Design of an Autonomous Intelligent Demand-Side Management System by using Electric Vehicles as Mobile Energy Storage Units by Means of Evolutionary Algorithms

International audienceEvolutionary Algorithms (EAs), or Evolutionary Computation, are powerful algorithms that have been used in a range of challenging real-world problems. In this paper, we are interested in their applicability on a dynamic and complex problem borrowed from Demand-Side Management (DSM) systems, which is a highly popular research area within smart grids. DSM systems aim to help both end-use consumer and utility companies to reduce, for instance, peak loads by means of programs normally implemented by utility companies. In this work, we propose a novel mechanism to design an autonomous intelligent DSM by using (EV) electric vehicles' batteries as mobile energy storage units to partially fulfill the energy demand of dozens of household units. This mechanism uses EAs to automatically search for optimal plans, representing the energy drawn from the EVs' batteries. To test our approach, we used a dynamic scenario where we simulated the consumption of 40 and 80 household units over a period of 30 working days. The results obtained by our proposed approach are highly encouraging: it is able to use the maximum allowed energy that can be taken from each EV for each of the simulated days. Additionally, it uses the most amount of energy whenever it is needed the most (i.e., high-peak periods) resulting into reduction of peak loads

Optimized energy consumption model for smart home using improved differential evolution algorithm

Abstract: This paper proposes an improved enhanced differential evolution algorithm for implementing demand response between aggregator and consumer. The proposed algorithm utilizes a secondary population archive, which contains unfit solutions that are discarded by the primary archive of the earlier proposed enhanced differential evolution algorithm. The secondary archive initializes, mutates and recombines candidates in order to improve their fitness and then passes them back to the primary archive for possible selection. The capability of this proposed algorithm is confirmed by comparing its performance with three other wellperforming evolutionary algorithms: enhanced differential evolution, multiobjective evolutionary algorithm based on dominance and decomposition, and non-dominated sorting genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective optimization problem representing a smart home with demand response aggregator. Shiftable and non-shiftable loads are considered for the smart home which model energy usage profile for a typical household in Johannesburg, South Africa. In this study, renewable sources include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed algorithm is able to optimize energy usage by balancing load scheduling and contribution of renewable sources, while maximizing user comfort and minimizing peak-to-average ratio