and community resilience, Valley Children’s Healthcare has also committed to extensive community health initiatives, including developing a 10-year plan through the Valley Children’s Guilds Center for Community Health to address regional health disparities, promote sustainable food practices and local sourcing, and engage in community partnerships to enhance environmental and health outcomes.

Beyond the initial microgrid, Valley Children’s Healthcare has undertaken several sustainability initiatives. These measures include:

• Conducting an energy audit of the facility to identify opportunities for improvement
• Replacing the aging vehicle fleet with more efficient models to reduce environmental impact
• Implementing measures to reduce food waste
• Developing a water management plan
• Addressing the environmental impact of anesthetic gases
• Establishing an employee-led "Green Team" stewardship program to promote sustainability initiatives

Additionally, Valley Children’s has been selected to receive a significant grant from the U.S. Department of Energy (DOE) totaling $30 million for long-duration energy storage demonstrations, with an additional $25 million from the California Energy Commission (CEC), further supporting the microgrid expansion. This grant is designated for a future phase of the project, enhancing clean energy storage capabilities.

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Ensuring uninterrupted patient care amidst power shutdowns due to wildfires and aging energy infrastructure.

Establish a self-sustaining renewable energy microgrid to reduce reliance on the traditional power grid.

Once operational, the microgrid will cover 80% of hospital energy needs, reduce 50.5% GHG emissions, and save $15 million over 25 years.

Ørsted’s renewable-energy transformation

To stop climate change, companies in every industry must rapidly reduce their carbon emissions. That is no easy task, but a few businesses show it can be done. Ørsted, an energy company based in Denmark, stands out as an example. Twelve years ago, when it was called DONG Energy, the company earned most of its revenues by selling heat and power, 85 percent of which came from coal. Then, in 2009, management announced a major strategic shift: the company would seek to generate 85 percent of heat and power from renewable sources by 2040.

Ørsted invested aggressively in offshore wind and phased out coal. By 2019, it had become the world’s largest producer of offshore-wind energy. The company also raised its renewable-generation share to 86 percent—hitting its target 21 years ahead of schedule. In an interview with McKinsey, the CEO of Ørsted’s offshore-wind business, Martin Neubert, tells the story of the company’s transformation: the strategic decision that started it all, the changes it went through, and the outlook for the future. (The remarks below have been condensed and edited for clarity.)

McKinsey: Back in 2008, DONG Energy was a profitable and stable conventional-energy company. How did the idea of pivoting to renewables come up?

Martin Neubert: At that time, DONG Energy was largely a domestic Danish energy company. Eighty-five percent of our power and heat production was powered by coal, and 15 percent by renewables. For us, one key factor supporting the decision to rethink our strategy in favor of renewables was the failed attempt to develop a 1,600-megawatt coal-fired power plant project, called Lubmin, in Northeast Germany.

We had made substantial investments in this greenfield project during the more than six years we spent trying to develop it. And while the project was supported by the German federal government, we experienced strong local opposition against the idea of building a coal-fired power plant on the Mecklenburg-Vorpommern coastline. This was the first clear sign telling us that the world was beginning to move in a different direction, and we concluded that there was no sustainable way of realizing the project. Also, in 2009, the global renewable-energy agenda was positioned strongly at the United Nations COP15 [15th Conference of the Parties] climate summit in Copenhagen, supported both by the Danish government and by our board of directors.

McKinsey: How did management assess the company’s position and its ability to shift toward renewables?

Martin Neubert: In 2008–09, we formulated a new strategy and vision called 85/15, stating that we wanted to change our generation mix from 85 percent conventional, 15 percent renewable to 85 percent renewable, 15 percent conventional. The 85/15 split, which was decided on by executive management, reflected the ambition to conduct a complete turnaround of our generation mix. It also took into account that DONG Energy had spent three decades establishing itself as a company focused on the generation of conventional fuels. So the expectation was that such a turnaround would have to be completed within one generation, or the equivalent of 30 years.

At the time, I don’t think anyone thought we would turn our generation mix upside down within only ten years. But that was not the discussion then. Instead, we discussed what our future growth areas should be: areas where we had critical mass, where we had the right competences, and where we could differentiate ourselves. It became clear that one was wind power, which three of the six companies that merged to become DONG Energy in 2006 had already pursued.

Onshore wind was well established. We had a sizeable portfolio of projects in Poland and Sweden, and we had been involved in projects in Spain and Greece. As for offshore wind, we had early-stage operating projects in Denmark and the United Kingdom and large-scale development projects. That gave us critical mass in wind when we formulated our vision.

We also had a team of 50 or 60 people working on renewable-energy projects. Some had spent their careers on these technologies, particularly onshore wind. That gave us substantial in-house expertise, backed by a clear understanding of what it would take to develop wind power, technology-wise.

The 85/15 split, which was decided on by executive management, reflected the ambition to conduct a complete turnaround of our generation mix. It also took into account that DONG Energy had spent three decades establishing itself as a company focused on the generation of conventional fuels. So the expectation was that such a turnaround would have to be completed within one generation, or the equivalent of 30 years.

McKinsey: Back then, the technology landscape for offshore wind looked very different from what it looks like now. How did that factor into your thinking?

Martin Neubert:  At the time, no offshore-wind projects bigger than 160 megawatts had been built. So we had to ask how we could build large-scale offshore-wind projects in a different way. Could we move from building one highly customized offshore-wind project every two or three years to building one or two more standardized projects every year? What would it take to go from handcrafting to serial production?

Answering that question involved a 360-degree review: the supply chain, our competencies, the financing models. We concluded that we could not do it alone. One challenge was installation. The installation companies in the market were small. We found a considerable risk that they could go bankrupt during a project. That led us to acquire A2SEA as an installation supplier.

We would also need strong partnerships with suppliers of turbines, foundations, and cables. Turbines were a particular issue. Since no purpose-built installation vessels existed, we reasoned that we would benefit from working with a manufacturer on the design, layout, and funding of second-generation installation vessels. Siemens quickly realized that offshore wind could develop into a large industry. We entered a partnership with them, which included the delivery of 500 3.6-megawatt turbines. At the time, it was one of the largest energy agreements Siemens had ever made.

McKinsey: How did executives and staff react to the decision to take the company in a new direction?

Martin Neubert: There was internal pressure to keep DONG Energy the same. It wasn’t unexpected, because we had spent three decades turning the company into a traditional fossil-fuel company. Fossil fuels were our core competence and the focus of our growth strategy. Our employees also perceived that we were the world’s best at running coal-fired power plants, and a benchmark for the industry. The skepticism was broad and profound.

Ultimately, though, internal skepticism receded. In 2012, when Henrik Poulsen had just joined as CEO, our portfolio of assets and activities had high exposure to gas and gas-fired power plants. As gas prices dropped in the United States, vast amounts of surplus American coal ended up in Europe, where it replaced gas as the preferred fuel for power generation. That caused us financial difficulties, which made it easier for people to accept the new focus on offshore wind and on the exploration and production of oil and gas, and the moves to divest noncore businesses.

We began implementing the new strategy by establishing a wind-power business unit. I think those of us who were asked to join this business unit saw it as the beginning of an interesting journey. A group of strong European utilities was active in UK offshore wind at the time. We all thought that something big was going on and that the UK would be the right place to pursue offshore-wind projects at industrial scale.

That proved to be the case when the UK government strengthened its support for offshore wind to help make these projects financially viable. If that hadn’t happened, I’m not sure that we would have progressed as fast as we did.

McKinsey: Getting into offshore wind required a multiyear effort to sell holdings and build up new assets. How did management secure the necessary capital even as the company was exiting businesses that were reliable sources of cash?

Martin Neubert: We had multiple new projects in the UK that needed funding. One model would have involved financing them with external debt and then divesting once the projects were operational. But raising debt for each project would not have worked well with our group-level funding strategy. Another approach, partnering with electric utilities, would have been too complicated, because these companies had their own asset portfolios and strategies.

We needed financial partners that could deliver capital and manage their investments while relying upon our experience constructing and operating offshore-wind projects. One structural issue, however, was that we did not want to use project financing, whereas many of our financial investors preferred or were even required to leverage their investment via project financing.

This led us to develop the “farm down” model, in which we could fund our half of a project on our balance sheet and partners could use project financing to fund the rest. With farm-downs happening before commissioning, we provided investors with turnkey project offerings, which would protect them from risks we can manage best, including development, construction, and operating risks. That model resonated with the Danish pension funds, and later with Dutch and Canadian pension funds and other investors.

Had we not developed the farm-down model, we couldn’t have funded all these projects in Europe. And the structure that we innovated became widely used in the industry.

Powering up sustainable energy

McKinsey: What organizational changes took place as Ørsted’s portfolio shifted toward renewables?

Martin Neubert: By 2012, our wind-power business unit had grown to hundreds of employees. But it was still working like a start-up. To support new projects, we added whatever resources were needed, which led to inefficiencies. We lacked a proper organizational structure and operating model.

Correcting that was one of the key accomplishments of my predecessor, Samuel Leupold. He introduced our first real operating model, establishing global functions, clear project governance, and a product-line organization that systematically reduced the cost of offshore-wind electricity by eliminating ad hoc or project-specific sourcing and procurement.

During the past three years, Ørsted has also cultivated a “one company” approach spanning our business units. For example, we have established a management-team forum, consisting of all EVPs and SVPs, who meet four times a year to talk about our strategy and strategic enablers such as talent and digital. That forum facilitates open discussions to break silos, align our approach, and build a strong network among senior leaders. In addition, we have reestablished our leadership-forum meetings for our top 400 leaders.

McKinsey: Ørsted has made significant moves in recent years. Can you talk about those, and the rationale for them?

Martin Neubert: The strategic steps we’ve taken during the past three to five years have focused on turning Ørsted into a global renewable-energy major. The first step was divesting our oil and gas business, which concentrated our business almost entirely on renewables. We also invested in the conversion of our domestic heat and power plants, enabling them to move away from coal toward biomass. As a result, we will exit coal in 2023, and our power generation will be carbon neutral in 2025.

In 2016, we completed our IPO, and DONG Energy, which we were still called at the time, became a publicly listed company. The IPO provided us with the flexibility and access to equity that we need to fund growth. The IPO also gave institutional and retail investors an opportunity to take part in our green transition, while sharpening our profile as a renewable pure-play.

Within the past couple of years, we have reentered the onshore-wind market and moved into solar PV [photovoltaic] and storage solutions. These moves will help diversify our technology mix so we can better meet the demands of our customers. What’s important to note is that we are moving into these technologies at scale. North America, for example, is a large market for onshore wind and storage solutions, and we are investing there. Everything we do reflects our vision to create a world that runs entirely on green energy. And while offshore wind has the potential to power the world, we’re convinced that a broader technology mix will support the growth of our company even better.

Within the past couple of years, we have reentered the onshore-wind market and moved into solar PV and storage solutions. These moves will help diversify our technology mix so we can better meet the demands of our customers. Everything we do reflects our vision to create a world that runs entirely on green energy.

McKinsey: Ørsted’s transformation into an offshore-wind leader has been complete for some time. What opportunities do you see for growth in that market?

Martin Neubert:  Our ambition is to remain the global leader in offshore wind. In the past two to three years, offshore wind has expanded from a predominantly European market to a global market. We’ve been a first mover as that shift has occurred. We were the first European developer that went into large-scale offshore wind in the US. We were also the first foreign offshore-wind developer to enter Taiwan. Within a few years, we have developed sizable project portfolios in both markets.

To support our growth, we recently reorganized our offshore-wind business and established four new regions. Moving closer to different markets is important for navigating their development. It also helps with commercial matters like owning wind farms. At the same time, we want to keep the scale advantages, leverage, and standards that our global operations and EPC [engineering, procurement, construction] functions deliver, and so they work closely with our regions.

McKinsey: New horizons for change in the energy sector are coming into view. How does management keep working hard to ensure that Ørsted remains a leader in offshore wind, while challenging itself to gain a strong position in the energy industry’s next evolutionary phase?

Martin Neubert: We ask ourselves that regularly. And I have been asked many times, by investors, by the media, and by people within our organization, if we are at risk, considering that bottom-fixed offshore wind is our bread and butter. We value our global leadership position in offshore wind, and we want to retain that. Obviously, we don’t want to miss out on major developments—for example, in floating offshore wind. But we must respond as the needs of our customers change.

The ability to reinvent ourselves has proven to be key. In 2006, DONG Energy consisted of some oil and gas licenses. Then it reinvented itself through the merger of six domestic energy companies. A few years later, the company reinvented itself again by establishing a wind-power business unit that became a global leader within a few years. Scanning new horizons and spotting new business areas are essential to Ørsted’s strategy and our ambition to become a global renewable-energy major.

Martin Neubert is executive vice president and CEO of offshore wind at Ørsted. This interview was conducted by Christer Tryggestad , a senior partner in McKinsey’s Oslo office.

This article was edited by Josh Rosenfield, an executive editor in the New York office.

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• ORIGINAL ARTICLE
• Open access
• Published: 06 March 2017

A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile

• H. Abos 2 ,
• M. Ave   ORCID: orcid.org/0000-0001-7386-4828 1 &
• H. Martínez-Ortiz 2  

Energy, Sustainability and Society volume  7 , Article number:  7 ( 2017 ) Cite this article

11k Accesses

3 Citations

Metrics details

Renewable energy resources show variabilities at different characteristic time scales. For a given resource and demand pro le, there is an absolute maximum achievable coverage (when limiting the fraction of energy lost during production and delivery to a reasonable value). To reach larger coverage factors, two plausible paths can be taken: a mix of resources with different time variabilities and/or an energy storage system. The case treated in this paper is the electricity supply of an Astronomical Center in the North of Chile. The economical feasibility of both possibilities is explored and compared to a grid connected alternative.

