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Modeling, Design and Engineering Optimization of Energy Systems

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A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 26 July 2024 | Viewed by 9691

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Special Issue Editors

Dr. Yamin Yan

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Guest Editor

School of New Energy, North China Electric Power University, Beijing 102206, China
Interests: energy system modeling; design and operation optimization; data driven

Dr. Qi Liao

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Guest Editor

School of Mechanical and Storage Engineering, China University of Petroleum, Beijing 102249, China
Interests: scheduling; supply chain optimization; mathematical programming
Special Issues, Collections and Topics in MDPI journals

Dr. Jie Yan

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Guest Editor

School of New Energy, North China Electric Power University, Beijing 102206, China
Interests: intelligent wind power forecast; wind farm control
Special Issues, Collections and Topics in MDPI journals

Dr. Haoran Zhang

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Guest Editor

School of Urban Planning and Design, Peking University, Shenzhen 518055, China
Interests: urban energy system; urban logistics system; urban flow space
Special Issues, Collections and Topics in MDPI journals

Dr. Wanshui Yu

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Guest Editor

Carbon Neutrality Research Center of Power System, China Electric Power Research Institute, Beijing 100192, China
Interests: carbon assessment of power system; product carbon footprint; carbon monitoring and accounting

Special Issue Information

Dear Colleagues,

Comprehensive energy systems will play vital roles in promoting improvements in energy efficiency and renewable energy accommodation, which is an important strategy for achieving the goal of carbon neutrality.

Comprehensive energy systems aim to integrate various energy sources such as natural gas, electricity, and biomass within the region, achieving the collaborative planning, operation, and interactive response of heterogeneous energy sources. On the premise of meeting diversified energy demands, such systems can reduce costs and improve energy efficiency, potentially bringing economic and environmental benefits to different energy sectors.

This Special Issue aims to collect innovative research on state-of-the-art energy system modeling, design optimization, and engineering optimization technologies to support the sustainable development of integrated energy systems. We welcome articles and reviews focused on, but not limited to, the following topics:

Design optimization and engineering optimization in energy systems considering various uncertain factors;
Energy system modeling considering data privacy protection;
Energy system modeling and design optimization considering flexibility and fairness;
Trading strategies for multisource heterogeneous energy systems;
Techno-economic and environmental analysis of heterogeneous energy systems;
Intelligent solutions for resilient energy systems.

Dr. Yamin Yan
Dr. Qi Liao
Dr. Jie Yan
Dr. Haoran Zhang
Dr. Wanshui Yu
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. Processes is an international peer-reviewed open access monthly 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 2400 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

energy systems
modeling
design optimization
engineering optimization
techno-economic assessment
flexibility and fairness
data privacy protection
trading strategies

Published Papers (10 papers)

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Research

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24 pages, 6762 KiB

Open AccessArticle

Charging and Discharging Optimization of Vehicle Battery Efficiency for Minimizing Company Expenses Considering Regular User Travel Habits

by Jiaoyang Lu, Shan Liu, Jing Zhang, Shuang Han, Xingyuan Zhou and Yongqian Liu

Processes 2024, 12(3), 435; https://doi.org/10.3390/pr12030435 - 21 Feb 2024

Viewed by 789

Abstract

With the increasing popularity and development of electric vehicles, the demand for electric vehicle charging is also constantly increasing. To meet the diverse charging needs of electric vehicle users and improve the efficiency of charging infrastructure, this study proposes an optimization strategy for electric vehicle charging and discharging. This method considers both the user’s travel mode and the operational efficiency of the charging pile. Firstly, a probability model based on travel spatiotemporal variables and Monte Carlo algorithm were used to simulate the travel trajectory of electric vehicles, providing a data foundation for optimizing the charging and discharging schemes of electric vehicles. Then, with the dual objective of minimizing the operating costs of charging piles and user charging costs, a linear programming model was constructed to optimize the charging and discharging strategies of electric vehicles. Finally, the model was validated using an apartment building as an example. The results indicate that, under the normal travel habits of users, with the goal of minimizing company expenses, the annual cost of the company reaches its minimum at a certain number of charging piles. When the cost of electric vehicle users dominates the objective function, they will pay more attention to battery degradation, significantly reducing their willingness to participate in discharge. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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17 pages, 25464 KiB

