Policy, Markets, and Analytics
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Item A Graph-theoretic Approach to Scenario Reduction for Stochastic Generation Expansion Problems(2025-01-07) Blumsack, Seth; Shaddel, SaraIn operations research and optimization, stochastic programming plays a pivotal role in decision-making under uncertainty. However, solving complex stochastic programs, especially with many scenarios, is computationally challenging. This paper introduces a novel graph-based scenario reduction approach, using bipartite graph theory and community detection algorithms to create a smaller, representative set of scenarios. Unlike many traditional methods, this approach determines the optimal number of scenarios endogenously, improving computational efficiency and robustness. We applied this graph-based method to a two-stage stochastic programming model for power generation expansion planning (GEP), initially comprising 2000 scenarios. Our approach successfully reduced the scenario set while maintaining solution quality. We compare our method with four other techniques—K-means clustering, the Approximate Latent Factor Algorithm (ALFA), Backward Reduction, and Forward Selection. On the GEP problem, the graph-based method yields improved robustness as compared to other methods.Item Designing Carbon Policy with Profit-Maximising Energy Storage(2025-01-07) Siddiqui, Afzal; Sioshansi, RamteenWe examine carbon-policy design for a power system with energy storage as well as renewable and fossil-fuelled generation. A central-planning solution internalises the environmental externality of carbon emissions and curbs fossil-fuelled generation in proportion to the marginal cost of damage. By contrast, a decentralised solution leads to a bi-level setup: an upper-level welfare-maximising policymaker sets a carbon tax to impose upon lower-level profit-maximising generators. For completely efficient storage, an optimal carbon tax in this bi-level setting renders the first-best outcome. However, with inefficient storage, an infinitesimal increase in the carbon tax induces prices to increase at the same rate. As a result, storage shifts energy to the off-peak period to offset the loss in the value of stored energy. Hence, relative to the marginal cost of damage from emissions under central planning, the optimal carbon tax for the decentralised case is lower and may be nonmonotonic in energy storage’s inefficiency.Item Evaluating the Benefits of Long Duration Storage: Modeling and Analysis of a Seasonal Energy Storage System against a Decarbonizing Power System(2025-01-07) Kumthekar, Ninad; Rudkevich, Aleksandr; Silvers, Joseph; Tabors, Richard; Swearingen, SidneyLong Duration Energy Storage (LDES) will play a critical role in successful decarbonization of the electric sector. LDES is needed to manage weather-driven energy needs and resources across time and will significantly reduce the required investment in energy production and transmission. LDES benefits accrue at multiple time scales ranging from days to seasons to years. An adequate evaluation of these benefits is a challenging analytical task as it requires the use of multiple modeling techniques: (1) Capacity Expansion Modeling to size and site LDES based on its impact on future generation and transmission; (2) Operational Scheduling to ration use of storage inventory across time; and (3) Energy and Ancillary Services Modeling to emulate storage operations at daily and intra-day timescales. In this paper, we discuss the coordination of these three models to assess LDES within a large regional electricity market. Our methodology uses sequentially optimized and coordinated decision cycles. From this, we provide a range of simulated metrics assessing benefits of a large-scale LDES at various time scales.Item Trade-offs between Battery Energy Storage and Hydrogen Storage in Off-Grid Green Hydrogen Systems(2025-01-07) Peng, Jing; Vijayshankar, Sanjana; King, Jennifer; Mathieu, JohannaGreen hydrogen, produced using renewables through electrolysis, can be used to reduce emissions in the hard-to-abate industrial sector. Efficient production and large-scale deployment require storage to mitigate electrolyzer degradation and ensure stable hydrogen supply. This paper explores the impacts and trade-offs of battery and hydrogen storage in off-grid wind-to-hydrogen systems, considering degradation of batteries and electrolyzers. Utilizing an optimization model, we examine system performance and costs over a wide range of storage capacities and wind profiles. Our results show that batteries smooth short-term fluctuations and minimize electrolyzer degradation but can experience significant degradation resulting from frequent charge/discharge cycles. Conversely, hydrogen storage provides long-term energy buffering, essential for sustained hydrogen production, but can increase electrolyzer cycling and degradation. Combining battery and hydrogen storage enhances system reliability, reduces component degradation, and reduces operational costs. This highlights the importance of strategic storage investments to improve the performance and costs of green hydrogen systems.Item Stochastic Nodal Adequacy Pricing (SNAP) Platform: A Methodology for Dealing with Weather and Operational Uncertainty in Market