Advanced Operation, Control, and Planning of Intelligent Energy Systems

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electricity electricity	-	-	2020	20.3 Days	CHF 1000	Submit
Electronics electronics	2.9	4.7	2012	15.6 Days	CHF 2400	Submit
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600	Submit
Processes processes	3.5	4.7	2013	13.7 Days	CHF 2400	Submit
Resources resources	3.3	7.7	2012	23.8 Days	CHF 1600	Submit

16 pages, 4390 KiB

Open AccessArticle

An Optimized Fault-Ride-Through Control Strategy of Hybrid MMC with Fewer FBSMs

by Yue Chen, Chenglin Ren, Junyi Sheng, Jinyu Wang, Yuebin Zhou, Wanyu Cao, Runtian Ding and Wujun Wang

Electronics 2024, 13(10), 1797; https://doi.org/10.3390/electronics13101797 - 07 May 2024

Viewed by 154

Abstract

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the [...] Read more.

The modular multilevel converter (MMC) has many advantages of low switching losses, good harmonic performance and high modularity structure in state-of-the-art HVDC applications. The full-bridge submodules (FBSMs) of the hybrid MMC can inherently output negative voltage to absorb fault currents, and consequently the hybrid MMC can ride through severe DC faults without blocking. During the DC fault-ride-through process, the submodule capacitor voltage and arm current of the MMC will be temporarily increased. These characteristics limit the proportion of the FBSMs, which should not be too low and thus increase the cost and operating losses of the hybrid MMC. In this paper, an improved sorting algorithm of SM capacitor voltage is established, and a novel virtual damping control strategy is proposed that can effectively suppress the increase in submodule capacitor voltage and arm current of the hybrid MMC during the DC fault-ride-through process. By adopting this optimization control, the proportion of FBSMs can be reduced significantly without deteriorating the fault-ride-through capability or safety of the MMC. The effectiveness of the proposed control is verified by careful theoretical analysis and detailed simulation results. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 1722 KiB

Open AccessArticle

Distribution System State Estimation Based on Enhanced Kernel Ridge Regression and Ensemble Empirical Mode Decomposition

by Xiaomeng Chu and Jiangjun Wang

Processes 2024, 12(4), 823; https://doi.org/10.3390/pr12040823 - 19 Apr 2024

Viewed by 344

Abstract

In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced [...] Read more.

In the case of strong non-Gaussian noise in the measurement information of the distribution network, the strong non-Gaussian noise significantly interferes with the filtering accuracy of the state estimation model based on deep learning. To address this issue, this paper proposes an enhanced kernel ridge regression state estimation method based on ensemble empirical mode decomposition. Initially, ensemble empirical mode decomposition is employed to eliminate most of the noise data in the measurement information, ensuring the reliability of the data for subsequent filtering. Subsequently, the enhanced kernel ridge regression state estimation model is constructed to establish the mapping relationship between the measured data and the estimation residuals. By inputting the measured data, both estimation results and estimation residuals can be obtained. Finally, numerical simulations conducted on the standard IEEE-33 node system and a 78-node system in a specific city demonstrate that the proposed method exhibits high accuracy and robustness in the presence of strong non-Gaussian noise interference. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 7018 KiB

Open AccessArticle

Non-Intrusive Load Monitoring Based on Multiscale Attention Mechanisms

by Lei Yao, Jinhao Wang and Chen Zhao

Energies 2024, 17(8), 1944; https://doi.org/10.3390/en17081944 - 19 Apr 2024

Viewed by 684

Abstract

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to [...] Read more.

With the development of smart grids and new power systems, the combination of non-intrusive load identification technology and smart home technology can provide users with the operating conditions of home appliances and equipment, thus reducing home energy loss and improving users’ ability to demand a response. This paper proposes a non-intrusive load decomposition model with a parallel multiscale attention mechanism (PMAM). The model can extract both local and global feature information and fuse it through a parallel multiscale network. This improves the attention mechanism’s ability to capture feature information over long time periods. To validate the model’s decomposition ability, we combined the PMAM model with four benchmark models: the Long Short-Term Memory (LSTM) recurrent neural network model, the Time Pooling-based Load Disaggregation Model (TPNILM), the Extreme Learning Machine (ELM), and the Load Disaggregation Model without Parallel Multi-scalar Attention Mechanisms (UNPMAM). The model was trained on the publicly available UK-DALE dataset and tested. The models’ test results were quantitatively evaluated using a confusion matrix. This involved calculating the F1 score of the load decomposition. A higher F1 score indicates better model decomposition performance. The results indicate that the PMAM model proposed in this paper maintains an F1 score above 0.9 for the decomposition of three types of electrical equipment under the same household user, which is 3% higher than that of the other benchmark models on average. In the cross-household test, the PMAM also demonstrated a better decomposition ability, with the F1 score maintained above 0.85, and the mean absolute error (MAE) decreased by 5.3% on average compared with that of the UNPMAM. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 879 KiB

Open AccessArticle

Fully Distributed Economic Dispatch with Random Wind Power Using Parallel and Finite-Step Consensus-Based ADMM

by Yuhang Zhang and Ming Ni

Electronics 2024, 13(8), 1437; https://doi.org/10.3390/electronics13081437 - 11 Apr 2024

Viewed by 371

Abstract

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated [...] Read more.

In this paper, a fully distributed strategy for the economic dispatch problem (EDP) in the smart grid is proposed. The economic dispatch model considers both traditional thermal generators and wind turbines (WTs), integrating generation costs, carbon trading expenses, and the expected costs associated with the unpredictability of wind power. The EDP is transformed into an equivalent optimization problem with only an equality constraint and thus can be solved by an alternating-direction method of multipliers (ADMM). Then, to tackle this problem in a distributed manner, the outer-layer framework of the proposed strategy adopts a parallel ADMM, where different variables can be calculated simultaneously. And the inner-layer framework adopts a finite-step consensus algorithm. Convergence to the optimal solution is achieved within a finite number of communication iterations, which depends on the scale of the communication network. In addition, leveraging local and neighbor information, a distributed algorithm is designed to compute the eigenvalues of the Laplacian matrix essential for the finite-step algorithm. Finally, several numerical examples are presented to verify the correctness and effectiveness of the proposed strategy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 3820 KiB

Open AccessArticle

A Unified Design Method for Multi-Source Complementary Heating Systems Based on a Transient Simulation

by Linyang Zhang, Jianxiang Guo, Jianan Li, Xinran Yu, Gang Hui, Jinqin Zhong, Na Liu, Dongdong Ren and Jijin Wang

Electronics 2024, 13(7), 1206; https://doi.org/10.3390/electronics13071206 - 25 Mar 2024

Viewed by 418

Abstract

Combining various sources to create a complementary system plays a key role in utilizing clean energy sources economically and mitigating air pollution during the heating season in Northern China. However, there is a lack of unified and reasonable design methods for such systems, [...] Read more.

