Economic Dispatch of Microgrid Incorporating Demand Response Using Dragonfly Algorithm

Economic dispatch has a significant importance in power system operation and control. This research presents a novel technique dragonfly algorithm (DA) for solving economic dispatch (ED) problem incorporating demand response (DR) model. In this work, an incentive based demand response model is incor...

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Bibliographic Details
Published in2021 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) pp. 59 - 68
Main Authors Imtiaz, Bisma, Cui, Yuanhui, Zafar, Imran
Format Conference Proceeding
LanguageEnglish
Published IEEE 27.08.2021
Subjects
Computational modeling
Costs
Demand response
dragonfly algorithm
economic dispatch
Generators
Linear programming
microgrid
Microgrids
Renewable energy sources
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Summary:Economic dispatch has a significant importance in power system operation and control. This research presents a novel technique dragonfly algorithm (DA) for solving economic dispatch (ED) problem incorporating demand response (DR) model. In this work, an incentive based demand response model is incorporated in grid-connected microgrid which includes, renewable energy sources (solar photovoltaic sources and wind power sources) and conventional generators (diesel). The objective of the presented economic dispatch is to get minimum fuel cost, minimum transferable power cost and maximum demand response benefit of microgrid operator. The DR model and DA algorithm is verified with the help of two test cases. To validate the efficacy of the proposed algorithm, the results obtained from DA are compared with other well-known algorithms including crow search algorithm (CSA), ant lion optimizer (ALO), particle swarm optimization (PSO) and genetic algorithm (GA). Results have proved that the inclusion of demand response model is effective for optimal economic dispatch for consumers, as well as the utility. DA has outperformed other algorithms in terms of finding best cost for objective function, due to its improved exploration rate. The best generation cost obtained from DA (215.1622/h in test case 1 and 670263.0445/h in test case 2) is minimum. Furthermore the convergence rate of DA (14 s for test case 1 and 23.6 s for test case 2) is found to be faster as compared to other algorithms due to high exploitation rate of DA.
DOI:10.1109/AEECA52519.2021.9574430