Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources
Creators
- 1. Metro College, University of Applied Science and Technology, Tehran (Iran, Islamic Republic of)
- 2. Power Control Center (PCC), Tehran Metro, Tehran Urban and Suburban Railway Operation Co (TUSRC), Tehran (Iran, Islamic Republic of)
Description
Highlights: • A comprehensive analysis on new structures of AC and DC systems is provided. • An intelligent method based on multi-objective particle swarm optimization is used. • To increase the availability and reduce network costs, the capacity of a smart microgrid with hybrid RESs is determined. • Optimal design of an AC-DC hybrid microgrid is presented. -- Abstract: Hybrid renewable system is a particular type of energy systems which can be used as Distributed Generation (DG) resources to reduce network losses and increase its efficiency. Overall, at design phase, there are two major constraints: first, availability, and second, the cost of equipment. In this paper, considering these constraints and using DGs as Renewable Energy Sources (RES) including wind turbines and photovoltaics, an intelligent method based on multi-objective particle swarm optimization is utilized. Besides, battery bank has been used as a backup unit and energy storage of the hybrid system to reduce the volatility of RESs. The purposes of this paper are: to provide a comprehensive analysis on new structures of AC and DC systems, and then, to determine the capacity and optimal design with hybrid RESs in a smart microgrid to increase the availability and reduce network costs. In order to demonstrate the possibility of proposed approach, an optimized method is designed and implemented in two scenarios (Basic, and Maximum Renewable). Effectiveness of the proposed approach is applied over a real study case. By comparing the proposed method with multi-objective genetic algorithm, simulation results show that the proposed method has effective performance in reducing costs and improving availability.
Additional details
Identifiers
- DOI
- 10.1016/j.energy.2018.12.083;
- PII
- S0360544218324496;
Publishing Information
- Journal Title
- Energy (Oxford)
- Journal Volume
- 169
- Journal Page Range
- p. 496-507
- ISSN
- 0360-5442
- CODEN
- ENEYDS
INIS
- Country of Publication
- United Kingdom
- Country of Input or Organization
- International Atomic Energy Agency (IAEA)
- INIS RN
- 55017976
- Subject category
- S29: ENERGY PLANNING, POLICY AND ECONOMY;
- Descriptors DEI
- COMPUTERIZED SIMULATION; DC SYSTEMS; DESIGN; ENERGY EFFICIENCY; ENERGY STORAGE; GENETIC ALGORITHMS; HYBRID SYSTEMS; OPTIMIZATION; PERFORMANCE; PHOTOVOLTAIC EFFECT; RENEWABLE ENERGY SOURCES; SOLAR CELLS; WIND TURBINES
- Descriptors DEC
- ALGORITHMS; DIRECT ENERGY CONVERTERS; EFFICIENCY; ENERGY SOURCES; ENERGY SYSTEMS; EQUIPMENT; MACHINERY; MATHEMATICAL LOGIC; PHOTOELECTRIC CELLS; PHOTOELECTRIC EFFECT; PHOTOVOLTAIC CELLS; POWER SYSTEMS; SIMULATION; SOLAR EQUIPMENT; STORAGE; TURBINES; TURBOMACHINERY
Optional Information
- Copyright
- Copyright (c) 2018 Elsevier Ltd. All rights reserved.