Integrated North American energy markets under different futures of cross-border energy infrastructure
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Siddiqui, Sauleh↗
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Conceptual Design of Integrated Energy Systems via Multiscale Market Simulations and Surrogate Models for Market Interactions
This final report describes novel capabilities developed as part of DISPATCHES, Design Integration and Synthesis Platform to Advance Tightly Coupled Hybrid Energy Systems, for designing hybrid energy systems (HES) a.k.a. integrated energy systems (IES) in the context of a larger electricity market. Capabilities are demonstrated on case studies for nuclear and renewable power.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Optimal Sizing and Dispatch of Solar Power with Storage
Designers of utility-scale solar plants with storage, seeking to maximize some aspect of plant performance, face multiple challenges. In many geographic locations, there is significant penetration of photovoltaic generation, which depresses energy prices during the hours of solar availability. An energy storage system affords the opportunity to dispatch during higher-priced time periods, but complicates plant design and dispatch decisions. Solar resource variability compounds these challenges, because determining optimal system sizes requires simultaneously considering how the plant will be operated under the imposed market and weather conditions. We develop an approach to analyze the economic performance of hybrid and single-technology solar power plants, which incorporates optimal dispatch, and considers the expected electricity market and weather conditions. We utilize the System Advisor Model software package to simulate the operation of multiple renewable generation and energy storage technologies, in conjunction with hourly-fidelity generation decisions determined by a revenue-maximizing, mixed-integer linear program. We show that, under our assumed market and weather conditions, the lifetime benefit-to-cost ratio can be improved by 6 to 19 percent, relative to a baseline design without optimizing, and that a concentrating solar power with thermal energy storage design produces significantly more energy per year, but is less profitable under our cost assumptions.
black-box optimization↗
Peer-to-Peer Energy Trading under Network Constraints Based on Generalized Fast Dual Ascent
We report the wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Decentralized Failure-Tolerant Optimization of Electric Vehicle Charging
We present a decentralized failure-tolerant algorithm for optimizing electric vehicle (EV) charging, using charging stations as computing agents. The algorithm is based on the alternating direction method of multipliers (ADMM) and it has the following features: (i) It handles capacity, peak demand, and ancillary services coupling constraints. (ii) It does not require a central agent collecting information and performing coordination (e.g. an aggregator), instead all agents exchange information and computations are carried out in a fully decentralized fashion. (iii) It can withstand the failure of any number of computing agents, as long as the remaining computing agents are in a connected communications network. We construct this algorithm by reformulating the optimal EV charging problem in a decomposable form, amenable to ADMM, and then developing efficient decentralized solution methods for the subproblems dealing with coupling constraints. We conduct numerical experiments on industry-scale synthetic EV charging datasets, with up to 1,152 charging stations, using a high performance computing cluster. The experiments demonstrate that the proposed algorithm can solve the optimal EV charging problem fast enough to permit the integration of EV charging with real-time electricity markets, even in the presence of failures.
42 ENGINEERING↗
A Penalty Scheme for Mitigating Uninstructed Deviation of Generation Outputs From Variable Renewables in a Distribution Market
With rapid growth of distributed renewable generation, the establishment of electricity distribution markets has attracted widespread concerns. Different from existing transmission grid-scale electricity markets, an electricity distribution market is featured by numerous small-scale prosumers, and zero marginal cost and intermittency of renewable generation units. Against this background, this paper first extends an average pricing market (APM) mechanism for pricing renewable generation outputs with zero marginal cost in the distribution network concerned. Then, to mitigate the uninstructed volatility of renewable generation outputs and power demand, a penalty scheme is proposed for deviations between the real-time demand/output and market cleared bid/offer, with frequency regulation service (FRS) from energy storage systems (ESSs) considered. It is proved that the market volatility can be well controlled within an expected limit through properly setting the penalty prices for load demand and generation output fluctuations. Also, with this mechanism a non-negative market surplus could always be attained. Case studies are carried out to demonstrate the feasibility and efficiency of the proposed distribution market mechanism and penalty scheme.
electricity distribution market↗
Robust Optimization for the Day-Ahead Scheduling of Cascaded Hydroelectric Systems
Uncertain electricity prices resulting from the re-structuring of electricity market have brought new opportu-nities and challenges for hydroelectric producers. This paper presents a data-driven robust optimization approach for the day-ahead scheduling of cascaded hydroelectric systems (CHS) with electricity price uncertainty. In this paper, a minimum volume enclosing ellipsoid (MVEE) is adopted to construct an ellipsoidal uncertainty set that fully identifies and exploits the historical characteristics data. Here, a second-order conic optimization formulation of the robust counterpart is derived for efficient computation. A real-world case study is conducted to demonstrate the capability of the proposed optimization approach compared with the traditional robust optimization approach.
