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Energies
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Demand Response Optimization Techniques for Smart Power Grids 2023
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Demand Response Optimization Techniques for Smart Power Grids 2023

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

Deadline for manuscript submissions: 31 October 2024 | Viewed by 1851

Special Issue Editors

Dr. Islam Safak Bayram

E-Mail Website
Guest Editor
Department of Electronic & Electrical Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Interests: electric vehicle grid integration; demand-side management; energy storage systems; sustainability; stochastic networks; optimization and control
Special Issues, Collections and Topics in MDPI journals
Dr. Muhammad Ismail

E-Mail Website
Guest Editor
Department of Computer Science, Faculty of Engineering, Tennessee Tech University, Cookeville, TN, USA
Interests: smart grids; networking; cyber-physical security; blockchain; resource allocation; machine learning; optimization; stochastic modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As part of the net-zero emission goals, the future of electric power grids is currently shaped by higher penetration levels of renewable energy sources, increasing adoption rates of plug-in electric vehicles (PEVs), and electrification of heating and cooling appliances. This transformation calls for dynamic energy management and scheduling of demand-side activities that can be realized by employing a set of enabling technologies such as wireless networks, smart meters, internet-of-things (IoT)-based sensors, and intelligent load switches. Demand response (DR) schemes have emerged as a way to shape electricity consumption profiles to optimize the operational costs typically defined as a combination of electricity prices, customer comfort, and load flexibility. 

While there is a growing body of literature on the optimization of DR in single residential units or microgrids, in this Special Issue, we are particularly interested in multi-dimensional joint optimization problems. For instance, DR can be used as a tool to enhance power quality, adjust the self-consumption levels of photovoltaic (PV) rooftop systems, increase PV handling capacity of distribution grids, and optimally schedule PEV loads to reduce solar “duck curves” or wind energy curtailment. Another key area of interest is the application of data analytics to load aggregation, managing houses at scale, and devising dynamic pricing methodologies. Furthermore, since DR schemes rely on information exchange among utility companies and customers, security and privacy issues of DR systems should receive appropriate consideration. 

This Special Issue is an ideal venue to make innovative contributions to novel architectures, optimization, and control of DR. We invite field experiments, simulation-based, and/or analytical studies with well-elaborated realistic case studies and real-world datasets.

Dr. Islam Safak Bayram
Dr. Muhammad Ismail
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

  • linear and integer programming for demand response
  • dynamic and stochastic optimization for demand response
  • game-theoretic optimization for demand response
  • data management, analytics, and machine learning for demand response
  • dynamic pricing for demand response
  • electric vehicle load management and smart charging
  • electric vehicle charging and discharging coordination for demand response
  • charging and discharging of energy storage units for demand response
  • renewable energy integration for demand response

Published Papers (2 papers)

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Research

20 pages, 3475 KiB  
Article
Price-Based Demand Response: A Three-Stage Monthly Time-of-Use Tariff Optimization Model
by Peipei You, Sitao Li, Chengren Li, Chao Zhang, Hailang Zhou, Huicai Wang, Huiru Zhao and Yihang Zhao
Energies 2023, 16(23), 7858; https://doi.org/10.3390/en16237858 - 30 Nov 2023
Viewed by 643
Abstract
In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on [...] Read more.
In this research, we developed a three-stage monthly time-of-use (TOU) tariff optimization model to address the concerns of confusing time period division, illogical price setting, and incomplete seasonal element consideration in the previous TOU tariff design. The empirical investigation was conducted based on load, power generation, and electricity pricing data from a typical northwest region in China in 2022. The findings indicate the following: (1) In producing the typical net load curves, the employed K-means++ technique outperformed the standard models in terms of the clustering effect by 4.27–26.70%. (2) Following optimization, there was a decrease of 1900 MW in the maximum monthly abandonment of renewable energy, a decrease of 0.31–53.94% in the peak–valley difference, and a decrease of 2.03–17.27% in the monthly net load cost. (3) By taking extra critical peak and deep valley time periods into account, the average net load cost decreased by 10.36% compared with conventional peak–flat–valley time period division criteria. Full article
(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids 2023)
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16 pages, 11340 KiB  
Article
Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm
by Liyuan Sun, Yuang Lin, Nan Pan, Qiang Fu, Liuyong Chen and Junwei Yang
Energies 2023, 16(23), 7714; https://doi.org/10.3390/en16237714 - 22 Nov 2023
Cited by 1 | Viewed by 644
Abstract
With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data [...] Read more.
With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data support for load management in diverse stations, this study proposes a load sequence noise reduction method. Initially, wavelet noise reduction is performed on the multiple types of load sequences collected by the power system. Subsequently, the northern goshawk optimization is employed to optimize the parameters of variational mode decomposition, ensuring the selection of the most suitable modal decomposition parameters for different load sequences. Next, the SSA–KELM model is employed to independently predict each sub-modal component. The predicted values for each sub-modal component are then aggregated to yield short-term load prediction results. The proposed load forecasting method has been verified using actual data collected from various types of power terminals. A comparison with popular load forecasting methods demonstrates the proposed method’s higher prediction accuracy and versatility. The average prediction results of load data in industrial stations can reach RMSE = 0.0098, MAE = 0.0078, MAPE = 1.3897%, and R2 = 0.9949. This method can be effectively applied to short-term load forecasting in multiple types of power stations, providing a reliable basis for accurate demand-side power load management and decision-making. Full article
(This article belongs to the Special Issue Demand Response Optimization Techniques for Smart Power Grids 2023)
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