Operations(2025-01-07) Tabors, Richard; Yanikara, Sel; Rudkevich, Aleksandr; Philbrick, RussA probabilistic extension of electricity production cost minimization tools that supports the use of high-fidelity models is introduced. These tools introduced are able to accurately simulate the temporal and spatial relationships affecting system physics and economics. The ability to use high-fidelity models enables accurate calculation of dual variables and their use in defining reliability metrics that accurately represent the economic and engineering characteristics of all resources. In particular, the use of dual variables captures impacts of time-coupled resources and constraints such as storage and limited fuel supply. By bringing economic metrics directly into reliability analysis, we can supplement traditional reliability metrics with economically justified operations. These techniques and their computational performance are illustrated using a high-fidelity model of a real-sized US market, more specifically ERCOT (Electric Reliability Council of Texas). The model includes MIP based security-constrained unit commitment, realistic operational details, and co-optimization of energy and reserves.Item An Econometric Analysis of Large Flexible Cryptocurrency-mining Consumers in Electricity Markets(2025-01-07) Majumder, Subir; Aravena, Ignacio; Xie, LeIn recent years, power grids have seen a surge in large cryptocurrency mining firms, with individual consumption levels reaching 700MW. This study examines the behavior of these firms in Texas, focusing on how their consumption is influenced by cryptocurrency conversion rates, electricity prices, local weather, and other factors. We transform the skewed electricity consumption data of these firms, perform correlation analysis, and apply a seasonal autoregressive moving average model for analysis. Our findings reveal that, surprisingly, short-term mining electricity consumption is not directly correlated with cryptocurrency conversion rates. Instead, the primary influencers are the temperature and electricity prices. These firms also respond to avoid transmission and distribution network (T&D) charges - commonly referred to as four Coincident peak (4CP) charges - during the summer months. As the scale of these firms is likely to surge in future years, the developed electricity consumption model can be used to generate public, synthetic datasets to understand the overall impact on the power grid. The developed model could also lead to better pricing mechanisms to effectively use the flexibility of these resources towards improving power grid reliability.Item AC-Network-Informed DC Optimal Power Flow for Electricity Markets(2025-01-07) Constante Flores, Gonzalo; Quisaguano, André; Conejo, Antonio; Li, CanThis paper presents a parametric quadratic approximation of the AC optimal power flow (AC-OPF) problem for time-sensitive and market-based applications. The parametric approximation preserves the physics-based representation provided by the DC-OPF model and leverages market and physics information encoded in the data-driven demand-dependent parameters. To enable the deployment of the proposed model for real-time applications, we propose a supervised learning approach to predict near-optimal parameters, given a certain metric concerning the dispatch quantities and locational marginal prices (LMPs). The training dataset is generated based on the solution of the AC-OPF problem and a bilevel optimization problem, which calibrates parameters satisfying two market properties: cost recovery and revenue adequacy. We show the proposed approach's performance in various test systems in terms of cost and dispatch approximation errors, LMPs, market properties satisfaction, dispatch feasibility, and generalizability with respect to N-1 network topologies.Item Winners and Losers from Vertical Integration Between Natural-Gas and Electricity Markets(2025-01-07) Morey, Danielle; Fischer, Michelle; Sioshansi, RamteenElectricity systems in many parts of the world are becoming more dependent upon natural gas as an electricity-generation fuel. As such, electricity and natural-gas markets are becoming more interconnected. Contemporaneously, some electricity and natural-gas markets are vertically integrating, through the merger of electricity and natural-gas suppliers. The market-efficiency impacts of this vertical integration is unclear. On one hand, vertical integration could exacerbate market power, whereas on another it could mitigate double marginalization. To study this question, this paper develops a Nash-Cournot model of the two interconnected markets. The model is converted into a linear complementarity problem, which allows deriving Nash equilibria readily. The model is applied to a stylized example with a range of parameter values. We find that integration is social-welfare enhancing---which implies that mitigating double marginalization outweighs the exercise of market power. In most cases, the effects of merger can give rise to a prisoner's-dilemma-type outcome, whereby firms have strong incentives to merge but merger is detrimental to producers. Overall, our results suggest that vertical integration in energy markets may be socially beneficial.Item Introduction to the Minitrack on Policy, Markets, and Analytics(2025-01-07) Oren, Shmuel; Parvania, Masood