Special Issue "Virtual Power Plants: ICT-Based Control and Optimization"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Energy Sources".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editors

Prof. Dr. S. M. Muyeen
E-Mail Website1 Website2
Guest Editor
1. Department of Electrical Engineering, Qatar University, Doha, Qatar
2. School of Electrical Engineering and Computing, Faculty of Science and Engineering, Curtin University, Perth, WA, Australia
Interests: renewable energy; energy storage technologies; power system stability; smart grid
Special Issues and Collections in MDPI journals
Dr. Sajal K. Das
E-Mail Website
Guest Editor
Department of Mechatronic Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh
Interests: renewable energy; micro-grid; control; optimization techniques; energy storage systems; and electric vehicles
Special Issues and Collections in MDPI journals
Prof. Dr. Frede Blaabjerg
grade E-Mail Website
Guest Editor
Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Interests: power electronics and its applications in motor drives; wind turbines; PV systems; harmonics; reliability of power electronic systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleague,

The high-volume incorporation of low inertia distributed energy resources of diverse ranges in the power grid to meet the growing energy demand of this era of a fourth industrial revolution has led to an urgent need for us to redesign the centralized power generation and distribution model of electricity into a new decentralized safe and secured structure for the bidirectional flow of electrical energy in order to protect our planet. The preface of this innovative arrangement paves the way for the latest trademark of the energy management framework, the virtual power plant (VPP) that intelligently combines and individually optimizes multiple discrete generations, energy storages, and loads exploiting an ICT-based control system which can map and regulate production vigorously and deal wisely in the energy marketplace. The flourishing action of the VPP necessitates precise scheduling, pricing, forecasting, and demand responses for endeavors, which are constrained by cybersecurity, information networks, protocols, maintenance, and technical and economic aspects. Researchers, scientists, and engineers in the academic world and industry call for solutions to deal with the abovementioned problems, especially from an ICT-based control and optimization point of view. This Special Issue is aimed at addressing the advancement of emerging technologies for future-generation virtual power plants in the context of ICT-based control and optimization.         

Topics of interest:

  • Advancements in the architectural model of the future-generation virtual power plant;
  • VPP communication protocol and network security;
  • ICT-based innovative energy management for VPP;
  • ICT-based demand response management of VPP;
  • Data analytics for VPP;
  • Big data management and control;
  • Critical design issues of VPP;
  • Intelligent and modern control design for VPP;
  • Machine learning and artificial intelligence technologies for VPP;
  • Advancing grid-forming inverter technology for VPP;
  • Software development and management for VPP;
  • Networked communication of VPP;
  • Operational flexibility and scalability of VPP;
  • Market modeling and social benefits of VPP;
  • Emerging technologies for VPP.

Dr. S. M. Muyeen
Dr. Sajal K. Das
Prof. Dr. Frede Blaabjerg
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 papers will be 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 2000 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.

Published Papers (3 papers)

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Research

Article
Evaluation of Communication Infrastructures for Distributed Optimization of Virtual Power Plant Schedules
Energies 2021, 14(5), 1226; https://doi.org/10.3390/en14051226 - 24 Feb 2021
Cited by 1 | Viewed by 578
Abstract
With the transition towards renewable energy resources, the impact of small distributed generators (DGs) increases, leading to the need to actively stabilize distribution grids. DGs may be organized in virtual power plants (VPPs), where DGs’ schedules must be coordinated to enable the VPP [...] Read more.
With the transition towards renewable energy resources, the impact of small distributed generators (DGs) increases, leading to the need to actively stabilize distribution grids. DGs may be organized in virtual power plants (VPPs), where DGs’ schedules must be coordinated to enable the VPP to act as a single plant. One approach to solving this problem is using multi-agent systems (MAS) to offer autonomous, robust, and flexible control methods. The coordination of such systems requires communication between agents. The time required for this depends on communication characteristics, determined by the underlying communication infrastructure. In this paper, we investigate communication influences for the wireless technologies CDMA450 and LTE Advanced on the fully distributed optimization heuristic COHDA, which is used to perform optimized scheduling for a VPP. The use case under consideration is the adaptation of schedules to provide flexibility for regional congestion management for delivery on a regionalized ancillary service market (rAS). We investigate the scalability of the VPP and the effects of disturbances in the communication infrastructure. The results show that the optimization duration of COHDA can be influenced by the underlying communication infrastructure and that this optimization method is applicable to a limited extent for product delivery of rASs. Full article
(This article belongs to the Special Issue Virtual Power Plants: ICT-Based Control and Optimization)
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Article
A Probabilistic Ensemble Prediction Method for PV Power in the Nonstationary Period
Energies 2021, 14(4), 859; https://doi.org/10.3390/en14040859 - 07 Feb 2021
Viewed by 727
Abstract
Due to the large number of grid connection of distributed power supply, the existing scheduling methods can not meet the demand gradually. The proposed virtual power plant provides a new idea to solve this problem. The photovoltaic power prediction provides the data basis [...] Read more.
Due to the large number of grid connection of distributed power supply, the existing scheduling methods can not meet the demand gradually. The proposed virtual power plant provides a new idea to solve this problem. The photovoltaic power prediction provides the data basis for the scheduling of the virtual power plant. Prediction intervals of photovoltaic power is a powerful statistical tool used for quantifying the uncertainty of photovoltaic power generation in power systems. To improve the interval prediction accuracy during the non-stationary periods of photovoltaic power, this paper proposes a probabilistic ensemble prediction model, which combines the modules of data preprocessing, non-stationary period discrimination, feature extraction, deterministic prediction, uncertainty prediction, and optimization integration into a general framework. More specifically, in the non-stationary period discrimination module, the method of discriminating the difference of the power ratio difference is introduced and applied for identifying the non-stationary period of the data of photovoltaic output; in the deterministic point prediction module, a stacking- long-short-term memory neural network model is used for point forecasts; in the uncertainty interval prediction module, a BAYES neural network is introduced for probabilistic forecasts; in the optimization integration module, an optimization algorithm named Non-dominated Sorting Genetic Algorithm-II is applied for integrating and optimizing the results of the point forecast and probabilistic forecast. The proposed model is tested using two photovoltaic outputs and weather data measured from a grid-connected photovoltaic system. The results show that the proposed model outperforms conventional forecast methods to predict short-term photovoltaic power outputs and associated uncertainties. The interval width is reduced by 10–20%, and the prediction accuracy is improved by at least 10%; this can be a useful tool for photovoltaic power forecasting. Full article
(This article belongs to the Special Issue Virtual Power Plants: ICT-Based Control and Optimization)
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Article
Virtual Visualization of Generator Operation Condition through Generator Capability Curve
Energies 2021, 14(1), 185; https://doi.org/10.3390/en14010185 - 01 Jan 2021
Viewed by 470
Abstract
Besides achieving an optimal scheduling generator, the operation safety of the generator itself needs to be focused on. The development of the virtual visualization of a generator capability curve simulation to visualize the operation condition of a generator is proposed in this paper. [...] Read more.
Besides achieving an optimal scheduling generator, the operation safety of the generator itself needs to be focused on. The development of the virtual visualization of a generator capability curve simulation to visualize the operation condition of a generator is proposed in this paper. In this paper, a neural network is applied to redraw the original generator’s capability curve. The virtual visualization of a generator’s capability curve can simulate the generator’s operating condition considering the limitation of the constraints on the various elements of the generator. Furthermore, it is able to show the various possibilities that occur in the operation of a generator in reality, and it can even simulate special conditions which are based on various conditions. Full article
(This article belongs to the Special Issue Virtual Power Plants: ICT-Based Control and Optimization)
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