Interval multi-objective optimization of hydrogen storage based intelligent parking lot of electric vehicles under peak demand management

Uncertainty, a familiar concept for power system operators has been set to be one of important topics in the industry of electricity systems. This circumstance is mainly caused by uncertain behavior of some parameters like price. Since the forecasting techniques are usually unable to guarantee a fixed and accurate value of such parameters therefore uncertainty modeling becomes essential. This work has applied an interval based optimization model for optimal performances of intelligent parking lot (IPL) of electric vehicles (EVs) within severe uncertainty of upper grid price under demand response program (DRP). In fact, DRP is used to enable IPL reduce its daily operation cost by shifting some parts of load demand from peak time intervals to off-peak time intervals. It should be mentioned that interval approach does not solve single objective problem and instead of that it generates a multi-objective optimization problem within which average and deviation costs are minimized as the bi-objective model. To do this, weighted sum and fuzzy approached are applied to solve the bi-objective problem. A sample system containing IPL, local dispatchable generation (LDG) units, non-renewable and renewable generation systems is studied under uncertainty of upper grid price through mentioned techniques and the results proving efficiency of employed techniques are investigated for comparison. According to the compared results, under DRP, average cost of IPL is reduced up to 4.37% while deviation cost representing uncertainty impact is also decreased up to 10.93%.