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DOE OSTI2024

Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region

As electricity systems transition to higher levels of solar and wind generation, electric system operators will likely need to hold additional reserves to manage the forecast error associated with these resources. Because wind and solar forecast errors tend to be poorly correlated across space, system operators can reduce their reserve requirements by sharing reserves. This paper examines the value of forecast error reserve sharing among balancing areas in the Southeast United States. It finds that forecast error reserve requirements increase linearly with growth in solar and wind generation capacity but that reserve sharing can significantly reduce physical (MW) reserve requirements (from 25%-26% to 18%-19% of average load in high solar scenarios). It finds that the value of forecast error reserve sharing declines with higher levels of solar and wind generation, due to lower wholesale energy and reserve prices. Even with declines in wholesale prices, forecast error reserve sharing can still provide substantial value (as much as $\$$400 million per year in a high solar scenario), though with higher levels of solar, wind, and electricity storage, this value is increasingly tied to avoiding scarcity prices. The results suggest the importance of coordinated capacity expansion planning for forecast error reserve sharing.

14 SOLAR ENERGY↗

DOE OSTI2022

Nuclear's Role in the U.S. Electricity System: A Multi-Model Inter-Comparison Analysis

Multiple capacity expansion models (CEMs) for the U.S. power system represent the balance of options among generation, transmission, and storage assets that can satisfy electric loads, operating and planning reserves, and policy requirements. These models are typically set up to find the least-cost portfolio of assets that meet specified requirements, and model decisions can include both investments in new, and retirement of existing, resources. The scenarios explored by CEMs can help inform strategies for meeting future electricity and energy needs under a range of future conditions. However, projections can differ between models, sometimes dramatically, for a seemingly similar scenario. Differences in model coverage, structure, and input assumptions contribute to the range of model outcomes. Understanding what drives the biggest differences in model outputs improves model insights and provides context for interpreting results. This summary presents analysis that was performed through a forum of analysts who own, update, and apply CEMs, as well as nuclear experts from national laboratories, industry, and the research community. The following sections describe methods, results, and findings from an original, innovative inter-model comparison that provides insights into what drives the greatest differences in nuclear retirement and deployment projections across models and a range of technology, market, and policy conditions.

capacity expansion model↗

DOE OSTI2022

Operational Probabilistic Tools for Solar Uncertainty (OPTSUN) (Final Project Report for DOE Solar Forecasting II Project)

Increasing levels of solar PV can challenge system operations and may require novel methods to operate the power system reliably and efficiently. Power system operating plans generally use deterministic forecasts, in which the variable energy resources are represented by the expected value for each interval of the decision horizon. Probabilistic forecasts are relatively new but have the potential to address the shortfalls of deterministic forecasts. However, understanding how best to use such forecasts is still a key gap in industry and was the focus of this project. The project had three workstreams. In a forecasting workstream, improvements were made to baseline probabilistic forecasts using a number of new approaches such as machine learning methods and improved input data. In a design workstream, advanced simulation tools used these forecasts to investigate newly proposed reserve determination methods. Lastly, in a demonstration workstream a scheduling management platform (SMP) was developed to leverage probabilistic forecasts in a modular and customizable manner. In order to study the benefits that could be accrued, the project team collaborated with three utility partners (Duke Energy, Southern Company and Hawaiian Electric) to deliver improved probabilistic forecasts for each region and to model each region in case studies using advanced production cost modeling tools. Different methods to determine operating reserve requirements from probabilistic forecasts were developed, simulated, and tested across each region. The benefits of using these newly proposed methods varied by utility, but, in general, using probabilistic forecasts as well as historical data to set the reserve requirements seems to improve reliability related results, with less risk of reserve or supply shortfalls. The cost implications were not always straightforward; in some cases the new methods could show a reduction in expected operating costs, but often the increase in reserves associated with better risk mitigation using probabilistic forecasts could result in an increase in operating costs in the simulations. The SMP tool was developed to process probabilistic forecasts from their initial receipt through to scheduling decisions. This open-source tool consists of several modules for scenario development, reserve requirements calculation, and visualization. The SMP tool was demonstrated to a wide range of operators and stakeholders at all three utilities and further improved based on their feedback. The tool will be available on www.epri.com/optsun. The proposed probabilistic information-based reserve determination approaches have the potential to be implemented by different regions to ensure an economic and reliable power system operation on power systems integrating increasing levels of variable renewable resources. The innovative yet practical methods developed in this project demonstrated tangible benefits from using probabilistic forecasts beyond just study-based assessments to include three unique balancing areas. The demonstrated benefits across the multiple utility environments, are expected to provide system operators in all regions the confidence required and a platform to adopt the new forecasting and operating methods.

