WeChat Mini Program
Old Version Features

Convex-hull Pricing of Ancillary Services for Power System Frequency Regulation with Renewables and Carbon-Capture-Utilization-and-Storage Systems

IEEE Transactions on Power Systems(2024)

Xi An Jiao Tong Univ

Cited 2|Views27
Abstract
In pursuit of achieving carbon neutrality goals, modern power systems are increasingly characterized by low-carbon and low-inertia properties, leading to significant concerns regarding the security of system frequency. These ancillary services for providing frequency regulation (FR) can contribute to the system inertia, FR reserve capacity, and the response rate of FR reserves. However, it could be challenging to motivate low-carbon resources, like carbon-capture-utilization-and-storage (CCUS) systems and grid-forming inverter-based renewable energy systems (RESs), to participate in FR ancillary service markets. A critical focus of this paper lies in assessing the marginal value of diverse FR ancillary services for improving the performance of frequency-secured systems in under-frequency and over-frequency cases. Given the tight relations among energy and FR ancillary services, the frequency-secured performance criteria are introduced, including maximum rate of change of frequency (RoCoF), maximum frequency deviation, and quasi-steady-state (Q-S-S) frequency which are devised in a joint energy, carbon, and FR ancillary service market. To solve this nonconvex and nonlinear market problem, while minimizing the uplift payment, a tractable shrunken convex hull pricing method is presented. Multiple case studies confirm the proposed method's effectiveness in enhancing the system frequency stability, reducing total costs, and curtailing carbon emissions.
More
Translated text
Key words
Ancillary services for frequency-regulation,low-carbon and low-inertia power systems,CCUS,RES,and convex hull pricing,Ancillary services for frequency-regulation,low-carbon and low-inertia power systems,CCUS,RES,and convex hull pricing
求助PDF
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined