Combining Genetic and Gravitational Search Algorithms for the Optimal Management of Battery Energy Storage Systems in Real-Time Pricing Markets

Determining the optimal management in terms of operative decisions (charging, discharging, or disconnection) as well as their magnitudes (charging/discharging current/power), considering the nonlinearities of battery energy storage system (BESS) is a crucial process on the successful acceptance of energy storage technologies. This work presents an optimization model for the management of BESS operating in real-time electricity markets in order to maximize the economic profits by energy arbitrage. The optimization model proposed combines genetic algorithm (GA) with gravitational search algorithm (GSA). On one hand, GA uses an integer codification, where charging, discharging, and disconnection are represented. On the other hand, GSA optimizes the maximum charging or discharging energy. The proposed combination of optimization algorithms allows determining the integer and continuous variables involved in the management problem, taking into account the nonlinear behavior of BESS. The proposed approach was implemented considering lead acid and vanadium redox flow batteries under the conditions of Spanish electricity market.