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Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 13744

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Dr. Yushuai Li

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Department of Informatics, University of Oslo, 0316 Oslo, Norway
Interests: distributed optimization; energy management; machine learning; digitial twin
Special Issues, Collections and Topics in MDPI journals

Dr. Ning Zhang

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School of Electrical Engineering and Automation, Anhui University, Anhui 230601, China
Interests: distributed control; energy internet; energy management system; machine learning
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Dr. Jiayue Sun

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School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: multiagent; consensus; adaptive dynamic programming; smart grid
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Special Issue Information

Dear Colleagues,

Increasing pressure in the move toward the protection of resources and the environment has made research on renewable energies urgent. The integration of a high proportion of renewable energy resources into our energy systems will enable sustainable energy development and a low-carbon society. To this end, this Special Issue focuses on new architecture, methodologies and technologies for renewable and sustainable energy systems (RSESs). From the perspective of energy type, the deep integration of multiple-energy networks offers diversified energy utilization forms, resulting in improved energy efficiency, enhanced system resilience and increased utilization of renewable energy. From the perspective of equipment, the electrification of energy devices (e.g., electric vehicle) can effectively reduce dependency on traditional fossil fuels, thus decreasing carbon emissions. From the perspective of cyber technology, advances in communication and big data analysis methods benefit the intelligent detection, coordination and management of energy systems.

There are many challenges that require further research and development on policy, architecture, modelling, planning, operation, optimization and control for renewable and sustainable energy systems. This Special Issue aims to address and disseminate state-of-the-art research and opportunities regarding applications of innovative solutions to achieve low-carbon and sustainable energy development. We welcome the submission of original papers with novel research contributions in all aspects of tools, models and methods of relevance and impact for RSESs.

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

The design of energy market and policies for RSESs;
Energy management architecture and method for RSESs;
Smart planning, operation and control for RSESs;
Spatiotemporal data analytics for RSESs;
Digital twin for RSESs;
Power electronics for RSESs;
Stability analysis for RSESs;
New algorithms and convergence analysis for RSESs;
The applications of AI, IoT and 6G/5G technologies in

We look forward to receiving your contributions.

Dr. Yushuai Li
Dr. Ning Zhang
Dr. Jiayue Sun
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. Sustainability 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

renewable energy
intelligent operation systems
multiple-energy systems
sustainable energy development

Related Special Issue

Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology, 2nd Edition in Sustainability

Published Papers (9 papers)

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Research

18 pages, 4361 KiB

Open AccessArticle

A Multi-Agent Integrated Energy Trading Strategy Based on Carbon Emission/Green Certificate Equivalence Interaction

by Jiaqi Tian, Bonan Huang, Qiuli Wang, Pengbo Du, Yameng Zhang and Bangpeng He

Sustainability 2023, 15(22), 15766; https://doi.org/10.3390/su152215766 - 09 Nov 2023

Viewed by 826

Abstract

To meet the demand for constructing a market mechanism that adapts to the integrated energy system and promotes market-oriented reforms in the energy sector, in-depth research on integrated energy trading strategies is required. This study focused on the integrated energy trading problem and clarify the relationships among participants in the integrated energy market. A regional integrated energy system model was established that enables trading of electricity, gas, heat, and cold, and propose a integrated energy trading strategy based on the carbon emissions/green certificate equivalence interaction. Firstly, the trading process of carbon emissions and green certificates, the underlying representation of green attributes, and market transaction prices are analyzed. Combining with a tiered carbon trading system that includes rewards and penalties, a carbon emissions/green certificate equivalence interaction mechanism is constructed. Secondly, the paper utilized the flexible characteristics of loads within the industrial park to establish a integrated energy demand response model for electricity, heat, and cold. Finally, with the objective of minimizing regional operating costs, a integrated energy trading model considering the carbon emissions/green certificate equivalence interaction mechanism was developed. In the simulation, the operating cost of the system is reduced by 4%, and the carbon emission is reduced by 11.4%, which verifies the effectiveness of the model. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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18 pages, 3604 KiB

Open AccessArticle

Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling

by Jinke Yao, Jiachen Xu, Ning Zhang and Yajuan Guan

Sustainability 2023, 15(12), 9235; https://doi.org/10.3390/su15129235 - 07 Jun 2023

