A multi-objective market-driven framework for power matching in the smart grid. (April 2018) - Azar, Armin Ghasem; Afsharchi, Mohsen; Davoodi, Mansoor; Bigham, Bahram Sadeghi
Abstract: Smart grids, to facilitate the electricity production, distribution, and consumption, employ information and communication technologies simultaneously. Electricity markets, through stabilizing the electricity prices, attempt to alleviate the challenges of power exchange. On one hand, buyers, by considering their full demand satisfaction, endeavor to purchase the electricity cost-effectively. On the other hand, sellers, by taking their limited electricity generation capacity into account, are interested in increasing their financial benefits. To address this challenge, this paper introduces a highly-functional semi-decentralized power matching framework based on multi-objective optimization techniques executing in a day-ahead electricity market. A two-stage price updating mechanism to continuously balance the electricity prices is also provided. At each time interval, buyers and sellers submit their individual electricity price offers to the market operator. The market operator tunes them and then, announces the electricity market price. A robust multi-objective power matching algorithm is developed to make the matching contracts considering buyers' and sellers' objectives along with grid stability constraints imposed by distribution system operators. It also considers the minimization of electricity distribution loss in the matching procedure. Simulation results indicate that the framework successfully reaches a reasonable balance of aforementioned conflicting objectives while conducing negotiating electricity price offers to an equilibrium. It is shown that the proposed algorithm behaves better compared to well-known multi-objective evolutionary algorithms in terms of both optimizing the social welfare and computational complexity (scalability). Finally, effects of the two-stage price updating mechanism on the stability of the proposed evolutionary algorithm is discussed. Performance comparisons show that the proposed framework outperforms the similar approaches available in the literature.