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Community Energy Storage Systems (CESSs) emerge as an innovative way to integrate batteries into Low Voltage (LV) and Medium Voltage (MV) distribution networks to provide ancillary services and improve the quality of energy received by the end user. However, since CESSs are still emerging technologies, there is much research space in this field for proposing innovative and economic control algorithms for such devices. Thus, this paper presents a study of four control strategies applied to an MV distribution network, i.e., peak shaving, line losses control, line congestion reduction, and system voltage control, through an Aggregated Community Energy Storage System (ACESS), which is represented as the sum of multiple CESSs connected in LV systems, viewed from MV side. The proposed strategies are based on Model Predictive Control (MPC), a technique that, using the future demand forecast data, calculates the dispatch of ACESS. The results show, with respect to the base case, an improvement between 12.8&#x0025; and 15.1&#x0025; for the line losses control strategy, a reduction of 31.5&#x0025; on the maximum demand for the peak shaving control strategy, a maximum lowering of 12&#x0025; on the currents of some lines for the congestion control strategy, and an enhancement of 0.15&#x0025; for the voltage control strategy. Moreover, for estimating the service lifetime of the ACESS after applying the control algorithms, the Rainflow Counting Algorithm (RCA) is used, exhibiting that, regardless of the control strategy, the degradation is inversely proportional to the storage capacity.

A Study on Control Strategies for Aggregated Community Energy Storage Systems in Medium Voltage Distribution Networks