Novel Electricity Spot Market Trading Mechanism Considering Stochastic Scenarios of Renewable Energy

With the continuous improvement of China’s electricity trading mechanism, encouraging renewable-energy sources to jointly participate in the energy-only spot market represents an inevitable trend for future development. Nevertheless, when renewable-energy power producers take part in the spot market, they are confronted with dual uncertainties originating from volatile renewable-energy output and fluctuating spot electricity prices, which degrade the stability of their revenue. To address these challenges, this paper designs a novel electricity spot market trading mechanism that accounts for stochastic scenarios of renewable energy. First, for the stochastic-scenario generation of renewable-energy output, both the interval and temporal characteristics of prediction errors are taken into consideration. Kernel density estimation and Markov chain modeling are adopted, followed by scenario reduction using an improved K-means algorithm to generate day-ahead stochastic scenarios of renewable-energy output for calculation. Subsequently, an operation optimization model for a multi-scenario coordinated complementary system consisting of wind, photovoltaic and thermal power is established. To enhance computational efficiency, a heuristic ant colony algorithm is employed for global-space search. Dynamic parameter adjustment of the pheromone volatilization factor is implemented to strengthen path searching capability and search probability to achieve accurate global-space search. Different from deterministic and robust scheduling frameworks, the proposed trading mechanism embeds high-precision stochastic scenarios into spot market optimization to balance renewable-energy revenue stability, system operation cost and rational market price signals. Finally, numerical case studies are carried out to verify and analyze the robustness and effectiveness of the proposed model. Numerical results demonstrate that compared with benchmark approaches, the proposed method reduces time-series RMSE by 11.29 MW, raises the scenario coverage rate by 8.73%, increases the comprehensive revenue of the renewable-energy base by 31.43%, and cuts the wind–solar curtailment rate by 6.95%.