Adaptive Dynamic Programming-Based Optimal Control Scheme for Energy Storage Systems with Solar Rene

Abstract

In this paper, a novel optimal energy storage control scheme is investigated in smart grid environments with solar renewable energy. Based on the idea of adaptive dynamic programming (ADP), a self-learning algorithm is constructed to obtain the iterative control law sequence of the battery. Based on the data of the real-time electricity price (electricity rate in brief), the load demand (load in brief), and the solar renewable energy (solar energy in brief), the optimal performance index function, which minimizes the total electricity cost and simultaneously extends the batterys lifetime, is established. A new analysis method of the iterative ADP algorithm is developed to guarantee the convergence of the iterative value function to the optimum under iterative control law sequence for any time index in a period. Numerical results and comparisons are presented to illustrate the effectiveness of the developed algorithm.

Existing System

Neural Network.

Proposed System

In this paper, a new iterative ADP algorithm is developed to obtain the optimal battery control for energy storage systems with solar renewable energy. According to the data of the electricity rate, the load, and the solar energy, the energy storage system is described and the optimization objective is established. Based on the established system, the iterative ADP algorithm is implemented, where in each iteration, an iterative control law sequence of a period is obtained instead of a single control law. Next, the properties of the iterative ADP algorithm are analyzed. As the iteration index increases to infinity, we emphasize that the iterative value function for any time index in the period is proven to converge to the optimal performance index function. Finally, numerical experiments and comparisons are presented to show the effectiveness of the iterative ADP algorithm.

Architecture

Energy storage system with solar energy