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The Impact of Technological Innovation on Renewable Energy Production: Simulation and Control of New Energy Power Generation Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 1 July 2024 | Viewed by 12749

Special Issue Editors

College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Interests: integrated modeling; simulation and control of renewable energy generation system; new energy power generation forecast; artificial intelligence application
Department of Electrical Information, Osaka University, Osaka 5650871, Japan
Interests: chance constrained optimization; data-driven optimal control theory and their applications
Special Issues, Collections and Topics in MDPI journals
College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China
Interests: smart grids; energy optimization; power systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the energy structure adjustment around the world in recent years, the development of renewable energy has attracted broad attention in many countries. Developing renewable energy, such as wind power, photovoltaic power and hydroelectric power, can not only improve the environmental problems but also reduce the production cost. However, with new energy generations connected to the grid, more challenging control issues will arise, including the control of hybrid power generation systems consisting of multiple new energy sources, the control between renewable energy and grid-connected hybrid energy storage, and the complex energy management control of distributed power generation systems, etc. Therefore, advanced control strategies are expected to improve the stability and sustainability of hybrid power generation systems, and new energy management schemes are needed for grid-connected hybrid energy storage in different modes to improve the power quality characteristics in the AC grid. This Special Issue aims to promote the development and research of control strategies and simulation techniques for new energy power generation systems so that the stability of new energy power generation systems and the power quality of AC grids can be improved.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Modeling and simulation of new energy power generation system
  • Modeling and simulation of energy storage system
  • Controller of hybrid power generation/energy storage system
  • Energy management and control
  • Distributed control
  • Control of grid-connected hybrid energy storage system
  • Distributed generating units
  • Real-time digital simulation platform
  • Hybrid photovoltaic and wind power generation system
  • Smart distribution network
  • Renewable power generation technology
  • Integrated energy system and microgrid
  • Optimal operation and planning for smart grid
  • Electric Vehicle and carbon reduction technology

We look forward to receiving your contributions.

Dr. Wenlong Fu
Dr. Xun Shen
Dr. Nan Yang
Guest Editors

Manuscript Submission Information

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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

  • wind power
  • photovoltaic power
  • energy storage system
  • renewable energy
  • hybrid power generation system
  • energy management system
  • smart grid
  • simulation and control
  • optimal operation
  • distributed control

Published Papers (11 papers)

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Research

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26 pages, 7427 KiB  
Article
Research on Renewable Energy Trading Strategies Based on Evolutionary Game Theory
by Fei Huang, Hua Fan, Yunlong Shang, Yuankang Wei, Sulaiman Z. Almutairi, Abdullah M. Alharbi, Hengrui Ma and Hongxia Wang
Sustainability 2024, 16(7), 2671; https://doi.org/10.3390/su16072671 - 25 Mar 2024
Viewed by 641
Abstract
The authors construct a tripartite evolutionary game model that considers renewable energy, traditional coal-fired power plants, and market users. We propose multiple income matrices under different strategies, conduct evolutionary stability analysis, and form a series of assumptions that meet the stability of the [...] Read more.
The authors construct a tripartite evolutionary game model that considers renewable energy, traditional coal-fired power plants, and market users. We propose multiple income matrices under different strategies, conduct evolutionary stability analysis, and form a series of assumptions that meet the stability of the game. We also simulate and analyze the impact of key factors—such as assessment costs, different pricing behaviors of coal-fired power plants, electricity prices of renewable energy, and green electricity demand—on the stability of the game. In addition, the market equilibrium points that can be achieved by optimizing trading strategies and their optimization status in promoting renewable energy consumption are analyzed. Based on the operational characteristics of the Guangxi electricity market in China and the trading situation of renewable energy, an evolutionary game method is applied to conduct empirical research. The trading behavior and evolution of all parties in the market are fully analyzed and are then applied to the construction and mechanism improvement of the electricity market. Full article
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20 pages, 3442 KiB  
Article
Multi-Objective Optimal Power Flow Calculation Considering Carbon Emission Intensity
by Gangfei Wang, Hengrui Ma, Bo Wang, Abdullah M. Alharbi, Hongxia Wang and Fuqi Ma
Sustainability 2023, 15(24), 16953; https://doi.org/10.3390/su152416953 - 18 Dec 2023
Cited by 1 | Viewed by 805
Abstract
In keeping with China’s dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power [...] Read more.
In keeping with China’s dual carbon goals, optimal low-carbon power system dispatch has become a necessary component of the greening of the power system. However, typically, research considers only the economics of such efforts. Based on our power flow analysis of the power grid and the correlation properties of carbon emission flow, an optimal power flow calculation model targeting the total carbon emission rate of the power system’s power generation cost, active network loss, and load and network loss was constructed. Next, the NSGA-III algorithm was used to solve the model, and the decision was to coordinate and optimize the output schemes of various types of power plants, such as wind, water, and thermal. The modified IEEE39 node simulation system was built with Matlab software (MATLAB R2020b). The results of the calculation showed that, compared to the traditional method of determining the optimal power flow, the proposed method reduced the system carbon emissions by 20% while the power generation cost increased by less than 2%, which proves the effectiveness and practicability of the proposed method. Full article
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23 pages, 6444 KiB  
Article
Integrated Energy System Based on Isolation Forest and Dynamic Orbit Multivariate Load Forecasting
by Shidong Wu, Hengrui Ma, Abdullah M. Alharbi, Bo Wang, Li Xiong, Suxun Zhu, Lidong Qin and Gangfei Wang
Sustainability 2023, 15(20), 15029; https://doi.org/10.3390/su152015029 - 18 Oct 2023
Cited by 1 | Viewed by 881
Abstract
Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes [...] Read more.
Short-term load forecasting is a prerequisite for achieving intra-day energy management and optimal scheduling in integrated energy systems. Its prediction accuracy directly affects the stability and economy of the system during operation. To improve the accuracy of short-term load forecasting, this paper proposes a multi-load forecasting method for integrated energy systems based on the Isolation Forest and dynamic orbit algorithm. First, a high-dimensional data matrix is constructed using the sliding window technique and the outliers in the high-dimensional data matrix are identified using Isolation Forest. Next, the hidden abnormal data within the time series are analyzed and repaired using the dynamic orbit algorithm. Then, the correlation analysis of the multivariate load and its weather data is carried out by the AR method and MIC method, and the high-dimensional feature matrix is constructed. Finally, the prediction values of the multi-load are generated based on the TCN-MMoL multi-task training network. Simulation analysis is conducted using the load data from a specific integrated energy system. The results demonstrate the proposed model’s ability to significantly improve load forecasting accuracy, thereby validating the correctness and effectiveness of this forecasting approach. Full article
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15 pages, 2797 KiB  
