Premature ageing of lithium-ion battery energy storage systems (BESS) is a common problem in applications with or without renewable energy sources (RES) in the household sector. It can result to significant issues for such systems such as inability of the system to cover load demand for a long period of time. Consequently, the necessity of limiting the degradation effects at a BESS leads to the development and application of energy management strategies (EMS). In this work, EMSs are proposed in order to define optimal operation of a BESS without RES under time-of-use (ToU) tariff conditions. The objective of the developed EMSs is to reduce the capacity loss at the BESS in order to extend its lifetime expectancy and therefore increase the economic profit in the long-term. The EMSs utilize a widely used battery mathematical model which is experimentally validated for a specific BESS and a battery degradation mathematical model from the literature. Indicative simulation results of the proposed strategies are presented. The outcomes of these simulated scenarios illustrate that the objectives are achieved. The BESS operates efficiently by preventing premature ageing and ensuring higher economic profit at the long term.

This paper examines the issue of energy management in a residential Battery Energy Storage System (BESS). The development of the energy management strategy (EMS) involves the modeling of the BESS and the modeling of a prediction aging mechanism for the Lithium Ion battery stack. In addition, EMS comprises the identification of the needs and requirements for energy management in a dynamic pricing environment where the Power Supplier (GRID) is the monadic source of power to the resident and to the BESS. The main objective of the proposed EMS is to prevent the fast aging of the Lithium-Ion batteries with respect to the maximum financial benefits for the consumer. Moreover, due to the dynamic pricing environment and the uncertainty that the forecast energy demand will match the actual energy demand, the ability of the EMS to update the proposed strategy during the day on hourly basis can be deemed vital. Indicative results of the proposed method are presented in order to demonstrate the ability of the BESS's to provide an optimal solution for covering the maximum energy demand with the minimum degradation effects for the batteries.

This paper discusses non-linear model predictive control (NMPC) for preventing premature aging in a Battery Energy Storage System (BESS). The BESS can be used in both residential and commercial buildings in order to reduce the cost of energy consumption. Apart from maintaining the BESS's life expectancy, the NMPC is also responsible for securing the maximum possible economic profit. The implementation of the NMPC requires the modeling of the BESS, the modeling of an aging prediction mechanism for the 15-Lithium Ion batteries stack and the identification of the needs and requirements for energy management in a dynamic pricing environment where the GRID is the unique source of power to the building and to the BESS. The NMPC utilizes forecasted profiles for the energy demand and the energy prices which are retrieved from a data knowledge warehouse in order to propose an optimal discharge profile for the BESS. Furthermore, the intra-day alternations of the energy prices and the uncertainty that the day-ahead energy demand profile will match the actual demand profile, makes the necessity of the controller to update the proposed discharge profile during the day inevitable. Indicative results of the proposed method are presented in order to demonstrate the ability of the NMPC to provide an optimal solution for achieving a specific capacity loss target for the batteries stack and simultaneously ensuring high financial profit.