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首页> 外文期刊>International review of electrical engineering >An Approach on Non-Discriminatory Losses Charge Allocation for Deregulated Power Market Using Meta-Heuristic-Optimization- Based-Electricity-Tracing (MOET)
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An Approach on Non-Discriminatory Losses Charge Allocation for Deregulated Power Market Using Meta-Heuristic-Optimization- Based-Electricity-Tracing (MOET)

机译:基于元启发式优化的电力跟踪(MOET)的非管制性电力市场非歧视性损失费用分配方法

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摘要

This paper presents an alternative technique for losses charge allocation using electricity tracing theory. The meta-heuristic optimization technique was incorporated to perform the tracing process of powers and losses contributed by individual generator and load. In producing a better optimization engine, hybridization between the well known Genetic Algorithm (GA) and continuous domain Ant Colony Optimization (ACO_R) was performed for fast optimization process with optimal solution. The proposed method gives reliable perspective when allocating the losses charge on consumers as it is free from assumption and also applicable in any conditions of power system. Comparative studies and analysis have justified the capability of the proposed method for non-discriminatory losses charge allocation in deregulated power market. Furthermore, comparison between various meta-heuristic algorithms and analysis on their performance has reduced the doubt for real system application.
机译:本文提出了一种使用电力追踪理论进行损失电荷分配的替代技术。合并了元启发式优化技术,以执行由单个发电机和负载贡献的功率和损耗的跟踪过程。为了产生更好的优化引擎,执行了众所周知的遗传算法(GA)和连续域蚁群优化(ACO_R)之间的杂交,以实现具有最佳解决方案的快速优化过程。所提出的方法在为用户分配损失费用时提供了可靠的观点,因为它无需假设,也适用于电力系统的任何情况。通过比较研究和分析,证明了所提方法在放松管制的电力市场中进行非歧视性损失费用分配的能力。此外,各种元启发式算法之间的比较以及对其性能的分析减少了对实际系统应用的怀疑。

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