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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 22964

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Special Issue Editor

Prof. Dr. Yagang Zhang

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Guest Editor

Institute of Data Science and Statistical Analysis, North China Electric Power University, Baoding 071003, China
Interests: renewable energy sources; wind power; wide area backup protection; WAMS and nonlinear complex system theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the continuous improvement and development of science and technology and information technology, the rapid development of artificial intelligence technology has been promoted. At the same time, with global climate change, countries are paying more attention and importance to carbon emissions than ever before, so the global requirements for power and energy systems are also increasing. Today, not only research scholars in related fields but also companies, including energy giants such as General Electric and AES, an independent power company in the United States, are actively exploring the application of AI technologies within power and energy systems. Artificial intelligence technologies have played an active role in improving grid driving capabilities, ensuring energy security, reducing energy waste, promoting the development of new energy, and better serving economic and social development. Strengthening the application of AI in power and energy systems is conducive to improving the operational efficiency and energy utilization of power systems, promoting the reform of power systems, promoting the intelligence of power systems, and achieving carbon emission reduction goals, and is also an important trend in the current development of energy systems.

This Special Issue is dedicated to provide a communication platform for the application of artificial intelligence in power and energy systems. This Special Issue welcomes original research articles and reviews discussing the latest research on theories, methods, techniques, and applications of AI in power and energy systems. Research areas may include (but are not limited to) the following: smart energy markets in the context of carbon emission reduction, smart grid operation optimization under the background of carbon emission reduction, smart grids, big data and power systems, smart forecasting and scheduling, green power automation, energy-oriented data mining and artificial intelligence technologies, energy internet, information and communication technologies, smart machines in power systems, techno-economic aspects of smart energy systems, and related topics.

I look forward to receiving your contributions.

Dr. Yagang Zhang
Guest Editor

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Keywords

artificial intelligence
energy system
power system
carbon emission reduction
data mining
energy forecast
power scheduling
smart machine
smart grids.

Published Papers (12 papers)

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Research

Jump to: Review

19 pages, 1659 KiB

Open AccessArticle

Towards Sustainable Energy Grids: A Machine Learning-Based Ensemble Methods Approach for Outages Estimation in Extreme Weather Events

by Ulaa AlHaddad, Abdullah Basuhail, Maher Khemakhem, Fathy Elbouraey Eassa and Kamal Jambi

Sustainability 2023, 15(16), 12622; https://doi.org/10.3390/su151612622 - 21 Aug 2023

Cited by 5 | Viewed by 1461

Abstract

The critical challenge of enhancing the resilience and sustainability of energy management systems has arisen due to historical outages. A potentially effective strategy for addressing outages in energy grids involves preparing for future failures resulting from line vulnerability or grid disruptions. As a result, many researchers have undertaken investigations to develop machine learning-based methodologies for outage forecasting for smart grids. This research paper proposed applying ensemble methods to forecast the conditions of smart grid devices during extreme weather events to enhance the resilience of energy grids. In this study, we evaluate the efficacy of five machine learning algorithms, namely support vector machines (SVM), artificial neural networks (ANN), logistic regression (LR), decision tree (DT), and Naive Bayes (NB), by utilizing the bagging ensemble technique. The results demonstrate a remarkable accuracy rate of 99.98%, with a true positive rate of 99.6% and a false positive rate of 0.01%. This research establishes a foundation for implementing sustainable energy integration into electrical networks by accurately predicting the occurrence of damaged components in the energy grid caused by extreme weather events. Moreover, it enables operators to manage the energy generated effectively and facilitates the achievement of energy production efficiency. Our research contributes to energy management systems using ensemble methods to predict grid vulnerabilities. This advancement lays the foundation for developing resilient and dependable energy infrastructure capable of withstanding unfavorable weather conditions and assisting in achieving energy production efficiency goals. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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16 pages, 4157 KiB

Open AccessArticle

Precise Modeling of Proton Exchange Membrane Fuel Cell Using the Modified Bald Eagle Optimization Algorithm

by Alaa A. Zaky, Rania M. Ghoniem and F. Selim

Sustainability 2023, 15(13), 10590; https://doi.org/10.3390/su151310590 - 5 Jul 2023

