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Energy Management Based on IoT
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Energy Management Based on IoT

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

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 14633

Special Issue Editor

Prof. Dr. Sehyun Park

E-Mail Website
Guest Editor
Department of Intelligent Energy and Industry, Chung-Ang University, Seoul 06974, Republic of Korea
Interests: smart energy; carbon neutrality; digital platform; AI-based data; digital twins; smart buildings and cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, many intelligent services are emerging for energy management in homes, buildings, and cities in order to realize a sustainable society worldwide. However, there are challenges to implementing an innovative and advanced active sustainable energy society. Currently, it is limited to simple energy saving based on stand-alone, only by controlling simple passive elements (simple air conditioning scheduling and algorithms, replacement of old equipment, etc.).

Accordingly, suggestions are being made to achieve more intelligent energy saving by applying IoT (Internet of Things), AI (artificial intelligence), and cloud technology to build a smart environment based on new ICT technology. IoT, AI, and cloud technology analyse and predict energy data to be used in the future, and establish appropriate energy saving policies to apply various technologies for energy optimization management.

This call for papers proposes an active and dynamic scheduling algorithm that collects, analyses, and infers energy data, and intelligently saves energy in a smart space based on IoT, AI, and cloud technology. It is necessary to propose energy saving plans in various regional, environmental, and service aspects, such as the home, buildings, and cities, by applying this algorithm. This method collects energy data based on IoT installed in a smart space, analyses/infers data based on artificial intelligence, and provides future energy saving system policies. The following shows detailed descriptions related to this.

IoT-based intelligent energy data analysis technology

  • IoT-, edge-, and cloud-based intelligent energy data collection, management, and processing technology
  • Smart energy digital twins for intelligent energy data modelling and simulation
  • AI-based intelligent data analysis algorithms

Distributed energy IoT-based home/building energy management system

  • Distributed energy IoT-based intelligent home/building energy management system and application services
  • Small community connection model based on smart edge technology
  • Cloud-based large-scale smart energy city platform for efficient energy management

 Distributed energy IoT-based smart energy city model

  • Intelligent energy management model for the home community (households, apartments, etc.)
  • Intelligent energy management model for the building community (small buildings, large buildings, factories, etc.)
  • Intelligent energy management model in the city (new cities, residential complexes, industrial complexes, etc.)

Distributed energy IoT-based energy transaction service and business model

  • Blockchain-based smart energy transaction system model for distributed energy resources
  • Intelligent energy prosumer model linking renewable energy–ESS–smart building
  • Intelligent V2G model linking renewable energy–ESS–EV

Smart energy IoT-based eco-friendly energy service and business model

  • Smart energy IoT-based intelligent smart farm management systems
  • Smart energy IoT-based smart green hydrogen systems
  • Smart energy IoT-based intelligent (renewable energy–heat grid–smart farm–fuel cell, etc.) eco-friendly energy linkage model

IoT-related energy big data for small-platform to small-platform

  • Small-platform to small-platform linkage model for intelligent linkage between energy elements and optimization for integrated energy management
  • Small-platform to small-platform linkage system through energy big data analysis
  • Energy optimization technology through energy big data map
  • Small-platform customization technology for integrated energy platforms

Prof. Dr. Sehyun Park
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

  • distributed energy-IoT (Internet of Things)
  • EMS (energy management system)
  • small-platform
  • digital-twin
  • energy simulation
  • renewable energy
  • green energy
  • active management
  • smart space
  • energy transaction system

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Published Papers (3 papers)

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20 pages, 866 KiB  
Article
Energy Trading among Power Grid and Renewable Energy Sources: A Dynamic Pricing and Demand Scheme for Profit Maximization
by Yoon-Sik Yoo, Seung Hyun Jeon, S. H. Shah Newaz, Il-Woo Lee and Jun Kyun Choi
Sensors 2021, 21(17), 5819; https://doi.org/10.3390/s21175819 - 30 Aug 2021
Cited by 9 | Viewed by 4033
Abstract
With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to [...] Read more.
With the technical growth and the reduction of deployment cost for distributed energy resources (DERs), such as solar photovoltaic (PV), energy trading has been recently encouraged to energy consumers, which can sell energy from their own energy storage system (ESS). Meanwhile, due to the unprecedented rise of greenhouse gas (GHG) emissions, some countries (e.g., Republic of Korea and India) have mandated using a renewable energy certificate (REC) in energy trading markets. In this paper, we propose an energy broker model to boost energy trading between the existing power grid and energy consumers. In particular, to maximize the profits of energy consumers and the energy provider, the proposed energy broker is in charge of deciding the optimal demand and dynamic price of energy in an REC-based energy trading market. In this solution, the smart agents (e.g., IoT intelligent devices) of consumers exchange energy trading associated information, including the amount of energy generation, price and REC. For deciding the optimal demand and dynamic pricing, we formulate convex optimization problems using dual decomposition. Through a numerical simulation analysis, we compare the performance of the proposed dynamic pricing strategy with the conventional pricing strategies. Results show that the proposed dynamic pricing and demand control strategies can encourage energy trading by allowing RECs trading of the conventional power grid. Full article
(This article belongs to the Special Issue Energy Management Based on IoT)
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21 pages, 857 KiB  
Article
Privacy-Preserving Energy Management of a Shared Energy Storage System for Smart Buildings: A Federated Deep Reinforcement Learning Approach
by Sangyoon Lee, Le Xie and Dae-Hyun Choi
Sensors 2021, 21(14), 4898; https://doi.org/10.3390/s21144898 - 19 Jul 2021
Cited by 15 | Viewed by 3382
Abstract
This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning [...] Read more.
This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning (DRL) framework using the FRL method, which consists of a global server (GS) and local building energy management systems (LBEMSs). In the framework, the LBEMS DRL agents share only a randomly selected part of their trained neural network for energy consumption models with the GS without consumer’s energy consumption data. Using the shared models, the GS executes two processes: (i) construction and broadcast of a global model of energy consumption to the LBEMS agents for retraining their local models and (ii) training of the SESS DRL agent’s energy charging and discharging from and to the utility and buildings. Simulation studies are conducted using one SESS and three smart buildings with solar photovoltaic systems. The results demonstrate that the proposed approach can schedule the charging and discharging of the SESS and an optimal energy consumption of heating, ventilation, and air conditioning systems in smart buildings under heterogeneous building environments while preserving the privacy of buildings’ energy consumption. Full article
(This article belongs to the Special Issue Energy Management Based on IoT)
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26 pages, 8364 KiB  
Article
Distributed Energy IoT-Based Real-Time Virtual Energy Prosumer Business Model for Distributed Power Resource
by Sanguk Park, Keonhee Cho, Seunghwan Kim, Guwon Yoon, Myeong-In Choi, Sangmin Park and Sehyun Park
Sensors 2021, 21(13), 4533; https://doi.org/10.3390/s21134533 - 1 Jul 2021
Cited by 11 | Viewed by 5185
Abstract
Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related [...] Read more.
Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established. Full article
(This article belongs to the Special Issue Energy Management Based on IoT)
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