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Sustainability, Volume 16, Issue 17 (September-1 2024) – 19 articles

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25 pages, 14771 KiB  
Article
Model Predictive Controlled Parallel Photovoltaic-Battery Inverters Supporting Weak Grid Environment
by Fatma Selim, Mokhtar Aly, Tamer F. Megahed, Masahito Shoyama and Sobhy M. Abdelkader
Sustainability 2024, 16(17), 7261; https://doi.org/10.3390/su16177261 (registering DOI) - 23 Aug 2024
Abstract
The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive [...] Read more.
The hybrid photovoltaic (PV) with energy storage system (ESS) has become a highly preferred solution to replace traditional fossil-fuel sources, support weak grids, and mitigate the effects of fluctuated PV power. The control of hybrid PV-power systems as generation-storage and their injected active/reactive power for the grid side present critical challenges in optimizing their performance. Therefore, this paper introduces hybrid PV-battery parallel inverters employing a finite control set model predictive control (FCSMPC) method. The proposed FCSMPC-based controller and inverter system achieves multiple functionalities, including maximum power extraction from PV, proper charging/discharging commands for ESS, support for weak grid conditions, support during low-voltage ride-through (LVRT) by increasing reactive power injection to counteract the drop in grid voltage, and economic management based on feed-in-tariff (FiT). The controller significantly improves the performance of the PV-battery system under faulty LVRT conditions and unbalanced grid voltages, satisfying grid code requirements while continuously supplying the microgrid’s delicate local load. A real-time simulation hardware-in-the-loop (HiL) setup, utilizing the OPAL-RT platform, is employed to implement the proposed hybrid PV–ESS with its controller. The results affirm the superior ability of FCSMPC in weak-grid conditions and its capability to achieve multiple objectives simultaneously. Full article
(This article belongs to the Special Issue Advanced Control Techniques for Renewable Energy Systems and Power Electronics Volume II)
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21 pages, 1540 KiB  
Article
Study on the Theme Evolution and Synergy Assessment of China’s New Energy Vehicle Policy Texts
by Shasha Wang and Sheng Mai
Sustainability 2024, 16(17), 7260; https://doi.org/10.3390/su16177260 (registering DOI) - 23 Aug 2024
Abstract
Drawing on data from 133 Chinese New Energy Vehicle (NEV) policy documents from 2007 to 2023, this study utilizes Dynamic Topic Modelling (DTM), social network analysis and a quantitative model to investigate the evolutionary path of policy themes and the coordination effects. The [...] Read more.
Drawing on data from 133 Chinese New Energy Vehicle (NEV) policy documents from 2007 to 2023, this study utilizes Dynamic Topic Modelling (DTM), social network analysis and a quantitative model to investigate the evolutionary path of policy themes and the coordination effects. The following results were obtained. (1) A thematic cross-sectional analysis identified six core policy themes, namely, coordinated promotion of technology and finance, industry development and safety standardisation, market service and technical support systems, promotion strategy and urban cluster development, industrial capital and safety supervision mechanisms, and policy support and market expansion. The analysis also mapped the distribution of hot spots within these themes. (2) The keyword co-occurrence network of the NEV policy indicated that the network structure evolved from an initial ‘overall dispersion–theme concentration’, comprising 16 policy themes, to an ‘overall stability–theme coordination’, consisting of 14 policy themes. (3) The coordination degrees across the three types of policies exhibited a consistent upward spiral, with the comprehensive coordination index surging from 30 in 2007 to 951 in 2023, underscoring the complementary effects among policy instruments. These conclusions offer valuable insights for government departments to understand NEV development trends and dynamically adjust policy themes accordingly. Full article
(This article belongs to the Special Issue Energy Saving and Emission Reduction from Green Transportation)
27 pages, 5063 KiB  
Article
Predicting Energy Consumption for Hybrid Energy Systems toward Sustainable Manufacturing: A Physics-Informed Approach Using Pi-MMoE
by Mukun Yuan, Jian Liu, Zheyuan Chen, Qingda Guo, Mingzhe Yuan, Jian Li and Guangping Yu
Sustainability 2024, 16(17), 7259; https://doi.org/10.3390/su16177259 (registering DOI) - 23 Aug 2024
Abstract
Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. [...] Read more.
