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DOE OSTI2023

Flexibility Requirements for Energy Systems with Renewable Generation under Forecast Uncertainties

Energy systems with high fractions of renewable energy-based resources require adequate assets providing flexibility in electricity usage to maximize the benefits of renewable energy. In this paper, we provide an analytical approach to estimate the flexibility requirements of such energy systems, with forecast uncertainties in both demand and generation. Our analytical results show that even with forecast errors, the expected system operating cost decreases with an increase in the amount of flexibility capacity -- however, there is an inflection point, beyond which addition of further flexibility capacity does not reduce expected system cost any further. Additionally, an enumeration-based approach is presented to estimate the maximum flexibility capacity needed to optimize the operating cost. Numerical experiments conducted on a network-abstracted modified IEEE 30-bus system are used for empirical validation and gaining additional insights on the effect of prosumers' willingness to offer flexibility on the dispatch performance.

Bhattacharya, Saptarshi↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Applying the Risk Management Framework: The Distributed Energy Resource Risk Manager

As part of a multiyear effort, the National Renewable Energy Laboratory (NREL) has dedicated resources to understand and identify cybersecurity weaknesses in distributed energy resources (DERs) by performing assessments. Due to a lack of standardization and rapidly increasing adoption of DERs, there is a critical need to address cybersecurity needs for DER systems in an interactive way. Furthermore, federal agencies, which are required to obtain an authority to operate, are challenged by the complexities of including their DERs. To help meet this need, in early 2020, NREL released the Distributed Energy Resources Cybersecurity Framework (DERCF) and accompanying Web application. This process is supported by the Risk Management Framework (RMF) developed by the National Institute of Standards and Technology. This project, referred to as the DERCF RMF application, expands on the existing DERCF work to include methods that support walking a user through the seven RMF steps. The tool will be available for download at no cost from [link ]. The purpose of this paper is to describe the steps the DERCF team at NREL took to understand Steps 1-5 of the RMF process. Additionally, this document will identify future work on the first five steps as well as a plan for Steps 6 and 7.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Circular Bioeconomy Concepts—A Perspective

Circular economy concepts—including a circular bioeconomy—aim to transition the current, essentially linear, economic system to a more sustainable one. However, organizations and researchers currently define the circular economy concept differently, resulting in inconsistencies and difficulty in effectively implementing the framework. In this paper, we provide our perspective on the conceptual definitions of the circular economy, bioeconomy, and circular bioeconomy, outlining potential overlaps and differences and proposing a harmonized interpretation that stresses the importance of the carbon cycle. We conclude that the key goal of a circular economy is to slow, narrow, and close material resource loops, built on the foundation of renewable energy and non-toxic materials. Further, a sustainable bioeconomy goes beyond simply switching fossil resources with renewable, biological resources. It requires low-carbon energy inputs, sustainable supply chains, and promising disruptive conversion technologies for the sustainable transformation of renewable bioresources to high-value bio-based products, materials, and fuels. The bio-based circular carbon economy, in particular, stresses capturing atmospheric carbon via photosynthesis and exploiting this unique feature to the fullest extent possible. It sits at the intersection between the circular economy and the bioeconomy concept, resulting in a framework that focuses on closing the carbon cycle and stressing the opportunity to create an additional carbon sink capability in the technosphere by utilizing biogenic carbon for products and materials that are circulated in same or improved use cycles. Lastly, a sustainable circular bioeconomy transition will necessitate a set of consistent metrics that fit all products and industries.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Integrated low carbon H 2 conversion with in situ carbon mineralization from aqueous biomass oxygenate precursors by tuning reactive multiphase chemical interactions

