Innovative Techniques for Smart Grids

Topic Information

Dear Colleagues,

We would like to invite submissions to this Topic on the subject of Smart Grids and Microgrids entitled “Innovative Techniques for Smart Grids”.

Due to environment concerns, energy security risks, and fossil fuel problems, many countries around the world have decided to increase the penetration level of renewable energy resources (RERs) in their energy networks. In addition to this, many countries are moving toward implementation of the smart grid concept including microgrid and deregulation in their power systems to achieve reliable and secure operation of their power systems with high penetration level of renewable energy resources. This topic focuses on providing the latest innovative techniques for enhancing the planning, operation, control and stability in modern and future smart grids and microgrids.

The topics include but are not limited to:

•    Smart grids and microgrids;
•    The design, modeling, and management of smart grids and microgrids;
•    Smart grid and microgrid reliability, sustainability, flexibility, and resiliency;
•    Smart grid and microgrid dynamics, stability, protection and security;
•    Methodologies and applications of modern methods for the operation and control of smart grids;
•    Intelligent systems, solving methods, optimization, and advanced heuristics;
•    The modeling, planning, and operating of renewable energy resources;
•    Business models for different electricity market players;
•    Demand side management and demand response;
•    The sizing, placement, and operation of energy storage systems and electric vehicles;
•    Smart homes and building energy management;
•    Electricity market, electrical power, and energy systems;
•    The modeling, forecasting, and management of uncertainty in smart grids;
•    Microgrids and islanded networks;
•    Smart cities, smart energy, and IoT;
•    Modern power systems and renewable energy resources.

Prof. Dr. Pierluigi Siano
Prof. Dr. Hassan Haes Alhelou
Prof. Dr. Amer Al-Hinai
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400
Energies energies	3.0	6.2	2008	16.8 Days	CHF 2600
Inventions inventions	2.1	4.8	2016	18.5 Days	CHF 1800
Smart Cities smartcities	7.0	11.2	2018	28.4 Days	CHF 2000
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400

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Published Papers (25 papers)

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19 pages, 5092 KiB  

Open AccessArticle

Compressed Sensing Super-Resolution Method for Improving the Accuracy of Infrared Diagnosis of Power Equipment

by Yan Wang, Jialin Zhang and Lingjie Wang

Appl. Sci. 2022, 12(8), 4046; https://doi.org/10.3390/app12084046 - 16 Apr 2022

Cited by 2 | Viewed by 2353

Abstract

The infrared image of power equipment plays a crucial role in identifying faults, monitoring equipment condition, and so on. The low resolution and low definition of infrared images in applications contribute to the low accuracy of infrared diagnosis. A super-resolution reconstruction method of [...] Read more.

The infrared image of power equipment plays a crucial role in identifying faults, monitoring equipment condition, and so on. The low resolution and low definition of infrared images in applications contribute to the low accuracy of infrared diagnosis. A super-resolution reconstruction method of infrared image, based on compressed sensing theory, is proposed. Firstly, by analyzing the variation of high-frequency information in infrared images with different blurring degrees, the image gradient norm ratio is introduced to estimate the blur kernel matrix in the degradation model a priori. Then, in the process of image reconstruction, we add the full variational regularization term to the traditional compressed sensing model, and design a two-step full variational sparse reconstruction algorithm. Experimental results verify the effectiveness of the method. Compared with the existing classical super-resolution methods, this method offers improvement in subjective visual effect and objective evaluation index. In addition, the final image recognition and infrared diagnosis experiments show that this method is helpful to improve the accuracy of infrared diagnosis of power equipment. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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22 pages, 4976 KiB  

Open AccessArticle

Digital Twin Concept Developing on an Electrical Distribution System—An Application Case

by Sabryna V. Fernandes, Diogo V. João, Beatriz B. Cardoso, Marcos A. I. Martins and Edgar G. Carvalho

Energies 2022, 15(8), 2836; https://doi.org/10.3390/en15082836 - 13 Apr 2022

Cited by 23 | Viewed by 4586

Abstract

Through the transformation that the electrical sector has been passing by, improvements in asset management and the guarantee of sustainable and quality services have become essential aspects for power companies. Thus, the digitalization of energy utilities presents itself as an important and crucial [...] Read more.

