文章摘要
黄伟,葛良军,华亮亮,等.基于概率潮流的主动配电网日前-实时两级优化调度[J].电力系统自动化. DOI: 10.7500/AEPS20170913001.
Huang Wei,Ge Liangjun,Hua Liangliang, et al.Two-level Optimal Scheduling Constituted by Day-ahead and Real-time for ADN Based on Probabilistic Power Flow[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20170913001.
基于概率潮流的主动配电网日前-实时两级优化调度
Two-level Optimal Scheduling Constituted by Day-ahead and Real-time for ADN Based on Probabilistic Power Flow
DOI:10.7500/AEPS20170913001
关键词: 主动配电网,日前—实时调度,不确定性,概率潮流,安全裕度
KeyWords: Active distribution network, day-ahead and real-time scheduling, uncertainty, probabilistic power flow, safety margin
上网日期:2018-05-11
基金项目:
作者单位E-mail
黄伟 华北电力大学电气与电子工程学院 huangwei@ncepu.edu.cn 
葛良军 华北电力大学电气与电子工程学院 2218173428@qq.com 
华亮亮 国网蒙东电力通辽供电公司 tlhualiangliang@163.com 
杨舒文 华北电力大学电气与电子工程学院 530631140@qq.com 
刘明昌 国网蒙东电力通辽供电公司 tldylmc@126.com 
摘要:
      为有效应对主动配电网(Active Distribution Network, ADN)中间歇性电源、虚拟微网与柔性负荷等不确定变量给电网安全经济运行带来的挑战,基于概率潮流技术建立了ADN日前-实时两级优化调度模型。日前调度以ADN中各单元日前功率预测结果为依据,以运行成本最小为目标,确定次日各时段内各调度单元运行计划;在此基础上,针对实际运行中各单元的波动性和超短期预测结果进行实时调度,以不确定单元作为随机变量进行概率潮流计算,对日前调度计划进行调整,使得ADN中各节点电压和支路功率在3倍标准差内波动仍满足约束条件,提高ADN的安全裕度。最后结合引力搜索算法和粒子群算法对调度模型进行求解,并通过算例验证模型的有效性。
Abstract:
      For effective handling the challenges of safe operation brought by the intermittent power supply, virtual micro-network, flexible load and other uncertain variables in the active distribution network, a two-level optimal scheduling model based on probabilistic power flow is proposed, which is constituted by day-head and real–time optimal scheduling model. The day-ahead scheduling determines the operation plan of each unit to minimum the operation cost of ADN according to the power forecasting results. On the basis of this, real-time scheduling is performed for the volatility of each unit in the actual operation and the results of the short-term forecast to adjust the plan made by day-ahead scheduling in accordance to the probabilistic power flow which regards the uncertain units as random variables to improve the safety margin of ADN. The gravitational search algorithm and particle swarm optimization algorithm are applied to solve the scheduling model. In the end, the effectiveness of the scheduling model is verified by case study.
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