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

机译:智能电网的模型预测驱动Volt / var控制在主动分配网络中的CVR

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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:通过电压减少方法的节能现在变得流行了一天。通过智能电网技术实现减少电压减少(CVR),这是非正式和有效的方式。但是,分布式能源资源(DER)在主动分发网格中的集成面临着一些操作问题。此外,通过传统方案的实现CVR的能够受到限制的电压降低,并且在间歇性期间无法应对。本文提出了一种用于在太阳能光伏(PV)动力系统的存在下使能CVR操作的智能电网的模型预测驱动伏/瓦控制(VVC)方案。在预测模型中纳入了PV发电中不确定性对PV发电和误差的影响。基于场景的Volt / VAR优化(VVO)方法用于CVR部署。 VVO目的是最小化负载节点电压和预期CVR电压之间的电压偏差。通过粒子群优化解决了VVO问题。为了避免由于云瞬变引起的电压违规,PV逆变器的VAR已被调度为局部电压控制。在径向分配网络的修改IEEE 13节点馈线上验证了所提出的CVR方法。仿真结果陶醉了通过具有合适的馈线电压曲线的CVR方案来降低了显着的能量需求和峰值负载功率。

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