Combining various sources to create a complementary system plays a key role in utilizing clean energy sources economically and mitigating air pollution during the heating season in Northern China. However, there is a lack of unified and reasonable design methods for such systems, resulting in the excessive capacity of equipment and the waste of energy. In this work, a unified design method is proposed to solve this problem. A generalized structure and its mathematical model are firstly established, enabling transient simulations on the TRNSYS platform. Then, a preliminary screening criterion for the system composition a general operation strategy is proposed. Finally, the system configuration is optimized by using the genetic algorithm. The method is successfully applied in a demonstration project in China. The results show that the coupling system consisting of a biomass boiler (384 kW), an air-source heat pump (430 kW) and a ground-source heat pump (369 kW) is the most economical, and the annual cost is 26.7% lower than that of a single-equipment system. Additionally, the sensitive factors that strongly affect the optimization results are explored. The establishment of the generalized structure and its mathematical model enables the quick calculation and convenient comparison of various schemes, and simplifies the complicated optimization problem of the capacity optimization of each piece of equipment. The proposed design method can reduce the annual cost to a minimum value, and thus it provides a theoretical basis for the large-scale application of clean energy sources for heating. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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21 pages, 3537 KiB

Open AccessArticle

Low-Carbon-Oriented Capacity Optimization Method for Electric–Thermal Integrated Energy System Considering Construction Time Sequence and Uncertainty

by Yubo Wang, Xingang Zhao and Yujing Huang

Processes 2024, 12(4), 648; https://doi.org/10.3390/pr12040648 - 24 Mar 2024

Viewed by 640

Abstract

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider [...] Read more.

The interdependence of various energy forms and flexible cooperative operation between different units in an integrated energy system (IES) are essential for carbon emission reduction. To address the planning problem of an electric–thermal integrated energy system under low-carbon conditions and to fully consider the low carbon and construction sequence of the integrated energy system, a low-carbon-oriented capacity optimization method for the electric–thermal integrated energy system that considers construction time sequence (CTS) and uncertainty is proposed. A calculation model for the carbon transaction cost under the ladder carbon trading mechanism was constructed, and a multi-stage planning model of the integrated energy system was established with the minimum life cycle cost, considering carbon transaction cost as the objective function, to make the optimal decision on equipment configuration in each planning stage. Finally, a case study was considered to verify the advantages of the proposed capacity optimization method in terms of economy and environmental friendliness through a comparative analysis of different planning cases. Simulation results show that, compared with the scenario of completing planning at the beginning of the life cycle at one time, the proposed low-carbon-oriented capacity optimization method that considers construction time sequence and uncertainty can not only reduce the cost of the integrated energy system, but also help to enhance renewable energy utilization and reduce the system’s carbon emissions; the total cost of phased planning is reduced by 11.91% compared to the total cost of one-time planning at the beginning of the year. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 5236 KiB

Open AccessArticle

Path Optimization of Aircraft-Gear-Tooth-Surface Detection Based on Improved Genetic Algorithm

by Xiaomeng Chu and Zhiji Zhou

Processes 2024, 12(4), 627; https://doi.org/10.3390/pr12040627 - 22 Mar 2024

Viewed by 516

Abstract

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface [...] Read more.

Aiming at the problems of low detection efficiency and complexity of aircraft gear tooth surfaces, a path optimization algorithm based on an improved genetic algorithm is proposed. The detection area of the tooth surface is planned, the sampling points of the tooth surface are determined based on the digital technology of the tooth surface, and the sampling mesh is obtained by the truncated plane method to reduce the sampling distortion of the shape and improve the sampling efficiency. Adaptive crossover and mutation probability are used to improve the convergence speed and accuracy of the genetic algorithm. The selected individuals of the binary tournament are used to guide the global optimal search by a simulated annealing algorithm, and the local optimal is avoided by the Metropolis criterion. In the simulation experiment, the proposed method and other algorithms are used to optimize the detection path. The optimized tooth-surface-detection path has the shortest distance and the shortest time, that is, the tooth-surface-detection path efficiency is improved, verifying the practicability of the algorithm. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 3659 KiB

Open AccessArticle

Revolutionizing Wind Power Prediction—The Future of Energy Forecasting with Advanced Deep Learning and Strategic Feature Engineering

by Md. Ahasan Habib and M. J. Hossain

Energies 2024, 17(5), 1215; https://doi.org/10.3390/en17051215 - 03 Mar 2024

Viewed by 548

Abstract

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make [...] Read more.

This paper introduces an innovative framework for wind power prediction that focuses on the future of energy forecasting utilizing intelligent deep learning and strategic feature engineering. This research investigates the application of a state-of-the-art deep learning model for wind energy prediction to make extremely short-term forecasts using real-time data on wind generation from New South Wales, Australia. In contrast with typical approaches to wind energy forecasting, this model relies entirely on historical data and strategic feature engineering to make predictions, rather than relying on meteorological parameters. A hybrid feature engineering strategy that integrates features from several feature generation techniques to obtain the optimal input parameters is a significant contribution to this work. The model’s performance is assessed using key metrics, yielding optimal results with a Mean Absolute Error (MAE) of 8.76, Mean Squared Error (MSE) of 139.49, Root Mean Squared Error (RMSE) of 11.81, R-squared score of 0.997, and Mean Absolute Percentage Error (MAPE) of 4.85%. Additionally, the proposed framework outperforms six other deep learning and hybrid deep learning models in terms of wind energy prediction accuracy. These findings highlight the importance of advanced data analysis for feature generation in data processing, pointing to its key role in boosting the precision of forecasting applications. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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0 pages, 2703 KiB

Open AccessArticle

Prediction of Energy Consumption in a Coal-Fired Boiler Based on MIV-ISAO-LSSVM

by Jiawang Zhang, Xiaojing Ma, Zening Cheng and Xingchao Zhou

Processes 2024, 12(2), 422; https://doi.org/10.3390/pr12020422 - 19 Feb 2024

Viewed by 522

Abstract

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the [...] Read more.