First, data from local weather stations was collected to have a realistic evaluation of the variability of the solar/wind resource at all time scales. Then, we developed a scalable design of a solar/wind plant and a pumped hydro energy storage system. The free parameters of the design are the maximum installed power for each resource and the capacity of the storage system. Finally, the electricity production is calculated to determine the coverage factor and losses for different values of these parameters.

We found that a coverage factor of 64% is economically feasible for systems without storage. The associated total losses are 24%. To reach larger coverage factors is not economically possible and a storage system must be introduced. If this is done, there is a quantum increase of the total cost of about 30%. However, losses are reduced to about 5% and the coverage factor reaches almost 90%. The cost increase is marginally economically feasible, but it has some other advantages: the consumer is independent of the volatility of electricity prices, and is more sustainable.

The time variability of renewable energy resources difficults reaching coverage levels larger than 60%. Energy storage systems are a requirement. Periods of zero net production seem unavoidable unless the renewable energy and storage system are largely overdimensioned. Back up systems based on fossil fuels seem to be unavoidable. Both the energy storage and back up system add an extra cost that has to be paid if such high coverage levels are a requirement.

The case treated in this paper is the electricity supply of an Astronomical Center in the North of Chile. The ESO is the European Organization for Astronomical research in the Southern hemisphere. It operates the VLT (very large telescope), located at Cerro Paranal in the Atacama desert, North of Chile. The E-ELT (European extremely large optical/infrared telescope) in Cerro Armazones (20 km away from Cerro Paranal) is in advanced design phase and will be the largest optical telescope in the world. Finally, the CTA collaboration (Cherenkov Telescope Array) has chosen the Armazones-Paranal site for construction of its Southern Observatory.

When the two new observatories enter in operation, the peak power demand of the Armazones-Paranal site is estimated to be ∼ 8.5 MW and the total annual energy consumption ∼ 70 GWh. Currently, the VLT is generating its own electricity using fossil fuel-based generators.

The two main characteristics of this consumption center are the strong requirements on the stability of the electricity supply, and the relatively large power demanded. Due to these two factors, the use of liquid fossil fuels is economically un-viable. The only two non-renewable solutions plausible are connection to the Chilean national grid or self production of electricity using generators run with natural gas from a nearby pipeline.

The main renewable energy resources available at the site are wind and solar. In this work, we consider a wind-solar PV plant with Pumped Hydro Energy Storage (PHES). We calculate the coverage factor for different values of total Power, Maximum Energy Storage and wind to solar fraction to find energy systems that maximize coverage but with costs below the non-renewable energy solutions. Embedded in this procedure is the fact that renewable energy time variability can be diminished by considering a mixture. An important ingredient of this procedure is the relative cost of each technology. Government estimates are taken when possible.

Additionally, a concentrated solar power (CSP) plant with thermal energy storage is analyzed. This technology is considered separately since the storage system cannot be used by the wind farm.

The design of the systems is not detailed but all sources of inefficiencies are taken into account. The wind and solar input data used is from local weather stations, which provides realistic time series that account for all possible sources of the variability of the resources. Overall, the estimates of electricity production and cost are as realistic as possible so they can be used as a guide if such energy systems are eventually implemented. The total cost of each system includes operation and maintenance over the 25 year lifetime of the astronomical center.

The paper is organized as follows: in “ Energy demand ” section, the energy demand is described; in “ Non-renewable energy systems ” section, the non-renewable energy systems and their cost are analyzed; in “ Renewable energy resources available in the site: solar and wind ” section, the solar and wind data used in our calculations is described; in “ Renewable energy systems ” section, the methodology to calculate the time series of electricity production for the Wind-Solar PV plant with PHES is presented, together with a modular design of each of the subsystems and their cost; in “ Results ” section, an algorithm to find the optimum system is presented and compared to the non-renewable energy alternatives. The CSP with thermal storage design and cost are presented in the Appendix .

Energy demand

The energy demand of the VLT is known [ 1 ]. The power demand changes from day to night but is rather constant along the year (less than 5% variability). The projected E-ELT (CTA) consumption is taken from ESO estimates [ 2 ]. All the sub-systems, including lodging, offices and workshops are included. A simplified model is adopted: a constant power with different day/night values. The start/end for day/night will be calculated using the sunrise and sunset, even though the start/end of astronomical observations is typically later/earlier.

Table 1 shows a summary of the site energy demand. Night consumption is smaller than day for the E-ELT and VLT due to the strict thermal control system.

All observatories work in slow tracking mode during the night. In between observational windows, telescopes are re-positioned to track new objects. The instantaneous power required for re-positioning is large compared to the average power: 700, 3200, and 2000 kW for the VLT, CTA, and E-ELT compared to 1000, 2750, and 4250 kW. However, the total energy for repositioning is small (<5%). The extra power for repositioning can be supplied by energy storage systems with extremely fast responses like flywheels, STATCOMs or a battery system.

Non-renewable energy systems

Connection to the chilean electrical network.

The grid connection alternative envisages the connection of the Armazones-Paranal site to the Paposo substation. It requires the construction of a ∼ 60 km 66 kV line, one 220–66 kV transformer (at Paposo) and one 66–23 kV substation (located halfway between Cerro Armazones and Paranal). The projected investment cost or CAPEX is 12.5 MUSD ( ∼ 11 M e ). The OPEX is calculated multiplying the total annual energy consumed (70 GWh) by a nominal price. Two cases are considered: no inter-annual increase and 1% inflation.

The electrical network in Chile consist of four independent networks, the two most important being Sistema Interconectado Central (SIC) and Sistema Interconectado del Norte Grande (SING). The electricity market is liberalized but there is a distinction between regulated ( P < 2000 kW) and special clients ( P > 2000 kW). Special clients can negotiate directly electricity prices with the producers and/or produce its own energy. Regulated clients are subject to prices fixed twice a year by the government based on the liberalized market prices. Figure 1 shows the time evolution of the mean market price in Chile for the SING/SIC in Chilean Pesos per kWh and e per MWh [ 3 ]. Prior the 2007 crisis, prices were around 40 e /MWh, and during the last 5 years have been stable around 80 e /MWh with 15% oscillations. This is the nominal price that will be considered in this work.

Time evolution of the mean market electricity price in Chile for the SING/SIC in Chilean Pesos per kWh and e per MWh

Multifuel generators

A 8.5 MW combined cycle gas turbine (CCGT) is considered in this case: it has high efficiencies ∼ 55% and fast time responses. Since there is already a 2.5 MW generator with these characteristics in the site, it will be only necessary to upgrade it with 6 MW more. We consider an investment cost of 1000 e /kW, i.e., a CAPEX of ∼ 6 M e . Natural Gas supplied by Gas Atacama, whose pipeline passes through the middle of the Armazones-Paranal site, can be used to run these generators. The expected connection cost is ∼ 2.5 M e : a gas sub-station, a low capacity (7000 m 3 per day) 5-km pipeline and a low capacity tank for regulation. In total, the CAPEX of the back-up system is 8.5 M e .

The OPEX is mainly due to the purchase of natural gas. The natural gas prices are high in Chile. The projections from the Chilean government are taken to correct the world market prices to the special case of Chile. The following equation is adopted to estimate the time-dependent price of a kWh generated by CCGT:

where C gas is given by (1+ f N years )·9, N years is the number of years since 2015 and f takes into account the interannual increase of prices. We consider two values: f =0.01 and f =0.1. This equation yields 0.07 e /kWh for 2015.

Due to the strong requirements on the stability of the supply, this system is also a requirement for all renewable energy systems considered.

Cost estimation

The total cost normalized to year 0 is estimated using:

where k is the interest rate, 3%. The lifetime of the observatories and the renewable energy system is taken as 25 years. Table 2 shows the results.

Renewable energy resources available in the site: solar and wind

The Armazones-Paranal site is located in the Atacama desert, 130 km south from Antofagasta and 1200 km north of Santiago de Chile. The Cerro Paranal and Cerro Armazones have a height of 2635 and 3000 m respectively, and they are 22 km apart. The Cerro Paranal is 15 km away from the coast.

The topography in the North of Chile is dominated by the Central Andes, characterized by four topographical segments from West to East: the coast mountain range, the central hollow, the pre Cordillera, and the Cordillera. The Armazones-Paranal site is located in the coast mountain range, 20–40 km wide and with mean heights of 1500–2000 m. The coast mountain range falls rapidly into the sea with active segments of sea abrasion where sea cliffs are present and inactive segments where there is an emerged platform.

The climate is typical of a desert region: day/night thermal differences of up to 10 o C , rainfall smaller than 30 mm and relative humidities in the 5–20% range. The average temperature is ∼ 15 with ∼ 5 o C seasonal variations.

The solar resource

The solar resource is characterized using the 2011 data from a weather station installed in the area [ 4 ]. The measurements available are global and diffuse irradiance in horizontal plane and one axis tracking mode (North-South orientation), temperature. Only 25 days have missing measurements. This data is directly used in the estimation of the electricity production of a solar based renewable energy system. This data contains all sources of time variability and in that sense is more suited for our purpose than satellite based models.

In some special cases, e.g., for missing data periods or to evaluate the inter-annual variability of a wind-solar plant, a simplified model of solar irradiance is used:

where I G is the global solar irradiance incident on a surface that subtends an angle Φ with the sun direction, f d is the fraction of diffuse irradiance and I D is the direct irradiance in the sun direction:

where θ s is the solar zenith angle, I 0 the irradiance when the sun is in the zenith and τ is an atmospheric extinction parameter. Adopting f d =0.05, I 0 =1200 W/m 2 1 and τ =0.1, a good description of the data is found. 2

Figure 2 shows the global irradiance incident on a horizontal and a one-axis tracking surface from data (dashed lines) compared to the model (solid lines) for the 23rd of June 2011. Figure 3 shows the same for the accumulated day irradiance. 34 days out of the 340 analyzed has a predicted irradiance 10% larger than measured (“Cloudy Days”) but only 5 are consecutive.

Global irradiance incident on a horizontal and a one-axis tracking surface from data ( dashed lines ) compared to the model ( solid lines ) for the 23rd of June 2011

Same as Fig. 2 but for the accumulated day irradiance

The temperature is also an important factor that determines the performance of solar plants. The weather station temperature time series is used in our calculations.

The wind resource

Wind and speed direction from the VLT meteo mast is used to characterized the wind resource [ 5 ]. Measurements at 10 and 30 m from the last 15 years exist. Table 3 shows the average wind speeds at 30 m for the last 10 years. Figure 4 shows the wind speed distribution for the year 2011 at 30 m.

Wind speed distribution at 30 m in Cerro Paranal for the year 2011

Renewable energy systems

In this section, the methodology to calculate the time series of electricity production for the wind-solar PV plant with PHES is presented. Then, a modular design of each of the subsystems is described. Finally, the procedure to calculate the cost given any value of installed power, wind to solar fraction and size of the storage system is described.

Electricity production time series: methodology

The following definitions will be adopted:

P P ( t ) MW : time series of power produced by solar/wind plant.

P D ( t ) MW : time series of power demand.

P A ( t ) MW : time series of power available to satisfy the demand (either from wind/solar plant or storage system).

E S and E MSC MWh: storage level and maximum storage capacity.

P to store and P \(_{max}^{to~store}\) : power to store and maximum instantaneous power that the storage system is able to store.

s 1 /s 2 : efficiency of the storage system to store/deliver electricity. It can depend on load.

t 1 /t 2 /t 3 : transport efficiencies (transformer and lines) between solar/wind plant-storage system (t 1 ), storage system-demand site (t 2 ) and solar/wind plant-demand site (t 3 ). t 1 /t 2 /t 3 depends on the location of each subsystem and transmission line type. For our case and using standard calculations they are: 97, 97.5, and 98%.

Time series are calculated in 10 min intervals. If P P > P D energy is stored with efficiency s 1 × t 1 , unless P to store > P \(_{max}^{to~store}\) or the storage system is full. If P P < P D energy is extracted from the storage system with efficiency s 2 × t 2 until depleted. The efficiency t 3 is also applied to the fraction of P P that directly satisfy the demand.

E \(_{loss}^{Stg}\) accounts for the energy lost because of P \(_{max}^{to~store}\) and E MSC . E \(_{loss}^{Eff}\) accounts for losses due to s 1 / s 2 . E \(_{loss}^{Transport}\) accounts for losses in transport. E \(_{loss}^{Avail}\) accounts for availability: it is included assuming that on the 15th day of each month all systems are stopped for maintenance (3.3%). It is only applied to the annual energy production.

E P , E D , and E A are the annual sum P P , P D , and P A . Other definitions:

f cover =E A /E D : energy coverage.

\(f_{loss}^{Stg}\) = E \(_{loss}^{Stg}\) /E P : energy loss due to storage size and storage maximum power.

\(f_{loss}^{All}\) =( E \(_{loss}^{Stg}\) + E \(_{loss}^{Eff}\) + E \(_{loss}^{Transport}\) + E \(_{loss}^{Avail.}\) )/ E P : total energy loss.

Solar PV plant

We present a modular design of a solar PV plant. The unit cells corresponds to ∼ 1 MWp. The components of the Solar PV plant selected are the following:

Solar panels: Jinko Solar JKM300M. This is a silicon poly-crystalline 300 Wp panel. These modules have the IEC61215 certification which is the standard in Europe.

Inverter/transformer: the Sunny Central SC1000MV. This is a central inverter optimal for large system where production is uniform across the array

Trackers: the ExoSun ExoTrackHZ, suitable for large plants deployed in flat areas. This is a one axis tracker (axis orientation North-South).

