Evaluating the Impact of Population Growth and Applying Decarbonization Regulations on the Energy Market in the ERCOT Interconnection Area

TL;DR

This paper assesses how population growth and decarbonization policies could influence ERCOT's energy system by 2030, highlighting the importance of demographic factors and carbon pricing in long-term planning.

Contribution

It introduces a capacity expansion model analyzing multiple population and decarbonization scenarios, emphasizing the role of demographic uncertainty and carbon pricing in energy planning.

Findings

Population growth raises system costs and unmet demand.

Carbon pricing shifts generation toward renewables and reduces natural gas use.

Emissions limits have minimal impact due to ERCOT's expected compliance.

Abstract

Texas ERCOT is experiencing rapid population growth that increases electricity demand and creates new reliability challenges. At the same time, decarbonization policies are being considered, adding uncertainty about future generation portfolios and system costs. This study evaluates how population driven load growth and proposed decarbonization regulations may affect ERCOT in 2030 using a capacity expansion model. Three population scenarios are analyzed: baseline, lower growth, and higher growth. For each scenario, additional cases introduce a CO2 emissions limit and a carbon price to assess their effects on system costs, generation mix, and non served energy. Results show that population growth increases total system costs and unmet demand, while storage needs and transmission transfer levels remain relatively stable. A CO2 emissions limit has almost no effect because ERCOT is already expected to meet the 2030 threshold. In contrast, carbon pricing produces a meaningful shift toward renewable generation and reduced use of natural gas, although cost impacts vary by scenario. These findings show the importance of accounting for demographic uncertainty in long term planning and suggest that carbon pricing may be a more effective decarbonization mechanism than static emissions limits. The study also provides a basis for future stochastic modeling that incorporates uncertainty in demand, renewable output, and fuel prices.