Through the transformation that the electrical sector has been passing by, improvements in asset management and the guarantee of sustainable and quality services have become essential aspects for power companies. Thus, the digitalization of energy utilities presents itself as an important and crucial process. A concept that involves a variety of innovative trends is the digital twin. It consists of a 3D virtual replica of existing physical objects and real-time monitoring of certain measures. By developing a digital twin in the electrical power grid, a virtual replica of the network is obtained providing network virtual maps, 3D asset models, dynamic and real-time data of grid assets, and IoT sensing. All these data can feed a platform where AI-based models and advanced field operation technologies and solutions will be applied. With a Network Digital Twin©development, applications involving on-field activities can be improved through augmented reality (AR) and virtual reality (VR) to enhance workforce operations. This paper discusses the best practices for the development of a digital twin for the electrical power sector. These practices were found during the development of a project carried out by Enel Distribuição São Paulo, applying a living lab concept in the densest region of Brazil. The results of this paper present 3D images captured with specialized tools, and how they influence the workforce activities of human interface operation. Furthermore, financial and operational returns are presented through a cost–benefit analysis for each relevant aspect. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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15 pages, 3987 KiB  

Open AccessArticle

A New Method of Conductor Galloping Monitoring Using the Target Detection of Infrared Source

by Yufeng Gao, Jun Yang, Ke Zhang, Huaizhen Peng, Yin Wang, Na Xia and Gang Yao

Electronics 2022, 11(8), 1207; https://doi.org/10.3390/electronics11081207 - 11 Apr 2022

Cited by 7 | Viewed by 2505

Abstract

Because the galloping of iced conductors is one of the main disasters in the State Grid, resulting in huge economic and property losses every year, the research on relevant monitoring methods is of great significance. The existing galloping monitoring technology is mainly based [...] Read more.

Because the galloping of iced conductors is one of the main disasters in the State Grid, resulting in huge economic and property losses every year, the research on relevant monitoring methods is of great significance. The existing galloping monitoring technology is mainly based on the contact detection method, which presents potential electrical hazards and power supply problems. In this paper, a conductor galloping monitoring method based on the target detection of infrared sources is put forward to overcome the shortcomings of existing methods. In other words, an infrared source label is installed on the conductor spacer, high-definition night vision infrared cameras are installed on electric power towers to take video of the infrared source labels, and the characteristic amplitude of conductor galloping is calculated by an image recognition and tracking algorithm. The practical application results indicate that the method has the advantages of non-contact detection, safety and reliability, and high detection accuracy. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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13 pages, 1561 KiB  

Open AccessArticle

Firefly Algorithm-Based Optimization of the Additional Energy Yield of Bifacial PV Modules

by Ibukun Damilola Fajuke and Atanda K. Raji

Energies 2022, 15(7), 2651; https://doi.org/10.3390/en15072651 - 5 Apr 2022

Cited by 7 | Viewed by 2429

Abstract

A solar bifacial photovoltaic (PV) module is designed so that it permits the addition of the back electrode to the prevailing silicon PV on the front side. Hence, it has the ability to harvest energy using its front and back faces. This study [...] Read more.

A solar bifacial photovoltaic (PV) module is designed so that it permits the addition of the back electrode to the prevailing silicon PV on the front side. Hence, it has the ability to harvest energy using its front and back faces. This study presents an optimization model for calculating the extra energy yield (EY) that can be harvested from the backside of a bifacial PV module using the Firefly Algorithm (FA). Mathematical modelling of the various parameters that influence the extra EY of the backside of a bifacial module was carried out using SIMULINK. Moreover, the mathematical model of the EY of the module was also carried out and then optimized using FA. The optimization model was confined to two orientation states namely the vertical south–north and vertical east–west at Ogbomosho (8.1227° N, 4.2436° E), Nigeria, with different values of albedo and mounting heights. The simulation result shows that the vertical east–west oriented modules outperform the vertical south–north oriented modules in terms of the EY generated. The result also showed that the maximum value of the EY is harvested at a mounting height of 1 m above the ground with row spacing of 2.5 m and a tilt angle of 25 degrees. Therefore, an optimal selection of the mounting surface (albedo) and mounting elevation values can harvest an extra EY of 5 to 45 per cent and help minimize the cost of energy generated using bifacial PV modules for electricity generation. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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18 pages, 5186 KiB  

Open AccessArticle

A Risk Assessment Method of Power Transformer Based on Three-Parameter Interval Grey Number Decision-Making

by Hongbo Yu, Wei Xiong, Kui Xu, Yunwen Yu, Xufeng Yuan, Xiaosong Zou and Ling Xiao

Appl. Sci. 2022, 12(7), 3480; https://doi.org/10.3390/app12073480 - 29 Mar 2022

Cited by 6 | Viewed by 3371

Abstract

In the process of power transformer risk assessment, the loss degree index is difficult to accurately quantify due to the influence of uncertain factors, leading to the deviation of risk judgment. A power transformer risk assessment method based on the three-parameter interval grey [...] Read more.

