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Advances in Power System Dynamics, Stability, Control and Dispatch with...
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Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 5579

Special Issue Editors

Dr. Gao Qiu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: power system stability; power system operational planning; artificial intelligence applications in power systems
Dr. Youbo Liu

E-Mail Website
Guest Editor
Department of Electrical Engineering, Sichuan University, Chengdu 610065, China
Interests: intelligent control; power system stability and control; active distribution network dispatch and control; energy storage; artificial intelligence applications in power systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mao Yang

E-Mail Website
Guest Editor
Electrical Engineering Department, Northeast Electric Power University, Jilin 132011, China
Interests: automatic control; intelligent scheduling; microgrid optimal dispatch; integrated energy system; renewable energy
Dr. Yuxiong Huang

E-Mail Website
Guest Editor
School of Electric Engineering, Xi’an Jiaotong University, Xi’an 713599, China
Interests: power system control; optimal control; reliability evaluation and machine learning technologies in power systems

Special Issue Information

Dear Colleagues,

Low-carbon ambitions around the world have motivated a great number of advancements in the utilization of renewable energy. As a pivotal carrier for renewable energy, electric power systems are currently undergoing a groundbreaking evolution. For example, the stochasticity of renewable energy triggers the significant operational variability of power systems. In this case, unpredictable system oscillations, such as power oscillation, frequency instability, etc., can occur at any time once any inapplicable control or dispatch exists. Unfavorably, these improper controls occur more easily since it can be intensely challenging to find a “one-size-fits-all” strategy to relieve oscillations or instability situations within such a tremendous operational space of the renewable energy-penetrated power system. On the other hand, a high proportion of power electronic devices necessitate electromagnetic transient analysis to better understand their impacts on power system stability. Thus, an overwhelming computing workload can emerge to make it more complicated to efficiently provide power system control or a dispatch strategy. Furthermore, renewable energy has widely supplanted conventional rotary generators. Given this scenario, newly appearing challenges (e.g., low-inertia and weak AC systems, etc.) are becoming key concerns for power system control and stability.

In this Special Issue, we encourage contributions addressing the barriers existing in renewable energy-penetrated power system stability analysis, control, and dispatch. Recent advances in real-time simulation, intelligent control, and artificial intelligence-based optimization and control are also welcome. The topics of interest include, but are not limited to:

(1) Stability analysis in renewable energy-penetrated power systems;

(2) Stability analysis in microgrids, active distribution networks, and integrated energy systems;

(3) Fast control or dispatch of renewable energy-penetrated power systems;

(4) Fast control or dispatch of microgrids, active distribution network, and integrated energy systems;

(5) Control of power electronic devices;

(6) Control of flexible AC/DC transmission systems;

(7) Artificial intelligence application in power system stability analysis, control, and dispatch;

(8) Simulation techniques of transmission system microgrids, active distribution networks, and integrated energy systems with penetration of renewable energy.

Dr. Gao Qiu
Dr. Youbo Liu
Prof. Dr. Mao Yang
Dr. Yuxiong Huang
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. Electronics 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 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • power system stability and control
  • power system dynamics
  • intelligent-based power system analysis
  • power system simulation
  • artificial intelligence application

Published Papers (6 papers)

