دانلود مقاله ISI انگلیسی شماره 141906
ترجمه فارسی عنوان مقاله

یک سیستم عامل چند منظوره برای اندازه گیری بهینه یک میکروپرداز چندپارچه پایدار همکاری خود پایدار

عنوان انگلیسی
A multi-agent system for optimal sizing of a cooperative self-sustainable multi-carrier microgrid
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
141906 2018 26 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Sustainable Cities and Society, Volume 38, April 2018, Pages 452-465

ترجمه کلمات کلیدی
سیستم عامل چندگانه، اندازه گیری مطلوب، ریزشبکه، منابع انرژی توزیع شده، مدیریت تقاضای جانبی،
کلمات کلیدی انگلیسی
Multi-agent system; Optimal sizing; Microgrid; Distributed energy resources; Demand-side management;
پیش نمایش مقاله
پیش نمایش مقاله  یک سیستم عامل چند منظوره برای اندازه گیری بهینه یک میکروپرداز چندپارچه پایدار همکاری خود پایدار

چکیده انگلیسی

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.