Robust Optimal Energy Management of a Residential Microgrid Under Uncertainties on Demand and Renewable Power Generation

• Published in: IEEE transactions on automation science and engineering 2021-04, Vol.18 (2), p.618-637
• Main Authors: Hosseini, Seyed Mohsen, Carli, Raffaele, Dotoli, Mariagrazia
• Format: Article
• Language: English
• Subjects: Alternative energy sources; Batteries; Budgets; Comfort; Decision making; Demand-side management (DSM); Distributed generation; Electric power demand; Electric vehicles; Energy costs; Energy management; Energy storage; Energy transfer; microgrid; Microgrids; Mixed integer; mixed-integer quadratic programming (MIQP); Multiple objective analysis; optimal energy scheduling; Optimal scheduling; Optimization; Quadratic programming; Renewable energy sources; Residential energy; robust control; Robustness; Scheduling; Systems design; Uncertainty; User satisfaction
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2020.2986269

Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users' behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users' demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints' violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners -This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users' payment and constraints' violation rate considering the energy cost saving, the system technical limitations and the users' comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncert