摘要
以风电、光伏、光热构成新能源互补发电模式,计及风、光资源消纳,建立将高载能负荷作为可调度资源参与新能源互补发电系统的二层优化调度模型,采用NSGA-Ⅱ和二进制粒子群算法求解模型。上层优化模型以互补系统输出功率波动最小和并网效益最大为优化指标确定各机组的出力,并且计算出弃风弃光量。在此基础上,下层优化模型针对上层优化模型造成的弃风弃光量,选取能够有效消纳风、光的高载能负荷参与电网调度。最后,以甘肃地区实际数据为例,验证了所提方法的可行性和有效性。仿真结果表明,光热电站的参与增加了44.3%的经济效益,减小了65.8%的输出功率波动,高载能负荷的参与减小了86.3%的弃风弃光量。
This paper makes a new energy hybrid power generation model,composed of wind power,photovoltaics,and concentrating solar power(CSP).Considering the consumption of wind and solar resources,a two-tier optimal scheduling model that uses high-load energy loads as schedulable resources to participate in the new energy complementary power generation system has been established.The fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)with elite strategy and binary particle swarm algorithm are used to solve the two-layer model.The upper-level optimization model uses the minimum output power fluctuation of the hybrid system and the maximum grid connection benefit as the optimization indicators to determine the output of each unit,to calculates the amount of wind and solar abandonment.On this basis,the lower-level optimization model selects the high-energy loads that can effectively absorb wind and light to participate in the grid dispatching according to the amount of wind and light abandonment caused by the upper-level optimization model.Finally,the actual data in Gansu district of China verifies the feasibility and effectiveness of the proposed method.The simulation results show that the participation of CSP stations increases the economic benefits by 44.3%,the output power fluctuations drops by 65.8%,and the participation of high-energy loads reduces the amount of wind and solar abandonment by 86.3%。
作者
熊伟
马志程
张晓英
王琨
周强
陈伟
Xiong Wei;Ma Zhicheng;Zhang Xiaoying;Wang Kun;Zhou Qiang;Chen Wei(College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,China;State Grid Gansu Electric Power Research Institute,Lanzhou 730000,China;State Grid Gansu Electric Power Company Marketing Service Center,Department of Metering,Lanzhou 730000,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2022年第7期39-48,共10页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(51867015)
甘肃省科技重大专项计划(19ZD2GA003)。
关键词
调度
遗传算法
多目标优化
消纳
二层规划
高载能负荷
scheduling
genetic algorithms
multi objective optimization
consumption
two-layer optimization
high energy load