The number of panels to be placed in series is calculated using: N series = V op,inv / V mpp,panel , where V op,inv is 450–820 V and V mpp,panel is 35–40 V depending on irradiance. This gives between 11 and 23 panels per string . The open circuit voltage of a string ( N series x45 V) should not exceed the maximum operating voltage of the inverter (880 V). For that reason 18 panels per string are chosen. 30 string s will be connected to a tracker forming a block , fulfilling the tracker specifications. All strings within a block are connected in parallel to an inverter. The number of blocks to be connected in parallel to reach the nominal inverter power is given by \(\frac {P_{inverter}}{N_{blocks}\times 30 \times 18 \times P_{nom,panel}}\) . This yields six blocks per inverter, which also complies with the restriction that the short circuit current does not exceed the maximum allowable current of the inverter.

Each string is a 2 x 18 m rectangle. 30 of them are placed consecutively (with a spacing of 7 m) to form a block. The spacing is chosen to minimize shading losses. 3 x 3 blocks are placed side by side to minimize DC cabling forming a unit cell, a rectangle of ∼ 280 x 64 m.

The power produced by the solar PV plant in a given time period is given by:

where I G ( Φ ) is the global solar irradiance on a surface with an incidence angle Φ , I stc the irradiance in standard conditions 1000 W/m 2 , the factors f therm . and f shading take into account the thermal and shading losses that depend on irradiance, ambient temperature and sun position, the factor f cte are losses that have no dependencies on the time period considered. The angle Φ is calculated for each period so the solar vector lies within the plane perpendicular to the aperture. The only exception is when the required solar panel elevation is smaller than what trackers allow (40 o , since trackers can rotate ±50 o ). In that case, the incidence angle is calculated for a fixed elevation of 40 o .

The thermal losses are calculated using:

where g is the thermal losses coefficient (0.4% per o C ), T std is the temperature in standard conditions (25 o C ) and T panel is the panel temperature that can be calculated using:

where T c is the characteristic temperature of the panel, 45 o C in our case, and T ambient is the ambient temperature taken from the weather station.

The shading losses are estimated by geometric calculations for each time period considered. The constant losses are 7%, see Table 4 .

Panel degradation is 20% over 25 years. Only the production of the first year is calculated. To maintain it over 25 years, extra power will be deployed that will be accounted in the OPEX of the plant.

Using the meteo-mast data and a topographic map of the area, we followed the standard procedure to design a wind farm. The software WASP is used to generate a wind resource map (WRG), see Fig. 5 . Then, the OpenWind Software is used to design wind farms with two, five, and ten turbines. The location is 15 km to the west of Cerro Paranal in the Coastal Cliff, where the wind power density is the highest. The wind turbine chosen is the Alstom ECO 80 2.0 Class 2. It is a pitch regulated 2 MW wind turbine, with a hub height of 80 m, a cut-in wind of 4 m/s and a cut-off wind of 25 m/s.

Map of the wind power density to the west of Cerro Paranal. The wind power density is higher in the pink areas . We also show the wind direction rose at the location of the Cerro Paranal. The areas with high wind density on the left correspond to the Coastal Cliff, about 15 km away from the Cerro Paranal

The mentioned software does not provide a time series of the produced electricity. This is a problem for our study: an storage system cannot be dimensioned without them. To overcome this problem, we use the following assumption to characterized the time series:

where P Turbine is the turbine power as a function of air density and wind speed at hub height:

where v 30 ( t ) is the measured temporal series of the meteo mast at 30 m, f vertical is a factor to extrapolate measurements to different heights:

and f horizontal is a factor that takes into account the geographical variations of the wind speed. The value of f horizontal is adjusted so Eq. 8 gives the same duty factor as OpenWind.

The PV and Wind plant requires an electricity based storage system that fulfills the following criteria:

Power: ∼ 10 MW.

Discharge time at output power: more than 12 h.

Response time: ∼ 10–30 min.

Lifetime 25 years.

Efficiency: high, at least 75%.

Technologically mature.

The only technology that matches these criteria is the pumped hydro energy storage (PHES). The site is located in the Atacama desert where water is scarce. Due to the proximity to the coast, there is the possibility to use sea water as storage medium. However, due to the size of the facility and plausible technological and environmental problems, it is advised the use of desalinated water either self produced or bought.

The PHES plant consist in an upper and lower water reservoir connected by penstocks, and a system of turbines and pumps than convert gravitational energy into electricity or vice versa. The system is closed, so filling of the reservoirs has to be done only once. A separate turbine and pumping system is planned, so typical elapsed times to go from pumping to full load generation are of the order of minutes. Water evaporation 4 and filtration of water are important and will be taken into account in the design. P \(_{max}^{to~store}\) is fixed to 14 MW, so hydraulic losses does not severely affect the design.

The hydro power in W is given by:

where ρ is the water density in kg/m 3 , g is the gravity acceleration constant in m/s 2 , Q is the water flow rate through the penstocks in m 3 /s, and δ h n is the net height difference given by:

where δ h g is the gross height difference and δ h ( Q ) are the hydraulic losses in the whole system that depend on the flow rate. The electric power in generation mode is given by:

where η turb and η gen is the efficiency of the turbine (that depends on load) and the generator. The electric power in storage mode is given by:

where η pump and η mot is the efficiency of the pump system (that depends on load) and the motor.

The required value of P e turb / P e pump is 8.5/14 MW.

The design of the system proceeds in two phases:

Site selection.

Plant design.

The site selection implies indirectly choosing two important variables: δ h g and penstock length. The second variable is crucial when determining the hydraulic losses, and is an important contributor to the total cost of the system. As a general rule, larger values of δ h g and smaller penstock length yield smaller investment costs. However, other factors have been analyzed:

Existence of infrastructures like roads and transmission lines.

Existence of hydro resources or possibilities to obtain them.

Earthquake risks.

Detritus removal: short but intense rainfalls can generate detritus removal that can affect the integrity of the PHES.

Topographic maps have been used to choose four possible sites. All sites have similar availability of water/infrastructures and geological risks. Therefore, the site with larger height difference and the smaller penstock length was chosen. Figure 6 shows a detailed topographic map of the site. It is located in the Coastal Cliff, close to the Wind Farm location.

3D map of the selected PHES site together with the elevation contour

Our choice for the turbine system is the use of two Pelton turbines with one injector that can work in parallel to provide the maximum power. The Pelton turbines can work up to 10% of the nominal load, have efficiencies around 90% and are adequate for the site height differences and required nominal flows. The turbines will be coupled to two generators with nominal power 5 MW, AC output voltage of 6 kV and 98% efficiency.

Regarding the pumping system configuration, our choice is the use of multistage centrifugal pumps: 6 of 2 MW and 2 of 0.5 MW. To simplify the calculations an efficiency of 90% for all loads is considered. The motors that drive the pumps work at 6 kV with an efficiency of 98%.

Steel penstocks have rugosities of ∼ 0.6 mm. The hydraulic losses are calculated using standard formulas for different pipe diameters. For each case, the nominal flow rate in production and storage mode is calculated by solving iteratively Eq. 13 /Eq. 14 . The hydraulic losses drop below 5% in both modes at nominal conditions for a tube diameter of 0.85 m. Losses because of other hydraulic components like valves, bypasses, contractions/expansions, etc. are small (10% of Penstock losses) and taken into account. Table 5 gives the final nominal flow rate and hydraulic losses in both modes. Using these calculations the storage efficiencies s 1 and s 2 are calculated.

The penstock wall thickness required to withstand the hydrostatic pressure is given by:

where e s is extra thickness in meters to allow for corrosion, k f is the weld efficiency (0.9), D is the pipe diameter in meters, σ f is the allowable tensile stress in Pascals (1400 kgf/cm 2 , i.e., 1.373 10 5 Pa) and P is the hydrostatic pressure in Pascals. Since the hydrostatic pressure changes from the upper to the lower reservoir, the penstock is built in sections of 100 m with decreasing thickness (10–30 mm). The total weight of the penstock is ∼ 1000 tons.

The surge pressure for the water-hammer effect at the pumping nominal load is 450 m, which would require a substantial increase of the thickness walls that would yield to a doubling of the total penstock weight, i.e. its cost. For that reason, the installation of a surge tower or relief valves is necessary.

The free parameter of the design is the maximum storage capacity, E MSC MWh. For a given value of E MSC , the volume of the water reservoirs is calculated by multiplying the flow rate in generation mode by E MSC /8.5 h, adding a 20% safety margin. In the selected site, there is room for reservoirs with storage capacities up to 1000 MWh.

The reservoirs will be constructed following the scheme of an Earth/Rock filled dam. The depth of the reservoir will be 14 m, leaving 1.3 m between the maximum water level and the top of the dam. The digged material will be reused to build the trapezoidal perimetral dike (3:1), which fixes the dimensions of the reservoir. The surface in contact with the water and the air-water layer is covered by a geotextile cloth.

To build and maintain the upper reservoir it is necessary to construct a 12 km access road. In the case of the lower reservoir there is a nearby access road, so only a short and flat connection to it is necessary. It will be also necessary to build a housing for the electromechanical equipment.

The total cost after 25 years of the wind-solar PV plant with PHES storage is estimated using Eq. 2 . The CAPEX in that equation has the following components:

Solar PV and wind plant: total power installed times a unitary cost of 1,700 e /kW.

PHES: the cost of a PHES system with E MSC =110 MWh is estimated to be 26.4 M e . Table 6 shows a breakdown. The PHES cost for different values of E MSC is estimated using:

where C 1 =3.8 M e is the baseline cost of water and reservoir and C 2 =19.51 M e is the cost of the rest of the subsystems.

Back up system: 8.5 M e .

Electrical infrastructures: 3.5 M e , see Table 7 .

The OPEX has the following components:

Insurance and O&M: we assume 2% of CAPEX with 3% inter annual increase.

Gas purchases: (1− f cover )·70 GWh times the unitary price given by Eq. ??.

Solar PV: the required annual enhancement of the power installed to reach the same nominal production as the first year 5 .

The electricity production is simulated for the following parameters:

P total MW : 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, and 40.

f solar : 0, 0.25, 0.5, 0.75, and 1.

E MSC MWh: 0, 20, 40, 60, 80, 100, 120, 240, 480, 1000.

An example of the time series is shown in Fig. 7 for P total =20 MW, f solar =0.5 and E MSC =100 MWh. For each simulated case, the annual value of f cover and \(f_{loss}^{All}\) is calculated. The left (right) panel in Fig. 8 show an example: f cover ( \(f_{loss}^{All}\) ) as a function of the total installed power for E MSC =0 MWh and three cases of f solar , 0, 0.5, and 1. For this value of E MSC , the optimum system in terms of coverage is neither purely wind or solar, but a mixture. \(f_{loss}^{All}\) for small P total is due to availability and transport.

An example of the time series of electricity production for P total =20 MW, f solar =0.5 and E MSC =100 MWh

f cover and \(f_{loss}^{All}\) as a function of the total installed power for E MSC =0 MWh and three cases of f solar , 0, 0.5, and 1

On the basis of the costs shown in Table 2 , we select two target maximum costs: 100 and 130 M e . For each simulated case, the total cost over 25 years is calculated as in “ Cost estimation ” section. The case with a cost below the target and with maximum coverage is kept. The two cases selected for the two targets are shown in Table 8 . The coverage factors are as large as 64 and 88%. It should be mentioned that the losses for the high cost target are driven by the storage efficiency, transport losses, and availability.

Finally, the case of a concentrated solar power (CSP) plant with thermal energy storage is analyzed. This technology is considered separately since the storage system cannot be used simultaneously by the Wind farm 6 . The design and costs are presented in Appendix . The total cost is 124 M e and f cover 72.5%. This alternative is within the high cost target, but it has lower coverage factor than the case presented in this section.

1 I 0 is only 12% smaller than the irradiance outside the atmosphere (1370 W/m 2 ), which is an indicator of the quality of the site.

2 The electricity production using the model and the raw data for the reference year agrees within 5%.

3 α =0.08 from the ratio of the measurements at 10 and 30 m. A conservatively smaller value is taken: measurements at 10 m can be affected by the surrounding buildings

4 According to our estimations, it can be severe, reducing the water level by almost 3 m per year.

5 It is calculated assuming: PV system prices will decrease at a rate of 20% over 25 years; PV module degradation is 20% over 25 years.

6 Electricity from the Wind Farm would have to be converted into thermal energy. To convert back to electricity the efficiency is given by the steam turbine, ∼ 32%.

Concentrated solar power (CSP) plant with thermal energy storage

The CSP is a technology that needs to be considered when there is plenty available land, the cloudy fraction is small and the fraction of direct irradiance is high. The dessert characteristics of the site fulfill these three criteria. The technology considered in this work is the parabolic trough collectors (PTC), widely considered in a stage of maturity.

In a CSP plant, an oil is heated in the solar field from 293 o C to 393 o C and sent either to the thermal storage system or to a heat exchanger that produces water vapour at 380 o C and 104 bar. The vapour is then conducted to a steam turbine coupled to a generator. After the turbine, the vapour is taken to a condenser and fed again into the loop. Due to the scarcity of water in the site, aerocondensers are considered. The efficiency to convert thermal energy into electricity depends on the nominal power of the turbine and for a 10 MW steam turbine is ∼ 32%.

The solar field is an array of PTCs. The mirrors have a one axis tracking system (North-South) that ensures that at all moments the solar vector lies within the plane perpendicular to the aperture of the collector. Alignment is a strong requirement in PTCs, and also cleaning.

