Bi-level Programming of Retailer and Prosumers' Aggregator to Clear the Energy of the Day Ahead Using the Combined Method of Mixed Integer Linear Programming and Mayfly Optimization in Smart Grid

In the restructured electricity industry, the electricity retailer, as a profit-oriented company, buys electricity from wholesale electricity markets and sells it to end customers. On the other hand, with the move of the electricity networks towards smart grids, small customers who, in addition to receiving energy from the distribution network, can generate power on a small scale, have emerged as prosumers in the electricity market environment. Therefore, the prosumers' aggregator is defined to maximize the profit of a set of prosumers in this environment. In this paper, the energy exchange between the retailer and the aggregator has been modeled as a bi-level game. At a higher level, the retailer, as a leader to maximize its profit or minimize its expenses, offers a price to buy or sell energy to the prosumers' aggregator. The aggregator also decides on the amount of exchange energy to buy or sell, to minimize the energy supply costs required of its consumers according to the retailer's bid price. In this paper, a combined method based on~MILP (Mixed Integer Linear Programming)~and MO (Mayfly Optimization) has been used to find the optimal point of this modeled game. To evaluate the efficiency of the proposed method, the three pricing methods FP (Fixed Pricing),~TOU (Time Of Using), and RTP (Real Time Pricing) as price-based demand response programs have been compared using the proposed algorithm. The simulation results show that among the three pricing methods for customers, the RTP pricing method has the highest profit for the retailer and the lowest cost for the aggregator.