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IT Applications for Optimal System Design of Microgrid
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IT Applications for Optimal System Design of Microgrid

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 13023

Special Issue Editors

Prof. Dr. Il-Yop Chung

E-Mail Website1 Website2
Guest Editor
School of Electrical Engineering, Kookmin University, Seoul 02707, Republic of Korea
Interests: power system control and operation; renewable energy integration to grids; microgrids; power distribution systems; shipboard power systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yun-Su Kim

E-Mail Website
Guest Editor
Graduate School of Energy Convergence, Gwangju Institute of Science and Technology, Gwangju, Korea
Interests: distribution network; microgrid; renewable energy; distributed generation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Microgrids can provide various advantages to customers and system operators, such as improvement of energy resiliency and reliability, integration of renewable energy resources, and management of local controllable loads and energy storage devices. There are many successful cases of field demonstrations and commercial operation of microgrids connected to the grids or islanded in the world.

To deal with various objectives for microgrid operation, real-time management of microgrids and coordinated control of its components are essential. Advanced IT technologies are needed to implement energy management functions such as system monitoring, state estimation, optimization, decision-making algorithms, and so on. Especially, microgrid management systems can host state-of-the-art IT technologies such as artificial intelligence, Internet-of-Things (IoT), big data, and cloud computing to provide sophisticated grid-supportive functions like demand response programs, virtual power plants and so on for better performance.

This Special Issue focuses on state-of-the-art IT applications and their system design for microgrid control and management systems. The scope of this Special Issue includes (but is not limited to) the following:

  • Energy data management techniques and IT infrastructure for microgrids
  • Intelligent coordination and control system of distributed energy resources (DER)
  • Intelligent data processing and decision-making structure for microgrids
  • Optimal design and analysis techniques for microgrid system design
  • Energy data management and service platform for microgrids
  • Advanced sensors and data networks for microgrids

Prof. Il-Yop Chung
Prof. Yun-Su Kim
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

  • Optimal system design of microgrid control and operation
  • IT system applications to microgrids
  • Artificial intelligence system for microgrids
  • Intelligent energy data management infrastructure
  • Coordination control system of distributed energy resources
  • Renewable energy forecasting infrastructure

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Published Papers (4 papers)

