Dynamic Energy Trading and Load Scheduling Algorithm for the End-User in Smart Grid

• Published in: IEEE access Vol. 8; pp. 189632 - 189645
• Main Authors: Liu, Didi, Xiao, Jiawen, Liu, Junxiu, Yuan, Xiaoming, Zhang, Suping
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Batteries; Delay time; Delays; Dynamic scheduling; Electrical loads; Electricity pricing; Energy; energy trading; flexible loads; Heuristic algorithms; Load; load scheduling; Optimal scheduling; Optimization; Renewable energy sources; Scheduling; Smart grid; Smart grids
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3031325

In the smart grid, the end-users have the opportunity to integrate renewable energy sources (RESs) and participate in two-way energy trading. At the same time, an increasing number of flexible loads (FLs) have been developed for use on the demand side. Thus, this article considers joint energy trading and load scheduling at a end-user with integrated renewable generation. With unknown statistics on renewable generation, loads and electricity prices, we aim at optimizing both energy trading and load scheduling to maximize the long-term average profit of the end-user, subject to load delay constraints. We employ the Lyapunov optimization theory to solve this stochastic problem with a series of problem corrections and transformations that enable us to design a dynamic energy trading and load scheduling algorithm. Within the performance analysis of the algorithm, we further demonstrate that the algorithm not only provides a bounded performance guarantee to the optimal solution that has complete future information, but is also asymptotically equivalent to it as the battery capacity or the delay time of FLs tend to infinity. The simulation results show that the proposed algorithm is superior to other algorithms both in terms of performance and service delay. Moreover, we can achieve a trade-off between comfort and total profit by adjusting the values of the parameters, and analyze the effect of battery capacity on algorithm performance to provide a theoretical basis for the end-user to determine battery capacity.