A hierarchical energy management strategy for interconnected microgrids considering uncertainty. (July 2019)

• Record Type: Journal Article
• Authors: Bazmohammadi, Najmeh; Tahsiri, Ahmadreza; Anvari-Moghaddam, Amjad; Guerrero, Josep M.
• Is Part Of: International journal of electrical power & energy systems. Volume 109(2019)
• Page Start: 597
• Page End: 608
• Publication Date: 2019-07
• Subjects: Energy management -- Multi-microgrid system -- Chance-constrained model predictive control -- Uncertainty management -- Monte-Carlo algorithm; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2019.02.033
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Developing a hierarchical energy management system for a network of microgrids. Modeling the effects of uncertainty in exchanged power among the microgrids. Modeling the energy management problem in the framework of chance-constrained MPC. Proposing a communication-based strategy to benefit from updated information. Making a comparative analysis to evaluate the robustness characteristics. Abstract: Coordinating the operation of neighboring microgrids is a promising solution for the problem of growing penetration of renewable-based microgrids into the power system. In this paper, a hierarchical stochastic energy management system is proposed for operation management of interconnected microgrids. At the upper-level, a central entity is responsible for coordinating the operation of microgrids. Based on the energy scheduling made at this level, the power reference values to be exchanged within the microgrids network and between the microgrids and the main grid are calculated and communicated with the local energy management systems. At the lower-level, a decision making approach based on chance-constrained model predictive control is adopted for local operation management of each microgrid taking into account different sources of uncertainties. The results show that the proposed strategy provides the microgrids with the opportunity of exploiting maximum available capacity in the network. Consequently, the microgrids dependency on the main grid will be reduced and some important performance indices such as multi-microgrid system cost and real-time power deviations will be improved.