Articles written in Sadhana

    • A hybrid genetic algorithm (GA)–particle swarm optimization (PSO) algorithm for demand side management in smart grid considering wind power for cost optimization


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      Demand side management (DSM) program is a key feature of smart grid and extensively used for its reliable functionalities and benefits of customers on electricity bill reduction. A DSM approach based on load shifting for a typical day is considered in this paper for a hierarchical smart grid structure. Renewable energy source such as wind energy is considered here along with conventional power generators. All the participants of electricity market: utility operator, customers and aggregator, wish to get monitory benefits in electricity market.It is quite challenging to ensure benefits to each participant simultaneously. To address the challenge, a multi-objective problem is framed. Further, using weighted sum technique, the multi-objective problem is transformedto a single objective. In this paper, a hybrid genetic algorithm (GA)–particle swarm optimization (PSO) (hybrid GA–PSO) algorithm is proposed to solve the problem developed. The objective of the proposed algorithm is to minimize cost of electricity bill and optimally allocate generation and load demand of a day-ahead market. The proposed hybrid algorithm is used to combine the strength of both GA and PSO algorithms and to help improve its performance by increasing the convergence speed and avoid trapping into local minima. In order to balancebetween exploration and exploitation a decision parameter: fusion factor, is introduced in this algorithm. The simulation results prove that the current approach is able to give financial advantage to all the participants of theelectricity market simultaneously while optimally allocating load and generation profile for a day. It also helps to reduce peak to average ratio (PAR) of load demand and improves the efficiency and economy of smart grid. Theresults have also been compared with a few existing optimization techniques to show effectiveness of the current optimization algorithm.

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