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Energies
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Modeling and Analysis of Active Distribution Networks and Smart Grids
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Modeling and Analysis of Active Distribution Networks and 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 (20 June 2023) | Viewed by 11735

Special Issue Editors

Dr. Eleftherios O. Kontis

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
Interests: power system dynamics; modeling and analysis of active distribution networks; real-time analysis and control of power systems through field measurements; application of system identification techniques to power systems; control and optimal operation of distributed energy resources
Prof. Dr. Theofilos A. Papadopoulos

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Interests: earth conduction effects in overhead lines and cable systems; modelling and analysis of electromagnetic transients; electromagnetic compatibility and transmission line modeling; power line communication; power systems load and active distribution network modeling; application of system identification techniques to power systems; integration of buildings into smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy sources (RESs) will constitute the backbone of future energy systems and, due to their different characteristics and dependence on geography and climate, they will be scattered throughout the power system. In this context, a vast amount of distributed energy resources (DERs), such as large- and small-scale RES power plants, energy storage systems, electric vehicles, and smart loads will be integrated into low and medium voltage distribution networks, posing unprecedented challenges to both transmission and distribution system operators (TSOs-DSOs). These challenges include both local problems as well as system-wide ones related to the overall system stability. Local problems mainly include power quality, overvoltage and congestion issues caused due to the intermittent nature of RESs and mainly due to reverse power flow phenomena during high generation periods. System-wide issues include, among others, abnormal frequency deviations, subsynchronous oscillations, fast dynamic phenomena as well as complex interactions among transmission systems (TSs) and active distribution networks (ADNs). These are mainly caused because DERs are connected to the grid via inertialess converters.

The above-mentioned challenges therefore necessitate the development of new control methods and monitoring techniques to enhance the secure and reliable operation of future power systems. Towards this direction:

  • New control functionalities must be developed for converter-interfaced DERs, targeting to improve their dynamic behaviour. Additionally, new centralized and/or distributed architectures should be developed to tackle power quality issues in ADNs.
  • The participation of low and medium voltage level ADNs to the voltage control procedure and congestion management of TSs should also be explored. In this context, coordinated strategies, aiming to control the flexibility of several DERs in order to provide aggregated ancillary services (ASs) from ADNs to the TS are required. This way, full utilization of existing DERs will be ensured, while higher energy efficiency and resiliency will be achieved.
  • New simplified aggregated, yet accurate, equivalent models must be introduced to facilitate the quasi-static, dynamic, and electromagnetic transient (EMT) analysis of ADNs. Such models can be optimally tuned using real field measurements, acquired through smart meters, phasor measurement units or frequency disturbance recorders.
  • New monitoring architectures must be also proposed to assess close-to-real-time the dynamic performance and the stability margins of power systems. The performance of these monitoring architectures can be further improved by adopting machine learning and big-data analysis techniques.
  • Numerical methods targeting to simulate complex interactions between ADNs and TSs should also be developed. These methods can be used to facilitate the modelling, operation, and control of ADNs for the coordination of TSO-DSO interactions and for the provision of aggregated ASs from ADNs to the TS.

The main objective of this special issue is to seek quality publications that highlight recent advances in the areas of monitoring, modelling, analysis, control, optimization, and simulation of ADNs as well as quality publications targeting to quantify the impact of converter-interfaced DERs on TSO-DSO interactions, system stability margins, and dynamic performance of ADNs. The topics of interest include, but are not limited to:

  • Modeling and control functionalities of converter-interfaced DERs
  • DER control principles for local and system-wide needs
  • Flexibility in ADNs from battery storage systems, electric vehicles, and RESs
  • Coordinated provision of ASs from ADNs to the TS
  • Steady-state, quasi-static, dynamic and EMT analysis of ADNs
  • Equivalencing of ADNs
  • Centralized and distributed architectures for data-driven monitoring, analysis, and control of ADNs and smart grids
  • Applications of machine learning and big data techniques for smart grid analysis
  • Numerical techniques and models for the analysis of complex interactions between ADNs and TSs
  • Impacts of converter-interfaced DERs on power system stability and dynamics
  • Application of real-time simulation and power hardware-in-the-loop analysis of ADNs
Dr. Eleftherios O. Kontis
Prof. Theofilos A. Papadopoulos
Guest Editors

