Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F1: Electrical Power System".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 16953

Share This Special Issue

Special Issue Editors

Dr. Qin Wang

E-Mail Website
Guest Editor

Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Interests: vehicle-to-X; grid integration of hydrogen resources; AI for energy; inverter control; microgrid
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mingjian Cui

E-Mail Website
Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: renewable energy; smart grid; power system operation; machine learning; power system cybersecurity; optimization modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Ershun Du

E-Mail Website
Guest Editor

Laboratory of Low Carbon Energy, Tsinghua University, Beijing 100084, China
Interests: power system planning; renewable energy integration; energy policy; electrical power engineering; power engineering; power systems modelling; renewable energy; electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of electrical power systems has undergone significant advancements in recent years, driven by the need for efficient and sustainable energy solutions. Simulation and analysis techniques play a crucial role in understanding, optimizing, and enhancing the performance of electrical power systems. This Special Issue aims to explore the latest advances in simulation and analysis methodologies and their applications in enhancing the efficiency, reliability, and sustainability of power systems.

The primary aim of this Special Issue is to provide a platform for researchers, practitioners, and experts in the field of electrical power systems to share their insights, innovations, and findings related to simulation and analysis. By assembling diverse perspectives, the Special Issue seeks to foster interdisciplinary collaborations, promote knowledge exchange, and contribute to the development of more efficient and sustainable electrical power systems.

The Special Issue invites contributions that address various aspects of simulation and analysis in electrical power systems. Topics of interest include, but are not limited to, the following:

Advanced simulation techniques for power system modeling and analysis
Optimization algorithms and tools for power system operation and planning
Integration of renewable energy sources in power system simulations
Simulation-based studies on grid stability, reliability, and resilience
Analysis of power system dynamics and control strategies
Simulation and analysis of smart grid technologies and architectures
Cybersecurity analysis and simulation for power system protection
Simulation-based studies on demand response and energy management systems
Impact analysis of electric vehicles and energy storage systems on power grids
Simulation and analysis of microgrids and distributed energy resources

Authors are encouraged to present original research, case studies, and review articles that contribute to the understanding and advancement of simulation and analysis techniques in electrical power systems. This Special Issue welcomes both theoretical contributions and practical applications, aiming to bridge the gap between academia and industry.

By exploring these topics, this Special Issue seeks to inspire innovative solutions, foster sustainable practices, and facilitate the transition towards a more reliable, efficient, and environmentally friendly electrical power system.

Dr. Qin Wang
Prof. Dr. Mingjian Cui
Dr. Ershun Du
Guest Editors

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

simulation
analysis
electrical power systems
efficiency
reliability
sustainability

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

20 pages, 3989 KiB

Open AccessArticle

Multi-Objective Optimization for the Low-Carbon Operation of Integrated Energy Systems Based on an Improved Genetic Algorithm

by Yao Duan, Chong Gao, Zhiheng Xu, Songyan Ren and Donghong Wu

Energies 2025, 18(9), 2283; https://doi.org/10.3390/en18092283 - 29 Apr 2025

Viewed by 318

Abstract

As global climate change and energy crises intensify, the pursuit of low-carbon integrated energy systems (IESs) has become increasingly important. This paper proposes an improved genetic algorithm (IGA) designed to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, which not only preserve advantageous traits from the parent population but also enhance genetic diversity, enabling comprehensive exploration of potential solutions. Additionally, the algorithm selects parent populations based on individual fitness and dominance, retaining successful chromosomes and eliminating those that violate constraints. This process ensures that subsequent generations inherit superior genetic traits while minimizing constraint violations, thereby enhancing the feasibility of the solutions. To evaluate the effectiveness of the proposed algorithm, we tested it on three different IES scenarios. The results demonstrate that the IGA successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES’s power demand in each time slot. We compared its performance against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm. Compared to conventional genetic algorithms, the IGA achieved maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm, demonstrating its superior performance in multi-objective optimization for low-carbon IESs. These outcomes underscore the algorithm’s reliability and practical applicability. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

29 pages, 5658 KiB

Open AccessArticle

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

by Haala Haj Issa, Moein Abedini, Mohsen Hamzeh and Amjad Anvari-Moghaddam

Energies 2024, 17(21), 5372; https://doi.org/10.3390/en17215372 - 29 Oct 2024

Viewed by 1105

Abstract

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

18 pages, 4860 KiB

Open AccessArticle

Research on Carbon-Reduction-Oriented Demand Response Technology Based on Generalized Nodal Carbon Emission Flow Theory

by Shixu Zhang, Yaowang Li, Ershun Du, Wei Wang, Min Wang, Haoran Feng, Yi Xie and Qiuyu Chen

