Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization - Jokar, Mohammad Reza; Shahmoradi, Saeid; Mohammed, Adil Hussein; Foong, Loke Kok; Le, Binh Nguyen; Pirouzi, Sasan (Journal of energy storage, 2023)
This paper presents the planning of a hybrid renewable system with wind turbines and bio-waste energy units along with stationary (i.e., batteries) and mobile (i.e., electric vehicles) energy storage. This model minimizes the cost of construction, maintenance and storage degradation. In this model, the energy supply priority is given to renewable sources. Then the mentioned storage devices are used to cover the gap between the load profile and the renewable generation power. The proposed scheme addresses the uncertainties of loads, renewable power and energy consumption of mobile storage devices. Therefore, robust optimization based on information-gap decision theory (IGDT) is utilized to obtain a robust solution against the prediction error of the aforementioned uncertainties. Also, the hybrid optimization algorithm of honey bee mating and artificial bee colony is derived, showing the optimal solution with low dispersion in the final response. The proposed design is applied to the data of the city of Espoo, Finland. The obtained numerical results show the capability of the proposed design in deriving robust economic planning for the proposed hybrid system. So, the used hybrid solution algorithm is able to obtain the best solution in a lower computation time compared to non-hybrid solvers, and it has a low standard deviation around 0.94 % in the final response. The presence of the aforementioned renewable sources leads to attaining an environment-friendly hybrid system. The optimal performance of the storage devices leads to the robustness of the optimal solution against the maximum error of 18.19 % in the prediction of uncertainties. This occurs considering the smart charging management of mobile storage, but the maximum uncertainty level of 11.12 % holds to non-smart management of these storage devices in the proposed scheme. The energy management of mobile storage devices based on smart (non-smart) charging strategy also reduces (increases) the planning cost of the off-grid system by 7.62 % (39.68 %) compared to their absence. •Planning an off-grid system with 100% renewable sources such as WT, BEU and stationery and mobile storage devices.•Formulating the function of aggregating EVs in an island hybrid system to minimize planning cost.•Deriving robust planning of off-grid system against forecasting error using IGDT model of uncertainties.•Obtaining optimal solution in low computing time with low dispersion in final solution by HBMO+ABC algorithm.