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Research

29 pages, 7956 KiB  
Article
Novel Mode Adaptive Artificial Neural Network for Dynamic Learning: Application in Renewable Energy Sources Power Generation Prediction
by Muhammad Ahsan Zamee and Dongjun Won
Energies 2020, 13(23), 6405; https://doi.org/10.3390/en13236405 - 3 Dec 2020
Cited by 25 | Viewed by 3404
Abstract
A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of [...] Read more.
A reasonable dataset, which is an essential factor of renewable energy forecasting model development, sometimes is not directly available. Waiting for a substantial amount of training data creates a delay for a model to participate in the electricity market. Also, inappropriate selection of dataset size may lead to inaccurate modeling. Besides, in a multivariate environment, the impact of different variables on the output is often neglected or not adequately addressed. Therefore, in this work, a novel Mode Adaptive Artificial Neural Network (MAANN) algorithm has been proposed using Spearman’s rank-order correlation, Artificial Neural Network (ANN), and population-based algorithms for the dynamic learning of renewable energy sources power generation forecasting model. The proposed algorithm has been trained and compared with three population-based algorithms: Advanced Particle Swarm Optimization (APSO), Jaya Algorithm, and Fine-Tuning Metaheuristic Algorithm (FTMA). Also, the gradient descent algorithm is considered as a base case for comparing with the population-based algorithms. The proposed algorithm has been applied in predicting the power output of a Solar Photovoltaic (PV) and Wind Turbine Energy System (WTES). Using the proposed methodology with FTMA, the error was reduced by 71.261% and 80.514% compared to the conventional fixed-sized dataset gradient descent-based training approach for Solar PV and WTES, respectively. Full article
(This article belongs to the Special Issue IT Applications for Optimal System Design of Microgrid)
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17 pages, 3908 KiB  
Article
MILP-PSO Combined Optimization Algorithm for an Islanded Microgrid Scheduling with Detailed Battery ESS Efficiency Model and Policy Considerations
by Rae-Kyun Kim, Mark B. Glick, Keith R. Olson and Yun-Su Kim
Energies 2020, 13(8), 1898; https://doi.org/10.3390/en13081898 - 13 Apr 2020
Cited by 31 | Viewed by 3999
Abstract
This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is [...] Read more.
This paper presents the optimal scheduling of a diesel generator and an energy storage system (ESS) while using a detailed battery ESS energy efficiency model. Optimal scheduling has been hampered to date by the nonlinearity and complexity of the battery ESS. This is due to the battery ESS efficiency being a multiplication of inverter and battery efficiency and the dependency of an inverter and any associated battery efficiencies on load and charging and discharging. We propose a combined mixed-integer linear programming and particle swarm optimization (MILP-PSO) algorithm as a novel means of addressing these considerations. In the algorithm, MILP is used to find some initial points of PSO, so that it can find better solution. Moreover, some additional algorithms are added into PSO to modify and, hence, improve its ability of dealing with constraint conditions and the local minimum problem. The simulation results show that the proposed algorithm performs better than MILP and PSO alone for the practical microgrid. The results also indicated that simplification or neglect of ESS efficiency when applying MILP to scheduling may cause a constraint violation. Full article
(This article belongs to the Special Issue IT Applications for Optimal System Design of Microgrid)
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20 pages, 3804 KiB  
Article
A New Power Sharing Scheme of Multiple Microgrids and an Iterative Pairing-Based Scheduling Method
by Hong-Chao Gao, Joon-Ho Choi, Sang-Yun Yun and Seon-Ju Ahn
Energies 2020, 13(7), 1605; https://doi.org/10.3390/en13071605 - 1 Apr 2020
Cited by 3 | Viewed by 2324
Abstract
As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution [...] Read more.
As the numbers of microgrids (MGs) and prosumers are increasing, many research efforts are proposing various power sharing schemes for multiple MGs (MMGs). Power sharing between MMGs can reduce the investment and operating costs of MGs. However, since MGs exchange power through distribution lines, this may have an adverse effect on the utility, such as an increase in peak demand, and cause local overcurrent issues. Therefore, this paper proposes a power sharing scheme that is beneficial to both MGs and the utility. This research assumes that in an MG, the energy storage system (ESS) is the major controllable resource. In the proposed power sharing scheme, an MG that sends power should discharge at least as much power from the ESS as the power it sends to other MGs, in order to actually decrease the total system demand. With these assumptions, methods for determining the power sharing schedule are proposed. Firstly, a mixed integer linear programming (MILP)-based centralized approach is proposed. Although this can provide the optimal power sharing solution, in practice, this method is very difficult to apply, due to the large calculation burden. To overcome the significant calculation burden of the centralized optimization method, a new method for determining the power sharing schedule is proposed. In this approach, the amount of power sharing is assumed to be a multiple of a unit amount, and the final power sharing schedule is determined by iteratively finding the best MG pair that exchange this unit amount. Simulation with a five MG scenario is used to test the proposed power sharing scheme and the scheduling algorithm in terms of a reduction in the operating cost of MGs, the peak demand of utility, and the calculation burden. In addition, the interrelationship between power sharing and the system loss is analyzed when MGs exchange power through the utility network. Full article
(This article belongs to the Special Issue IT Applications for Optimal System Design of Microgrid)
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26 pages, 4273 KiB  
Article
The Impact of Policy and Technology Parameters on the Economics of Microgrids for Rural Electrification: A Case Study of Remote Communities in Bolivia
by Munir Husein, Hyung-Ju Kim and Il-Yop Chung
Energies 2020, 13(4), 877; https://doi.org/10.3390/en13040877 - 17 Feb 2020
Cited by 8 | Viewed by 2719
Abstract
Throughout the developing world, most remote and isolated communities are still without reliable electricity in the twenty-first century, and this is primarily due to the high cost of grid extensions. In communities that do have electricity, they usually rely on diesel generators, though [...] Read more.
Throughout the developing world, most remote and isolated communities are still without reliable electricity in the twenty-first century, and this is primarily due to the high cost of grid extensions. In communities that do have electricity, they usually rely on diesel generators, though these have high operating and maintenance costs, while also polluting the environment. A more sustainable approach is to deploy microgrids, however, microgrids have a high upfront cost, which is a major obstacle, especially in rural areas of developing countries. This study aims to investigate the parameters that can be influenced to make microgrids more economical for rural electrification. Through sensitivity analyses, five key policy and technology parameters were identified. They include real discount rates, diesel prices, grants, battery chemistry, and operating strategies. The system was then redesigned using scenarios formulated by varying these parameters. Results show that the parameters affect the configuration, levelized cost of energy (LCOE), renewable energy penetration (REP), and pollutant emissions. The study uses three remote communities in the Beni Department of Bolivia as case studies. MDSTool was used as a modeling framework to design the microgrids. The unique insights and lessons learned during the design process are discussed at length because these may be valuable for future microgrid designs for remote communities. Full article
(This article belongs to the Special Issue IT Applications for Optimal System Design of Microgrid)
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