Manuscript Submission Information

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Keywords

  • Active distribution networks
  • Smart grids
  • Distributed energy resources
  • Quasi-static analysis
  • Dynamic analysis
  • Control functionalities of converter-interfaced DERs
  • Ancillary services
  • Coordinated operation of ADNs
  • Optimization of ADNs
  • Dynamic equivalencing
  • Data-driven modeling
  • Artificial intelligence
  • TSO/DSO interactions
  • Stability analysis of converter-based power systems
  • Power hardware-in-the-loop testbeds

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Published Papers (5 papers)

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Research

20 pages, 3523 KiB  
Article
Reliability Improvement of a Hybrid Electric Vehicle Integrated Distribution System
by Ramalingam Sripriya, Chandrasekaran Kumar, Felix Joseph Xavier, Jeyaraj Senthil Kumar, Panos Kotsampopoulos and Hady H. Fayek
Energies 2023, 16(10), 3984; https://doi.org/10.3390/en16103984 - 9 May 2023
Cited by 1 | Viewed by 1661
Abstract
The recent trend in hybrid electric vehicles (HEV) has increased the need for vehicle charging stations (VCS) in the distribution system. In this condition, the additional load in the system leads to an increase in power loss, reduction in voltage and reliability of [...] Read more.
The recent trend in hybrid electric vehicles (HEV) has increased the need for vehicle charging stations (VCS) in the distribution system. In this condition, the additional load in the system leads to an increase in power loss, reduction in voltage and reliability of the system. The drawbacks of introducing this additional load can be rectified by integrating distributed generation (DG) into the distribution system. In this paper, the ideal location for placing DG is identified through the voltage stability index. The power loss minimization objective function is formulated with all the required constraints to estimate the size of DG required for the distribution system. Moreover, loss of load probability is used as a reliability assessment technique, through which the system reliability is analyzed after assessing the impact of integrating VCS and DG. Simulations are carried out to compare the following cases: a system without VCS and DG, a system that has only VCS and a system that has both VCS and DG. The IEEE 12-bus and 33-bus test systems are considered. In the 12-bus system with both VCS and DG, the power loss is reduced by 56% when compared with the system with only VCS, while the net reliability is also improved. The reliability of the system is evaluated for a 24 h load variation. The proposed work provides an efficient tool to improve the reliability of the system with support from DG. Full article
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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42 pages, 35310 KiB  
Article
Realistic μPMU Data Generation for Different Real-Time Events in an Unbalanced Distribution Network
by Abdul Haleem Medattil Ibrahim, Madhu Sharma and Vetrivel Subramaniam Rajkumar
Energies 2023, 16(9), 3842; https://doi.org/10.3390/en16093842 - 29 Apr 2023
Cited by 2 | Viewed by 1738
Abstract
Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the [...] Read more.
Monitoring, protection, and control processes are becoming more complex as distributed energy resources (DERs) penetrate distribution networks (DNs). This is due to the inherent nature of power DNs and the bi-directional flow of current from various sources to the loads. To improve the system’s situational awareness, the grid dynamics of the entire DER integration processes must be carefully monitored using synchronized high-resolution real-time measurement data from physical devices installed in the DN. μPMUs have been introduced into the DN to help with this. In comparison to traditional measurement devices, μPMUs can measure voltage, current, and their phasors, in addition to frequency and rate of frequency change (ROCOF). In this study, an approach to generating realistic event data for a real utility DN utilizing strategically installed μPMUs is proposed. The method employs an IEEE 34 test feeder with 12 μPMUs installed in strategic locations to generate real-time events-based realistic μPMU data for various situational awareness applications in an unbalanced DN. The node voltages and line currents were used to analyze the various no-fault and fault events. The author generated the data as part of his PhD research project, utilizing his real-time utility grid operation experience to be used for various situational awareness and fault location studies in a real unbalanced DN. The DN was modeled in DIgSILENT PowerFactory (DP) software. The generated realistic μPMU data can be utilized for developing data-driven algorithms for different event-detection, classification and section-identification research works. Full article
