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Sensors and Energy Management Applications for Smart Grid
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Sensors and Energy Management Applications for Smart Grid

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 9468

Special Issue Editors

Prof. Dr. Ciwei Gao

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: electricity market; demand response and demand side management; integrated energy system
Prof. Dr. Xun Dou

E-Mail Website
Guest Editor
College of Electrical Engineering and Control Science, Nanjing TECH University, Nanjing 211816, China
Interests: optimisation; distributed power generation; demand side management; power generation dispatch; power generation scheduling; cost reduction; electric vehicle charging; power generation economics; automobiles; cost-benefit analysis; costing; energy consumption; energy storage; investment; load (electric) ;optimal control; pollution control; power consumption; power distribution economics; power generation planning; power grids; power markets; pricing; renewable energy sources; tanks (containers)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the rise of the IoT and the progress of information technology, the traditional electrical power grid is also transforming into a smart grid. Recently, innovative sensing products, services, and technologies such as intelligent monitoring, control, and communication have been adopted by smart grids to maintain and improve services. The behaviors and actions of all users are connected together by smart grids based on sensors. However, different types of users have different ways of participating in grid scheduling and control, which leads to the need for smart grids to flexibly adjust their own regulation and control strategies according to the dynamic response of demand-side resources. In addition, power trading can also be used to supplement and optimize the smart grid load dispatching operation, and to distribute, control, and monitor power more effectively. In this scenario, new approaches in sensor deployments and new algorithms to analyze the information obtained from them are becoming an essential tool to support the trading, scheduling, and control.

In this sense, in order to face the aforementioned challenges, we are proposing this Special Issue titled “Sensors and Energy Management Applications for the Smart Grid”. The main goal of this Special Issue is to give academics, researchers, and industry professionals an opportunity to highlight their current work and define future research directions.

Topics that can be addressed include (but are not limited to) the following:

  1. Power demand-side management and sensors
  • Electric vehicles as a sensor in a smart grid
  • Power demand response and sensors
  • Energy storage control technology
  • Virtual power plant operation optimization
  • Demand-side load forecasting and assessment
  • Regulated load telemetry control technology
  • Terminal energy optimization control
  1. Energy internet and power planning
  • Integrated energy system planning
  • Energy storage allocation methods
  • Source-load prediction based on neural networks and AI
  • Sensors in renewable energy systems
  • System control and power quality analysis
  • Design of key equipment/subsystems in a smart grid

Prof. Dr. Ciwei Gao
Prof. Dr. Xun Dou
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. Sensors 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

  • smart grid
  • power demand-side management
  • electricity market
  • electricity regulation
  • energy internet
  • power planning

Related Special Issue

Published Papers (5 papers)