Aiming at the problem that the energy consumption of the boiler system varies greatly under the flexible peaking requirements of coal-fired units, an energy consumption prediction model for the boiler system is established based on a Least-Squares Support Vector Machine (LSSVM). First, the Mean Impact Value (MIV) algorithm is used to simplify the input characteristics of the model and determine the key operating parameters that affect energy consumption. Secondly, the Snow Ablation Optimizer (SAO) with tent map, adaptive t-distribution, and the opposites learning mechanism is introduced to determine the parameters in the prediction model. On this basis, based on the operation data of an ultra-supercritical coal-fired unit in Xinjiang, China, the boiler energy consumption dataset under variable load is established based on the theory of fuel specific consumption. The proposed prediction model is used to predict and analyze the boiler energy consumption, and a comparison is made with other common prediction methods. The results show that compared with the LSSVM, BP, and ELM prediction models, the average Relative Root Mean Squared Errors (aRRMSE) of the LSSVM model using ISAO are reduced by 2.13%, 18.12%, and 40.3%, respectively. The prediction model established in this paper has good accuracy. It can predict the energy consumption distribution of the boiler system of the ultra-supercritical coal-fired unit under variable load more accurately. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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22 pages, 4160 KiB

Open AccessArticle

Optimization of Topological Reconfiguration in Electric Power Systems Using Genetic Algorithm and Nonlinear Programming with Discontinuous Derivatives

by Giovanny Andrés Diaz Vargas, Darin Jairo Mosquera and Edwin Rivas Trujillo

Electronics 2024, 13(3), 616; https://doi.org/10.3390/electronics13030616 - 01 Feb 2024

Cited by 2 | Viewed by 620

Abstract

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the [...] Read more.

This article addresses a comprehensive analysis of power electrical systems, employing a combined approach of genetic algorithms and mathematical optimization through nonlinear programming with discontinuous derivatives (DNLP) in GAMS. The primary objective is to minimize economic losses and associated costs faced by the network operator following disruptive events. The analysis is divided into two fundamental aspects. Firstly, it addresses the topological reconfiguration of the network, involving the addition of lines and distributed energy resources such as distributed generation. To determine the optimal topological reconfiguration, a genetic algorithm was developed and implemented. This approach aims to restore electrical service to the maximum load within the system. Secondly, an optimal energy dispatch was performed for each generator, considering the variation in load throughout the day. The system’s load curve is taken into account to determine the optimal energy distribution. Thus, the problem of economic losses is approached from two perspectives: the minimization of costs due to nonsupplied electrical energy and the determination of efficient energy dispatch for each generator after network reconfiguration. For the analysis and case studies, simulations were conducted on the IEEE 9- and 30-node test systems. The results demonstrate the effectiveness of the proposed solution, evaluated in terms of reduced load shedding and economic losses. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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25 pages, 44711 KiB

Open AccessArticle

Low Energy Cost Synchronization Strategy for Markovian Switching Complex Systems/Networks: Multiple Perspectives Comparative Analysis

by Qian Xie, Haolan Xu, Jian Dang and Zhe Wang

Processes 2024, 12(1), 232; https://doi.org/10.3390/pr12010232 - 21 Jan 2024

Viewed by 733

Abstract

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on [...] Read more.

In this paper, the low energy cost synchronization control strategy of Markovian switching complex systems/networks is mainly studied and analyzed through multiple perspectives. Firstly, in order to achieve synchronization of Markovian switching complex networks with low energy cost, a control scheme based on the optimal node selection strategy that does not depend on the network coupling strength is improved, and a finite-time controller with a simpler structure is constructed. Secondly, based on the event-triggered control strategy an effective trigger event is designed to achieve the low energy cost synchronization of Markovian switching complex networks on the basis of reducing the information transmission and interaction between networks. Finally, the two control strategies mentioned in this paper are compared and analyzed from multiple perspectives through numerical simulations to better guide practical engineering. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 3820 KiB

Open AccessArticle

Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed

by Muxi Li and Fengting Li

Electronics 2024, 13(2), 279; https://doi.org/10.3390/electronics13020279 - 08 Jan 2024

Viewed by 566

Abstract

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia [...] Read more.

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 2489 KiB

Open AccessArticle

Multi-Objective Capacity Optimization of Grid-Connected Wind–Pumped Hydro Storage Hybrid Systems Considering Variable-Speed Operation

by Yang Li, Outing Li, Feng Wu, Shiyi Ma, Linjun Shi and Feilong Hong

Energies 2023, 16(24), 8113; https://doi.org/10.3390/en16248113 - 17 Dec 2023

Cited by 1 | Viewed by 1007

Abstract

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation [...] Read more.

The coordination of pumped storage and renewable energy is regarded as a promising avenue for renewable energy accommodation. Considering wind power output uncertainties, a collaborative capacity optimization method for wind–pumped hydro storage hybrid systems is proposed in this work. Firstly, considering the fluctuation of wind power generation caused by the natural seasonal weather and inherent uncertainties of wind power outputs, a combined method based on the generative adversarial network and K-means clustering algorithm is presented to construct wind power output scenarios. Then, a multi-objective wind–pumped storage system capacity optimization model is established with three objectives consisting of minimizing the levelized cost of energy, minimizing the net load peak–valley difference of regional power grids, and minimizing the power output deviation of hybrid systems. An inner and outer nested algorithm is proposed to obtain the Pareto frontiers based on the strength of the Pareto evolutionary algorithm II. Finally, the complementarity of wind power and pumped storage is illustrated through an analysis of numerical examples, and the advantages of variable-speed pumped storage in complementary operation with wind power over fixed-speed units are verified. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 1960 KiB

Open AccessArticle

Reliability Evaluation of Cabled Active Distribution Network Considering Multiple Devices—A Generalized MILP Model

by Jiaxin Zhang, Bo Wang, Hengrui Ma, Yifan He, Yiwei Wang and Zichun Xue

Processes 2023, 11(12), 3404; https://doi.org/10.3390/pr11123404 - 11 Dec 2023

Viewed by 666

Abstract

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast [...] Read more.

With the rapid development of power electronic equipment, the automation and intelligence level of active distribution networks (ADNs) continues to improve. Against this background, soft open points (SOPs) are gradually replacing traditional segmented switches and interconnection switches. The voltage support capability and fast response characteristics of SOPs can shorten power outage time and expand load recovery range. However, the widespread integration of distributed renewable energy and new power electronic devices has made the fault characteristics of ADNs more complex, significantly increasing the computational complexity of ADN reliability assessment. At present, there are few studies that comprehensively consider ADNs with multiple devices. Therefore, this paper proposes a reliability evaluation method for ADNs that considers multiple devices. Firstly, the impact of circuit breakers, SOPs, and segmented switches on the load recovery process is analyzed. Secondly, an improved virtual fault flow model based on the action mechanisms of circuit breakers, SOPs, and segmented switches is established. The virtual fault flow is represented by logical variables to simulate post fault network reconstruction strategies that include circuit breaker tripping, SOP power supply recovery, and segmented switch isolation actions. Then, with the goal of minimizing the system average outage time after network reconstruction, a generalized mixed integer linear programming (MILP) model is given. Finally, taking the IEEE 33-node testing system as a case, the effectiveness and feasibility of the proposed method are demonstrated. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 4358 KiB

Open AccessArticle

Multi-Objective Co-Operative Game-Based Optimization for Park-Level Integrated Energy System Based on Exergy-Economic Analysis

by Lili Mo, Zeyu Deng, Haoyong Chen and Junkun Lan

Energies 2023, 16(24), 7945; https://doi.org/10.3390/en16247945 - 07 Dec 2023

Viewed by 568

Abstract

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of [...] Read more.