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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27 pages, 2478 KiB  
Article
Validation of a Holistic System for Operational Analysis and Provision of Ancillary Services in Active Distribution Networks
by Theofilos A. Papadopoulos, Kalliopi D. Pippi, Georgios A. Barzegkar-Ntovom, Eleftherios O. Kontis, Angelos I. Nousdilis, Christos L. Athanasiadis and Georgios C. Kryonidis
Energies 2023, 16(6), 2787; https://doi.org/10.3390/en16062787 - 17 Mar 2023
Cited by 3 | Viewed by 1792
Abstract
The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such [...] Read more.
The advent of distributed renewable energy sources (DRESs) has led to the progressive transformation of traditional distribution networks to active components of the power system. This transformation, however, may jeopardize the reliable grid operation due to the advent of new technical problems, such as network overloading, over-/under-voltage events, abnormal frequency deviation and dynamic instability. In this challenging scenery, the installation of a modern measuring infrastructure has created new sources of data and information that facilitate the provision of ancillary services (ASs) via measurement-based analysis. The ACTIVATE (ancillary services in active distribution networks based on monitoring and control techniques) project aims to design innovative AS solutions for power system operators. These solutions aim to tackle the technical issues emerged by the ever-increasing DRES penetration and their volatile nature. In this context, in ACTIVATE, a holistic system is proposed comprising centralized and decentralized control features to enhance the overall network performance. Additionally, a network monitoring system is designed to support a number of online and offline dynamic analysis applications by exploiting measurements obtained at the transmission, primary and secondary distribution network. This paper presents a validation of the overall system, which is performed by using simulation and power-hardware-in-the-loop results in combined transmission and distribution network models. Full article
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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22 pages, 5651 KiB  
Article
Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
by Sepideh Radhoush, Trevor Vannoy, Kaveen Liyanage, Bradley M. Whitaker and Hashem Nehrir
Energies 2023, 16(5), 2288; https://doi.org/10.3390/en16052288 - 27 Feb 2023
Cited by 15 | Viewed by 2828
Abstract
Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real [...] Read more.
Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data. Full article
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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18 pages, 993 KiB  
Article
Optimum Parallel Processing Schemes to Improve the Computation Speed for Renewable Energy Allocation and Sizing Problems
by Soheil Younesi, Bahman Ahmadi, Oguzhan Ceylan and Aydogan Ozdemir
Energies 2022, 15(24), 9301; https://doi.org/10.3390/en15249301 - 8 Dec 2022
Cited by 1 | Viewed by 1585
Abstract
The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper [...] Read more.
The optimum penetration of distributed generations into the distribution grid provides several technical and economic benefits. However, the computational time required to solve the constrained optimization problems increases with the increasing network scale and may be too long for online implementations. This paper presents a parallel solution of a multi-objective distributed generation (DG) allocation and sizing problem to handle a large number of computations. The aim is to find the optimum number of processors in addition to energy loss and DG cost minimization. The proposed formulation is applied to a 33-bus test system, and the results are compared with themselves and with the base case operating conditions using the optimal values and three popular multi-objective optimization metrics. The results show that comparable solutions with high-efficiency values can be obtained up to a certain number of processors. Full article
(This article belongs to the Special Issue Modeling and Analysis of Active Distribution Networks and Smart Grids)
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