Autonomous Demand-Side Management in the Future Smart Grid

Abstract

Group Autonomous Demand-Side Management (ADSM) programs provide practical mechanisms to coordinate energy consumption to achieve smart grid-wide objectives, such as reducing the energy cost, reducing the Peak-to-Average Ratio (PAR), and increasing the penetration of Renewable Energy Sources (RESs). In this work, a group ADSM program, where the customers cooperate to reduce their energy cost payment through scheduling the future energy consumption profiles, is investigated. First, an aggregative game is formulated to model the strategic behavior of the customers. Subsequently, in order to consider the computational complexity and limitations of the group ADSM programs, an efficient energy consumption scheduling algorithm based on Tabu Search (TS) is proposed. In addition to the ability of achieving the near-optimal energy schedules, the computational time is reduced to a large extent as compared to the energy scheduling algorithm based on Parallel Monte Carlo Tree Search (P-MCTS) and the benchmark energy scheduling algorithm based on Branch and Bound (BB). Moreover, a billing mechanism that charges customers fairly based on their energy consumption and commitment to abide by the assigned schedules and program rules is developed. Two systems are considered; Single-Source Multiple-Customers (SSMC) system and Multiple-Sources Multiple-Customers (MSMC) system. In the SSMC system, a central energy source is shared among customers, while the MSMC system consists of a central energy source, distributed RESs, and Distributed Storage Elements (DSEs). Simulation results confirm that the proposed billing mechanism enhances the fairness level of the system and the proposed algorithm ensures a considerable reduction in the computational complexity. In addition, due to the utilization of distributed RESs and DSEs in the MSMC system, both the level of greenhouse emissions and the total system cost are guaranteed to be reduced compared to the SSMC system.