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Multi-objective based economic environmental dispatch with stochastic solar-wind-thermal power system

机译:随机太阳能 - 风电系统的多目标经济环境派出

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This paper presents an evolutionary based technique for solving the multi-objective based economic environmental dispatch by considering the stochastic behavior of renewable energy resources (RERs). The power system considered in this paper consists of wind and solar photovoltaic (PV) generators along with conventional thermal energy generators. The RERs are environmentally friendlier, but their intermittent nature affects the system operation. Therefore, the system operator should be aware of these operating conditions and schedule the power output from these resources accordingly. In this paper, the proposed EED problem is solved by considering the nonlinear characteristics of thermal generators, such as ramp rate, valve point loading (VPL), and prohibited operating zones (POZs) effects. The stochastic nature of RERs is handled by the probability distribution analysis. The aim of proposed optimization problem is to minimize operating cost and emission levels by satisfying various operational constraints. In this paper, the single objective optimization problems are solved by using particle swarm optimization (PSO) algorithm, and the multi-objective optimization problem is solved by using the multi-objective PSO algorithm. The feasibility of proposed approach is demonstrated on six generator power system.
机译:本文介绍了一种基于进化的技术,用于通过考虑可再生能源资源的随机行为(RERS)来解决基于多目标的经济环境派遣。本文考虑的电力系统由风和太阳能光伏(PV)发电机以及传统的热能发生器组成。 RERS是环保的,但它们间歇性的性质会影响系统操作。因此,系统操作员应该了解这些操作条件,并相应地安排从这些资源的功率输出。在本文中,通过考虑热发生器的非线性特性,例如斜坡率,阀点加载(VPL)和禁止的操作区(POZS)效应来解决所提出的EED问题。 RERS的随机性质由概率分布分析处理。所提出的优化问题的目的是通过满足各种操作约束来最小化运营成本和发射水平。在本文中,通过使用粒子群优化(PSO)算法来解决单个客观优化问题,并且通过使用多目标PSO算法来解决多目标优化问题。在六个发电机电力系统上证明了所提出的方法的可行性。

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