WILMAR Planning Tool

WILMAR was developed by an international consortium in the EU funded WILMAR project [1]. It is used to analyse the optimal operation of power systems treating wind power production forecasts and load forecasts as stochastic input parameters. So far two versions of the model have been created but the number of users is unknown. The first version was created in 2006, and the model will be commercially available in the future but a price has not been decided. To use the model, 2 to 3 months of training is necessary.

WILMAR consists of a number of sub-models and databases. It is typically used to simulate international energy systems over a one year time-horizon. WILMAR’s functionality is embedded in a Scenario Tree Tool (STT) and a Scheduling model (SM). The Scenario Tree Tool generates stochastic scenario trees containing three input parameters to the Scheduling Model: the demand for positive reserves with activation times longer than 5 minutes and for forecast horizons from 5 minutes to 36 hours ahead (in the following named replacement reserve), wind power production forecasts and load forecasts. The main input data for the Scenario Tree Tool is wind speed and/or wind power production data, historical electricity demand data, assumptions about wind production forecast accuracies and load forecast accuracies for different forecast horizons, and data of outages and the mean time to repair power plants. The demand for replacement reserves corresponds to the total forecast error of the power system considered which is defined according to the hourly distribution of wind power and load forecast errors and according to forced outages of conventional power plants. The calculation of the replacement reserve demand by the Scenario Tree Tool enables WILMAR to quantify the effect that partly predictable wind power production has on the replacement reserve requirements for different planning horizons (forecast horizons). All thermal generation, electricity storage and renewable technologies are considered by WILMAR except solar thermal and geothermal. The Scheduling model is a mixed integer, stochastic, optimisation model with the demand for replacement reserves, wind power production forecasts and load forecasts as the stochastic input parameters, and hourly time-resolution. The model minimises the expected value of the system operation costs consisting of fuel costs, start-up costs, costs of consuming CO2 emission permits and variable operation and maintenance costs. The expectation of the system operation costs is taken over all given scenarios of the stochastic input parameters. Thereby it has to optimise the operation of the whole power system without the knowledge which one of the scenarios will be closest to the realisation of the stochastic input parameter, for example the actual wind power generation. Hence, some of the decisions, notably start-ups of power plants, have to be made before the wind power production and load (and the associated demand for replacement reserve) is known with certainty. The methodology ensures that these unit commitment and dispatch decisions are robust towards different wind power prediction errors and load prediction errors as represented by the scenario tree for wind power production and load forecasts. Finally, all types of electric vehicles can be simulated using the model but no hydrogen technologies are accounted for.

WILMAR has previously been used to analyse the change in operation costs within the electricity sector due to increased wind penetrations [2], to simulate the integration of wind power onto the Nordic energy-system [3], to evaluate how electric boilers and heat pumps can improve the feasibility of large wind-penetrations [4], to identify the consequences of increased wind power on the island of Ireland [5], and to analyse the importance of rolling planning and stochastic optimisation [6].


  1. WILMAR: WInd Power Integrated in Liberalised Electricity Markets, Risoe National Laboratory, 27th April 2009, http://www.wilmar.risoe.dk/
  2. Meibom, P., Weber, C., Barth, R. & Brand, H., Operational costs induced by fluctuating wind power production in Germany and Scandinavia. IET Renewable Power Generation, 3(1), pp. 75-83, 2009.
  3. Sørensen, P., Norheim, I., Meibom, P. & Uhlen, K., Simulations of wind power integration with complementary power system planning tools. Electric Power Systems Research, 78(6), pp. 1069-1079, 2008.
  4. Meibom, P., Kiviluoma, J., Barth, R., Brand, H., Weber, C. & Larsen, H. V., Value of Electric Heat Boilers and Heat Pumps for Wind Power Integration. Wind Energy, 10(4), pp. 321-337, 2007.
  5. Meibom, P., Barth, R., Brand, H., Hasche, B., Ravn, H. & Weber, C. All-Island Grid Study: Wind Variability Management Studies. Department of Communications Energy and Natural Resources (Ireland), 2008,http://www.dcenr.gov.ie/Energy/Latest+News/All-Island+Grid+Study+Published.htm.
  6. Tuohy, A., Meibom, P., Denny, E. & O’Malley, M., Unit Commitment for Systems With Significant Wind Penetration. Power Systems, IEEE Transactions on, 24(2), pp. 592-601, 2009.