Cited by 1 | Viewed by 1084

Abstract

The proton exchange membrane fuel cell (PEMFC) is a green energy converter that is based on the chemical reaction process. The behavior of this system can change with time due to aging and operating conditions. Knowing the current state of this system requires an accurate model, and an exact PEMFC model requires precise parameters. These parameters should be identified and used to properly fit the polarization curve in order to effectively replicate the PEMFC behavior. This work suggests a precise unknown PEMFC parameter extraction based on a new metaheuristic optimization algorithm called the modified bald eagle search algorithm (mBES). The mBES is an optimization algorithm based on the principles of bald eagle behavior that combines local search and global search to achieve a balance between the exploration and exploitation of search spaces. It is a powerful and efficient technique for optimization problems where accurate and near-optimal solutions are desired. To approve the accuracy of the proposed identification approach, the proposed algorithm is compared to the following metaheuristic algorithms: bald eagle search algorithm (BES), artificial ecosystem-based optimization (AEO), leader Harris Hawk’s optimization (LHHO), rain optimization algorithm (ROA), sine cosine algorithm (SCA), and salp swarm algorithm (SSA). This evaluation process is applied to two commercialized PEMFC stacks: BCS 500 W PEMFC and Avista SR-12 PEM. The extracted parameters’ accuracy is measured as the sum of square errors (SSE) between the results produced by the optimizer and the experimental data in the objective function. As a result, the proposed PEMFC optimizing model outperforms the comparison models in terms of system correctness and convergence. The proposed extraction strategy, mBES, obtained the best results, with a fitness value of 0.011364 for the 500 W BCS and 0.035099 for the Avista SR-12 500 W PEMFC. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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28 pages, 4793 KiB

Open AccessArticle

A Model for Determining the Optimal Decommissioning Interval of Energy Equipment Based on the Whole Life Cycle Cost

by Biao Li, Pengfei Wang, Peng Sun, Rui Meng, Jun Zeng and Guanghui Liu

Sustainability 2023, 15(6), 5569; https://doi.org/10.3390/su15065569 - 22 Mar 2023

Cited by 1 | Viewed by 1254

Abstract

An appropriate technical overhaul strategy is very important for the development of enterprises. Most enterprises pay attention to the design life of the equipment, that is, the point when the equipment can no longer be used as stipulated by the manufacturer. However, in the later stage of the equipment, the operation and maintenance costs may be higher than the benefit of the equipment. Therefore, only the design life of the equipment may cause a waste of funds, so as to avoid the waste of funds, the enterprise’s strategy of technical reform and overhaul are optimized. This paper studies the optimal decommissioning life of the equipment (taking into account both the safety and economic life of the equipment), and selects the data of a 35 kV voltage transformer in a powerful enterprise. The enterprise may have problems with the data due to recording errors or loose classification. In order to analyze the decommissioning life of the equipment more accurately, it is necessary to first use t-distributed stochastic neighbor embedding (t-SNE) to reduce the data dimension and judge the data distribution. Then, density-based spatial clustering of applications with noise (DBSCAND) is used to screen the outliers of the data and mark the filtered abnormal data as a vacancy value. Then, random forest is used to fill the vacancy values of the data. Then, an Elman neural network is used for random simulation, and finally, the Fisher orderly segmentation is used to obtain the optimal retirement life interval of the equipment. The overall results show that the optimal decommissioning life range of the 35 kV voltage transformer of the enterprise is 31 to 41 years. In this paper, the decommissioning life range of equipment is scientifically calculated for enterprises, which makes up for the shortage of economic life. Moreover, considering the “economy” and “safety” of equipment comprehensively will be conducive to the formulation of technical reform and overhaul strategy. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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23 pages, 15745 KiB

Open AccessArticle

Unified Power Quality Conditioner Using Recent Optimization Technique: A Case Study in Cairo Airport, Egypt

by Sally E. Abdel Mohsen, Ahmed M. Ibrahim, Z. M. Salem Elbarbary and Ahmed I. Omar

Sustainability 2023, 15(4), 3710; https://doi.org/10.3390/su15043710 - 17 Feb 2023

