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AI, IoT and Block Chain Assisted Intelligent Solutions to Energy...
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AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems

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

Deadline for manuscript submissions: closed (28 February 2021) | Viewed by 33174

Special Issue Editors

Prof. Dr. Jens Bo Holm-Nielsen

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark
Interests: renewable energy systems; smart energy systems; biomass resources; biogas production; bio-refineries
Special Issues, Collections and Topics in MDPI journals
Dr. Padmanaban Sanjeevikumar

E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 6700 Esbjerg, Denmark
Interests: power electronics; electrical drives; renewable energy; power electronics converter; modulation techniques; grid connected system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy efficiency in digital scenario and fast computing methodologies has become one of the most vital parameters in determining the success of a product in the market. To stay globally competitive, every manufacturer and service provider invests huge amounts of money in research and development for artificial intelligence solutions in assisting the energy efficiency and sustainability of their products. Recently, artificial intelligence (AI), internet-of-things (IoT), big data analytics, machine learning, deep learning, cloud computing and block chain technologies have been intelligently applied with various applications in networking, manufacturing, building management, transportation and shipping to build energy efficient and sustainable systems.

This Special Issue focuses on the recent advancements in product-based design and manufacturing for achieving energy efficiency using artificial intelligence technologies. Topics of interest for this special issue include, but are not limited to,

  • Intelligent energy efficient solutions for future applications.
  • AI and block chain assisted energy efficient product designs.
  • Optimization of energy assets using machine learning and deep learning algorithms.
  • Smart IoT sensor design and optimal utilization in smart cities.
  • Industrial Revolution 4.0 focused automation using block chain technologies in energy harvest (photovoltaic (PV), wind, solar, batteries, electric vehicles (EVs)).
  • Real-time energy resource management and efficient internet of medical things.
  • Applications of artificial intelligence, block chain IoT for sustainable manufacturing and service.
  • AI based intelligent solutions for energy efficiency.
  • Artificial neural networks applied to energy systems.
  • Energy solutions to intelligent transportation systems.
  • Big data supported energy efficient systems.
  • Energy efficient cloud computing designs.
  • Energy efficient fog networks.
  • Hardware optimization for energy efficiency.
  • Advanced sensor technologies for sustainable systems.
  • Future perspectives of energy efficient and sustainable products.
  • Digital quality monitoring of energy assets (photovoltaic (PV), wind, solar, batteries, electric vehicles (EVs)) for preventive/predictive management and cost optimization.

Prof. Dr. Jens Bo Holm-Nielsen
Dr. Padmanaban Sanjeevikumar
Guest Editors

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Published Papers (5 papers)

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Research

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14 pages, 2636 KiB  
Article
A Lightweight Secure and Energy-Efficient Fog-Based Routing Protocol for Constraint Sensors Network
by Khalid Haseeb, Naveed Islam, Yasir Javed and Usman Tariq
Energies 2021, 14(1), 89; https://doi.org/10.3390/en14010089 - 26 Dec 2020
Cited by 19 | Viewed by 2625
Abstract
The Wireless Sensor Network (WSN) has seen rapid growth in the development of real-time applications due to its ease of management and cost-effective attributes. However, the balance between optimization of network lifetime and load distribution between sensor nodes is a critical matter for [...] Read more.
The Wireless Sensor Network (WSN) has seen rapid growth in the development of real-time applications due to its ease of management and cost-effective attributes. However, the balance between optimization of network lifetime and load distribution between sensor nodes is a critical matter for the development of energy-efficient routing solutions. Recently, many solutions have been proposed for constraint-based networks using the cloud paradigm. However, they achieve network scalability with the additional cost of routing overheads and network latency. Moreover, the sensors’ data is transmitted towards application users over the uncertain medium, which leads to compromised data security and its integrity. Therefore, this work proposes a light-weight secure and energy-efficient fog-based routing (SEFR) protocol to minimize data latency and increase energy management. It exploits the Quality of Service (QoS) factors and facilitates time-sensitive applications with network edges. Moreover, the proposed protocol protects real-time data based on two levels of cryptographic security primitives. In the first level, a lightweight data confidentiality scheme is proposed between the cluster heads and fog nodes, and in the second level, a high-performance asymmetric encryption scheme is proposed among fog and cloud layers. The analysis of simulation-based experiments has proven the significant outcomes of the proposed protocol compared to existing solutions in terms of routing, security, and network management. Full article
(This article belongs to the Special Issue AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems)
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17 pages, 3021 KiB  
Article
Enhancement of Security and Handling the Inconspicuousness in IoT Using a Simple Size Extensible Blockchain
by B. Saravana Balaji, P. Vishnu Raja, Anand Nayyar, P. Sanjeevikumar and Sanjeevi Pandiyan
Energies 2020, 13(7), 1795; https://doi.org/10.3390/en13071795 - 8 Apr 2020
Cited by 26 | Viewed by 3800
Abstract
Blockchain technology is increasingly used worldwide to enhance the performance and profit of any environment through its defining characteristics, such as security, auditability, immutability, and inconspicuousness. Owing to these characteristics, the blockchain can be used in various non-financial operations of some domains, such [...] Read more.
Blockchain technology is increasingly used worldwide to enhance the performance and profit of any environment through its defining characteristics, such as security, auditability, immutability, and inconspicuousness. Owing to these characteristics, the blockchain can be used in various non-financial operations of some domains, such as the Internet of Things (IoT) and distributed computing. However, implementing blockchain technology in IoT is not always a feasible solution because blockchain deployment is costly, it has limited extensibility and provides irregular bandwidth and latency. In this regard, a simple size extensible (SSE) blockchain has been proposed to provide an optimal solution for IoT environments by satisfying the needs of the IoT environment as well as ensuring end-to-end security. The implementation of the proposed blockchain develops an overlay network to obtain a distributed environment where the blockchain is handled by the resources present therein. Two novel algorithms were introduced into the proposed system to minimize the irregularity and latency on one hand, and to maximize the throughput of the system on the other. The shared-time depending agreement algorithm (STD) minimizes the irregularity in the extraction operation and latency. The other, the shared throughput administration algorithm (STA) justifies the overall collection of the transmission load in the network and maintains the performance of the blockchain. The proposed system was applied to smart home IoT appliances to test the performance of the proposed system. The experimental results show that the proposed blockchain system minimizes nearly 70% of the data irregularity, latency, and furthermore, 30% of the blockchain extensibility is maximized as compared to the existing systems. Full article
(This article belongs to the Special Issue AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems)
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Review