Hybrid energy supply systems are widely utilized in modern manufacturing processes, where accurately predicting energy consumption is essential not only for managing productivity but also for driving sustainable development. Effective energy management is a cornerstone of sustainable manufacturing, reducing waste and enhancing efficiency. However, conventional studies often focus solely on predicting single types of energy consumption and overlook the integration of physical laws and information, which are essential for a comprehensive understanding of energy dynamics. In this context, this paper introduces a multi-task physics-informed multi-gate mixture-of-experts (pi-MMoE) model that not only considers multiple forms of energy consumption but also incorporates physical principles through the integration of physical information and multi-task modeling. Specifically, a detailed analysis of manufacturing processes and energy patterns is first conducted to study various energy types and extract relevant physical laws. Next, using industry insights and thermodynamic principles, key equations for energy balance and conversion are derived to create a physics-based loss function for model training. Finally, the pi-MMoE model framework is constructed, featuring multi-expert networks and gating mechanisms to balance cross-task knowledge sharing and expert learning. In a case study of a textile factory, the pi-MMoE model reduced electricity and steam prediction errors by 14.28% and 27.27%, respectively, outperforming traditional deep learning methods. This demonstrates that the model can improve prediction performance, providing a novel approach to intelligent energy management and promoting sustainable development in manufacturing. Full article
(This article belongs to the Special Issue Energy Management System and Sustainability)
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18 pages, 4142 KiB  
Article
Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load
by Sijiang Ma, Jin Ning, Ning Mao, Jie Liu and Ruifeng Shi
Sustainability 2024, 16(17), 7258; https://doi.org/10.3390/su16177258 (registering DOI) - 23 Aug 2024
Abstract
To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user [...] Read more.
To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R2 coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R2 coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance. Full article
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17 pages, 476 KiB  
Article
Conceptualization of ESG Management Values of Professional Sports Clubs: From Consumers’ Perspective
by Wangsung Myung
Sustainability 2024, 16(17), 7257; https://doi.org/10.3390/su16177257 (registering DOI) - 23 Aug 2024
Abstract
Discussions and practices of sustainability are actively underway around the world. In this social context, this study conceptualized ESG management values focused on sports organizations, especially professional sports clubs in Korea. Utilizing Q methodology, which is suitable for research on individuals’ subjectivity, we [...] Read more.
Discussions and practices of sustainability are actively underway around the world. In this social context, this study conceptualized ESG management values focused on sports organizations, especially professional sports clubs in Korea. Utilizing Q methodology, which is suitable for research on individuals’ subjectivity, we explored ESG management values accepted by consumers of Korean professional sports. As a result, the ESG management values were confirmed as follows: “Type I: Trust Management Emphasis”, “Type II: Local Community Emphasis”, and “Type III: Safety–Respect Culture Emphasis”. While each of the three types accepted governance, social responsibility, safety, and respect culture as their main values, they tended to deny environment-related values. Accordingly, this study provided two discussions: “professional sports consumers’ low awareness of the environment” and “reinterpretation of environmental values among the ESG management values of professional sports clubs”. In conclusion, the three types are significant in that (1) they reflect well the social and cultural context of Korean professional sports and consumers’ perceptions, and (2) they provide a new perspective on the ESG management value of professional sports teams. Full article
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21 pages, 9062 KiB  
Article
Spatial-Temporal Evolution and Environmental Regulation Effects of Carbon Emissions in Shrinking and Growing Cities: Empirical Evidence from 272 Cities in China
by Xinhang Tang, Shuai Shao and Jia Cui
Sustainability 2024, 16(17), 7256; https://doi.org/10.3390/su16177256 (registering DOI) - 23 Aug 2024
Abstract
Shrinking and growing cities are categories of cities characterized by population loss or add, and the issue of carbon emissions in these cities is often neglected. Environmental regulation, as an important influence on carbon emissions, plays an important role in promoting the low-carbon [...] Read more.