Article
Reliability Analysis of Nuclear Power Plant Electrical System Considering Common Cause Failure Based on GO-FLOW
by Zhijian Wang, Yao Sun, Jie Zhao, Xuzhu Dong, Chen Chen, Bo Wang and Haocheng Wu
Sustainability 2023, 15(19), 14071; https://doi.org/10.3390/su151914071 - 22 Sep 2023
Cited by 1 | Viewed by 1116
Abstract
The reliability of nuclear power plant electrical systems is an important guarantee of nuclear safety, and the common fault failure problem arising from redundant design and intelligent control may greatly affect reliability assessment results. Combined with the features of repairability, multi-state characteristics, and [...] Read more.
The reliability of nuclear power plant electrical systems is an important guarantee of nuclear safety, and the common fault failure problem arising from redundant design and intelligent control may greatly affect reliability assessment results. Combined with the features of repairability, multi-state characteristics, and common fault failure of nuclear power plant electrical systems, a reliability analysis method of nuclear power plant electrical systems based on the GO-FLOW method considering common fault failure is proposed. This study firstly constructs the algorithmic model of combining operators of repairable components and the equivalent model of reliability parameters of multi-mode repairable components, then establishes a probability calculation model of common fault failure for repairable systems by considering the quantitative computation of the common signaling system model, and finally, quantitatively calculates the reliability of nuclear power plant electrical systems and their influencing factors. The example simulation calculates the reliability of the external power supply system and the electrical system of the nuclear power plant, analyzes the influence of the common signal processing and the common fault failure factors on the reliability of the electrical system of the nuclear power plant, and verifies the validity of the proposed method. The results show that the common fault failure factors have a large impact on the system reliability analysis; the common fault failure of the standby diesel generator set will seriously reduce the reliability of the electrical system, which can be improved by installing additional standby diesel generators. Full article
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20 pages, 2493 KiB  
Article
Integrated Reactive Power Optimisation for Power Grids Containing Large-Scale Wind Power Based on Improved HHO Algorithm
by Jie Zhao, Mingcheng Zhang, Biao Zhao, Xiao Du, Huaixun Zhang, Lei Shang and Chenhao Wang
Sustainability 2023, 15(17), 12962; https://doi.org/10.3390/su151712962 - 28 Aug 2023
Cited by 2 | Viewed by 723
Abstract
Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm [...] Read more.
Large-scale wind power grid integration will greatly change the system current distribution, making it difficult for the reactive power regulator to adjust to the optimal state. In this paper, an integrated reactive power optimisation method based on the improved Harris Hawk (HHO) algorithm is proposed. Firstly, a reactive power regulation model is constructed to solve the reactive power regulation interval of wind turbines, and the reactive power margin of wind turbines is used to participate in the system’s reactive power optimisation. Finally, a reactive power compensation capacity allocation optimisation model considering nodal voltage deviation, line loss and equipment investment cost, is established, and a reactive power optimisation scheme is obtained using the Harris Hawk optimisation algorithm on the basis of considering the constraints of the wind turbine reactive power output interval. The improved HHO algorithm is used to solve the reactive power optimisation scheme considering the constraints of tidal power, machine end voltage, a conventional generator and wind farm reactive power. In the simulation, the effects of the improved Harris Hawk optimisation algorithm and the particle swarm optimisation algorithm are compared, and the experimental results prove that compared to the particle swarm algorithm, the optimisation result of the improved Harris Hawk optimisation algorithm reduces the average loss of the system by 42.6% and reduces the average voltage deviation by 30.3%, which confirms that the improved Harris Hawk intelligent optimisation algorithm is effective in proving its superiority and solving the multi-objective model for reactive power optimisation. Full article
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19 pages, 2710 KiB  
Article
An Intelligent Algorithm for Solving Unit Commitments Based on Deep Reinforcement Learning
by Guanglei Huang, Tian Mao, Bin Zhang, Renli Cheng and Mingyu Ou
Sustainability 2023, 15(14), 11084; https://doi.org/10.3390/su151411084 - 15 Jul 2023
Viewed by 1036
Abstract
With the reform of energy structures, the high proportion of volatile new energy access makes the existing unit commitment (UC) theory unable to satisfy the development demands of day-ahead market decision-making in the new power system. Therefore, this paper proposes an intelligent algorithm [...] Read more.
