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Applied Sciences
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Energy Management and Smart Grids
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Energy Management and Smart Grids

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Energy Science and Technology".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 29308

Special Issue Editors

Prof. Dr. Catalina Spataru

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Guest Editor
Energy Institute, University College London, London WC1H 0NN, UK
Interests: resource nexus (water–energy–land–food–materials); governance; scenarios analysis; sustainability; systems modelling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Ding

E-Mail Website
Guest Editor
College of Electrical Engineering, Zhejiang University, Hangzhou 310012, China
Interests: smart grids; cities; technologies; energy systems modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pierluigi Siano

grade E-Mail Website
Guest Editor
Department of Management and Innovation Systems, University of Salerno, 84084 Salerno, Italy
Interests: smart grids; energy management; power systems; demand response
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zita Vale

E-Mail Website
Guest Editor
GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-465 Porto, Portugal
Interests: artificial intelligence; demand response; electric vehicles; electricity markets; power and energy systems; renewable and sustainable energy; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Electricity demand is rapidly increasing. Smart grids allow the management of energy and real-time balancing between electrical energy supply and demand. There is an urgent need for innovative business models and policies and new mathematical models and approaches for the efficient integration of distributed energy resources in power and energy systems according to the smart grid paradigm.

This call aims at bringing together the latest research in the area of energy management and smart grids.

Topics of interest for this Special Issue include, but are not limited to:

  • Energy management concepts;
  • Smart grids visions of power system innovation, reliability and efficiency of the distribution grid;
  • Demand flexibility;
  • Generation flexibility;
  • Modeling of smart grids;
  • Demand side response;
  • Algorithms and approaches on the consumer side;
  • Energy trading among interconnected microgrids;
  • Control applications to distributed energy management systems;
  • Energy management systems for smart grids;
  • Stochastic optimization methods for energy management;
  • Energy management approaches for demand-side applications;
  • Smart energy storage;
  • Load balancing in smart grids;
  • Smart cities, case studies, business models, and innovative applications;
  • Emerging technologies and end-user systems.

Prof. Dr. Catalina Spataru
Prof. Dr. Yi Ding
Prof. Dr. Pierluigi Siano
Prof. Dr. Zita A. Vale
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences 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 2400 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

  • demand flexibility
  • demand management
  • energy management
  • power quality
  • smart cities
  • smart grids

Published Papers (7 papers)

