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首页> 外文会议>CIGRE Canada Conference >Optimal placement and sizing of distributed generation units by multi-objective genetic algorithm in Chilean electrical networks
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Optimal placement and sizing of distributed generation units by multi-objective genetic algorithm in Chilean electrical networks

机译：智利电网多目标遗传算法的分布生成单位最佳放置和尺寸

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In recent years, the optimization of distributed generation (DG) has become a major issue for electrical power system quality and reliability. The vast majority of papers dealing with this problem use operational research and artificial intelligence techniques, such as genetic algorithms (GA), particle swarm optimization (PSO) and tabu search (TS). This studies have solved the problem by achieving convergence at the best possible solution. However, most of them are only applied on the IEEE test systems and only consider technical and/or economic objectives. This study aims to deliver sustainable solutions for electrical power systems, by an effective method that maximizes technical, economic, social and environmental benefits. For the placement and sizing of DG units in electrical distribution networks, an approach based on a multi-objective optimization model and a genetic algorithm is proposed. Two objective functions are defined. The first aims to minimize network power losses and is defined in the framework of operation and security distribution network constraints. The second aims to minimize the total network CO_(2eq) emissions, using a carbon footprint database of generation technologies, calculated using a life cycle assessment (LCA) method. In addition, an evaluation function is used to evaluate the economic benefits delivered by the solutions for the electrical distribution company, based on the reduction of losses and local social acceptance improvement using a stated willingness to pay more for renewable electricity, from local individuals. The proposed method is assessed in two Chilean electrical networks. The simulation results illustrate the capacity of the proposed method for real case studies, and concluded that the method provides an effective balanced approach for the integration of distributed generation units in the electrical power systems.
机译：近年来，分布式发电（DG）的优化已成为电力系统质量和可靠性的主要问题。占该问题的绝大多数论文使用操作研究和人工智能技术，例如遗传算法（GA），粒子群优化（PSO）和禁忌搜索（TS）。通过在最佳解决方案中实现收敛，本研究解决了该问题。然而，大多数人仅应用于IEEE测试系统，并且只考虑技术和/或经济目标。本研究旨在通过一种最大化技术，经济，社会和环境效益的有效方法为电力系统提供可持续解决方案。对于电配电网络中DG单元的放置和尺寸，提出了一种基于多目标优化模型和遗传算法的方法。定义了两个目标函数。首先旨在最大限度地减少网络功率损耗，并在操作框架和安全分布网络约束中定义。第二个旨在利用生成技术的碳足迹数据库最小化全网络CO_（2EQ）排放，使用生命周期评估（LCA）方法计算。此外，评估函数用于评估配电公司解决方案提供的经济效益，基于使用该公司的损失和当地社会验收改善的损失和当地社会接受改善，从当地个人提供更多的愿意为可再生电力支付。在两个智利电网中评估了所提出的方法。仿真结果说明了实际案例研究的所提出方法的能力，并且得出的方法提供了一种有效的平衡方法，用于电力系统中的分布式发电单元的集成。

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