Open AccessArticle

An Integrated Risk Assessment Methodology of In-Service Hydrogen Storage Tanks Based on Connection Coefficient Algorithms and Quintuple Subtraction Set Pair Potential

by Xiaobin Liang, Fan Fei, Lei Wang, Daibin Mou, Weifeng Ma and Junming Yao

Processes 2024, 12(2), 420; https://doi.org/10.3390/pr12020420 - 19 Feb 2024

Viewed by 687

Abstract

At present, there have been a number of hydrogen storage tank explosions in hydrogen filling stations, causing casualties and property losses, and having a bad social impact. This has made people realize that the risk assessment and preventive maintenance of hydrogen storage tanks are crucial. Therefore, this paper innovatively proposes a comprehensive risk assessment model based on connection coefficient algorithms and quintuple subtractive set pair potential. First of all, the constructed index system contains five aspects of corrosion factors, material factors, environmental factors, institutional factors and human factors. Secondly, a combined weighting analysis method based on FAHP and CRITIC is proposed to determine the weight of each indicator. The basic indicators influencing hydrogen storage tanks are analyzed via the quintuple subtraction set pair potential and full partial connection coefficient. Finally, the risk level and development trend of hydrogen storage tanks in hydrogen filling stations are determined by a combination of the three-category connection coefficient algorithms and the risk level eigenvalue method. The results of our case analysis show that the proposed risk assessment model can identify the main weak indicators affecting the safety of hydrogen storage tanks, including installation quality, misoperation and material quality. At the same time, it is found that the risk of high-pressure hydrogen storage tanks is at the basic safety level, and the development trend of safety conditions holds a critical value. The evaluation results can help establish targeted countermeasures for the prevention and maintenance of hydrogen storage tanks. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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18 pages, 3606 KiB

Open AccessArticle

Economical Design of Drip Irrigation Control System Management Based on the Chaos Beetle Search Algorithm

by Yue Zhang and Chenchen Song

Processes 2023, 11(12), 3417; https://doi.org/10.3390/pr11123417 - 13 Dec 2023

Viewed by 696

Abstract

In the realm of existing intelligent drip irrigation control systems, traditional PID control encounters challenges in delivering satisfactory control outcomes, primarily owing to issues related to non-linearity, time-varying behavior, and hysteresis. In order to solve the problem of the unstable operation of the drip irrigation system in an intelligent irrigation system, this paper proposes chaotic beetle swarm optimization (CBSO) based on the BAS (beetle antennae search) longicorn search algorithm, with inertial weights, variable learning factors, and logistic chaos initialization improving global search capabilities. This was accomplished by formulating the optimization objective, which involved integrating the control input’s time integral term, the square term, and the absolute value of the error. Subsequently, PID parameter tuning was performed. In order to verify the actual effect of the CBSO algorithm on the PID drip irrigation control system, MATLAB was used to simulate and compare PID control optimized by the GA algorithm, PSO algorithm, and BSO (beetle search optimization) algorithm. The results show that PID control based on CBSO optimization has a short response time, small overshoot, and no oscillation in the steady state process. The performance of the controller is improved, which provides a basis for PID parameter setting for a drip irrigation control system. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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32 pages, 12425 KiB

Open AccessArticle

Optimal Allocation of Hybrid Energy Storage System Based on Smoothing Wind Power Fluctuation and Improved Scenario Clustering Algorithm

by Jinhua Zhang, Tianyi Zhang, Peng Cheng, Dongzheng Yang, Jie Yan and Xinpei Tian

Processes 2023, 11(12), 3407; https://doi.org/10.3390/pr11123407 - 11 Dec 2023

Viewed by 718

Abstract

Against the backdrop of the global energy transition, wind power generation has seen rapid development. However, the intermittent and fluctuating nature of wind power poses a challenge to the stability of grid operation. To solve this problem, a solution based on a hybrid energy storage system is proposed. The hybrid energy storage system is characterized by fast and precise control and bidirectional energy throughput, which can improve the impact of wind power fluctuations on grid stability. An ensemble empirical modal decomposition method was used to assign the raw wind power data to the grid-connected power and energy storage power commands with two reasonable corrections to meet the power allocation of the hybrid energy storage characteristics. In addition, a hybrid energy storage system model considering the whole life cycle cost was developed, and the optimal energy storage power cutoff was determined by exhaustively enumerating the high- and low-frequency power cutoffs. Finally, a comparison with a single storage capacity optimization model was carried out to verify the technical and economic advantages of hybrid energy storage in smoothing wind power fluctuations. To address the shortcomings of the traditional fuzzy c-means clustering algorithm, such as the need to specify the number of clusters in advance and sensitivity to the selection of the initial clustering centers, a combination of the cloud modeling theory and fuzzy c-means was used to make the process more automated and efficient. The improved clustering method algorithmic scheme had capacity error, power error, and cost error of around 3%, and the computational time was also significantly reduced and was computationally efficient compared to the full-year time series simulation. Through MATLAB (2020b) experimental simulation, it was found that the algorithm had a better balance of computational accuracy and efficiency. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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20 pages, 2919 KiB