In the process of power transformer risk assessment, the loss degree index is difficult to accurately quantify due to the influence of uncertain factors, leading to the deviation of risk judgment. A power transformer risk assessment method based on the three-parameter interval grey number decision-making is proposed. Firstly, the fault probability of the transformer is quantified based on the condition evaluation results. Secondly, considering the uncertainty of DG output and load, the Nataf transform and Cholesky decomposition were used to eliminate the correlation of random variables, and a three-point estimation method combined with a DC cut load model was introduced to calculate the probability distribution of the loss degree caused by the transformer fault. Finally, the origin moment of each order was obtained based on the calculation formula of risk value, and the risk probability distribution was obtained through the Cornish–Fisher series expanding. The decision method of the three-parameter interval grey number distance measure was used to judge the risk grade of the equipment. The results show that the proposed method fully considers the influence of uncertainty on equipment risk judgment, can realize the full use of the equipment risk value interval number to judge the risk, and avoids the decision-making defects of the traditional certain risk quantification method. Meanwhile, the influence of different factors on the risk evaluation results is in line with the actual operation condition of the transformer. The results also verify the effectiveness and accuracy of the proposed method, which provides a new judgment idea for power grid equipment risk quantitative assessment. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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14 pages, 3750 KiB  

Open AccessArticle

Modular Combined DC-DC Autotransformer for Offshore Wind Power Integration with DC Collection

by Yuanjian Song, Zheren Zhang and Zheng Xu

Appl. Sci. 2022, 12(4), 1810; https://doi.org/10.3390/app12041810 - 10 Feb 2022

Cited by 4 | Viewed by 1849

Abstract

Offshore wind farms (OWFs) integration are attractive extensively for furnishing more robust power than land wind farms. This paper introduces a modular combined DC-DC autotransformer (MCAT), which contributes to the offshore wind power integration of DC grids with different voltage levels. Traditional DC [...] Read more.

Offshore wind farms (OWFs) integration are attractive extensively for furnishing more robust power than land wind farms. This paper introduces a modular combined DC-DC autotransformer (MCAT), which contributes to the offshore wind power integration of DC grids with different voltage levels. Traditional DC transformers contains medium- or high-frequency converter transformers, which have the disadvantages of high manufacturing difficulty and cost. These shortcomings seriously affect the progress of commercial application of DC transformers. To solve these problems, in the proposed MCAT, converter transformers are replaced with a DC-isolation capacitor and a compensation inductor in series to reduce the footprint of offshore platforms and improve economy. Theoretical analysis is carried out for the MCAT operation principle. Selection methods of main circuit parameters for the MCAT are discussed in detail. Then, corresponding control strategies of the MCAT are proposed. Finally, the effectiveness of the proposed MCAT and its control strategies are validated by time domain simulations in PSCAD/EMTDC. The time-domain simulation results show the correctness of the main circuit parameters and the rationality of the MCAT control strategies. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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22 pages, 8373 KiB  

Open AccessArticle

Home Energy Management Strategy-Based Meta-Heuristic Optimization for Electrical Energy Cost Minimization Considering TOU Tariffs

by Rittichai Liemthong, Chitchai Srithapon, Prasanta K. Ghosh and Rongrit Chatthaworn

Energies 2022, 15(2), 537; https://doi.org/10.3390/en15020537 - 12 Jan 2022

Cited by 13 | Viewed by 2800

Abstract

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power [...] Read more.

It is well documented that both solar photovoltaic (PV) systems and electric vehicles (EVs) positively impact the global environment. However, the integration of high PV resources into distribution networks creates new challenges because of the uncertainty of PV power generation. Additionally, high power consumption during many EV charging operations at a certain time of the day can be stressful for the distribution network. Stresses on the distribution network influence higher electricity tariffs, which negatively impact consumers. Therefore, a home energy management system is one of the solutions to control electricity consumption to reduce electrical energy costs. In this paper, a meta-heuristic-based optimization of a home energy management strategy is presented with the goal of electrical energy cost minimization for the consumer under the time-of-use (TOU) tariffs. The proposed strategy manages the operations of the plug-in electric vehicle (PEV) and the energy storage system (ESS) charging and discharging in a home. The meta-heuristic optimization, namely a genetic algorithm (GA), was applied to the home energy management strategy for minimizing the daily electrical energy cost for the consumer through optimal scheduling of ESS and PEV operations. To confirm the effectiveness of the proposed methodology, the load profile of a household in Udonthani, Thailand, and the TOU tariffs of the provincial electricity authority (PEA) of Thailand were applied in the simulation. The simulation results show that the proposed strategy with GA optimization provides the minimum daily or net electrical energy cost for the consumer. The daily electrical energy cost for the consumer is equal to 0.3847 USD when the methodology without GA optimization is used, whereas the electrical energy cost is equal to 0.3577 USD when the proposed methodology with GA optimization is used. Therefore, the proposed optimal home energy management strategy with GA optimization can decrease the daily electrical energy cost for the consumer up to 7.0185% compared to the electrical energy cost obtained from the methodology without GA optimization. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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17 pages, 2149 KiB  

Open AccessArticle

Evaluation of Electric Vehicle Integrated Charging Safety State Based on Fuzzy Neural Network

by Hui Gao, Binbin Zang, Lei Sun and Liangliang Chen

Appl. Sci. 2022, 12(1), 461; https://doi.org/10.3390/app12010461 - 4 Jan 2022

Cited by 8 | Viewed by 2516

Abstract

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of [...] Read more.