Viewed by 1271

Abstract

Due to the uncertainty and randomness of clean energy, microgrid operation is often prone to instability, which requires the implementation of a robust and adaptive optimization scheduling method. In this paper, a model-based reinforcement learning algorithm is applied to the optimal scheduling problem of microgrids. During the training process, the current learned networks are used to assist Monte Carlo Tree Search (MCTS) in completing game history accumulation, and updating the learning network parameters to obtain optimal microgrid scheduling strategies and a simulated environmental dynamics model. We establish a microgrid environment simulator that includes Heating Ventilation Air Conditioning (HVAC) systems, Photovoltaic (PV) systems, and Energy Storage (ES) systems for simulation. The simulation results show that the operation of microgrids in both islanded and connected modes does not affect the training effectiveness of the algorithm. After 200 training steps, the algorithm can avoid the punishment of exceeding the red line of the bus voltage, and after 800 training steps, the training result converges and the loss values of the value and reward network converge to 0, showing good effectiveness. This proves that the algorithm proposed in this paper can be applied to the optimization scheduling problem of microgrids. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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27 pages, 9790 KiB

Open AccessArticle

Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM

by Bo Gu, Xi Li, Fengliang Xu, Xiaopeng Yang, Fayi Wang and Pengzhan Wang

Sustainability 2023, 15(8), 6538; https://doi.org/10.3390/su15086538 - 12 Apr 2023

Cited by 5 | Viewed by 1499

Abstract

Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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15 pages, 7174 KiB

Open AccessArticle

A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy

by Yuanzhuo Du, Kun Zhang, Qianzhi Shao and Zhe Chen

Sustainability 2023, 15(7), 6285; https://doi.org/10.3390/su15076285 - 06 Apr 2023

Cited by 1 | Viewed by 872

Abstract

Wind power generation is a type of renewable energy that has the advantages of being pollution-free and having a wide distribution. Due to the non-stationary characteristics of wind power caused by atmospheric chaos and the existence of outliers, the prediction effect of wind power needs to be improved. Therefore, this study proposes a novel hybrid prediction method that includes data correlation analyses, power decomposition and reconstruction, and novel prediction models. The Pearson correlation coefficient is used in the model to analyze the effects between meteorological information and power. Furthermore, the power is decomposed into different sub-models by ensemble empirical mode decomposition. Sample entropy extracts the correlations among the different sub-models. Meanwhile, a long short-term memory model with an asymmetric error loss function is constructed considering outliers in the power data. Wind power is obtained by stacking the predicted values of subsequences. In the analysis, compared with other methods, the proposed method shows good performance in all cases. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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12 pages, 3013 KiB

Open AccessArticle

Transmission Line Equipment Infrared Diagnosis Using an Improved Pulse-Coupled Neural Network

by Jie Tong, Xiangquan Zhang, Changyu Cai, Zhouqiang He, Yuanpeng Tan and Zhao Chen

Sustainability 2023, 15(1), 639; https://doi.org/10.3390/su15010639 - 30 Dec 2022

Cited by 2 | Viewed by 1045

Abstract

In order to detect the status of power equipment from infrared transmission line images under the spatial positioning relationship of the transmission line equipment, such as corridor, substation equipment, and facilities, this paper presents an improved PCNN model which merges an optimized parameter setting method. In this PCNN model, the original iteration mechanism is abandoned, and instead, the thresholding model is built by the maximum similarity thresholding rule. To ensure similarity during classifying neighboring neurons into cluster centers, a local clustering strategy is used for setting the linking coefficient, thus improving the efficiency of the method to detect the power equipment in infrared transmission line images. Finally, experimental results on transmission line infrared images show that the proposed method can provide the basis for the diagnosis of power equipment, preventing the casualties and property damage caused by the thermal damage of power equipment, and effectively improving the safety risk identification and operation control ability of power grid engineering. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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22 pages, 640 KiB

Open AccessArticle

Energy Management Strategy for Seaport Integrated Energy System under Polymorphic Network

by Fei Teng, Qing Zhang, Tao Zou, Jun Zhu, Yonggang Tu and Qian Feng

Sustainability 2023, 15(1), 53; https://doi.org/10.3390/su15010053 - 21 Dec 2022

Cited by 5 | Viewed by 1586

Abstract

This paper studies the energy management problem of a seaport integrated energy system under the polymorphic network. Firstly, with the diversity of energy devices, a seaport integrated energy system based on the polymorphic network is established to ensure information exchange and energy interaction between heterogeneous devices, including the service layer, control layer, and data layer. Secondly, by analyzing the characteristics of different loads and the energy conversion hub, such as the power to gas (P2G) and combined cooling heating and power (CCHP), the energy management model for the seaport integrated energy system is constructed. Finally, we obtain the optimal solution by mixed integer linear programming, and the proposed strategy is used to a seaport integrated energy system including CCHP, P2G, clean energy and energy storage device. By comparing four different cases, the simulation results show a reduction in the cost of energy purchase and carbon emissions when applying our strategy with various device types and device failures. Moreover, considering the application of the proposed energy management strategy under seasonal variations, the optimal solution for the energy management problem of the seaport integrated energy system is obtained. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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11 pages, 1959 KiB

