Value-oriented forecast reconciliation for renewables in electricity markets

2025-01-27

AI Summary

In renewable energy electricity markets, existing multi-agent forecasting coordination approaches neglect individual decision-making value, leading to unfair profit allocation and strategic conflicts among agents. Method: This paper proposes an agent-centric forecasting coordination framework that integrates Nash bargaining theory into the coordination modeling process to simultaneously ensure individual rationality and system-wide consistency. We further design a weighted profit allocation mechanism based on primal-dual optimization and empirical risk minimization to guarantee fair and equitable gains for all agents. Contribution/Results: Empirical evaluation in a wind power aggregation trading scenario demonstrates that the proposed method significantly improves the expected profits of all participating agents—without compromising overall forecasting accuracy—while ensuring stable and predictable revenue growth. The framework thus achieves both fairness in profit distribution and alignment of individual incentives with collective objectives.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Forecast reconciliation is considered an effective method for achieving coherence and improving forecast accuracy. However, the value of reconciled forecasts in downstream decision-making tasks has been mostly overlooked. In a multi-agent setup with heterogeneous loss functions, this oversight may lead to unfair outcomes, hence resulting in conflicts during the reconciliation process. To address this, we propose a value-oriented forecast reconciliation approach that focuses on the forecast value for individual agents. Fairness is ensured through the use of a Nash bargaining framework. Specifically, we model this problem as a cooperative bargaining game, where each agent aims to optimize their own gain while contributing to the overall reconciliation process. We then present a primal-dual algorithm for parameter estimation based on empirical risk minimization. From an application perspective, we consider an aggregated wind energy trading problem, where profits are distributed using a weighted allocation rule. We demonstrate the effectiveness of our approach through several numerical experiments, showing that it consistently results in increased profits for all agents involved.

Problem

• Renewable Energy Forecasting
• Fairness in Prediction Benefits Allocation
• Decision-making in Electricity Markets

Innovation

• Nash Bargaining Theory
• Renewable Energy Forecasting
• Fairness in Wind Energy Trading