A Two-Stage Stochastic Programming Approach to Unit Commitment with Wind Power Integration: A Novel Pricing Scheme

With high wind power penetration, power system operations face significant uncertainty, rendering traditional pricing mechanisms inadequate for stochastic dispatch environments and hindering the sustainable development of power systems with high renewable energy integration. This paper systematically compares three electricity pricing schemes—system marginal pricing, conservative pricing, and the proposed average pricing—within a two-stage stochastic unit commitment framework. It is found that system marginal pricing behaves as an ex post pricing method dependent on scenario realizations and lacks stability, whereas conservative pricing degenerates into a scheme based on the minimum wind output scenario, leading to higher and more volatile prices. To address these issues, this paper proposes a novel “Average Pricing” method, in which the day-ahead price is defined as the expected value of marginal prices across all wind power scenarios. Theoretical analysis and numerical simulations on the IEEE 39-bus system demonstrate that the proposed method offers both economic interpretability and numerical stability, with mean prices ranging from 14.0739 to 15.9825 and standard deviations ranging from 16.6323 to 19.9471 across four seasonal cases. Compared with conservative pricing, it achieves lower mean prices in three seasons and lower price volatility in three seasons while maintaining a unique day-ahead price and providing a novel and sustainable pathway for pricing design in power systems with high renewable energy integration.