A retrospective analysis of the market price response to distributed photovoltaic generation in California
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Craig, Michael T.↗
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Rethinking the Price Formation Problem–Part 2: Rewarding Flexibility and Managing Price Risk
In this study, part 1 of this two-part paper describes the impact that uncertainty has on the design and analysis of price formation policies in the non-convex auctions conducted by U.S. wholesale electricity market operators. Using first a toy model and then a large-scale test system, Part 2 demonstrates the difference in prices under the idealized benchmark of ex ante convex hull pricing defined in Part 1 versus existing methods, in particular documenting the potential for suppression of volatility and therefore under-compensation of flexibility by existing methods. The examples highlight that inefficient spot price formation can induce inefficient forward commitments of generators, necessitating out-of-market intervention to restore a reliable and efficient operating plan.Given the potential side effects of existing policies for investment and operation, we suggest two elements in a reoriented approach to the price formation problem: first ensuring that prices exhibit full-strength volatility, and second ensuring that risk-averse market participants have sufficient ability to manage this volatility.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Beyond Price Taker: Conceptual Design and Optimization of Integrated Energy Systems Using Machine Learning Market Surrogates
Future electricity generation systems must be optimized to provide flexibility that counteracts the variability of non-dispatchable renewable energy sources and ensures the reliability and safety of critical infrastructure, including the electric grid. The current state-of-the-art is to co-optimize the design and operation of integrated energy systems (IES) treating historical or predicted time-series electricity prices as fixed parameters. Recent literature has shown the limitations of this price taker assumption, which neglects how IES optimization decisions influence market outcomes. As such, this paper proposes a new optimization formulation that uses machine learning surrogate models, trained from a library of annual market operation simulations, to embed IES market interactions into the co-optimization problem directly. Using a thermal generator example built in the open-source IDAES computational environment, we show that the price taker approach routinely over-predicts annual revenues by 8% or more compared to a validation simulation, where the proposed approach has a typical relative error of 1% or less.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Oil price states and drivers: An analysis of the second-month spot-futures price differential
Oil remains a dominant component of global energy use, and its price, characterized by frequent changes and an ever-present potential for large swings, continues to be a focus of industry participants, policymakers and analysts attention. Here, this study examines the behavior of future spot oil prices using a continuous-time hidden Markov model (HMM) and daily price data spanning years 2007 to 2024. We identify six states in the second-month WTI spot-futures price differential and assess the roles of eleven futures price, quantity, financial market, and geopolitical/volatility variables in each state. The model yields several insights into the workings of the oil market and the relative roles of these drivers. We find support for several theoretical and empirical findings in the oil market literature, including the role of inventory, volatility/risk, and market responses to contango/backwardation in futures markets. A novel finding is that “normal contango” conditions represent a significant portion of next-day states in our in-sample data. Under the most volatile normal contango state, many of the oil market drivers differ markedly in coefficient signs and magnitudes from those in other states. The resulting model also performed well out-of-sample and would, in addition to understanding the impact of market drivers, be useful for short-term forecasting. Overall, the findings highlight the highly non-linear, regime-dependent interactions of the oil price and its drivers, emphasizing the importance of detailed information to market stakeholders.
Oladosu, Gbadebo A. [Oak Ridge National Laboratory↗
Beyond Price-Taker: Multiscale Optimization of Wind and Battery Integrated Energy Systems
Integrating renewable energy into the electric grid is challenging due to the intermittency and variability of wind and other non-dispatchable resources. Integrated energy systems (IESs) combine multiple energy technologies (e.g., fossil, nuclear, renewables, storage) to reduce costs and improve flexibility and reliability. However, standard techno-economic analysis (TEA) methods often overestimate the benefits of IESs because they fail to account for energy market adjustments. This paper systematically studies the limitations of the prevailing price-taker assumption for TEA and optimization of hybrid energy systems. As an illustrative case study, we retrofit an existing wind farm in the RTS-GMLC test system (which loosely mimics the Southwest U.S.) with battery energy storage to form an IES. We show that the standard price-taker model overestimates the electricity revenue and the net present value (NPV) of the IES up to 178% and 30.4%, respectively, compared to our more rigorous multiscale optimization. These differences arise because introducing storage creates a more flexible resource that impacts the larger wholesale electricity market. Moreover, this work highlights the impact of the IES has on the market via various strategic bidding, and underscores the importance of moving beyond price-taker for optimal storage sizing and TEA of IESs. We conclude by discussing opportunities to generalize the proposed framework to other IESs, and highlight emerging research questions regarding the complex interactions between IESs and markets.
