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The relatively short lifetime of batteries is one of the crucial factors that affects its economic viability in current electricity markets. Thus, to make batteries a more viable technology in real power market from life cycle cost assessment perspective, full understanding of battery ageing parameters and which operating control strategies cause slower degradation rate is essential and still an open problem. This study deals with the 32 different battery operating control strategies to evaluate their importance on cyclic and calendric degradation, lifetime, and life cycle cost assessment of a battery system in a grid-connected residential application. In other words, it is evaluated that at which operating control strategy the system simulation results in a more beneficial system from techno-economic perspective. A battery modelling scenario is proposed to accurately estimate battery performance, degradation, and lifetime under real operational condition given different operating control strategies. An operational strategy, which benefits from the dynamic real-time electricity price scheme, is conducted to simulate the system operation. The key results show that selecting a proper state-of-charge control strategy positively affects the battery lifetime and consequently its net-present-value, in which the best strategy led to 30% improvement in net-present-value compared to the worst strategy.

Battery storage in solar residential applications has the potential to improve system flexibility under high renewable energy penetration. A better understanding of the dynamic operational conditions of batteries is of high importance for the technical and economic feasibility of the associated system. This study evaluates key parameters for the proper battery management design, control, and optimization of a battery system integrated into a grid-connected, solar-powered building. Three different battery modelling scenarios are proposed in terms of battery ageing and lifetimes, internal states, and control strategies. Each proposed scenario consists of a set of specific methods for the estimation of battery voltage-current characteristics, capacity degradation, remaining lifetime, states of charge, states of health, and states of power. A criteria-based operational strategy linked to a nondominated sorting genetic algorithm (NSGA_II) is constructed for the simulation and multiobjective optimization of the system. The self-sufficiency ratio and life-cycle cost of a battery are considered the technical and economic goals, which are influenced by the capacity degradation and achievable lifetime of the battery. Moreover, the annual battery degradation cost and self-consumption ratio are calculated over the project lifetime. The comparison between the techno-economic optimization results obtained under three battery modelling scenarios indicate that a more realistic design and a superior techno-economic assessment are obtained under Model 3, which is able to simulate battery degradation considering all ageing influence parameters under real operational conditions. In comparison with Model 3, Model 1 which neglects the battery degradation, techno-economically leads an overly optimistic result and also Model 2, which was based on linear capacity degradation regardless of the observed dynamic operational conditions, leads an excessively pessimistic result, implying that applying several simplifying assumptions for a battery operation simulation in a real-life application greatly affects the resulting battery state of charge, state of power, and state of health estimations, leading to an improper battery management system and consequently to the misestimation of techno-economic objective functions. The results prove that the real design and techno-economic assessment of a battery in a solar-powered application highly depend on battery operations in which the seasonal photovoltaic (PV) power production affects the rates of calendric and cyclic battery degradation. 

A battery storage has emerged as the most widely-used storage option, due to its flexible and complementary functionality for renewable energy systems such as solar PV and wind power. In order to ensure the efficient operation of batteries in energy systems, a proper battery model is essential in predicting realistic battery performance under various operating conditions. Accurate knowledge of the state of charge, state of power, and battery efficiency is a necessity for the development of advanced grid management applications. This paper investigates the techno-economic impacts of two battery modelling scenarios on the sizing and optimization of a grid-connected PV-battery system. Scenario 1 is based on a common simple battery model and control strategy which represents the battery status without reflecting dynamic behavior. By contrast, Scenario 2 is based on a complex battery model involving estimation of battery current-voltage characteristics under various operating conditions. A rule-based operational strategy linked to a non-dominated sorting genetic algorithm is further employed for the simulation and multi-objective optimization of a grid-connected hybrid PV-battery system. The battery life cycle cost and the self-sufficiency ratio are analyzed and optimized as objective functions, and battery capacity constitutes as a decision variable. The results show that in order to reach the same self-sufficiency ratio, the optimization of a hybrid energy system based on Scenario 1 leads to solutions with a higher life cycle cost and requiring bigger battery capacity, compared to that of Scenario 2. Moreover, under the same design parameters, the system optimization based on Scenario 2 delivers more power to the end-user, which leads to a higher selfsufficiency ratio compared to when the system is simulated based on Scenario 1. This study proves that an efficient battery model with sufficient accuracy is techno-economically more beneficial, and leads to more accurate battery sizing.

Stationary battery storages become a promising solution for improving flexibility of renewable energy system to balance the fluctuating of power production and demand. However, each application has a specific operational strategy, consequently a specific dynamic operational profile which leads to a different estimated battery lifetime due to the degradation of battery capacity over its operation in the application. An accurate knowledge about battery lifetime, and battery state of health at different operational conditions is important to ensure a feasible techno-economic assessment. This paper deals with the techno-economic evaluation of a battery system integrated into a residential grid-connected PV system considering two battery models with and without battery degradation. The battery life cycle cost, the self-sufficiency ratio and battery lifetime are analyzed for techno-economic assessment of a residential grid-connected hybrid PV-battery system. The results show that the simulation without battery degradation gives 31.43% lower life cycle cost and 7.4% higher self-sufficiency ratio, compared to the modeling with battery degradation. This proves the importance of battery aging model for assessing a battery integrated into a renewable PV system.