With the reform of energy structures, the high proportion of volatile new energy access makes the existing unit commitment (UC) theory unable to satisfy the development demands of day-ahead market decision-making in the new power system. Therefore, this paper proposes an intelligent algorithm for solving UC, based on deep reinforcement learning (DRL) technology. Firstly, the DRL algorithm is used to model the Markov decision process of the UC problem, and the corresponding state space, transfer function, action space and reward function are proposed. Then, the policy gradient (PG) algorithm is used to solve the problem. On this basis, Lambda iteration is used to solve the output scheme of the unit in the start–stop state, and finally a DRL-based UC intelligent solution algorithm is proposed. The applicability and effectiveness of this method are verified based on simulation examples. Full article
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17 pages, 1753 KiB  
Article
Optimal Allocation of Energy Storage Capacity in Microgrids Considering the Uncertainty of Renewable Energy Generation
by Wei Wei, Li Ye, Yi Fang, Yingchun Wang, Xi Chen and Zhenhua Li
Sustainability 2023, 15(12), 9544; https://doi.org/10.3390/su15129544 - 14 Jun 2023
Cited by 4 | Viewed by 1240
Abstract
The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the [...] Read more.
The high dimensionality and uncertainty of renewable energy generation restrict the ability of the microgrid to consume renewable energy. Therefore, it is necessary to fully consider the renewable energy generation of each day and time period in a long dispatching period during the deployment of energy storage in the microgrid. To this end, a typical multi-day scenario set is used as the simulation operation scenario, and an optimal allocation method of microgrid energy storage capacity considering the uncertainty of renewable energy generation is designed. Firstly, the historical scenarios are clustered into K types of daily state types using the K-means algorithm, and the corresponding probability distribution is obtained. Secondly, the Latin hypercube sampling method is used to obtain the state type of each day in a multi-day scenario set. Then, the daily scenario generation method based on conditional generative adversarial networks is used to generate a multi-day scenario set, combining the day state type as a condition, and then the typical scenario set is obtained using scenario reduction. Furthermore, a double-layer optimization allocation model for the energy storage capacity of microgrids is constructed, in which the upper layer optimizes the energy storage allocation capacity and the lower layer optimizes the operation plans of microgrids in each typical scenario. Finally, the proposed model is solved using the PSO algorithm nested with the CPLEX solver. In the microgrid example, the proposed method reduces the expected annual total cost by 19.66% compared with the stochastic optimal allocation method that assumes the scenic power obeys a specific distribution, proving that it can better cope with the uncertainty of renewable energy generation. At the same time, the expected annual total cost is reduced by 6.99% compared with the optimal allocation method that generates typical daily scenarios based on generative adversarial networks, which proves that it can better cope with the high dimensionality of renewable energy generation. Full article
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17 pages, 3190 KiB  
Article
Multistep Wind Power Prediction Using Time-Varying Filtered Empirical Modal Decomposition and Improved Adaptive Sparrow Search Algorithm-Optimized Phase Space Reconstruction–Echo State Network
by Chao Tan, Wenrui Tan, Yanjun Shen and Long Yang
Sustainability 2023, 15(11), 9107; https://doi.org/10.3390/su15119107 - 5 Jun 2023
Cited by 5 | Viewed by 1177
Abstract
Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of wind power prediction, in this study, a hybrid prediction model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction [...] Read more.
Accurate wind power prediction is vital for improving grid stability. In order to improve the accuracy of wind power prediction, in this study, a hybrid prediction model combining time-varying filtered empirical modal decomposition (TVFEMD), improved adaptive sparrow search algorithm (IASSA)-optimized phase space reconstruction (PSR) and echo state network (ESN) methods was proposed. First, the wind power data were decomposed into a set of subsequences by using TVFEMD. Next, PSR was used to construct the corresponding phase space matrix for sequences, which were then divided into training sets, validation sets, and testing sets. Then, ESN was used for subsequence prediction. Finally, the predicted values of all the subseries were used to determine the final predicted power. To enhance the model performance, the sparrow search algorithm was improved in terms of the discoverer position update strategy, the follower position update strategy, and the population structure. IASSA was employed to synchronously optimize multiple parameters of PSR-ESN. The results revealed that the proposed model has higher applicability and prediction accuracy than existing models. Full article
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14 pages, 1325 KiB  
Article
Research on Power System Day-Ahead Generation Scheduling Method Considering Combined Operation of Wind Power and Pumped Storage Power Station
