EXploration of PAtterns in Near-optimal Energy ScEnarios (EXPANSE)

EXPANSE (EXploration of PAtterns in Near-optimal Energy ScEnarios) has the structure of the conventional, bottom-up, technology rich, cost optimization model with perfect foresight. In addition, it has two state-of-the-art features. First, it systematically explores near-optimal energy scenarios. Even with a single set of input assumptions, the model generates a wanted number of near-optimal scenarios, whose total system costs do not exceed the predefined threshold above the costs of the optimal scenario. Despite slightly higher total system costs, such near-optimal scenarios can vary significantly in the composition of the energy system that might be preferable due to other, unmodeled objectives. Second, Monte Carlo technique is used to understand the influence of parametric uncertainty.

In addition to the basic EXPANSE model (Trutnevyte, 2013, 2014), several other versions exist and include the dynamic version D-EXPANSE (Trutnevyte, under review), spatially-explicit version (Trutnevyte et al., 2012), and an interactive version for stakeholder engagement (Trutnevyte, 2014). D-EXPANSE model has been validated through a retrospective modelling exercise of the UK electricity sector 1990-2014 (Trutnevyte, under review) and multi-model comparison of the future UK electricity transition 2010-2050 (Trutnevyte et al., 2014).

Model inputs

  • Technology characterization (capital and operational costs, efficiency, lifetime, availability etc.)
  • Renewable energy resources potential
  • International energy prices (natural gas, oil, imported electricity, etc.)
  • Electricity demands
  • Policies (e.g. nuclear phase out, emission targets, energy security objectives)
  • Discount rate
  • Deviation from cost-optimal scenarios

Model outputs

  • Installed generation capacity (GW)
  • Generated and supplied electricity (TWh)
  • Primary energy demand (TWh)
  • Fuel use in the electricity sector (TWh)
  • Renewable electricity generation (TWh)
  • International electricity trade volume: import and exports (TWh)
  • Hourly dispatch of electricity supply
  • Electricity system cost (fuel, investment, etc.) (CHF)
  • CO2 emissions (Mt-CO2)
  • Heat and electricity generation from CHPs (TWh)


  • Trutnevyte E. Does cost optimization approximate the real-world energy transition? Under review.
  • Trutnevyte E. The allure of energy visions: are some visions better than others? Energy Strategy Reviews 2014, 2, 211-219.
  • Trutnevyte E., Barton J., O'Grady A., Ogunkunle D., Pudjianto D., Robertson E. Linking a storyline with multiple models: a cross-scale analysis of the UK power system transition. Technological Forecasting and Social Change 2014, 89, 26-42.
  • Trutnevyte E. EXPANSE methodology for evaluating the economic potential of renewable energy from an energy mix perspective. Applied Energy 2013, 111, 593-601.
  • Trutnevyte E., Stauffacher M., Schlegel M., Scholz R. W. Context-specific energy strategies: coupling energy system visions with feasible implementation scenarios. Environmental Science & Technology 2012, 46(17), 9240-9248.
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