The nonlinear, equilibrium ENPEP-BALANCE model matches the demand for energy with available resources and technologies. It was developed by Argonne National Laboratory in the USA in 1999. The exact number of users is not known but it is in use in over 50 countries. The model can be downloaded for free from [1], and takes approximately one week of training for basic applications, and two weeks of training for advanced applications. There is support forum specifically for ENPEP-BALANCE at [2].

ENPEP-BALANCE uses a market-based simulation approach to determine the response of various segments of the energy system to changes in energy prices and demand levels. The analysis is carried out on an annual basis for up to a maximum of 75 years, typically on national energy-systems. The model relies on a decentralized decision-making process in the energy sector and can be calibrated to the different preferences of energy users and suppliers. Basic input parameters include information on the entire energy-system structure; these include base year energy statistics, including production and consumption levels, and prices; which include projected energy demand growth; and any technical and policy constraints. In this process, an energy network is designed to trace the flow of energy from primary resources to useful energy demands in the end-use sectors. ENPEP-BALANCE networks are constructed using different nodes and links, which represent various energy system components. Nodes in the network represent diminishable and renewable resources, various conversion processes, refineries, thermal and hydro power stations, cogeneration units, boilers and furnaces, marketplace competition, taxes and subsidies, and energy demands. The only technologies not accounted for are pumped-hydro, battery and compressed-air energy-storage. Links connect the nodes and transfer information among nodes. The energy network is not hardwired but rather designed by the user on-screen. The model employs a market share algorithm to estimate the penetration of supply alternatives. The market share of a specific commodity is sensitive to the commodity’s price relative to the price of alternative commodities. User-defined constraints, consumer preferences, and the ability of markets to respond to price signals over time also affect the market share of a commodity. ENPEP-BALANCE simultaneously finds the intersection of supply and demand curves for all energy supply forms and all energy uses included in the energy network. Equilibrium is reached when the model finds a set of market clearing prices and quantities that satisfy all relevant equations and inequalities. The model employs the Jacobi iterative technique to find the solution that is within a user-defined convergence tolerance.

Some of the case studies which ENPEP-BALANCE has been used for include analysing Mexico’s future energy needs and estimating the associated environmental burdens [3], developing GHG emissions projections for Turkey [4], and a GHG mitigation analyses for Bulgaria [5]. A full range of other publications that ENPEP-BALANCE participated in is available throughout the world is available at [6]. Finally, ENPEP-BALANCE has been used to simulate near 20% of the electricity production [7] from renewable-energy sources within an energy system.


  1. Energy and Power Evaluation Program (ENPEP-BALANCE), Argonne National Laboratory, 23rd April 2009,http://www.dis.anl.gov/projects/Enpepwin.html.
  2. ADICA Energy Forum, ADICA, 22 April 2010, http://www.adicasupport.com/wrapper/energy_forum.html.
  3. Comparative assessment of energy options and strategies in Mexico until 2025, International Atomic Energy Agency, 2005, http://www.dis.anl.gov/news/MexicoEnergy.html.
  4. Conzelmann, G. & Koritarov, V. Turkey Energy and Environmental Review. Argonne National Laboratory, 2002, http://www.dis.anl.gov/news/TurkeyUndp.html.
  5. Republic of Bulgaria: The First National Communication on Climate Change, The United Nations, 1996,http://unfccc.int/resource/docs/natc/bulnc1.pdf.
  6. ENPEP Applications, Argonne National Laboratory, 23rd April 2009,http://www.dis.anl.gov/news/EnpepwinApps.html.
  7. Mirsagedis, S., Conzelmann, G., Georgopoulou, E., Koritarov, V. & Sarafidis, Y., Long-Term GHG Emissions Outlook for Greece, Proc. of the 6th IAEE European Conference on Modelling in Energy Economics and Policy, Zurich, Switzerland, 2-3 September.