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Research

13 pages, 956 KiB  
Article
A Novel Scalable Reconfiguration Model for the Postdisaster Network Connectivity of Resilient Power Distribution Systems
by Ahmed Imteaj, Vahid Akbari and Mohammad Hadi Amini
Sensors 2023, 23(3), 1200; https://doi.org/10.3390/s23031200 - 20 Jan 2023
Cited by 1 | Viewed by 1278
Abstract
The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage [...] Read more.
The resilient operation of power distribution networks requires efficient optimization models to enable situational awareness. One of the pivotal tools to enhance resilience is a network reconfiguration to ensure secure and reliable energy delivery while minimizing the number of disconnected loads in outage conditions. Power outages are caused by natural hazards, e.g., hurricanes, or system malfunction, e.g., line failure due to aging. In this paper, we first propose a distribution-network optimal power flow formulation (DOPF) and define a new resilience evaluation indicator, the demand satisfaction rate (DSR). DSR is the rate of satisfied load demand in the reconfigured network over the load demand satisfied in the DOPF. Then, we propose a novel model to efficiently find the optimal network reconfiguration by deploying sectionalizing switches during line outages that maximize resilience indicators. Moreover, we analyze a multiobjective scenario to maximize the DSR and minimize the number of utilized sectionalizing switches, which provides an efficient reconfiguration model preventing additional costs associated with closing unutilized sectionalizing switches. We tested our model on a virtually generated 33-bus distribution network and a real 234-bus power distribution network, demonstrating how using the sectionalizing switches can increase power accessibility in outage conditions. Full article
(This article belongs to the Special Issue Sensors and Energy Management Applications for Smart Grid)
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19 pages, 5013 KiB  
Article
A Multi-Port Hardware Energy Meter System for Data Centers and Server Farms Monitoring
by Giuseppe Conti, David Jimenez, Alberto del Rio, Sandra Castano-Solis, Javier Serrano and Jesus Fraile-Ardanuy
Sensors 2023, 23(1), 119; https://doi.org/10.3390/s23010119 - 23 Dec 2022
Cited by 5 | Viewed by 2486
Abstract
Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) [...] Read more.
Nowadays the rationalization of electrical energy consumption is a serious concern worldwide. Energy consumption reduction and energy efficiency appear to be the two paths to addressing this target. To achieve this goal, many different techniques are promoted, among them, the integration of (artificial) intelligence in the energy workflow is gaining importance. All these approaches have a common need: data. Data that should be collected and provided in a reliable, accurate, secure, and efficient way. For this purpose, sensing technologies that enable ubiquitous data acquisition and the new communication infrastructure that ensure low latency and high density are the key. This article presents a sensing solution devoted to the precise gathering of energy parameters such as voltage, current, active power, and power factor for server farms and datacenters, computing infrastructures that are growing meaningfully to meet the demand for network applications. The designed system enables disaggregated acquisition of energy data from a large number of devices and characterization of their consumption behavior, both in real time. In this work, the creation of a complete multiport power meter system is detailed. The study reports all the steps needed to create the prototype, from the analysis of electronic components, the selection of sensors, the design of the Printed Circuit Board (PCB), the configuration and calibration of the hardware and embedded system, and the implementation of the software layer. The power meter application is geared toward data centers and server farms and has been tested by connecting it to a laboratory server rack, although its designs can be easily adapted to other scenarios where gathering the energy consumption information was needed. The novelty of the system is based on high scalability built upon two factors. Firstly, the one-on-one approach followed to acquire the data from each power source, even if they belong to the same physical equipment, so the system can correlate extremely well the execution of processes with the energy data. Thus, the potential of data to develop tailored solutions rises. Second, the use of temporal multiplexing to keep the real-time data delivery even for a very high number of sources. All these ensure compatibility with standard IoT networks and applications, as the data markup language is used (enabling database storage and computing system processing) and the interconnection is done by well-known protocols. Full article
(This article belongs to the Special Issue Sensors and Energy Management Applications for Smart Grid)
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28 pages, 6678 KiB  
Article
A Multi-Stage Planning Method for Distribution Networks Based on ARIMA with Error Gradient Sampling for Source–Load Prediction
by Sheng Yan and Minqiang Hu
Sensors 2022, 22(21), 8403; https://doi.org/10.3390/s22218403 - 01 Nov 2022
Cited by 4 | Viewed by 1338
Abstract
As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generation and user demand. The smart distribution [...] Read more.
