Abstract
The optimal operation of microgrids is challenging due to the presence of various uncertain factors, i.e., renewable energy sources, loads, market price signals, and arrival and departure times of electric vehicles (EVs). In order to incorporate these uncertainties into the operation model of microgrids, an adaptive robust optimization-based operation method is proposed in this paper. In particular, the focus is on the uncertainties in arrival and departure times of EVs. The optimization problem is divided into inner and outer problems and is solved iteratively by introducing column and constraint cuts. The unit commitment status of dispatchable generators is determined in the outer problem. Then, the worst-case realizations of all the uncertain factors are determined in the inner problem. Based on the values of uncertain factors, the generation amount of dispatchable generators, the amount of power trading with the utility grid, and the charging/discharging amount of storage elements are determined. The performance of the proposed method is evaluated using three different cases, and sensitivity analysis is carried out by varying the number of EVs and the budget of uncertainty. The impact of the budget of uncertainty and number of EVs on the operation cost of the microgrid is also evaluated considering uncertainties in arrival and departure times of EVs.
Highlights
Microgrids are generally composed of various distributed energy sources, such as distributed controllable generators (DGs), energy storage systems (ESSs), and renewable energy sources (RESs), along with local loads
electric vehicles (EVs) can be charged by buying power from the grid during off-peak price intervals and can be discharged to feed local loads during peak price intervals
Due to the availability of plenty of literature on the prevailing uncertainties, this study focuses more on the arrival and departure time uncertainties in microgrids
Summary
Microgrids are generally composed of various distributed energy sources, such as distributed controllable generators (DGs), energy storage systems (ESSs), and renewable energy sources (RESs), along with local loads. With the increased use of electric vehicles (EVs), coordinated operation of microgrids with EVs is gaining popularity [1,2]. The integration of EVs can provide additional storage to microgrids. EtaVs can be used to increase the utilization of RESs by absorbing excess power during peak generation intervals and releasing it during peak load intervals. EVs can be charged by buying power from the grid during off-peak price intervals and can be discharged to feed local loads during peak price intervals. In this way, EVs can be utilized to reduce greenhouse gas emissions and reduce the operation cost of the microgrid. EVs can be used as a storage element to enhance service reliability, improve power quality, and assist participation in demand response programs [3,4]
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