An investment behavioral modeling framework for advancing power system transformation toward renewable energy integration

Financial incentives, such as carbon credits and feed-in tariffs, are effective policy tools to mobilize renewable energy investment for combating climate change. However, climate and policy uncertainties also induce substantial financial risks to power companies' investments. A company may view renewable energy as an opportunity or a risky business depending on its perception of how renewable technologies and energy policies evolve. To explore how the diverse response from individual companies affects the power system's adoption of renewables, this study develops an agent-based modeling framework that includes renewable technology advancement, market conditions, and changes in incentive programs in the agents' decision-making. Power companies (i.e., agents) are assumed profit-driven and have different risk attitudes toward climate and energy policy uncertainty. For illustration, we applied the method to the Texas power system as a case study where a group of agents are randomly generated to represent the power companies' aggregated behaviors. Agents' risk attitudes are inferred based on a survey, historical data, and model diagnosis. Results of future scenarios highlight renewable adoption prediction uncertainties and the need to develop holistic modeling approaches to facilitate energy policy and power system planning. This modeling framework creates a flexible representation of the power industry and serves as a building block of our vision toward holistic power system modeling and planning. We discuss future research directions that extend the framework through model coupling for system reliability assessment and improve agent representation regarding risk perception and market dynamics.