Forecasting Indonesia's electricity load through 2030 and peak demand reductions from appliance and lighting efficiency

被引:67
|
作者
McNeil, Michael A. [1 ]
Karali, Nihan [1 ]
Letschert, Virginie [1 ]
机构
[1] Lawrence Berkeley Natl Lab, Energy Technol Area, One Cyclotron Rd,MS 90R2121, Berkeley, CA 94720 USA
关键词
Energy efficiency; Indonesia; Lighting; Appliances; Electricity demand forecasting; Peak load; IMPROVEMENT OPPORTUNITIES; TERM; CONSUMPTION; MODELS;
D O I
10.1016/j.esd.2019.01.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Indonesia's electricity demand is growing rapidly, driven by robust economic growth combined with unprecedented urbanization and industrialization. Energy-efficiency improvements could reduce the country's electricity demand, thus providing monetary savings, greenhouse gas and other pollutant reductions, and improved energy security. Perhaps most importantly, using energy efficiency to lower peak electricity demand could reduce the risk of economically damaging power shortages while freeing up funds that would otherwise be used for power plant construction. We use a novel bottom-up modeling approach to analyze the potential of energy efficiency to reduce Indonesia's electricity demand: the LOAD curve Model (LOADM) combines total national electricity demand for each end use-as modeled by the Bottom-Up Energy Analysis System (BUENAS)-with hourly end-use demand profiles. We find that Indonesia's peak demand may triple between 2010 and 2030 in a business-as-usual case, to 77.3 GW, primarily driven by air conditioning and with important contributions from lighting and refrigerators. However, we also show that appliance and lighting efficiency improvements could hold the peak demand increase to a factor of two, which would avoid 26.5 GW of peak demand in 2030. These results suggest that well-understood programs, such as minimum efficiency performance standards, could save Indonesia tens of billions of dollars in capital costs over the next decade and a half. (C) 2019 The Authors. Published by Elsevier Inc. on behalf of International Energy Initiative.
引用
收藏
页码:65 / 77
页数:13
相关论文
共 10 条
  • [1] Daily Peak Load Forecasting for Electricity Demand by Time series Models
    Lee, Jeong-Soon
    Sohn, H. G.
    Kim, S.
    KOREAN JOURNAL OF APPLIED STATISTICS, 2013, 26 (02) : 349 - 360
  • [2] Reducing household electricity consumption through demand side management: the role of home appliance scheduling and peak load reduction
    Laicane, Ilze
    Blumberga, Dagnija
    Blumberga, Andra
    Rosa, Marika
    INTERNATIONAL SCIENTIFIC CONFERENCE ENVIRONMENTAL AND CLIMATE TECHNOLOGIES, CONECT 2014, 2015, 72 : 222 - 229
  • [3] Forecasting China's electricity demand up to 2030: a linear model selection system
    Zhu, Xinzhi
    Yang, Shuo
    Lin, Jingyi
    Wei, Yi-Ming
    Zhao, Weigang
    JOURNAL OF MODELLING IN MANAGEMENT, 2018, 13 (03) : 570 - 586
  • [4] A method for distinguishing appliance, lighting and plug load profiles from electricity 'smart meter' datasets
    George, Dane
    Swan, Lukas G.
    ENERGY AND BUILDINGS, 2017, 134 : 212 - 222
  • [5] DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?
    Yilmaz, S.
    Rinaldi, A.
    Patel, M. K.
    ENERGY POLICY, 2020, 139 (139)
  • [6] ECONOMICS OF MEETING PEAK ELECTRICITY DEMAND USING HYDROGEN AND OXYGEN FROM BASE-LOAD NUCLEAR OR OFF-PEAK ELECTRICITY
    Forsberg, Charles W.
    NUCLEAR TECHNOLOGY, 2009, 166 (01) : 18 - 26
  • [7] Energy Savings Through Refrigeration Load Control With Assessment of Commercial Potential: Lowering peak power and electricity bills through optimal demand scheduling
    Wilson, Vince
    Li, Fangxing
    Thornburg, Jesse
    Mohammadi, Javad
    Martinez, Justin
    IEEE ELECTRIFICATION MAGAZINE, 2024, 12 (01): : 66 - 76
  • [8] Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method
    Owusu, Frank Kofi
    Amoako-Yirenkyi, Peter
    Frempong, Nana Kena
    Omari-Sasu, Akoto Yaw
    Mensah, Isaac Adjei
    Martin, Henry
    Sakyi, Adu
    HELIYON, 2023, 9 (08)
  • [9] Analysis and Modeling for China's Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission
    Liang, Yi
    Niu, Dongxiao
    Cao, Ye
    Hong, Wei-Chiang
    ENERGIES, 2016, 9 (11)
  • [10] A Hybrid bVAR-NARX Wind Power Forecasting Model Based on Wind and Load Demand Correlation: A Case Study of ERCOT's System from an ISO's Perspective
    Heistrene, Leena
    Mishra, Poonam
    Lokhande, Makarand
    ELECTRIC POWER COMPONENTS AND SYSTEMS, 2018, 46 (14-15) : 1634 - 1649