13 HYDRO ENERGY↗
Market-Integrated Optimization of Wind-Battery-Hydrogen Hybrids for Peaking Capacity via Storage
As Integrated Energy Systems (IES) combine multiple energy and storage technologies to provide potentially more value and less risk via resource diversification, complementary overbuild, increased flexibility, and revenue-stacking, IES value is dependent on electricity market dispatch and grid interactions should play an important role in IES design and operation. This study hybridizes and retrofits wind and combustion turbine plants to study the impacts of replacing gas generation capacity with wind, battery, PEM electrolysis, hydrogen tanks and hydrogen turbines. The optimized design is co-simulated in a production cost model with different bidding strategies in order to compare performance and highlight the importance of grid-interactions. We analyze the revenue and dispatch changes as well as the price and cost implications of wind-battery-hydrogen IESs.
electrolysis↗
Report on the Use and Function of the Integrated Operations Capability Analysis Platform (ICAP) and the LWRS Innovation Portal (IP)
The commercial nuclear power industry has achieved excellent safety and reliability performance but is struggling to survive in an electricity market that is increasingly dominated by subsidized renewables and cheap natural gas. These challenges have forced utilities to explore previously uncharted avenues to drastically reduce the operations and maintenance costs of their plants which are the primary drivers of the total cost to produce electricity. The petrochemical industry faced similar challenges some years ago as the costs of extraction and processing were rising while their reservoirs were being depleted along with a drop-in commodity prices that resulted in unsustainable operations. In this challenging climate, they developed a business model called Integrated Operations (IO) that sought to utilize technology to enable news ways of working through the integration of people, technology, process and governance changes. The Light Water Reactor Sustainability (LWRS) program, working with IFE have developed an operating model via transferable learnings from the North Sea O&G industry. This framework is termed “Integrated Operations for Nuclear” (ION). ION is a transformation model that integrates the benefits and features of four principal factors: People, Technology, Process and Governance. The purpose of this report is to describe how to generate an ION business process analysis and utilize this information to reduce O&M costs. In order to make this job easier, DOE LWRS has created a suite of tools that will allow a person who is involved in a transformation effort at their company to build a solid documented business case for embarking on a major transformation effort. These tools, the Integrated Operations Capability Analysis Model (ICAP) and the LWRS Innovation Portal (IP) are described herein with instructions on their use. Instructions on how the interface with the EPRI Business Case Analysis Method (BCAM) are also provided.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Models and Strategies for Optimal Demand Side Management in the Chemical Industries
Deregulation and the increase of renewable electricity generation from wind and solar photovoltaics have transformed the U.S. electricity market. Economic and environmental benefits notwithstanding, the presence of renewables has increased variability and uncertainty on the supply side of the grid. Managing demand, rather than generation – a strategy referred to as “demand response (DR)” – is an attractive approach for mitigating this imbalance. DR efforts aim to reduce electricity usage during peak demand times, lessening stress on the grid. Industrial users are particularly attractive entities for DR participation since they present large, localized loads that can provide significant relief on grid demand and –unlike other large loads, such as buildings – are minimally dependent on human needs and preferences. In this project, we accomplished three main objectives. (1) We developed data-driven low-order DR scheduling-relevant dynamic models of chemical processes. Concurrently, we studied the formulation and solution of the associated optimal DR production scheduling problems. (a) A prototype air separation unit (ASU) model was used to generate simulated operating data for initial modeling efforts, which enabled the later use of industrial data for data-driven modeling. (b) We utilized Hammerstein-Wiener (HW) and Finite Step Response (FSR) models to represent nonlinear plant dynamics. (c) The HW models were linearized using exact linearization so they could potentially be embedded in power system models, which are formulated as mixed integer linear programs (MILPs). (d) We solved DR optimization problems under uncertainty and found that even naïve predictions of electricity price and product demand led to significant cost savings benefits. (2) Our DR scheduling optimization problem formulations are amenable to real-time solution. (a) We utilized Lagrangian Relaxation (LR) to efficiently solve the optimization problem by decoupling subproblems linked by complicating constraints. (b) We have achieved computation times for the 3-day DR scheduling problem of an ASU as low as 1.88 minutes. (3) Our representations of the DR behavior of chemical process as grid-level batteries were embedded in power system models. (a) For a small-scale grid, we found that incorporating the dynamics of the chemical plant in the optimal power flow calculations resulted in better resource management leading to up to 15% and 46% cost reduction for the grid and chemical plant operations, respectively, during periods of power line congestion. We have published several works dedicated to modeling and solving DR optimization problems from the user side. These were published in top peer-reviewed journals and are summarized in this report. The most recent work (and papers in preparation) considers DR scheduling from the grid side. Future efforts will consider networked plants (e.g., air separation units operating on a common pipeline) for DR participation, which is expected to amplify the capabilities of industrial DR participants to perform load-shifting. Our consideration of uncertainty in DR has inspired future directions in this area as well: we plan to develop multistage methods to fully account for the effects of uncertainty in DR scheduling.
