Modeling Investment Decisions from Heterogeneous Firms under Imperfect Information and Risk in Wholesale Electricity Markets

Muhammad Anwar, Gord Stephen, Sourabh Dalvi, Bethany Frew, Sean Ericson, Maxwell Brown, Mark O'Malley

Research output: Contribution to journalArticlepeer-review

27 Scopus Citations

Abstract

Investment decisions in the electricity sector are complex and depend on wholesale market and policy structures, attributes of investor firms that impact risk and financing, and the location-specific economics of investment options. This paper introduces the Electricity Markets and Investment Suite - Agent-Based Simulation (EMIS-AS), which models the evolution of the electricity generation mix under various market structures while explicitly capturing the aforementioned investment factors and imperfect information. EMIS-AS advances the state-of-the-art of generation expansion planning and agent-based modeling by incorporating various aspects of investor heterogeneity (e.g., differences in financial characteristics, technology preferences, and attitudes towards risk under uncertainty), a robust price prediction methodology, a methodology for updating investors’ forecast parameters using Kalman Filters, and endogenous representation of a customizable set of wholesale electricity markets including energy, ancillary services, capacity, and renewable energy certificate markets. Implementation of EMIS-AS on a test system highlights the strong role that firms’ heterogeneous attributes have on the investment decisions, generation portfolio, and resulting resource adequacy. In multiple instances, investment and retirement results diverge not only due to each firm's own parameters, but also due to the actions and characteristics of other firms. Results also demonstrate how imperfect information and risk preferences can lead to suboptimal investment outcomes, which can require firm-level recourse actions with severe profitability implications. In addition, a comparison with a traditional generation expansion planning model highlights the ability of EMIS-AS to capture resource scarcity and early retirements caused by real-world imperfections that traditional models cannot represent.

Original languageAmerican English
Article number117908
Number of pages16
JournalApplied Energy
Volume306
Issue numberPart A
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

NREL Publication Number

  • NREL/JA-6A40-81490

Keywords

  • Agent-based models
  • Capacity expansion
  • Electricity investments
  • Generation expansion planning
  • Heterogeneous agents
  • Risk aversion
  • Wholesale electricity markets

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