Real-Time Energy Storage Management for Renewable Integration in Microgrid: An Off-Line Optimization Approach

• Published in: IEEE transactions on smart grid Vol. 6; no. 1; pp. 124 - 134
• Main Authors: Rahbar, Katayoun, Jie Xu, Rui Zhang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Convex optimization; distributed storage; Dynamic programming; Energy management; Energy storage; Load modeling; microgrid; Microgrids; online algorithm; Optimization; Prediction algorithms; Real-time systems; renewable energy; Renewable energy sources; Renewable resources; smart grid; renewable energy; smart grid; distributed storage; Convex optimization; microgrid; online algorithm; energy management
• ISSN: 1949-3053; 1949-3061
• DOI: 10.1109/TSG.2014.2359004

Microgrid is a key enabling solution to future smart grids by integrating distributed renewable generators and storage systems to efficiently serve the local demand. However, due to the random and intermittent characteristics of renewable energy, new challenges arise for the reliable operation of microgrids. To address this issue, we study in this paper the real-time energy management for a single microgrid system that constitutes a renewable generation system, an energy storage system, and an aggregated load. We model the renewable energy offset by the load over time, termed net energy profile, to be practically predictable, but with finite errors that can be arbitrarily distributed. We aim to minimize the total energy cost (modeled as sum of time-varying strictly convex functions) of the conventional energy drawn from the main grid over a finite horizon by jointly optimizing the energy charged/discharged to/from the storage system over time subject to practical load and storage constraints. To solve this problem in real time, we propose a new off-line optimization approach to devise the online algorithm. In this approach, we first assume that the net energy profile is perfectly predicted or known ahead of time, under which we derive the optimal off-line energy scheduling solution in closed-form. Next, inspired by the optimal off-line solution, we propose a sliding-window based online algorithm for real-time energy management under the practical setup of noisy predicted net energy profile with arbitrary errors. Finally, we conduct simulations based on the real wind generation data of the Ireland power system to evaluate the performance of our proposed algorithm, as compared with other heuristically designed algorithms, as well as the conventional dynamic programming based solution.