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Improved Demand Side Management Scheme for Renewable-Energy-Integrated Smart Grid with Short-Term Load Forecasting

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Abstract

Demand side management (DSM) is widely utilized in smart grid for its reliable features, flexibility, and cost benefits that it offers to customers on reduction of the energy bill. In the smart grid, demand response aggregator, power customers, and utility operator all strive to increase their individual profits. However, it is extremely challenging to guarantee profits for all of the candidates simultaneously. In this paper, these criteria are employed to execute a problem with multiple objectives by combining the concept of DSM and dynamic economic emission dispatch considering uncertainties of renewable energy sources (RESs). The uncertainties of RESs can bring adverse effects on the grid operator. Thus, Weibull probability distribution function and lognormal probability distribution function are used to model the uncertainties of wind speed and solar irradiation, respectively, in this paper. Further, the multi-objective improved DSM scheme is optimized by Class Topper optimization algorithm. The objective here is to optimally schedule load demand and generation pattern simultaneously to improve load factor, minimize electricity bill and maximize the profits of all candidates of the day-ahead electricity market simultaneously. Error in the load forecasting model may lead to growing operational cost of energy generation. In this paper, a machine learning model using linear regression is used for short-term load forecasting (STLF) to forecast day-ahead load demand. The simulation results show the importance of the improved DSM scheme and the advantage of STLF, which further help to improve the economy and efficiency of the smart grid.

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Data will be made available on reasonable request to the corresponding authors.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of EEE, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, India

    Chitrangada Roy

  2. Department of EEE, NIT Nagaland, Dimapur, India

    Dushmanta Kumar Das

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  1. Chitrangada Roy
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  2. Dushmanta Kumar Das
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Contributions

CR was involved in methodology, software, validation, formal analysis, investigation, writing—original draft. Dr. DKD did conceptualization, methodology, supervision, writing—review & editing.

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Correspondence to Dushmanta Kumar Das.

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Roy, C., Das, D.K. Improved Demand Side Management Scheme for Renewable-Energy-Integrated Smart Grid with Short-Term Load Forecasting. J Control Autom Electr Syst 35, 74–91 (2024). https://doi.org/10.1007/s40313-023-01047-9

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