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Smart Sensors, Smart Grid and Energy Management
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Smart Sensors, Smart Grid and Energy Management

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2304

Special Issue Editors

Prof. Dr. Emmanuel Karapidakis

E-Mail Website
Guest Editor
School of Engineering, Hellenic Mediterranean University, GR-71410 Heraklion, Greece
Interests: energy policy; power systems operation; diverse and dispersed generation; micro-grids; renewable energy sources; energy trading
Special Issues, Collections and Topics in MDPI journals
Dr. Pompodakis Evangelos

E-Mail Website
Guest Editor
Institute of Energy, Environment and Climatic Change, Hellenic Mediterranean University, 714 10 Iraklio, Greece
Interests: distribution system modeling; power converters; power system protection; smart grids; microgrids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart sensors play a crucial role in modern energy management systems, particularly in the context of smart grids. These sensors have advanced functionalities, such as real-time data collection, analysis, and communication capabilities. By integrating smart sensors into the grid infrastructure, utilities can monitor energy consumption patterns, detect faults or abnormalities, and optimize energy distribution more efficiently. Key features of smart sensors include remote monitoring, predictive maintenance, and the ability to integrate with other smart grid components like smart meters and control systems. Implementing smart sensors enhances grid reliability, improves energy efficiency, and facilitates the integration of renewable energy sources.

Prof. Dr. Emmanuel Karapidakis
Dr. Pompodakis Evangelos
Guest Editor

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 sensors
  • smart grids
  • energy management
  • real-time data monitoring
  • optimization
  • fault detection
  • renewable energy integration
  • grid efficiency and reliability

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Published Papers (3 papers)

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Research

22 pages, 6483 KiB  
Article
Demand Response Strategy Considering Industrial Loads and Energy Storage with High Proportion of Wind-Power Integration
by Chongyi Tian, Julin Li, Chunyu Wang, Longlong Lin and Yi Yan
Sensors 2024, 24(22), 7335; https://doi.org/10.3390/s24227335 - 17 Nov 2024
Viewed by 307
Abstract
To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources [...] Read more.
To address the challenges of reduced grid stability and wind curtailment caused by high penetration of wind energy, this paper proposes a demand response strategy that considers industrial loads and energy storage under high wind-power integration. Firstly, the adjustable characteristics of controllable resources in the power system are analyzed, and a demand response scheduling framework based on energy storage systems and industrial loads is established. Building on this foundation, a multi-scenario stochastic programming approach is employed to develop a day-ahead and intra-day multi-time-scale optimization scheduling model, aimed at maximizing economic benefits. In response to the challenges of wind-power fluctuations with high temporal resolution, a strategy for smoothing intra-day wind-power variability is further proposed. Finally, simulations are conducted to derive optimal demand response strategies for different stages. As verified by the comparison of different scheduling strategies, the demand response strategy proposed in this paper can reduce wind curtailment when there is sufficient wind power and reduce load shedding when there is insufficient wind power, which effectively reduces the system operation cost. Full article
(This article belongs to the Special Issue Smart Sensors, Smart Grid and Energy Management)
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25 pages, 9704 KiB  
Article
Towards Automated Model Selection for Wind Speed and Solar Irradiance Forecasting
by Konstantinos Blazakis, Nikolaos Schetakis, Paolo Bonfini, Konstantinos Stavrakakis, Emmanuel Karapidakis and Yiannis Katsigiannis
Sensors 2024, 24(15), 5035; https://doi.org/10.3390/s24155035 - 3 Aug 2024
Cited by 1 | Viewed by 792
Abstract
Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation [...] Read more.
Given the recent increase in demand for electricity, it is necessary for renewable energy sources (RESs) to be widely integrated into power networks, with the two most commonly adopted alternatives being solar and wind power. Nonetheless, there is a significant amount of variation in wind speed and solar irradiance, on both a seasonal and a daily basis, an issue that, in turn, causes a large degree of variation in the amount of solar and wind energy produced. Therefore, RES technology integration into electricity networks is challenging. Accurate forecasting of solar irradiance and wind speed is crucial for the efficient operation of renewable energy power plants, guaranteeing the electricity supply at the most competitive price and preserving the dependability and security of electrical networks. In this research, a variety of different models were evaluated to predict medium-term (24 h ahead) wind speed and solar irradiance based on real-time measurement data relevant to the island of Crete, Greece. Illustrating several preprocessing steps and exploring a collection of “classical” and deep learning algorithms, this analysis highlights their conceptual design and rationale as time series predictors. Concluding the analysis, it discusses the importance of the “features” (intended as “time steps”), showing how it is possible to pinpoint the specific time of the day that most influences the forecast. Aside from producing the most accurate model for the case under examination, the necessity of performing extensive model searches in similar studies is highlighted by the current work. Full article
(This article belongs to the Special Issue Smart Sensors, Smart Grid and Energy Management)
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19 pages, 4380 KiB  
Article
Optimization of Distributed Energy Resources Operation in Green Buildings Environment
by Safdar Ali, Khizar Hayat, Ibrar Hussain, Ahmad Khan and Dohyeun Kim
Sensors 2024, 24(14), 4742; https://doi.org/10.3390/s24144742 - 22 Jul 2024
Viewed by 768
Abstract
Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize [...] Read more.
Without a well-defined energy management plan, achieving meaningful improvements in human lifestyle becomes challenging. Adequate energy resources are essential for development, but they are both limited and costly. In the literature, several solutions have been proposed for energy management but they either minimize energy consumption or improve the occupant’s comfort index. The energy management problem is a multi-objective problem where the user wants to reduce energy consumption while keeping the occupant’s comfort index intact. To address the multi-objective problem this paper proposed an energy control system for a green environment called PMC (Power Management and Control). The system is based on hybrid energy optimization, energy prediction, and multi-preprocessing. The combination of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) is performed to make a fusion methodology to improve the occupant comfort index (OCI) and decrease energy utilization. The proposed framework gives a better OCI when compared with its counterparts, the Ant Bee Colony Knowledge Base framework (ABCKB), GA-based prediction framework (GAP), Hybrid Prediction with Single Optimization framework (SOHP), and PSO-based power consumption framework. Compared with the existing AEO framework, the PMC gives practically the same OCI but consumes less energy. The PMC framework additionally accomplished the ideal OCI (i-e 1) when compared with the existing model, FA–GA (i-e 0.98). The PMC model consumed less energy as compared to existing models such as the ABCKB, GAP, PSO, and AEO. The PMC model consumed a little bit more energy than the SOHP but provided a better OCI. The comparative outcomes show the capability of the PMC framework to reduce energy utilization and improve the OCI. Unlike other existing methodologies except for the AEO framework, the PMC technique is additionally confirmed through a simulation by controlling the indoor environment using actuators, such as fan, light, AC, and boiler. Full article
(This article belongs to the Special Issue Smart Sensors, Smart Grid and Energy Management)
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