The park-level integrated energy system (PIES) can realize the gradient utilization of energy and improve the efficiency of energy utilization through the coupling between multiple types of energy sub-networks. However, energy analysis and exergy analysis cannot be used to evaluate the economics of PIES. In addition, conflicts of interest among integrated energy suppliers make the economic scheduling of the PIES more difficult. In this paper, we propose a multi-objective collaborative game-based optimization method based on exergy economics, in which the introduction of exergy economics realizes the economic assessment of any link within the PIES, and the optimization model constructed based on the potential game solves the problem of conflict of interest among multiple energy suppliers and improves the benefits of each supplier. Finally, taking a PIES in Guangzhou as an example, the rationality of the optimization scheme proposed in this paper is demonstrated by comparing it with the classical optimization scheme. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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16 pages, 5724 KiB

Open AccessArticle

Improved Methodology for Power Transformer Loss Evaluation: Algorithm Refinement and Resonance Risk Analysis

by Mantas Plienis, Tomas Deveikis, Audrius Jonaitis, Saulius Gudžius, Inga Konstantinavičiūtė and Donata Putnaitė

Energies 2023, 16(23), 7837; https://doi.org/10.3390/en16237837 - 29 Nov 2023

Viewed by 763

Abstract

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the [...] Read more.

The decline in power quality within electrical networks is adversely impacting the energy efficiency and safety of transmission elements. The growing prevalence of power electronics has elevated harmonic levels in the grid to an extent where their significance cannot be overlooked. Additionally, the increasing integration of renewable energy sources introduces heightened fluctuations, rendering the prediction and simulation of working modes more challenging. This paper presents an improved algorithm for calculating power transformer losses attributed to harmonics, with a comprehensive validation against simulation results obtained from the Power Factory application and real-world measurements. The advantages of the algorithm are that all evaluations are performed in real-time based on single-point measurements, and the algorithm was easy to implement in a Programmable Logic Controller (PLC). This allows us to receive the exchange of information to energy monitoring systems (EMSs) or with Power factor Correction Units (PFCUs) and control it. To facilitate a more intuitive understanding and visualization of potential hazardous scenarios related to resonance, an extra Dijkstra algorithm was implemented. This augmentation enables the identification of conditions, wherein certain branches exhibit lower resistance than the grid connection point, indicating a heightened risk of resonance and the presence of highly distorted currents. Recognizing that monitoring alone does not inherently contribute to increased energy efficiency, the algorithm was further expanded to assess transformer losses across a spectrum of Power Factory Correction Units power levels. Additionally, a command from a PLC to a PFCU can now be initiated to change the capacitance level and near-resonance working mode. These advancements collectively contribute to a more robust and versatile methodology for evaluating power transformer losses, offering enhanced accuracy and the ability to visualize potentially critical resonance scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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20 pages, 11202 KiB

Open AccessArticle

Competitive Equilibrium Analysis of Power Generation Transaction Subjects Considering Tradable Green Certificates

by Jiaxing Wen, Rong Jia, Xin Gao, Ge Cao, Jian Dang, Wei Li and Peihang Li

Processes 2023, 11(10), 3008; https://doi.org/10.3390/pr11103008 - 19 Oct 2023

Cited by 1 | Viewed by 727

Abstract

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of [...] Read more.

The implementation of renewable portfolio standards (RPS) and tradable green certificate schemes will exert significant influences on the market equilibrium outcomes and generation firms’ strategic behaviors. To quantitatively investigate these influences, firstly, considering the difference in power generation cost and the uncertainty of renewable energy power generation, the equilibrium model for various trade subjects in the electricity market is established. Secondly, the nondominated sorting genetic algorithm II is adopted for solving the equilibrium model to find well-distributed Pareto-optimal solutions. Finally, the grey relational projection method is used to calculate the priority membership of each decision-making scheme so as to determine the optimal compromise solution. The simulation focuses on analyzing the impact of RPS on the equilibrium results and market behavior of power generators and introduces the Lerner index to quantify the market power of generators. The results show that: (1) An increase in the quota ratio can effectively increase power generation in renewable energy generators. The game between thermal power generators and renewable energy generators raises the prices of both markets at the same time. (2) Improving the forecasting accuracy is conducive to alleviating the market power behavior of various power generators, thereby ensuring the healthy operation of the power market. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 2650 KiB

Open AccessArticle

Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory

by Wengang Chen, Ying Zhang, Jiajia Chen and Bingyin Xu

Electronics 2023, 12(20), 4327; https://doi.org/10.3390/electronics12204327 - 18 Oct 2023

Cited by 2 | Viewed by 641

Abstract

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading [...] Read more.

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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23 pages, 8125 KiB

Open AccessArticle

A Data-Driven Approach for the Ultra-Supercritical Boiler Combustion Optimization Considering Ambient Temperature Variation: A Case Study in China

by Zhi Wang, Guojia Yao, Wenyuan Xue, Shengxian Cao, Shiming Xu and Xianyong Peng

Processes 2023, 11(10), 2889; https://doi.org/10.3390/pr11102889 - 30 Sep 2023

Cited by 3 | Viewed by 773

Abstract

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the [...] Read more.

To reduce coal consumption, nitrogen oxide (NOx), and carbon emissions for coal-fired units, combustion optimization has become not only a hot issue for scientists but also a practical engineering for engineers. A data-driven multiple linear regression (MLR) model is proposed to solve the time-consuming problems of boiler online combustion optimization systems. Firstly, A whole year’s worth of the historical operating data preprocessing procedure of a coal-fired boiler in a power station including data resampling, data cleaning, steady-state selection, and cluster analysis is performed. In order to meet the applicable conditions of the linear model, the historical operating data are divided into different sub-datasets (combination mode of coal mills, main steam flow, ambient temperature, lower heating value of coal). Secondly, the multi-objective optimization strategy of economical, carbon, and NOx emissions indexes is employed to select operating optimum data packets, and a new dataset is established that is better than the average value of the optimization target in each sub-dataset. On this basis, a stepwise regression algorithm (SRA) is used to select the specific manipulated variables (MVs) that are significant to the multiple optimization targets from 47 candidate MVs in each sub-dataset (different partitions have different types of MVs), and an MLR prediction model is developed. In order to further realize combustion optimization control, the MVs are optimized by employing the MLR model. According to the deviation between the optimal value and the real-time value of the MVs, a boiler combustion closed-loop control system is developed, which is connected with the DCS using the sum of the deviation signal and the corresponding original one. Then, a boiler combustion application test was carried out under some working conditions to verify the feasibility and effectiveness of the approach. The update time of the system signals running on industrial computers is less than 1 s and suitable for online applications. Finally, a full-scale test of the combustion optimization online control system (OCS) is executed. The results show that the boiler thermal efficiency increased by 0.39% based on standard coal, the NOx emissions reduced by 2.85% and the decarbonization effect is significant. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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25 pages, 2063 KiB

Open AccessArticle

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

by Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Masahiro Furukakoi, Paras Mandal and Tomonobu Senjyu

Energies 2023, 16(19), 6838; https://doi.org/10.3390/en16196838 - 27 Sep 2023

Viewed by 931

Abstract

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and [...] Read more.

