2019 journal article

Repurposing an energy system optimization model for seasonal power generation planning

ENERGY, 181, 1321–1330.

By: A. Queiroz  n, D. Mulcahy  n, A. Sankarasubramanian  n, J. Deane *, G. Mahinthakumar  n, N. Lu n , J. DeCarolis n

author keywords: Power generation planning; Unit commitment; Energy system optimization; Seasonal demand forecasts; Mathematical programming
TL;DR: 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. (via Semantic Scholar)
UN Sustainable Development Goal Categories
13. Climate Action (OpenAlex)
Source: Web Of Science
Added: August 12, 2019

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.