Open AccessArticle

A Novel Design of Centrifugal Pump Impeller for Hydropower Station Management Based on Multi-Objective Inverse Optimization

by Yue Zhang and Chenchen Song

Processes 2023, 11(12), 3335; https://doi.org/10.3390/pr11123335 - 30 Nov 2023

Viewed by 1253

Abstract

The impeller, regarded as the central component of a centrifugal pump, plays a pivotal role in dictating overall performance. Overcoming challenges arising from the complexity of design parameters and the time-intensive nature of the design process has been a persistent obstacle to widespread adoption. In this study, we integrated ANSYS-CFX 2023 software with innovative inverse design techniques to optimize the impeller design within a centrifugal pump system. Our investigation reveals groundbreaking insights, highlighting the significant influence of both blade load and shaft surface geometry on impeller performance. Notably, through load optimization, substantial enhancements in centrifugal pump efficiency were achieved, demonstrating improvements of 1.8% and 1.7% under flow conditions of 1.0 Q and 0.8 Q, respectively. Further, the efficiency gains of 0.44% and 0.36% were achieved in their corresponding flow conditions. The optimization of blade load and shaft surface configuration notably facilitated a more homogenized internal flow pattern within the impeller. These novel findings contribute substantively to the theoretical foundations underpinning centrifugal pump impeller design, offering engineers a valuable reference to elevate their performance. Our utilization of ANSYS-CFX software in conjunction with inverse design methodologies showcases a promising avenue for advancing impeller design, ultimately culminating in superior efficiency and performance for centrifugal pumps. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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20 pages, 7062 KiB

Open AccessArticle

Optimization of Energy Storage Allocation in Wind Energy Storage Combined System Based on Improved Sand Cat Swarm Optimization Algorithm

by Jinhua Zhang, Xinzhi Xue, Dongfeng Li, Jie Yan and Peng Cheng

Processes 2023, 11(12), 3274; https://doi.org/10.3390/pr11123274 - 22 Nov 2023

Viewed by 928

Abstract

In order to improve the operation reliability and new energy consumption rate of the combined wind–solar storage system, an optimal allocation method for the capacity of the energy storage system (ESS) based on the improved sand cat swarm optimization algorithm is proposed. First, based on the structural analysis of the combined system, an optimization model of energy storage configuration is established with the objectives of the lowest total investment cost of the ESS, the lowest load loss rate and the lowest new energy abandonment rate, which not only takes into account the economy of energy storage construction for investors and builders, but also reduces the probability of blackout for users to protect their interests and improves the utilization rate of the natural resources of wind and light, which can achieve a multi-win–win situation. The model can realize the win–win situation in many aspects. Secondly, an improved k-means clustering algorithm is used to cluster the renewable energy power and load data to realize the typical day data extraction. Then, for the proposed multi-objective optimization model, an SCSO is proposed based on the triangular wandering strategy, Lévy flight strategy and lens imaging reverse learning improvement, which can help the algorithm to jump out of the local optimum while improving its global optimization ability, and these improvements can significantly improve the optimization effect of the SCSO. Finally, simulation analysis is carried out in combination with typical daily extraction data, and the results verify the advantages and effectiveness of the proposed model and algorithm. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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19 pages, 4557 KiB

Open AccessArticle

A Multi-Period Model of Compressor Scheme Optimization for the Shale Gas Gathering and Transportation System

by Kunyi Wu, Jianying Yang, Yu Lin, Pan Zhou, Yanli Luo, Feng Wang, Shitao Liu and Jun Zhou

Processes 2023, 11(11), 3101; https://doi.org/10.3390/pr11113101 - 29 Oct 2023

Viewed by 686

Abstract

In the process of shale gas production, with the change in gas productive parameters, the pressurization demand for the shale gas gathering and transportation system (SGGTS) also changes, which affects the choice of pressurizing location and timing. Our purpose is to effectively respond to the impact of parameter changes during shale gas production and to better select the pressurization schemes. Therefore, we considered the modularization of the compressors and established a mixed-integer nonlinear programming (MINLP) model to minimize the total cost of the SGGTS. Taking an actual shale gas field as an example, by discretizing the time during a given production period to solve the model under multi-period and single-period conditions, the optimal pressurization scheme for the SGGTS in the specified production period is obtained. It indicates that the results obtained under a multi-period condition are more conducive to actual production. Compared with the results obtained under the single-period condition, the cumulative cost obtained in the multi-period condition is reduced by 17.19%. By deploying the MINLP model in the specified production period, the pressurization demand is met in each time period. This greatly improves the utilization rate of modular compressors, reduces the total cost, and improves the economic benefits of the SGGTS. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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22 pages, 5461 KiB

