  Seasonal climate variations affect electricity demand, which in turn affects
month-to-month electricity planning and operations. Electricity system planning
at the monthly timescale can be improved by adapting climate forecasts to
estimate electricity demand and utilizing energy models to estimate monthly
electricity generation and associated operational costs. The objective of this
paper is to develop and test a computationally efficient model that can support
seasonal planning while preserving key aspects of system operation over hourly
and daily timeframes. To do so, an energy system optimization model is
repurposed for seasonal planning using features drawn from a unit commitment
model. Different scenarios utilizing a well-known test system are used to
evaluate the errors associated with both the repurposed energy system model and
an imperfect load forecast. The results show that the energy system
optimization model using an imperfect load forecast produces differences in
monthly cost and generation levels that are less than 2% compared with a unit
commitment model using a perfect load forecast. The enhanced energy system
optimization model can be solved approximately 100 times faster than the unit
commitment model, making it a suitable tool for future work aimed at evaluating
seasonal electricity generation and demand under uncertainty.
