{"title":"Development of a coupled agent-based generation expansion planning tool with a power dispatch model","authors":"","doi":"10.1016/j.egycc.2024.100156","DOIUrl":null,"url":null,"abstract":"<div><p>Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.</p></div>","PeriodicalId":72914,"journal":{"name":"Energy and climate change","volume":null,"pages":null},"PeriodicalIF":5.8000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and climate change","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666278724000321","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.