Energies 2024, 17(18), 4672; https://doi.org/10.3390/en17184672 - 19 Sep 2024

Cited by 1 | Viewed by 1144

Abstract

The decarbonization of power systems plays a great part in the carbon neutrality goal. Currently, researchers have explored reducing carbon in power systems in terms of the optimization of energy supply structure and operation strategies, but ignored the carbon reduction potential of users. To investigate the carbon reduction capability of users and further promote power system decarbonization through the active response of electricity loads, this paper proposes a carbon-reduction-oriented demand response (CRODR) technology based on generalized nodal carbon emission flow theory. First, the framework of the CRODR mechanism is established to provide an interaction baseline for users facing carbon reduction guiding signals. Secondly, the generalized nodal carbon emission flow theory is introduced to provide a calculation method for the guiding signals, considering dynamic electricity carbon emission factors with various spatiotemporal resolutions. Then, a matrix-based method is proposed to efficiently solve the carbon emission flow and obtain the guiding signals. On this basis, an optimal load-regulating model to help users meet their carbon reduction goals is built, and a carbon reduction benefit-evaluation method is proposed. Case studies on China’s national power system and a textile company verify that CRODR technology can realize efficient carbon reduction through load shifting while maintaining the total power consumption of users. The proposed CRODR technology is expected to provide a theoretical basis and guiding mechanism for promoting carbon reduction throughout the entire power system. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

13 pages, 6868 KiB

Open AccessArticle

Storage Regulation Mechanism and Control Strategy of a Hydraulic Wave Power Generation System

by Jianjun Peng, Chenchen Huang, Meng Xue, Run Feng, Erhao Zhou, Zhidan Zhong and Xiangchen Ku

Energies 2024, 17(16), 4151; https://doi.org/10.3390/en17164151 - 21 Aug 2024

Cited by 1 | Viewed by 877

Abstract

Based on a mechanism study, the regulation and control mechanism of the hydraulic energy storage system is elaborated in detail, and the regulation and control strategy is formulated for the hydraulic power generation system under the condition of a stable random wave, and the working mode of the wave power generation system is deeply studied. According to the characteristics of a hydraulic system, a control strategy of a three-position four-way electromagnetic directional valve suitable for adaptive energy storage system is proposed. In order to verify the feasibility of the control strategy, a mathematical model of the hydraulic cylinder displacement control system is designed based on the Matlab/Simulink platform, and a PID control strategy is introduced to build a wave simulation loop. Amesim and Simulink co-simulation is used to verify the performance of the wave simulation circuit and the hydraulic power generation system. The simulation results show that the maximum error rate is only 0.8% after PID control is added to the wave power generation simulation loop, the displacement of the hydraulic cylinder can reach the expected value, and the random wave motion can be simulated effectively. In the hydraulic system of wave energy generation, the proposed adaptive control strategy can accelerate the system stability process, reduce the power overshoot significantly, and convert wave energy into electric energy more effectively. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

11 pages, 3132 KiB

Open AccessArticle

Characteristics and Simulation of Icing Thickness of Overhead Transmission Lines across Various Micro-Terrains

by Guosheng Huang, Mingli Wu, Zhen Qiao, Songping Fu, Qiujiang Liu, Xiaowei Huai and Pengcheng Yan

Energies 2024, 17(16), 4024; https://doi.org/10.3390/en17164024 - 14 Aug 2024

Viewed by 909

Abstract

The hazard of ice accretion on overhead power circuits is significant, yet predicting it is very difficult. The key reason lies in the shortage of sufficient observational data on ice thickness, and previous studies have also rarely taken into account micro-terrain and micro-meteorological conditions. In response to the challenge of simulating overhead line icing, this study introduces a new icing simulation technique that fully considers the effects of micro-terrain and micro-meteorology. For this technique, typical micro-terrains of overhead line areas are first identified by using high-resolution elevation data, and the icing thickness characteristics in different micro-terrains are analyzed. Subsequently, icing thickness simulations for different micro-terrains are conducted. The results indicate that during the icing process, the icing thickness ranges from 5 mm to 8 mm under three types of micro-terrain, namely, “uplift type”, “alpine drainage divide type” and “canyon wind channel type”, whereas the icing thickness is less than 5 mm in the “flat type” of micro-terrain. This finding suggests that the first three micro-terrain types facilitate icing on overhead transmission lines due to the condensation and uplifting effects of water vapor caused by terrain. However, flat terrain lacks the conditions necessary for water vapor accumulation and thus is not easy to form icing. The results are advantageous for the deployment of overhead power lines in intricate terrain. It is advisable to steer clear of regions susceptible to icing, and endeavor to install circuits in level territories whenever feasible. In addition, the simulated icing thickness under different terrains is in good agreement with the observations. Specifically, the correlation coefficient between simulated and observed icing thickness is significant at the 0.99 confidence level, and the deviations between them are within 0.5 mm. This signifies that the forecasting methodologies employed are dependable and possess significant implications as a reference for disaster prevention and mitigation efforts. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