Open AccessArticle

Optimal Scheduling Strategy of Wind Farm Active Power Based on Distributed Model Predictive Control

by Jiangyan Zhao, Tianyi Zhang, Siwei Tang, Jinhua Zhang, Yuerong Zhu and Jie Yan

Processes 2023, 11(11), 3072; https://doi.org/10.3390/pr11113072 - 26 Oct 2023

Viewed by 773

Abstract

In recent years, the development and utilization of China’s wind energy resources have been greatly developed, but the large-scale wind power grid connection has brought threats to the safe and stable operation of the power grid. In order to ensure the stability of the power grid, it is necessary to reduce wind power output fluctuation and improve the tracking accuracy of dispatch instructions. Therefore, based on the distributed model predictive control of wind farm active power distribution strategy, an ultra-short-term wind power hybrid deep learning predictive model is proposed. The prediction results of a wind farm in North China show that the hybrid neural network model can achieve high ultra-short-term wind power prediction accuracy and is suitable for active power control prediction models. A two-layer distributed model is proposed to predict the active power control architecture of wind farms by implementing the clustering process with the Crow Search Algorithm. The distributed model predictive control strategy is proposed in the upper layer, and the centralized model predictive control algorithm is adopted in the lower control structure and optimized. The results show that the dual-layer distributed model predictive control strategy can better track the active power distribution instructions, reduce output fluctuation and scheduling value changes, and enhance the robustness of active power regulation, which is suitable for active power online control in wind farms. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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19 pages, 6118 KiB

Open AccessArticle

Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model

by Feng Yan, Kaituo Jiao, Chaofei Nie, Dongxu Han, Qifu Li and Yujie Chen

Processes 2023, 11(9), 2666; https://doi.org/10.3390/pr11092666 - 6 Sep 2023

Viewed by 791

Abstract

The heat transfer assessment of a buried hot oil pipe is essential for the economical and safe transportation of the pipeline, where the basis is to determine the temperature field surrounding the pipe quickly. This work proposes a novel method to efficiently predict the temperature field surrounding a hot oil pipe, which combines the proper orthogonal decomposition (POD) method and the backpropagation (BP) neural network, named the POD-BP model. Specifically, the BP neural network is used to establish the mapping relationship between spectrum coefficients and the preset parameters of the sample. Compared with the classical POD reduced-order model, the POD-BP model avoids solving the system of reduced-order governing equations with spectrum coefficients as variables, thus improving the prediction speed. Another advantage is that it is easy to implement and does not require tremendous mathematical derivation of reduced-order governing equations. The POD-BP model is then used to predict the temperature field surrounding the hot oil pipe, and the sample matrix is obtained from the numerical results using the finite volume method (FVM). In validation cases, both steady and unsteady states are investigated, and multiple boundary conditions, thermal properties, and even geometry parameters (different buried depths and pipe diameters) are tested. The mean errors of steady and unsteady cases are 0.845~3.052% and 0.133~1.439%, respectively. Appealingly, almost no time, around 0.008 s, is consumed in predicting unsteady situations using the proposed POD-BP model, while the FVM requires a computational time of 70 s. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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Review

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18 pages, 5060 KiB

Open AccessReview

Integration of Carbon Dioxide Removal (CDR) Technology and Artificial Intelligence (AI) in Energy System Optimization

by Guanglei Li, Tengqi Luo, Ran Liu, Chenchen Song, Congyu Zhao, Shouyuan Wu and Zhengguang Liu

Processes 2024, 12(2), 402; https://doi.org/10.3390/pr12020402 - 17 Feb 2024

Viewed by 1611

Abstract

In response to the urgent need to address climate change and reduce carbon emissions, there has been a growing interest in innovative approaches that integrate AI and CDR technology. This article provides a comprehensive review of the current state of research in this field and aims to highlight its potential implications with a clear focus on the integration of AI and CDR. Specifically, this paper outlines four main approaches for integrating AI and CDR: accurate carbon emissions assessment, optimized energy system configuration, real-time monitoring and scheduling of CDR facilities, and mutual benefits with mechanisms. By leveraging AI, researchers can demonstrate the positive impact of AI and CDR integration on the environment, economy, and energy efficiency. This paper also offers insights into future research directions and areas of focus to improve efficiency, reduce environmental impact, and enhance economic viability in the integration of AI and CDR technology. It suggests improving modeling and optimization techniques, enhancing data collection and integration capabilities, enabling robust decision-making and risk assessment, fostering interdisciplinary collaboration for appropriate policy and governance frameworks, and identifying promising opportunities for energy system optimization. Additionally, this paper explores further advancements in this field and discusses how they can pave the way for practical applications of AI and CDR technology in real-world scenarios. Full article

(This article belongs to the Special Issue Modeling, Design and Engineering Optimization of Energy Systems)

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