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Techno-Economic Feasibility Analysis of Hybrid Renewable Energy System Using Improved Version of Particle Swarm Optimization

机译:改进的粒子群算法在混合可再生能源系统的技术经济可行性分析

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The present paper presents an improved version of particle swarm optimization method for obtaining unit sizing and techno-economic feasibility analysis of off-grid hybrid energy system for Sundarban region, world's largest mangrove forest located partly in West Bengal, India considering the real load data and other meteorological parameters. Initially the hybrid energy system is designed keeping in mind the load pattern and the availability of the renewable sources of that specific location. The hybrid system is designed with the combination of different renewable energy sources like wind turbines, solar panels along with battery and diesel generator to meet the localized load demand in different hours. Net present cost (NPC) and cost of energy (COE) for power generation have been considered to obtain the optimal unit sizing of the system. Emission from the hybrid system is also considered and compared with a conventional energy system in terms of emission per unit of generation and it is seen that with the implementation of this hybrid system, 0.688Kg of CO_2 emission per unit generation of electricity can be reduced while meeting the local demand. It is also seen that the improved version of particle swarm optimization technique is quite capable of solving this complex non-linear optimization problem quite efficiently.
机译:本文提出了一种粒子群优化方法的改进版本,该方法用于获取Sundarban地区的离网混合能源系统的单元大小并进行技术经济可行性分析,该地区是世界最大的红树林,部分位于印度西孟加拉邦,考虑了实际负荷数据和其他气象参数。最初,在设计混合能源系统时要牢记特定位置的负载模式和可再生资源的可用性。混合动力系统的设计结合了不同的可再生能源,例如风力涡轮机,太阳能电池板以及电池和柴油发电机,可以满足不同时间的局部负荷需求。已经考虑了用于发电的净现成本(NPC)和能源成本(COE),以获得系统的最佳单位大小。还考虑了混合动力系统的排放并将其与常规能源系统的单位发电量进行比较,可以看出,通过实施该混合动力系统,可以减少单位发电量的0.688Kg CO_2排放,同时满足当地需求。还可以看到,改进的粒子群优化技术版本能够相当有效地解决这个复杂的非线性优化问题。

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