A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid. (April 2018)

• Record Type: Journal Article
• Authors: Mohseni, Soheil; Moghaddas-Tafreshi, Seyed Masoud
• Is Part Of: Sustainable cities and society. Volume 38(2018)
• Page Start: 452
• Page End: 465
• Publication Date: 2018-04
• Subjects: Multi-agent system -- Optimal sizing -- Microgrid -- Distributed energy resources -- Demand-side management; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2018.01.016
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Optimal sizing of a cooperative self-sustainable multi-carrier microgrid that includes multiple stakeholders is considered. A multi-agent system (MAS) with five different agents that are organized in three levels is developed. According to the considered demand-side management strategy, the optimal sizes of the microgrid components are reduced. Using the proposed MAS reduces the overall cost of the microgrid. Abstract: In this paper, an interactive multi-agent system (MAS) is applied to the problem of optimal sizing of a cooperative self-sustainable multi-carrier microgrid that includes various privately-owned entities. The proposed microgrid includes photovoltaic (PV) arrays, batteries, an electrolyzer, a hydrogen tank, a fuel cell (FC), a reactor-reformer system, a hydrogen compressor-dispenser system, a converter, residential electrical loads, and a charging/refilling station. The proposed MAS enables information exchange required for the application of demand-side management (DSM) strategy and has five agents, namely generation agent (GA), electrical load agent (LA), charging/refilling station agent (SA), control agent (CA), and design agent (DA). The GA is responsible for managing the distributed energy resources of the microgrid. The LA aggregates the residential electrical loads. The SA is responsible for charging of plug-in hybrid electric vehicles (PHEVs) and refilling of fuel cell electric vehicles (FCEVs). The CA coordinates the interactions between the field level agents. The DA finds the optimal sizes of the system's components by minimizing the total cost of the system through particle swarm optimization (PSO) algorithm. Simulation results demonstrate that the proposed system can reduce the overall cost of the microgrid in comparison with non-interactive methods.