Cited by 6 | Viewed by 1711

Abstract

This article offers a power quality (PQ) strategy to reduce light intensity flickers, voltage enhancements, and harmonics mitigation of the grid current in extensive networks of LED lighting at Cairo airport, Egypt. A transformerless unified power quality conditioner (TL-UPQC) with its controls is presented to address the majority of PQ issues in a network. The TL-UPQC comprises a dynamic voltage restorer (DVR) as a series compensator, which quickly maintains the load voltage when there is a voltage decrease, surge, or flickering in the network and an active power filter (APF) acts as a shunt compensator that reduces harmonic currents and injects reactive currents. The gain values of the PI controller are obtained using an extended bald eagle search (EBES) optimizer. In addition, a comparative study of three optimizers, namely, moth flame (MFO), cuckoo search (CSA), and salp swarm algorithm (SSA), is presented to test the performance of the PI controller and fast dynamic response. The results showed that the APF nearly obtained unity PF and that the harmonics produced as THD by LED light bulbs for current at the grid were abolished that becomes 3.29%. Additionally, the results verified that TL-UPQC could cancel voltage fluctuations at grid problems so that UPQC’s performance is successfully achieved to provide a flicker-free LED lighting network and this appeared clearly when used in LED lighting network at Cairo airport. MATLAB simulation has been employed to confirm the proposed TL-UPQC’s effectiveness. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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20 pages, 4364 KiB

Open AccessArticle

Effective Load Frequency Control of Power System with Two-Degree Freedom Tilt-Integral-Derivative Based on Whale Optimization Algorithm

by Preeti Ranjan Sahu, Kumaraswamy Simhadri, Banaja Mohanty, Prakash Kumar Hota, Almoataz Y. Abdelaziz, Fahad Albalawi, Sherif S. M. Ghoneim and Mahmoud Elsisi

Sustainability 2023, 15(2), 1515; https://doi.org/10.3390/su15021515 - 12 Jan 2023

Cited by 14 | Viewed by 1904

Abstract

Nowadays, the operation and control of power systems are a big challenge. An essential part of the power system (PS) control is load frequency control (LFC). Different secondary controllers are implemented for the frequency control problem. Hence, cascaded two-degree freedom and a tilt-integral-derivative controller having a filter (2DOFTIDF) are intended in this paper and implemented for load frequency control. In order to determine the efficiency of the 2DOFTIDF controller, a well-known non-reheat thermal system with/without a governor dead band is considered. A new whale optimization algorithm (WOA) is used to enhance the suggested controller parameters. The predominance of the presented method is exhibited by comparing the consequences with different heuristic techniques tuned to controllers published recently. Further, the simulation results for two test cases indicate that system enactments are enhanced by introducing the suggested controller and are also best suited in the presence of system nonlinearity. Finally, random load fluctuation along with noise and changing the system parameters are also used to determine the reliability of the suggested controller. Compared to the WOA-tuned TIDF controller, the settling time of ΔF₁, ΔF_2, and ΔP_Tie is improved by 45.45%, 56.77%, and 20.26%, respectively, with the WOA-tuned 2DOFTIDF controller and by 40%, 48.27%, and 20%, respectively, with the DE-tuned TIDF controller. Experimental validation using the hardware-in-the-loop real-time simulation based on OPAL-RT has been carried out to confirm the viability of the proposed approach. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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16 pages, 3666 KiB

Open AccessArticle

Prediction of Main Parameters of Steam in Waste Incinerators Based on BAS-SVM

by Lianhong Chen, Chao Wang, Rigang Zhong, Zhuoge Li, Zheng Zhao and Ziyu Zhou

Sustainability 2023, 15(2), 1132; https://doi.org/10.3390/su15021132 - 6 Jan 2023

Cited by 3 | Viewed by 1245

Abstract

The main steam parameters of a waste-to-energy plant are the key indicator of the safety and stability of its combustion process. Accurate prediction of the main steam parameters can help the control system to reasonably analyze the combustion conditions and, thus, to greatly improve the combustion efficiency. In this paper, we propose an optimized method for predicting the main steam parameters of waste incinerators. Firstly, a grey relational analysis (GRA) is used to obtain the ranking of the correlation degree between 114 characteristic variables in the furnace and the main steam parameters, and 13 characteristic variables are selected as model inputs. A Spearman-based time delay compensation method is proposed to effectively overcome the influence of time asynchrony on the prediction accuracy. At last, the beetle antennae search (BAS) optimized support vector machine (SVM) model is proposed. Taking advantage of the fast iteration of the beetle antennae search algorithm to find the key hyperparameters of the support vector machine, the optimized main steam parameter prediction model is finally obtained. The simulation results show that the prediction accuracy of this model is greatly improved compared with traditional neural network models, such as long short-term memory neural networks (LSTMs) and convolutional neural networks (CNNs), as well as a single SVM. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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18 pages, 1827 KiB