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 1481 KiB

Open AccessArticle

Economic Dispatch of AC/DC Power System Considering Thermal Dynamics

by Xiping Ma, Chen Liang, Xiaoyang Dong and Yaxin Li

Processes 2023, 11(9), 2522; https://doi.org/10.3390/pr11092522 - 23 Aug 2023

Viewed by 779

Abstract

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a [...] Read more.

Advance in renewable penetration has promoted the interaction between the AC and DC power system and multi-energy sources. However, the disparate nature of different energy utilization technologies presents strong challenges for the economic dispatch of such a complex system. This paper proposes a comprehensive characterization of the AC/DC power system considering multi-energy and renewable integration. Detailed models are elaborated for the AC/DC power grid, the district heating system (DHS), coupling units, and renewables to describe their inner interactions accurately. On this basis, an economic dispatch method is developed to minimize the operation cost and renewable’s abandonment. Simulations indicate that the interaction between the AC/DC power systems and multi-energy sources can enhance voltage levels and improve operational economy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 5096 KiB

Open AccessArticle

Counting Abalone with High Precision Using YOLOv3 and DeepSORT

by Duncan Kibet and Jong-Ho Shin

Processes 2023, 11(8), 2351; https://doi.org/10.3390/pr11082351 - 04 Aug 2023

Viewed by 992

Abstract

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras [...] Read more.

In this research work, an approach using You Only Look Once version three (YOLOv3)-TensorFlow for abalone detection and Deep Simple Online Real-time Tracking (DeepSORT) for abalone tracking in conveyor belt systems is proposed. The conveyor belt system works in coordination with the cameras used to detect abalones. Considering the computational effectiveness and improved detection algorithms, this proposal is promising compared to the previously proposed methods. Some of these methods have low effectiveness and accuracy, and they provide an incorrect counting rate because some of the abalones tend to entangle, resulting in counting two or more abalones as one. Conducting detection and tracking research is crucial to achieve modern solutions for small- and large-scale fishing industries that enable them to accomplish higher automation, non-invasiveness, and low cost. This study is based on the development and improvement of counting analysis tools for automation in the fishing industry. This enhances agility and generates more income without the cost created by inaccuracy. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 8636 KiB

Open AccessArticle

Distributed Fixed-Time Secondary Control for MTDC Systems Using Event-Triggered Communication Scheme

by Xiaoyue Zhang, Xinghua Liu and Peng Wang

Processes 2023, 11(8), 2329; https://doi.org/10.3390/pr11082329 - 02 Aug 2023

Cited by 1 | Viewed by 648

Abstract

Multi-terminal DC transmission (MTDC) systems have attracted much attention due to their significant advantages in long-distance and high-capacity transmission. To improve their reliability and operation performance, a distributed fixed-time secondary control of frequency restoration and active power sharing is proposed under event-triggered communication, [...] Read more.

Multi-terminal DC transmission (MTDC) systems have attracted much attention due to their significant advantages in long-distance and high-capacity transmission. To improve their reliability and operation performance, a distributed fixed-time secondary control of frequency restoration and active power sharing is proposed under event-triggered communication, which only depends on the states of each AC grid and its neighbors. By utilizing Lyapunov theory, we prove that the MTDC system with the fixed-time secondary control can be stable in a settling time, and the conditions of the settling time are established for fixed-time algorithms. In addition, we simulate a five-terminal MTDC system in Matlab/Simulink. Several cases of MTDC systems are exhibited to showcase how well the suggested controller works when dealing with load changes and attacks. The comparison of the number of event-triggered instants shows that the proposed control method can effectively reduce communication resources. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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64 pages, 13019 KiB

Open AccessReview

Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review

by Haoming Liu, Muhammad Yasir Ali Khan and Xiaoling Yuan

Energies 2023, 16(15), 5665; https://doi.org/10.3390/en16155665 - 27 Jul 2023

Cited by 5 | Viewed by 1127

Abstract

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: [...] Read more.

To efficiently and accurately track the Global Maximum Power Point (GMPP) of the PV system under Varying Environmental Conditions (VECs), numerous hybrid Maximum Power Point Tracking (MPPT) techniques were developed. In this research work, different hybrid MPPT techniques are categorized into three types: a combination of conventional algorithms, a combination of soft computing algorithms, and a combination of conventional and soft computing algorithms are discussed in detail. Particularly, about 90 hybrid MPPT techniques are presented, and their key specifications, such as accuracy, speed, cost, complexity, etc., are summarized. Along with these specifications, numerous other parameters, such as the PV panel’s location, season, tilt, orientation, etc., are also discussed, which makes its selection easier according to the requirements. This research work is organized in such a manner that it provides a valuable path for energy engineers and researchers to select an appropriate MPPT technique based on the projects’ limitations and objectives. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 6814 KiB

Open AccessArticle

Disturbance-Suppression Method of Direct-Driven PMSG-Based Wind Power System in Microgrids

by Xiuqi Xu, Liancheng Xiu, Jingxuan He and Rongxin Gong

Processes 2023, 11(7), 2189; https://doi.org/10.3390/pr11072189 - 21 Jul 2023

Viewed by 621

Abstract

In order to solve the current fluctuation problem in microgrids, a suppression method called the Direct-driven Permanent Magnet Synchronous Generator (DPMSG)-based Wind Power System (WPS) based on an adaptive enhanced moving average filter algorithm is proposed. Firstly, the mathematical model of the WPS [...] Read more.

