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Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (20 July 2020) | Viewed by 18259

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Prof. Dr. Pavlos S. Georgilakis

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School of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
Interests: power system planning; power system operation; power system analysis; power system control; active distribution systems; distributed energy resources
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Special Issue Information

Dear Colleagues,

We invite you to submit articles to a Special Issue of Energies related to the subject area of “Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques”.

This Special Issue will deal with novel heuristic optimization and artificial intelligence techniques for solving emerging problems of modern electric power transmission and distribution systems. Topics of interest for publication include, but are not limited to

Planning of active distribution systems;
Optimal operation and control of distribution systems with distributed energy resources;
Analysis of active distribution systems;
Planning of distributed energy resources: distributed generation, renewable energy sources, energy storage systems, and demand responses;
Optimal operation of distributed energy resources;
Planning of power transmission systems;
Optimal operation and control of transmission systems;
Analysis of transmission systems;
Flexible AC Transmission Systems (FACTS);
High Voltage Direct Current (HVDC) transmission systems.

Prof. Dr. Pavlos S. Georgilakis
Guest Editor

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

artificial intelligence techniques
heuristic optimization techniques
power distribution systems
distributed energy resources
distributed generation
renewable energy sources
energy storage systems
demand response
power transmission systems
Flexible AC Transmission Systems (FACTS)
High Voltage Direct Current (HVDC) transmission systems

Published Papers (6 papers)

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Research

Jump to: Review

15 pages, 2293 KiB

Open AccessArticle

Load Curtailment Optimization Using the PSO Algorithm for Enhancing the Reliability of Distribution Networks

by Laura M. Cruz, David L. Alvarez, Ameena S. Al-Sumaiti and Sergio Rivera

Energies 2020, 13(12), 3236; https://doi.org/10.3390/en13123236 - 22 Jun 2020

Cited by 14 | Viewed by 2554

Abstract

Power systems are susceptible to disturbances due to their nature. These disturbances can cause overloads or even contingencies of greater impact. In case of an extreme situation, load curtailment is considered the last resort for reducing the contingency impact, its activation being necessary to avoid the collapse of the system. However, load shedding systems seldom work optimally and cause either excessive or insufficient reduction of the load. To resolve this issue, the present paper proposes a methodology to enhance the load curtailment management in medium voltage distribution systems using Particle Swarm Optimization (PSO). This optimization seeks to minimize the amount of load to be cut off. Restrictions on the optimization problem consist of the security operation margins of both loading and voltage of the system elements. Heuristic optimization algorithms were chosen, since they are considered an online basis (allowing a short processing time) to solve the formulated load curtailment optimization problem. Best performances regarding optimal value and processing time were obtained using a PSO algorithm, qualifying the technique as the most appropriate for this study. To assess the methodology, the CIGRE MV distribution network benchmark was used, assuming dynamic load profiles during an entire week. Results show that it is possible to determine the optimal unattended power of the system. This way, improvements in the minimization of the expected energy not supplied (ENS) as well as the System Average Interruption Frequency Index (SAIDI) at specific hours of the day were made. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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24 pages, 6911 KiB

Open AccessArticle

Optimal Allocation of Static Var Compensators in Electric Power Systems

by Martin Ćalasan, Tatjana Konjić, Katarina Kecojević and Lazar Nikitović

Energies 2020, 13(12), 3219; https://doi.org/10.3390/en13123219 - 21 Jun 2020

Cited by 31 | Viewed by 3044

Abstract

In the current age, power systems contain many modern elements, one example being Flexible AC Transmission System (FACTS) devices, which play an important role in enhancing the static and dynamic performance of the systems. However, due to the high costs of FACTS devices, the location, type, and value of the reactive power of these devices must be optimized to maximize their resulting benefits. In this paper, the problem of optimal power flow for the minimization of power losses is considered for a power system with or without a FACTS controller, such as a Static Var Compensator (SVC) device The impact of location and SVC reactive power values on power system losses are considered in power systems with and without the presence of wind power. Furthermore, constant and variable load are considered. The mentioned investigation is realized on both IEEE 9 and IEEE 30 test bus systems. Optimal SVC allocation are performed in program GAMS using CONOPT solver. For constant load data, the obtained results of an optimal SVC allocation and the minimal value of power losses are compared with known solutions from the literature. It is shown that the CONOPT solver is useful for finding the optimal location of SVC devices in a power system with or without the presence of wind energy. The comparison of results obtained using CONOPT solver and four metaheuristic method for minimization of power system losses are also investigated and presented. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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13 pages, 4235 KiB

Open AccessArticle

Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

by Konstantinos V. Blazakis, Theodoros N. Kapetanakis and George S. Stavrakakis

Energies 2020, 13(12), 3110; https://doi.org/10.3390/en13123110 - 16 Jun 2020

Cited by 28 | Viewed by 3637

Abstract

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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19 pages, 2001 KiB

Open AccessArticle

A Chance-Constrained Multistage Planning Method for Active Distribution Networks

by Nikolaos Koutsoukis and Pavlos Georgilakis

Energies 2019, 12(21), 4154; https://doi.org/10.3390/en12214154 - 31 Oct 2019

Cited by 13 | Viewed by 1937

Abstract

This paper introduces a multistage planning method for active distribution networks (ADNs) considering multiple alternatives. The uncertainties of load, wind and solar generation are taken into account and a chance constrained programming (CCP) model is developed to handle these uncertainties in the planning procedure. A method based on a k-means clustering technique is employed for the modelling of renewable generation and load demand. The proposed solution methodology, which is based on a genetic algorithm, considers multiple planning alternatives, such as the reinforcement of substations and distribution lines, the addition of new lines, and the placement of capacitors and it aims at minimizing the net present value of the total operation cost plus the total investment cost of the reinforcement and expansion plan. The active network management is incorporated into planning method in order to exploit the control capabilities of the output power of the distributed generation units. To validate its effectiveness and performance, the proposed method is applied to a 24-bus distribution system. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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17 pages, 1153 KiB

Open AccessArticle

Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources

by Fatma Yaprakdal, Mustafa Baysal and Amjad Anvari-Moghaddam

Energies 2019, 12(10), 1858; https://doi.org/10.3390/en12101858 - 15 May 2019

Cited by 19 | Viewed by 2859

Abstract

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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Review

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37 pages, 549 KiB

Open AccessReview

Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research

by Pavlos S. Georgilakis

Energies 2020, 13(1), 186; https://doi.org/10.3390/en13010186 - 01 Jan 2020

Cited by 19 | Viewed by 3241

Abstract

The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operation of local energy markets, classifying and analyzing current and future research directions in this area. Full article

(This article belongs to the Special Issue Planning, Operation, and Analysis of Electric Power Transmission and Distribution Systems by Artificial Intelligence Techniques)

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