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Model Predictive Driven Volt/VAr Control for Smart Grid Enabled CVR in Active Distribution Network

机译:有源配电网中基于智能电网的CVR的模型预测驱动电压/ VAr控制

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Energy conservation through voltage reduction methodology is becoming popular now a days. Enabling of conservation of voltage reduction (CVR) through smart grid technologies makes informal and effective way. However, the integration of distributed energy resources (DERs) in active distribution grids are facing some operational issues. Moreover, the enabling of CVR through conventional schemes are restricted with limited voltage reduction and unable to cope up during intermittency. This paper proposes a model predictive driven Volt/VAR Control (VVC) scheme for smart grid enabled CVR operation in presence of solar photovoltaic (PV) power system. The impact of uncertainty in PV power generation and error in load forecasting are incorporated in predictive model. Scenario based Volt/VAR optimization (VVO) approach is utilized to CVR deployment. The VVO objective is to minimize the voltage deviations between load node voltage and expected CVR voltage. The VVO problem is solved through particle swarm optimization. In order to avoid the voltage violation due to cloud transients, PV inverter-based VAR has been dispatched as a local voltage control. The proposed CVR method is validated on modified IEEE 13 node feeder of radial distribution network. The simulation results revels that the remarkable energy demand and peak load power is reduced through proposed CVR scheme with suitable feeder voltage profile.
机译:如今,通过降压方法实现节能一直很流行。通过智能电网技术实现节能降压(CVR)保护成为一种非正式而有效的方法。但是,将分布式能源(DER)集成到有源配电网中面临一些操作问题。而且,通过常规方案的CVR的启用受到有限的电压降低的限制,并且在间歇期间无法应付。本文提出了一种模型预测驱动的伏/无功控制(VVC)方案,用于在太阳能光伏(PV)电力系统存在的情况下,智能电网使CVR运行。在预测模型中纳入了光伏发电不确定性的影响以及负荷预测中的误差。基于场景的Vol / VAR优化(VVO)方法用于CVR部署。 VVO的目的是使负载节点电压和预期CVR电压之间的电压偏差最小。通过粒子群优化解决了VVO问题。为了避免由于云瞬变而引起的电压违规,基于光伏逆变器的VAR已被派遣为本地电压控制。改进的CVR方法在改进的径向配电网IEEE 13节点馈线上得到了验证。仿真结果表明,通过提出的具有合适馈线电压曲线的CVR方案,可降低显着的能源需求和峰值负载功率。

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