Framework to Evaluate Power Portfolio Dispatch Considering Balancing Market Participation Show others and affiliations
2021 (English) In: 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings, Institute of Electrical and Electronics Engineers (IEEE) , 2021, article id 9494917Conference paper, Published paper (Refereed)
Abstract [en]
The power grid is rapidly experiencing a transformation driven by renewable and climate targets which pose a huge challenge to maintain the system stability and reliability, thus balancing services become more crucial now than ever. To provide balancing services in a cost-efficient way, it is necessary to develop predictive models which can optimize power portfolio in an online manner. This paper presents the development of an Online Predictive Dispatch Optimizer and its connection with a grid model that simulates power and frequency control within interconnected power systems. The performance of the dispatch optimizer and its connection with the grid model is tested by simulating several cases where the adequacy of the model is confirmed, especially regarding its ability to manage energy imbalance in real-time. The outcomes and flexibility of this framework can be used to quantitatively evaluate the operation of power portfolio owners in the power grid.
Place, publisher, year, edition, pages Institute of Electrical and Electronics Engineers (IEEE) , 2021. article id 9494917
Keywords [en]
balancing markets, power balance, Power portfolio management, predictive dispatch optimization, receding horizon control, Electric load dispatching, Electric power transmission networks, Online systems, Predictive analytics, System stability, Balancing market, Climate targets, Cost-efficient, Energy imbalances, Power grids, Power portfolio, Predictive models, Stability and reliabilities, Electric power system control
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
Identifiers URN: urn:nbn:se:kth:diva-311069 DOI: 10.1109/PowerTech46648.2021.9494917 ISI: 000848778000167 Scopus ID: 2-s2.0-85112362628 OAI: oai:DiVA.org:kth-311069 DiVA, id: diva2:1654408
Conference 2021 IEEE Madrid PowerTech, PowerTech 2021, 28 June 2021 through 2 July 2021
Note QC 20220926
Part of proceedings: ISBN 978-166543597-0
2022-04-272022-04-272022-09-26 Bibliographically approved