25 ENERGY STORAGE↗
Three-Phase AC Optimal Power Flow Based Distribution Locational Marginal Price
Designing market mechanisms for electricity distribution systems has been a hot topic due to the increased presence of smart loads and distributed energy resources (DERs) in distribution systems. The distribution locational marginal pricing (DLMP) methodology is one of the real-time pricing methods to enable such market mechanisms and provide economic incentives to active market participants. Determining the DLMP is challenging due to high power losses, the voltage volatility, and the phase imbalance in distribution systems. Existing DC Optimal Power Flow (OPF) approaches are unable to model power losses and the reactive power, while single-phase AC OPF methods cannot capture the phase imbalance. To address these challenges, in this paper, a three-phase AC OPF based approach is developed to define and calculate DLMP accurately. The DLMP is modeled as the marginal cost to serve an incremental unit of demand at a specific phase at a certain bus, and is calculated using the Lagrange multipliers in the three-phase AC OPF formulation. Extensive case studies have been conducted to understand the impact of system losses and the phase imbalance on DLMPs as well as the potential benefits of flexible resources.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Evaluating resource adequacy impacts on energy market prices across wind and solar penetration levels
Not Available
Frew, Bethany↗
Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model
The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.
batteries↗
State Approaches to Demand Reduction Induced Price Effects: Examining How Energy Efficiency Can Lower Prices for All
Effects (DRIPE) as a real, quantifiable benefit of energy efficiency and demand response programs. DRIPE is a measurement of the value of demand reductions in terms of the decrease in wholesale energy prices, resulting in lower total expenditures on electricity or natural gas across a given grid. Crucially for policymakers and consumer advocates, DRIPE savings accrue not only to the subset of customers who consume less, but to all consumers. Rate-paying customers realize DRIPE savings when price reductions across an electricity or natural gas system are passed on to all retail customers as lower rates (depending upon regulation and market structure, residual savings may be wholly or partially retained by utilities). DRIPE savings, though seemingly small in terms of percent price reductions or dollars per household, can amount to hundreds of millions of dollars per year across entire states or grids. Therefore, accurately assessing DRIPE benefits can help to ensure appropriate programs are designed and implemented for energy efficiency measures. This paper reviews the existing knowledge and experience from select U.S. states regarding DRIPE (including New York and Ohio), and the potential for expanded application of the concept of DRIPE by regulators. Policymakers and public utility commissions have a critical role to play in setting the methodology for determining DRIPE, encouraging its capture by utilities, and allocating DRIPE benefits among utilities, various groups of customers, and/or society at large. While the methodologies for estimating DRIPE benefits are still being perfected, policymakers can follow the examples of states such as Maryland and Vermont in including conservative DRIPE estimates in their resource planning.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Impact of Wildfires on Solar Generation, Reserves and Energy Prices
Wildfire seasons in the Western U.S. become more prolonged and intense in recent years bringing significant variability and uncertainty of solar generation. It greatly challenges the bulk power system and electricity market operation managed by California Independent System Operator (CAISO) as California leads both the wildfire records and the solar power integration. This study presents a screening-level analysis of the impact of wildfires on solar generation, operating reserve and energy prices applying historical real-world wildfire and market operation data. To the best of the authors knowledge, it is a first-of-its-kind study and will lay the foundation for market impact quantification and wildfire mitigation strategies design based on projected wildfire activities in future years.
electricity price↗
Pricing Strategy of Electric Vehicle Aggregators Based on Locational Marginal Price to Minimize Photovoltaic (PV) Curtailment
The global climate crisis demands urgent action to mitigate global warming. Using renewable energy sources, such as solar and wind power, for electricity generation is crucial. This shift from centralized to distributed power systems, however, brings challenges, including voltage fluctuations and renewable energy curtailment. The rapid growth of the electric vehicle (EV) industry adds complexity, increasing overall electricity demand and straining the power supply during peak charging times. This paper proposes a scheduling strategy for EV aggregators to reduce renewable energy curtailment and stabilize grid operation by strategically scheduling EV charging. Using Multi -Agent Transport Simulation (MATSim), a traffic simulation tool, EV driving data in Denver, Colorado, USA, were modeled. The EV aggregator adjusts charging fees based on locational marginal prices, encouraging EVs to charge at different stations according to pricing. Simulations on an IEEE 33-bus system with distributed energy resources and EV charging stations validate the proposed algorithm, demonstrating its effectiveness in reducing curtailment by 12.55% and stabilizing grid operation.
33 ADVANCED PROPULSION SYSTEMS↗
Technology advances needed for photovoltaics to achieve widespread grid price parity: Widespread grid price parity for photovoltaics
To quantify the potential value of technological advances to the photovoltaics (PV) sector, this paper examines the impact of changes to key PV module and system parameters on the levelized cost of energy (LCOE). The parameters selected include module manufacturing cost, efficiency, degradation rate, and service lifetime. NREL's System Advisor Model (SAM) is used to calculate the lifecycle cost per kilowatt-hour (kWh) for residential, commercial, and utility scale PV systems within the contiguous United States, with a focus on utility scale. Different technological pathways are illustrated that may achieve the Department of Energy's SunShot goal of PV electricity that is at grid price parity with conventional electricity sources. In addition, the impacts on the 2015 baseline LCOE due to changes to each parameter are shown. These results may be used to identify research directions with the greatest potential to impact the cost of PV electricity.
14 SOLAR ENERGY↗
Evaluating the Impact of Third-Party Price Reporting and other Drivers on Residential Photovoltaic Price Estimates
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14 SOLAR ENERGY↗
The relation between petroleum product prices and crude oil prices
Not Available
Ederington, Louis H.↗