In order to solve the current fluctuation problem in microgrids, a suppression method called the Direct-driven Permanent Magnet Synchronous Generator (DPMSG)-based Wind Power System (WPS) based on an adaptive enhanced moving average filter algorithm is proposed. Firstly, the mathematical model of the WPS is established. On this basis, the suppression method under unbalanced conditions is derived by the instantaneous power equation to ensure the stable operation of the microgrid. In order to improve the dynamic compensation capability of the DPMSG-based WPS, an enhanced moving average filtering algorithm with frequency adaptability is proposed. The positive and negative sequence components are obtained in the dq frame by this filtering algorithm. Subsequently, the angular frequency of the microgrid is obtained according to the changing phase, which realizes the high-performance control of the WPS and avoids the complicated parameter adjustment of traditional methods. The correctness of this method is verified by the simulation results. The DPMSG-based WPS with the proposed method can improve the stability of the microgrid. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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28 pages, 6659 KiB

Open AccessArticle

A New Industry 4.0 Approach for Development of Manufacturing Firms Based on DFSS

by Alhadi Khlil, Zhanqun Shi, Abubakar Umar and Botong Ma

Processes 2023, 11(7), 2176; https://doi.org/10.3390/pr11072176 - 21 Jul 2023

Cited by 1 | Viewed by 1222

Abstract

The adoption of Industry 4.0 is attracting manufacturing companies, but the implementation barriers they expect to face, such as huge investment costs, and lack of skilled workers and infrastructure, make many of them hesitate to go through with implementation. The lack of a [...] Read more.

The adoption of Industry 4.0 is attracting manufacturing companies, but the implementation barriers they expect to face, such as huge investment costs, and lack of skilled workers and infrastructure, make many of them hesitate to go through with implementation. The lack of a standardization approach also adds more difficulties in the implementation of advanced key technologies. Based on the design for the six sigma (DFSS) method, a new decision-making and implementation approach (DM&I) is proposed to address some implementation barriers and provide strategic guidance to implement the Industry 4.0 advanced key technologies. In this study, a systematic literature review was conducted to determine the impact of the implementation barriers for Industry 4.0 adoption. The DM&I approach has been applied in a bearing ring production line as a real case. The proposed method consists of two steps: decision-making, which consists of the define phase in determining the exact system weak point in order to reduce the improvement risk and system operation disturbance; the evaluate phase, which is the determining of the level of use of key technologies; the visualize phase, which is the stage of designing and modeling the proposed system and creating the virtual environment to simulate the system in real-time in order to support the improved decision-making process and avoid the fear of high costs; and then the optimize phase, where the optimal level of use of key technologies is determined. Then, the implementation phase consists of the develop phase, which encompasses the stage of physical system construction and hardware software development, followed by the validate phase and the integrate phase. These phases support infrastructure improvement. However, the proposed approach can be used by manufacturing companies to improve production efficacy and competitiveness. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 2472 KiB

Open AccessArticle

Multi-Agent Reinforcement Learning-Based Decentralized Controller for Battery Modular Multilevel Inverter Systems

by Ali Mashayekh, Sebastian Pohlmann, Julian Estaller, Manuel Kuder, Anton Lesnicar, Richard Eckerle and Thomas Weyh

Electricity 2023, 4(3), 235-252; https://doi.org/10.3390/electricity4030014 - 06 Jul 2023

Cited by 1 | Viewed by 1871

Abstract

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central [...] Read more.

The battery-based multilevel inverter has grown in popularity due to its ability to boost a system’s safety while increasing the effective battery life. Nevertheless, the system’s high degree of freedom, induced by a large number of switches, provides difficulties. In the past, central computation systems that needed extensive communication between the master and the slave module on each cell were presented as a solution for running such a system. However, because of the enormous number of slaves, the bus system created a bottleneck during operation. As an alternative to conventional multilevel inverter systems, which rely on a master–slave architecture for communication, decentralized controllers represent a feasible solution for communication capacity constraints. These controllers operate autonomously, depending on local measurements and decision-making. With this approach, it is possible to reduce the load on the bus system by approximately 90 percent and to enable a balanced state of charge throughout the system with an absolute maximum standard deviation of

1.1 \times 10^{- 5}

. This strategy results in a more reliable and versatile multilevel inverter system, while the load on the bus system is reduced and more precise switching instructions are enabled. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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19 pages, 1706 KiB

Open AccessArticle

Merchant Energy Storage Investment Analysis Considering Multi-Energy Integration

by Long Wang

Energies 2023, 16(12), 4695; https://doi.org/10.3390/en16124695 - 14 Jun 2023

Cited by 1 | Viewed by 865

Abstract

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology [...] Read more.

In this paper, a two-stage model of an integrated energy demand response is proposed, and the quantitative relationship between the two main concerns of investors, i.e., investment return and investment cycle and demand response, is verified by the experimental data. Energy storage technology is a key means through which to deal with the instability of modern energy sources. One of the key development paths in the electricity market is the development by energy merchants of energy storage power plants in the distribution network to engage in a grid demand response. This research proposes a two-stage energy storage configuration approach for a cold-heat-power multi-energy complementary multi-microgrid system. Considering the future bulk connections of distributed power generation, the two most critical points of energy storage station construction are the power generation equipment and specific scenarios for serving the community, as well as the purchase and sale price of electricity for serving the community microgrid (which directly affects the investment revenue). Therefore, this paper focuses on analyzing the different impacts caused by these two issues; namely, the two most important concerns for the construction of energy storage configurations. First, the basic model enabling wholesale electricity traders to construct energy storage power plants is presented. Second, for a multi-microgrid system with a complementary cold-heat-power multi-energy scenario, a two-stage optimum allocation model is constructed, whereby the upper model calculates the energy storage allocation problem and the lower model calculates the optimal dispatch problem. The lower model’s dispatch computation validates the upper configuration model’s reasonableness. Finally, the two-layer model is converted to a single-layer model by the KKT condition, and the nonlinear problem is converted to a linear problem with the big-M method. The validity is proved via mathematical examples, and it is demonstrated that the planned energy storage plants by merchants may accomplish resource savings and mutual advantages for both users and wholesale power traders. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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17 pages, 3855 KiB

Open AccessArticle

Optimal Scheduling Strategy for Multi-Energy Microgrid Considering Integrated Demand Response

by Long Wang

Energies 2023, 16(12), 4694; https://doi.org/10.3390/en16124694 - 14 Jun 2023

Cited by 4 | Viewed by 1049

Abstract

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. [...] Read more.

Research on energy storage plants has gained significant interest due to the coupled dispatch of new energy generation, energy storage plants, and demand-side response. While virtual power plant research is prevalent, there is comparatively less focus on integrated energy virtual plant station research. This study aims to contribute to the integrated energy virtual plant station research by exploring the relationship between the integrated energy electro-thermal coupling capacity, various forms of electro-thermal integrated energy response, and electro-thermal integrated energy storage. Analyzing the attributes of an integrated energy microgrid, including energy storage characteristics, time-of-use tariffs, and electric and thermal loads, is crucial. A grid-connected microgrid with cogeneration systems, electric boilers, fuel cells, and energy storage systems is used as an illustrative example. The dispatching method prioritizes multiple complementary energy sources while considering the integrated energy demand response. The study presents different models for the electricity demand and thermal energy demand response and introduces the design of a wholesale power trader involved in building energy storage facilities and participating in the demand response. To verify the feasibility and rationality of the integrated energy demand response scenario, three different schemes are compared, and an economic analysis is conducted. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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26 pages, 17941 KiB

Open AccessArticle

Detecting Plant-Wide Oscillation Propagation Effects of Disturbances and Faults in a Chemical Process Plant Using Network Topology of Variance Decompositions

by Dhan Lord B. Fortela and Ashley P. Mikolajczyk

Processes 2023, 11(6), 1747; https://doi.org/10.3390/pr11061747 - 07 Jun 2023

Viewed by 961

Abstract

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to [...] Read more.

