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A probabilistic unit commitment model for optimal operation of plug-in electric vehicles in microgrid

机译:微电网中插电式电动汽车最优运行的概率单位承诺模型

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摘要

This paper presents a probabilistic Unit Commitment (UC) model for optimal scheduling of wind power, load forecasts and controllability of vehicles in a microgrid using a stochastic programming framework. The microgrid is made up of microturbines, wind turbine, boiler, Plug-in Electric Vehicles (PEVs), thermal storage and battery storage. The proposed model will help the power grid operators with optimal day ahead planning even with variable operating conditions in respect of load forecasts, controllability of vehicles and wind generation. A set of valid scenarios is assigned for the uncertainties of wind sources, load and PEVs and objective function in the form of expected value. The objective function is to maximize the expected total profit of the UC schedule for the set of scenarios from the viewpoint of microgrid management. The probabilistic unit commitment optimizes the objective function using Particle Swarm Optimization (PSO) algorithm. In order to verify the effectiveness of the stochastic modelling and make a comparison with a simple deterministic one, a typical microgrid is used as a case study. The results can be used to evaluate the effect of integration of PEVs on the economic operation of the microgrid. The results also confirm the necessity to consider the key uncertainties of the microgrid; otherwise the results could overly misrepresent the real world operation of the system. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文提出了一种概率单位承诺(UC)模型,用于使用随机编程框架对微电网中的风能,负荷预测和车辆的可控性进行优化调度。微电网由微型涡轮机,风力涡轮机,锅炉,插电式电动汽车(PEV),储热器和电池储器组成。所提出的模型将帮助电网运营商进行最佳的提前计划,即使在负荷预测,车辆可控制性和风力发电方面的可变运行条件下也是如此。针对风源,负载和PEV的不确定性以及目标函数,以预期值的形式分配了一组有效方案。从微电网管理的角度来看,目标功能是针对一组方案最大化UC计划的预期总利润。概率单元承诺使用粒子群优化(PSO)算法优化目标函数。为了验证随机建模的有效性并与简单的确定性模型进行比较,以典型的微电网为案例研究。该结果可用于评估PEV集成对微电网经济运行的影响。结果还证实了考虑微电网关键不确定性的必要性。否则,结果可能会误导系统的实际操作。 (C)2016 Elsevier Ltd.保留所有权利。

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