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Energies
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Planning and Operation of Distributed Energy Resources in Smart Grids
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Planning and Operation of Distributed Energy Resources in Smart Grids

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 24155

Special Issue Editors

Prof. Stefania Conti

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Civile e architettura, University of Catania, Catania, Italy
Interests: power systems analysis; renewable sources; integration of distributed generation; smart grids
Dr. Fabio Bignucolo

E-Mail Website
Guest Editor
Dipartimento di ingeneria Industriale, University of Padova, Padova, Italy
Interests: distribution networks; smart grids; renewable generation; electrical system
Dr. Emilio Ghiani

E-Mail Website
Guest Editor
Department of Electrical and Electronic Engineering, University of Cagliari, 09124 Cagliari, Italy
Interests: smart grids; renewable energy; energy storage devices; energy distribution systems
Special Issues, Collections and Topics in MDPI journals
Dr. Santi A. Rizzo

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica Elettronica e Informatica, University of Catania, 95125 Catania, Italy
Interests: distribution network; power electronics; power systems; renewable energy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The management of distributed energy resources (DERs) in smart grids is the most promising solution to cope with the steadily increasing demand of electrical energy all over the world, accounting for the requirement of planning a sustainable future from both environmental and economic viewpoints. In fact, the presence of renewable generation injecting power into the electrical networks, as well as the presence of various types of DERs (e.g., electric vehicles, responsive loads, and distributed storage), leads to several critical conditions of unpredictability and insecurity, which require researchers and utilities to develop innovative approaches, such as the smart grid.

Within the smart grid framework, the centralized and distributed generation, transmission, and distribution, as well as the final users, communicate with each other and cooperate so as to enhance the efficiency and reliability of the networks, while new factors introduce security and predictability issues. This can be achieved by using ICT and sensoring technologies to implement intelligent monitoring and control functions. Then, the smart grid concept plays a crucial role in order to integrate high shares of distributed generators based on variable renewable energies sources.

Smart grids require the following challenging characteristics in order to be implemented effectively: safety, reliability, efficiency, affordability, environmental “cleanliness”, technical and economical optimization, interaction with electricity markets, self-healing ability, and the presence of an appropriate regulatory framework. Therefore, researchers are still involved in many studies and experimental implementations in order to find solutions to the technical, economic, and regulatory issues.

Any scientific work dealing with the aforementioned topics regarding the planning and operation of smart grids are welcome in this Special Issue.

Prof. Stefania Conti
Dr. Fabio Bignucolo
Dr. Emilio Ghiani
Dr. Santi A. Rizzo
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. Energies 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

  • renewable generation
  • distributed energy resources
  • smart grids
  • microgrids
  • power quality
  • optimal planning and operation
  • smart protections
  • power electronics
  • reliability
  • adequacy

Published Papers (6 papers)