Open AccessArticle

Modal Identification of Low-Frequency Oscillations in Power Systems Based on Improved Variational Modal Decomposition and Sparse Time-Domain Method

by Lei Liu, Zheng Wu, Ze Dong and Shaojie Yang

Sustainability 2022, 14(24), 16867; https://doi.org/10.3390/su142416867 - 15 Dec 2022

Cited by 1 | Viewed by 1485

Abstract

Power systems have an increasing demand for operational condition monitoring and safety control aspects. Low-frequency oscillation mode identification is one of the keys to maintain the safe and stable operation of power systems. To address the problems of low accuracy and poor anti-interference of the current low-frequency oscillation mode identification method for power systems, a low-frequency oscillation mode feature identification method combining the adaptive variational modal decomposition and sparse time-domain method is proposed. Firstly, the grey wolf optimization algorithm (GWO) is used to find the optimal number of eigenmodes and penalty factor parameters of the variational modal decomposition (VMD). And the improved method (GWVMD) is used to decompose the measured signal with low-frequency oscillations and then reconstruct the signal to achieve a noise reduction. Next, the processed signal is used as a new input for the identification of the oscillation modes and their parameters using the sparse time-domain method (STD). Finally, the effectiveness of the method is verified by the actual low-frequency oscillation signal identification in the Hengshan power plant and numerical signal simulation experiments. The results show that the proposed method outperforms the conventional methods such as Prony, ITD, and HHT in terms of modal discrimination. Meanwhile, the overall reduction in the frequency error is 34, 44, and 21%, and the overall reduction in the damping error is 37, 41, and 18%, compared with the recently proposed methods such as the EFEMD-HT, RDT-ERA, and TLS-ESPRIT. The effectiveness of the methods in suppressing the modal confusion and noise immunity is demonstrated. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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25 pages, 6082 KiB

Open AccessArticle

Economic Planning of Energy System Equipment

by Biao Li, Tao Wang, Zhen Dong, Qian Geng and Yi Sun

Sustainability 2022, 14(18), 11464; https://doi.org/10.3390/su141811464 - 13 Sep 2022

Viewed by 958

Abstract

The asset wall (AW) model is widely used by energy companies to forecast the retirement size of equipment. The AW model is a method of arranging historical data in chronological order and then using extrapolation to predict trends in asset size volumes over time. However, most studies using the AW model treat all equipment as a whole and perform a flat extrapolation mechanically, ignoring the impact of technological improvements and price fluctuations. Furthermore, there are relatively few studies on the assetization of equipment replacement scale. This paper fits a Weibull distribution density function and uses Monte Carlo stochastic simulation to determine the retirement age of each piece of equipment, reducing the ambiguity and randomness generated by the AW approach of treating all equipment as a whole. This modified model is noted in this paper as the Weibull–Monte Carlo stochastic simulation asset model wall (WMCAW). The paper then investigated the assetization of equipment replacement size, comparing the three error indicators Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) in order to select the appropriate optimization model for price forecasting from several combinations of models. Finally, the paper verified the feasibility of the WMCAW model using various types of equipment decommissioned in 1970 and compared the forecasting effects of AW and WMCAW. It is found that the curve of the equipment replacement scale predicted by WMCAW is smoother than that of AW, and the forecasting results are more stable and scientific. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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23 pages, 2511 KiB

Open AccessArticle

MSSA-DEED: A Multi-Objective Salp Swarm Algorithm for Solving Dynamic Economic Emission Dispatch Problems

by Mohamed H. Hassan, Salah Kamel, José Luís Domínguez-García and Mohamed F. El-Naggar

Sustainability 2022, 14(15), 9785; https://doi.org/10.3390/su14159785 - 8 Aug 2022

