新能源功率预测综合评价与预测-决策一体化OA
Comprehensive Evaluation for Renewable Energy Power Forecasting and Forecasting-Decision Making Integration
在新能源占比逐渐提高的新型电力系统背景下,新能源功率预测是支撑新能源高比例消纳的关键技术之一,被广泛应用于安全分析、优化调度、稳定控制、电力市场交易等.首先,对新能源功率预测技术和评价指标进行概述,分析现有评价指标的特征和适用性;其次,对现有体系下功率预测-决策的一致性问题进行分析,进而构建预测-决策-评估闭环架构;然后,总结新能源功率预测的应用场景,探讨预测价值量化评估体系,构建价值导向的新能源功率预测;最后,探讨了预测价值量化、综合评价指标、价值导向的预测技术和预测-决策一体化等未来的研究方向,以期对新型电力系统背景下新能源功率预测技术的发展提供实践思路.
In the context of new power systems with an increasing proportion of renewable energy,the renewable energy power forecasting has become one of the key technologies supporting high-proportion accommodation of renewable energy,which is widely used in such aspects as security analysis,optimal dispatching,stability control,and electricity market trading.Firstly,the renewable energy power forecasting technologies and evaluation indicators are outlined,and the characteristics and applicability of the existing evaluation indicators are analyzed.Secondly,the consistency problem between power forecasting and decision-making in the existing system is discussed,and the closed-loop forecasting-decision making-evaluation framework is constructed.Then,the application scenarios of renewable energy power forecasting are summarized;the quantitative evaluation system of forecasting values is discussed;and the value-oriented renewable energy power forecasting is constructed.Finally,future research directions are explored concerning forecasting value quantification,comprehensive evaluation indicators,value-oriented forecasting technologies,and forecasting-decision making integration,in order to provide practical ideas for the development of renewable energy power forecasting technologies in the context of new power systems.
万灿;陈燕惠;鞠平
浙江大学电气工程学院,浙江省 杭州市 310027浙江大学电气工程学院,浙江省 杭州市 310027浙江大学电气工程学院,浙江省 杭州市 310027
新能源功率预测综合评价指标价值导向决策一致性一体化
renewable energypower forecastingcomprehensive evaluation indicatorvalue-orienteddecision-makingconsistencyintegration
《电力系统自动化》 2026 (12)
16-30,15
国家自然科学基金优秀青年科学基金资助项目(52422706)国家自然科学基金面上项目(52277130)国家自然科学基金委员会-国家电网公司智能电网联合基金资助项目(U2066601). This work is supported by National Natural Science Foundation of China(No.52422706,No.52277130)and National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid(No.U2066601).
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