Shrinking and growing cities are categories of cities characterized by population loss or add, and the issue of carbon emissions in these cities is often neglected. Environmental regulation, as an important influence on carbon emissions, plays an important role in promoting the low-carbon transition in Chinese cities. This study focused on the carbon emissions of 272 cities in China from 2012–2021, constructed a comprehensive indicator to classify four city types, and calculated carbon emissions. Spatial-temporal characteristics and evolution of carbon emissions and impacts of environmental regulation were investigated. Carbon emissions of rapidly growing cities showed a downward trend, whereas those of slightly growing, rapidly shrinking, and slightly shrinking cities showed upward trends. The more rapidly a city grew or shrunk, the higher its average carbon emissions. Growing cities’ center of gravity of their carbon emissions migrated northwest. Carbon emissions of rapidly and slightly shrinking cities were high in the northeast, and their carbon emission centers migrated northeast and southwest, respectively, with obvious spatial autocorrelation of city types. Strengthening environmental regulations significantly positively affected carbon emission reduction. The impact of environmental regulation on carbon emissions reduction was temporally and spatially heterogeneous and more significant in non-resource cities. Full article
(This article belongs to the Special Issue Sustainable Urban Development and Carbon Emission Efficiency)
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19 pages, 1860 KiB  
Article
A Cooperative Game Approach for Optimal Design of Shared Energy Storage System
by Qin Wang, Jincan Zeng, Beibei Cheng, Minwei Liu, Guori Huang, Xi Liu, Gengsheng He, Shangheng Yao, Peng Wang and Longxi Li
Sustainability 2024, 16(17), 7255; https://doi.org/10.3390/su16177255 (registering DOI) - 23 Aug 2024
Abstract
The energy sector’s long-term sustainability increasingly relies on widespread renewable energy generation. Shared energy storage embodies sharing economy principles within the storage industry. This approach allows storage facilities to monetize unused capacity by offering it to users, generating additional revenue for providers, and [...] Read more.
The energy sector’s long-term sustainability increasingly relies on widespread renewable energy generation. Shared energy storage embodies sharing economy principles within the storage industry. This approach allows storage facilities to monetize unused capacity by offering it to users, generating additional revenue for providers, and supporting renewable energy prosumers’ growth. However, high investment costs and long payback periods often hinder the development of battery storage. To address this challenge, we propose a shared storage investment framework. In this framework, a storage investor virtualizes physical storage equipment, enabling prosumers to access storage services as though they owned the batteries themselves. We adopt a cooperative game approach to incorporate storage sharing into the design phase of energy systems. To ensure a fair distribution of cooperative benefits, we introduce a benefit allocation mechanism based on contributions to energy storage sharing. Utilizing realistic data from three buildings, our simulations demonstrate that the shared storage mechanism creates a win–win situation for all participants. It also enhances the self-sufficiency and self-consumption of renewable energy. This paper provides valuable insights for shared storage investors regarding optimal design and benefit allocation among multiple stakeholders. Full article
(This article belongs to the Special Issue Renewable Energy and Sustainable Energy Systems)
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14 pages, 3301 KiB  
Article
Characteristics of Microplastic Pollution in Agricultural Soils in Xiangtan, China
by Cong Ye, Jing Lin, Zhenguo Li, Guanghuai Wang and Zeling Li
Sustainability 2024, 16(17), 7254; https://doi.org/10.3390/su16177254 (registering DOI) - 23 Aug 2024
Abstract
Microplastic pollution in agricultural soils has drawn significant attention in recent years. The objective of this study is to investigate the forms and characteristics of microplastic pollution in agricultural soils, specifically focusing on rice and vegetable soil in Xiangtan City. Various analytical techniques [...] Read more.
Microplastic pollution in agricultural soils has drawn significant attention in recent years. The objective of this study is to investigate the forms and characteristics of microplastic pollution in agricultural soils, specifically focusing on rice and vegetable soil in Xiangtan City. Various analytical techniques including stereomicroscopy, SEM, and FTIR spectroscopy were used to analyze the color, particle size, abundance, and types of microplastics in the study area. The findings indicated that the average abundance of microplastics in the soils in the study area was 4377.44 items/kg, with a maximum of 12,292.33 items/kg. Microplastics with smaller particle sizes were more prevalent, with their colors mainly being yellow, transparent, and black. The shapes of the microplastics were mainly thin-filmy and fibrous, and the types mainly included PE and PP. The abundance of microplastics in the vegetable soil with agricultural films applied was four times more than that without agricultural films. In the research area, the use of agricultural films was the most significant source of microplastics. The study’s findings describe the characteristics of microplastic pollution in agricultural soils in Xiangtan City. The findings could serve as a reference for establishing standardized assessments of microplastic pollution in agricultural soils, in addition to offering data support for Xiangtan City’s future efforts to safeguard agricultural soils and regulate microplastic pollution. Full article
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15 pages, 4321 KiB  
Article
Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks
by Krishnamoorthy Suresh, Raju Kannadasan, Stanley Vinson Joshua, Thangaraj Rajasekaran, Mohammed H. Alsharif, Peerapong Uthansakul and Monthippa Uthansakul
Sustainability 2024, 16(17), 7253; https://doi.org/10.3390/su16177253 (registering DOI) - 23 Aug 2024
Abstract
Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) [...] Read more.
Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%. Full article
(This article belongs to the Section Sustainable Engineering and Science)
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22 pages, 3190 KiB  
Article
Sustainable Impact of Stance Attribution Design Cues for Robots on Human–Robot Relationships—Evidence from the ERSP
by Dong Lv, Rui Sun, Qiuhua Zhu, Jiajia Zuo and Shukun Qin
Sustainability 2024, 16(17), 7252; https://doi.org/10.3390/su16177252 (registering DOI) - 23 Aug 2024
Abstract
With the development of large language model technologies, the capability of social robots to interact emotionally with users has been steadily increasing. However, the existing research insufficiently examines the influence of robot stance attribution design cues on the construction of users’ mental models [...] Read more.
With the development of large language model technologies, the capability of social robots to interact emotionally with users has been steadily increasing. However, the existing research insufficiently examines the influence of robot stance attribution design cues on the construction of users’ mental models and their effects on human–robot interaction (HRI). This study innovatively combines mental models with the associative–propositional evaluation (APE) model, unveiling the impact of the stance attribution explanations of this design cue on the construction of user mental models and the interaction between the two types of mental models through EEG experiments and survey investigations. The results found that under the influence of intentional stance explanations (compared to design stance explanations), participants displayed higher error rates, higher θ- and β-band Event-Related Spectral Perturbations (ERSPs), and phase-locking value (PLV). Intentional stance explanations trigger a primarily associatively based mental model of users towards robots, which conflicts with the propositionally based mental models of individuals. Users might adjust or “correct” their immediate reactions caused by stance attribution explanations after logical analysis. This study reveals that stance attribution interpretation can significantly affect users’ mental model construction of robots, which provides a new theoretical framework for exploring human interaction with non-human agents and provides theoretical support for the sustainable development of human–robot relations. It also provides new ideas for designing robots that are more humane and can better interact with human users. Full article
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21 pages, 307 KiB  
Article
Environmental, Social, and Governance Performance, Platform Governance, and Value Creation of Platform Enterprises
by Ruixin Su and Na Li
Sustainability 2024, 16(17), 7251; https://doi.org/10.3390/su16177251 (registering DOI) - 23 Aug 2024
Abstract
Under the concepts of sustainable development and a sharing economy, the ESG performance of platform enterprises has played a significant role in measuring the operating status and responsible investment of platform enterprises. Platform enterprises have different typical characteristics from traditional enterprises. The mechanisms [...] Read more.
Under the concepts of sustainable development and a sharing economy, the ESG performance of platform enterprises has played a significant role in measuring the operating status and responsible investment of platform enterprises. Platform enterprises have different typical characteristics from traditional enterprises. The mechanisms of ESG and financial performance needs to be further explored. The empirical analysis finds that: (1) the ESG performance of platform enterprises and its S index and G index has a positive impact on corporate financial performance. (2) Media attention plays a positive moderating role between the ESG and ROA. (3) Platform data governance and platform reputation governance are two internal and external paths for platform enterprises’ ESG performance to improve financial performance. (4)There is heterogeneity in the relationship between ESG and ROA in terms of platform enterprise scale and platform type. Based on the above conclusions, this paper provides reference experience for the ESG governance and value creation of platform enterprises. Full article
(This article belongs to the Special Issue ESG, Sustainability and Competitiveness: A Serious Reflection)
21 pages, 2817 KiB  
Article
Measurement and Evaluation of the Modernization Development Level of Higher Education in China: Based on Panel Data Analysis of 31 Provinces from 2012 to 2022
by Qingqing Liang and Fang Yin
Sustainability 2024, 16(17), 7250; https://doi.org/10.3390/su16177250 (registering DOI) - 23 Aug 2024
Abstract
The scale and quality of higher education are key indicators of a country’s development level and its potential for future growth. This study utilizes literature analysis and the core functions of higher education institutions to construct an evaluation index system for the modernization [...] Read more.
