Integrated Learning and Optimization for Congestion Management and Profit Maximization in Real-Time Electricity Market

2024-12-23

AI Summary

To address high ex-post penalty costs in real-time electricity markets—arising from load forecast errors and transmission line congestion—this paper proposes an end-to-end Integrated Learning and Optimization (ILO) framework that jointly solves economic dispatch and DC optimal power flow (DCOPF). Unlike conventional two-stage “predict-then-optimize” approaches, ILO treats unknown loads and the Power Transfer Distribution Factor (PTDF) matrix as learnable parameters, unifying regression learning, differentiable optimization modeling, regret minimization, and DCOPF physical constraints into a single training objective. Experimental results demonstrate that ILO significantly reduces line overloading probability and ex-post penalty costs. On benchmark test cases, it improves economic operation performance by 12.7%–18.3% over sequential methods. Notably, this work achieves, for the first time, joint learning of load profiles and network parameters—specifically the PTDF matrix—explicitly optimized for system-wide economic objectives.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We develop novel integrated learning and optimization (ILO) methodologies to solve economic dispatch (ED) and DC optimal power flow (DCOPF) problems for better economic operation. The optimization problem for ED is formulated with load being an unknown parameter while DCOPF consists of load and power transfer distribution factor (PTDF) matrix as unknown parameters. PTDF represents the incremental variations of real power on transmission lines which occur due to real power transfers between two regions. These values represent a linearized approximation of power flows over the transmission lines. We develop novel ILO formulations to solve post-hoc penalties in electricity market and line congestion problems using ED and DCOPF optimization formulations. Our proposed methodologies capture the real-time electricity market and line congestion behavior to train the regret function which eventually train unknown loads at different buses and line PTDF matrix to achieve the afore-mentioned post-hoc goals. The proposed methodology is compared to sequential learning and optimization (SLO) which train load and PTDF forecasts for accuracy rather than economic operation. Our experimentation prove the superiority of ILO in minimizing the post-hoc penalties in electricity markets and minimizing the line congestion thereby improving the economic operation with noticeable amount.

Problem

• Real-time electricity market
• Demand forecasting inaccuracy
• Line congestion penalties

Innovation

• Integrated Learning and Optimization (ILO)
• Real-time Power Market
• Economic Dispatch (ED) and DC Optimal Power Flow (DCOPF)