An optimal charging scheduler-based charging strategy of a real life hybrid energy storage for a solar and wind equipped student residence

Abstract

With increasing renewable energy penetration globally, Energy Storage Systems (ESS) act as a vital component for transforming the current energy sector. In the form of Grid connected ESS, Lithium-Ion Battery (LIB) technology is presently the most popular form of ESS, especially because of its fast response capability, efficiency, and reducing market prices, but is not always preferred for long-term storage, due to its relatively shorter lifetime. A Redox Flow Battery (RFB) on the other hand has a higher lifetime and better long-term storage capability, but has a higher upfront cost and reduced round trip efficiency. A Hybrid ESS (HESS) consisting of LIB and RFB offers the advantages of both the technologies, thus making the individual ESS more economical and flexible to use while also improving its cycle lifetime. Such a grid-connected HESS is planned and installed for a student residence at Bruchsal accommodating 150 students and equipped with 220kWp photovoltaics (PV) and 10.5kWp wind-power. In order to control this conglomerate, an Energy Management System (EMS) is deployed which not only controls but also optimizes its operations in real-time. The EMS aims at achieving multiple objectives which include reducing the ESS aging, operating the system at reduced losses, and the most important, improving building self-sufficiency. This paper focuses on the charging strategy of the HESS which is optimized in two folds. First the HESS is operated with a fixed priority-based strategy where the operation efficiency of the High Energy ESS, i.e. RFB is improved. Secondly, based on generation and consumption forecasts of the setup the EMS optimizes the charging of the High Power ESS, i.e. LIB. With the forecasts available, the EMS strategically schedules delayed charging of the individual ESS, which avoids longer relaxation periods at higher SOC and thus the aging caused due to it. Additionally, the optimization algorithm iteratively searches and operates with the possible optimal operation point of the ESS, where conversion losses are minimal under the given circumstances. Results of real-life operation of the setup based on these operation strategies are provided in this work.