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Research

15 pages, 2826 KiB  
Article
Fast Coordinated Predictive Control for Renewable Energy Integrated Cascade Hydropower System Based on Quantum Neural Network
by Xi Ye, Zhen Chen, Tong Zhu, Wei Wei and Haojin Peng
Electronics 2024, 13(4), 732; https://doi.org/10.3390/electronics13040732 - 11 Feb 2024
Viewed by 506
Abstract
The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It [...] Read more.
The increasing penetration of renewable energy poses intractable uncertainties in cascade hydropower systems, such that excessively conservative operations and unnecessary curtailment of clean energies can be incurred. To address these challenges, a quantum neural network (QNN)-based coordinated predictive control approach is proposed. It manipulates coordinated dispatch of multiple clean energy sources, including hydro, wind, and solar power, leverages QNN to conquer intricate multi-uncertainty and learn intraday predictive control patterns, by taking renewable power, load, demand response (DR), and optimal unit commitment as observations. This enables us to exploit the stability and exponential memory capacity of QNN to extrapolate diversified dispatch policies in a reliable manner, which can be hard to reach for traditional learning algorithms. A closed-loop warm start framework is finally presented to enhance the dispatch quality, where the decisions by QNN are fed to initialize the optimizer, and the optimizer returns optimal solutions to quickly evolve the QNN. A real-world case in the ZD sub-grid of the Sichuan power grid in China demonstrates that the proposed method hits a favorable balance between operational cost, accuracy, and efficiency. It realizes second-level elapsed time for intraday predictive control. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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22 pages, 3005 KiB  
Article
Data-Driven Distributionally Robust Optimization-Based Coordinated Dispatching for Cascaded Hydro-PV-PSH Combined System
by Shuai Zhang, Gao Qiu, Youbo Liu, Lijie Ding and Yue Shui
Electronics 2024, 13(3), 667; https://doi.org/10.3390/electronics13030667 - 05 Feb 2024
Viewed by 449
Abstract
The increasing penetration of photovoltaic (PV) and hydroelectric power generation and their coupling uncertainties have brought great challenges to multi-energy’s coordinated dispatch. Traditional methods such as stochastic optimization (SO) and robust optimization (RO) are not feasible due to the unavailability of accurate probability [...] Read more.
The increasing penetration of photovoltaic (PV) and hydroelectric power generation and their coupling uncertainties have brought great challenges to multi-energy’s coordinated dispatch. Traditional methods such as stochastic optimization (SO) and robust optimization (RO) are not feasible due to the unavailability of accurate probability density function (PDF) and over-conservative decisions. This limits the operational efficiency of the clean energies in cascaded hydropower and PV-enriched areas. Based on data-driven distributionally robust optimization (DRO) theory, this paper tailors a joint optimization dispatching method for a cascaded hydro-PV-pumped storage combined system. Firstly, a two-step model for a Distributed Renewable Optimization (DRO) dispatch is developed to create the daily dispatch plan, taking into account the system’s complementary economic dispatch cost. Furthermore, the inclusion of a complementary norm constraint is implemented to restrict the confidence set of the probability distribution. This aims to identify the optimal adjustment scheme for the day-ahead dispatch schedule, considering the adjustment cost associated with real-time operations under the most unfavorable distribution conditions. Utilizing the MPSP framework, the Column and Constraint Generation (CCG) algorithm is employed to resolve the two-stage dispatch model. The optimal dispatch schedule is then produced by integrating the daily dispatch plan with the adjustive dispatch scheme. Finally, the numerical dispatch results obtained from an actual demonstration area substantiate the effectiveness and efficiency of the proposed methodology. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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13 pages, 4382 KiB  
Article
A Multichannel-Based CNN and GRU Method for Short-Term Wind Power Prediction
by Jian Gao, Xi Ye, Xia Lei, Bohao Huang, Xi Wang and Lili Wang
Electronics 2023, 12(21), 4479; https://doi.org/10.3390/electronics12214479 - 31 Oct 2023
Cited by 3 | Viewed by 918
Abstract
Incorporating wind energy on a large scale into power systems presents challenges for the operation and control of the grid. To enhance the safety of power grid operation, accurate short-term forecasting of wind power is necessary, as it minimizes the impact of randomness. [...] Read more.
Incorporating wind energy on a large scale into power systems presents challenges for the operation and control of the grid. To enhance the safety of power grid operation, accurate short-term forecasting of wind power is necessary, as it minimizes the impact of randomness. Considering the uncertainty and prediction issues associated with wind power, this paper introduces a CNN–GRU ultra-short-term wind power prediction model. This model relies on multichannel signals, including data such as wind speed, wind direction, climate conditions, and historical power outputs collected from wind farms. These data types contribute to the formation of a comprehensive multichannel signal for wind power. Following that, the CNN method extracts both global and partial features from these signals. Concurrently, features are extracted from past power outputs based on their time series. These features are then combined with the ones obtained from the convolution process. Subsequently, these combined features are input into a fully connected network. This step is crucial for blending multichannel information and generating forecast results. To validate the model, it was tested using data from a wind farm located in a specific region of Sichuan Province. According to our experimental results, the model demonstrates a high level of accuracy in computation and robust generalization ability. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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17 pages, 11679 KiB  
