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Optimal Allocation of Wind and Solar Capacity in Regional Power Grids Considering Hydropower Regulation Capability Under Electricity-Carbon Coupling Environment
ZHAO Yishen, ZHONG Hao, DU Tao, LI Xun, WANG Zhen, OUYANG Zhenhui
Electric Power Construction ›› 2025, Vol. 46 ›› Issue (11) : 158-172.
PDF(2389 KB)
PDF(2389 KB)
Optimal Allocation of Wind and Solar Capacity in Regional Power Grids Considering Hydropower Regulation Capability Under Electricity-Carbon Coupling Environment
[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 market mechanism effectively inhibits the operational behavior of high-carbon emission units through price signals and promotes the optimization of the carbon emission structure of thermal power units. The CCER market friction factor had a significant weakening effect on carbon returns， which can be reduced by up to 33.5%.[Conclusions] This model highlights the key role of hydropower regulation capacity and carbon market signals in wind and solar consumption， which is helpful for new energy development and low carbonization of power structures and provides a theoretical basis for the optimization of carbon market policy.
hydropower regulation capability / electricity-carbon coupling / cooperative game / electricity market / wind and solar capacity configuration / carbon price sensitivity / market friction factors
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Reasonable planning of new-energy installed capacity is the foundation for achieving efficient consumption of new energy. The volatility of new energy sources poses significant challenges for the safe and stable operation of the system. To mitigate the volatility of wind and solar outputs, energy storage equipment must be added to improve system stability. Current research models for new energy installation planning mostly set a fixed energy storage capacity, which makes it difficult to achieve optimal economic benefits. This study proposes a joint planning and operation optimization method for wind, solar, and storage capacities in regional power grids, considering source load matching. First, based on k-means clustering, the most representative 24×7 h typical daily data out of 8760 h in the whole year is obtained as the basic data；second, a joint optimization model for regional grid wind, solar, and energy storage capacity planning and operation is developed considering source load matching. In the model, the optimization goal is to minimize the daily average planning cost, and a source load matching constraint is added to achieve consistency in changing trends between the total generation output and the load demand to reduce the peak shaving pressure. A matching deviation penalty mechanism is designed to reduce the source load matching deviation. Finally, a case study is conducted based on a regional power grid in Jiangsu Province, and the results show that the model can reduce system operating costs, source load matching deviation, and peak pressure. This verifies the effectiveness and feasibility of the proposed model.
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In this paper, the optimal allocation model of the whole process from the feasibility evaluation to the optimal allocation decision is established. Based on the source-load matching idea, the model establishes the load matching index of wind-solar combined output; considering the new peak shaving demand caused by grid connection, the carbon reduction benefit of renewable energy power generation, and the penalty cost of electricity abandonment, the average power cost model of wind-solar combined output is constructed. Taking the maximum load matching degree and the minimum average power cost as the optimization objectives, the NSGA-Ⅱ algorithm is used to optimize the capacity allocation of wind and solar installation. The COWA operator is introduced and the game theory method is used to determine the index weight to further optimize the optimization solution set to assist decision making. This paper takes a certain region as an example to verify the validity of the model. The model is suitable for the planning and construction of regional power grid and micro-grid. By optimizing the capacity configuration of wind and solar installation, the matching degree of renewable energy output to load can be improved, and the average power cost can be reduced.
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