1 Introduction
One of the biggest challenges in the current power system operation is caused by the large scale integration of distributed energy resources (DERs) that have high volatility generations (Uzum et al., 2021). Communication and control technologies are significantly improved to provide direct interaction between agents and customers, such as in peer-to-peer frameworks. In addition, the recent developments in monitoring, sensor networks, and advanced metering infrastructure (AMI) greatly enhance the variety, volume, and speed of measurement data in electricity transmission and distribution networks. By harnessing these technologies, the application of big data, artificial intelligence, and machine learning methods can be implemented to overcome the challenges from massive DERs integration in power systems. However, these technologies require a large amount of capital to operate, which can lead to financial loss if used without an appropriate strategy.
In this context, the topics of interest of this Research Topic address market-based DER operations, regulation, and decision-making, and analyze the impact of market-based DER operation on power systems.
2 Research Topic papers summary
In this section, we have summarized the manuscripts published in this Research Topic.
Xu et al. in addressed the issue of EV via a price-based demand response for economic operation in distribution systems. Existing price-based demand response algorithms fail on spatial-temporal distribution of large-scale EVs connected to the distribution network. In this study, a price-based demand response was proposed for a day-ahead economic dispatch strategy. Simulation cases were implemented in IEEEE 33-bus systems to demonstrate the effectiveness of this strategy.
Kocer et al. addressed the different characteristics of charging and discharging for EVs. In this study’s new context, they explored how a battery swap station (BSS), implemented as an alternative solution to a charging station, can decrease the long waiting time for charging as well as battery degradation due to fast charging. The main objective of this study was to optimize the charging–discharging schedule for several BSSs in the ancillary services of the public transportation system in Berlin, Germany. In addition to BSSs, a mobile swapping station (MSS) concept is introduced to increase flexibility in power systems. The results show that while a BSS is a great tool in the ancillary services market, an MSS concept provides more advantages in power systems.
Oh et al. addressed the matter of DR participation strategy as an aggregator that offers appropriate DR programs to customers with flexible loads. This study formalized the DR strategy as a Markov decision process (MDP) and used the reinforcement learning (RL) framework. In the RL framework, the DR aggregator and each customer are allocated to an agent that interacts with the environment and is trained to make optimal decisions. The proposed method was validated using actual demand and market price profiles. Simulation results demonstrated that the proposed method could optimize DR participation strategy.
Noorfatima et al. addressed the issue of managing local market transactions with many participants through community-based peer-to-peer (P2P) energy trading. This study implements discriminatory pricing-based P2P energy trading by aggregating participants’ trading profiles based on K-means clustering to improve the P2P energy trading results and the computation process. To overcome fairness issues between members of each cluster, the participants’ trading results and contributions to network usages due to P2P energy trading are taken into account and the resulting Shapley value is incorporated to distribute profit. The results show that the proposed method is a suitable option to obtain an optimal local electricity market in a distribution system with an increasing number of participants.
3 Conclusion
Four high-quality manuscripts have been published in this Research Topic. We would like to submit our new editorial to continue the research on the subject.
Statements
Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Acknowledgments
We would like to thank all authors, reviewers, editors and Frontiers in Energy Research staff for their kind and enormous efforts.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
UzumB.OnenA.HasanienH. M.MuyeenS. M. (2021). Rooftop solar PV penetration impacts on distribution network and further growth factors—a comprehensive review. Electronics10, 55. 10.3390/electronics10010055
Summary
Keywords
renewable resources, distributed energy source, electricity market, big data application, artificial intelligence
Citation
Onen A, Jung J, Guerrero JM, Lee C-H and Hossain MA (2022) Editorial Market-based distributed energy resources operation for future power systems. Front. Energy Res. 10:1100740. doi: 10.3389/fenrg.2022.1100740
Received
17 November 2022
Accepted
30 November 2022
Published
13 December 2022
Volume
10 - 2022
Edited and reviewed by
ZhaoYang Dong, Nanyang Technological University, Singapore
Updates
Copyright
© 2022 Onen, Jung, Guerrero, Lee and Hossain.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ahmet Onen, a.onen@squ.edu.om
This article was submitted to Smart Grids, a section of the journal Frontiers in Energy Research
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.