Article
Optimal Scheduling of Virtual Power Plant Considering Revenue Risk with High-Proportion Renewable Energy Penetration
by Zhen Zhang, Yan Zhao, Wen Bo, Donglai Wang, Dong Zhang and Jiaqi Shi
Electronics 2023, 12(21), 4387; https://doi.org/10.3390/electronics12214387 - 24 Oct 2023
Cited by 1 | Viewed by 922
Abstract
Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a [...] Read more.
Distributed power supplies have gradually become a new trend in power supply development, but access to a large number of distributed energy sources has a certain impact on the stable operation of the power grid. A virtual power plant (VPP) can integrate a variety of distributed power sources for coordination and optimization; thus, it can effectively solve the difficulties faced by a distributed energy grid connection and promote the complementarity of energy sources. However, renewable energy often has a degree of volatility and randomness when distributed, which can bring certain risks to the operation of the VPP. In order to consider the risks brought by renewable energy, an optimal scheduling model of the VPP, based on an improved generative adversarial network (GAN) and the conditional value at risk (CVaR), was proposed to measure the relationship between the benefits and risks. Firstly, the uncertainty of new energy is analyzed, and wind power and photovoltaic scenarios are generated by the improved GAN; then, typical scenarios are generated by the k-medoids method. Finally, based on the CVaR, the optimal scheduling model of the VPP is established to study the effect of risk weight on VPP revenue. The results show that the model can effectively measure the relationship between the benefits and risks and can provide some references for the VPP to make reasonable operational decisions with different risk preferences. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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14 pages, 2724 KiB  
Article
Wind Power Group Prediction Model Based on Multi-Task Learning
by Da Wang, Mao Yang and Wei Zhang
Electronics 2023, 12(17), 3683; https://doi.org/10.3390/electronics12173683 - 31 Aug 2023
Viewed by 671
Abstract
Large-scale wind power grid connection increases the uncertainty of the power system, which reduces the economy and security of power system operations. Wind power prediction technology provides the wind power sequence for a period of time in the future, which provides key technical [...] Read more.
Large-scale wind power grid connection increases the uncertainty of the power system, which reduces the economy and security of power system operations. Wind power prediction technology provides the wind power sequence for a period of time in the future, which provides key technical support for the reasonable development of the power generation plan and the arrangement of spare capacity. For large-scale wind farm groups, we propose a cluster model of wind power prediction based on multi-task learning, which can directly output the power prediction results of multiple wind farms. Firstly, the spatial and temporal feature matrix is constructed based on the meteorological forecast data provided by eight wind farms, and the dimensionality of each attribute is reduced by the principal component analysis algorithm to form the spatial fusion feature set. Then, a network structure with bidirectional gated cycle units is constructed, and a multi-output network structure is designed based on the Multi-gate Mixture-of-Experts (MMoE) framework to design the wind power group prediction model. Finally, the data provided by eight wind farms in Jilin, China, was used for experimental analysis, and the predicted average normalized root mean square error is 0.1754, meaning the prediction precision meets the scheduling requirement, which verifies the validity of the wind power prediction model. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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25 pages, 12738 KiB  
Article
Mechanism Analysis of Multiple Disturbance Factors and Study of Suppression Strategies of DFIG Grid-Side Converters Caused by Sub-Synchronous Oscillation
by Dong-Yang Sun, Zi-Jie Qian, Wen-Qiang Shen, Kai Zhou, Ning-Zhi Jin and Qing-Guo Chen
Electronics 2023, 12(10), 2293; https://doi.org/10.3390/electronics12102293 - 18 May 2023
Cited by 1 | Viewed by 723
Abstract
With the increasing utilization of electronic equipment in the power system, sub-synchronous oscillation (SSO) has occurred many times and caused off-grid accidents because of power oscillation. SSO has become one of the main problems that restrict the development of new energy. In this [...] Read more.
With the increasing utilization of electronic equipment in the power system, sub-synchronous oscillation (SSO) has occurred many times and caused off-grid accidents because of power oscillation. SSO has become one of the main problems that restrict the development of new energy. In this paper, power oscillation in grid-side converters (GSCs) in doubly-fed induction generators (DFIGs) under SSO is studied. Firstly, the influence mechanism of SSO on GSC multipath disturbance is studied. Secondly, the problem of coupling oscillation caused by PLL output errors after coordinate transformation is studied, and the mathematical model of GSC output power considering SSO multipath disturbance is established. By analyzing the oscillation suppression ability of the quasi-resonant controller under variable SSO states, the key influencing factors of SSO for GSC power oscillation suppression strategies are determined. Furthermore, based on the above theoretical analysis and research, an improved PLL is designed to eliminate the influence of its output errors on the disturbance of GSC. At the same time, a DFIG-GSC power oscillation suppression strategy using an adaptive quasi-resonant controller is designed to eliminate the influence of SSO on the multi-path disturbance of GSC. Finally, the effectiveness of the proposed suppression strategy is verified using simulation and experimental results. Full article
(This article belongs to the Special Issue Advances in Power System Dynamics, Stability, Control and Dispatch with Large-Scale Renewable Energy Penetrated)
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