Representation of wind power generation in economic models for long-term energy planning

Anotacija

The representation of wind power plants electricity generation in economic models for energy planning is problematic, since generation in wind power plants is variable and it is not possible to predict accurately enough wind fluctuations for more than a few days. Often, wind power plants generation patterns from a single historical year are repeated throughout the modelled time period. Typically, this method is used when analysing power system operation in hourly time intervals for all days in a year for each year. However, hourly time resolution is not feasible in large-scale models, which tend to require considerable amounts of computing power. Thus, some kind of time aggregation is needed. On the other hand, currently used methods for models with less than hourly temporal resolution are becoming inadequate because of increasing share of fluctuating electricity production from wind in total power generation. In this article, a methodology for evaluation of wind power plants stochastic electricity generation in power system development models is described. The methodology is based on evaluation of how much time single or multiple wind power plants generate a certain output range during a season or some time period within a year, modelling of output distribution for a typical day of the selected time period, and preparation of electricity output curves. These electricity output curves when modelling wind power plants in models with aggregated time allow to assess fluctuations in generation, observable regularities and enhance the objectivity of balancing power demand assessment, also ensure that electricity generated during a typical day corresponds to electricity generated during the selected time period. This methodology will help to determine the rational perspective power generation mix more accurately and make a better assessment of cost-effectiveness of wind power plants in economic models for energy planning, without significantly increasing the size of already large-scale models.