Meeting our rising demand for clean energy carriers such as H 2 from renewable biomass resources is challenged by the co-emission of CO 2 and CH 4 . To address this challenge, we design novel reactive separation pathways that integrate multiphase chemical reactions by harnessing Ca and Mg bearing minerals as a sorbent to capture CO 2 released during the hydrothermal deconstruction of aqueous biomass oxygenates to produce H 2 and solid carbonates via low temperature aqueous phase reforming and thermodynamically downhill carbon mineralization. Earth abundant catalysts such as Ni/Al 2 O 3 are effective in producing H 2 yields as high as 79% and 74% using ethylene glycol and methanol in the presence of Ca(OH) 2 as an alkaline sorbent, without contaminating or deactivating the catalyst. H 2 yields with in situ carbon mineralization using a Ni or Pt/Al 2 O 3 catalyst are enhanced based on the following order of reactivity: acetate < glycerol < methanol < formate < ethylene glycol. These studies demonstrate that the multiphase chemical interactions can be successfully tuned to enhance H 2 yields through the selective cleavage of C–C bonds using Ni/Al 2 O 3 catalysts to deconstruct biomass oxygenates for producing H 2 and CO 2 , and in situ carbon mineralization by harnessing abundant alkaline materials, as demonstrated using ladle slag. This approach unlocks new scientific possibilities for harnessing multiple emissions including abundant organic-rich wastewater streams and alkaline industrial residues to co-produce low carbon H 2 and carbonate-bearing materials for use in construction by using renewable solar thermal energy resources.

09 BIOMASS FUELS↗

DOE OSTI2020

A convective wind resource model for Solar Vortex power generation

Climate change has increased the need for clean, nonpolluting energy sources to decrease dependence on fossil fuels. Alternative energy sources, mainly solar and horizontal wind, have been the primary focus for producing clean energy. New technologies are being developed, such as the Solar Vortex (SoV), which was developed at the Georgia Institute of Technology, and relies on a vertical wind resource to generate power. The National Renewable Energy Lab (NREL) has resource models representing solar and horizontal wind resources across the 48 United States. This research developed a vertical wind resource model that is comparable in resolution to NREL’s solar and horizontal wind resource models and uses the model for estimating power output for the SoV. This model complements NREL’s existing resource models and supports the deployment of an additional clean energy generation technology. The model was applied to Mesa, Arizona to find feasible sites for a small-scale vertical wind farm.

14 SOLAR ENERGY↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Stochastic Continuous-time Flexibility Scheduling and Pricing in Wholesale Electricity Markets

Large-scale integration of intermittent renewable energy sources (RES) is calling for additional flexibility resources as well as more advanced modeling and optimization techniques to account for the increasing uncertainty and variability in power systems operation. As the RES integration gains momentum, the magnitude and frequency of their variations increase, which may trigger ramping scarcity events in real-time power systems operation. This necessitates revisiting the present definition of power systems flexibility and reserve services to reflect their robustness and adequacy towards sub-interval variations of the load and RES, as well as adjusting the operation models to accommodate the new reserve services. This project took a fundamental approach and aimed at developing continuous-time scheduling and pricing model that accurately models the continuous-time variations of load and RES and efficiently deploys the ramping capability of flexible resources to compensate the sources of variability and uncertainty in the market. In this regard, this project pursued the following goals: Developing stochastic multi-fidelity continuous-time optimization models for scheduling of energy storage (ES) systems and flexible loads in wholesale energy markets; Developing the theory and practices of continuous-time locational marginal pricing for valuating energy storage systems and flexible loads in wholesale energy markets; Developing function space solution approach to convert the proposed stochastic multi-fidelity continuous-time optimization models into tractable mixed-integer linear optimization models; and Defining flexibility reserve as a new type of reserve in markets that would enable ultimate participation of energy storage devices in provision of services to compensate the variability and uncertainty of RES in electricity markets. This project successfully completed all five major tasks defined in the SOPO, and produced 8 high-impact journal papers, 6 conference papers, 3 published U.S. patents, and one web-based software for continuous-time operation optimization of power systems. The application of the proposed flexibility reserve and the stochastic multi-fidelity continuous-time operation scheduling models would modify the forward commitment and schedule of generating units, ES devices and flexible loads, and would line up the resources in such a way that the composition of available resources is better prepared to respond to the sub-hourly variations of the load and renewable resources in real-time operation. Therefore, this project paves the way to sustainable, reliable, and economic integration of renewable energy resources in power system, supporting the progress towards reaching the national targets on energy independence. Even if the proposed models offers a radically different point of view as compared to existing models, it does not alter fundamentally the architecture of power systems operations, nor the complexity of the scheduling problem, so the integration of this project in power systems is extremely practical.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Nepal Himalaya offers considerable potential for pumped storage hydropower

