Particle Swarm Optimization Of a Hybrid Wind/Tidal/PV/Battery Energy System. Application To a Remote Area In Bretagne, France

Omar Hazem Mohammed , Yassine Amirat , Mohamed Benbouzid
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引用次数: 64

Abstract

A new method proposed in this work to optimize the power generated by a hybrid renewable energy system which consists of Wind turbine/Tidal turbine/PV module/Batteries. This system has been designed to satisfy a stand-alone area in Brittany, France, as an example of load demand. The Particle Swarm Optimization technique (PSO) was proposed and developed to minimize the cost of energy. Where the ability of this algorithm was developed to reach the best results in double speeds, at a time rate better than 80% of conventional technology time and less than 20 repetitions only. The problem is defined as an economic problem, taking into consideration the optimal sizing of the system, high reliability, planning expansion for future development, the state of charge of the battery. The total net present cost (TNPSC) is introduced as the objective function, taking into consideration the minimum fitness values in the particle swarm process. The (PSO) algorithm developed has several characteristics and advantages over other traditional techniques and algorithms. In fact, it allows to achieve the optimal solution and to reduce the overall cost with high speed and accuracy. The PSO algorithm program was developed using MATLAB software.

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风能/潮汐能/光伏/电池混合能源系统的粒子群优化。在法国布列塔尼偏远地区的应用
本文提出了一种新的方法来优化由风力涡轮机/潮汐涡轮机/光伏组件/电池组成的混合可再生能源系统的发电量。该系统设计用于满足法国布列塔尼的一个独立地区,作为负载需求的一个例子。提出并发展了粒子群优化技术(PSO),使能量成本最小化。该算法能够以双倍速度达到最佳结果,时间速率优于传统技术时间的80%,并且重复次数少于20次。该问题被定义为一个经济问题,考虑到系统的最优规模、高可靠性、规划未来发展的扩展、电池的充电状态。引入总净当前成本(TNPSC)作为目标函数,考虑粒子群过程中的最小适应度值。与其他传统的技术和算法相比,所开发的粒子群算法具有许多特点和优点。事实上,它可以实现最佳解决方案,并以高速度和准确性降低整体成本。利用MATLAB软件开发了粒子群算法程序。
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