16 pages, 7659 KiB

Open AccessArticle

The Effect of the Vertical Layout on Underground Cable Current Carrying Capacity

by Ahmet Ozyesil, Burak Altun, Yunus Berat Demirol and Bora Alboyaci

Energies 2024, 17(3), 674; https://doi.org/10.3390/en17030674 - 31 Jan 2024

Cited by 3 | Viewed by 2577

Abstract

Underground cable installation in historical areas, natural protected areas, narrow streets, or residential areas with high traffic flows is very difficult due to both legal permits and the conditions of the work sites. The trefoil layout requires a smaller channel than the flat layout. However, the trefoil layout carries some risks, such as damage to the cables together in the event of short circuit faults and reduced ampacity in single-side-bonded systems. This study’s scope examines the current carrying capacities and thermal effects of directly buried underground cables in trefoil and vertical layouts using CYMCAP power cable analysis software. A field investigation was also carried out to verify the analysis results. The performance of the recommended method was evaluated by considering current and temperature measurements from the fieldwork and analysis. According to the studied cable design, the current carrying capacities of the cables in flat and vertical layouts are similar and higher than in the trefoil layout. However, it should be taken into consideration that these results will vary depending on a cable system’s design parameters. As a result, this article emphasizes that a vertical layout can be considered as a layout option in certain areas. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

23 pages, 8995 KiB

Open AccessArticle

Evaluation Method for Hosting Capacity of Rooftop Photovoltaic Considering Photovoltaic Potential in Distribution System

by Yilin Xu, Jie He, Yang Liu, Zilu Li, Weicong Cai and Xiangang Peng

Energies 2023, 16(22), 7677; https://doi.org/10.3390/en16227677 - 20 Nov 2023

Cited by 1 | Viewed by 1497

Abstract

Regarding the existing evaluation methods for photovoltaic (PV) hosting capacity in the distribution system that do not consider the spatial distribution of rooftop photovoltaic potential and are difficult to apply on the actual large-scale distribution systems, this paper proposes a PV hosting capacity evaluation method based on the improved PSPNet, grid multi-source data, and the CRITIC method. Firstly, an improved PSPNet is used to efficiently abstract the rooftop in satellite map images and then estimate the rooftop PV potential of each distribution substation supply area. Considering the safety, economy, and flexibility of distribution system operation, we establish a multi-level PV hosting capacity evaluation system. Finally, based on the rooftop PV potential estimation of each distribution substation supply area, we combine the multi-source data of the grid digitalization system to carry out security verification and indicator calculation and convert the indicator calculation results of each scenario into a comprehensive score through the CRITIC method. We estimate the rooftop photovoltaic potential and evaluate the PV hosting capacity of an actual 10 kV distribution system in Shantou, China. The results show that the improved PSPNet solves the hole problem of the original model and obtains a close-to-realistic rooftop photovoltaic potential estimation value. In addition, the proposed method considering the photovoltaic potential in this paper can more accurately evaluate the rooftop PV hosting capacity of the distribution system compared with the traditional method, which provides data support for the power grid corporation to formulate a reasonable PV development and hosting capacity enhancement program. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

17 pages, 5390 KiB

Open AccessArticle

Evaluation of Rooftop Photovoltaic Power Generation Potential Based on Deep Learning and High-Definition Map Image

by Wenbo Cui, Xiangang Peng, Jinhao Yang, Haoliang Yuan and Loi Lei Lai

Energies 2023, 16(18), 6563; https://doi.org/10.3390/en16186563 - 12 Sep 2023

Cited by 7 | Viewed by 2065

Abstract

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m², a physical potential of 25,186,181.7 m², and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon “3060” plan for the future of China. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

Review

Jump to: Research

29 pages, 1607 KiB

Open AccessReview

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

by Michael Meiser and Ingo Zinnikus

Energies 2024, 17(9), 1992; https://doi.org/10.3390/en17091992 - 23 Apr 2024

Cited by 3 | Viewed by 2316

Abstract

To achieve the energy transition, energy and energy efficiency are becoming more and more important in society. New methods, such as Artificial Intelligence (AI) and Machine Learning (ML) models, are needed to coordinate supply and demand and address the challenges of the energy transition. AI and ML are already being applied to a growing number of energy infrastructure applications, ranging from energy generation to energy forecasting and human activity recognition services. Given the rapid development of AI and ML, the importance of Trustworthy AI is growing as it takes on increasingly responsible tasks. Particularly in the energy domain, Trustworthy AI plays a decisive role in designing and implementing efficient and reliable solutions. Trustworthy AI can be considered from two perspectives, the Model-Centric AI (MCAI) and the Data-Centric AI (DCAI) approach. We focus on the DCAI approach, which relies on large amounts of data of sufficient quality. These data are becoming more and more synthetically generated. To address this trend, we introduce the concept of Synthetic Data-Centric AI (SDCAI). In this survey, we examine Trustworthy AI within a Synthetic Data-Centric AI context, focusing specifically on the role of simulation and synthetic data in enhancing the level of Trustworthy AI in the energy domain. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Figure 1

17 pages, 572 KiB

Open AccessReview

Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review

by Bozhen Jiang, Qin Wang, Shengyu Wu, Yidi Wang and Gang Lu

Energies 2024, 17(6), 1381; https://doi.org/10.3390/en17061381 - 13 Mar 2024

Cited by 4 | Viewed by 2628

Abstract

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. Full article

(This article belongs to the Special Issue Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability)

► Show Figures

Journal Menu

Journal Browser

Advances in Simulations and Analysis of Electrical Power Systems: Enhancing Efficiency, Reliability and Sustainability

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI