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Computational Methods and Artificial Intelligence Studies in Smart Grids

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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 (30 December 2022) | Viewed by 11307

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Special Issue Editors

Dr. Rammohan Mallipeddi

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Guest Editor

Department of Artificial Intelligence, Kyungpook National University, Daegu, Korea
Interests: computational intelligence; NILM; smart farm; smart grid; smart home

Dr. Abhishek Kumar

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Guest Editor

Department of Artificial Intelligence, Kyungpook National University, Daegu, Korea
Interests: evolutionary computation; constraint handling techniques; power flow algorithms; power system optimization

Dr. Swagatam Das

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Guest Editor

Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India
Interests: artificial intelligence; evolutionary algorithm; differential evolution; swarm intelligence; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the latest advancements in computational and artificial intelligence methodologies in smart grids. We invite scientists from around the world to contribute to developing a comprehensive collection of papers on the application of computational and artificial intelligence techniques on smart grids. Novel algorithms, new applications and problem formulations, comparative analysis of models, case studies, and state-of-the-art review papers are particularly welcomed.

Smart grids are the future of the electric power system with integrated communication, protection, control, and sensing technologies. They are expected to provide an affordable, reliable, and sustainable supply of electricity. With the introduction of new technologies which constitute the smart grid, such as demand response, demand side management, electric vehicles, energy storage systems, distributed energy resources, integration of renewable energy resources, and forecasting methods such as artificial neural networks, deep learning methods, and evolutionary computation, the scope of planning and operation of a smart grid has broadened. The new technologies bring the need for better tools for solving planning and operation problems. Due to the recent penetration of electric vehicles into the market, more pressure is being added to the smart grid due to the drastic increase in load. Current carbon neutrality programs across the world also force the efficient usage of electricity

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent.

As a response to the recent advancements in this domain, the objective of this collection is to present notable methods and applications of smart grid. Topics of interest for publication include, but are not limited to:

Machine learning-based applications in smart grids and microgrids;
Deep learning-based applications in smart grids and microgrids;
Deep reinforcement learning-based applications in smart grids and microgrids;
Transfer learning and federated learning for applications in smart grids and microgrids;
Explainable artificial intelligence (XAI)-based applications in smart grids and microgrids;
Optimization techniques, mathematic programming methods, and metaheuristics to solve problems of smart grids;
Artificial Intelligence, metaheuristics, and optimization techniques for smart grids;
Artificial Intelligence, metaheuristics, and optimization techniques for Internet of energy;
Artificial Intelligence, metaheuristics, and optimization techniques for sharing energy and energy trading;
Artificial Intelligence, metaheuristics, and optimization techniques for distributed energy;
Artificial Intelligence, metaheuristics, and optimization techniques for energy storage systems;
Artificial Intelligence, metaheuristics, and optimization techniques for renewable energy;
Artificial Intelligence, metaheuristics, and optimization techniques for green energy and carbon footprint;
Novel applications of smart grids for planning smart city.

Dr. Rammohan Mallipeddi
Dr. Abhishek Kumar
Dr. Swagatam Das
Guest Editors

Manuscript Submission Information

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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.

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Keywords

machine learning-based applications in smart grids and microgrids
deep learning-based applications in smart grids and microgrids
deep reinforcement learning-based applications in smart grids and microgrids
transfer learning and federated learning for applications in smart grids and microgrids
explainable artificial intelligence (XAI)-based applications in smart grids and microgrids
optimization techniques, mathematic, programming methods, and metaheuristics to solve problems of smart grids
artificial intelligence, metaheuristics, and optimization techniques for smart grids
artificial intelligence, metaheuristics, and optimization techniques for internet of energy
artificial intelligence, metaheuristics, and optimization techniques for sharing energy and energy trading
artificial intelligence, metaheuristics, and optimization techniques for distributed energy
artificial intelligence, metaheuristics, and optimization techniques for energy storage systems
artificial intelligence, metaheuristics, and optimization techniques for renewable energy
artificial intelligence, metaheuristics, and optimization techniques for green energy and carbon footprint
novel applications of smart grids for planning smart city

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Published Papers (4 papers)

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Research

16 pages, 511 KiB

Open AccessArticle

Adaptive Backward/Forward Sweep for Solving Power Flow of Islanded Microgrids

by Abhimanyu Kumar, Abhishek Kumar, Rammohan Mallipeddi and Dong-Gyu Lee

Energies 2022, 15(24), 9348; https://doi.org/10.3390/en15249348 - 9 Dec 2022

Cited by 6 | Viewed by 2574

Abstract

This paper presents an algorithm for solving the power flow (PF) problem of droop-regulated AC microgrids (DRACMs) operating in isolated mode. These systems are based on radial distribution networks without having a slack bus to facilitate conventional computations. Moreover, distributed generation units have to distribute the power and voltage regulation among themselves as a function of operating frequency and voltage droop rather than having a slack bus that regulates voltage and power demands. Based on the conventional backward/forward sweep algorithm (BFS), the proposed method is a derivative-free PF algorithm. To manage the absence of a slack bus in the system, the BFS algorithm introduces new loops, equations, and self-adaptation procedures to its computation procedures. A comparison is presented between the proposed BFS algorithm and other state-of-the-art PF algorithms, as well as PSCAD/EMTDC. In comparison to existing algorithms, the proposed approach is fast, simple, accurate, and easy to implement, and it can be considered an effective tool for planning and analyzing islanded DRACMs. Full article

