考虑负荷方差的智能微网用户侧分时电价优化模型
投稿时间:2020-02-04  修订日期:2020-03-03  点此下载全文
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作者单位邮编
杨国山* 国网甘肃省电力公司经济技术研究院 730000
杨德州 国网甘肃省电力公司经济技术研究院 
杨昌海 国网甘肃省电力公司经济技术研究院 
徐慧慧 国网甘肃省电力公司经济技术研究院 
刘永成 国网甘肃省电力公司经济技术研究院 
中文摘要:为调整用户用电行为,提升智能微网安全性和稳定性,需构建考虑负荷方差的智能微网用户侧分时电价优化模型。通过需求价格弹性系数描述用户对电价的反应情况,依照智能微网中负荷恒定状态和波动状态,计算线路可变损耗,取样采点一定周期内负荷,计算负荷方差,探析负荷方差对线路损耗的影响。依照负荷方差和用户负荷峰谷差构建智能微网用户侧分时电价优化的目标函数,设置峰平谷电价、用户利益、峰谷倒置等五个约束条件,结合约束条件和目标函数获取最终的优化模型。经过实验分析发现,该种方法构建的用户侧分时电价优化模型后,用户侧分时电价最大峰段负荷为14109KW,峰谷差是7963KW,显著增加售电侧和发电侧利润。
中文关键词:负荷方差  智能  微电网  用户侧  分时电价  优化模型
 
Power price optimization model for intelligent micro-network user-side sub-division considering load variance
Abstract:In order to adjust the power consumption behavior of users and improve the security and stability of intelligent microgrid, it is necessary to construct an optimization model of time-sharing electricity price on the user side of intelligent microgrid considering load variance. The response of users to electricity price is described by demand price elasticity coefficient. According to the constant and fluctuating state of load in intelligent microgrid, the variable loss of line is calculated, the load within a certain period of sampling point is calculated, the load variance is calculated, and the influence of load variance on line loss is analyzed. According to the load variance and the peak and valley difference of user load, the objective function is constructed, and the five constraint conditions, such as peak flat valley electricity price, user interest and peak and valley inversion, are set up, and the final optimization is obtained by combining the constraint conditions and the objective function. Model。 Through the experimental analysis, it is found that after the optimization model of the time-sharing electricity price on the user side, the maximum peak load of the time-sharing electricity price on the user side is 16324kW, and the difference between the peak and valley is 8143kW, which significantly increases the profit of the selling side and the generating side.
keywords:Load variance  Intelligence  Microgrid  User side  Time-sharing electricity price  Optimization model
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