14 SOLAR ENERGY↗

DOE OSTI2020

Flexible Service Contracting for Risk Management within Integrated Transmission and Distribution Systems

The general objective of our project has been to investigate the ability of Independent Distribution System Operators (IDSOs), functioning as linkage agents for Integrated Transmission and Distribution (ITD) systems, to facilitate the flexible availability and usage of reserve in support of ITD system operations. This general objective is in accordance with Order 2222 of the U.S. Federal Energy Regulatory Commission (FERC), titled “Participation of Distributed Energy Resource Aggregations in Markets Operated by Regional Transmission Organizations and Independent System Operators”. The Final Rule for FERC Order 2222 was issued on September 17, 2020. The primary contribution of our project is that we have formulated an innovative energy management Transactive Energy Design (TES) approach for ITD systems that provides promising support for our project objective. Specifically, we have developed a new type of IDSO-managed TES design for distribution system operations, as well as new types of swing contracts permitting IDSOs to participate in transmission system operations as providers of reserve harnessed from distribution system resources in return for appropriate compensation. Together, these design elements constitute a scalable market-based approach facilitating efficient reserve procurement for ITD system operations from a fuller range of power resources. The efficacy of our approach has been demonstrated by means of detailed conceptual analyses as well as test case simulations. To implement the latter, we have developed the ITD TES Platform V2.0, a computational platform that permits the modeling and software implementation of ITD system operations over time. The Electric Reliability Council of Texas (ERCOT) energy region has been used as the empirical anchor for the development of this platform. Our conceptual and test-case work has been reported in a Wiley/IEEE Press book, two refereed book chapters, and seven refereed journal articles. The key components comprising the ITD TES Platform V2.0 have been released as documented open-source software at online GitHub repositories.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Design and Operation of Energy Systems with Large Amounts of Variable Generation: IEA Wind TCP Task 25 (Final Summary Report)

This report summarizes findings on wind integration from the 17 countries or sponsors participating in the International Energy Agency Wind Technology Collaboration Program (IEA Wind TCP) Task 25 from 2006-2020. Both real experience and studies are reported. Many wind integration studies incorporate solar energy, and most of the results discussed here are valid for other variable renewables in addition to wind. The national case studies address several impacts of wind power on electric power systems. In this report, they are grouped under long-term planning issues and short-term operational impacts. Long-term planning issues include grid planning and capacity adequacy. Short-term operational impacts include reliability, stability, reserves, and maximizing the value of wind in operational timescales (balancing related issues). The first section presents the variability and uncertainty of power system-wide wind power, and the last section presents recent studies toward 100% shares of renewables. The appendix provides a summary of ongoing research in the national projects contributing to Task 25 for 2021-2024. The design and operation of power and energy systems is an evolving field. As ambitious targets toward net-zero carbon energy systems are announced globally, many scenarios are being made regarding how to reach these future decarbonized energy systems, most of them involving large amounts of variable renewables, mainly wind and solar energy. The secure operation of power systems is increasingly challenging, and the impacts of variable renewables, new electrification loads together with increased distribution system resources will lead to somewhat different challenges for different systems. Tools and methods to study future power and energy systems also need to evolve, and both short-term operational aspects (such as power system stability) and long-term aspects (such as resource adequacy) will probably see new paradigms of operation and design. The experience of operating and planning systems with large amounts of variable generation is accumulating, and research to tackle the challenges of inverter-based, nonsynchronous generation is on the way. Energy transition and digitalization also bring new flexibility opportunities, both short and long term.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Solar and Wind Forecast Error Reserve Sharing in a Multi-Utility Region

