DESIGN OF MICROGRIDS AS A COST ECONOMY ENERGY SAVINGS SIMULATION MODEL: MONTE CARLO METHOD

被引:2
|
作者
Straka, M. [1 ]
机构
[1] Tech Univ Kosice, Inst Logist & Transport, BERG Fac, Pk Komenskeho 14, Kosice 04200, Slovakia
关键词
Energy Savings Simulation Model; Monte Carlo Method; Local Energy Systems; Microgrids; Simulation; ExtendSim; OPTIMIZATION; SYSTEMS; LOGISTICS; OPERATION; STORAGE; FLOW;
D O I
10.2507/IJSIMM22-4-659
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The article examines the creation of a Cost Economy Energy Savings Simulation Model (CEESS Model) as an economic scenario generator for energy-independent structures using the Monte Carlo method. The CEESS model is a continuous simulation model created on the ExtendSim simulation system platform. The problem is related to the constantly changing environmental parameters for the purpose of energy security for buildings as modern, energy-independent and self-sufficient systems. In terms of the implementation of the defined part of the research, a logistical approach was applied: system analysis, coordination, algorithm work, planning, efficiency. We define logistics as a system, principle, philosophy of management of flows. The numerous simulation experiments carried out show that the return on investment of the option with an initial investment of 5000 euros is in the range of 2422 to 4978 days, the return on investment of the option with an initial investment of 10000 euros is in the range of 4233 to 7902 days and the return on investment of the option with an initial investment of 15000 euros is in the range of 5691 to 10073 days. (Received in May 2023, accepted in August 2023. This paper was with the author 1 month for 2 revisions.)
引用
收藏
页码:586 / 597
页数:202
相关论文
共 50 条
  • [1] A novel stochastic method to dispatch microgrids using Monte Carlo scenarios
    Fioriti, Davide
    Poli, Davide
    ELECTRIC POWER SYSTEMS RESEARCH, 2019, 175
  • [2] Energy and cost savings in household refrigerating appliances: A simulation-based design approach
    Negrao, Cezar O. R.
    Hermes, Christian J. L.
    APPLIED ENERGY, 2011, 88 (09) : 3051 - 3060
  • [3] The Monte Carlo method and the multiagent system simulation
    Martins Ferreira, Ricardo Poley
    ABAKOS, 2012, 1 (01): : 89 - 99
  • [4] Simulation Design and Testing of Gallium Oxide Ultraviolet Detector Based on Monte Carlo Method
    Lu, Shuxiang
    Zhang, Zhihao
    LASER & OPTOELECTRONICS PROGRESS, 2025, 62 (07)
  • [5] Monte Carlo Simulation of Energy Distribution of Radiation Field
    Liu Yi
    Chen Xiao-Bai
    Wang Chun-Yan
    CEIS 2011, 2011, 15
  • [6] Cost sensitivity analysis by Monte Carlo simulation
    Woinaroschy, Alexandru
    ENVIRONMENTAL ENGINEERING AND MANAGEMENT JOURNAL, 2008, 7 (03): : 173 - 177
  • [7] Design Method of Longitudinal Bracing Force Based on Monte Carlo Simulation
    Zhao Jinyou
    Guo Xun
    Liu Wancheng
    Zuo Hongliang
    ADVANCES IN CIVIL ENGINEERING, PTS 1-6, 2011, 255-260 : 1944 - +
  • [8] Application of Monte Carlo Simulation to Reliability Design
    Hanaki, Satoshi
    Kurashiki, Tetsusei
    JOURNAL OF JAPANESE SOCIETY OF TRIBOLOGISTS, 2011, 56 (11) : 686 - 691
  • [9] Stochastic Model Predictive Control for Microgrids Based on Monte Carlo Simulations
    Sezgin, Mustafa Erdem
    Pouraltafi-Kheljan, Soheil
    Beyarslan, Mehmet
    Gol, Murat
    2022 57TH INTERNATIONAL UNIVERSITIES POWER ENGINEERING CONFERENCE (UPEC 2022): BIG DATA AND SMART GRIDS, 2022,
  • [10] MEMS yield simulation with Monte Carlo method
    Xiong, Xingguo
    Wu, Yu-Liang
    Jone, Wen-Ben
    INNOVATIVE ALGORITHMS AND TECHNIQUES IN AUTOMATION, INDUSTRIAL ELECTRONICS AND TELECOMMUNICATIONS, 2007, : 501 - +