There is a pressing need for a transition from fossil fuel to renewable energy to meet the increasing energy demands and reduce greenhouse gas emissions. The Himalayan region, with its unique topography and abundant water resources, offers substantial renewable energy potential, particularly through hydropower generation. However, the current exploitation rate is low owing to the predominance of run-of-river hydropower systems to support the power system. The utility-scale storage facility is crucial in the load scenario of an integrated power system to manage diurnal variation, peak demand, and penetration of intermittent energy sources. In this study, we assess the potential of pumped storage hydropower across Nepal, a central Himalayan country, under multiple configurations by pairing lakes, rivers, and available flat terrains. We then identify technically feasible pairs from those of potential locations. Infrastructural, environmental, operational, and other technical constraints govern the choice of feasible locations. Here, we show that 42% of the theoretical potential of 3000 GWh is technically feasible. We find the flat land-to-river configuration more promising than other configurations. Our findings provide insight into the potential of pumped storage hydropower and are of practical importance in planning sustainable power systems in the Himalayas and beyond.

13 HYDRO ENERGY↗

NASA NTRS1982

Seattle's System for Evaluating Energy Options

In 1975, the City Council developed a blueprint called "Energy 1990" for meeting Seattle's future electric energy needs. Priorities for addressing or offsetting expected growth in demand are in order: (1) conservation; (2) hydroelectricity; (3) other renewable sources such as wind, biomass, solar, and geothermal energy; (4) abundant nonrenewable resources such as coal, and (5) other renewables. An energy resources planning group was formed and a data base was established. Resource options were investigated and the recommendations were published.

Logie, P.↗

DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗

DOE OSTI2025

Resilience of the Electric Grid Through Trustable IoT-Coordinated Assets

The electricity grid has evolved from a physical system to a cyberphysical system with digital devices that perform measurement, control, communication, computation, and actuation. The increased penetration of distributed energy resources (DERs) including renewable generation, flexible loads, and storage provides extraordinary opportunities for improvements in efficiency and sustainability. However, they can introduce new vulnerabilities in the form of cyberattacks, which can cause significant challenges in ensuring grid resilience. We propose a framework in this paper for achieving grid resilience through suitably coordinated assets including a network of Internet of Things devices. A local electricity market is proposed to identify trustable assets and carry out this coordination. Situational Awareness (SA) of locally available DERs with the ability to inject power or reduce consumption is enabled by the market, together with a monitoring procedure for their trustability and commitment. With this SA, we show that a variety of cyberattacks can be mitigated using local trustable resources without stressing the bulk grid. Multiple demonstrations are carried out using a high-fidelity cosimulation platform, real-time hardware-in-the-loop validation, and a utility-friendly simulator.

distributed energy resources↗

DOE OSTI2025

Virtual Power Plant Profiles and Inventory

Increased adoption of distributed energy resources (DERs), growing quantities of renewable energy on the grid, and load growth from buildings, industry, and data centers are driving the need for increased demand flexibility and solutions to reliably and cost-effectively balance electricity supply and demand. In order to deploy DERs on a similar scale as conventional supply-side resources, utilities and state decision-makers are exploring a range of programmatic approaches and market designs, including Virtual Power Plants (VPP). Based on interviews with more than twenty subject matter experts on VPPs, the Insights into Scaling Virtual Power Plants report and appendix outlines actions that utilities and regulators can take to expand VPPs, organized around successful VPP designs, deployment, and operation. The companion report, Virtual Power Plant Profiles and Inventory describes three conceptual profiles that can be used as a starting point to facilitate clear discussions among regulators, utilities, solution providers, and stakeholders about various VPP strategies. It also includes details on more than 790 demand response programs and net energy metering solar plus battery programs, and 180 VPP programs. The Inventory is provided below as a separate file.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Mexico Clean Energy Report

