A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. (October 2015)

• Record Type: Journal Article
• Authors: Karavas, Christos-Spyridon; Kyriakarakos, George; Arvanitis, Konstantinos G.; Papadakis, George
• Is Part Of: Energy conversion and management. Volume 103(2016)
• Page Start: 166
• Page End: 179
• Publication Date: 2015-10
• Subjects: APM Autonomous Polygeneration Microgrid -- CEMS centralized energy management system -- DEMS decentralized energy management system -- EMS energy management system -- FCM Fuzzy Cognitive Map -- MAS Multi Agent System -- NPC Net Present Cost (€) -- PAGE Percepts, Actions, Goals and the Environment -- PSO Particle Swarm Optimization -- RES renewable energy sources -- SCADA supervisory control and data acquisition system; Microgrids -- Polygeneration -- Multi agent systems -- Decentralized energy management -- Fuzzy Cognitive Maps; Direct energy conversion -- Periodicals; Energy storage -- Periodicals; Energy transfer -- Periodicals; Énergie -- Conversion directe -- Périodiques; Direct energy conversion; Periodicals; 621.3105
• DOI: 10.1016/j.enconman.2015.06.021
• Languages: English
• ISSNs: 0196-8904

Abstract

Highlights: A decentralized energy management system based on multi agent systems theory. A decentralized energy management system is technically feasible. A decentralized approach utilizes the devices better than a centralized one. A decentralized energy management system is economically competitive. Abstract: The autonomous polygeneration microgrid topology has been developed in order to cover holistically needs in a remote area such as electrical energy, space heating and cooling, potable water through desalination and hydrogen as fuel for transportation. The existence of an advanced energy management system is essential for the operation of an autonomous polygeneration microgrid. So far, energy management systems based on a centralized management and control have been developed for the autonomous polygeneration microgrid topology based on computational intelligence approaches. A decentralized management and control energy management system can have important benefits, when taking into consideration the autonomous character of these microgrids. This paper presents the design and investigation of a decentralized energy management system for the autonomous polygeneration microgrid topology. The decentralized energy management system gives the possibility to control each unit of the microgrid independently. The most important advantage of using a decentralized architecture is that the managed microgrid has much higher chances of partial operation in cases when malfunctions occur at different parts of it, instead of a complete system breakdown. The designed system was based on a multi-agent system and employed Fuzzy Cognitive Maps for its implementation. It was then compared through a case study with an existing centralized energy management system. The technical performance of the decentralized solution performance is on par with the existing centralized one, presenting improvements in financial and operational terms for the implementation and operation of an autonomous polygeneration microgrid.