The PTCs have lengths between 100 and 150 m. The 8 module EuroTrough collector with PTR-70 Schott tubes is selected. N series of these modules are placed in series to form a group. N parallel groups are connected in parallel in central feeding configuration to minimize pipe lengths. The separation between rows of collectors is three times the width of the parabola to ensure that annual shadowing losses are below 1%.

The thermal power captured by the collector is given by:

where \(\eta _{opt \phi =0^{0}}~K(\Phi)\phantom {\dot {i}\!}\) is a parameterization of the optical and geometrical losses of the collector, A c is the aperture area, I D is the direct irradiance in W/m 2 at the period considered, F e is a factor that takes into account the dirt in the mirrors (0.95), and P losses are the thermal losses parameterized with its dependence on the temperature difference between the fluid and the ambient, as well as on the direct irradiance and incidence angle.

The collected power can also be written as:

where Q m is the fluid mass flow in kg/s, C p is the specific heat in J/K Kg and T in /T out is the start/final temperature of the fluid. The thermal fluid chosen is an oil called Therminol VP1. Its maximum working temperature is 398 o and solidification temperature is 12 o . This fluid has to be pressurized to 10.5 bar so it is not gas phase at the maximum working temperature. The specific heat and density depends on temperature and is taken from a parameterization provided by the manufacturer.

N series of collectors have to rise the fluid temperature from T in =293 o C to T out =393 o C. The necessary value of Q m is calculated iteratively by equating Eqs. 17 and 18 in 1 m intervals.

The fluid must circulate in a regime turbulent enough to avoid thermal gradients between the external/internal face of the tube that can cause fractures. The optimum value of N series is calculated by imposing a condition on the Reynolds number of the circulating fluid for the time of maximum direct irradiance. In our design, N series must be 4.

The hydraulic losses are calculated for each configuration of the system ( N series , N parallel ) and time period considered using the oil and tube characteristics and ambient conditions. Losses in the pipes that connect the collectors with the heat exchanger and the losses in the pump are also taken into account. 7 .

The required electrical pumping power is given by:

where η m ( ∼ 70%) and η e ( ∼ 99%) are the mechanical and electrical efficiency of the pump.

The electrical power produced by the plant is given by:

where η is the efficiency to convert thermal to electrical energy (32%). The storage efficiencies considered are s 1 = s 2 =96% (Round trip efficiency of 92%). Transport losses are only applicable to t 3 (2%). The availability is included as described before.

The electricity production described in “ Electricity production time series: methodology ” section is calculated in 10 min intervals during a period of 48 hours around the summer solstice. N parallel is increased until f cover =100%. The required value of N parallel is 33. E MSC is given by the maximum storage level during the design period (100 MWh).

The storage system must be able to store 100 MWh, i.e., 312 MWth. This capacity is increased by a safety margin of 8%, i.e., 337.5 MWhth. The temperature in the hot/cold tank corresponds to the temperature of the oil before/after the heat exchanger. Nitrate salt (60% by weight NaNO 3 and 40% KNO 3 ) is considered as storage medium. The mass required can be calculated using:

which yields 8530 tons of salt to store 337.5 MWth. The corresponding volume of the hot and cold tank is different due to temperature. The volume required for the cold/hot tank is 4471 and 4618 m 3 . Fast fluctuations of the solar resource are easily tracked by the thermal storage system by controlling the flow from the solar field that is diverted to the heat exchanger of the storage system.

The electricity production is then calculated for the whole year. The results are shown in Table 9 together with the main design parameters.

A flat area is necessary to ease installation of the solar field. A possible site has been found 10 km away from Cerro Paranal. The access road to the Cerro Paranal passes by the solar field, so no extra civil works are planned. For electrical infrastructures and their cost, see Table 10 .

The investment cost (CAPEX) of the CSP plant is estimated to be 58.5 M e . Table 11 shows the breakdown. The OPEX considered is 2% of the CAPEX with a 3% inter annual increase. The total cost after 25 years normalized to year 0 is 124 M e .

Abbreviations

British thermal unit

Capital expenditure

Combined cycle gas turbine

• Concentrated solar power

Cherenkov telescope array

European extremely large telescope

European southern observatory

Million US dollars

Open software to design Wind Farms

Operating expenditure

Pumped hydro energy storage

• Photovoltaic

Parabolic Trough Collectors

Sistema interconectado central

Sistema interconectado del Norte Grande

Static synchronous compensator

Very large telescope

Wind energy industry-standard software

Wind Resource Map (Power density)

Interest rate

Global solar irradiance in a surface W/m 2

Direct irradiance in the sun direction

Diffuse irradiance in a surface expressed as a fraction of the direct irradiance

Angle subtended by the normal of a surface with the sun direction

Solar zenith angle

Atmospheric extinction parameter

Time series of power produced by solar/wind plant in MW

Time series of power demand in MW

Time series of power available to satisfy the demand in MW

Annual sum P P ,P D and P A E S and E MSC : Storage Level and Maximum Storage Capacity in MWh

Power to store and Maximum Instantaneous Power that the storage system is able to store

Efficiency of the storage system to store/deliver electricity

Transport efficiencies (transformer and lines) between solar/wind plant-storage system (t 1 ), storage system-demand site (t 2 ) and solar/wind plant-demand site (t 3 )

Energy lost during storage operations due to P \(_{max}^{to~store}\) and E MSC

Energy lost during storage operations due to s 1 / s 2

Energy lost due to transport inefficiencies

Energy lost due to operation and maintenance (availability)

E A /E D energy coverage

E \(_{loss}^{Stg}\) /E P , energy loss due to storage size and storage maximum power

(E \(_{loss}^{Stg}\) +E \(_{loss}^{Eff}\) +E \(_{loss}^{Transport}\) +E \(_{loss}^{Avail.}\) )/E P , total energy loss

The irradiance in standard conditions 1000 W/m 2

Watt Peak, solar panel power for I stc

The solar panel temperature in standard conditions (25 o C )

Solar panel losses, thermal, shading and those that do not depend on solar irradiance

Solar panel thermal loss coefficient

Inverter input voltage range

Solar panel volage at maximum power

Wind speed at hub height

Wind speed height coefficient

Air density

Water density

Gravity acceleration constant

Water flow rate through the penstocks in m 3 /s

Net height difference between upper and lower reservoir in a PHES

Gross height difference

Q dependent hydraulic losses

Efficiencies of turbine, generator, pump and motor in a PHES

Hydro power

Electrical power

Penstock diameter

Length of penstock

Penstock weld efficiency

Allowable tensile stress in Pascals

Hydrostatic pressure in penstock

PTC thermal losses

PTC losses due to dirtying

Thermal power captured by a PTC

Optical and geometrical losses of the collector

Specific heat of PTC thermal fluid

Mass flow in kg/s of the thermal fluid

Temperature in/out of the thermal fluid

Towards a Green Observatory. https://www.eso.org/sci/libraries/SPIE2010/7737-73.pdf . Accessed Feb 2017.

The E-ELT construction proposal. http://www.eso.org/public/products/books . Accessed Feb 2017.

Comision Nacional de Energia de Chile. www.cne.cl. Accessed Feb 2017.

Ministerio de Energía de Chile. http://antiguo.minenergia.cl . Accessed May 2015.

European Southern Observatory. http://archive.eso.org . Accessed Feb 2017.

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Acknowledgements

This work would not be possible without the financial support of the CNPq, FAPESP (PROCESSO 2015/15897-1) and the resources of the Instituto de Física de São Carlos. We thank Vitor de Souza for the careful reading of the manuscript, Eduardo Zarza for his guidance with CSP technology, Marcos Blanco for providing the WASP simulations needed to estimate the Wind Resource, Marc Sarazin for his help with the Wind data and ESO water supply, and Natalia Serre for all the information she provided concerning CTA power supply. Finally, we also thank all the Escuela de Organizacion Industrial (EOI) staff for their support.

Authors’ contributions

All authors contributed to the development of the work. The corresponding author prepared the manuscript, but all authors read and approved the final manuscript.

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Abos, H., Ave, M. & Martínez-Ortiz, H. A case study of a procedure to optimize the renewable energy coverage in isolated systems: an astronomical center in the North of Chile. Energ Sustain Soc 7 , 7 (2017). https://doi.org/10.1186/s13705-017-0109-0

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Received : 25 November 2016

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Published : 06 March 2017

DOI : https://doi.org/10.1186/s13705-017-0109-0

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As more countries, companies and individuals seek energy sources beyond fossil fuels, interest in renewable energy continues to rise.

In fact, world-wide capacity for energy from solar, wind and other renewable sources increased by 50% in 2023  (link resides outside ibm.com). More than 110 countries at the United Nations’ COP28 climate change conference agreed to triple that capacity by 2030, and global investment in clean energy transition hit a record high of USD 1.8 trillion in 2023  (link resides outside ibm.com).

But with all of this new capacity, how are renewable energy resources really being used? Here, we will look at examples and applications of renewable energy across a variety of industries, its impact on energy systems and the energy technologies that will drive its use in the future.

Renewable energy, sometimes called green energy, refers to energy generated from natural resources such as sun, wind, rain, geothermal heat and ocean tides. While fossil fuels—including non-renewable energy sources such as oil, coal and natural gas—are finite resources, renewable resources are replenished over time and considered inexhaustible (that is, they are not in danger of being depleted or used up entirely.) These power sources generally have a lower impact on the environment than fossil fuels, which released carbon dioxide and other harmful greenhouse gas emissions (GHGs) that contribute to global warming and are widely considered to be the main driver of climate change .

Types of renewable energy sources include:

• Solar: Sunlight is converted into electricity and heat in two ways. The most common method of producing solar energy, photovoltaics (PV), collects sunlight via solar panels and converts it to electricity. For larger-scale uses, the concentrating solar-thermal power (CSP) method uses mirrors to collect sunlight for fluid-filled receivers, which generate thermal energy for power.
• Wind: Individual wind turbines and large-scale wind farms harness kinetic energy from the air for electricity generation. Wind power can be generated by turbines on land, as well as offshore wind farms over water.
• Hydro: Water-generated hydroelectric power is produced using tidal power, bodies of water and dams to move electricity-creating turbines. Some 60% of all renewable electricity (link resides outside ibm.com) comes from hydropower sources, making it the largest contributor to renewable electricity worldwide.
• Geothermal: Hot steam and hydrocarbon vapor from geothermal reservoirs within the Earth can be harnessed for energy production. Geothermal heat pumps (GHPs) are used to heat, cool and provide hot water to homes and offices.

Biomass is sometimes considered a source of renewable energy. The term biomass energy refers to the conversion of organic materials and byproducts (including organic matter like wood or waste) into either electrical energy or biofuels such as ethanol or biodiesel. However, producing these forms of bioenergy can contribute to greenhouse gas emissions and deforestation; as a result, some do not consider them completely renewable sources of energy. Additionally, while nuclear power is often considered a “clean” energy source due to its low carbon emissions, it is not renewable; nuclear energy requires uranium, which is a finite resource.

Governments around the world are taking strides to increase production and use of alternative energy to meet energy consumption demands. Reducing dependence on fossil fuels and diversifying the energy mix can help countries lower their carbon footprints and contribute to international efforts to limit global warming, thus protecting ecosystems and biodiversity. It also appeals to those trying to boost their energy security and independence, as renewable sources are locally available and less impacted by price volatility and geopolitical tensions. Additionally, many governments see renewable energy as a way to improve their economies through job creation and investment, and public health by reducing air pollution.

• Iceland: Known for its unique geothermal landscapes, Iceland is a world leader in harnessing geothermal energy. More than 85% (link resides outside ibm.com) of Iceland’s electricity comes from local renewable resources, including hydropower and geothermal power.
• Portugal: The country was one of the first in Europe to pledge carbon neutrality by 2050  (link resides outside ibm.com). Portugal set a record last year for most consecutive days powered solely by renewable energy— for 149 straight hours  (link resides outside ibm.com), or more than six days, energy generated from renewable sources exceeded the country’s consumption needs.
• Uruguay: Uruguay has made massive investments in wind and solar power and now gets nearly 98% of its electricity from renewables  (link resides outside ibm.com). The country’s decarbonization efforts and swift transition to renewable energy was prompted by rising fuel prices in the early 2000s.

Cities, towns and other communities are also evaluating their environmental impact and incorporating renewables into their energy-generation plans. Local programs are using renewable sources of energy to offset electricity costs and provide greater reliability. Through decentralized energy systems, microgrids and smart grids, communities are diversifying their options for sourcing electricity and monitoring systems for more efficient use. These systems can be especially useful during natural disasters, cyberattacks or other events that may disrupt the power supply in a region.

Some cities are requiring new construction to include energy-efficient green buildings or offering incentives to prompt older buildings to modernize for renewable capacity. Others are working renewables into municipal infrastructure by installing solar-powered streetlights or purchasing electric school buses and other vehicles.

Companies and organizations seeking more sustainable energy sources have a number of ways to procure renewable energy . They can invest in and install their own equipment, from solar panels to wind turbines, for on-site generation. Many utilities offer the option for companies to purchase green power by paying a premium for electricity generated from renewable sources. Other companies use power purchase agreements (PPAs), or long-term agreements with renewable electricity producers, such as solar power plants or wind farms. These offer cost savings for the purchaser and stability for the provider.

They’re using that renewable energy for a wide variety of things, including:

Powering operations: In manufacturing, wind energy and solar power are fueling warehouses and factories. In the agriculture sector, innovations such as solar-powered irrigation systems are reducing reliance on fossil fuels and decreasing operating costs. And as growing use of artificial intelligence (AI) and other new technologies increase demand for energy-intensive data centers, major tech companies are using renewable power sources to limit their environmental impact.

