Multiobjective optimization of smart grids considering market power

Abstract

Smart grids gain acceptance for promoting the efficient use of energy resources, based on market prices. These include energy storage systems and electric vehicles; in terms of operation they are complex for controlling the loading / unloading of energy or the buy / sell of it. These networks also encourage demand response programs, that is, according to the price, the users decide how much energy they consume. In addition to promoting the use of renewable energy. This research presents two contributions: 1) The implementation of market power indicators to a mathematical model of smart microgrid and 2) The implementation of a new multiobjective hybrid algorithm called "variable neighborhood search: the differential evolutionary particle swarm". The results are close to the Pareto front with a uniform distribution. Then, the smart microgrid is evaluated with two restrictions: the Herfindahl-Hirschman index and the three biggest bidders' index, the first contribution allows no bidder to exercise market power during the 24 hours, which guarantees a competitive electricity market. The second contribution consists in converting this single objective algorithm to a multiobjective version. The performance of the new multiobjective algorithm is verified with the test problems showing good performance.