The scale and quality of higher education are key indicators of a country’s development level and its potential for future growth. This study utilizes literature analysis and the core functions of higher education institutions to construct an evaluation index system for the modernization level of higher education in China, using data from 2012 to 2022. The results reveal the following: (1) From 2012 to 2022, the modernization level of higher education across China’s 31 provinces generally increased, despite some fluctuations. Beijing consistently maintained the highest level of modernization, while Hainan demonstrated the fastest growth rate. (2) The modernization level of higher education exhibited uneven distribution across the provinces: eleven provinces were at a low level, eight at a moderate level, eight at a relatively high level, and four at a high level. (3) The development level of higher education modernization shows a clear correlation with geographic location and economic development, characterized by a distinct ‘high in the east, low in the west’ pattern. (4) There is a urgent need to enhance the internationalization of higher education development in China. (5) The overall index of higher education development in China displayed a slow decline with fluctuations from 2012 to 2022, reflecting ongoing efforts toward balanced development across the 31 provinces and cities. The most significant disparities in higher education development remain between the western and eastern regions. Full article
(This article belongs to the Topic Education and Learning in the Context of the New Normal: Sustainable Development and the Path to Sustainability)
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21 pages, 3545 KiB  
Article
CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors
by Moaaz Elkabalawy, Abobakr Al-Sakkaf, Eslam Mohammed Abdelkader and Ghasan Alfalah
Sustainability 2024, 16(17), 7249; https://doi.org/10.3390/su16177249 (registering DOI) - 23 Aug 2024
Abstract
The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs [...] Read more.
The significant energy consumption associated with the built environment demands comprehensive energy prediction modelling. Leveraging their ability to capture intricate patterns without extensive domain knowledge, supervised data-driven approaches present a marked advantage in adaptability over traditional physical-based building energy models. This study employs various machine learning models to predict energy consumption for an office building in Berkeley, California. To enhance the accuracy of these predictions, different feature selection techniques, including principal component analysis (PCA), decision tree regression (DTR), and Pearson correlation analysis, were adopted to identify key attributes of energy consumption and address collinearity. The analyses yielded nine influential attributes: heating, ventilation, and air conditioning (HVAC) system operating parameters, indoor and outdoor environmental parameters, and occupancy. To overcome missing occupancy data in the datasets, we investigated the possibility of occupancy-based Wi-Fi prediction using different machine learning algorithms. The results of the occupancy prediction modelling indicate that Wi-Fi can be used with acceptable accuracy in predicting occupancy count, which can be leveraged to analyze occupant comfort and enhance the accuracy of building energy models. Six machine learning models were tested for energy prediction using two different datasets: one before and one after occupancy prediction. Using a 10-fold cross-validation with an 8:2 training-to-testing ratio, the Random Forest algorithm emerged superior, exhibiting the highest R2 value of 0.92 and the lowest RMSE of 3.78 when occupancy data were included. Additionally, an error propagation analysis was conducted to assess the impact of the occupancy-based Wi-Fi prediction model’s error on the energy prediction model. The results indicated that Wi-Fi-based occupancy prediction can improve the data inputs for building energy models, leading to more accurate energy consumption predictions. The findings underscore the potential of integrating the developed energy prediction models with fault detection systems, model predictive controllers, and energy load shape analysis, ultimately enhancing energy management practices. Full article
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15 pages, 3858 KiB  
Article
Enhancing Distribution Networks with Optimal BESS Sitting and Operation: A Weekly Horizon Optimization Approach
by Diego Jose da Silva, Edmarcio Antonio Belati and Jesús M. López-Lezama
Sustainability 2024, 16(17), 7248; https://doi.org/10.3390/su16177248 (registering DOI) - 23 Aug 2024
Abstract
The optimal sitting and operation of Battery Energy Storage Systems (BESS) plays a key role in energy transition and sustainability. This paper presents an optimization framework based on a Multi-period Optimal Power Flow (MOPF) for the optimal sitting and operation of BESS alongside [...] Read more.
The optimal sitting and operation of Battery Energy Storage Systems (BESS) plays a key role in energy transition and sustainability. This paper presents an optimization framework based on a Multi-period Optimal Power Flow (MOPF) for the optimal sitting and operation of BESS alongside PV in active distribution grids. The model was implemented in AMPL (A Mathematical Programming Language) and solved using the Knitro solver to minimize power losses over one week, divided into hourly intervals. To demonstrate the applicability of the proposed model, various analyses were conducted on a benchmark 33-bus distribution network considering 1, 2 and 3 BESS. Along with the reduction in power losses of up to 17.95%, 26% and 29%, respectively. In all cases, there was an improvement in the voltage profile and a more uniform generation curve at the substation. An additional study showed that operating over a one-week horizon results in an energy gain of 1.08 MWh per day compared to single daily operations. The findings suggest that the proposed model for optimal sitting and operation of BESS in the presence of Renewable Energy Sources (RES) applies to real-world scenarios. Full article
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15 pages, 284 KiB  
Article
The Impact of Digital Governance on Entrepreneurial Activity in Deprived Areas: Evidence from Tibet, China
by Aiyan Xu, Pengji Li and Xin Xin
Sustainability 2024, 16(17), 7247; https://doi.org/10.3390/su16177247 (registering DOI) - 23 Aug 2024
Abstract
This paper examines the impact of digital governance on entrepreneurial activity in deprived areas from both theoretical and empirical perspectives. Our study is twofold. First, we utilize a economic geography model to theoretically analyze the influence of digital governance on regional entrepreneurial endeavors [...] Read more.