Optimizing energy efficiency: Companies are also investing in technologies to optimize their energy use and further reduce their carbon emissions. By integrating smart grids and Internet of Things (IoT) devices, businesses can better manage their energy use.

Building sustainable supply chains: Companies are looking beyond their own operations to their supply chains , recognizing that they can make a significant impact on Scope 3 emissions . They are increasingly requiring their suppliers to use renewable energy and adopt energy-efficient practices.

Meeting compliance and sustainability reporting requirements: Using renewable energy can help businesses meet mandatory reporting requirements and contribute to local and international goals in the fight against climate change.

Enhancing brand reputation: More and more consumers prefer to support businesses that prioritize sustainability and offer green products. By harnessing renewable energy, companies can position themselves as leaders in their industry and attract environmentally conscious customers.

Creating new income streams: Businesses that generate more renewable energy than they consume can sell the surplus back to the grid through feed-in tariffs or net metering arrangements. They can also earn Renewable Energy Certificates (RECs) for the power they generate. Some are adopting an “Energy as a Service” (EaaS) model, opening up opportunities to manage energy systems and efficiency for other companies.

Going forward, innovations in renewable energy storage and grid integration will open new doors for ways to make use of green power, while artificial intelligence and machine learning aid in optimizing energy use. Countries, corporations, communities and even individuals are showing that integrating renewable power into business operations can drive both sustainability and innovation—and pave the way for a more sustainable future. How can your organization make its own contribution?

Get the most value from your enterprise assets with IBM Maximo® Application Suite . Through AI, IoT and analytics, this cloud-based platform can optimize performance and reduce operational costs.

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SURE Stories and Case Studies

How a circular economy can cut greenhouse gas emissions by one-third.

USAID has a unique opportunity to help reshape the renewable energy ecosystem and promote a circular economy approach to renewable energy, plasstics, and other sectors. Read the story

Low-cost renewable energy has the potential to lift up economies, strengthen energy security, mitigate the impacts of climate change, and promote climate-positive investments. Getting it right empowers partner countries to deliver cleaner, more affordable, and more reliable electricity to homes and businesses.

Accelerating the Clean Energy Transition through Auctions

View data on USAID-supported renewable energy auctions worldwide and learn how partner countries use auctions to achieve sustainable cost reductions, reduce emissions, and increase energy security. View the Story Map

Green Bonds in India

India faced a massive capital investment challenge and needed affordable, long-term financing to scale up renewable energy. USAID supported Green Bonds as a solution. Read the story

Mexico’s Auction Program Injects Competition to Energy Market

With support from USAID, Mexico’s auctions injected competition, private investment, efficiency, and transparency into the sector while achieving some of the world’s lowest renewable energy prices. Read the case study

Kazakhstan Renewable Energy Auctions

Kazakhstan’s renewable energy auctions resulted in private investments and bid prices 25–40 percent below previous renewable tariff ceilings. Read the case study

Reliable Electricity is Critical to a Pandemic Response

Reliable electricity can mean life or death for a patient, especially during a pandemic. USAID resources help health facilities power life-saving medical equipment and preserve perishable supplies like vaccines. Read the story

USAID Reveals the Top 5 Renewable Energy Auction Resources

In today’s rapidly evolving renewable energy marketplace, it’s hard to keep track of current best practices. USAID shares resources that can help drive energy prices down and advance energy goals. Read the story

Colombia Engages Private Sector and Forges Energy Future

After awarding nine power purchase agreements, Colombia incorporates a private sector engagement strategy to ensure renewable energy projects are completed utilizing local labor. Read the case study

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Scaling up renewable energy (sure).

Body GLOBAL, 2017–ONGOING – Through the SURE program, USAID helps partner countries power economies, meet international climate commitments, and strengthen energy security via private investment in, and competitive procurement of, clean electricity.

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100% Clean Electricity by 2035 Study

An NREL study shows there are multiple pathways to 100% clean electricity by 2035 that would produce significant benefits exceeding the additional power system costs.

For the study, funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, NREL modeled technology deployment, costs, benefits, and challenges to decarbonize the U.S. power sector by 2035, evaluating a range of future scenarios to achieve a net-zero power grid by 2035.

The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Scenario Approach

To examine what it would take to achieve a net-zero U.S. power grid by 2035, NREL leveraged decades of research on high-renewable power systems, from the Renewable Electricity Futures Study , to the Storage Futures Study , to the Los Angeles 100% Renewable Energy Study , to the Electrification Futures Study , and more.

NREL used its publicly available flagship  Regional Energy Deployment System   capacity expansion model to study supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies.

The scenarios apply a carbon constraint to:

• Achieve 100% clean electricity by 2035 under accelerated demand electrification
• Reduce economywide, energy-related emissions by 62% in 2035 relative to 2005 levels—a steppingstone to economywide decarbonization by 2050.

For each scenario, NREL modeled the least-cost option to maintain safe and reliable power during all hours of the year.

Key Findings

Technology deployment must rapidly scale up.

In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar.

To achieve those levels would require rapid and sustained growth in installations of solar and wind generation capacity. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today’s installed capacity by 2035.

Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure demand for electricity is met during all hours of the year.

Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday to seasonal mismatch of variable renewable supply and demand. Across the scenarios, seasonal capacity in 2035 ranges about 100–680 gigawatts.

Significant additional research is needed to understand the manufacturing and supply chain associated with the unprecedent deployment envisioned in the scenarios.

Significant Additional Transmission Capacity

In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today’s capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026.

Climate and Health Benefits of Decarbonization Offset the Costs

NREL finds in all modeled scenarios the health and climate benefits associated with fewer emissions offset the power system costs to get to 100% clean electricity.

Decarbonizing the power grid by 2035 could total $330 billion to $740 billion in additional power system costs, depending on restrictions on new transmission and other infrastructure development. However, there is substantial reduction in petroleum use in transportation and natural gas in buildings and industry by 2035. As a result, up to 130,000 premature deaths are avoided by 2035, which could save between $390 billion to $400 billion in avoided mortality costs.

When factoring in the avoided cost of damage from floods, drought, wildfires, and hurricanes due to climate change, the United States could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion.

Necessary Actions To Achieve 100% Clean Electricity

The transition to a 100% clean electricity U.S. power system will require more than reduced technology costs. Several key actions will need to take place in the coming decade:

• Dramatic acceleration of electrification and increased efficiency in demand
• New energy infrastructure installed rapidly throughout the country
• Expanded clean technology manufacturing and the supply chain
• Continued research, development, demonstration, and deployment to bring emerging technologies to the market.

Failing to achieve any of the key actions could increase the difficulty of realizing the scenarios outlined in the study.

Study Resources

Full report, supporting materials.

Download the technical report, Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Download the report overview infographic and a 1-slide summary brief deck or a 10-slide summary brief deck .

Paul Denholm

Principal Energy Analyst

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Sustainability Case Study: Renewable Energy

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Brookfield Real Estate Is Committed to Executing Clean Energy Initiatives Across Its Portfolio, Driving Its Overall Carbon Footprint Reduction

As we execute our long-term strategy to deliver net-zero emissions across our business by 2050 or sooner, we are reducing our Scope 2 and 3 emissions by powering 100% of Brookfield’s U.S. Office portfolio with predominantly clean energy sources by 2026. Brookfield will predominantly leverage power purchase agreements (PPAs) to decarbonize its operations, ensuring that we source electricity from the same power grid in which we will use the electricity, further incentivizing the development of new clean energy sources. Clean energy sources for procurement include hydropower, solar, wind and nuclear power. Brookfield’s U.S. office portfolio will procure 600 GWh of clean electricity, reducing GHG emissions by 260,000 mtCO 2 e annually (the equivalent of avoiding burning 300 million pounds of coal). Clean Power Sources by City:

• Denver: Renewable electricity from local wind power facilities.
• Houston: Newly built, Texas-based solar power plant, with its construction initiated by Brookfield Properties
• Los Angeles: Newly built, California-based solar power plant, with its construction initiated by Brookfield Properties.
• New York: In-state, run-of-river hydropower facilities.
• San Francisco: Solar and wind farms through the CleanPowerSF SuperGreen program.
• Washington D.C.: Nuclear power facilities equipped by Brookfield’s Westinghouse Electric Corp.

Accelerating our progress, in 2022, Brookfield Properties India committed to reach net-zero emissions by 2040 across its entire portfolio of 50 million square feet in India, which includes locations in which we have an operating presence. Brookfield Properties India’s strategy to achieve net zero is focused on energy efficiency, reducing water consumption, promoting recycling, and improving indoor air quality, which benefits all its tenants.

Brookfield Renewable will supply Brookfield Properties’ Canary Wharf with clean energy beginning in 2026, providing 80 GWh of annual electricity needed with power generated from the development of our new, onshore windfarm in Scotland.

Brookfield’s newly opened mixed-use complex in Shanghai, One East, uses renewable energy to collaborate on net zero-strategies with tenants. During construction, Brookfield installed solar panels on the roof of the complex’s retail area, which is visible to the tenants of both office towers. The solar panels generate 260 MWh of energy per year, which reduces the building’s GHG emissions by over 5,000 metric tons a year. Beyond supplying a portion of the complex’s energy, the solar panels are a conversation starter on how we develop sustainable real estate in alignment with China’s National Energy Administration policy.

Explore More Insights

Library » Publication

Case studies of renewable thermal energy, a report to the renewable thermal collaborative by the center for climate and energy solutions.

This series of case studies showcases successful outcomes from the use of renewable thermal technologies at several different large companies and in a major city. It also provides some understanding of the potential benefits and challenges when considering different renewable heating and cooling technologies. In each of the case studies, significant cost and emissions savings were generated by investments in renewable thermal solutions.

One key theme across all of the case studies was that each organization had clearly established sustainability goals that supported a renewable approach. Other factors that facilitated implementation of renewable thermal solutions included high and volatile fossil fuel costs or the phaseout of older capital investments, which offered an opportunity to review renewable options for heating and cooling needs.

Another common theme shared by each of the case studies was the availability of a local resource. This makes the projects more difficult to replicate since local circumstances can greatly vary the project economics or viability of a certain technology for a given application. However, facilities co-located with each other may offer expanded possibilities for renewable thermal solutions.

Most projects included in this report were self-financed and achieved their expected return on investment. However, one technology facing more economic barriers is renewable natural gas (RNG). RNG projects in the United States have been stalled due to low domestic natural gas prices. In the RNG case study included in this report, the market for the renewable fuel standard program was used to mitigate this cost barrier, but broader federal programs may be needed to help support RNG over the long term. The introduction of a thermal renewable energy certificate could also make tracking and claims easier and more standardized for these types of projects.

The Renewable Thermal Collaborative (RTC) is facilitated by the  Center for Climate and Energy Solutions ,  David Gardiner and Associates , and  World Wildlife Fund . The goal of the RTC is to raise awareness and build greater supply and demand for renewable thermal options. Increasing the availability and cost competitiveness of these solutions is key to deploying them at scale. With greater scale, more organizations in the industrial and commercial sectors will be able to make dramatic cuts in their carbon emissions.

Download Publication (pdf, 3 MB)

Renewable Energy Policy in Cities: Selected Case Studies

Browse by theme.

• Dezhou, China   which has actively supported the establishment of renewable energy industries with the Dezhou Economic Development Zone for solar technology
• Chemnitz, Germany  where the local government enabled the formulation of strategies to use renewable sources and in 2008 developed the Integrated Climate Protection Programme (Integriertes Klimaschutzprogramm).
• Belo Horizonte, Brazil which has reduced greenhouse gas (GHG) emission substantially and, since 2007, turning a closed landfill site into a waste-to-energy facility.
• Austin, US where the GreenChoice Program active since 2001 has stimulated the initial demand for renewable-based electricity, facilitating municipal and commu­nity procurement of renewable energy.
• Sydney, Australia, and Nagpur, India where energy efficiency and renewable energy have reduced emissions from public street lights.
• Sao Paolo, Brazil where a local regulation requires new residential, commercial and industrial buildings to install solar water heating systems (SWH) to cover at least 40% of the energy used for heating water.
• Malmo, Sweden which set targets significantly more ambitious than either the European Union target for Sweden (49% by 2020) or the national plan (50% by 2020), so that the city is expected to be climate neutral with municipal operations run on 100% renewable energy by 2030.

Additional analyses

Sub-saharan africa: policies and finance for renewable energy deployment, 100% renewable energy scenarios: supporting ambitious policy targets, green hydrogen for sustainable industrial development: a policy toolkit for developing countries, north africa: policies and finance for renewable energy, renewable energy market analysis: mano river union region, related content.

Key Enablers to Triple Renewables by 2030: Policy and Regulations

The Role of Sustainable Bioenergy in Supporting Climate and Development Goals

Record Growth in Renewables, but Progress Needs to be Equitable

Innovative Policymaking is Crucial to Drive Green Hydrogen Market and Ensure its Sustainable Production

Renewables Jobs Nearly Doubled in Past Decade, Soared to 13.7 Million in 2022

Planning Renewable Energy Communities in Intermediate Areas. A Case of Study

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https://ror.org/00x27da85Department of Mathematics and Computer Science, University of Perugia, Perugia, Italy

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What would it take to decarbonize the electric grid by 2035?  A new report  by the National Renewable Energy Laboratory (NREL) examines the types of clean energy technologies and the scale and pace of deployment needed to achieve 100% clean electricity, or a net-zero power grid, in the United States by 2035. This would be a major stepping stone to economy-wide decarbonization by 2050.

The study, done in partnership with the U.S. Department of Energy and with funding support from the Office of Energy Efficiency and Renewable Energy, is an initial exploration of the transition to a 100% clean electricity power system by 2035—and helps to advance understanding of both the opportunities and challenges of achieving the ambitious goal.

