Topic Editors

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Dr. Xinshan Zhu
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China

Energy and Environmental Situation Awareness

Abstract submission deadline
closed (31 July 2024)
Manuscript submission deadline
closed (31 October 2024)
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2287

Topic Information

Dear Colleagues,

Due to the rapid development of emerging information and communication technologies as well as advanced metering infrastructure, planning and operation (P&O) costs are an economic factor that energy management (EM) must consider. The P&O level of multi-terminals directly connected to each user determines the power quality of end users. Among multiple P&O technologies, situation awareness (SA) emerges and is gradually integrated into energy management. Facing a high proportion of RES, the adequate monitoring, analysis, and prediction of the energy management operating status are urgent. Therefore, comprehensive SA, which contains detection, comprehension, and projection, becomes a significant guarantee for the optimal operation of EM. Due to the strong adaptability, SA can dynamically evolve with future EM technology development to provide higher quality P&O of EM.

Dr. Leijiao Ge
Dr. Xinshan Zhu
Topic Editors

Keywords

  • energy management
  • planning
  • operation
  • situation awareness
  • detection
  • comprehension
  • projection

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.3 2011 17.8 Days CHF 2400
Energies
energies
3.0 6.2 2008 17.5 Days CHF 2600
Forecasting
forecasting
2.3 5.8 2019 24.2 Days CHF 1800
Information
information
2.4 6.9 2010 14.9 Days CHF 1600
Sensors
sensors
3.4 7.3 2001 16.8 Days CHF 2600

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Published Papers (2 papers)

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19 pages, 2719 KiB  
Article
Optimal Operation of Generation Company’s Participating in Multiple Markets with Allocation and Exchange of Energy-Consuming Rights and Carbon Credits
by Hanyu Yang, Mengru Ding, Muyao Li, Shilei Wu, Ye Zhang and Xun Dou
Energies 2024, 17(23), 5884; https://doi.org/10.3390/en17235884 - 23 Nov 2024
Viewed by 315
Abstract
The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the [...] Read more.
The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the carbon emission reduction accurately for the GenCos that participate in electricity, carbon, and ECR markets simultaneously, this paper proposes a market framework where a flexible exchange mechanism between the ECR and carbon markets is specially considered. To investigate the influence of the allocation and exchange of ECR and carbon credits on the behavior of GenCos that participate in multi-type markets, a bi-level model based on the leader–follower game theory is proposed. In the upper level of the proposed model, a decision problem for maximizing the profit of GenCos is developed, which is especially constrained to the primary allocation of ECR and carbon credits. While the multi-type market clearing model and an exchange mechanism between the ECR and carbon credits are proposed in the lower level of the model. The bi-level problem is converted into the mathematical program with equilibrium constraints (MPECs) through the Karush–Kuhn–Tucker (KKT) condition to solve. The results illustrate that the interaction between the ECR market and the carbon market can improve the energy efficiency and reduce the carbon emissions of GenCos. Full article
(This article belongs to the Topic Energy and Environmental Situation Awareness)
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37 pages, 5194 KiB  
Article
Meteorological Data Mining and Synthesis for Supplementing On-Site Data for Regulatory Compliance
by Ben Sonpon, Shoaib Usman, Joseph Smith, Sarah Kovaleski and Jason Wibbenmeyer
Energies 2024, 17(15), 3691; https://doi.org/10.3390/en17153691 - 26 Jul 2024
Viewed by 639
Abstract
Many regulatory requirements add significant delay in the licensing of new nuclear power stations. One area of particular interest is the environmental impact of potential atmospheric release. The purpose of this research is to demonstrate effectiveness of meteorological data mining and synthesis from [...] Read more.
Many regulatory requirements add significant delay in the licensing of new nuclear power stations. One area of particular interest is the environmental impact of potential atmospheric release. The purpose of this research is to demonstrate effectiveness of meteorological data mining and synthesis from offsite locations to reduce need for onsite data, hence allowing rapid licensing. The automated procedures tested for data mining and extraction of meteorological data from multiple offsite sources and the data analytic tool developed for data fusion are presented here. Three important meteorological parameters from regulatory compliance are considered for this analysis: wind velocity, wind direction, and atmospheric stability. Callaway Nuclear Power Plant (CNPP) is used as our reference site. CNPP uses the ΔTΔz approach while we use the Vogt method to estimated stability for the offsite locations. Stability classification correlation coefficients between the reference plant and Columbia Regional Airport range from −0.087 to 0.826 for raw with an average of 0.317 ± 0.313. With travel time, correction changed slightly, i.e., a 10 m observation 0.064 ± 0.249 and 0.028 ± 0.123 and a 60 m observation 0.103 ± 0.265 and 0.063 ± 0.155 for the wind from the reference plant to the airport and vice versa, respectively. For Jefferson City Memorial Airport, raw data correlation was from −0.083 to 0.771, with an average of 0.358 ± 0.321. With travel time, correction changed slightly, i.e., 10 m observation 0.075 ± 0.208 and −0.073 ± 0.255 and 60 m observation 0.018 ± 0.223 and −0.032 ± 0.248 for wind from the reference plant to the airport and vice versa, respectively. Stability classification correlation coefficients between the reference plant and St. Louis Lambert International Airport ranged from −0.083 to 0.763 for raw with an average of 0.314 ± 0.295. With travel time, correction changed slightly, i.e., 10 m observation −0.003 ± 0.307 and −0.030 ± 0.277 and 60 m observation −0.030 ± 0.193 and −0.005 ± 0.215 for wind from the reference plant to the airport and vice versa, respectively. It is important to observe that mathematically. stability class correlation coefficients were not great, but in most cases the predicted and observed values were only off by one stability class. Similar correlations were calculated for wind direction and velocities. Our result, when applied to a proposed nuclear power station, can significantly reduce time and effort to prepare a robust environmental protection plan required for license application. Full article
(This article belongs to the Topic Energy and Environmental Situation Awareness)
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