Two-Layer Optimization Model for Virtual Power Plants Participating in Node-Based Spot Market
- DOI
- 10.2991/978-94-6463-256-9_139How to use a DOI?
- Keywords
- virtual power plants; spot market
- Abstract
Virtual power plants (VPP) can aggregate decentralized resources that are small in scale and lack the ability to optimize management, and follow the market price to enhance the flexible regulating ability of the system. At present, there are few cases of virtual power plants participating in the market in China, and the relevant rules are still in the process of improvement. After the large-scale development of virtual power plants, they face the situation where the same power plant resources are distributed at different nodes and exhibit different operating characteristics. In this study, a two-layer coordination optimization model for multi-types of VPPs to participate in the node-based spot market is proposed, and simulation cases verified the effectiveness of the method, which is able to improve the interaction ability between virtual power plants and the power grid, and optimize market operating costs and efficiency.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Zheng Zhao AU - Shijun Tian AU - Chenghui Tang PY - 2023 DA - 2023/10/09 TI - Two-Layer Optimization Model for Virtual Power Plants Participating in Node-Based Spot Market BT - Proceedings of the 2023 4th International Conference on Management Science and Engineering Management (ICMSEM 2023) PB - Atlantis Press SP - 1379 EP - 1388 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-256-9_139 DO - 10.2991/978-94-6463-256-9_139 ID - Zhao2023 ER -