This work demonstrates for the first time the application of network topology of variance decompositions in analyzing the connectedness of chemical plant process variable oscillations arising from disturbances and faults. Specifically, the time-based connectedness and frequency-based connectedness of variables can be used to compute the net pairwise dynamic connectedness (NPDC), which originated as a volatility spillover index for financial markets studies in the field of econometrics. This work used the anomaly-detection benchmark Tennessee-Eastman chemical process (TEP) dataset, which consists of 41 measured variables and 11 manipulated variables subjected to various faulty operating conditions. The data analytics was performed using key functions from the R-package ‘ConnectednessApproach’ that implements connectedness computations based on time and frequency. The NPDC coefficient matrices were then transformed into network adjacency matrices for the rendering of the network topology of connectedness for TEP. The resulting network topologies allow a comprehensive analysis of oscillation effects across all plant-measured and manipulated variables. Analyzing the directed connectedness of the system dynamics at short-range, mid-range, and long-range frequencies showed how the oscillation effects of disturbances and faults propagate and dissipate in the short-term, mid-term, and long-term periods. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 3563 KiB

Open AccessArticle

Data-Driven Operation of Flexible Distribution Networks with Charging Loads

by Guorui Wang, Zhenghao Qian, Xinyao Feng, Haowen Ren, Wang Zhou, Jinhe Wang, Haoran Ji and Peng Li

Processes 2023, 11(6), 1592; https://doi.org/10.3390/pr11061592 - 23 May 2023

Viewed by 895

Abstract

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging [...] Read more.

The high penetration of distributed generators (DGs) and the large-scale charging loads deteriorate the operational status of flexible distribution networks (FDNs). A soft open point (SOP) can deal with operational issues, such as voltage violations and the high electricity purchasing cost of charging stations. However, the absence of accurate parameters poses challenges to model-based methods. This paper proposes a data-driven operation method of FDNs with charging loads. First, a data-driven model-free adaptive predictive control (MFAPC) approach is proposed to fully involve charging loads in the control of FDN without accurate network parameters. Then, a multi-timescale coordination control model of an SOP with charging loads is established to satisfy the demand of charging loads and improve the control performance. The effectiveness of the proposed method is numerically demonstrated on the modified IEEE 33-node distribution network. The results indicate that the proposed method can effectively reduce the electricity purchasing cost of charging stations and improve the operational performance of FDNs. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 2409 KiB

Open AccessArticle

Interval Forecasting Method of Aggregate Output for Multiple Wind Farms Using LSTM Networks and Time-Varying Regular Vine Copulas

by Yanwen Wang, Yanying Sun, Yalong Li, Chen Feng and Peng Chen

Processes 2023, 11(5), 1530; https://doi.org/10.3390/pr11051530 - 17 May 2023

Cited by 2 | Viewed by 1113

Abstract

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output [...] Read more.

Interval forecasting has become a research hotspot in recent years because it provides richer uncertainty information on wind power output than spot forecasting. However, compared with studies on single wind farms, fewer studies exist for multiple wind farms. To determine the aggregate output of multiple wind farms, this paper proposes an interval forecasting method based on long short-term memory (LSTM) networks and copula theory. The method uses LSTM networks for spot forecasting firstly and then uses the forecasting error data generated by LSTM networks to model the conditional joint probability distribution of the forecasting errors for multiple wind farms through the time-varying regular vine copula (TVRVC) model, so as to obtain the probability interval of aggregate output for multiple wind farms under different confidence levels. The proposed method is applied to three adjacent wind farms in Northwest China and the results show that the forecasting intervals generated by the proposed method have high reliability with narrow widths. Moreover, comparing the proposed method with other four methods, the results show that the proposed method has better forecasting performance due to the consideration of the time-varying correlations among multiple wind farms and the use of a spot forecasting model with smaller errors. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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18 pages, 6609 KiB

Open AccessArticle

Integrating Risk Preferences into Game Analysis of Price-Making Retailers in Power Market

by Chen Zhao, Jiaqi Sun, Ping He, Shaohua Zhang and Yuqi Ji

Energies 2023, 16(8), 3339; https://doi.org/10.3390/en16083339 - 09 Apr 2023

Cited by 2 | Viewed by 1048

Abstract

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, [...] Read more.

In the restructured electricity market, retailers are intermediaries between the electricity wholesale market and consumers. Considering the uncertainty of wholesale market price, retailers should consider the risks of their profit caused by the uncertain wholesale price when participating in the retail competition. Indeed, retailers’ risk preferences will impact their price bidding strategies. To examine the effects of retailers’ risk preferences on their strategies and equilibrium outcomes in the retail market, an equilibrium model for price-making retailers is proposed by employing the mean–variance utility theory to model the risk preferences of retailers. The market share function is used to characterize consumers’ price-elasticity and switching behavior in the retail market. Few works in the literature address the issue of bidding strategies of retailers with different risk preferences in the electricity market with switchable consumers. Moreover, the existence and uniqueness of the Nash equilibrium are theoretically proved. A theoretical analysis is presented to investigate the impacts of wholesale price uncertainty and retailer’s risk preference on the bidding strategy. By adopting the nonlinear complementarity approach, the proposed game model is transformed into a set of nonlinear equations, which is further solved by the Levenberg–Marquardt algorithm. Finally, examples are included to verify the effectiveness of the proposed theory, and the results show that the bidding price of a retailer will increase with the increasing uncertainty of the wholesale price and the increasing risk-averse levels of itself and its rivals. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 236 KiB

Open AccessArticle

Electricity Supply Unreliability and Technical Efficiency: Evidence from Listed Chinese Manufacturing Companies

by Presley K. Wesseh, Jr., Yuqing Zhong and Chin Hui Hao

Energies 2023, 16(7), 3283; https://doi.org/10.3390/en16073283 - 06 Apr 2023

Cited by 1 | Viewed by 1015

Abstract

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) [...] Read more.