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Research

15 pages, 33835 KiB  
Article
Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin
by Hyang-A Park, Gilsung Byeon, Wanbin Son, Hyung-Chul Jo, Jongyul Kim and Sungshin Kim
Energies 2020, 13(20), 5504; https://doi.org/10.3390/en13205504 - 20 Oct 2020
Cited by 51 | Viewed by 5131
Abstract
Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, [...] Read more.
Due to the recent development of information and communication technology (ICT), various studies using real-time data are now being conducted. The microgrid research field is also evolving to enable intelligent operation of energy management through digitalization. Problems occur when operating the actual microgrid, causing issues such as difficulty in decision making and system abnormalities. Using digital twin technology, which is one of the technologies representing the fourth industrial revolution, it is possible to overcome these problems by changing the microgrid configuration and operating algorithms of virtual space in various ways and testing them in real time. In this study, we proposed an energy storage system (ESS) operation scheduling model to be applied to virtual space when constructing a microgrid using digital twin technology. An ESS optimal charging/discharging scheduling was established to minimize electricity bills and was implemented using supervised learning techniques such as the decision tree, NARX, and MARS models instead of existing optimization techniques. NARX and decision trees are machine learning techniques. MARS is a nonparametric regression model, and its application has been increasing. Its performance was analyzed by deriving performance evaluation indicators for each model. Using the proposed model, it was found in a case study that the amount of electricity bill savings when operating the ESS is greater than that incurred in the actual ESS operation. The suitability of the model was evaluated by a comparative analysis with the optimization-based ESS charging/discharging scheduling pattern. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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21 pages, 3382 KiB  
Article
Comparative Study on Optimization Solvers for Implementation of a Two-Stage Economic Dispatch Strategy in a Microgrid Energy Management System
by Gi-Ho Lee, Jae-Young Park, Seung-Jun Ham and Young-Jin Kim
Energies 2020, 13(5), 1096; https://doi.org/10.3390/en13051096 - 02 Mar 2020
Cited by 5 | Viewed by 2615
Abstract
A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization [...] Read more.
A microgrid energy management system (MEMS) optimally schedules the operation of dispatchable distributed energy resources to minimize the operation costs of microgrids (MGs) via an economic dispatch (ED). Actual ED implementation in the MEMS relies on an optimization software package called an optimization solver. This paper presents a comparative study of optimization solvers to investigate their suitability for ED implementation in the MEMS. Four optimization solvers, including commercial as well as open-source-based ones, were compared in terms of their computational capability and optimization results for ED. Two-stage scheduling was applied for the ED strategy, whereby a mixed-integer programming problem was solved to yield the optimal operation schedule of battery-based energy storage systems. In the first stage, the optimal schedule is identified one day before the operating day; in the second stage, the optimal schedule is updated every 5 min during actual operation to compensate for operational uncertainties. A modularized programming strategy was also introduced to allow for a comparison between the optimization solvers and efficient writing of codes. Comparative simulation case studies were conducted on three test-bed MGs to evaluate the optimization results and computation times of the compared optimization solvers. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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14 pages, 5143 KiB  
Article
Planning of a Smart Local Energy Community: The Case of Berchidda Municipality (Italy)
by Emilio Ghiani, Andrea Giordano, Andrea Nieddu, Luca Rosetti and Fabrizio Pilo
Energies 2019, 12(24), 4629; https://doi.org/10.3390/en12244629 - 05 Dec 2019
Cited by 51 | Viewed by 6948
Abstract
Recent strategic policies and regulations dealing with market liberalization and decarbonization plans, such as the European directives contained in the recent EU Clean Energy for All Europeans Package, are seeking to promote new roles for citizens in the management of the self-produced renewable [...] Read more.
Recent strategic policies and regulations dealing with market liberalization and decarbonization plans, such as the European directives contained in the recent EU Clean Energy for All Europeans Package, are seeking to promote new roles for citizens in the management of the self-produced renewable energy and the development of local energy markets. In this context, this paper aims at presenting the planning actions for the transition of the current passive distribution system of the Municipality of Berchidda (Italy) towards a smart local energy community. This planning study represents the first stage of a development action financed by the Sardinian Region, whose Regional Energetic and Environmental Plan identifies the Municipality of Berchidda as a priority area to focus the experimental actions for innovative smart grids and intelligent energy management. The project, named “Berchidda Energy 4.0”, focuses on increasing the energy efficiency of the community by boosting local renewable generation production and maximizing its self-consumption, also with the support of storage systems, as well as increasing the active involvement of the consumers that will be equipped with a smart home automation system for demand response applications. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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18 pages, 1823 KiB  