by Zhi Zhang, Dan Xu, Xuezhen Chan and Guobin Xu
Sustainability 2023, 15(7), 6208; https://doi.org/10.3390/su15076208 - 4 Apr 2023
Cited by 5 | Viewed by 1177
Abstract
In the proposed wind-storage combined operation technology, the storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on the operational characteristics of pumped storage power stations, the day-ahead dispatching method of a power system with wind [...] Read more.
In the proposed wind-storage combined operation technology, the storage side is foreseen to play a significant role in power system day-ahead generation scheduling. Based on the operational characteristics of pumped storage power stations, the day-ahead dispatching method of a power system with wind farms and pumped storage power stations is studied. The dispatching mode that aims at the lowest operating cost is proposed, taking into consideration the coordination relationship between the scheduling benefit of pumped storage power stations and the total peak-shaving economy of the system and the fluctuation of new energy output. First, taking the constraint of reservoir capacity, the output power, and the daily pumping power of the pumped storage power station into account, a day-ahead generation scheduling model is constructed, with the objective of minimizing costs. Then, the imperial competition algorithm is applied to the proposed model. Finally, the algorithm is compared with the standard particle swarm optimization algorithm. The simulation results based on standard 4-unit and 10-unit systems indicate that the proposed method is effective and robust for a power system with wind power and pumped storage power stations. Full article
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23 pages, 3127 KiB  
Article
Research on Day-Ahead Optimal Scheduling Considering Carbon Emission Allowance and Carbon Trading
by Jiangnan Li, Tian Mao, Guanglei Huang, Wenmeng Zhao and Tao Wang
Sustainability 2023, 15(7), 6108; https://doi.org/10.3390/su15076108 - 1 Apr 2023
Cited by 4 | Viewed by 1409
Abstract
In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access [...] Read more.
In the context of the marketization of carbon trading in the power system, it is of great theoretical and practical significance to study a scientific and effective carbon emission quota allocation strategy. To solve this problem, under the current situation of large-scale access to new energy, considering the limitations of the carbon emissions from different emission subjects plus the construction of a carbon trading model among the emission subjects, a day-ahead optimal scheduling method that takes carbon emission quotas and carbon trading into account is proposed. Firstly, carbon transaction cost models of thermal power and wind power are constructed, respectively, and a carbon emission quota allocation strategy based on the entropy method is proposed to redistribute the weights of baseline emission factors for the regional power grid. Then, considering the additional carbon emissions of conventional thermal power units caused by wind power access, the carbon trading costs of different types of generation units are calculated on the basis of carbon trading price prediction. Thereafter, a day-ahead optimal scheduling model considering carbon emissions trading is constructed with the objective of minimizing the total cost of the system in the scheduling period. The model is solved as an MINLP problem based on MATLAB 2016a software utilizing CPLEX 12.4. Simulation results verify the correctness and effectiveness of the proposed method. Full article
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11 pages, 2335 KiB  
Brief Report
Modeling and Simulation of a Transient Process and an Analysis of Transient Characteristics in the Switching Operation of Disconnectors
by Yue Tong, Qi Wang, Yuqing Wang and Xiang Liu
Sustainability 2023, 15(8), 6822; https://doi.org/10.3390/su15086822 - 18 Apr 2023
Viewed by 985
Abstract
High-voltage switchgears can generate high-frequency and high-amplitude electromagnetic interference during their operation, which may lead to abnormal outputs of the measurement devices, thus giving rise to measurement errors or protective relay malfunctions and impacts on the stable running of the power grid. Considering [...] Read more.
High-voltage switchgears can generate high-frequency and high-amplitude electromagnetic interference during their operation, which may lead to abnormal outputs of the measurement devices, thus giving rise to measurement errors or protective relay malfunctions and impacts on the stable running of the power grid. Considering the air breakdown delay, a dynamic reignition model consisting of the hyperbolic model, the static model and the improved Mayr model is proposed to analyze the transient process during the on/off operation of open-type disconnectors. Meanwhile, the influence of arc resistance, line length and breakdown delay on the model were analyzed, and an experimental circuit was built to verify the simulation results through a comparison. It was found that the proposed model could reflect the high-frequency voltage and current during the on/off operation more accurately and thus provide an effective method for the precise modeling of the switching operation of the disconnector. Full article
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