Electric vehicles have been promoted worldwide because of their high energy efficiency and low pollution. However, frequent charging safety accidents have to a certain extent restricted the development of electric vehicles. Therefore, it is extremely important to accurately evaluate the safety state of EV charging. The paper presents an integrated safety assessment method for electric vehicle charging safety based on fuzzy neural network. The integrated fault model was established by analyzing the correlation between truck–pile–grid. Then the integrated evaluation index was analyzed and sorted out, and the comprehensive fuzzy evaluation method used to evaluate. Following this, the improved GA_BP neural network algorithm was used to calculate the weight. Compared with the evaluation effect before and after the improvement, the simulation results show that the GA_BP neural network has higher accuracy and smaller error than the ordinary BP neural network. Finally, the feasibility and effectiveness of the evaluation method was verified by a case study. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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19 pages, 6228 KiB  

Open AccessArticle

Graph Modeling for Efficient Retrieval of Power Network Model Change History

by Ivana Dalčeković, Aleksandar Erdeljan, Nikola Dalčeković and Jelena Marjanović

Energies 2021, 14(24), 8351; https://doi.org/10.3390/en14248351 - 11 Dec 2021

Cited by 1 | Viewed by 2685

Abstract

Power grids are constantly evolving, and data changes are increasing. Operational technology (OT) is controlled by IT technologies in smart grids, where changes in the physical world impose changes in the software data model, as well as the continuous generation of data points, [...] Read more.

Power grids are constantly evolving, and data changes are increasing. Operational technology (OT) is controlled by IT technologies in smart grids, where changes in the physical world impose changes in the software data model, as well as the continuous generation of data points, resulting in time series datasets. The increased need for processing large amounts of data combined with requirements to maintain and increase overall performances has created a significant challenge for traditional database solutions and relational database models. The main idea of this paper was to find and propose a graph model that will allow the retrieval of historical connectivity in a reduced time complexity. Furthermore, the research question was addressed by evaluating three different approaches where the results provide a foundation for the proposed design guidelines related to optimizing graph-based databases for a modern smart grid system. The results of the experiments demonstrated reduced time complexities from 3 to 5 times depending on the typical industry usage patterns and the selected graph model. This suggests that the design decision may severely affect the outcome for given smart grid use cases when using historical features in OT technologies. Therefore, the main contribution of the research is the proposed guidelines on how to design an optimal graph model that satisfies the described smart grid requirements. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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28 pages, 7815 KiB  

Open AccessArticle

Design of a Partially Grid-Connected Photovoltaic Microgrid Using IoT Technology

by Mahmoud Shaban, Imed Ben Dhaou, Mohammed F. Alsharekh and Mamdouh Abdel-Akher

Appl. Sci. 2021, 11(24), 11651; https://doi.org/10.3390/app112411651 - 8 Dec 2021

Cited by 13 | Viewed by 3839

Abstract

This study describes the design and control algorithms of an IoT-connected photovoltaic microgrid operating in a partially grid-connected mode. The proposed architecture and control design aim to connect or disconnect non-critical loads between the microgrid and utility grid. Different components of the microgrid, [...] Read more.

This study describes the design and control algorithms of an IoT-connected photovoltaic microgrid operating in a partially grid-connected mode. The proposed architecture and control design aim to connect or disconnect non-critical loads between the microgrid and utility grid. Different components of the microgrid, such as photovoltaic arrays, energy storage elements, inverters, solid-state transfer switches, smart-meters, and communication networks were modeled and simulated. The communication between smart meters and the microgrid controller is designed using LoRa communication protocol for the control and monitoring of loads in residential buildings. An IoT-enabled smart meter has been designed using ZigBee communication protocol to evaluate data transmission requirements in the microgrid. The loads were managed by a proposed under-voltage load-shedding algorithm that selects suitable loads to be disconnected from the microgrid and transferred to the utility grid. The simulation results showed that the duty cycle of LoRa and its bit rate can handle the communication requirements in the proposed PV microgrid architecture. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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34 pages, 4821 KiB  

Open AccessReview

Cooperative Control of Microgrids: A Review of Theoretical Frameworks, Applications and Recent Developments

by Edward Smith, Duane Robinson and Ashish Agalgaonkar

Energies 2021, 14(23), 8026; https://doi.org/10.3390/en14238026 - 1 Dec 2021

Cited by 7 | Viewed by 3024

Abstract

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by rapid advances in wireless communications technology [...] Read more.

