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Multi-objective hydro-thermal-wind coordination scheduling integrated with large-scale electric vehicles using IMOPSO

机译:基于IMOPSO的大型电动汽车多目标水热风协调调度

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Since the intermittency and volatility of wind power has restricted its penetration into power grid, coordination scheduling of flexible resources and wind energy becomes a promising technique for promoting wind power utilization. Hence, this paper integrates large-scale electric vehicles (EVs) with wind power generation to formulate multi-objective hydro-thermal-wind with EVs scheduling (MOHTWES) problem. And what's more, an improved multi-objective particle swarm optimization (IMOPSO) algorithm is proposed for solving the above problem with various constraints. By introducing a unique dual population evolution mechanism and a hierarchical elitism preserving strategy based on crowding entropy, IMOPSO can achieve excellent and well-distributed Pareto optimal solutions in objective space. Furthermore, a set of constraint handling strategies are utilized to guarantee that the solutions obtained are in feasible region. Finally, a daily scheduling problem of hydro-thermal system is used to verify the performance of IMOPSO, the numerical results of which shows the Pareto optimal solutions obtained by IMOPSO have greater advantages than the comparison algorithms. Furthermore, it can be concluded from the simulation results for MOHTWES problem that, smart scheduling of EVs integrated with wind energy can promote wind power utilization and reduce the generation cost and emission simultaneously. (C) 2018 Elsevier Ltd. All rights reserved.
机译:由于风电的间歇性和波动性限制了其向电网的渗透,因此灵活的资源和风能的协调调度成为促进风电利用的有前途的技术。因此,本文将大型电动汽车与风力发电相结合,以提出带有电动汽车调度问题的多目标水热风问题。并提出了一种改进的多目标粒子群算法(IMOPSO)来解决上述各种约束条件。通过引入独特的双重种群进化机制和基于拥挤熵的等级精英保护策略,IMOPSO可以在目标空间中获得出色且分布均匀的帕累托最优解。此外,利用一组约束处理策略来确保获得的解在可行区域内。最后,利用水热系统的日调度问题验证了IMOPSO的性能,其数值结果表明,IMOPSO所获得的帕累托最优解比比较算法具有更大的优势。此外,从MOHTWES问题的仿真结果可以得出结论,与风能集成的电动汽车的智能调度可以促进风能的利用,同时降低发电成本和排放。 (C)2018 Elsevier Ltd.保留所有权利。

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