Abstract The basic objectives of demand side management (DSM) are shifting load from peak hours to off-peak hours and reducing consumption during peak hours. The DSM operation is cleared when deregulated electricity market is considered where the retailer purchases electricity from the electricity market to cover the end users requirements of energy. In this paper, DSM techniques (load shifting and peak clipping) are used to maximize the profit for retailer company by reducing total demand at peak hours and achieve an optimal daily load schedule using linear programing (LP) and genetic algorithm (GA). These techniques are implemented on the 33-bus radial network included wind generation penetration. A short term artificial neural network technique (ANN) is used to get forecasted wind speed and forecasted users load for date 25-March-2018. The ANN uses an actual hourly load data and an actual hourly wind speed data. Then the forecasted data is used in the optimization to get optimal daily load schedule to maximize the profit for retailer company. Finally, comparison is carried out between profit using LP and GA. The optimized DSM succeeded to increase the profits of the company by around 4.5 times its old profit using LP and around 2.5 times using GA.

中文翻译：

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具有高风电渗透率的配电系统的 DSM 方法

摘要 需求侧管理（DSM）的基本目标是将负荷从高峰时段转移到非高峰时段，减少高峰时段的消耗。当考虑解除管制的电力市场，即零售商从电力市场购买电力以满足终端用户对能源的需求时，DSM 操作被清除。在本文中，DSM 技术（负载转移和削峰）用于通过减少高峰时段的总需求来最大化零售商公司的利润，并使用线性规划 (LP) 和遗传算法 (GA) 实现最佳的每日负载计划。这些技术在包括风力发电渗透的 33 总线辐射网络上实施。短期人工神经网络技术 (ANN) 用于获取 2018 年 3 月 25 日的预测风速和预测用户负载。ANN 使用实际每小时负荷数据和实际每小时风速数据。然后将预测数据用于优化以获得最佳的每日负载计划，以最大化零售商公司的利润。最后，比较了使用LP和GA的利润。优化后的 DSM 成功地将公司的利润增加了大约 4.5 倍使用 LP 的旧利润和大约 2.5 倍使用 GA。