电碳耦合环境下考虑水电调节能力的区域电网风光容量优化配置OA北大核心
Optimal Allocation of Wind and Solar Capacity in Regional Power Grids Considering Hydropower Regulation Capability Under Electricity-Carbon Coupling Environment
[目的]针对当前大规模风电与光伏电站投资中普遍存在的上网受限、投资回报率偏低及碳市场激励机制缺失等问题,在电碳耦合环境下,构建了一种考虑水电调节能力的区域电网风光容量双层优化配置模型.[方法]上层模型以风电与光伏的投资回报率最大为优化目标,在综合考虑电力市场与碳市场收益的基础上,制定风光容量配置策略;下层模型中,小水电、风电与光伏组成的可再生能源群体以购电成本最小化为目标参与电力市场出清,同时考虑风光在核证自愿减排量(China certified emission reduction,CCER)市场中的碳交易结果,实现电力与碳市场的联合优化出清,模型引入合作博弈与Shapley值,量化风光水各自收益,并采用改进粒子群优化算法(improved particle swarm optimization,IPSO)嵌套CPLEX求解器实现双层结构的协同求解.[结果]仿真结果表明,不同水文场景下,风光的收益随水电调节能力的变化而变化,丰水期收益最大,枯水期则相应减少,进而影响风光全年的最优配置.引入电碳耦合市场模式后,风光系统收益显著提升,配置容量比传统电力市场模式增长约24%;碳市场机制通过价格信号有效抑制了高碳排放机组的运行行为,促进了火电机组碳排放结构的优化.CCER市场摩擦因素对碳收益存在显著削弱效应,收益最高可下降33.5%.[结论]模型突出了水电调节能力和碳市场信号对风光消纳的关键作用,有助于新能源开发和电源结构低碳化,为碳市场政策优化提供了理论依据.
[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.
赵义深;钟浩;杜涛;李迅;王振;欧阳臻辉
三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002三峡大学梯级水电站运行与控制湖北省重点实验室,湖北省 宜昌市 443002||三峡大学电气与新能源学院,湖北省 宜昌市 443002
动力与电气工程
水电调节能力电碳耦合合作博弈电力市场风光容量配置碳价敏感性市场摩擦因素
hydropower regulation capabilityelectricity-carbon couplingcooperative gameelectricity marketwind and solar capacity configurationcarbon price sensitivitymarket friction factors
《电力建设》 2025 (11)
158-172,15
湖北省自然科学基金联合基金项目(2022CFD167)This work is supported by Natural Science Foundation of Hubei Province(No.2022CFD167).
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