This report provides an assessment of Mexico's clean energy resource potential and pathways for rapidly deploying renewable energy technologies to enable Mexico to reach its goal of 35% renewable energy by 2024 within the current legal and regulatory framework. An appendix to the report includes the results of the 2024 Renewable Energy Integration Study, and technology chapters on wind, solar, geothermal, hydropower, transport electrification, and green hydrogen, detailing current state, resource potential, and deployment opportunities and recommendations. Additional sections include transmission, a section on the challenges and solutions for the integration of variable generation resources, and a summary of benefits.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Decentralized Voltage Control with Peer-to-peer Energy Trading in a Distribution Network

Utilizing distributed renewable and energy storage resources via peer-to-peer (P2P) energy trading has long been touted as a solution to improve energy system’s resilience and sustainability. Consumers and prosumers (those who have energy generation resources), however, do not have expertise to engage in repeated P2P trading, and the zero-marginal costs of renewables present challenges in determining fair market prices. To address these issues, we propose a multi-agent reinforcement learning (MARL) framework to help automate consumers’ bidding and management of their solar PV and energy storage resources, under a specific P2P clearing mechanism that utilizes the so-called supply-demand ratio. In addition, we show how the MARL framework can integrate physical network constraints to realize decentralized voltage control, hence ensuring physical feasibility of the P2P energy trading and paving ways for real-world implementations.

Feng, Chen↗

DOE OSTI2024

Impacts of Renewable Energy and Green Hydrogen Policies on Uttar Pradesh's Power Sector Future

This report is part of a broader program focused on supporting Indian states with long-term power system planning. More information about this program can be found at the National Renewable Energy Laboratory's "Supporting India's States With Renewable Energy Integration" web page at https://www.nrel.gov/international/india-renewable-energy-integration.html. The power sector in Uttar Pradesh, India's most populous state, is poised to transform over the next few decades due to a combination of national and state-level policies impacting both the supply and demand of electricity. The Government of Uttar Pradesh has policies and plans to develop in-state solar PV, pumped storage hydropower, and green hydrogen. Power system policymakers and utilities in Uttar Pradesh are faced with the challenges of planning a system that incorporates increasing amounts of renewable energy and storage resources, meets rising electricity demand due to economic development and green hydrogen production, and satisfies operational and reliability requirements. To support these various objectives, the National Renewable Energy Laboratory (NREL), RMI, and the Uttar Pradesh New and Renewable Energy Development Agency (UPNEDA) evaluated the least-cost pathways for the state's power sector through 2050. NREL developed a capacity expansion model that identifies investment and operational decisions for every year (2024-2050) for all of India, with detailed representation for the state of Uttar Pradesh, which can provide a framework for recurring planning studies. The main insights from this study can also help inform policy development and investment decisions.

08 HYDROGEN↗

DOE OSTI2024

Impacts of Renewable Energy and Green Hydrogen Policies on Uttar Pradesh's Power Sector Future: Additional Modeling Scenarios to Explore Hydrogen Flexibility [Slides]

This slide deck is part of a broader program focused on supporting Indian states with long-term power system planning. More information about this program can be found at the National Renewable Energy Laboratory's "Supporting India's States With Renewable Energy Integration" web page at https://www.nrel.gov/international/india-renewable-energy-integration.html. The power sector in Uttar Pradesh, India's most populous state, is poised to transform over the next few decades due to a combination of national and state-level policies impacting both the supply and demand of electricity. The Government of Uttar Pradesh has policies and plans to develop in-state solar PV, pumped storage hydropower, and green hydrogen. Power system policymakers and utilities in Uttar Pradesh are faced with the challenges of planning a system that incorporates increasing amounts of renewable energy and storage resources, meets rising electricity demand due to economic development and green hydrogen production, and satisfies operational and reliability requirements. To support these various objectives, the National Renewable Energy Laboratory (NREL), RMI, and the Uttar Pradesh New and Renewable Energy Development Agency (UPNEDA) evaluated the least-cost pathways for the state's power sector through 2050. NREL developed a capacity expansion model that identifies investment and operational decisions for every year (2024-2050) for all of India, with detailed representation for the state of Uttar Pradesh, which can provide a framework for recurring planning studies. The purpose of this slide deck is to supplement the main study (published in May 2024) with additional modeling scenarios to explore hydrogen flexibility.

08 HYDROGEN↗