Real-time demand response strategy base on price and incentive considering multi-energy in smart grid: A bi-level optimization method

Abstract

Real-time pricing (RTP) is an efficient approach for demand side management, which mainly aims at reducing peak-to-average ratio (PAR) and balancing power supply and demand. However, in real life, users’ real power consumption sometimes deviates significantly from the ordering power, which results in waste of power resources. Only the price-based demand response (FBDR) is difficult to tackle the deviation problem, especially in the multi-energy generation system. To address this issue, a two-stage hybrid demand response (DR) strategy, which combines RTP and real-time incentive, has been proposed in this paper to minimize the difference between the real power consumption and the ordering power. The first stage is to determine the optimal ordering power via RTP strategy, and then the power supplier and users reach an agreement of power ordering (APO). The second stage is to minimize the difference between the real power consumption and the ordering power via real-time incentive and APO. According to the random dynamic interaction between users and the power supplier, a bi-level model is constructed. Multi-energy generation and energy storage system (ESS) on the supply side, and photovoltaic (PV) generation on the demand side are also considered in this model. Considering the characteristics of the upper and lower objectives and the benefits of both supply and demand sides, the corresponding distributed optimization algorithm is designed to solve the model. Finally, the real-time price, optimal power consumption and optimal power supply are obtained. The simulation results show that the proposed DR strategy and algorithm not only fully guarantee the benefits of users and the power supplier, but also have a good performance in shaving peak and filling valley.