Open AccessArticle

Deep Pose Graph-Matching-Based Loop Closure Detection for Semantic Visual SLAM

by Ran Duan, Yurong Feng and Chih-Yung Wen

Sustainability 2022, 14(19), 11864; https://doi.org/10.3390/su141911864 - 21 Sep 2022

Cited by 6 | Viewed by 1805

Abstract

This work addresses the loop closure detection issue by matching the local pose graphs for semantic visual SLAM. We propose a deep feature matching-based keyframe retrieval approach. The proposed method treats the local navigational maps as images. Thus, the keyframes may be considered keypoints of the map image. The descriptors of the keyframes are extracted using a convolutional neural network. As a result, we convert the loop closure detection problem to a feature matching problem so that we can solve the keyframe retrieval and pose graph matching concurrently. This process in our work is carried out by modified deep feature matching (DFM). The experimental results on the KITTI and Oxford RobotCar benchmarks show the feasibility and capabilities of accurate loop closure detection and the potential to extend to multiagent applications. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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14 pages, 1136 KiB

Open AccessArticle

Distributed Optimization of Joint Seaport-All-Electric-Ships System under Polymorphic Network

by Wenjia Xia, Qihe Shan, Geyang Xiao, Yonggang Tu and Yuan Liang

Sustainability 2022, 14(16), 9914; https://doi.org/10.3390/su14169914 - 11 Aug 2022

Cited by 2 | Viewed by 1189

Abstract

As a result of the trend towards auto intelligence and greening of vehicles and with the concept of polymorphic network being put forward, the power transmission mode between seaports and all-electric ships (AESs) is likely to be converted to “peer-to-peer” transmission. According to practical shore power systems and carbon trade mechanisms, an advanced peer-to-peer power dispatching model-joint seaport-AESs microgrid(MG) system has been proposed in the paper. The joint seaport–AES system model is proposed to minimize the total operational cost of power production and marketing, including distributed generation (DG) cost, electricity trading cost, and carbon emissions, and the boundary conditions are given as well. A parameter projection distributed optimization (PPDO) algorithm is utilized to solve the distributed optimization power operation planning of the proposed joint seaport–AES MG system under a polymorphic network and to guarantee the precision of power dispatching, which compensates for the insufficiency of the computing power. Finally, a case study of a five-node polymorphic joint seaport-AESs system is conducted. The feasibility of the parameter projection approach and the peer-to-peer power dispatching model are verified via the convergence of all the agents within the constraint sets. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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16 pages, 5647 KiB

Open AccessArticle

Research on Pedestrian Detection and DeepSort Tracking in Front of Intelligent Vehicle Based on Deep Learning

by Xuewen Chen, Yuanpeng Jia, Xiaoqi Tong and Zirou Li

Sustainability 2022, 14(15), 9281; https://doi.org/10.3390/su14159281 - 28 Jul 2022

Cited by 13 | Viewed by 2303

Abstract

In order to improve the tracking failure caused by small-target pedestrians and partially blocked pedestrians in dense crowds in complex environments, a pedestrian target detection and tracking method for an intelligent vehicle was proposed based on deep learning. On the basis of the YOLO detection model, the channel attention module and spatial attention module were introduced and were joined to the back of the backbone network Darknet-53 in order to achieve weight amplification of important feature information in channel and space dimensions and improve the representation ability of the model for important feature information. Based on the improved YOLO network, the flow of the DeepSort pedestrian tracking method was designed and the Kalman filter algorithm was used to estimate the pedestrian motion state. The Mahalanobis distance and apparent feature were used to calculate the similarity between the detection frame and the predicted pedestrian trajectory; the Hungarian algorithm was used to achieve the optimal matching of pedestrian targets. Finally, the improved YOLO pedestrian detection model and the DeepSort pedestrian tracking method were verified in the same experimental environment. The verification results showed that the improved model can improve the detection accuracy of small-target pedestrians, effectively deal with the problem of target occlusion, reduce the rate of missed detection and false detection of pedestrian targets, and improve the tracking failure caused by occlusion. Full article

(This article belongs to the Special Issue Renewable and Sustainable Energy Systems: Architecture, Methodology and Technology)

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