24 POWER TRANSMISSION AND DISTRIBUTION↗
SCUC-DER Integration Report: Integrating Distributed Energy Resources (DER) Using Advanced Unit Commitment Models and DER Aggregation Methodologies
Distributed energy resources (DERs) are continuing to grow due to regulatory, policy, and market shifts, and it needs to be ensured that small DERs are given a level playing field with traditional resources. Legacy market processes such as unit-commitment problems were designed for a power grid consisting largely of centralized power plants. In contrast, DERs consist of many small devices with distinct operating characteristics that may or may not be connected at the transmission interconnection points, limiting their visibility to the independent system operators (ISOs) who operate wholesale electricity markets. This report details the development and initial results from a simulation platform that integrates state-of-the-art security-constrained unit commitment software, detailed feeder models, and a DER aggregator model to quantify potential DER integration issues. Quantitative results to date illustrate potential infeasible scheduling solutions from SCUC when the DERA includes aggregations of energy-limited energy storage resources. Likewise, if aggregations are not penalized for dispatch deviations, they may have incentives to deviate from the SCUC-determined resource schedules. Assumptions about the amount of aggregated demand response resources (DRRs) and the ability of DERAs to follow profit incentives have an important impact – DRRs have significant flexibility and can typically feasibly meet their SCUC schedule, but on the other hand, their profit incentives can cause unscheduled increases in load before and after DRR dispatch. Computation time results on the SCUC solver and simulation platform only show a modest increase in SCUC solution time as the number of DERAs is increased, but results to date only reflect the RTS-GMLC test system; results may show more significant solver slowdown in larger transmission systems. The largest contribution to simulation time is attributed to the DERA offer generation method, which is suggested for future improvements.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Financial Analysis of Experimental Releases Conducted at Glen Canyon Dam during Water Year 2019
This report was prepared by Argonne National Laboratory in support of a financial analysis of the Glen Canyon Dam high-flow experimental release that is intended to mobilize the sand in the Colorado River with high-volume water releases from the dam and redeposit it downstream as sandbars along the river. These sandbars serve, among other things, as habitat for wildlife. This experimental release was conducted during the period from November 5 to November 8, 2018. This analysis was funded by the Colorado River Storage Project (CRSP) Office of the U.S. Department of Energy Western Area Power Administration (WAPA). CRSP markets electricity produced by hydroelectric facilities collectively known as the Salt Lake City Area Integrated Projects including dams equipped for power generation on the Colorado, Green, Gunnison, and Rio Grande Rivers and on Plateau Creek in the states of Arizona, Colorado, New Mexico, Utah, and Wyoming. Staff members in Argonne’s Energy Systems Division prepared this technical report with assistance from the WAPA CRSP and Energy Marketing and Management Offices.
13 HYDRO ENERGY↗
Financial Analysis of the 2019 Glen Canyon Dam Bug Flow Experiment
This report was prepared by Argonne National Laboratory (Argonne) in support of a financial analysis of the Glen Canyon Dam (GCD) flow experiment that was intended to support downstream macroinvertebrate production that are a primary food supply for fishes in the Colorado River. Also known as “bug flow” experiments, these experimental water releases were conducted on the weekends and holidays from the beginnings of May 2019 through the end of August 2019. This analysis was funded by the Colorado River Storage Project (CRSP) Office of the U.S. Department of Energy’s Western Area Power Administration (WAPA). CRSP markets electricity produced by hydroelectric facilities collectively known as the Salt Lake City Area Integrated Projects including dams equipped for power generation on the Colorado, Green, Gunnison, and Rio Grande Rivers and on Plateau Creek in the states of Arizona, Colorado, New Mexico, Utah, and Wyoming. Staff members in Argonne’s Energy Systems Division prepared this technical memorandum with assistance from WAPA’s CRSP and Energy Marketing and Management Offices (EMMO).