As the scale of distributed renewable energy represented by wind power and photovoltaic continues to expand and load demand gradually changes, the future evolution of the smart distribution network will be directly driven by both distributed generation and user demand. The smart distribution network contains a wide range of flexible resources, and its flexibility and uncertainty will bring great challenges to grid data acquisition and control feedback. To adapt to the precise control and feedback of smart distribution network access equipment under the high proportion of new energy access and to ensure the safe operation of the system, it is urgent to accelerate the study of the evolution of the future distribution grid based on the existing distribution grid. Hence, a multi-stage planning method for distribution networks based on source–load prediction is proposed in this paper. Firstly, a distribution network source–load prediction method based on the autoregressive integrated moving average model (ARIMA) and error gradient sampling is proposed, using ARIMA to predict the scale of source–load development and error gradient sampling based on the generation of source–load scenarios with error intervals. K-means is further used for scenario reduction, to explore multiple operating scenarios of China’s distribution network source–load, and the unit’s output forecast interval and load demand from 2021 to 2030 for typical regions are derived using rolling forecasts by combining the unit’s output, end-demand and clean energy share over the years. Secondly, the planning model of distribution grid evolution in different stages is constructed to analyze the future evolution form of the distribution grid considering the distribution network’s load cross-section, respectively, and to provide a development path reference for the future construction of distribution grid form in China. Full article
(This article belongs to the Special Issue Sensors and Energy Management Applications for Smart Grid)
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13 pages, 1165 KiB  
Article
The Optimal Sizing of Substation Capacity in a Distribution Network, Considering a Dynamic Time-of-Use Pricing Mechanism
by Dajun Xu, Qiang Fu, Lun Ye, Wenyu Lin, Yuzhou Qian, Kebo Zhang and Jiangang Yao
Sensors 2022, 22(19), 7173; https://doi.org/10.3390/s22197173 - 21 Sep 2022
Viewed by 1304
Abstract
Application of sensors in the smart grid has promoted the development of demand side management (DSM). However, the incentives of DSM such as peak–valley time-of-use (TOU) price will change the load pattern in the future; the substation capacity sizing will be further influenced [...] Read more.
Application of sensors in the smart grid has promoted the development of demand side management (DSM). However, the incentives of DSM such as peak–valley time-of-use (TOU) price will change the load pattern in the future; the substation capacity sizing will be further influenced accordingly. This paper proposes a substation capacity sizing method in distribution network considering DSM and establishes a peak-valley TOU pricing method based on the cost–benefit analysis of each participant in the TOU price. Compared with the conventional fixed peak–valley ratio, a dynamic division method is proposed to calculate the optimal pull-off ratio for the TOU pricing. By considering the proposed TOU pricing method, a substation capacity sizing model for the distribution network is further proposed. Finally, the economic benefits of the two substation capacity sizing schemes are compared and evaluated according to the selected economic indicators. The results of the case study demonstrate that under the premise of reasonable pricing, considering the impact of TOU on substation capacity sizing, the construction investment and the user cost of power supply companies can be saved while meeting the power demand. The economy and rationality of the planning scheme have been significantly improved. Full article
(This article belongs to the Special Issue Sensors and Energy Management Applications for Smart Grid)
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28 pages, 6496 KiB  
Article
Retailing Strategies for Power Retailers with a Generator Background Considering Energy Conservation Services under the Internet of Things
by Xun Dou, Jiazhe Zhou, Yanbo Ding, Jiacheng Li, Yang Cao, Maohua Shan and Hao Yuan
Sensors 2022, 22(17), 6612; https://doi.org/10.3390/s22176612 - 01 Sep 2022
Cited by 3 | Viewed by 1533
Abstract
Facing the electricity market environment, in which the traditional power grid is transformed into a smart grid, power retailers with a generator background are designing new business models of cold-heat-electricity multi-energy supply based on the Internet of Things data collection, interconnection, computing and [...] Read more.
Facing the electricity market environment, in which the traditional power grid is transformed into a smart grid, power retailers with a generator background are designing new business models of cold-heat-electricity multi-energy supply based on the Internet of Things data collection, interconnection, computing and other technical supports. On the other hand, through internet of things real-time monitoring technology, the necessity of setting up energy security for power retailers is explored to enhance the control’s ability to deal with the risks of electricity sales. Firstly, based on internet of things data analysis, retail strategies such as cooling-heat-electricity multi-energy packages, desulphurization and carbon emissions and energy conservation are designed. Then, a revenue cost measurement model based on the generator background of the power retailers is established. A source of data for the expansion of power retailers and the proliferation of load users is provided through the real-time monitoring of new business models that consider the operation of energy conservation on the supply and use side. Finally, an analysis based on the detection of operation under the scenarios constructed in the example of coal price market fluctuations and proliferation stagnation of user-side packages is conducted. It is verified that the power retailers with a generator background can effectively weaken the adverse impact of upward fluctuations in the coal price market in the peak season of energy consumption on the cost of power retailers by setting energy conservation. At the same time, the diffusion of a new business model in the user side is improved, and the revenue source of power retailers is further expanded. Therefore, taking energy conservation as an important innovation technique of retail strategy can enhance the market competitiveness and risk control ability of power retailers. Full article
(This article belongs to the Special Issue Sensors and Energy Management Applications for Smart Grid)
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