As electricity systems transition to higher levels of solar and wind generation, electric system operators will likely need to hold additional reserves to manage solar and wind forecast error. Because solar and wind forecast errors tend to be weakly correlated across space, system operators can reduce their reserve requirements by sharing reserves. This paper examines the benefits of forecast error reserve sharing among balancing areas in the Southeastern United States, in scenarios in which solar and wind generation ranges from 34% to 65% of total generation. It finds that day-ahead forecast error reserve requirements increase linearly with growth in solar and wind generation capacity (6%-10% of total capacity), but that reserve sharing can significantly reduce these requirements (by 6%-29%). It finds that, in economic terms, the value of forecast error reserve sharing ($\$$0.09-$\$$1.24 billion per year, $\$$0.12-$\$$1.68/MWh of load across scenarios) tends to decline with higher levels of solar and wind generation, due to lower reserve and energy prices. Even with declines in reserve prices, forecast error reserve sharing can still provide substantial value, though with higher levels of solar, wind, and electricity storage this value is increasingly tied to avoiding scarcity prices.

14 SOLAR ENERGY↗

DOE OSTI2023

Protecting Customer Privacy Through Distributed Energy Resource Anonymization

Due to their stochastic nature, the increase of Renewable Energy Resources (RERs) as a primary source of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in Demand Response (DR) programs is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and the Grid Service Provider (GSP). This paper discusses the use of flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of the DERs by making it more difficult to determine the individual characteristics of the device. We discuss an example of this using simulated DER load profile data and examine the resulting effectiveness by using a machine learning algorithm for classification, called Support Vector Machine (SVM).

Distributed Energy Resource, Anonymization, Renewa↗

DOE OSTI2022

Hierarchical Control of Utility-Scale Solar PV Plants for Mitigation of Generation Variability and Ancillary Service Provision

This paper presents a hierarchical control system to mitigate the variability of solar photovoltaic (PV) power plant and provide ancillary services to the electric grid without the need for additional non-solar resources. With coordinated management of each inverter in the system, the control system commands the power plant to proactively curtail a small fraction of its instantaneous maximum power potential, which gives the plant enough headroom to ramp up production from the overall power plant, for a service such as regulation reserve. This control system is practical for continuously changing cloud cover conditions in partially cloudy days. A case study from a site in Hawaii with one-second resolution solar irradiance data is used to verify the efficacy of the proposed control system. The proposed control algorithm is subsequently compared with the alternative control technology from the literature, the grouping control algorithm; the results show that the proposed hierarchical control system is over 10 times more effective in reducing generator mileage to support power fluctuations from solar PV power plants.

14 SOLAR ENERGY↗

DOE OSTI2021

Multiscale Effects Masked the Impact of the COVID-19 Pandemic on Electricity Demand in the United States

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implications for electricity sector investment and operating decisions that maintain reserve margins and provide grid reliability. This study reveals that understanding this uncertainty requires an understanding of the underlying drivers at the customer-class scale. This paper utilizes three datasets to compare the impacts of COVID-19 on electricity consumption across a range of spatiotemporal and customer scales. At the utility/customer-class scale, COVID-19-induced shutdowns in the spring of 2020 shifted weekday residential load profiles to resemble weekend profiles from previous years. Total commercial loads declined, but the commercial diurnal load profile was unchanged. With only total loads available at the balancing authority scale, the apparent impact of COVID-19 was smaller during the summer due in part to phased re-opening and spatial variability in re-opening, but there were still clear variations once total loads were broken down zonally. Monthly data at the state scale showed an increase in state-level residential electricity sales, a decrease in commercial sales, and a small net decrease in total sales in most states from April-August 2020. Analyses that focus on total load or a single scale may miss important changes that become apparent when the load is broken down regionally or by customer class.

