Multi-retailer energy procurement in smart grid environment with the presence of renewable energy resources and energy storage system

An electricity retailer, as a profit-oriented company, is an intermediary between large producers and end consumers of electricity. The smart grid structure provides retailers with facilities such as telecommunications infrastructure, energy management systems, distributed generation resources, and energy storage systems to meet the needs of end consumers. Therefore, energy procurement of retailer in smart grid environment is a big problem and challenge. In a way, it is possible to use the capacities of other retailers to achieve the goals of each retailer in such an environment. In this paper, the structure of the network is considered, with several retailers present in the smart grid environment. Therefore, a model has been proposed in which each retailer in his area to settle energy and also in accordance with a cooperative game with other retailers, to reach an agreement with them on the amount of exchangeable power and its price. A distributed method is used to day-ahead energy procurement of retailers. Each retailer clears energy in their area by solving a Mixed Integer Linear Programing (MILP). The Mayfly optimization (MO) algorithm is used to find the best energy exchange price between retailers. To evaluate the efficiency of the proposed method, a network with the presence of three retailers has been studied. Also, 4 different case studies have been used to compare the proposed method with the state without energy exchange between retailers and also to investigate the effect of the presence of renewable energy resources and energy storage system on the profitability of retailers. In the absence of distributed generation resources and energy storage system, the use of the proposed method compared to the method of non-exchange of energy between retailers has increased the total profit of retailers by 2.1 %. This rate of profit increase using the proposed method in the presence of distributed generation sources and energy storage system was 7.8 %. Additionally, to evaluate the efficiency of using MO in the method proposed in this paper, this algorithm has compared with Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) algorithms. Also in this paper, the effect of considering the uncertainty of the pool market price in the proposed method has investigated. In this paper, the effect of parameters of storage systems and wind power generation on retailers' profits has evaluated.