考虑收益公平性，提升风电交易中预测协调的实效性。
Value-oriented forecast reconciliation for renewables in electricity markets
- 用纳什谈判框架确保各主体预测收益公平
- 在风电交易中使所有参与方利润均提升
- 适合多主体电力市场中的协同决策场景
预测协调被广泛认为是实现预测层级内一致性并提升预测质量的有效方法。然而，协调后预测在下游决策任务中的实际价值常被忽视。在损失函数异质的多智能体设置下，这种忽视可能导致不公平结果，从而引发协调过程中的冲突。为此，本文提出一种面向价值的预测协调方法，聚焦于各主体自身的预测价值。通过纳什谈判框架保障公平性，将问题建模为合作博弈，各智能体在优化自身收益的同时贡献于整体协调。我们基于经验风险最小化提出了一个原始-对偶算法用于参数估计。从应用角度看，研究了聚合风电交易问题，采用加权分配规则分配利润。数值实验表明，该方法能持续提升所有参与方的利润。
原文摘要 · Abstract (English)
Forecast reconciliation is considered an effective method to achieve coherence (within a forecast hierarchy) and to improve forecast quality. 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 all 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.
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