Overall, NREL finds multiple pathways to 100% clean electricity by 2035 that would produce significant benefits, but the exact technology mix and costs will be determined by research and development (R&D), manufacturing, and infrastructure investment decisions over the next decade.

"There is no one single solution to transitioning the power sector to renewable and clean energy technologies," said Paul Denholm, principal investigator and lead author of the study. "There are several key challenges that we still need to understand and will need to be addressed over the next decade to enable the speed and scale of deployment necessary to achieve the 2035 goal."

The new report comes on the heels of the enactment of the landmark  Inflation Reduction Act  (IRA), which—in tandem with the  Bipartisan Infrastructure Law  (BIL)—is estimated to reduce economy-wide greenhouse gas emissions in the United States to 40% below 2005 levels by 2030. The impact of the IRA and BIL energy provisions are expected to be most pronounced for the power sector, with  initial analyses  estimating that grid emissions could decline to 68%–78% below 2005 levels by 2030. The longer-term implications of the new laws are uncertain, but they likely will not get us all the way to 100% carbon-free electricity by 2035.

None of the scenarios presented in the report include the IRA and BIL energy provisions, but their inclusion is not expected to significantly alter the 100% systems explored—and the study's insights on the implications of achieving net-zero power sector decarbonization by 2035 are expected to still apply.

Future Scenarios To Answer the Key Questions

To examine what it would take to fully decarbonize the U.S. power sector by 2035, NREL leveraged decades of research on high-renewable power systems, from the  Renewable Electricity Futures Study , to the  Storage Futures Study , to the  Los Angeles 100% Renewable Energy Study , to the  Electrification Futures Study , and more.

Using its publicly available flagship  Regional Energy Deployment System (ReEDS)  capacity expansion model, NREL evaluated supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies.

Unlike other NREL studies, the 2035 study scenarios consider many new factors: a 2035 full decarbonization timeframe, higher levels of electrification and an associated increase in electricity demand, increased electricity demand from carbon dioxide removal technologies and clean fuels production, higher reliance on existing commercial renewable energy generation technologies, and greater diversity of seasonal storage solutions.

For each scenario, NREL modeled the least-cost generation, energy storage, and transmission investment portfolio to maintain safe and reliable power during all hours of the year.

"For the study, ReEDS helped us explore how different factors—like siting constraints or evolving technology cost reductions—might influence the ability to accelerate renewable and clean energy technology deployment," said Brian Sergi, NREL analyst and co-author of the study.

Technology Deployment Must Rapidly Scale Up

In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar.

To achieve those levels would require an additional 40–90 gigawatts of solar on the grid per year and 70–150 gigawatts of wind per year by the end of this decade under this modeled scenario. That's more than four times the current annual deployment levels for each technology. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today's installed capacity by 2035.

Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure that demand for electricity is met during all hours of the year.

Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday-to-seasonal mismatch of variable renewable supply and demand. Seasonal storage is represented in the study as clean hydrogen-fueled combustion turbines, but it could also include a variety of emerging technologies.

Across the scenarios, seasonal storage capacity in 2035 ranges from about 100 gigawatts to 680 gigawatts. Achieving seasonal storage of this scale requires substantial development of infrastructure, including fuel storage, transportation and pipeline networks, and additional generation capacity needed to produce clean fuels.

Other emerging carbon removal technologies, like direct air capture, could also play a big role in 2035 if they can achieve cost competitiveness.

"The U.S. can get to 80%–90% clean electricity with technologies that are available today, although it requires a massive acceleration in deployment rates," Sergi said. "To get from there to 100%, there are many potentially important technologies that have not yet been deployed at scale, so there is uncertainty about the final mix of technologies that can fully decarbonize the power system. The technology mix that is ultimately achieved will depend on advances in R&D in further improving cost and performance as well as the pace and scale of investment."

In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the Eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today's capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026.

The Benefits Exceed the Costs of a Net-Zero Power Grid

Overall, NREL finds in all modeled scenarios that the health and climate benefits associated with fewer emissions exceed the power system costs to get to 100% clean electricity.

To decarbonize the grid by 2035, the total additional power system costs between 2023 and 2035 range across scenarios from $330 billion to $740 billion. The scenarios with the highest cost have restrictions on new transmission and other infrastructure development. In the scenario with the highest cost, the amount of wind that can be delivered to population centers is constrained and more storage and nuclear generation are deployed.

However, in all scenarios there is substantial reduction in fossil fuels used to produce electricity. As a result of the improved air quality, up to 130,000 premature deaths are avoided in the coming decades, which could save $390 billion to $400 billion—enough to exceed the cost to decarbonize the electric grid.

When factoring in the avoided cost of damage from the impacts of climate change, a net-zero grid could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion.

"Decarbonizing the power system is a necessary step if the worst effects of climate change are to be avoided," said Patrick Brown, NREL analyst and co-author of the study. "The benefits of a zero-carbon grid outweigh the costs in each of the more than 100 scenarios modeled in this study, and accelerated cost declines for renewable and clean energy technologies could lead to even larger benefits."

Critical Hurdles to Decarbonizing the Power Sector

Reduced technology costs alone cannot achieve the transformational change outlined in the study. NREL also identifies four key challenges that must be addressed in the next decade, through further research and other societal efforts, to enable full power sector decarbonization.

1. Dramatic acceleration of electrification and increased efficiency in demand

Electrification of some end-use energy services in the buildings, transportation, and industrial sectors is a key strategy for decarbonizing those sectors. Increased electrification, in turn, increases overall electricity demand and the scale of the power system that needs to be decarbonized. Enabling more efficient use of electricity in the buildings, transportation, and industrial sectors could enable a cost-effective transition.

2. New energy infrastructure installed rapidly throughout the country

This includes siting and interconnecting new renewable and storage plants at a rate three to six times greater than recent levels, which would set the stage for doubling or tripling the capacity of the transmission system, upgrading the distribution system, building new pipelines and storage for hydrogen and carbon dioxide, and/or deploying nuclear and carbon management technologies. The Inflation Reduction Act could jumpstart the deployment needed by making it more cost-effective.

3. Expanded clean energy manufacturing and supply chains

The unprecedented deployment rates require a corresponding growth in raw materials, manufacturing facilities, and a trained workforce throughout clean energy supply chains. Further analysis is needed to understand how to rapidly scale up manufacturing.

4. Continued research, development, demonstration, and deployment support to bring emerging technologies to the market

Technologies that are being deployed widely today can provide most of U.S. electricity by 2035 in a deeply decarbonized power sector, but achieving a net-zero electricity sector at the lowest cost will take advances in R&D into emerging technologies—particularly to overcome the last 10% to full decarbonization.

A growing body of research has demonstrated that cost-effective high-renewable power systems are possible, but costs increase as systems approach 100% carbon-free electricity, also known as the "last 10% challenge." The increase in costs is driven largely by the seasonal mismatch between variable renewable energy generation and consumption.

NREL has been studying how to solve the last 10% challenge, including outlining  key unresolved technical and economic considerations  and modeling  possible pathways and system costs  to achieve 100% clean electricity.

Still, getting from a 90% clean grid to full decarbonization could be accelerated by developing large-scale, commercialized deployment solutions for clean hydrogen and other low-carbon fuels, advanced nuclear, price-responsive demand response, carbon capture and storage, direct air capture, and advanced grid controls. These areas are ripe for continued R&D.

"Failing to achieve any of the ambitious tasks outlined in the study will likely make it harder to realize a net-zero grid by 2035," said Trieu Mai, NREL analyst and co-author of the study. "The study identifies research questions that we want to further explore. At NREL, we will continue to examine these complex questions to understand the most feasible path for the great challenge ahead."

Significant future research is needed to better understand the implications for power system operations, grid reliability, impacts on the distribution system, electrification and efficiency investment costs and adoption, and clean fuels production infrastructure investment costs. Requirements and limitations of resources, including land and water; supply chain and workforce requirements; and other economy-wide decarbonization considerations will also need to be considered.

Learn more about NREL's  energy analysis  and  grid modernization research .

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Challenges of using ‘variable’ renewables in power systems are surmountable, IEA report says Analysis of eight case studies shows that greater technical potential exists than is commonly assumed

News 24 May 2011

A new book from the International Energy Agency (IEA), Harnessing Variable Renewables: a Guide to the Balancing Challenge , presents a novel method of assessing the resources needed to balance supply and demand in power systems with large shares of ‘variable’ renewables, such as solar photovoltaic, wind and tidal energy. The report, which features case studies of eight geographic regions with sharply different power attributes, shows that there is a greater technical potential for balancing variable renewable energy output than is commonly assumed. Wind and solar energy have been growing at double-digit rates for at least five years – a trend that must continue if a more secure, diverse and sustainable energy mix is to be achieved. The IEA World Energy Outlook, for example, foresees that 45% of global electricity supply will need to come from renewable sources by 2035 if the level of carbon dioxide in the atmosphere is to be limited to 450 parts per million – roughly consistent with a global temperature rise of no more than 2 degrees C. Under this scenario, around 17% of electricity would need to come from variable renewables, up from 1% in 2008. But this raises a host of important challenges, particularly because of the uncertainties that are inherent in variable electricity supply. How can such large shares of variable electricity best be integrated without jeopardising the stability of existing power systems? What is the maximum share of electricity that can come from variable sources? The answer to these questions is that it depends on the flexibility of the power system in question. The new IEA book provides a tool to assess this flexibility, and in the process serves to reassure policy makers that the challenges of integrating large shares of variable renewables in power systems are far from insurmountable. Assessing flexible resources Harnessing Variable Renewables: a Guide to the Balancing Challenge lays out a four-step method for assessing existing flexible resources, which can then be used to balance increasingly variable supply and demand. Step one of this Flexibility Assessment (FAST) method assesses the ability of the different flexible resources to change their production or consumption; step two examines the aspects of the power system that will constrain them from doing so; step three calculates the maximum requirement for flexibility of a given system resulting from fluctuating demand and output from wind plants and the like; and step four identifies how much more variability can be balanced with existing flexible resources. The book features eight case studies in which the FAST Method is applied to eight geographic areas with very different characteristics. The resulting analysis shows that each region has the technical resources to balance large shares of variable renewable energy. Potentials range from 19% in the least flexible area assessed (Japan) to 63% in the most flexible area (Denmark). The IEA also assessed the resources of the British Isles (Great Britain and Ireland together), 31%; the Iberian Peninsula (Spain and Portugal together), 27%; Mexico, 29%; the Nordic Power Market (Denmark, Finland, Norway and Sweden), 48%; the Western Interconnection of the United States, 45%; and the area operated by the New Brunswick System Operator in Eastern Canada, 37%. This range of results is due to the different flexible resources found in these areas. Norway, for example, has extensive hydropower, which is a very flexible resource; while Japan’s power plants, many of which run on nuclear and coal, are not as flexible (e.g. it takes longer for these sources to respond to fluctuations in demand). “While some areas are clearly more flexible than others, all power areas assessed show that greater technical potential for balancing variable renewable energy output exists than is commonly supposed,” said Richard Jones, the IEA Deputy Executive Director. He launched the book at EREC 2011, Europe’s Renewable Energy Policy Conference , on 24 May in Brussels. “The results from these case studies demonstrate that variability needs not be an impediment to deployment,” Ambassador Jones said. “As long as power systems and markets are properly configured so they can get the best use of their flexible resources, large shares of variable renewables are entirely feasible from the balancing perspective.” About the IEA The International Energy Agency (IEA) is an autonomous organisation which works to ensure reliable, affordable and clean energy for its 28 member countries and beyond. Founded in response to the 1973/4 oil crisis, the IEA’s initial role was to help countries co-ordinate a collective response to major disruptions in oil supply through the release of emergency oil stocks to the markets. While this continues to be a key aspect of its work, the IEA has evolved and expanded. It is at the heart of global dialogue on energy, providing reliable and unbiased research, statistics, analysis and recommendations.

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Study on the evolutionary process and balancing mechanism of net load in renewable energy power systems.

1. Introduction

2. quantitative analysis indicators for net load characteristics, 2.1. net load fluctuation coefficient, nlfc, 2.2. net load variation coefficient, nlvc, 2.3. net load percentile distribution, nlpd, 2.4. net load rate of change index, nlrc.

• Selecting the window size and polynomial order:

3. The Evolution of Net Load

4. mechanisms of net load balancing, 4.1. impact of variable renewable energy on power system balancing mechanisms, 4.2. modeling of regulation resources, 4.3. turning point in the demand for power system regulation, 4.4. optimal load balancing method considering the turning point of regulation demand, 5. case study analysis, 5.1. system main parameters, 5.2. main parameters settings, 5.3. analysis of net load balancing mechanism in different stages of new energy development, 5.3.1. low-proportion new energy power system, 5.3.2. medium-to-high-proportion new energy power systems, 5.3.3. high-proportion new energy power systems, 5.3.4. ultra-high-proportion new energy power systems, 6. conclusions, author contributions, data availability statement, conflicts of interest.