The development of cooperative control strategies for microgrids has become an area of increasing research interest in recent years, often a result of advances in other areas of control theory such as multi-agent systems and enabled by rapid advances in wireless communications technology and power electronics. Though the basic concept of cooperative action in microgrids is intuitively well-understood, a comprehensive survey of this approach with respect to its limitations and wide range of potential applications has not yet been provided. The objective of this paper is to provide a broad overview of cooperative control theory as applied to microgrids, introduce other possible applications not previously described, and discuss recent advances and open problems in this area of microgrid research. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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14 pages, 1289 KiB  

Open AccessArticle

Comparison of Baseline Load Forecasting Methodologies for Active and Reactive Power Demand

by Edgar Segovia, Vladimir Vukovic and Tommaso Bragatto

Energies 2021, 14(22), 7533; https://doi.org/10.3390/en14227533 - 11 Nov 2021

Cited by 7 | Viewed by 2900

Abstract

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the [...] Read more.

Forecasting the electricity consumption is an essential activity to keep the grid stable and avoid problems in the devices connected to the grid. Equaling consumption to electricity production is crucial in the electricity market. The grids worldwide use different methodologies to predict the demand, in order to keep the grid stable, but is there any difference between making a short time prediction of active power and reactive power into the grid? The current paper analyzes the most usual forecasting algorithms used in the electrical grids: ‘X of Y’, weighted average, comparable day, and regression. The subjects of the study were 36 different buildings in Terni, Italy. The data supplied for Terni buildings was split into active and reactive power demand to the grid. The presented approach gives the possibility to apply the forecasting algorithm in order to predict the active and reactive power and then compare the discrepancy (error) associated with forecasting methodologies. In this paper, we compare the forecasting methodologies using MAPE and CVRMSE. All the algorithms show clear differences between the reactive and active power baseline accuracy. ‘Addition X of Y middle’ and ‘Addition Weighted average’ better follow the pattern of the reactive power demand (the prediction CVRMSE error is between 12.56% and 13.19%) while ‘Multiplication X of Y high’ and ‘Multiplication X of Y middle’ better predict the active power demand (the prediction CVRMSE error is between 12.90% and 15.08%). Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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13 pages, 1343 KiB  

Open AccessReview

A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges

by Shitu Zhang, Zhixun Zhu and Yang Li

Energies 2021, 14(21), 7238; https://doi.org/10.3390/en14217238 - 2 Nov 2021

Cited by 32 | Viewed by 3878

Abstract

Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are [...] Read more.

Transient stability assessment (TSA) has always been a fundamental means for ensuring the secure and stable operation of power systems. Due to the integration of new elements such as power electronics, electric vehicles and renewable power generations, dynamic characteristics of power systems are becoming more and more complex, which makes TSA an increasingly urgent task. Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry. This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and prospects for future research; finally, draws the conclusions of this review. This review will be beneficial for relevant researchers to better understand the research status, key technologies, and existing challenges in the field. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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28 pages, 6099 KiB  

Open AccessArticle

Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN

by Yuhong Wang, Xu Zhou, Yunxiang Shi, Zongsheng Zheng, Qi Zeng, Lei Chen, Bo Xiang and Rui Huang

Energies 2021, 14(19), 6073; https://doi.org/10.3390/en14196073 - 24 Sep 2021

Cited by 10 | Viewed by 2747

Abstract

This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of [...] Read more.

This paper presents a multi-agent Double Deep Q Network (DDQN) based on deep reinforcement learning for solving the transmission network expansion planning (TNEP) of a high-penetration renewable energy source (RES) system considering uncertainty. First, a K-means algorithm that enhances the extraction quality of variable wind and load power uncertain characteristics is proposed. Its clustering objective function considers the cumulation and change rate of operation data. Then, based on the typical scenarios, we build a bi-level TNEP model that includes comprehensive cost, electrical betweenness, wind curtailment and load shedding to evaluate the stability and economy of the network. Finally, we propose a multi-agent DDQN that predicts the construction value of each line through interaction with the TNEP model, and then optimizes the line construction sequence. This training mechanism is more traceable and interpretable than the heuristic-based methods. Simultaneously, the experience reuse characteristic of multi-agent DDQN can be implemented in multi-scenario TNEP tasks without repeated training. Simulation results obtained in the modified IEEE 24-bus system and New England 39-bus system verify the effectiveness of the proposed method. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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15 pages, 2213 KiB  

Open AccessArticle

Design of a Hybrid Energy System with Energy Storage for Standalone DC Microgrid Application

by Mwaka I. Juma, Bakari M. M. Mwinyiwiwa, Consalva J. Msigwa and Aviti T. Mushi

Energies 2021, 14(18), 5994; https://doi.org/10.3390/en14185994 - 21 Sep 2021

Cited by 22 | Viewed by 5005

Abstract

This paper presents microgrid-distributed energy resources (DERs) for a rural standalone system. It is made up of a solar photovoltaic (solar PV) system, battery energy storage system (BESS), and a wind turbine coupled to a permanent magnet synchronous generator (WT-PMSG). The DERs are [...] Read more.

This paper presents microgrid-distributed energy resources (DERs) for a rural standalone system. It is made up of a solar photovoltaic (solar PV) system, battery energy storage system (BESS), and a wind turbine coupled to a permanent magnet synchronous generator (WT-PMSG). The DERs are controlled by maximum power point tracking (MPPT)-based proportional integral (PI) controllers for both maximum power tracking and error feedback compensation. The MPPT uses the perturb and observe (P&O) algorithm for tracking the maximum power point of the DERs. The PI gains are tuned using the Ziegler–Nichols method. The developed system was built and simulated in MATLAB/Simulink under two conditions—constant load, and step-load changes. The controllers enabled the BESS to charge even during conditions of varying load and other environmental factors such as change of irradiance and wind speed. The reference was tracked extremely well by the output voltage of the DC microgrid. This is useful research for electrifying the rural islanded areas which are too far from the grid. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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22 pages, 497 KiB  

Open AccessReview

A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid

by Shahid Tufail, Imtiaz Parvez, Shanzeh Batool and Arif Sarwat

Energies 2021, 14(18), 5894; https://doi.org/10.3390/en14185894 - 17 Sep 2021

Cited by 89 | Viewed by 8401

Abstract

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it [...] Read more.

The world is transitioning from the conventional grid to the smart grid at a rapid pace. Innovation always comes with some flaws; such is the case with a smart grid. One of the major challenges in the smart grid is to protect it from potential cyberattacks. There are millions of sensors continuously sending and receiving data packets over the network, so managing such a gigantic network is the biggest challenge. Any cyberattack can damage the key elements, confidentiality, integrity, and availability of the smart grid. The overall smart grid network is comprised of customers accessing the network, communication network of the smart devices and sensors, and the people managing the network (decision makers); all three of these levels are vulnerable to cyberattacks. In this survey, we explore various threats and vulnerabilities that can affect the key elements of cybersecurity in the smart grid network and then present the security measures to avert those threats and vulnerabilities at three different levels. In addition to that, we suggest techniques to minimize the chances of cyberattack at all three levels. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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26 pages, 7363 KiB  

Open AccessArticle

Optimal Configuration with Capacity Analysis of PV-Plus-BESS for Behind-the-Meter Application

by Cheng-Yu Peng, Cheng-Chien Kuo and Chih-Ta Tsai

Appl. Sci. 2021, 11(17), 7851; https://doi.org/10.3390/app11177851 - 26 Aug 2021

Cited by 16 | Viewed by 4601

Abstract

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the [...] Read more.

As the cost of photovoltaic (PV) systems and battery energy storage systems (BESS) decreases, PV-plus-BESS applied to behind-the-meter (BTM) market has grown rapidly in recent years. With user time of use rates (TOU) for charging and discharging schedule, it can effectively reduce the electricity expense of users. This research uses the contract capacity of an actual industrial user of 7.5 MW as a research case, and simulates a PV/BESS techno-economic scheme through the HOMER Grid software. Under the condition that the electricity demand is met and the PV power generation is fully used, the aim is to find the most economical PV/BESS capacity allocation and optimal contract capacity scheme. According to the load demand and the electricity price, the analysis shows that the PV system capacity is 8.25 MWp, the BESS capacity is 1.25 MW/3.195 MWh, and the contract capacity can be reduced to 6 MW. The benefits for the economical solution are compared as follows: 20-year project benefit, levelized cost of energy (LCOE), the net present cost (NPC), the internal rate of return (IRR), the return on investment (ROI), discounted payback, total electricity savings, renewable fraction (RF), and the excess electricity fraction. Finally, the sensitivity analysis of the global horizontal irradiation, electricity price, key component cost, and real interest rate will be carried out with the most economical solution by analyzing the impacts and evaluating the economic evaluation indicators. The analysis method of this research can be applied to other utility users to program the economic benefit evaluation of PV/BESS, especially an example for Taiwan’s electricity prices at low levels in the world. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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21 pages, 7220 KiB  

Open AccessArticle

Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

by İsmail Hakkı Çavdar and Vahit Feryad

Energies 2021, 14(15), 4649; https://doi.org/10.3390/en14154649 - 30 Jul 2021

Cited by 12 | Viewed by 3665

Abstract

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using [...] Read more.