(This article belongs to the Special Issue Computational Methods and Artificial Intelligence Studies in Smart Grids)

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22 pages, 2330 KiB

Open AccessArticle

Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm

by Li Yan, Zhengyu Zhu, Xiaopeng Kang, Boyang Qu, Baihao Qiao, Jiajia Huan and Xuzhao Chai

Energies 2022, 15(14), 4942; https://doi.org/10.3390/en15144942 - 6 Jul 2022

Cited by 11 | Viewed by 1922

Abstract

Dynamic economic emission dispatch (DEED) in combination with renewable energy has recently attracted much attention. However, when wind power is considered in DEED, due to its generation uncertainty, some additional costs will be introduced and the stability of the dispatch system will be affected. To address this problem, in this paper, the energy-storage characteristic of electric vehicles (EVs) is utilized to smooth the uncertainty of wind power and reduce its impact on the system. As a result, an interaction model between wind power and EV (IWEv) is proposed to effectively reduce the impact of wind power uncertainty. Further, a DEED model based on the IWEv system (

{DEED}_{IWEv}

) is proposed. For solving the complex model, a self-adaptive multiple-learning multi-objective harmony-search algorithm is proposed. Both elite-learning and experience-learning operators are introduced into the algorithm to enhance its learning ability. Meanwhile, a self-adaptive parameter adjustment mechanism is proposed to adaptively select the two operators to improve search efficiency. Experimental results demonstrate the effectiveness of the proposed model and the superiority of the proposed method in solving the

{DEED}_{IWEv}

model. Full article

(This article belongs to the Special Issue Computational Methods and Artificial Intelligence Studies in Smart Grids)

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17 pages, 3639 KiB

Open AccessArticle

A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting

by Faisal Saeed, Anand Paul and Hyuncheol Seo

Energies 2022, 15(6), 2263; https://doi.org/10.3390/en15062263 - 20 Mar 2022

Cited by 23 | Viewed by 2980

Abstract

Smart grids provide a unique platform to the participants of energy markets to tweak their offerings based on demand-side management. Responding quickly to the needs of the market can help to improve the reliability of the system, as well as the cost of capital investments. Electric load forecasting is important because it is used to make and run decisions about the power grid. However, people use electricity in nonlinear ways, which makes the electric load profile a complicated signal. Even though there has been a lot of research done in this field, an accurate forecasting model is still needed. In this regard, this article proposed a hybrid cross-channel-communication (C3)-enabled CNN-LSTM model for accurate load forecasting which helps decision making in smart grids. The proposed model is the combination of three different models, i.e., a C3 block to enable channel communication of a CNN (convolutional neural networks) model, two convolutional layers to extract the features and an LSTM (long short-term memory network) model for forecasting. In the proposed hybrid model, Leaky ReLu (rectified linear unit) was used as activation function instead of sigmoid. The channel communication in CNN model makes the proposed model very light and efficient. Extensive experimentation was done on electricity load data. The results show the model’s high efficiency. The proposed model shows 98.3% accuracy and 0.4560 MAPE error. Full article

(This article belongs to the Special Issue Computational Methods and Artificial Intelligence Studies in Smart Grids)

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19 pages, 319 KiB

Open AccessArticle

Benchmarking Optimization-Based Energy Disaggregation Algorithms

by Oladayo S. Ajani, Abhishek Kumar, Rammohan Mallipeddi, Swagatam Das and Ponnuthurai Nagaratnam Suganthan

Energies 2022, 15(5), 1600; https://doi.org/10.3390/en15051600 - 22 Feb 2022

Cited by 6 | Viewed by 2687

Abstract

Energy disaggregation (ED), with minimal infrastructure, can create energy awareness and thus promote energy efficiency by providing appliance-level consumption information. However, ED is highly ill-posed and gets complicated with increase in number and type of devices, similarity between devices, measurement errors, etc. To design, test, and benchmark ED algorithms, the availability of open-access energy consumption datasets is crucial. Most datasets in the literature suit data-intensive pattern-based ED algorithms. Recently, optimization-based ED algorithms that only require information regarding the operational states of the devices are being developed. However, the lack of standard datasets and appropriate evaluation metrics is hindering the development of reproducible state-of-the-art optimization-based ED algorithms. Therefore, in this paper, we propose a dataset with multiple instances that are representative of the different challenges posed by ED in practice. Performance indicators to empirically evaluate different optimization-based ED algorithms are summarized. In addition, baseline simulation results of the state-of-the-art optimization-based ED algorithms are presented. The developed dataset, summarization of different metrics, and baseline results are expected to provide a platform for researchers to develop novel optimization-based frameworks, in general, and evolutionary computation-based frameworks in particular to solve ED. Full article

(This article belongs to the Special Issue Computational Methods and Artificial Intelligence Studies in Smart Grids)

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