Optimal Allocation of Wind and Solar Capacity in Regional Power Grids Considering Hydropower Regulation Capability Under Electricity-Carbon Coupling Environment
摘要
[Objective] In this study， a bi-level optimal configuration model of wind and solar capacity with hydropower regulation ability was constructed for a regional power grid in an environment of electric carbon coupling， to address the carbon market considerations for current large-scale wind power and photovoltaic power stations regarding limited access to the internet， low return on investment， and lack of incentive mechanisms.[Methods] The upper model takes the maximum return on investment of wind and photovoltaic power as the optimization goal and formulates the configuration strategy for wind and photovoltaic capacity by comprehensively considering the benefits of the electricity and carbon markets. In the lower-level model， the renewable energy group， composed of small hydropower， wind power， and photovoltaics， participates in clearing the electricity market to minimize the cost of purchasing electricity. Joint optimization clearing of the electricity and carbon markets was realized considering the carbon trading results for wind and solar energies in the Chinese certified emission reduction （CCER） market. The model introduces a cooperative game and Shapley value to quantify the respective benefits of wind， solar， and water and uses the improved particle swarm optimization （IPSO） nested CPLEX solver to realize the collaborative solution of the two-layer structure.[Results] The simulation results show that under different typical scenarios， the income of the scenery varies with changes in hydropower regulation capacity. The income is the largest in the wet season， and in the dry season， it is reduced accordingly， which in turn affects the optimal configuration of the scenery throughout the year. After introducing the electricity-carbon coupled market model， the revenue of the wind-solar system was significantly improved， with the configuration capacity reaching approximately 24%， which is higher than that of the traditional electricity market model. The carbon mark