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Advances in Low Carbon and Artificial Intelligence in Power Energy System

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

Deadline for manuscript submissions: 1 August 2024 | Viewed by 8807

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Special Issue Editors

Prof. Dr. Lingling Zhao

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Guest Editor

National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing, Jiangsu 210096, China
Interests: low-carbon technique in power energy; numerical modeling and visualization monitoring systems; energy saving and pollution control in power plants

Dr. Yue Cao

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Guest Editor

School of Energy and Environment, Southeast University, Nanjing, Jiangsu 210096, China
Interests: supercritical CO2 power generation systems; waste heat recovery; artificial intelligence control

Dr. Rui Guo

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Guest Editor

School of Energy and Environment, Southeast University, Nanjing, Jiangsu 210096, China
Interests: efficient utilization and transformation of thermal energy; waste heat recovery; fault detection and diagnosis in power energy systems

Special Issue Information

Dear Colleagues,

Regarding the long-term objective of carbon neutrality, traditional power energy technologies including coal-fired power plants and gas turbine combined cycles will play a new role in the power grid. Considering the development of renewable energies, such as solar and wind power, power energy systems, which have a strong ability to accommodate renewable energy, are widely used to achieve power balance in power grids.

In terms of the uncertainty of renewable energy, it is necessary to operate power energy systems under variable conditions. In order to achieve the objectives of low carbon use, economy, and speediness, artificial intelligence algorithms are introduced in the optimal operation of power energy systems. Undoubtedly, both renewable energy and artificial intelligence technologies have become the key to achieve the objective of low carbon for power energy systems.

This Special Issue aims to present the most recent advances related to the theory, design, modelling, numerical simulation, application, optimization, dynamic characteristics, performance assessment, and control of low-carbon and artificial intelligence technologies in power energy systems.

We invite you to bring us your contributions on topics including, but not limited to, the following:

Advanced power energy systems;
Renewable energy technologies;
Carbon neutrality;
Artificial intelligence;
Optimization algorithms;
Operating strategy on power energy systems;
Dynamic modelling;
Performance assessment;
Supercritical CO₂ cycle;
Numerical modelling;
Visualization monitor systems;
Energy saving;
Pollutant control;
Intelligent control;
Energy storage systems.

Prof. Dr. Lingling Zhao
Dr. Yue Cao
Dr. Rui Guo
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

• low carbon
• artificial intelligence
• power energy systems
• pollution control
• energy saving
• renewable energy
• optimization operation
• fault diagnosis and intelligent operation and maintenance

Published Papers (6 papers)

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Research

12 pages, 1662 KiB

Open AccessArticle

A Data-Driven Kernel Principal Component Analysis–Bagging–Gaussian Mixture Regression Framework for Pulverizer Soft Sensors Using Reduced Dimensions and Ensemble Learning

by Shengxiang Jin, Fengqi Si, Yunshan Dong and Shaojun Ren

Energies 2023, 16(18), 6671; https://0-doi-org.brum.beds.ac.uk/10.3390/en16186671 - 18 Sep 2023

Cited by 1 | Viewed by 614

Abstract

In light of the nonlinearity, high dimensionality, and time-varying nature of the operational conditions of the pulverizer in power plants, as well as the challenge of the real-time monitoring of quality variables in the process, a data-driven KPCA–Bagging–GMR framework for soft sensors using reduced dimensions and ensemble learning is proposed. Firstly, the methodology employs a Kernel Principal Component Analysis to effectively reduce the dimensionality of the collected process data in a nonlinear manner. Secondly, the reduced principal components are then utilized to reconstruct a refined set of input samples, followed by the application of the Bagging algorithm to obtain multiple subsets of the samples and develop corresponding Gaussian Mixture Regression models. Ultimately, the fusion output is achieved by calculating the weights of each local model based on Bayesian posterior probabilities. By conducting simulation experiments on the coal mill, the proposed approach has been validated as demonstrating superior predictive accuracy and excellent generalization capabilities. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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16 pages, 1648 KiB