13 HYDRO ENERGY↗
Financial Analysis of the 2020 Macroinvertebrate Production Flow Experiment at Glen Canyon Dam
This report was prepared by Argonne National Laboratory (Argonne) in support of a financial analysis of the Glen Canyon Dam (GCD) flow experiment that was intended to support downstream macroinvertebrate production that are a primary food supply for fishes in the Colorado River. Also known as “bug flow” experiments, these experimental water releases were conducted on the weekends and holidays from the beginnings of May 2020 through the end of August 2020. This analysis was funded by the Colorado River Storage Project (CRSP) Office of the U.S. Department of Energy’s Western Area Power Administration (WAPA). CRSP markets electricity produced by hydroelectric facilities collectively known as the Salt Lake City Area Integrated Projects including dams equipped for power generation on the Colorado, Green, Gunnison, and Rio Grande Rivers and on Plateau Creek in the states of Arizona, Colorado, New Mexico, Utah, and Wyoming. Staff members in Argonne’s Energy Systems Division prepared this technical memorandum with assistance from WAPA’s CRSP and Energy Marketing and Management Offices (EMMO).
13 HYDRO ENERGY↗
Financial Analysis of Experimental Releases Conducted at Glen Canyon Dam during Water Year 2021
This report was prepared by Argonne National Laboratory (Argonne) in support of a financial analysis of two Glen Canyon Dam (GCD) flow experiments that were conducted in Water Year 2021. The first experiment, called “spring disturbance flow”, was conducted from March 15 to 26, 2021. This disturbance flow experiment is designed to simulate a spring-timed runoff event so scientists can study its effects on the Colorado River ecosystem. The second experiment was a reduced and steady discharge from May 29 to June 4, 2021. This reduced steady flow experiment was conducted for the United States Geological Survey (USGS) to collect high-resolution aerial imagery over Grand Canyon National Park. This analysis was funded by the Colorado River Storage Project (CRSP) Office of the U.S. Department of Energy’s Western Area Power Administration (WAPA). CRSP markets electricity produced by hydroelectric facilities collectively known as the Salt Lake City Area Integrated Projects including dams equipped for power generation on the Colorado, Green, Gunnison, and Rio Grande Rivers and on Plateau Creek in the states of Arizona, Colorado, New Mexico, Utah, and Wyoming. Staff members in Argonne’s Energy Systems Division prepared this technical memorandum with assistance from WAPA’s CRSP and Energy Marketing and Management Offices (EMMO).
58 GEOSCIENCES↗
Financial Analysis of the 2022 Macroinvertebrate Production Flow Experiment at Glen Canyon Dam
This report was prepared by Argonne National Laboratory (Argonne) in support of a financial analysis of the Glen Canyon Dam (GCD) flow experiment that was intended to support downstream macroinvertebrate production that are a primary food supply for fishes in the Colorado River. Also known as “bug flow” experiments, these experimental water releases were conducted on the weekends and holidays from the beginnings of May 2022 through the end of August 2022. This analysis was funded by and prepared for the Colorado River Storage Project (CRSP) Office of the U.S. Department of Energy’s Western Area Power Administration (WAPA). CRSP markets electricity produced by hydroelectric facilities collectively known as the Salt Lake City Area Integrated Projects including dams equipped for power generation on the Colorado, Green, Gunnison, and Rio Grande Rivers and on Plateau Creek in the states of Arizona, Colorado, New Mexico, Utah, and Wyoming. Staff members in Argonne’s Energy Systems and Infrastructure Analysis Division prepared this technical memorandum with assistance from WAPA’s CRSP and Energy Marketing and Management Offices (EMMO).