COVID-19, electricity demand, multiscale, Commonwe↗

DOE OSTI2023

Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operations (SUMMER-GO): Project Final Report

The Solar Uncertainty Management and Mitigation for Exceptional Reliability in Grid Operation (SUMMER-GO) project was recently completed through a collaboration among the National Renewable Energy Laboratory, Maxar, the Electric Reliability Council of Texas (ERCOT), the University of Texas at Dallas, the University of California Berkeley, and the University of Colorado Boulder. The project made significant advances in probabilistic solar power forecasting, both through the development of Bayesian model averaging methods for ensemble forecasting and in bringing these and other advancements into practice with Maxar's delivery of operational forecasts to ERCOT. In addition to creating more reliable solar power forecasts, the project developed methods for their utilization in power system operations. These include the development of risk-aware unit commitment and economic dispatch algorithms and methods to reformulate probabilistic forecasts to be used in these power system operational models. Dynamic power system reserve methods were also developed, which have been shown in silico to create economic savings and reliability improvements on an ERCOT-like system as well as financial savings in the ERCOT system through more granular consideration of the uncertainty associated with solar power forecasts. Finally, a situational awareness tool to help grid operators better understand solar power forecast uncertainty in daily operations was developed and extensively vetted.

14 SOLAR ENERGY↗

DOE OSTI2024

On the role of Battery Energy Storage Systems in the day-ahead Contingency-Constrained Unit Commitment problem under renewable penetration

The integration of variable Renewable Energy Sources (vRES) to alleviate greenhouse gas emissions has introduced significant challenges for power systems operations. These challenges include high levels of uncertainty due to the intermittence associated with vRES and therefore impose the need to devise a reliable and cost-effective day-ahead unit commitment and power and reserves scheduling for real-time operations. Also, this increasing penetration of vRES requires higher ramping capabilities from units originally designed for other purposes (e.g., base-load generation), which might be exacerbated during contingency states. Hence, in this work, we propose a methodology to address the day-ahead Contingency-Constrained Unit Commitment (CCUC) problem that leverages the participation of Battery Energy Storage Systems (BESSs) to address load-following and post-contingency management, therefore alleviating the ramping burden on conventional thermal generators. To do so, we formulate a three-level optimization problem that represents the decision-making process of obtaining the least-cost commitment, generation and reserves scheduling, while restricting the Conditional Value-at-Risk (CVaR) of the system imbalance at real-time operations to user-defined tolerance levels. In addition, we devise a computationally efficient solution approach for the proposed problem based on the Column-and Constraint Generation (CCG) algorithmic framework. Two numerical experiments are conducted to empirically illustrate the benefits of the proposed methodology. Key results indicate a reduction in real-time ramping needs and a better usage of the system resources, with a reduction in the overall system commitment levels and reserve scheduling costs when compared to a benchmark case in which storage is not available.

Moreira, Alexandre↗

DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

What Is the Value of Alternative Methods for Estimating Ramping Needs?