This study examines the extent to which electricity shortage influences technical efficiency using data of 805 listed manufacturing companies in China from 2009 to 2020 collected from the CSMAR database. To achieve the objectives of this paper, first, a stochastic frontier analysis (SFA) is used to estimate the technical efficiency (TE) score of manufacturing companies. Subsequently, the TE score is used to evaluate the electricity shortage index and other factors that are postulated to affect enterprise productivity. Two estimation methods have been adopted including ordinary least squares (OLS), which is less robust to endogeneity and instrumental variable (IV) estimation, which turns out to be more robust to endogeneity in the data. The empirical results show that, under OLS estimation, electricity shortage has a significantly negative impact on the technical efficiency of the listed manufacturing companies. However, when IV regression is implemented to address endogeneity issues in the data, electricity shortages tend to have a significantly positive impact on the technical efficiency, underscoring the importance of capturing endogeneity in the data. Extending the baseline results, this study also finds that, while the size of an enterprise may have no bearing, state-owned companies are more likely to be negatively affected by electricity shortages compared to privately owned companies. These results have significant implications for industrial policy design in China in particular, and developing countries in general. Most importantly, the results of this study underscore the importance of policies and measures to promote a shift in the ownership structure towards the private sector. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

15 pages, 972 KiB

Open AccessArticle

A Bilevel Stochastic Optimization Framework for Market-Oriented Transmission Expansion Planning Considering Market Power

by Khalid A. Alnowibet, Ahmad M. Alshamrani and Adel F. Alrasheedi

Energies 2023, 16(7), 3256; https://doi.org/10.3390/en16073256 - 05 Apr 2023

Cited by 1 | Viewed by 993

Abstract

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in [...] Read more.

Market power, defined as the ability to raise prices above competitive levels profitably, continues to be a prime concern in the restructured electricity markets. Market power must be mitigated to improve market performance and avoid inefficient generation investment, price volatility, and overpayment in power systems. For this reason, involving market power in the transmission expansion planning (TEP) problem is essential for ensuring the efficient operation of the electricity markets. In this regard, a methodological bilevel stochastic framework for the TEP problem that explicitly includes the market power indices in the upper level is proposed, aiming to restrict the potential market power execution. A mixed-integer linear/quadratic programming (MILP/MIQP) reformulation of the stochastic bilevel model is constructed utilizing Karush−Kuhn−Tucker (KKT) conditions. Wind power and electricity demand uncertainty are incorporated using scenario-based two-stage stochastic programming. The model enables the planner to make a trade-off between the market power indices and the investment cost. Using comparable results of the IEEE 118-bus system, we show that the proposed TEP outperforms the existing models in terms of market power indices and facilitates open access to the transmission network for all market participants. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 1347 KiB

Open AccessArticle

Research on Load State Sensing and Early Warning Method of Distribution Network under High Penetration Distributed Generation Access

by Cailian Gu, Yibo Wang, Weisheng Wang and Yang Gao

Energies 2023, 16(7), 3093; https://doi.org/10.3390/en16073093 - 28 Mar 2023

Viewed by 1135

Abstract

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms [...] Read more.

Aiming at the problems of power flow fluctuation and voltage exceeding standard caused by high permeability distributed power supply access, this paper proposes a load state perception early warning method for distribution networks. Firstly, the random behavior characteristics and voltage early warning mechanisms of power supply and load in distribution networks are analyzed, the dynamic model of distribution networks based on complex network theory is established, and the risk index of voltage exceeding limits under the conditions of high permeability distributed power supply access is put forward. Secondly, the random power flow of distribution networks based on the Monte Carlo method is studied by sampling and analyzing the dynamic model of distribution networks. Then, the risk calculation and safety assessment of voltage exceeding limits are carried out on the currently extracted model, and the risk control strategy of distribution network operation is put forward. Finally, an improved IEEE30-node distribution network topology is proposed. Through simulation analysis, it is proven that the load situation awareness early warning method of distribution networks can effectively predict, improve the security of distribution networks, and provide timely early warning information for maintenance personnel. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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15 pages, 2087 KiB

Open AccessArticle

Decision-Making Method for Mine Cable Insulation Monitoring and Grounding Fault Diagnosis

by Chen Feng, Pingfeng Ye, Yanying Sun, Jingrui Li, Xiangyu Zang and Chenhao Sun

Processes 2023, 11(3), 795; https://doi.org/10.3390/pr11030795 - 07 Mar 2023

Cited by 1 | Viewed by 1059

Abstract

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground [...] Read more.

Real-time monitoring of the power cable state has tremendous significance for ensuring the safe and economic operation of mine power distribution systems. However, due to the harsh conditions of underground coal mines, it is difficult for the cable monitoring system operating in underground coal mines to carry out large-scale calculations to diagnose the grounding fault of the cable. Additionally, there are many types of cable grounding faults, such as single grounding fault and two-phase ground fault. Therefore, how to determine the type of grounding fault quickly through effective calculations and alarms to select the grounding cable is always a difficult task. In this study, to reduce the complexity of cable insulation state classification, we develop a novel classification method based on the decision tree algorithm. Concerning the zero-sequence network under different insulation conditions, the first calculation’s positive and negative values were generated to identify whether the cable insulation was symmetrical. Then, the insulation degradation phase is identified by the relationship between the three-phase voltage phase angle and the current difference variation between the beginning and end of the line. By substituting the correlation quantities collected by a wide-area synchronous measurement system into different equations, the whole grid’s decision tree was constructed in different insulation states. Then, the insulation state of each line was evaluated according to the conductance value. The effectiveness of the proposed method was verified using a 35/6 kV mine power distribution system model based on the MATLAB/Simulink platform. The test results illustrate that the method can accurately diagnose the power cable insulation state based on the decision tree of whether the grid three-phase loads operate in an ungrounded mode or not. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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14 pages, 9065 KiB

Open AccessArticle

Innovative Inertial Response Imitation and Rotor Speed Recovery Control Scheme for a DFIG

by Xiaocen Xue, Jiejie Huang and Shun Sang

Electronics 2023, 12(4), 1029; https://doi.org/10.3390/electronics12041029 - 18 Feb 2023

Viewed by 1018

Abstract

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric [...] Read more.

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric power grid. To achieve the first benefit, the coupling relationship between the control coefficient of DFIGs and the frequency deviation was established by using the exponential function so that the control coefficient becomes large with the increasing frequency deviations and sizes of disturbance. After supporting the system frequency, the exponential function was employed to schedule the dynamic control coefficient to alleviate the negative effects of RSR on the instantaneous system frequency. The benefits of the proposed IIR and RSR strategy were investigated in a test system under various scenarios of sizes of disturbance and wind speed conditions. Test results clearly demonstrate that the proposed IIR and RSR strategy is capable of boosting the maximum system frequency excursion and reducing the negative influences on the system frequency during the speed recovery period. Full article

(This article belongs to the Topic Advanced Operation, Control, and Planning of Intelligent Energy Systems)

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