Value-oriented forecast reconciliation for renewables in electricity markets
This work addresses fairness and conflict issues in multi-agent renewable energy forecasting, offering a domain-specific solution for electricity market participants.
The paper tackles the problem of forecast reconciliation in electricity markets by proposing a value-oriented approach that ensures fairness among agents with heterogeneous loss functions, using a Nash bargaining framework and demonstrating increased profits for all agents in numerical experiments.
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.