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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 December 2020) | Viewed by 18478

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Prof. Dr. Tek Tjing Lie

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Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1010, New Zealand
Interests: algorithm to determine the optimal capacity and cost of hybrid renewable resources in isolated power systems; energy management; power market operation and planning; sustainable energy systems; advanced control techniques and electric vehicles; AI applications to power systems; microgrids; smart grid; DC network architecture
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Dear colleagues,

Today, the flow of electricity is bidirectional, and not all electricity is centrally produced in large power plants. With the growing emergence of prosumers and microgrids, the amount of electricity produced by sources other than large, traditional power plants is ever-increasing. These alternative sources include photovoltaic (PV), wind turbine (WT), geothermal, and biomass renewable generation plants. Some renewable energy resources (solar PV and wind turbine generation) are highly dependent on natural processes and parameters (wind speed, wind direction, temperature, solar irradiation, humidity, etc.). Thus, the outputs are so stochastic in nature. New data-science-inspired real-time solutions are needed in order to co-develop digital twins of large intermittent renewable plants whose services can be globally delivered.

Due to the scale of energy networks and the amount of data that need to be digitized, new techniques in data mining and AI approaches are needed to analyze and predict the behavior of complex power systems.

Prof. Dr. Tek Tjing Lie
Guest Editor

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Keywords

intermittent renewable energy resources
data science
digital twins
AI
data mining

Published Papers (7 papers)

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Editorial

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3 pages, 144 KiB

Open AccessEditorial

Editorial to the Special Issue “AI Applications to Power Systems”

by Tek-Tjing Lie

Energies 2021, 14(18), 5667; https://doi.org/10.3390/en14185667 - 9 Sep 2021

Cited by 1 | Viewed by 1031

Abstract

This Special Issue consists of the successful invited submissions to Energies on the very topical subject area of “AI applications to power systems”. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

Research

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21 pages, 1434 KiB

Open AccessArticle

Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

by Alvaro Furlani Bastos and Surya Santoso

Energies 2021, 14(2), 463; https://doi.org/10.3390/en14020463 - 16 Jan 2021

Cited by 6 | Viewed by 2509

Abstract

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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17 pages, 4890 KiB

Open AccessArticle

Online Steady-State Security Awareness Using Cellular Computation Networks and Fuzzy Techniques

by Lili Wu, Ganesh K. Venayagamoorthy and Jinfeng Gao

Energies 2021, 14(1), 148; https://doi.org/10.3390/en14010148 - 30 Dec 2020

Cited by 4 | Viewed by 1619

Abstract

Power system steady-state security relates to its robustness under a normal state as well as to withstanding foreseeable contingencies without interruption to customer service. In this study, a novel cellular computation network (CCN) and hierarchical cellular rule-based fuzzy system (HCRFS) based online situation awareness method regarding steady-state security was proposed. A CCN-based two-layer mechanism was applied for voltage and active power flow prediction. HCRFS block was applied after the CCN prediction block to generate the security level of the power system. The security status of the power system was visualized online through a geographic two-dimensional visualization mechanism for voltage magnitude and load flow. In order to test the performance of the proposed method, three types of neural networks were embedded in CCN cells successively to analyze the characteristics of the proposed methodology under white noise simulated small disturbance and single contingency. Results show that the proposed CCN and HCRFS combined situation awareness method could predict the system security of the power system with high accuracy under both small disturbance and contingencies. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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20 pages, 7226 KiB

Open AccessArticle

Estimation of Modal Parameters for Inter-Area Oscillations Analysis by a Machine Learning Approach with Offline Training

by Carlo Olivieri, Francesco de Paulis, Antonio Orlandi, Cosimo Pisani, Giorgio Giannuzzi, Roberto Salvati and Roberto Zaottini

Energies 2020, 13(23), 6410; https://doi.org/10.3390/en13236410 - 4 Dec 2020

Cited by 13 | Viewed by 1542

Abstract

An accurate monitoring of power system behavior is a hot-topic for modern grid operation. Low-frequency oscillations (LFO), such as inter-area electromechanical oscillations, are detrimental phenomena impairing the development of the grid itself and also the integration of renewable sources. An interesting countermeasure to prevent the occurrence of such oscillations is to continuously identify their characteristic electromechanical mode parameters, possibly realizing an online monitoring system. In this paper an attempt to develop an online modal parameters identification system is done using machine learning techniques. An approach based on the development of a proper artificial neural network exploiting the frequency measurements coming from actual PMU devices is presented. The specifically developed offline training stage is fully detailed. The output results from the dynamic mode decomposition method are considered as reference in order to validate the machine learning approach. Some results are presented in order to validate the effectiveness of the proposed approach on data coming from recordings of real grid events. The main key points affecting the performance of the proposed technique are discussed by means of proper validation scenarios. This contribution is the first step of a more extended project whose final aim is the development of an artificial neural networks (ANN) architecture able to predict the system behavior (in a given time span) in terms of LFO modal parameters, and to classify the contingencies/disturbances based on an online training that has memory of the passed training samples. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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27 pages, 2388 KiB

