4.7 Article

Stationary and mobile storages-based renewable off-grid system planning considering storage degradation cost based on information-gap decision theory optimization

Journal

JOURNAL OF ENERGY STORAGE
Volume 58, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.est.2022.106389

Keywords

Bio-waste energy unit; Hybrid solution algorithm; Information -gap decision theory; Off -grid system; Stationary and mobile storage; Storage degradation cost

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This paper presents a hybrid renewable system planning that combines wind turbines, bio-waste energy units, stationary and mobile energy storage. The proposed model prioritizes the use of renewable sources and utilizes storage devices to bridge the gap between demand and renewable generation. It addresses uncertainties by employing robust optimization algorithms and demonstrates low computation time and high robustness. The presence of renewable sources contributes to an environmentally friendly system, and smart charging strategies further reduce the planning cost.
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 aforemen-tioned 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 al-gorithm 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 de-vices 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.

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