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Energies
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Big Data Analysis and Application in Power System
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Big Data Analysis and Application in Power System

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: 23 October 2024 | Viewed by 921

Special Issue Editors

Prof. Dr. Bo Wang

E-Mail Website
Guest Editor
School of Electrical Engineering, Wuhan University, Wuhan 430072, China
Interests: power safety image interpretation; big data and AI applications in energy systems; multimodal data fusion in power systems
Dr. Hengrui Ma

E-Mail Website
Guest Editor
New Energy Photovoltaic Industry Research Center, Qinghai University, Xining 810016, China
Interests: AI applications in energy systems; multi-energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Fuqi Ma

E-Mail Website
Guest Editor
School of Electrical Engineering, Xi'an University of Technology, Xi’an 710054, China
Interests: power safety image interpretation; power vision understanding; edge intelligence
Dr. Hongxia Wang

E-Mail Website
Guest Editor
Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, USA
Interests: big data and multimodal data fusion in power systems

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of power industry and the continuous construction of intelligent power systems, a large amount of information data is generated in the process of power production, marketing and service, and each of them will accumulate a large amount of historical data. The application of data in power system has developed from structured data to the unstructured data and even multi-physical field data.

This special issue aims to present and disseminate the latest development of big data in energy production, multi-energy system operation, and security risk analysis.

Topics of interest for this publication include, but are not limited to:

  • Microgrid architecture, monitoring and analysis
  • Multi-physical field fusion computational imaging technology
  • Production safety risk identification technology
  • Personal safety risk analysis technology driven by multi-source data
  • Energy forecasting, i.e. wind, solar, load, price
  • Optimization and control of low-carbon energy system
  • Demand response and resources analytics
  • Technologies, problems and applications of multimodal data in future power systems
  • Multimodal data based analysis of power equipment and energy systems interactions

Prof. Dr. Bo Wang
Dr. Hengrui Ma
Dr. Fuqi Ma
Dr. Hongxia Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data
  • multimodal data fusion
  • energy systems
  • power safety

Published Papers (1 paper)

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Research

15 pages, 489 KiB  
Article
Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties
by Zhiyong Li, Wenbin Wu, Yang Si and Xiaotao Chen
Energies 2023, 16(22), 7636; https://doi.org/10.3390/en16227636 - 17 Nov 2023
Viewed by 567
Abstract
Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which [...] Read more.
Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories, resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However, the output of photovoltaic power is stochastic, which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs, starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions, a deterministic optimization model is established to address the corresponding challenges. Furthermore, a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems, accounting for uncertainties. The objective of the two-stage stochastic planning model is to minimize the distribution system’s operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power, a data-driven algorithm is introduced to cluster historical data into representative scenarios, effectively reducing the planning model’s scale. To ensure an efficient solution, a Benders’ decomposition-based algorithm is proposed, which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments, demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment, paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM’s siting and sizing obtained by the proposed algorithm, which also reveals immense economic and environmental benefits. Full article
(This article belongs to the Special Issue Big Data Analysis and Application in Power System)
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