Cited by 15 | Viewed by 1588

Abstract

Due to the rising cost of fuel, increased demand for energy, and the stresses of environmental issues, dynamic economic emission dispatch (DEED), which is the most precise mode for actual dispatching conditions, has been a significant study topic in current years. In this article, the higher dimensional, deeply correlated, non-convex, and non-linear multi-objective DEED problem is designated, involving both the fuel costs and emissions objectives simultaneously. In addition, the valve point effect, transmission loss, as well as the ramping rate, are considered. The Salp Swarm Algorithm (SSA) is a well-established meta-heuristic that was inspired by the foraging behavior of salps in deep oceans and has proved to be beneficial in estimating the global optima for many optimization problems. The objective of this article is to evaluate the performance of the multi-objective Salp Swarm Algorithm (MSSA) for obtaining the optimal dispatching schemes. Furthermore, the fuzzy decision-making (FDM) approach is employed to achieve the best compromise solution (BCS). In order to confirm the efficacy of the MSSA, the IEEE 30-bus six-unit power system, standard 39-bus ten-unit New England power system, and IEEE 118-bus fourteen-unit power system were considered as three studied cases. The obtained results proved the strength and supremacy of the MSSA compared with two well-known algorithms, the multi-objective grasshopper optimization algorithm (MOGOA) and the multi-objective ant lion optimizer (MALO), and other reported methods. The BCS of the proposed MSSA for the six-unit power system was USD 25,727.57 and 5.94564 Ib, while the BCS was 2.520778 × USD 106 and 3.05994 × 105 lb for the ten-unit power system, and was 1.29200 × USD106 and 98.1415 Ib for the 14 generating units. Comparisons with the other well-known methods revealed the superiority of the proposed MSSA and confirmed its potential for solving other power systems’ multi-objective optimization problems. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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15 pages, 3516 KiB

Open AccessArticle

Wind Speed Prediction Model Based on Improved VMD and Sudden Change of Wind Speed

by Shijun Wang, Chun Liu, Kui Liang, Ziyun Cheng, Xue Kong and Shuang Gao

Sustainability 2022, 14(14), 8705; https://doi.org/10.3390/su14148705 - 15 Jul 2022

Cited by 6 | Viewed by 1727

Abstract

An accurate wind speed prediction system is of great importance prerequisite for realizing wind power grid integration and ensuring the safety of the power system. Quantifying wind speed fluctuations can better provide valuable information for power dispatching. Therefore, this paper proposes a deterministic wind speed prediction system and an interval prediction method based on the Lorentzian disturbance sequence. For deterministic forecasting, a variational modal decomposition algorithm is first used to reduce noise. The preprocessed data are then predicted by a long and short-term neural network, but there is a significant one-step lag in the results. In response to such limitation, a wind speed slope is introduced to revise the preliminary prediction results, and the final deterministic wind speed prediction model is obtained. For interval prediction, on the basis of deterministic prediction, the Lorenz disturbance theory is introduced to describe the dynamic atmospheric system. B-spline interpolation is used to fit the distribution of Lorenz disturbance theory series to obtain interval prediction results. The experimental results show that the model proposed in this paper can achieve higher forecasting accuracy than the benchmark model, and the interval prediction based on the Lorentzian disturbance sequence can achieve a higher ground truth coverage rate when the average diameter is small through B-spline interpolation fitting. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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15 pages, 2330 KiB

Open AccessArticle

Mid- to Long-Term Electric Load Forecasting Based on the EMD–Isomap–Adaboost Model

by Xuguang Han, Jingming Su, Yan Hong, Pingshun Gong and Danping Zhu

Sustainability 2022, 14(13), 7608; https://doi.org/10.3390/su14137608 - 22 Jun 2022

Cited by 11 | Viewed by 1850

Abstract

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. In this study, a hybrid algorithm (EMDIA) that combines empirical mode decomposition (EMD), isometric mapping (Isomap), and Adaboost to construct a prediction mode for mid- to long-term load forecasting is developed. Based on full consideration of the meteorological and economic factors affecting the power load trend, the EMD method is used to decompose the load and its influencing factors into multiple intrinsic mode functions (IMF) and residuals. Through correlation analysis, the power load is divided into fluctuation term and trend term. Then, the key influencing factors of feature sequences are extracted by Isomap to eliminate the correlations and redundancy of the original multidimensional sequences and reduce the dimension of model input. Eventually, the Adaboost prediction method is adopted to realize the prediction of the electrical load. In comparison with the RF, LSTM, GRU, BP, and single Adaboost method, the prediction obtained by this proposed model has higher accuracy in the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R²). Compared with the single Adaboost algorithm, the EMDIA reduces MAE by 11.58, MAPE by 0.13%, and RMSE by 49.93 and increases R² by 0.04. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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Review

Jump to: Research

27 pages, 1821 KiB

Open AccessReview

Review of Metaheuristic Optimization Algorithms for Power Systems Problems

by Ahmed M. Nassef, Mohammad Ali Abdelkareem, Hussein M. Maghrabie and Ahmad Baroutaji

Sustainability 2023, 15(12), 9434; https://doi.org/10.3390/su15129434 - 12 Jun 2023

Cited by 23 | Viewed by 5620

Abstract

Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field. Full article

(This article belongs to the Special Issue Artificial Intelligence Applications in Power and Energy Systems)

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