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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34 pages, 2340 KiB  

Open AccessReview

Issues and Challenges for HVDC Extruded Cable Systems

by Giovanni Mazzanti

Energies 2021, 14(15), 4504; https://doi.org/10.3390/en14154504 - 26 Jul 2021

Cited by 63 | Viewed by 7271

Abstract

The improved features of AC/DC converters, the need to enhance cross-country interconnections, the will to make massive remote renewable energy sources available, and the fear of populations about overhead lines have fostered HVDC cable transmission all over the world, leading in the last [...] Read more.

The improved features of AC/DC converters, the need to enhance cross-country interconnections, the will to make massive remote renewable energy sources available, and the fear of populations about overhead lines have fostered HVDC cable transmission all over the world, leading in the last two decades to an exponential increase of commissioned HVDC cable projects, particularly of the extruded insulation type. Comprehensive surveys of the issues to be faced by HVDC extruded cable systems appeared in the literature some years ago, but they are not so up-to-date, as HVDC extruded cable technology is developing fast. Therefore, the contribution this paper aims at giving is a systematic, comprehensive and updated summary of the main present and future issues and challenges that HVDC cable systems have to face to further improve their performance and competitiveness, so as to meet the growing quest for clean and available energy worldwide. The topics covered in this review–treated in alphabetical order for the reader’s convenience–are accessories, higher voltage and power, laying environment (submarine and underground cables), modeling, multiterminal HVDC, operation and diagnostics, recyclable insulation, space charge behavior, testing, thermal stability, transient voltages. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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20 pages, 6625 KiB  

Open AccessArticle

Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

by Mohamed Massaoudi, Ines Chihi, Lilia Sidhom, Mohamed Trabelsi, Shady S. Refaat and Fakhreddine S. Oueslati

Energies 2021, 14(13), 3992; https://doi.org/10.3390/en14133992 - 2 Jul 2021

Cited by 30 | Viewed by 3361

Abstract

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based [...] Read more.

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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15 pages, 5957 KiB  

Open AccessArticle

Research on Temperature Monitoring Method of Cable on 10 kV Railway Power Transmission Lines Based on Distributed Temperature Sensor

by Kai Chen, Yi Yue and Yuejin Tang

Energies 2021, 14(12), 3705; https://doi.org/10.3390/en14123705 - 21 Jun 2021

Cited by 21 | Viewed by 3793

Abstract

Railway power transmission lines (RPTL) are power lines that provide nontraction power supply for railways, such as communications and signals along the railway. With the advancement of technology, power cables are being used more and more widely. Operational experience has shown that during [...] Read more.

Railway power transmission lines (RPTL) are power lines that provide nontraction power supply for railways, such as communications and signals along the railway. With the advancement of technology, power cables are being used more and more widely. Operational experience has shown that during the operation of power cables, abnormal heat is often caused by fault factors such as poor joint crimping and severe partial discharge caused by insulation defects, leading to cable burns in extreme cases. Distributed temperature sensors (DTS), a kind of spatial continuous temperature sensor using sensing optical fiber, can measure the temperature along the cable and are expected to realize on-line monitoring and positioning of cable heating faults. This paper first builds a finite element model of the cable under various faults to calculate the distribution characteristics of the temperature field of the faulty cable. Then the results are verified through experiments with the external sensing fiber and the artificially manufactured heating points of the cable. The conclusions show that it is feasible to use a distributed sensing fiber to monitor and locate the heating fault of power cable. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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26 pages, 8091 KiB  

Open AccessArticle

Voltage Profile and Sensitivity Analysis for a Grid Connected Solar, Wind and Small Hydro Hybrid System

by Noah Serem, Lawrence K. Letting and Josiah Munda

Energies 2021, 14(12), 3555; https://doi.org/10.3390/en14123555 - 15 Jun 2021

Cited by 12 | Viewed by 3203

Abstract

Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a [...] Read more.

Due to increase in integration of renewable energy into the grid and power quality issues arising from it, there is need for analysis and power improvement of such networks. This paper presents voltage profile, Q-V sensitivity analysis and Q-V curves analysis for a grid that is highly penetrated by renewable energy sources; solar PV, wind power and small hydro systems. Analysis is done on IEEE 39 bus test system with Wind power injection alone, PV power injection alone, with PV and wind power injection and with PV, wind and micro hydro power injection to the grid. The analysis is used to determine the buses where voltage stability improvement is needed. From the results, it was concluded that injection of the modeled wind power alone helped in stabilizing the voltage levels as determined from voltage profiles and reactive power margins. Replacing some of the conventional sources with PV power led to reduction of voltages for weak buses below the required standards. Injection of power from more than one renewable energy source helped in slightly improving the voltage levels. Distribution Static compensators (D-STATCOMs) were used to improve the voltage levels of the buses that were below the required standards. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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15 pages, 4621 KiB  

Open AccessArticle

Deep-Reinforcement-Learning-Based Two-Timescale Voltage Control for Distribution Systems

by Jing Zhang, Yiqi Li, Zhi Wu, Chunyan Rong, Tao Wang, Zhang Zhang and Suyang Zhou

Energies 2021, 14(12), 3540; https://doi.org/10.3390/en14123540 - 14 Jun 2021

Cited by 15 | Viewed by 2514

Abstract

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions [...] Read more.

