In this paper, the optimal allocation of battery energy storage in the distribution network is performed for peak shaving and maximizing profitability. To this end, indicator shave been introduced using hourly load information, feeder upgrade cost and electricity sales
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In this paper, the optimal allocation of battery energy storage in the distribution network is performed for peak shaving and maximizing profitability. To this end, indicator shave been introduced using hourly load information, feeder upgrade cost and electricity sales price to various tariffs. Then, using the Analytic Hierarchy Process (AHP), the indicators are weighted and a suitable feeder is indicated for installing energy storage. Then, to achieve the maximum possible peak shaving and maximize profit, an economic objective function is defined to determine the optimal sizing and charge-discharge of the energy storage. The objective function includes the investment and operating cost of battery energy storage and the profits of energy arbitrage, deferring facility investment, environmental issues, and reducing the upstream access cost. Appropriate constraints are considered according to the peak shaving, range of battery power and energy capacity as well as balance in the amount of charge and discharge. Due to the nonlinearity of the objective function, the components involved in the nonlinearity of the objective function are determined according to the heuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Tabu Search (TS)) and then the objective function is solved by the Interior-point linear programming. The results provide the most suitable battery type and optimization method among the introduced batteries and methods while fulfilling the objectives.
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