LAUSR

The increase of Plug-in Electric Vehicles (PEVs) penetration in distribution systems necessitates processing strategic assets in order to deal with their energy needs. A careful investigation into matters related to PEV charging management under actual circumstances can be regarded as the critical step towards enabling this process. Accordingly, this paper intends to design a practical controller capable of performing charging scheduling under uncertainties related to the lack of access to crucial PEV information accounting for departure time, energy requirement, and power demand nonlinearity. Although such an issue can be encountered when developing charging models for real-world conditions, it has not been adequately taken into consideration. The proposed controller carries out charging scheduling through a procedure with a set of effective straightforward algorithms, essential for actual applications. Particularly, it takes advantage of a Bayesian forecasting model that is able to efficiently predict charging energy demand according to car owner’s behavior. In addition, it employs a stochastic optimization framework to schedule PEV charging based on the dynamic electricity price and user preference. Several case studies are conducted to examine the performance of suggested controller in optimal scheduling by exploiting real data. The evaluation process is executed through a comparative analysis by using a deterministic method, as the ideal case, which exploits a full-information space. The results show that the proposed procedure can offer competitive charging schedules, which can minimize the cost while satisfying user desires. The designed controller can successfully manage PEV charging in the presence of stochastic phenomena with limited information access, and thus, enable physical implementations.