Because of the high penetration of renewable energies and the installation of new control devices, modern distribution networks are faced with voltage regulation challenges. Recently, the rapid development of artificial intelligence technology has introduced new solutions for optimal control problems with high dimensions and dynamics. In this paper, a deep reinforcement learning method is proposed to solve the two-timescale optimal voltage control problem. All control variables are assigned to different agents, and discrete variables are solved by a deep Q network (DQN) agent while the continuous variables are solved by a deep deterministic policy gradient (DDPG) agent. All agents are trained simultaneously with specially designed reward aiming at minimizing long-term average voltage deviation. Case study is executed on a modified IEEE-123 bus system, and the results demonstrate that the proposed algorithm has similar or even better performance than the model-based optimal control scheme and has high computational efficiency and competitive potential for online application. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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16 pages, 1375 KiB  

Open AccessArticle

Optimization of IEDs Position in MV Smart Grids through Integer Linear Programming

by Francesco Bonavolontà, Vincenzo Caragallo, Alessandro Fatica, Annalisa Liccardo, Adriano Masone and Claudio Sterle

Energies 2021, 14(11), 3346; https://doi.org/10.3390/en14113346 - 7 Jun 2021

Cited by 4 | Viewed by 2657

Abstract

In the paper, an analytical method for determining the optimal positioning of intelligent electronic devices in medium voltage grids is proposed. Intelligent electronic devices are automated devices able to communicate one with each other and command the circuit breaker in order to localize [...] Read more.

In the paper, an analytical method for determining the optimal positioning of intelligent electronic devices in medium voltage grids is proposed. Intelligent electronic devices are automated devices able to communicate one with each other and command the circuit breaker in order to localize and isolate a line fault as fast as possible. However, the number of intelligent electronic devices to install has to be limited, due to the relevant installation costs and the reduction in the transmission bandwidth caused by the increased number of exchanged messages. So, the electrical distributor has to carefully detect the nodes of the grid where the intelligent electronic devices have to be installed. The authors propose a method based on integer linear programming, which, given the number of intelligent electronic devices to install, finds their optimal position, i.e., the one that minimizes the penalties associated with the power down experienced by customers. In order to highlight the offered advantages in terms of computational effort, the proposed approach has been assessed with a real medium voltage grid. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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20 pages, 3362 KiB  

Open AccessArticle

Optimal Scheduling Strategy of AC/DC Hybrid Distribution Network Based on Power Electronic Transformer

by Qingwen Peng, Lu Qu, Zhichang Yuan, Xiaorui Wang, Yukun Chen and Baoye Tian

Energies 2021, 14(11), 3219; https://doi.org/10.3390/en14113219 - 31 May 2021

Cited by 6 | Viewed by 2401

Abstract

The AC/DC hybrid distribution network is composed of a medium-voltage DC bus, a low-voltage DC bus, and a power electronic transformer, and has the characteristics of multi-voltage level, multi-DC bus, and multi-converter, so its operation mode and optimal scheduling strategy are more complex. [...] Read more.

The AC/DC hybrid distribution network is composed of a medium-voltage DC bus, a low-voltage DC bus, and a power electronic transformer, and has the characteristics of multi-voltage level, multi-DC bus, and multi-converter, so its operation mode and optimal scheduling strategy are more complex. Firstly, this paper constructs the AC/DC hybrid distribution network using an power electronic transformer. Then, a two-layer control structure including a scheduling management layer and a bus control layer is proposed, which simplifies the control structure and gives full play to the role of “energy routing” function of the power electronic transformer. Moreover, the minimum operation cost of the AC/DC hybrid distribution network in the whole scheduling cycle is taken as the optimization objective, considering the characteristics of various distributed generations, the structure of AC/DC hybrid distribution network, and the interaction of “source–load–storage”. Finally, the optimal scheduling model of the AC/DC hybrid distribution network based on power electronic transformer is established, and the feasibility of the optimal scheduling strategy is verified by the open-source solver, which can realize the complete absorption of renewable energy and the optimal coordinated control of “source–load–storage”. Full article

(This article belongs to the Topic Innovative Techniques for Smart Grids)

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