Open AccessArticle

Automatic P2P Energy Trading Model Based on Reinforcement Learning Using Long Short-Term Delayed Reward

by Jin-Gyeom Kim and Bowon Lee

Energies 2020, 13(20), 5359; https://doi.org/10.3390/en13205359 - 14 Oct 2020

Cited by 28 | Viewed by 3020

Abstract

Automatic peer-to-peer energy trading can be defined as a Markov decision process and designed using deep reinforcement learning. We consider prosumer as an entity that consumes and produces electric energy with an energy storage system, and define the prosumer’s objective as maximizing the profit through participation in peer-to-peer energy trading, similar to that of the agents in stock trading. In this paper, we propose an automatic peer-to-peer energy trading model by adopting a deep Q-network-based automatic trading algorithm originally designed for stock trading. Unlike in stock trading, the assets held by a prosumer may change owing to factors such as the consumption and generation of energy by the prosumer in addition to the changes from trading activities. Therefore, we propose a new trading evaluation criterion that considers these factors by defining profit as the sum of the gains from four components: electricity bill, trading, electric energy stored in the energy storage system, and virtual loss. For the proposed automatic peer-to-peer energy trading algorithm, we adopt a long-term delayed reward method that evaluates the delayed reward that occurs once per month by generating the termination point of an episode at each month and propose a long short-term delayed reward method that compensates for the issue with the long-term delayed reward method having only a single evaluation per month. This long short-term delayed reward method enables effective learning of the monthly long-term trading patterns and the short-term trading patterns at the same time, leading to a better trading strategy. The experimental results showed that the long short-term delayed reward method-based energy trading model achieves higher profits every month both in the progressive and fixed rate systems throughout the year and that prosumer participating in the trading not only earns profits every month but also reduces loss from over-generation of electric energy in the case of South Korea. Further experiments with various progressive rate systems of Japan, Taiwan, and the United States as well as in different prosumer environments indicate the general applicability of the proposed method. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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37 pages, 11143 KiB

Open AccessArticle

Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)

by Mahmoud G. Hemeida, Salem Alkhalaf, Al-Attar A. Mohamed, Abdalla Ahmed Ibrahim and Tomonobu Senjyu

Energies 2020, 13(15), 3847; https://doi.org/10.3390/en13153847 - 27 Jul 2020

Cited by 36 | Viewed by 3553

Abstract

Manta Ray Foraging Optimization Algorithm (MRFO) is a new bio-inspired, meta-heuristic algorithm. MRFO algorithm has been used for the first time to optimize a multi-objective problem. The best size and location of distributed generations (DG) units have been determined to optimize three different objective functions. Minimization of active power loss, minimization of voltage deviation, and maximization of voltage stability index has been achieved through optimizing DG units under different power factor values, unity, 0.95, 0.866, and optimum value. MRFO has been applied to optimize DGs integrated with two well-known radial distribution power systems: IEEE 33-bus and 69-bus systems. The simulation results have been compared to different optimization algorithms in different cases. The results provide clear evidence of the superiority of MRFO that defind before (Manta Ray Foraging Optimization Algorithm. Quasi-Oppositional Differential Evolution Lévy Flights Algorithm (QODELFA), Stochastic Fractal Search Algorithm (SFSA), Genetics Algorithm (GA), Comprehensive Teaching Learning-Based Optimization (CTLBO), Comprehensive Teaching Learning-Based Optimization (CTLBO (ε constraint)), Multi-Objective Harris Hawks Optimization (MOHHO), Multi-Objective Improved Harris Hawks Optimization (MOIHHO), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-Objective Particle Swarm Optimization (MOWOA) in terms of power loss, Voltage Stability Index (VSI), and voltage deviation for a wide range of operating conditions. It is clear that voltage buses are improved; and power losses are decreased in both IEEE 33-bus and IEEE 69-bus system for all studied cases. MRFO algorithm gives good results with a smaller number of iterations, which means saving the time required for solving the problem and saving energy. Using the new MRFO technique has a promising future in optimizing different power system problems. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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20 pages, 3161 KiB

Open AccessArticle

Distributed Machine Learning on Dynamic Power System Data Features to Improve Resiliency for the Purpose of Self-Healing

by Miftah Al Karim, Jonathan Currie and Tek-Tjing Lie

Energies 2020, 13(13), 3494; https://doi.org/10.3390/en13133494 - 6 Jul 2020

Cited by 9 | Viewed by 2696

Abstract

Numerous online methods for post-fault restoration have been tested on different types of systems. Modern power systems are usually operated at design limits and therefore more prone to post-fault instability. However, traditional online methods often struggle to accurately identify events from time series data, as pattern-recognition in a stochastic post-fault dynamic scenario requires fast and accurate fault identification in order to safely restore the system. One of the most prominent methods of pattern-recognition is machine learning. However, machine learning alone is neither sufficient nor accurate enough for making decisions with time series data. This article analyses the application of feature selection to assist a machine learning algorithm to make better decisions in order to restore a multi-machine network which has become islanded due to faults. Within an islanded multi-machine system the number of attributes significantly increases, which makes application of machine learning algorithms even more erroneous. This article contributes by proposing a distributed offline-online architecture. The proposal explores the potential of introducing relevant features from a reduced time series data set, in order to accurately identify dynamic events occurring in different islands simultaneously. The identification of events helps the decision making process more accurate. Full article

(This article belongs to the Special Issue AI Applications to Power Systems)

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