Improving Sequential Market Coordination via Value-oriented Renewable Energy Forecasting

2024-05-15

AI Summary

In power systems with high renewable energy source (RES) penetration, poor coordination between day-ahead (DA) and real-time (RT) markets leads to elevated total system costs. Method: This paper proposes a value-oriented end-to-end RES forecasting framework that directly embeds market clearing objectives into the prediction model’s loss function. We derive a piecewise-linear, differentiable joint optimization loss and enable co-training of RES forecasting and DA bidding decisions via differentiable linear programming. Unlike conventional expectation-based deterministic forecasting, our approach endogenously aligns RES bid quantities with market incentives and constraints. Contribution/Results: Numerical experiments demonstrate that the proposed method significantly reduces the total DA–RT operational cost compared to benchmark approaches, thereby enhancing market efficiency and improving RES utilization and economic value.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Large penetration of renewable energy sources (RESs) brings huge uncertainty into the electricity markets. The current deterministic clearing approach in the day-ahead (DA) market, where RESs participate based on expected production, has been criticized for causing a lack of coordination between the DA and real-time (RT) markets, leading to high overall operating costs. Previous works indicate that improving day-ahead RES entering quantities can significantly mitigate the drawbacks of deterministic clearing. In this work, we propose using a trained forecasting model, referred to as value-oriented forecasting, to determine RES Improved Entering Quantities (RIEQ) more efficiently during the operational phase. Unlike traditional models that minimize statistical forecasting errors, our approach trains model parameters to minimize the expected overall operating costs across both DA and RT markets. We derive the exact form of the loss function used for training, which becomes piecewise linear when market clearing is modeled by linear programs. Additionally, we provide the analytical gradient of the loss function with respect to the forecast, enabling an efficient training strategy. Numerical studies demonstrate that our forecasts significantly reduce overall operating costs for deterministic market clearing compared to conventional forecasts based on expected RES production.

Problem

• Reduce uncertainty in electricity markets from renewable energy sources
• Improve coordination between day-ahead and real-time market operations
• Minimize overall operating costs via value-oriented renewable energy forecasting

Innovation

• Value-oriented forecasting minimizes operating costs
• Piecewise linear loss function for market clearing
• Analytical gradient enables efficient model training