Open AccessArticle

Storage Optimization (r, Q) Strategy under Condition-Based Maintenance of Key Equipment of Coal-Fired Power Units in Carbon Neutrality Era

by Tao Sun, Qiang Zhang, Jing Ye, Rong Guo, Rongze Chen, Jianguo Chen, Rui Xiong, Jitao Zhu and Yue Cao

Energies 2023, 16(14), 5485; https://0-doi-org.brum.beds.ac.uk/10.3390/en16145485 - 19 Jul 2023

Cited by 1 | Viewed by 736

Abstract

For the safe, stable, and economic operation of thermal power units in new power systems, the condition-based maintenance mode and storage strategy of key equipment and materials for power generation enterprises were selected. According to the storage linkage demand of condition-based maintenance, a Weibull probability density function was used to calculate spare parts demand, and an intelligent storage optimization model with an availability constraint was established. The application cases of spare parts cost and availability of high-value key equipment and low-value key equipment of coal-fired thermal power units were analyzed, respectively, and the influence of different life spans and the number of covered units on the model were expounded. The results show that the cost of spare parts borne by a single unit is greatly reduced via the optimization of an intelligent inventory (r, Q) strategy on the premise that the availability of units is not less than 99.5%. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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30 pages, 6164 KiB

Open AccessArticle

Study on Combustion Characteristics and NOx Formation in 600 MW Coal-Fired Boiler Based on Numerical Simulation

by Bo Zhu, Bichen Shang, Xiao Guo, Chao Wu, Xiaoqiang Chen and Lingling Zhao

Energies 2023, 16(1), 262; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010262 - 26 Dec 2022

Cited by 8 | Viewed by 1677

Abstract

The variations in the boiler operation conditions have a great effect on the combustion characteristics and the pollutant formation in furnaces. This work aims to investigate the effects of operational parameters on NOx formation and its distribution in furnaces using the numerical simulation method to obtain the optimum control strategy for reducing NOx emissions. The numerical simulation models of pulverized coal combustion in furnaces involving flow, heat transfer, combustion and NOx formation are established. Taking a 600 MW supercritical opposed firing pulverized coal boiler as the study object, a full-scale three-dimensional physical model of the boiler is constructed with Gambit software. On this basis, the pulverized coal combustion and the NOx formation under various boiler loads are numerically simulated using the software of Ansys Fluent 2021R1, and the accuracy and the reliability of the models established are verified by comparing the simulation data with the field test data. According to the combustion numerical simulation of 128 groups of operating conditions, the effects of boiler load, the air rate and the air temperature on combustion and NOx formation have been emphatically investigated. The simulation results indicate that the formation of NOx and the NOx concentration distribution are mainly affected by the oxygen concentration and the temperature in the furnace. Especially, the effects of the variation in the excess air coefficient, the over-fire air (OFA) ratio, the primary air ratio and the internal secondary air ratio on NOx concentration distribution vary greatly. When the air temperature increases the overall NOx concentration in the furnace increases, and the influence of the secondary air temperature and the OFA temperature is greater than that of the primary air temperature. Large amounts of simulation data are a necessary data source for further study on the NOx prediction model at the economizer outlet, which can improve the prediction ability and the generalization ability of the NOx prediction model. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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15 pages, 2456 KiB

Open AccessArticle

Optimization of Steam Distribution Mode for Turbine Units Based on Governing Valve Characteristic Modeling

by Lei Zhang, Zongliang Qiao, Bingsen Hei, Youfei Tang and Shasha Liu

Energies 2022, 15(23), 9139; https://0-doi-org.brum.beds.ac.uk/10.3390/en15239139 - 02 Dec 2022

