Energy management in multi‐microgrids via an aggregator to override point of common coupling congestion. Issue 5 (1st March 2019)

• Record Type: Journal Article
• Authors: Khavari, Farshad; Badri, Ali; Zangeneh, Ali
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 5(2019)
• Page Start: 634
• Page End: 642
• Publication Date: 2019-03-01
• Subjects: power generation economics -- power generation scheduling -- optimisation -- probability -- distributed power generation -- profitability -- energy management systems; multimicrogrids -- microgrid aggregator -- energy management -- point of common coupling congestion -- PCC congestion -- day‐ahead scheduling -- MGA -- Karush‐Kuhn‐Tucker conditions -- virtual pricing algorithm -- Shapley value -- bilevel hierarchical structure; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.5922
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : Generally, the owners of microgrids are not identical; therefore, each microgrid tries to optimise its own profit and maximise its utilisation of the point of common coupling (PCC) capacity to boost its profitability, while being ignorant of its neighbouring microgrids. In the absence of information concerning neighbouring microgrids, data limitations in the PCC may be troublesome, especially in load peak hours. Hence, existence of an agent is vital to manage energy exchanges between microgrids and grid. This study introduces a bi‐level hierarchical structure to manage energy in a system composed of multimicrogrids while considering PCC congestion. In the first level, each microgrid implements its day‐ahead scheduling and declares its probable energy mismatch to an agent, entitled microgrid aggregator (MGA). In the second level, the MGA aims to maximise profit of microgrids considering PCC constraint through a two‐stage optimisation problem. In the first stage, Karush‐Kuhn‐Tucker conditions are used to present a novel virtual pricing algorithm exerted by aggregator for rescheduling of microgrids. After regulating generation of microgrids through a reciprocal process, the resultant profit is fairly divided among microgrids via Shapley value in the second stage. The simulation results reveal the efficiency and reliability of the proposed method.