摘要
风电、光伏的出力具有随机性、波动性以及间歇性,直接并网将会造成电站发电收益降低和电能并网波动性较大以及风、光弃电量较多等问题,降低碳减排量.抽水蓄能电站的加入可在一定程度上缓解上述问题.因此,针对风-光-抽水蓄能联合发电应用场景进行研究,建立综合考虑联合系统经济收益最大化、系统功率波动最小化以及碳减排量最大化3个目标的多目标优化模型,并通过归一化处理,将多目标问题转换成单一目标问题进行求解.采用能够实现局部搜索与全局搜索自适应调整的灰狼算法,对风电、光伏以及抽水蓄能的并网电量进行优化仿真.仿真结果表明:所建立的模型能够有效提高系统的经济收益,大大降低电能并网波动性.同时,新能源的高效利用也使得联合系统的碳减排能力显著提高,模型具备较高的可行性.
The output of wind power and photovoltaic has the characteristics of randomness,volatility,and intermittency.Direct grid connection will lead to a lower power generation income of the power station,a greater volatility of grid connection of electric energy,and more wind and photovoltaic power discards,resulting in lower carbon emission reductions.The addition of pumped storage power plants effectively reduces the above impacts.Therefore,this paper studies the application scenario of wind photovoltaic and pumped storage combined power generation,establishes a multi-objective optimization model that comprehensively considers the three objectives of maximizing the economic benefits of the combined system,minimizing the system power fluctuation,and maximizing carbon emission reduction,and converts the multi-objective problem into a single objective problem for solution by normalization.In this paper,the gray wolf algorithm,which can realize the adaptive adjustment of local search and global search,is used to simulate and optimize the grid connected power of wind power,photovoltaic,and pumped storage.The optimization results show that the established model can effectively improve the economic benefits of the system and greatly reduce the fluctuation of power grid connection.In addition,the efficient use of new energy also greatly improves the carbon emission reduction capacity of the joint system,which proves that the model has high feasibility.
作者
张良
郑丽冬
冷祥彪
吕玲
蔡国伟
ZHANG Liang;ZHENG Lidong;LENG Xiangbiao;L Ling;CAI Guowei(School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,Jilin,China;Southern Power Grid Energy Development Research Institute Co.,Ltd.,Guangzhou 510670,China)
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2024年第10期1554-1566,共13页
Journal of Shanghai Jiaotong University
基金
吉林省科技厅重点研发项目资助(20220203052SF)
吉林省科技厅国际科技合作项目(20210402080GH)。
关键词
碳减排
多目标优化
归一化处理
灰狼算法
carbon emission reduction
multi-objective optimization
normalization
gray wolf algorithm
