Data-Driven Analysis of the Interaction Between Storage Ownership and Market Behavior
With the Main Document advancement of market liberalization, energy storage, as a key technology for energy transition, has seen diversified investment entities. However, a significant unresolved issue in deregulated markets is how to assess the market power of energy storage, especially considering the diversity of ownership. This paper introduces a data-driven analysis framework to study the interaction between storage ownership and market behavior. The paper establishes a bi-level, agent-based model that effectively captures the dynamic relationship between strategic market behavior of storage and system operation. To enhance the scalability of the model, this paper designs a correlation analysis algorithm based on random forests, elucidating the relationship between different market boundary conditions and storage market behavior. Case study results demonstrate that energy storage exercises market power, particularly by creating flexibility scarcity, can obtain over 16% excess revenue by influencing inter-temporal electricity prices. Furthermore, the ownership of energy storage significantly impacts its market behavior and the transfer of social welfare, increasing approximately 11% excess revenue when collaborating with flexible units. It is hoped that the analysis framework proposed herein will offer valuable insights for the design of market mechanisms and policies pertaining to energy storage.
Authors
- Zun Guo (ORCID: https://orcid.org/0000-0002-1027-4983)
- Zhaoyuan Wu (ORCID: https://orcid.org/0000-0002-9269-0082)
- Ming Zhou (ORCID: https://orcid.org/0000-0002-9226-0496)
- Zili Chen (ORCID: https://orcid.org/0000-0002-2104-5369)
- Yi Ren (ORCID: https://orcid.org/0009-0007-0523-0197)
- Lin Chen
Institutions
- North China Electric Power University (CN)
- Economic Research Institute (BG)
- Economic Research Institute (KZ)
- Tsinghua University (CN)
Publication Details
- Journal
- IEEE Transactions on Industry Applications
- Published
- 2024-09-16
- DOI
- https://doi.org/10.1109/tia.2024.3462304
- Citations
- 7
- Primary Topic
- FinTech, Crowdfunding, Digital Finance
- Type
- article
- Field-Weighted Citation Impact
- 3.70
Funders
- National Natural Science Foundation of China