Repurposing an Energy System Optimization Model for Seasonal Power Generation Planning

TL;DR

This paper adapts an energy system optimization model for seasonal power planning, achieving near-accurate cost estimates and significantly faster computation, thus supporting efficient seasonal electricity system analysis under uncertainty.

Contribution

It introduces a computationally efficient adaptation of an energy system model for seasonal planning, maintaining key operational details while enabling faster analysis.

Findings

Less than 2% difference in cost and generation estimates compared to unit commitment models.

Model solves approximately 100 times faster than traditional unit commitment models.

Effective for evaluating seasonal electricity demand and generation under uncertainty.

Abstract

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.