TY - GEN
T1 - Profit Maximization based Optimal Scheduling of a Virtual Power Plant using Red Fox Optimizer
AU - Kumar Pandey, Anubhav
AU - Kumar Jadoun, Vinay
AU - Jayalakshmi, N. S.
N1 - Funding Information:
The first author positively acknowledges the funding organization: DST sponsored INSPIRE Fellowship [IF 190938] for providing continuous financial support. The authors also concede their gratefulness to the host University Manipal Academy of Higher Education for providing a conducive research environment to carry out the planned research work.
Funding Information:
VI. ACKNOWLEDGEMENT The first author positively acknowledges the funding organization: DST sponsored INSPIRE Fellowship [IF 190938] for providing continuous financial support. The authors also concede their gratefulness to the host University Manipal Academy of Higher Education for providing a conducive research environment to carry out the planned research work.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The management of electricity consumption is necessary to tackle the growing demand of electricity in a supervised and efficient manner. A virtual power plant (VPP) is the best way to solve this crisis with the use of technologically advanced software systems to ensure the supply and demand by avoiding the additional cost to setup the necessary distribution networking infrastructure. In this paper, optimal scheduling of the VPP is performed in which the main intention is to enhance the overall net profit that can be beneficial for all the participants involved in the VPP system. The resources considered for the VPP system under study are solar PV, wind power, fuel cell, electric load followed by electricity price and energy market. A newly developed metaheuristic technique Red Fox optimizer (RFO) is utilized to carry out the optimization and the obtained results are compared with various other well-established techniques i.e., genetic algorithm (GA), teaching learning based algorithm (TLBO), ant colony optimization (ACO) and particle swarm optimization (PSO). Numerical results are obtained after 100 independent trials and the comparative analysis shows the effectiveness of the selected algorithm. The computation time required to obtain the optimum value for the net profit is also reduced and the convergence trend is also improved by reaching to the desired solution in lesser number of iterations.
AB - The management of electricity consumption is necessary to tackle the growing demand of electricity in a supervised and efficient manner. A virtual power plant (VPP) is the best way to solve this crisis with the use of technologically advanced software systems to ensure the supply and demand by avoiding the additional cost to setup the necessary distribution networking infrastructure. In this paper, optimal scheduling of the VPP is performed in which the main intention is to enhance the overall net profit that can be beneficial for all the participants involved in the VPP system. The resources considered for the VPP system under study are solar PV, wind power, fuel cell, electric load followed by electricity price and energy market. A newly developed metaheuristic technique Red Fox optimizer (RFO) is utilized to carry out the optimization and the obtained results are compared with various other well-established techniques i.e., genetic algorithm (GA), teaching learning based algorithm (TLBO), ant colony optimization (ACO) and particle swarm optimization (PSO). Numerical results are obtained after 100 independent trials and the comparative analysis shows the effectiveness of the selected algorithm. The computation time required to obtain the optimum value for the net profit is also reduced and the convergence trend is also improved by reaching to the desired solution in lesser number of iterations.
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U2 - 10.1109/PIICON56320.2022.10045206
DO - 10.1109/PIICON56320.2022.10045206
M3 - Conference contribution
AN - SCOPUS:85149387872
T3 - 2022 IEEE 10th Power India International Conference, PIICON 2022
BT - 2022 IEEE 10th Power India International Conference, PIICON 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Power India International Conference, PIICON 2022
Y2 - 25 November 2022 through 27 November 2022
ER -
