Interpretable Hybrid Experiment Learning-Based Simulation Analysis of Power System Planning under the Spot Market Environment

The electricity spot market plays a significant role in promoting the self-improvement of overall resource utilization efficiency power system and advancing energy conservation emission reduction. This paper analyzes compares potential impacts operations on planning, considering differences between planning methods traditional environments through theoretical analysis model comparison. Furthermore, we conduct research grid under environment with goal maximizing social benefits. Unlike pricing approach based historical price data simulation processes, data-driven that combines experimental economics machine learning is proposed, specifically using mixed empirical to simulate unit bidding strategies transactions. A for trading constructed analyze performance results environment. case study indicate proposed can enable operate well environment, maintain relatively stable nodal prices, ensure integration high proportion clean energy.