Article
Microgrid Infrastructure Compendium Analysis with a Model Creation Tool and Guideline Based on Machine Learning Techniques
by Miguel Carpintero-Rentería, David Santos-Martín, Mónica Chinchilla and David Rebollal
Energies 2019, 12(23), 4509; https://doi.org/10.3390/en12234509 - 27 Nov 2019
Cited by 1 | Viewed by 2737
Abstract
A microgrid (MG) is an electric power distribution system that may provide a suitable ecosystem for distributed generation. Detailed information about the infrastructure layer in MG projects is available, so this study aimed to propose a compendium and a model creation guideline for [...] Read more.
A microgrid (MG) is an electric power distribution system that may provide a suitable ecosystem for distributed generation. Detailed information about the infrastructure layer in MG projects is available, so this study aimed to propose a compendium and a model creation guideline for MGs. The aggregated information based on 1618 MGs was summarized into different tables and analyzed based on various parameters. Two MG infrastructure model creation tools were developed. First, a simple guideline was created based on the information in the tables, and then a machine learning tool based on decision trees was proposed that generates more accurate MG models using two main inputs: latitude and the segment in which they operate. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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17 pages, 3198 KiB  
Article
Sizing and Siting of Distributed Generators and Energy Storage in a Microgrid Considering Plug-in Electric Vehicles
by Mingrui Zhang, Ming Gan and Luyao Li
Energies 2019, 12(12), 2293; https://doi.org/10.3390/en12122293 - 15 Jun 2019
Cited by 14 | Viewed by 2480
Abstract
This paper presents a sizing and siting model for distributed generators (DGs) and energy storage systems (ESS) towards the design of a cost-efficient and reliable microgrid considering electric vehicles (EVs). The proposed model exploits the coordinated energy dispatching of DGs, ESS, and EVs, [...] Read more.
This paper presents a sizing and siting model for distributed generators (DGs) and energy storage systems (ESS) towards the design of a cost-efficient and reliable microgrid considering electric vehicles (EVs). The proposed model exploits the coordinated energy dispatching of DGs, ESS, and EVs, aiming at minimizing the overall planning and operating cost as well as meeting power supply reliability requirements. This issue is addressed in a two-stage framework. The upper stage determines the sizes and sites of candidate DGs and ESS, and the lower stage optimizes the microgrid’s economic power dispatch. Since the two-stage model contains both planning and operational variables, a two-stage iterative heuristic algorithm is designed. The effectiveness of the proposed approach is validated by case studies, and corresponding results demonstrate that the planning approach that considers coordinated management of an EV fleet and economic power dispatch of microgrid achieves better economics. In addition, the suggested approach can also better match distributed generation and power demands as well as securing microgrid power supply. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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17 pages, 3978 KiB  
Article
Q-Learning-Based Operation Strategy for Community Battery Energy Storage System (CBESS) in Microgrid System
by Van-Hai Bui, Akhtar Hussain and Hak-Man Kim
Energies 2019, 12(9), 1789; https://doi.org/10.3390/en12091789 - 10 May 2019
Cited by 32 | Viewed by 3447
Abstract
Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the [...] Read more.
Energy management systems (EMSs) of microgrids (MGs) can be broadly categorized as centralized or decentralized EMSs. The centralized approach may not be suitable for a system having several entities that have their own operation objectives. On the other hand, the use of the decentralized approach leads to an increase in the operation cost due to local optimization. In this paper, both centralized and decentralized approaches are combined for managing the operation of a distributed system, which is comprised of an MG and a community battery storage system (CBESS). The MG is formed by grouping all entities having the same operation objective and is operated under a centralized controller, i.e., a microgrid EMS (MG-EMS). The CBESS is operated by using its local controller with different operation objectives. A Q-learning-based operation strategy is proposed for optimal operation of CBESS in both grid-connected and islanded modes. The objective of CBESS in the grid-connected mode is to maximize its profit while the objective of CBESS in islanded mode is to minimize the load shedding amount in the entire system by cooperating with the MG. A comparison between the Q-learning-based strategy and a conventional centralized-based strategy is presented to show the effectiveness of the proposed strategy. In addition, an adjusted epsilon is also introduced for epsilon-greedy policy to reduce the learning time and improve the operation results. Full article
(This article belongs to the Special Issue Planning and Operation of Distributed Energy Resources in Smart Grids)
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