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【目的】为解决高比例风电、光伏接入电网导致的功率间歇性、波动性及弃风弃光问题,提高系统运行经济性与可再生能源消纳能力,需要建立一套综合的优化调度模型。【方法】本文提出风-光-储综合能源系统日前调度框架,集成数据驱动预测与模型优化。利用MATLAB神经网络构建多输入时序模型,输入历史风电光伏出力、电负荷及气象数据,输出24 h的小时级预测值。并基于预测数据,以福建省泉州市某区域系统为对象,建立以最小化总运行成本为目标的调度模型,采用MATLAB+CPLEX求解方式进行综合分析验证。最后设计了5组场景(场景1:储能、需求响应和售电均无;场景2:只增加储能;场景3:增加储能和需求响应;场景4:增加储能和售电;场景5:增加储能、需求响应和售电),对比使用该模型后的关键指标。【结果】对比场景1和场景2,场景2总成本降低约500元,购售电成本减少约600元,降幅12%;场景2弃风弃光缓解,但受限于储能容量未完全消除弃风弃光。对比场景2和场景3,场景3通过分时电价引导负荷转移,总运行成本进一步降低约0.4%。对比场景3、场景4和场景5,场景5总成本显著降低约7 600元,引入售电机制很大程度上解决弃风弃光问题。综合对比,场景5效果最优,增加储能配置、价格型需求响应及向电网售电的组合策略可使系统运行总成本降低28%、弃风弃光降为0、负荷波动降低10%,提升了系统经济性与消纳能力。【结论】数据预测与优化协同的调度框架可有效应对可再生能源不确定性,储能配置、价格型需求响应及向电网售电的组合策略能客观降低系统运行成本、消除弃风弃光、平抑负荷波动,提升经济性与消纳能力。
Abstract：[Objective] In order to solve the problems of power intermittence, fluctuation and wind and light curtailment caused by high proportion of wind power and photovoltaic access to the power grid, and to improve the system operation economy and renewable energy consumption capacity, it is necessary to establish a comprehensive optimal scheduling model. [Methods] This paper proposes a day-ahead scheduling framework for wind-solar-storage integrated energy system, which integrates data-driven prediction and model optimization. The multi-input time series model is constructed by MATLAB neural network, and the historical wind power photovoltaic output, electric load and meteorological data are input, and the 24-hour hourly prediction value is output. Based on the forecast data, taking a regional system in Quanzhou City, Fujian Province as the object, a scheduling model with the goal of minimizing the total operating cost is established, and the MATLAB + CPLEX solution method is used for comprehensive analysis and verification. Finally, five sets of scenarios are designed( Scenario 1: no energy storage, demand response and electricity sales. Scenario 2 : only increase energy storage. Scenario 3: Increasing energy storage and demand response. Scenario 4: increase energy storage and electricity sales. Scenario 5: Increase energy storage, demand response and electricity sales), and compare the key indicators after using the model. [Results] Compared with Scenario 1 and Scenario 2, the total cost of Scenario 2 is reduced by about 500 yuan, and the cost of purchasing and selling electricity is reduced by about 600 yuan, a decrease of 12%; in Scenario 2, the curtailment of wind and light is alleviated, but limited by the energy storage capacity, the curtailment of wind and light is not completely eliminated. Compared with Scenario 2 and Scenario 3, Scenario 3 guides load transfer through time-of-use electricity price, and the total operating cost is further reduced by about 0.4%. Compared with Scenario 3, Scenario 4 and Scenario 5, the total cost of Scenario 5 is significantly reduced by about 7 600 yuan, and the introduction of electricity sales mechanism greatly solves the problem of wind and light abandonment. By comprehensive comparison, scenario 5 has the best effect. The combined strategy of increasing energy storage configuration, price-based demand response and selling electricity to the power grid can reduce the total operation cost of the system by 28%, reduce the wind and light curtailment to 0, and reduce the load fluctuation by 10%, which improves the economy and consumption capacity of the system. [Conclusion] The scheduling framework of data prediction and optimization can effectively deal with the uncertainty of renewable energy. The combination strategy of energy storage configuration, price-based demand response and power sales to the power grid can objectively reduce the system operation cost, eliminate the wind and light curtailment, stabilize the load fluctuation, and improve the economy and consumption capacity.
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基本信息:
DOI：10.19944/j.eptep.1674-8069.2025.06.002
中图分类号:TK01;TM73
引用信息:
[1]赵威翰,徐进,田新,等.考虑价格型需求响应的风-光-储综合能源系统日前优化调度研究[J].电力科技与环保,2025,41(06):878-887.DOI:10.19944/j.eptep.1674-8069.2025.06.002.
基金信息:
国家自然科学基金项目(52306242)