A Computationally Efficient Optimization Approach for Battery Systems in Islanded Microgrid

Abstract

In islanded microgrids, it is a challenge to optimize battery energy storage systems (BESSs) with other power supply units (e.g., renewable energy and traditional power generator) and achieve the minimum daily operational cost. In this paper, we propose a computationally efficient near optimal control approach to tackle this problem. Specifically, a new islanded microgrid model, including the power supply and demand as well as battery lifetime characteristics, has been formulated based on Markov decision process. Then, we propose an approximate dynamic programming (ADP) approach to solve this energy optimization problem, and achieve near minimum operational cost efficiently. We use linear programming (LP) and dynamic programming (DP) approaches to validate the percentage of optimality of our proposed approach for deterministic and stochastic case studies, respectively.

Existing System

Multi-Objective Optimization, Particle Swarm Optimization.

Proposed System

In this paper, the optimal operation of energy systems in an islanded microgrid is investigated. Extensive simulations were conducted to validate the effectiveness of the proposed ADP approach. The traditional LP and DP were used for the deterministic and stochastic case study, respectively. the main contributions of this paper are as follows: A new energy optimization problem for islanded microgrid is formulated as a Markov decision process (MDP), where the wind energy, the BESS, and the diesel generator models are taken into consideration. A proper control strategy for SOC is also developed for the healthy operation of the BESS. Different from the other prior works, the proposed model considers the operation of the islanded microgrid, and the uncertainty of wind energy and the battery lifetime characteristics are included. An efficient ADP approach is proposed to solve the energy optimization problem formulated above on both deterministic and stochastic cases. ADP can achieve the same optimality performance as that of linear programming (LP) in deterministic cases and competitive optimality performance as that of dynamic programming (DP) for stochastic cases.

Architecture

BLOCK DIAGRAM