A mixed-integer optimization model for electricity infrastructure development

Abstract

We formulate a mixed-integer program that can be used to analyze the decision between centralized and decentralized technologies for new energy infrastructure development. The formulation minimizes the cost of meeting both average and peak power demand in each specified demand node. We demonstrate our methodology with a case study of Rwanda, accounting for existing generation and transmission infrastructure. Thirteen ongoing or proposed projects are considered as potential new centralized generation facilities and the decentralized technology is modeled after a small (\(\sim \)50 W) solar home system. The case study is repeated using population data at four different resolutions while varying demand levels and decentralized technology cost. A tipping point effect is observed, where the optimal infrastructure tips from being primarily centralized to primarily decentralized under certain combinations of the demand and cost parameters. These tipping points are largely consistent across the four data resolutions. Finding a solution within 1 % of optimal was often found to be computationally expensive in formulations with greater than approximately 200 nodes and 800 edges. However, formulations using less dense population data are shown to accurately identify the same tipping points while requiring fewer computational resources. Examples of the minimum cost electricity infrastructure in Rwanda are also shown for several specific combinations of parameters.