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Research

20 pages, 3006 KiB  
Article
Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
by Jinying Yu, Yuchen Gao, Yuxin Wu, Dian Jiao, Chang Su and Xin Wu
Appl. Sci. 2019, 9(17), 3558; https://doi.org/10.3390/app9173558 - 30 Aug 2019
Cited by 9 | Viewed by 2866
Abstract
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. [...] Read more.
Non-intrusive load monitoring (NILM) is a core technology for demand response (DR) and energy conservation services. Traditional NILM methods are rarely combined with practical applications, and most studies aim to disaggregate the whole loads in a household, which leads to low identification accuracy. In this method, the event detection method is used to obtain the switching event sets of all loads, and the power consumption curves of independent unknown electrical appliances in a period are disaggregated by utilizing comprehensive features. A linear discriminant classifier group based on multi-feature global similarity is used for load identification. The uniqueness of our algorithm is that it designs an event detector based on steady-state segmentation and a linear discriminant classifier group based on multi-feature global similarity. The simulation is carried out on an open source data set. The results demonstrate the effectiveness and high accuracy of the multi-feature integrated classification (MFIC) algorithm by using the state-of-the-art NILM methods as benchmarks. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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26 pages, 6660 KiB  
Article
A Priority-Based Synchronous Phasor Transmission Protocol Extension Method for the Active Distribution Network
by Weiqing Tao, Mengyu Ma, Ming Ding, Wei Xie and Chen Fang
Appl. Sci. 2019, 9(10), 2135; https://doi.org/10.3390/app9102135 - 24 May 2019
Cited by 5 | Viewed by 2824
Abstract
With the advancement of active distribution network construction, to solve the shortcomings of the existing distribution network technology in distribution network perception and control, the relevant technologies of the Wide Area Measurement System (WAMS) in the transmission network have attracted more attention in [...] Read more.
With the advancement of active distribution network construction, to solve the shortcomings of the existing distribution network technology in distribution network perception and control, the relevant technologies of the Wide Area Measurement System (WAMS) in the transmission network have attracted more attention in terms of their usage in the distribution network. Micro Multifunction Phasor Measurement Unit (μMPMU), as an example, is being gradually utilized in the distribution network. However, the existing synchronous phasor transmission protocol is mainly designed for the transmission network, which requires an extension to meet the communication requirements to be directly used in the distribution network. In this work, the requirements of active distribution network communication are analyzed, and trade-offs between National Standard of the People’s Republic of China/Recommended (GB/T) 26865.2-2011 and International Electro technical Commission (IEC) 60870-5-101/104 protocol are compared. An extension method of the communication protocol is proposed, with the benefits of the prioritized transmission of important data, expanded remote control function of μMPMU, increased types of offline files, and reduced amount of network communication and data storage. The method is built upon the existing GB/T 26865.2-2011 protocol, and refers to the Application Service Data Unit (ASDU) of IEC 60870-5-101/104 to add an application extension frame. Application extension frames are used to transmit telemetry data, telesignalization, partial commands, and partial offline files. Finally, an experimental environment is set up, which includes a phasor measurement unit (PMU) Emulator, distribution network phasor data concentrator (PDC), and main station emulator to implement the standard GB/T 26865.2-2011 protocol and extension protocol. The feasibility and effectiveness of the method are confirmed by the superior performance of the extended protocol compared with the standard protocol. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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12 pages, 3869 KiB  
Article
Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches
by Juncheng Zhu, Zhile Yang, Yuanjun Guo, Jiankang Zhang and Huikun Yang
Appl. Sci. 2019, 9(9), 1723; https://doi.org/10.3390/app9091723 - 26 Apr 2019
Cited by 90 | Viewed by 6338
Abstract
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles [...] Read more.
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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17 pages, 1338 KiB  
Article
Subsidy and Pricing Model of Electric Vehicle Sharing Based on Two-Stage Stackelberg Game – A Case Study in China
by Jun Yang, Yangjia Lin, Fuzhang Wu and Lei Chen
Appl. Sci. 2019, 9(8), 1631; https://doi.org/10.3390/app9081631 - 19 Apr 2019
Cited by 18 | Viewed by 4165
Abstract
Electric vehicle sharing provides an effective way to improve the traffic situation and relieve environmental pressure. The government subsidy policy and the car-sharing operator’s pricing strategy are the key factors that affect the large-scale application of electric vehicle sharing. To address this issue, [...] Read more.
Electric vehicle sharing provides an effective way to improve the traffic situation and relieve environmental pressure. The government subsidy policy and the car-sharing operator’s pricing strategy are the key factors that affect the large-scale application of electric vehicle sharing. To address this issue, a subsidy and pricing model for electric vehicle sharing based on the two-stage Stackelberg game is proposed in this paper according to the current situation in China. First, an electric vehicle sharing operation mode under government participation is constructed. Then, a two-stage Stackelberg game model involving the government, the car-sharing operator and the consumers is proposed to determine the subsidy rates and pricing strategies. The improved particle swarm optimization algorithm is used to obtain the Nash equilibrium of the model. Also, the influence of private car cost and shared travel comfort on subsidy rates and pricing strategies is analyzed. Finally, the simulation of electric vehicle sharing in a town of China is carried out to investigate the performance of the proposed subsidy and price model. The simulation results show that the model rationally formulates subsidy policies and pricing strategies of the electric vehicle sharing to balance the interests of the three participants, mobilizing users’ enthusiasm while guaranteeing the benefits of the government and operator, making the overall benefit optimal. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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22 pages, 2810 KiB  
Article
Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation
by Xiangyu Kong, Yuting Wang, Xiaoxiao Yuan and Li Yu
Appl. Sci. 2019, 9(7), 1515; https://doi.org/10.3390/app9071515 - 11 Apr 2019
Cited by 21 | Viewed by 3816
Abstract
A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state [...] Read more.
A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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22 pages, 2188 KiB  
Article
Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function
by Pawan Singh, Baseem Khan, Ankit Vidyarthi, Hassan Haes Alhelou and Pierluigi Siano
Appl. Sci. 2019, 9(7), 1467; https://doi.org/10.3390/app9071467 - 08 Apr 2019
Cited by 10 | Viewed by 2738
Abstract
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data centers and battery-based computing devices. Practically important online non-clairvoyant job scheduling is studied less extensively than other algorithms. In this paper, an online non-clairvoyant scheduling algorithm Highest Scaled Importance [...] Read more.
Efficient job scheduling reduces energy consumption and enhances the performance of machines in data centers and battery-based computing devices. Practically important online non-clairvoyant job scheduling is studied less extensively than other algorithms. In this paper, an online non-clairvoyant scheduling algorithm Highest Scaled Importance First (HSIF) is proposed, where HSIF selects an active job with the highest scaled importance. The objective considered is to minimize the scaled importance based flow time plus energy. The processor’s speed is proportional to the total scaled importance of all active jobs. The performance of HSIF is evaluated by using the potential analysis against an optimal offline adversary and simulating the execution of a set of jobs by using traditional power function. HSIF is 2-competitive under the arbitrary power function and dynamic speed scaling. The competitive ratio obtained by HSIF is the least to date among non-clairvoyant scheduling. The simulation analysis reflects that the performance of HSIF is best among the online non-clairvoyant job scheduling algorithms. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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17 pages, 4416 KiB  
Article
Voltage/Frequency Deviations Control via Distributed Battery Energy Storage System Considering State of Charge
by Yongzhu Hua, Xiangrong Shentu, Qiangqiang Xie and Yi Ding
Appl. Sci. 2019, 9(6), 1148; https://doi.org/10.3390/app9061148 - 18 Mar 2019
Cited by 19 | Viewed by 5102
Abstract
In recent years, the installation of distributed generation (DG) of renewable energies has grown rapidly. When the penetration of grid-integrated DGs are getting high, the voltage and frequency of the power system may cause deviation. We propose an algorithm that reduces voltage and [...] Read more.
In recent years, the installation of distributed generation (DG) of renewable energies has grown rapidly. When the penetration of grid-integrated DGs are getting high, the voltage and frequency of the power system may cause deviation. We propose an algorithm that reduces voltage and frequency deviation by coordinating the control of multiple battery energy storage systems (BESSs). The proposed algorithm reduces the total number of charging and discharging times by calculating the sensitivity coefficient of BESS at different nodes and then selecting the appropriate BESSs to operate. The algorithm is validated on a typical distribution testing system. The results show that the voltage and frequency are controlled within the permissible range, the state of charge of BESSs are controlled within the normal range, and the total number of charging and discharging cycles of BESSs are reduced. Full article
(This article belongs to the Special Issue Energy Management and Smart Grids)
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