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Hu, S.; Yang, J.; Guo, Y.; Bi, Y.; Nan, J. Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems. Energies 2024 , 17 , 4654. https://doi.org/10.3390/en17184654

Hu S, Yang J, Guo Y, Bi Y, Nan J. Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems. Energies . 2024; 17(18):4654. https://doi.org/10.3390/en17184654

Hu, Sile, Jiaqiang Yang, Yu Guo, Yue Bi, and Jianan Nan. 2024. "Study on the Evolutionary Process and Balancing Mechanism of Net Load in Renewable Energy Power Systems" Energies 17, no. 18: 4654. https://doi.org/10.3390/en17184654

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• Published: 06 July 2023

Fundamental theory on multiple energy resources and related case studies

• A. J. Jin 1  

Scientific Reports volume  13 , Article number:  10965 ( 2023 ) Cite this article

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• Energy science and technology
• Engineering
• Renewable energy

Herein, I methodically optimize a distributed energy resource in terms of the production, management, utilization, and/or transaction of renewable energies during the deployment process. I deliver a theoretical mathematical model that allows users to visualize three critical output functions of their energy preference, including output power, energy economy, and carbon footprint. The model delivers three eigenstates derived by a power utility matrix (PUM) model. PUM transforms three-input parameters (3i) into three-output functions (3o) through 3i3o-transformation. It is ubiquitous, and its systematic characterization is discussed. Moreover, I discover a mathematical conversion relationship translating energy generation to carbon emissions. Various case-studies demonstrate the optimal energy resource utilization. Furthermore, an energy blockchain approach is employed for microgrid design, development, and carbon reduction. Finally, the authors demonstrate the energy–matter conversion relationship that improves carbon emissions for energy production, reducing the beta factor of carbon emissions to 0.22 kg/kilowatt hour for carbon peak and to zero for carbon neutrality.

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Introduction.

As a part of the industrial power goal to address emissions and climate change issues, the global scientific community has reached a consensus on the need to curb carbon emissions 1 , 2 , 3 . Scientists have dedicated great efforts for decades to both energy-efficient and carbon-free methods to address power industry needs. The field of distributed energy resources (DERs) has been very interesting and has gained considerable attention for its potential in helping reduce emissions 4 , 5 , 6 .

The Paris Agreement set goals to address climate change issues 1 , 2 , defined steps for governments and multiple technology sectors to achieve, and proposed ways to mitigate the currently large carbon emissions. Carbon greenhouse gas (GHG) emissions from energy production can lead to climate anomalies, and thus, there is an urgent need to reduce carbon emissions. Because GHGs increase solar irradiance absorption, which leads to rapid glacier melting and the disruption of fragile ecosystems 7 , a climate emergency has been declared 2 .

The desire to meet the carbon neutrality goal has resulted in unprecedented international collaboration between citizens, academics, industry leaders, and government officials. Ideally, all sources of electricity will be stable, economical, and environmentally friendly 5 . Over the past few decades, renewable energy (RE) technology that can definitively meet the world’s energy demands has been developed, such as solar photovoltaic (PV) energy, wind energy, ocean energy 8 , 9 , 10 , 11 , 12 , 13 , hydrogen fuel cells, and energy storage (ES) technologies 14 , 15 , 16 , 17 , 18 . For instance, governments have been able to achieve major reductions in carbon emissions across all major business sectors. California has introduced a series of energy-themed goals, policies, and programs. Carbon cap and trade initiatives have begun to gain traction as a system to lower economic barriers to carbon reduction measures 19 .

Recently, with rapid advances in commercialization, renewable energy technologies have been widely applied. Major commercial RE sources, including solar and wind power, are unstable or intermittent by nature. The best utilization of RE usually involves integrating various types of complementary power generation (PG), ES, and commercial grid power (GP). Researchers have innovated or advanced technology that enables customers to leverage the great value of RE sources. The Carbon Border Adjustment Mechanism (CBAM) is designed and abided by the European Union (EU) to put a fair price on the carbon emitted during the production of carbon intensive goods that are trading in the EU. It is critical for advanced renewable technologies to remain competitive when commercial rules for carbon pricing, such as CBAM, are met.

Carbon pricing scenarios include a wide range of low-cost and cost-saving options associated with high energy efficiency, schedule optimization, alternative energies, ES, and fuel switching, i.e., transitioning from less environmentally friendly energy sources to more RE sources. The recent exponential growth in energy consumption demand has resulted in an urgent need to identify RE sources that can meet this demand and be operationalized at large scales.

Many valuable commercial and technological advances in energy decarbonization have been achieved 20 , 21 . Despite the unforeseeable challenges of daily changes in solar power and wind power instability, many countries have developed advanced technologies that enable them to use RE sources 22 , 23 , 24 .

For those who cannot participate in public utilities with mainly RE grids, RE can include locally produced systems that form microgrids and integrate various types of complementary PG and storage processes 25 , 26 .

The energy generated in a microgrid can be monitored and distributed using meticulously derived and advanced algorithms 27 that match the supply of energy producers with the demands of consumers, ensuring that power stability and quality are maintained.

In addition to producing and consuming energy, producers and consumers can trade surplus energy with other users and/or profit from energy-related transactions. Blockchain technology encourages data sharing and collaboration through the Internet of Things, which enables buyers and sellers to conduct such energy transactions in an easy and transparent manner 28 , 29 .

Regarding RE sources, it is necessary to study how these systems perform and evolve both over time and space. For example, a detailed investigation of alternative energy sources in Sweden revealed that solar irradiance and wind speed are negatively correlated with one another on both hourly and annual scales 30 . To optimize the stability of the energy output on a seasonal basis, it was recommended that this system should operate with 70% solar power and 30% wind power. The complementary outputs of solar PV and wind power should be verified in the partition on a case-by-case basis when designed for tandem use. The fluctuations in these two energy sources are much smaller than when each energy source is used separately. As microgrids become more popular and efficient than ever before, the need for large datasets related to microgrid consumption, transactions, and management will correspondingly increase.

Transactions between end-users and prosumers 31 , 32 have been investigated by several research groups. For example, some researchers have explored data and machine learning 6 , while other researchers have published investigative results 33 regarding energy blockchain (EBC) applications involving homes and buildings and for distributed peer-to-peer (P2P) energy trading. In this study, I proposed a smart algorithm for a distributed energy resource (DER) that employs the inputs of utilization data to help achieve the stated optimal goals. Prior researchers have achieved significant progress in this area 30 , 35 , 36 ,–37 ; for example, Zhang et al. provided valuable technology components for RE systems. Here, I investigate a general approach to obtain a novel and valuable fundamental framework that will lay the groundwork for determining the process exergy. A novel framework that advances our knowledge in this field and the present cause for RE applications is imperative.

Power utility matrix (PUM) methods 34 provide a typical case of PUM design with a smart energy approach. The PUM is a key component for best use realizing a smart energy system.

In studies on extensive RE systems, there are several factors for system characterization. It is crucial to identify the best DER and/or energy management system for three kinds of critical outputs for any given microgrid: power output, smart energy use/transactions, and carbon footprint.

RE is becoming  a mainstream energy that contributes to the 20 TW of annual power required to entirely meet the world's current energy demand. Researchers have made significant progress in increasing our knowledge about RE and distributed energy resources 38 , 39 , 40 . This knowledge is applicable to the exploitation of the full use of RE capacity; for example, it is valuable to be able to fully benefit from the current installed solar-wind power capacity that can deliver 15%of total electricity in China. Based on the above knowledge, I proposed a novel mathematical framework to solve the challenge of multiple RE supplies. The multiple energy resource (MER) system can facilitate the smart energy of a smart energy system. An important application can be derived from the aforementioned mathematical framework. The energy–matter conversion relationship (EMCR) is inspired by the mass and energy conversion relationship and applied as an important concept 41 . In this article, the EMCR connects GHG production to energy production.

The focus in this article is to formulate a simple and ubiquitous framework of smart microgrids and energy blockchain. To achieve a stable power supply, the employment of the PUM model can reduce the power strain on the grid during peak demand periods. Additionally, users can employ commercially available control strategies such as load shedding, demand response, and energy storage to maintain a robust microgrid 42 .

Moreover, it is valuable to develop communication and networking technologies for smart microgrids and to leverage machine learning in smart microgrids 42 . Energy blockchain utilizes blockchain technology to manage energy transactions among producers, consumers, and microgrids. The decentralized system of EBC ensures secure and transparent transactions, making it an ideal solution for managing energy transactions between renewable energy producers and consumers, which is a very exciting research area 43 .

This article is Structured as follows. In Section I, I introduce the background and the urgent need for green and economical power resources. Then, REs and the PUM are introduced. In Section II, the mathematical model of the PUM and several applications are discussed. Furthermore, in Section III, the system characteristic matrix (K,ij) and systematic ways to solve PUM models with the PUM (K,ij) characteristics are discussed. In Section IV, the EBC approach is explored and studied. Finally, in Section VI, future work and the directions and valuable assets for solutions to meet carbon neutrality goals at a global scale are discussed.

Characteristics of common renewable energy sources

It is important to establish a foundational framework to provide a background for the general mathematical description below. The energy optimization scheduling of a microgrid is a multiobjective optimization problem with multiple constraints. The maximal energy utilization efficiency, which is known as the exergy of the DER system, can be achieved and requires the proper scheduling of the DERs, ES, and power. The objective of the optimization is to achieve the maximum overall benefit through reasonable coordination, where all parties representing sources, storage, grids, and loads communicate to coordinate utilization scheduling. The microgrid can operate in either the grid-connected mode or the independent mode, both of which require proper scheduling of the DERs, ES, and load. For instance, a microgrid can operate more reliably under the independent operation mode. However, the scheduling process for DERs is complex. A mathematical model may describe a ubiquitous law, using the PUM model, as described below.

Figure  1 illustrates a DER, microgrid, and simplified PUM model to address the power, cost, and carbon emissions of supplying energy. The left-hand side of Fig.  1 represents three inputs, namely PG, ES, and GP, which are large power resources. The right-hand side includes the power output, the user’s power economy, and carbon emission data. The focus of optimized energy scheduling is to achieve both economic and environmental goals while delivering the required power output.

Detailed schematic of the power input elements and triple outputs. The middle column illustrates a working model and its solutions that render a smart power utility matrix (PUM) system.

A microgrid usually offers much better predictability and reliability in the long term when operated in the grid-connected mode than in the independent mode. The PUM model for a microgrid matrix is investigated to produce stable output power to meet demand and to meet certain economic and environmental goals in terms of capacity. The PUM model can be expressed as follows:

where \(\tau \, = \,\frac{\Delta t}{T}\) , ∆t is the scheduling time, and T is 1 h. For example, if the scheduling time is 10 min, \(\uptau =1/6\) .

Pt,CP denotes the power output. Ct,ECO denotes the financial value of the power consumption to its user. et,CD represents the carbon emissions derived from both the DER and the power utility.

Following these formulae, one may derive the value dependency relationship 44 . In the microgrid economic and carbon emission calculation model, the first row of the matrix indicates that the power demand of the load in a microgrid must be met in total by the current level of energy production, the available stored energy, and the energy provided by an external power grid. In other words, consumers are ensured that they can use electricity without service disruptions.

The typical value of each of the PUM coefficients in the first row typically ranges from 0.95 to 1. Therefore, each K,1j is simply assigned a value of 1, as follows:

To maintain a specific power quality, the power balance constraint must be satisfied. Therefore, the first row of the PUM can be rewritten as follows:

where \({P}_{CP}^{t}\) stands for the electricity power demand of consumers, \({P}_{PG}^{t}\) for the power generated by a generator, \({\mathrm{P}}_{\mathrm{ES}}^{\mathrm{t}}\) for the power exchanged by the ES systems, and \({\mathrm{P}}_{\mathrm{GP}}^{\mathrm{t}}\) for the power exchanged by the microgrids and power grids.

The net electricity cost is the sum of income and expenses. Income is generated when users purchase electricity from the microgrid and/or from the power grid. By managing peaks, valleys, and storage, it should be possible to maintain a balance between supply and demand without costly power spikes or deficits. Based on current technological development, I demonstrate scientific principles that may be universally applicable in the field. Each principle is an applicable tool for experts in the field to aid in smart power system design. The net electricity cost of operating a microgrid is shown in the second row of Eq. ( 1 ).

\({K}_{C\_PG}\) is the cost coefficient of generating 1 kWh of electricity from a generator, which may include the equipment cost, depreciation, and operation maintenance.

The cost of operating an ES system is expressed as follows:

where \({K}_{C\_ES}\) stands for the cost coefficient of charging and discharging 1 kWh of electricity from the ES system, which may include the equipment cost, depreciation, and operation maintenance; \(K_{Bat - opm}\) for the operating and maintenance cost coefficient of charging and discharging 1 kWh of electricity from the ES system; \(K_{Bat - ll} \,\) for the equipment depreciation cost coefficient of charging and discharging 1 kWh of electricity from the ES system; \(K_{Bat - el}\) for the energy loss cost coefficient of charging and discharging 1 kWh of electricity from the ES system.

The income and the cost of purchasing energy from an external power grid are expressed as follows:

where \({f}_{s}\) is the price at which energy is sold to the power grid, and \({c}_{b}\) is the price at which electricity is purchased by the microgrid from the power grid.

The cumulative net electricity cost of operating a microgrid over a period \(t_{n}\) is given as follows:

Values of typical K-characteristics in the transformation matrix

The commercial cost structure of current technology with solar RE (i.e., lithium battery) grid power is presented as follows 44 . At the current commercial stage, some K-values are provided as follows:

K2,1 is the cost of PV power per kWh, which mainly includes the depreciation of the purchase and installation cost of the PV modules, inverters, transformers, brackets, and power distribution equipment, as well as daily maintenance costs. The fuel cost of the PV modules is zero; the ratio of equipment depreciation and maintenance costs (including the operation cost) is generally approximately 7:3. The actual equipment lifetime is approximately 20 years. The largest variable affecting the depreciation cost is the local optical resources. The richer the optical resources are, the lower the investment equivalent to the unit of installation is, and the lower the depreciation is. The value of K2,1 is derived from the average level in China, and for regions with extremely rich optical resources, such as India and Pakistan, the Middle East, North Africa, and Central America, K2,1 may be halved with the same equipment.