13 HYDRO ENERGY↗
Enabling Grid-Aware Market Participation of Aggregate Flexible Resources
Increasing integration of distributed energy resources (DERs) within distribution feeders provides unprecedented flexibility at the distribution-transmission interconnection. With the new FERC 2222 order, DER aggregations are allowed to participate in energy market. To enable market participation, these virtual power plants need to provide their generation cost curves. This paper proposes efficient optimization formulations and solution approaches for the characterization of hourly as well as multi-time-step generation cost curves for a distribution system with high penetration of DERs. Network and DER constraints are taken into account when deriving these cost curves, and they enable active distribution systems to bid into the electricity market. The problems of deriving linear and quadratic cost curves are formulated as robust optimization problems and tractable reformulation/solution algorithm are developed to facilitate efficient calculations. The proposed formulations and solution algorithm are validated on a realistic test feeder with high penetration of flexible resources.
aggregated distributed energy resources↗
A Technical and Economic Assessment of LWR Flexible Operation for Generation and Demand Balancing to Optimize Plant Revenue
With increased penetration of subsidized variable renewable energy (VRE) resources and competition from low natural gas prices, existing light water reactor (LWR) nuclear power plants (NPPs) are struggling to remain economically competitive. This work examines the potential economic competitiveness of various thermal energy storage (TES) technologies when coupled directly or indirectly with a NPP. To highlight their relative economic competitiveness, we contrast several energy storage solutions in stochastic dispatch optimization. We leverage data from recent work analyzing a range of TES technologies with varying capital costs, performance, and technology readiness level (TRL) to establish our case. We explore inserting these technologies into an electricity market with existing nuclear generation and large projected variable renewable energy (VRE) penetration. Although these technologies' projected capital costs may make them unlikely candidates in their current state, this analysis demonstrates a high-fidelity techno-economic analysis of energy storage. Furthermore, as the projected cost of energy storage technologies evolves, this analysis sets a precedent for similar future investigations. One region with projected trends that may be unfavorable for existing nuclear capacity is the New York Independent System Operator (NYISO) market. New York state’s baseload generation has been historically provided by fossil-fired and nuclear assets. However, amid economic pressures from subsidized VREs and low natural gas prices, the state has recently deactivated Indian Point nuclear power plant units 2 and 3. Furthermore, the state plans to meet its zero-emission generation target by 2040 by replacing fossil-fired capacity with significant investments in VRE resources like wind and solar photovoltaic (PV) and battery storage. Increased intermittent resource penetration lowers the baseload power requirement, adding further economic pressure to the state’s three remaining NPPs still in operation. With three NPPs still in operation in New York, this work analyzes potential economic benefits to NPPs on the New York grid when directly or indirectly coupled with various TES technologies. This work requires two modeling steps to analyze the potential economic benefits of various system configurations of the TES directly or indirectly coupled with nuclear. First, this analysis leverages capacity expansion modeling by experts at the Electric Power Research Institute (EPRI). Using their deterministic capacity expansion model, U.S. Regional Economy, Greenhouse Gas, and Energy (US-REGEN), EPRI analysts evaluated the capacity and generation evolution of the New York state energy market under four projection scenarios. These four projection scenarios were developed to represent the potential evolution of the capacity and generation in NYISO from 2015 to 2050 under various economic, technology, and policy constraints. The results from these capacity expansion models are then used as boundary conditions in the second modeling step. The second modeling step uses the Holistic Energy Resource Optimization Network (HERON) for a set of stochastic techno-economic analyses (STEAs) to investigate the potential increase in the economic viability of various configurations of the TES. With no current capacity expansion capabilities, HERON takes the data generated from US-REGEN for 2050 to generate synthetic load, solar, and wind data. Then HERON economically optimizes the capacity and dispatch of the various TES configurations. The potential economic benefit is the differential net present value (NPV) of the TES configurations from the no-TES baseline. As a stochastic techno-economic analysis package, HERON introduces uncertainty into the economic metrics, while US-REGEN trades resolution for reduced computational complexity. Using HERON also allows the modeling of direct thermal coupling, a feature not common in capacity and dispatch models. As expected, with high capital costs, the costs of introducing energy storage for all the technologies considered outweighed the potential economic benefit of this strategy for flexible plant operation. The benefit of this analysis is primarily in demonstrating a workflow that examines innovative solutions to increase NPP revenue via TES coupling. HERON’s stochastic capacity and dispatch optimization process used in this work has proven an effective tool in observing and evaluating the impact of introducing storage technologies in a grid energy system.
25 ENERGY STORAGE↗