Distributed genetic real-time pricing for multiseller-multibuyer smart grid based on bilevel programming considering random fluctuation of electricity consumption

Abstract

As reform and deregulation in the power industry proceed, more than one utility company has emerged in the power grid. In addition, due to the random fluctuation of electricity consumption, the power grid becomes more complex. New appropriate management strategies are required to handle the future smart grid. The real-time pricing (RTP) mechanism is an incentive way to increase energy usage efficiency. In this paper, considering the effect of the random fluctuation of electricity consumption, we propose a distributed genetic RTP scheme for smart grid with multiple utility companies and users based on expectation bilevel programming. We discuss the properties of the model and transform it into a deterministic optimization problem. Using a distributed genetic algorithm (GA), we find the optimal strategy for electricity supply and consumption and obtain the RTP. With this approach, subproblems can be solved individually. In addition, at each iteration, the information exchanged in the integrated system only consists of prices, the optimal electricity consumption, etc., which can be provided by each utility company and user respectively. Therefore, the distributed GA not only helps to protect the privacy of utility companies and users but also lowers the complexity of the computation. Simulation results validate the proposed distributed genetic RTP can significantly reduce peak time loading and efficiently balance system energy distribution while maximizing benefits for both utility companies and users.