Power system operators procure and deploy flexibility reserves or ramping products to address balancing needs caused by uncertainty and variability of load and generation. Existing methods estimate ramping needs using calendar information and historical forecast errors. Novel methods investigate if real-time weather information could inform ramping and other balancing requirements. This paper compares estimation methods for ramping requirements in theory and practice. The theoretical framework indicates when an alternative method could yield improved economic or reliability performance than existing methods by requiring lower or higher levels of ramping products. Preliminary simulations on a 118-bus test system for 4 days in May 2019 illustrate how system performance improves or deteriorates when ramping requirements are weather-informed (alternative) instead of calendar-based (baseline). Preliminary results suggest high variability in change of performance and underline the impact of additional factors, such as system conditions, on the realized performance change.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2024

Interregional Transmission Operational Coordination

This presentation will discuss challenges and opportunities on interregional coordination in the areas of interchange transaction, congestion management, reserve sharing and deliverability across multi-stage operational processes. It will then provide an overview of the interregional transmission operational coordination (IRTOC) project that currently focuses on congestion management, including Inter-regional transmission coordination in real time operations and operational planning, HVDC optimization (intra- and interregional), ancillary service deliverability (intra- and interregional). In addition, it will discuss the Sienna-Decomposition tool development to systematically study interregional coordination methods.

congestion management↗

DOE OSTI2023

Cost-Benefit Analysis of Grid-Supportive Loads for Fast Frequency Response

Flexibility in inverter-based loads could be used to support the converter-dominated power grid by offering a rapid, autonomous, and adjustable power reserve during system transients to help maintain system stability. Based on technical potential, ancillary service (AS) value, and implementation costs, this study illustrates the cost-benefit analysis of grid-supportive loads (GSLs) for the supply of fast frequency response (FFR). The net benefit for each GSL is demonstrated using a case study and relevant data sources. The findings suggest that implementation costs for enabling GSL features are low compared to the value that grid operators get from the acquisition of responsive reserve services. The authors believe that, given the rising popularity of renewable energy sources, GSLs can be a useful tool for grid stability in low-inertia systems.

cost-benefit analysis↗

DOE OSTI2021

Time-frequency based cyber security defense of wide-area control system for fast frequency reserve

Global power systems are transiting from conventional fossil fuel energy to renewable energies due to their environmental benefits. The increasing penetration of renewable energies presents challenges for power system operation. The efficiency and sufficiency of responsive reserves have become increasingly important for power systems with a high proportion of renewable energies. The Fast Frequency Reserve (FFR), especially the Wide-area Monitoring System (WAMS)-based FFR, is a promising and effective solution to secure and enhance the stability of power systems. However, cyber security has become a new challenge for the WAMS-based FFR system. Cyber attacks on the FFR control system may threaten the safety of power system operation due to the rapid power controllability requirement of FFR. Therefore, to address this problem, a time-frequency based cyber security defense framework is proposed to detect the cyber spoofing of synchrophasor data in WAMS-based FFR control systems. This paper first introduces the Continuous Wavelet Transforms (CWTs) to decompose spoofing signals. Then, the Dual-frequency Scale Convolutional Neural Networks (DSCNN) is proposed to identify the time-frequency domains matrix from two frequency scales. Integrating CWTs and DSCNN, an identification framework called CWTs-DSCNN is further proposed to detect the spoofing attacks in the WAMS-based FFR system. Multiple experiments using the actual data from FNET/GridEye are performed to verify the effectiveness of the framework in securing WAMS-based FFR systems.

25 ENERGY STORAGE↗

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

Fast Frequency Response using Reinforcement Learning-Controlled Wind Turbines

To fulfill the auxiliary grid services such as load regulation, spin and non-spin reserve, and frequency support during emergencies, power system operators often require certain wind farms to operate in de-loaded modes. By leveraging the fast response capability of wind farms, the reserved power in deloaded modes can significantly enhance the stability and reliability of power grids. This paper presents a novel methodology that incorporates wind turbines into reinforcement learning-based solutions for frequency response. The proposed approach employs the state-of-the-art reinforcement learning algorithm, surrogategradient- based evolution strategy (GSES), for continuous control of the wind farm output. Our methodology is tested on a modified IEEE-39 bus system, and simulation outcomes demonstrate that the proposed approach can reliably support the frequency of the power system and prevent unnecessary load shedding.

Gao, Wei↗