We consider joint design of energy storage management and load scheduling with integrated renewable energy. We aim at optimizing the energy flows and load scheduling simultaneously in order to minimize the overall system cost over a finite time horizon. Our model incorporates battery operational costs and assumes unknown arbitrary dynamics of renewable source, load, and pricing information. Loads are modeled by individual tasks with their own intensities, requested service durations, and maximum and average delay constraints. We use a sequence of problem modification and transformation and employ Lyapunov optimization technique to design the real-time joint control and scheduling policy. Our policy provides an closed-form solution for load scheduling and storage energy management. We show that our proposed algorithm guarantees a bounded performance from an optimal non-causal T-slot look-ahead control policy.

We consider the design of cost-effective management of energy storage with renewable integration for load supply. We take a finite time horizon approach and formulate the control optimization problem aimed at minimizing the system cost over a fixed time period. Recognizing the unpredictable and nonstationary stochastic nature of system dynamics, we assume unknown arbitrary dynamics of renewable generation, load, and electricity pricing in formulating our problem. Furthermore, we incorporate detailed battery operation cost into the system cost. Different from the infinite time horizon problems in existing works, the coupling of control decisions over time, due to finite battery capacity, is more challenging to manage. We develop a special technique to tackle the technical challenges in solving the problem. Through problem modification and transformation, we are able to apply Lyapunov optimization to design a real-time control algorithm that relies only on the current system dynamics. The proposed control solution has a closed-form expression and thus is simple to implement. Through analysis, the proposed algorithm is shown to have a bounded performance gap to the optimal noncausal T-slot lookahead control policy. Simulation studies show the effectiveness of our proposed algorithm as compared with two alternative real-time and noncausal algorithms.

In this work, we study the problem of energy storage management with renewable energy integration by designing online control policy to minimize the long-term time-averaged cost. We take into account system input dynamics, and incorporate the batter operation cost for energy storage into the control optimization. Applying Lyapunov optimization technique, we design an on-line control policy that jointly optimizes the decisions for storage from two energy sources and supply to the consumer, which has bounded performance from the optimal scheme. We provide a close-form solution to our control optimization which renders our policy implementation with minimum complexity. Simulations show that introducing renewable energy can effectively reduce the long-term cost and improve the efficiency of energy storage relative to the battery operation cost.