Cited by 1 | Viewed by 1535

Abstract

With the extensive application of renewable energy generation, thermal power units are required to participate in peak-regulating operations. The mode of steam distribution significantly influences the economy when the steam turbine operates at a low load. The turbine unit’s governing valve characteristics and steam distribution modes are studied in this paper, and the optimal sliding pressure operation curve is derived. Firstly, the theoretical model of the governing stage and the governing valve is derived, and the reliability is verified with field data. Secondly, the overall simulation model of the turbine unit is established, and the turbine off-design performance is analyzed with variable main steam pressure. Finally, the advantages and disadvantages of the three steam distribution modes are discussed thoroughly. The steam distribution modes and optimal main steam pressures are analyzed. The results show that a precise composite sliding pressure operation scheme is recommended, and a sliding pressure operation mode is adopted under 470 MW and constant pressure operation for others. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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16 pages, 3416 KiB

Open AccessArticle

Closed-Loop Combustion Optimization Based on Dynamic and Adaptive Models with Application to a Coal-Fired Boiler

by Chuanpeng Zhu, Pu Huang and Yiguo Li

Energies 2022, 15(14), 5289; https://0-doi-org.brum.beds.ac.uk/10.3390/en15145289 - 21 Jul 2022

Cited by 1 | Viewed by 1691

Abstract

To increase combustion efficiency and reduce pollutant emissions, this study presents an online closed-loop optimization method and its application in a boiler combustion system. To begin with, three adaptive dynamic models are established to predict NOx emission, the carbon content of fly ash (Cfh), and exhaust gas temperature (Teg), respectively. In these models, the orders of the input variables are considered to enable them to reflect the dynamics of the combustion system under load changes. Meanwhile, an adaptive least squares support vector machine (ALSSVM) algorithm is adopted to cope with the nonlinearity and the time-varying characteristics of the combustion system. Subsequently, based on the established models, an economic model predictive control (EMPC) problem is formulated and solved by a sequential quadratic programming (SQP) algorithm to calculate the optimal control variables satisfying the constraints on the control and control moves. The closed-loop optimization system is applied on a 600 MW boiler, and the performance analysis is conducted based on the operation data. The results show that the system can effectively increase boiler efficiency by about 0.5%. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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16 pages, 3847 KiB

Open AccessArticle

Flexible Operation of Concentrating Solar Power Plant with Thermal Energy Storage Based on a Coordinated Control Strategy

by Xianhua Gao, Shangshang Wei, Chunlin Xia and Yiguo Li

Energies 2022, 15(13), 4929; https://0-doi-org.brum.beds.ac.uk/10.3390/en15134929 - 05 Jul 2022

Cited by 7 | Viewed by 1638

Abstract

With the ambition of achieving carbon neutrality worldwide, renewable energy is flourishing. However, due to the inherent uncertainties and intermittence, operation flexibility of controllable systems is critical to accommodate renewables. Existing studies mainly focus on improving the flexibility of conventional plants, while no attention has been paid to the flexible operation of concentrating solar power with thermal energy storage (CSP-TES) systems. To this end, the ultimate goal of this work is to investigate the potentiality and realization of CSP-TES systems to flexibly operate in grid system regulation. With this goal, the dynamic characteristics of a 50 MW parabolic trough collector CSP plant with molten-salt-based TES is analyzed, and its dominant control characteristics are concluded to demonstrate the possibility of the ideal. After that, a coordinated control strategy is proposed. Specifically, a disturbance observer-based feedforward–feedback control scheme and a feedforward–feedback controller are designed, respectively, for the solar field and the energy storage subsystems, while the power block subsystem is regulated by a two-input and two-output decoupled controller. Based on the decentralized structure, three simulation cases are, respectively, performed to testify the capacity of the CSP-TES system to wide-range load variation tracking, strong disturbance rejection, or both. The results show that the CSP-TES system can adequately track the grid commands based on the proposed coordinated control strategy, even under strong fluctuation of irradiation, demonstrating the flexibility of CSP-TES participating in grid regulation. In the context of continuous penetration of renewable energy into the grid system, research on the role transition of the CSP-TES system from its own optimization to grid regulator is of great importance. Full article

(This article belongs to the Special Issue Advances in Low Carbon and Artificial Intelligence in Power Energy System)

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