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首页> 外文期刊>Journal of Energy Storage >Optimizing operation of a photovoltaic/diesel generator hybrid energy system with pumped hydro storage by a modified crow search algorithm
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Optimizing operation of a photovoltaic/diesel generator hybrid energy system with pumped hydro storage by a modified crow search algorithm

机译:改进的乌鸦搜索算法优化带抽水蓄能的光伏/柴油发电机混合能源系统的运行

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摘要

In a hybrid energy system composed of photovoltaic (PV), diesel generator and pumped hydro storage (PHS), to attain minimum fuel consumption, system operation should be optimized. This paper focuses on optimizing operation of a PV/diesel/PHS hybrid energy system by a modified crow search algorithm (CSA). For this aim, fuel consumption during the considered period is defined as the objective function. At deficit conditions (when PV generation is less than load demand), percent of deficit power which should be supplied by PHS is considered as decision variable. To effectively solve this difficult optimization problem, CSA with an adaptive chaotic awareness probability (CSA(AC-AP)) has been proposed. Simulated results reveal that the proposed CSA(AC-AP) produces more accurate and robust results than genetic algorithm (GA), particle swarm optimization (PSO) and original CSA. Moreover, optimal sharing of deficit power between diesel generator and PHS leads to having minimum operation cost.
机译:在由光伏(PV),柴油发电机和抽水蓄能(PHS)组成的混合能源系统中,为了获得最低的燃料消耗,应优化系统运行。本文致力于通过改进的乌鸦搜索算法(CSA)优化光伏/柴油/ PHS混合能源系统的运行。为此,将考虑期间的油耗定义为目标函数。在亏空条件下(当光伏发电量小于负荷需求时),应由PHS提供的亏空功率百分比被视为决策变量。为了有效地解决这一难题,提出了一种具有自适应混沌感知概率的CSA(CSA(AC-AP))。仿真结果表明,与遗传算法(GA),粒子群优化(PSO)和原始CSA相比,提出的CSA(AC-AP)产生了更准确,更可靠的结果。此外,柴油发电机和PHS之间的赤字功率的最佳分配导致运行成本最小。

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