K2,2 is the cost of energy storage, which mainly includes equipment depreciation and power loss. Maintenance costs are relatively low, mainly composed of personnel salaries, but solar/wind power distribution network energy storage power stations are generally maintained by solar/wind power station personnel part time. Under the condition of determining the technical conditions, the cost of energy storage is most affected by the utilization frequency of the energy storage system: a higher recycling frequency can reduce the apportioned depreciation cost of the latter two factors of equipment within a certain range, which is mainly due to the calendar and cycle lives of energy storage batteries. When any one of the calendar years or cycles reaches the design value, the energy storage equipment needs to be replaced; therefore, when the calendar life is reached before the cycle life, the increase in the frequency of use can proportionally reduce the depreciation and allocation, and vice versa, and the impact of reaching lifetime lmit is not significant. In addition, a higher recycling frequency can also reduce the power loss, which is mainly due to the internal thermal management, monitoring, and other supporting power consumption ratios of the energy storage system decreasing with increasing frequency of use. This K2,2 value is estimated based on the average charge and discharge frequency once a day.

K2,3 is the cost of the external purchase and sale of electricity. In general, the fs and Cb values are not equal during the trade, depending on whether the microgrid investor and the power grid company have signed a power purchase and sales agreement (generally, the absolute value of Cb will be equal to the absolute value of fs). The K2,3 value is the average of the market conditions of the previous two factors. In countries such as Germany and China, governments have implemented tariff policies over two decades that have provided substantial RE pricing incentives to increase RE sales on the grid.

As a result, the entire matrix is completed with estimates of a typical carbon emission case as follows. Energy production may vary. Thus, actual numbers depend, in part, on the procurement process within the industrial eco-chain.

The investigation leads to typical technical specifications at the current level.

The provided specifications based on technical data are extracted from commercially available products.

Furthermore, the typical values of all the PUM coefficients in the second row are as follows. The detailed model in the studies on K,2j shows time dependence. The third row of the matrix represents the carbon emissions generated by the operation of the microgrid 45 , 46 .

I selected typical values of CO2 emissions. For the current technological stage of development, some K-values in the specification are as follows:

The amount of carbon released (in commercial DERs) is dependent on the maturity of the related technology, which may show substantial dependence on the time, manufacturing approach, operating conditions, and location in the supply chain.

Power utility matrix for smart energy

Linear integrative model of smart energy.

In this study, a scientific description that may be universally applicable for engineers skilled in the smart grid field is provided. In accordance with Eq. ( 1 ), a more general mathematical equation is an integral equation. In another form of summarizing a typical overall PUM characterization, the PUM model (with the 3i3o model) and the PUM matrix are expressed as follows:

The energy, cost, and carbon emissions are obtained from Eq. ( 10 ). The PUM values are obtained from Eqs. ( 2 ), ( 8 ), and ( 9 ).

The general problem becomes an interesting mathematical formulation that can be solved using the following framework. The linear algebraic equation may be simplified by solving for the three eigenvalues and deriving the three eigenstates in the eigenspace. The linear algebraic problem leads to characteristic (eigenvalue) equations in the eigenspace that have a diagonalizable matrix and three orthogonal variables. The 3 × 3 square matrix is a diagonalizable matrix. I can derive such eigenstates from linear algebraic calculations of the matrix as the determinant, minors, and cofactors.

Simulation studies

Electricity from a microgrid can be transferred to users through a series of physical transactions; these transactions occur at both on-peak and off-peak times. There are many independent power producers and microgrids currently competing for customers in energy distribution networks.

Conducting power demand management (PDM) is imperative for a microgrid to ensure that it can meet the relevant energy demands during emergencies. Microgrid loads are generally classified as critical loads, controllable loads, or uncontrollable loads. Power systems must be able to meet the critical load requirement at any given moment.

Dual energy storage: a working mode

In the case of an emergency, a controllable load can be cut off or adjusted as needed. Under normal circumstances, the purpose of optimizing the load use and energy savings may be to manage the response to demand. For example, transferable loads such as heat loads can be used when electricity prices are low and the system is not experiencing peak demand. The load in a given household is directly related to users’ electricity preferences and comfort level. Users can carry out oversight of their power needs and consumption through the use of smart devices (e.g., smart switches and smart thermostats). ES is one of the key constituents of a regular microgrid; dual ES (DES) is both useful in grid operation and valuable for the battery lifetime. RE, such as solar PV, has variable availability that is approximately one quarter of the time; its actual time is dependent on the location of its operation. To supply power continuously, the remaining power must be provided through complementarity, such as ES and batteries. The working of the ES is discussed below.

The working caveats may be extended to a variety of applications. For example, I have designed an improvement in the utilization of ES.

ES is one of the critical components in a DER. Researchers have discovered that all ES batteries have optimal charge‒discharge cycle depths. Their battery lifetime depends on an important parameter: the depth of discharge of batteries (DoDb).

The smart microgrid architecture is shown in Fig.  2 below. Each ES device is programmed in the DES mode; it recurrently works in a full cycle of charge and discharge. Deep cycles are very beneficial for the ES lifetime by removing numerous insufficient charge–discharge cycles.

Microgrid architecture illustrating the dual energy storage (DES) mode.

Simulations of PUM model with distributed energy-resources

Figure  3 contains the test results for a simulation of a metro-transportation system. These test results demonstrate the typical energy in-and-out flow and are shown as follows: (a) scheduling of the distributed energy system with energy storage; (b) comparison at the point of common connection. In the DES system, DES can be used effectively to overcome the prediction error and to track the day-ahead trading plan of the microgrid system in real time.

Typical energy in-and-out flow: ( a ) scheduling of distributed energy system with energy storage and ( b ) comparison at the point of common connection.

A comparison of various methods is presented in Table 1 . The table shows a list of the economic costs of the methods based on simulations once per day, under peak rotation, and with various hardware configurations.

According to the analysis in the last section, the DES mode is utilized with benefits. The working schematic diagram is shown in Fig.  4 , along with its benefits. The energy supply appears the same for the outside viewer, providing ES, increasing the storage lifetime, and  including many operational nuances for potential benefits.

Schematic diagram of the charge‒discharge cycle showing the cooperative working mode for a DES system.

Microgrid system architecture and the transformation matrix

The mathematical model of the PUM can be readily applied to all microgrid systems. Equation ( 1 ) is rewritten as follows:

In the previous section, I deployed the PUM transformation matrix, which provides the output solutions based on the input DER variables. As a result, the derivation of the technically complex architecture of a DER becomes a simplified DER specification. The three-output functions (3o) in the simplified PUM design problem are as follows:

where 3i represents the three input parameters and PUM is a 3 × 3 square matrix.

According to Wolfram et al. 47 , the solution to Eq. ( 12 ) is readily obtained in orthogonal space and can be characterized by a set of three distinct eigenstates with three eigenvalues: λ, 1 , λ, 2 , and λ, 3 . Thus, the details of this derivation are omitted here.

In the relevant eigenspace, each output function is specifically related to each particular eigenstate with the corresponding input variable, which is called the orthogonal variable. Each of the critical outputs of the three eigenstates depends on its orthogonal variable. For example, the increment in the power output in an eigenspace is achieved by tuning its orthogonal eigenvariable, which will not affect the value of the carbon emissions within a specified range.

The carbon index of the PUM system is derived as follows:

The output function is built based on the critical values in the eigenstates within its critical variables in the eigenspace. The beta factor is the ratio of output energy and carbon emissions.

An artificial intelligence (AI) algorithm issues the command to set the output power values automatically. When the EBC is implemented, the power conversion will be connected to the RE and to the ES that has a point of common connection with an inverter/converter function.

A distributed energy approach is shown in the working diagram in Fig.  4 with a DES and in the 3i3o model illustrated in Fig.  1 . The model can be expanded in applications in the case of a DES.

Energy blockchain

In a decentralized energy system, energy supply contracts can be directly communicated between producers and consumers. Enabling an EBC can result in a considerable number of transactions between producers and consumers, which makes each transaction less expensive overall. Blockchains facilitate direct interactions and transactions between local energy producers and consumers by eliminating the need for a third-party monitoring platform.

 Software instructs the system connected to the output terminal, i.e., the client node. A ledger from the EBC may be implemented in 5 min upon request (or in a different agreed-upon set of time. There are several network blockchain options for energy-industry applications. Many researchers have discovered various scientific phenomena/data, and many studies have been reported 48 , 49 , 50 , 51 , 52 , 53 , 54 . Researchers have conducted investigations and have expanded the knowledge on this subject by referring to the results in the literature 55 , 56 , 57 , 58 , 59 .

The distributed energy resources can be traded with clients via the internet by choosing one of the blockchain options. The classic blockchain structure of EBC illustrates layers of provider-customer-clients and peer-to-peer gateway to the internet. For example, a smart grid can be applied for all digital electricity where the client nodes stand for all goods: AG1, AG2, AG3. Keyless blockchain-as-a-service interfaces (KBaaS) are presented in Fig.  5 . Its advantage is trust and it meets the security needs established for both supply and demand groups. Figure  5 provides microgrid-management data provenance based on a lightweight and keyless blockchain -as-a-service (KBaaS). The blockchain structure of EBC is illustrated as follows.

Flowchart of keyless blockchain-as-a-service interfaces.

The share of global electric generation is expected to reach a total of 20 TW soon. The shares of global RE generation should be increased: solar and wind power must reach 78% to achieve carbon neutrality 60 . To achieve the emission peak by 2025 and the carbon neutrality goals by 2050, the current policy calls for 47–78% RE in the primary energy supply. The world requires 20 TW to maintain quality of life, with approximately 78% accounted for by renewables. This corresponds to 6.2 and 9.4 TW of solar and wind power to achieve carbon neutrality.

I studied the beta factor, which is a metric defined as the rate of EMCR across multiple energy systems. It is 0.45 kg/kWh for traditional coal-fired power plants. However, it should be less than 0.22 kg/kWh to achieve the desired carbon emission peak; and it should be nearly zero to achieve carbon neutrality.

Researchers may be challenged to attain a pure categorical input (Cat). Each category may be composed of some combination of reated input entities. The current PUM in this article has advantages in deriving the eigenstates and eigenvalues because the PUM is a 3X3 square matrix. This PUM can deliver three eigenstates or eigenvalues. As a result, some simple metrics for carbon neutrality have emerged elegantly.

The current PUM utilized the essential classification of three input categories that may be expanded in every category as follows: Cat-1 has renewable energy resources; Cat-2 has energy storagies; and Cat-3 has microgrids. A remark is that the PUM model may encounter situations that exceed the basic PUM scheme in practice; each input parameter may be combination of multiple parameters, such as several renewable energy sources. Furthermore, customers may have more competing requests in an application while their knowledge of data and machine deep learning can be valuable for a good resolution. Each Cat may be properly bundled to meet the model requirements shown later.

To define the predicted RE power supplies as a single input in the PUM model, one would need to formulate Cat-1 as the single input. Moreover, the text at above has demonstrated an example of Cat-2 as illustrated in Fig.  2 ; the DES is managed in an algorithm that may be expanded in various applications. Furthermore, to formulate Cat-3 as the single input by supplier(s), I believe that the general categorical solution can be conveniently applied from the input power. In general, Cat-3 is provided by the power input, e.g., the grid provider. This input power may be composed of EBC and/or power supplier solutions of the microgrids. Our future work will collect data systematically across all the afore-mentioned categories so that the PUM model may be implemented for all types of smart microgrids.

In practice, one may handle complexity and employ a more complex system-solution 61 . The PUM model is an advantageous approach as an overall solution technique for microgrids. Several more complex systems are studied experimentally and methodically in a separate work 62 .

Conclusions

In conclusion, I have presented a theoretical study toward developing a predictive model that in turn realized a mathematical PUM specified by a 3i3o square matrix. Every element in the 3 × 3 PUM matrix is important in that it contributes to the specification of the total distributed energy system. Moreover, the PUM forms orthogonal variables based on linear algebraic operations of the input parameters. For example, a rule-of-algebra can be applied in the eigenspace to tune the output power in a specified range without affecting the other two functions that include cost and/or carbon emissions. Moreover, an important discovery is the proposal of a beta factor to illustrate the ratio of carbon dioxide emissions and the total output power. The beta factor was less than 0.22 kg/kWh when a carbon emissions peak was achieved, and it was nearly zero for the carbon neutral system.

I conclude the knowledge of the power utility matrix in detail is crucial to resolve critical outputs for the designer's tools, to enlist critical output parameters, and to identify the fundamental mathematical model. The related knowledge is very important to determine the direction and provide guidance to effectively achieve carbon neutrality with REs. The above framework provides important guidance for DER design, construction, and operation.

Data availability

The data that support the findings of this study are available from Ningbo University, China; but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available. Data are however available from us upon reasonable request and with permission from Ningbo University, China. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

Renewable energies

Distributed energy resource

Three-input-parameters

Three-input-functions

Greenhouse gas

Solar photovoltaic

Energy storage

Power generation

Carbon border adjustment mechanism

Internet of Things

Peer-to-peer

Power utility matrix

Energy matter conversion relationship

Multiple energy resources

Power demand management

Discharge of batteries

Energy management system

Artificial intelligence

Electric vehicle

Power management unit

Distributed energy prosumer

Distribution system operator

Tera Watt hour

Giga ton CO 2

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The author is very appreciative of Profs. G. Chen, C. Lee, Drs. D. Liu, S.W. Gao, and Mr. J. Su and for their valuable discussion and support.

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Jin, A.J. Fundamental theory on multiple energy resources and related case studies. Sci Rep 13 , 10965 (2023). https://doi.org/10.1038/s41598-023-37653-5

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