A scenario-based stochastic model for day-ahead energy management of a multi-carrier microgrid considering uncertainty of electric vehicles

Abstract

This paper presents a scenario-based stochastic management algorithm to deal with scheduling the optimal operation of a multi-carrier microgrid (MG). The proposed framework of the multi-carrier MG comprises of wind turbine (WT), photovoltaic (PV) panel, fuel cell (FC), microturbine (MT), boiler, combined heat and power (CHP) unit, electrical load, thermal load, hydrogen load, electrical energy storage, H2 storage, and thermal storage. To cope with the uncertainties arisen by the renewable resources, electricity price, electrical load, and electric vehicle (EV), we proposed a scenario-based approach to model the uncertain parameters (wind speed, solar irradiance, electricity price, electrical load, EV arrival time, EV departure time, and EV traveled distance). The proposed algorithm initially estimates the uncertain parameters through fitting a probability distribution function (PDF) with the time series of historical uncertain parameters. Then, the scenario generation and reduction approach is applied to find the finite scenarios according to the fitted PDFs. The stochastic management algorithm is formulated as a cost minimization problem, where the demand response (DR) program is embedded in the formulation. The proposed framework is tested on a sample multi-carrier MG for two cases, namely, the application of stochastic energy management for a Multi-carrier MG with and without DR. Also, we perform a comparative study to show the benefits of the stochastic management algorithm along with a deterministic approach.