Electric Power System Optimization

Últimos artículos

• PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets

  Zhanhua Pan, Xin Qin, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu · 30 de septiembre de 2026

  Power markets are a natural testbed for multi-agent reinforcement learning (MARL), where multiple self-interested participants repeatedly submit bids. A market-clearing mechanism then determines dispatch and prices subject to power grid constraints and market settlement rules. However, existing MARL…

• Functional Architecture of European Electricity Trading Markets: Requirements for AI Supported Trading Systems under Regulatory Constraints

  Walter Kurz, Wojtek Stricker · 25 de septiembre de 2026

  European electricity trading in the EU operates as a constrained multi-layer system in which legal design, exchange microstructure, and network physics are executed jointly across forward, day-ahead, intraday, and balancing horizons. This paper develops a functional architecture for AI-supported tra…

• Differentiable Electricity-Market Clearing for Gradient-Based Planning

  Luca Mungo, Maarten P. Scholl, Arnau Quera-Bofarull · 3 de septiembre de 2026

  Planning a large data center is difficult because a facility big enough to matter changes the electricity prices it will pay. Those prices are set by market clearing, a constrained optimization problem solved anew in every operating condition. However, simulating the market tells a planner how a can…

• AI agents in Algorithmic Electricity Markets: On the Emergence of Tacit Collusion

  Jakub Seredy\'nski, Georgios Tsaousoglou · 28 de agosto de 2026

  As electricity market participants increasingly adopt learning-based agents for their bidding strategies, electricity markets are becoming algorithmic. Evidence from algorithmic markets in other domains shows that tacit collusion can arise purely through independent learning. Moreover, electricity m…

• EU-ETS under attack? The impact of carbon price suppression on the decarbonization of the power sector

  Javier Gonzalez-Ruiz, Carlos Rodriguez-Pardo, Alice Di Bella, Paolo Mastropietro, Jose Pablo Chavez-Avila, Massimo Tavoni · 14 de agosto de 2026

  European countries are debating policies to mitigate the increased energy costs caused by renewed geopolitical tensions, while pursuing decarbonization and electrification. A notable example is Italy's 2026 Decreto Bollette package, which proposes to remove the carbon price equivalent from the bids …

• Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch

  Yangze Zhou, Yihong Zhou, Thomas Morstyn, Yi Wang · 8 de julio de 2026

  The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for robust power system dispatch. DRO relies on forecast scenarios to construct ambiguity sets, but conventional scenario generation pipelines are often trai…

• Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

  Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang · 25 de junio de 2026

  The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity. As traditional optimization methods struggle with such uncertainty and complexity of …

• Analysing drivers and interdependencies in European electricity markets using XAI

  Antoine Pesenti, Aidan O'Sullivan · 18 de junio de 2026

  Electricity markets are inherently complex systems characterised by strong nonlinearities, high-dimensional interactions, and increasing interdependence across regions. While deep neural networks (DNNs) have demonstrated strong predictive capabilities for electricity prices, their lack of interpreta…

• S3TS: Stochastic Scenario-Structured Tree Search for Advanced Planning Under Uncertainty

  Fabio Pavirani, Bert Claessens, Pierre Pinson, Chris Develder · 2 de junio de 2026

  Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance, optimizing the dispatch of generation units and storage systems. An effective planning strategy must (a) accommodate advanced and potentially non-l…

• Will the Carbon Border Adjustment Mechanism Impact European Electricity Prices? A GNN-Based Network Analysis

  Jiachen Shen, Jian Shi, Dan Wang, Han Zhu · 6 de mayo de 2026

  The European Union's Carbon Border Adjustment Mechanism (CBAM) creates a complex challenge for the interconnected European electricity market. Traditional static analyses often miss the cross-border spillover effects that are vital for understanding this policy. This paper addresses this gap by deve…

• Self-Certifying Primal-Dual Optimization Proxies for Large-Scale Batch Economic Dispatch

  Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck · 14 de abril de 2026

  Recent research has shown that optimization proxies can be trained to high fidelity, achieving average optimality gaps under 1% for large-scale problems. However, worst-case analyses show that there exist in-distribution queries that result in orders of magnitude higher optimality gap, making it dif…

• A Dual-Positive Monotone Parameterization for Multi-Segment Bids and a Validity Assessment Framework for Reinforcement Learning Agent-based Simulation of Electricity Markets

  Zunnan Xu, Zhaoxia Jing, Zhanhua Pan · 14 de abril de 2026

  Reinforcement learning agent-based simulation (RL-ABS) has become an important tool for electricity market mechanism analysis and evaluation. In the modeling of monotone, bounded, multi-segment stepwise bids, existing methods typically let the policy network first output an unconstrained action and …

• Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees

  Guangwen Wang, Jiaqi Wu, Yang Weng, Baosen Zhang · 6 de abril de 2026

  The growing number of individual generating units, hybrid resources, and security constraints has significantly increased the computational burden of network-constrained unit commitment (UC), where most solution time is spent exploring branch-and-bound trees over unit-hour binary variables. To reduc…

• Efficient reformulations of ReLU deep neural networks for surrogate modelling in power system optimisation

  Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg, Julian Lesmos-Vinasco · 22 de enero de 2026

  The ongoing decarbonisation of power systems is driving an increasing reliance on distributed energy resources, which introduces complex and nonlinear interactions that are difficult to capture in conventional optimisation models. As a result, machine learning based surrogate modelling has emerged a…

• Assessing Long-Term Electricity Market Design for Ambitious Decarbonization Targets using Multi-Agent Reinforcement Learning

  Javier Gonzalez-Ruiz, Carlos Rodriguez-Pardo, Iacopo Savelli, Alice Di Bella, Massimo Tavoni · 22 de diciembre de 2025

  Electricity systems are key to transforming today's society into a carbon-free economy. Long-term electricity market mechanisms, including auctions, support schemes, and other policy instruments, are critical in shaping the electricity generation mix. In light of the need for more advanced tools to …