Optimum Energy Management of Islanded Nanogrids through Nonlinear Stochastic Dynamic Programming

Abstract

Nanogrids (NGs) are small scale microgrids typically serving few buildings or loads. An islanded NG is an autonomous system that consists of generation units including renewable energy sources and traditional fuel generators, energy storage systems (ESS) and loads. This paper presents the design and validation of a new optimal energy management (EM) algorithm for an islanded NG. To minimize the generator's operating cost and maximize battery availability at each operating cycle, dynamic programming (DP) framework is employed to solve the underlying optimization problem. The goal of the proposed EM algorithm is to ensure both the use of maximum available solar power and optimal battery state of charge. To meet that goal, the management of the ESS is formulated as a stochastic optimal control problem, where nonlinearities in the battery charging and discharging process are considered. A Markov model is built in order to predict the probability distribution of the solar production used in the stochastic DP formulation. Simulation results are given to illustrate the efficacy of the proposed DP-based approach compared to a rule-based algorithm. Finally, a hardware-in-the-loop system is used to evaluate the real-time operation of the proposed EM algorithms.