Real-Time Residential-Side Joint Energy Storage Management and Load Scheduling with Renewable Integration
For residential energy management systems, this work provides a practical real-time algorithm with theoretical performance guarantees, though the approach is incremental.
This paper addresses joint energy storage management and load scheduling for residential sites with renewable integration, aiming to minimize system cost under unknown dynamics. The proposed real-time algorithm achieves a bounded performance guarantee from the optimal T-slot look-ahead solution and is asymptotically optimal as battery capacity and time horizon increase.
We consider joint energy storage management and load scheduling at a residential site with integrated renewable generation. Assuming unknown arbitrary dynamics of renewable source, loads, and electricity price, we aim at optimizing the load scheduling and energy storage control simultaneously in order to minimize the overall system cost within a finite time period. Besides incorporating battery operational constraints and costs, we model each individual load task by its requested power intensity and service durations, as well as the maximum and average delay requirements. To tackle this finite time horizon stochastic problem, we propose a real-time scheduling and storage control solution by applying a sequence of modification and transformation to employ Lyapunov optimization that otherwise is not directly applicable. With our proposed algorithm, we show that the joint load scheduling and energy storage control can in fact be separated and sequentially determined. Furthermore, both scheduling and energy control decisions have closed-form solutions for simple implementation. Through analysis, we show that our proposed real-time algorithm has a bounded performance guarantee from the optimal T-slot look-ahead solution and is asymptotically equivalent to it as the battery capacity and time period goes to infinity. The effectiveness of joint load scheduling and energy storage control by our proposed algorithm is demonstrated through simulation as compared with alternative algorithms.