Joint scheduling optimization of a microgrid with integration of renewable energy sources and electric vehicles considering energy and reserve minimization - Tao Hai; Jincheng Zhou; Jasni Mohamad Zain; Farah Jamali (Energy Science & Engineering, 2023)
Abstract To lower operational costs as well as emissions when wind and solar resources are available in a microgrid (MG), this study discusses the scheduling of electric vehicles (EVs) and responsive demands simultaneously. To mitigate the effects associated with undispatchable energy sources such as wind and solar, the proposed system makes use of EVs for peak shaving and load curve changes, while responsive demands provide the reserves required to do so. In addition, a two‐stage model is provided to evaluate MG's planned running costs (energy and reserve). Costs related to generating and reserving electricity are minimized in Stage 1, while costs related to adjusting unit scheduling to account for fluctuations in wind and photovoltaic output are minimized in Stage 2. Converged barnacles mating optimizer (CBMO) is a highly effective and powerful optimization tool that is used to handle the resultant objective optimization issue. An MG consisting of multiple dispersed generations is used to implement the proposed model. It is worth mentioning that three scenarios have been defined to analyze the impact of joint scheduling of EVs and controllable loads on the MG's day‐ahead operation. The three cost terms, that is, the generation cost, the reserve cost, and the startup cost of units in this scenario, are derived as $745.6913, $10.5278, and $6.35, respectively, remarkably less than the values reported in Scenarios 1 and 2. In Scenario 1, the CBMO algorithm yielded a lower MG operational cost than methods by a margin of 843.2 $/day. Costs per day of operation in Scenario 2 are derived to be $819.3 using the CBMO technique, whereas in Scenario 3, they are determined to be $743.1.