Abstract
In the assessment of the features of strategic bidding choice issues, this paper proposes a novel procedure that optimizes strategic bidding using Intelligent Gravitational Search Algorithm (IGSA) for profit maximization of power suppliers in an emerging power market. In this paper, two approaches are suggested. One suggests using the inverse agents in the assessment procedure of GSA. It empowers improved investigation of the exploration space and avoids trapping of the solution in a local optimum result. Another is a new gravity constant control procedure to avoid repetitive calculation and enhance the speed of convergence. The suggested procedure has been tested on the IEEE 30-bus system. The experimental solutions of both result qualities in terms of profit and calculation efficiency demonstrate the efficacy and strength of IGSA to other approaches such as Shuffled Frog Leaping Algorithm (SFLA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Monte Carlo (MC).
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Singh, S., Fozdar, M., Singh, A.K. (2020). Optimal Strategic Bidding Using Intelligent Gravitational Search Algorithm for Profit Maximization of Power Suppliers in an Emerging Power Market. In: Kalam, A., Niazi, K., Soni, A., Siddiqui, S., Mundra, A. (eds) Intelligent Computing Techniques for Smart Energy Systems. Lecture Notes in Electrical Engineering, vol 607. Springer, Singapore. https://doi.org/10.1007/978-981-15-0214-9_102
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DOI: https://doi.org/10.1007/978-981-15-0214-9_102
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