Disaggregating and Projecting Electricity Demand in China (2017-2018)


The decisions made today about the electric power sector will have profound and long-lasting implications for the economic and social well-being of citizens, and will affect the availability and prices of energy resources worldwide and the state of our natural environment.

Much of the world is in the midst of a decision about the future of electricity systems. However, there is an important element that separates the planning exercises of the developed countries from those of developing countries: projected growth of electricity demand.

Decisions about electric power development are difficult to make. Achieving energy and environmental goals while satisfying the electric power needs and supporting economic growth requires an information-driven planning process that considers the economic, environmental and reliability implications of using energy sources and key demand-side alternatives such as end-use energy efficiency and demand-side management.

Project Description

While a precise forecast of electricity demand is impossible, a projection of its many drivers and how these may lead to different consumption levels is a sine qua non for robust planning of capacity to generate, transmit and distribute electrical power. The goal of this Bass Connections project is to understand the determinants of electricity demand in order to project different electricity demand scenarios at a high temporal and spatial resolution in developing countries.

As the largest developing country in the world, China faces an even greater challenge in projecting future electricity demand. The project team will focus on a model development for China’s electricity demand projection; in the future this model can be applied to other developing countries.

The mechanistic model for estimating the uncertainty of electricity demand began in 2016 as part of the Bass Connections MOTESA project. The team collected, filtered, analyzed and synthetized high-resolution data on electricity consumption about multiple aspects, such as economic (GDP, industrial structure, income), social (urbanization rate, stocks of housing and appliances), meteorological (temperature and humidity) and technological (energy efficiency) indicators; and gathered information from other energy models for missing data.

The 2017-2018 project team seeks to understand the determinants of electricity demand in order to project different demand scenarios at a high temporal and spatial resolution based on the information gathered in 2016, and to develop a bottom-up framework for characterizing possible futures for electricity demand in China that considers the uncertainties in demand-side technologies and policies. This will be done using the concept of system dynamics, by developing dynamic energy models and then projecting changes in technologies used for different scenarios of socioeconomic conditions, regulatory framework and consumers’ behavioral trends.

Further, the team will take scenarios of projected determinants as key inputs to develop a modeling structure using a series of algorithms that will enable team members to generate a synthetic time series of hourly and sub-hourly electricity demand useful for designing end-use energy efficiency and demand side management programs for planning the future electricity system in China. 

Anticipated Outcomes

Computer-based dynamic model that projects determinants and disaggregating hourly electricity demand in multi-time-scale (short-, middle- and long-term); open-access tool based on the dynamic electricity demand model; report and corresponding database with basic information about the hourly electricity demand by province (2015-2050) and its ingredient in China; peer-reviewed journal articles that project China’s disaggregating electricity demand at a high temporal and spatial resolution under different scenarios


Summer 2017 – Fall 2017 or Spring 2018

This project is designed to be completed during Summer and Fall 2017. Involvement during the summer will be through internships, and involvement during the fall will be for academic credit (3 credit units). Students willing to continue their involvement in the project during Spring 2018 can do so as part of their senior thesis project or master’s project.

  • Summer 2017: Literature review on China’s power system; identification of databases for location screening in China; student contributions submitted and evaluated
  • Fall 2017: Dynamic model for Chinese electric power demand projection; student contributions submitted and evaluated
  • Spring 2018: Simulations of Chinese electricity consumption under different scenarios; student contributions submitted and evaluated; theses, master’s projects and papers based on the project submitted

Team Outcomes to Date

China’s Electricity Future: A Provincial Scenario Analysis towards 2050 (poster by Mingquan Li, Rui Shan, Mauricio Hernandez, Varun Mallampalli, Dalia Patiño-Echeverri), presented at Bass Connections Showcase, April 18, 2018

This Team in the News

Energy Student Profile: Aubrey Zhang (MPP ’18)

See earlier related team, Modeling Tools for Energy Systems Analysis (MOTESA) (2016-2017).

Professor Patino-Echeverri and Postdoctoral Researcher were invited to Peking University China to give a talk

/faculty/staff Team Members

  • Mark Borsuk, Pratt School of Engineering-Civil & Environmental Engineering
  • Mingquan Li, Nicholas School of the Environment-Environmental Sciences and Policy*
  • Varun Mallampalli, Pratt School - Civil and Environmental Engineering-Ph.D. Student*
  • Dalia Patino Echeverri, Nicholas School of the Environment-Environmental Sciences and Policy*

/graduate Team Members

  • Ziting Huang, Master of Environmental Management, Energy and Environment
  • Qingran Li, Environmental Policy-PHD
  • Chuwen Liang, Master of Environmental Management, Environmental Economics/Policy
  • Xuebei Tan, Master of Environmental Management, Energy and Environment
  • Jun Zhang, Master of Environmental Management, Energy and Environment
  • Aubrey (Yuchen) Zhang, Public Policy Studies-MPP

/undergraduate Team Members

  • Yanwen Chen, Economics (AB), Public Policy Studies (AB2)
  • Yutao Gong
  • Lin Zuo, Statistical Science (BS), Computer Science (AB2)