Research on wind-solar-storage system optimal scheduling based on multi-objective particle swarm algorithm

IF 4.6 Q1 OPTICS
Huitong Ru, Jisheng Zhao, Yi Sui, Zhe Huang, Zhouyang Chen, Bo Fan, Bo Wang
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引用次数: 0

Abstract

Abstract For the current problems that economic benefit and energy saving can not be considered in the existing optimal scheduling schemes of wind-solar-diesel-storage system, this paper proposes an optimal scheduling scheme of this system based on multi-objective particle swarm optimization. The mathematical models of active power output of wind turbines, solar turbines and energy storage units of wind-solar-diesel-storage system are established respectively. According to the historical data of wind power generation and photovoltaic power generation, the power prediction data of corresponding prediction models are adopted respectively On this basis, the objective function is constructed by minimizing the operating cost of the system and maximizing the wind-solar consumption ratio, and the system active power, energy storage battery capacity and diesel generator power are taken as constraints Using multi-objective particle swarm optimization algorithm to solve the output of each unit in the system. Simulation results illustrate the optimization scheme can well achieve the target of taking into account both economic benefits and energy saving in the optimal scheduling of wind-solar-diesel-storage system.
基于多目标粒子群算法的风能-太阳能-储能系统优化调度研究
摘要针对现有风能-太阳能-柴油-储能系统优化调度方案不能兼顾经济效益和节能的问题,提出了一种基于多目标粒子群算法的风能-太阳能-柴油-储能系统优化调度方案。分别建立了风力发电机组、太阳能发电机组和风能-太阳能-柴油-储能系统储能单元的有功输出数学模型。根据风电和光伏发电的历史数据,分别采用相应预测模型的功率预测数据。在此基础上,以系统运行成本最小、风光利用比最大为目标函数,构建系统有功功率,以蓄电池容量和柴油发电机组功率为约束条件,采用多目标粒子群优化算法求解系统中各机组的输出。仿真结果表明,该优化方案能够较好地实现风能-太阳能-柴油-储能系统优化调度中兼顾经济效益和节能的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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