Demand Side Management and Dynamic Economic Dispatch Using Genetic Algorithms

Khaled Dassa, A. Recioui
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引用次数: 3

Abstract

The purpose of this work is to find the optimal energy management mix in order to maximize the benefit for the client by minimizing the bill and reducing losses by optimizing the energy distribution in the network. There exist two smart grid management problems: demand side management (DSM) and dynamic economic dispatch (DED). DSM consists of modifying electricity consumption patterns with reference to the overall consumption picture, consumption time profile, and contractual supply parameters in order to achieve savings in electricity charges. DED aims at providing the ideal share of electricity produced corresponding to the overall energy request of users and the generated power. Research works in the literature dealt with DSM or DED issues independently. In this work, genetic algorithms will be used to solve DSM and DED problems, considering them as two complementary stages in the optimization process.
基于遗传算法的需求侧管理与动态经济调度
本研究的目的是找到最优的能源管理组合,通过优化网络中的能源分配,使客户的利益最大化,使账单最小化,减少损失。智能电网管理存在需求侧管理(DSM)和动态经济调度(DED)两个问题。用电需求管理包括根据整体用电情况、用电时间概况和合约供应参数修改用电模式,以节省电费。DED旨在提供与用户整体能源需求和发电功率相对应的理想电力份额。文献中的研究工作独立处理DSM或DED问题。在本工作中,遗传算法将用于解决需求侧需求和需求侧需求问题,将它们视为优化过程中的两个互补阶段。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
0.70
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