Optimal Multi-Microgrids Energy Management Through Information Gap Decision Theory and Tunicate Swarm Algorithm

Abstract

A multi-microgrid (MMG) consists of several individual microgrids (MGs) within a distribution system to improve the system’s stability and reliability. A MMG can operate in grid-connected or island mode and requires advanced control techniques and effective energy management. This paper proposes a novel energy management approach for a MMG at the tertiary level control (TLC) using an adaptive optimal control model. Operational costs of the MMG are minimized for short-term planning while satisfying operational constraints of the network; the influential indices, the energy not supplied (ENS) and fatigue life (FL), remain balanced. The information gap decision theory (IGDT) is used to consider uncertainties in power generation and consumptions. MATLAB and DigSilent are used simultaneously to model optimally connected individual MGs within a MMG. The Tunicate Swarm Algorithm (TSA) is used for TLC for cost calculation and forming optimal connection models of individual MGs. The proposed method is validated through several case studies, showing superior performance.

Keywords

• Energy management
• fatigue life
• information gap decision theory
• multi-microgrid
• tertiary-level control
• tunicate swarm algorithm