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46 pages, 9265 KiB  
Review
Systematic Categorization of Optimization Strategies for Virtual Power Plants
by Amit Kumer Podder, Sayemul Islam, Nallapaneni Manoj Kumar, Aneesh A. Chand, Pulivarthi Nageswara Rao, Kushal A. Prasad, T. Logeswaran and Kabir A. Mamun
Energies 2020, 13(23), 6251; https://doi.org/10.3390/en13236251 - 27 Nov 2020
Cited by 23 | Viewed by 5284
Abstract
Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate [...] Read more.
Due to the rapid growth in power consumption of domestic and industrial appliances, distributed energy generation units face difficulties in supplying power efficiently. The integration of distributed energy resources (DERs) and energy storage systems (ESSs) provides a solution to these problems using appropriate management schemes to achieve optimal operation. Furthermore, to lessen the uncertainties of distributed energy management systems, a decentralized energy management system named virtual power plant (VPP) plays a significant role. This paper presents a comprehensive review of 65 existing different VPP optimization models, techniques, and algorithms based on their system configuration, parameters, and control schemes. Moreover, the paper categorizes the discussed optimization techniques into seven different types, namely conventional technique, offering model, intelligent technique, price-based unit commitment (PBUC) model, optimal bidding, stochastic technique, and linear programming, to underline the commercial and technical efficacy of VPP at day-ahead scheduling at the electricity market. The uncertainties of market prices, load demand, and power distribution in the VPP system are mentioned and analyzed to maximize the system profits with minimum cost. The outcome of the systematic categorization is believed to be a base for future endeavors in the field of VPP development. Full article
(This article belongs to the Special Issue AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems)
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42 pages, 8725 KiB  
Review
Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids
by Nallapaneni Manoj Kumar, Aneesh A. Chand, Maria Malvoni, Kushal A. Prasad, Kabir A. Mamun, F.R. Islam and Shauhrat S. Chopra
Energies 2020, 13(21), 5739; https://doi.org/10.3390/en13215739 - 2 Nov 2020
Cited by 136 | Viewed by 14293
Abstract
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers [...] Read more.
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-way flow of electricity and data between the peers within the electricity system networks (ESN) and its clusters. The self-healing capabilities of SG allow the peers to become active partakers in ESN. In general, the SG is intended to replace the fossil fuel-rich conventional grid with the distributed energy resources (DER) and pools numerous existing and emerging know-hows like information and digital communications technologies together to manage countless operations. With this, the SG will able to “detect, react, and pro-act” to changes in usage and address multiple issues, thereby ensuring timely grid operations. However, the “detect, react, and pro-act” features in DER-based SG can only be accomplished at the fullest level with the use of technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and the Blockchain (BC). The techniques associated with AI include fuzzy logic, knowledge-based systems, and neural networks. They have brought advances in controlling DER-based SG. The IoT and BC have also enabled various services like data sensing, data storage, secured, transparent, and traceable digital transactions among ESN peers and its clusters. These promising technologies have gone through fast technological evolution in the past decade, and their applications have increased rapidly in ESN. Hence, this study discusses the SG and applications of AI, IoT, and BC. First, a comprehensive survey of the DER, power electronics components and their control, electric vehicles (EVs) as load components, and communication and cybersecurity issues are carried out. Second, the role played by AI-based analytics, IoT components along with energy internet architecture, and the BC assistance in improving SG services are thoroughly discussed. This study revealed that AI, IoT, and BC provide automated services to peers by monitoring real-time information about the ESN, thereby enhancing reliability, availability, resilience, stability, security, and sustainability. Full article
(This article belongs to the Special Issue AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems)
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21 pages, 1411 KiB  
Review
A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems
by Ali Thaeer Hammid, Omar I. Awad, Mohd Herwan Sulaiman, Saraswathy Shamini Gunasekaran, Salama A. Mostafa, Nallapaneni Manoj Kumar, Bashar Ahmad Khalaf, Yasir Amer Al-Jawhar and Raed Abdulkareem Abdulhasan
Energies 2020, 13(11), 2787; https://doi.org/10.3390/en13112787 - 1 Jun 2020
Cited by 55 | Viewed by 5274
Abstract
The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target [...] Read more.
The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem. Full article
(This article belongs to the Special Issue AI, IoT and Block Chain Assisted Intelligent Solutions to Energy Efficient and Sustainable Systems)
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