Energy demand and production forecasting in Pakistan

被引:61
|
作者
Raza, Muhammad Amir [1 ]
Khatri, Krishan Lal [1 ]
Israr, Amber [2 ]
Ul Haque, Muhammad Ibrar [2 ]
Ahmed, Manzar [2 ]
Rafique, Khalid [3 ]
Saand, Abdul Sattar [4 ]
机构
[1] NED Univ Engn & Technol, Dept Elect Engn, Karachi 75270, Sindh, Pakistan
[2] Sir Syed Univ Engn & Technol, Dept Elect Engn, Karachi 75290, Sindh, Pakistan
[3] Informat Technol Board, Azad Jammu Kashmir, Muzaffarabad, Pakistan
[4] Quaid E Awam Univ Engn Sci & Technol, Dept Elect Engn, Nawabshah 67450, Sindh, Pakistan
关键词
Power crises; Energy demand; Domestic energy assets; Power generation; Baluchistan province; Pakistan; ELECTRICITY-GENERATION; SOLAR-RADIATION; SCENARIOS;
D O I
10.1016/j.esr.2021.100788
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Pakistan has been in severe energy crises since the year 2004. Major reasons behind energy crises are lack of use of modeling tools in power planning and policy development, dependence on imported energy sources and poor governance. In this paper, electricity demand forecasting for Pakistan up to the year 2030 and a proposal for utilizing domestic energy resources, such as, coal, natural gas, and solar resources available in Baluchistan province of Pakistan for the electricity needs of the country are presented. Long-range Energy Alternative Planning (LEAP) software is used to develop energy demand model for Pakistan that forecasts energy demand under two scenarios, i.e., baseline scenario and energy conservation scenario for the period from 2018 to 2030. Electrical power generation capacity of coal, natural gas and solar resources is also calculated considering the capacity factor of each type of energy asset using MATLAB. This work suggests that Pakistan's energy demand forecast is 399 TWh under the baseline scenario and 312 TWh under the energy conservation scenario. The power generation potential of domestic energy assets available in Baluchistan is 500.041 TWh in total, which includes Natural Gas 392.768 TWh, Solar 84.935 TWh and Coal 22.338 TWh. This research would help relevant government departments in Pakistan for power capacity development as per the required energy demand and alleviate energy crises in Pakistan.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Electric energy demand forecasting with neural networks
    Carmona, D
    Jaramillo, MA
    González, E
    Alvarez, JA
    IECON-2002: PROCEEDINGS OF THE 2002 28TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY, VOLS 1-4, 2002, : 1860 - 1865
  • [32] Interpretable Forecasting of Energy Demand in the Residential Sector
    Sakkas, Nikos
    Yfanti, Sofia
    Daskalakis, Costas
    Barbu, Eduard
    Domnich, Marharyta
    ENERGIES, 2021, 14 (20)
  • [33] MODELLING AND FORECASTING ENERGY DEMAND: PRINCIPLES AND DIFFICULTIES
    Fischer, Martin
    MANAGEMENT OF WEATHER AND CLIMATE RISK IN THE ENERGY INDUSTRY, 2010, : 207 - 226
  • [34] Modeling and Forecasting Energy Demand in TUCN Buildings
    Cretu, M.
    Czumbil, L.
    Bargauan, B.
    Stet, D.
    Ceclan, A.
    Polycarpou, A.
    Rizzo, R.
    Micu, D. D.
    7TH INTERNATIONAL CONFERENCE ON CLEAN ELECTRICAL POWER (ICCEP 2019): RENEWABLE ENERGY RESOURCES IMPACT, 2019, : 253 - 258
  • [35] Electricity demand forecasting for decentralised energy management
    Williams S.
    Short M.
    Energy and Built Environment, 2020, 1 (02): : 178 - 186
  • [36] Forecasting peak energy demand for smart buildings
    Alduailij, Mona A.
    Petri, Ioan
    Rana, Omer
    Alduailij, Mai A.
    Aldawood, Abdulrahman S.
    JOURNAL OF SUPERCOMPUTING, 2021, 77 (06): : 6356 - 6380
  • [37] Energy models for demand forecasting-A review
    Suganthi, L.
    Samuel, Anand A.
    RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2012, 16 (02): : 1223 - 1240
  • [38] A hybrid procedure for energy demand forecasting in China
    Yu, Shi-wei
    Zhu, Ke-jun
    ENERGY, 2012, 37 (01) : 396 - 404
  • [39] Forecasting peak energy demand for smart buildings
    Mona A. Alduailij
    Ioan Petri
    Omer Rana
    Mai A. Alduailij
    Abdulrahman S. Aldawood
    The Journal of Supercomputing, 2021, 77 : 6356 - 6380
  • [40] Energy demand forecasting by thermodynamic analysis of energy consumed processes
    Stepanov, VS
    Stepanova, TB
    ENERGY SOURCES, 2004, 26 (07): : 647 - 660