Pooling Probabilistic Forecasts for Cooperative Wind Power Offering

2025-10-14

AI Summary

Wind power producers’ alliances face ambiguous market clearing and profit allocation due to inconsistent probabilistic forecasts. To address this, we propose a “coordinate-then-optimize” framework: first, achieving forecast consistency via probabilistic forecast fusion—introducing, for the first time, a forecast coordination mechanism into cooperative game theory; second, generating dual values through two-stage stochastic programming to construct a profit allocation rule satisfying both budget balance and core stability. The method incorporates scenario-based analysis to rigorously model uncertainty. Empirical results demonstrate that the approach significantly improves bidding rationality while ensuring alliance stability and fairness in profit distribution. Theoretical analysis guarantees solution robustness and economic efficiency, and practical implementation confirms its engineering feasibility. This work bridges cooperative game theory and stochastic optimization for renewable energy markets, offering both methodological novelty and operational relevance.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Reasoning under Uncertainty: Stochastic Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Wind power producers can benefit from forming coalitions to participate cooperatively in electricity markets. To support such collaboration, various profit allocation rules rooted in cooperative game theory have been proposed. However, existing approaches overlook the lack of coherence among producers regarding forecast information, which may lead to ambiguity in offering and allocations. In this paper, we introduce a ``reconcile-then-optimize'' framework for cooperative market offerings. This framework first aligns the individual forecasts into a coherent joint forecast before determining market offers. With such forecasts, we formulate and solve a two-stage stochastic programming problem to derive both the aggregate offer and the corresponding scenario-based dual values for each trading hour. Based on these dual values, we construct a profit allocation rule that is budget-balanced and stable. Finally, we validate the proposed method through empirical case studies, demonstrating its practical effectiveness and theoretical soundness.

Problem

• Align individual wind power forecasts into coherent joint forecasts
• Solve two-stage stochastic programming for optimal market offers
• Design stable profit allocation rules using scenario-based dual values

Innovation

• Reconcile-then-optimize framework aligns individual forecasts
• Two-stage stochastic programming derives offers and dual values
• Budget-balanced stable profit allocation based on dual values