This paper examines the impact of digital governance on entrepreneurial activity in deprived areas from both theoretical and empirical perspectives. Our study is twofold. First, we utilize a economic geography model to theoretically analyze the influence of digital governance on regional entrepreneurial endeavors and develop research hypotheses. Second, using county panel data from Tibet spanning from 2001 to 2021, we empirically examine the influence of digital governance on entrepreneurial activity. The results show that digital governance can significantly increase regional entrepreneurial activity, and that the effect exhibits an upward and then a downward trend over time, with some spatial spillover effects. We argue that differences in regional network infrastructure are an important heterogeneity factor affecting digital governance’s ability to increase entrepreneurial activity. Our conclusions remain robust to various tests. Full article
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16 pages, 6890 KiB  
Article
Characterization of Waste Biomass Fuel Prepared from Coffee and Tea Production: Its Properties, Combustion, and Emissions
by Shangrong Wu, Qingyue Wang, Weiqian Wang, Yanyan Wang and Dawei Lu
Sustainability 2024, 16(17), 7246; https://doi.org/10.3390/su16177246 (registering DOI) - 23 Aug 2024
Abstract
In order to reduce global warming, new energy fuels that use waste biomass to replace traditional coal are rapidly developing. The main purpose of this study is to investigate the feasibility behavior of different biomass materials such as spent coffee grounds (SCGs) and [...] Read more.
In order to reduce global warming, new energy fuels that use waste biomass to replace traditional coal are rapidly developing. The main purpose of this study is to investigate the feasibility behavior of different biomass materials such as spent coffee grounds (SCGs) and spent tea grounds (STGs) as fuel during combustion and their impact on the environment. This study involves using fuel shaping and co-firing methods to increase the fuel calorific value and reduce the emissions of pollutants, such as NOX and SO2, and greenhouse gas CO2. The produced gas content was analyzed using the HORIBA (PG-250) laboratory combustion apparatus. The results indicate that, among the measured formed particles, SCG:STG = 8:2, 6:4, and 4:6 had the lowest post-combustion pollutant gas emissions. Compared to using only waste coffee grounds as fuel, the NOx emissions were reduced from 166 ppm to 102 ppm, the CO emissions were reduced from 22 ppm to 12 ppm, and the CO2 emissions were reduced from 629 ppm to 323 ppm. In addition, the emission of SO2, the main component of acid rain, was reduced by 20 times compared to the combustion of traditional fuels. The SO2 emission of five different proportions of biomass fuels was 5 ppm, which is much lower than that of traditional coal fuels. Therefore, SCG and STG mixed fuels can replace coal as fuel while reducing harmful gasses. Full article
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26 pages, 510 KiB  
Article
Perceived Quality of Service in Tourist Transportation in the City of Baños de Agua Santa, Ecuador
by Rommel Velastegui-Hernández, Diego Melo-Fiallos, María Mayorga-Ases, Segundo Hernández-Del-Salto, Eduardo Manobanda-Tenelema and Marcelo V. Garcia
Sustainability 2024, 16(17), 7245; https://doi.org/10.3390/su16177245 (registering DOI) - 23 Aug 2024
Viewed by 98
Abstract
This study investigates the perceived quality of tourist transportation services in Baños de Agua Santa, Ecuador, utilizing the SERVQUAL model to assess service quality. Through an examination of the gap between tourists’ expectations and their actual experiences, the research aims to evaluate service [...] Read more.
This study investigates the perceived quality of tourist transportation services in Baños de Agua Santa, Ecuador, utilizing the SERVQUAL model to assess service quality. Through an examination of the gap between tourists’ expectations and their actual experiences, the research aims to evaluate service quality. A survey of 203 tourists who utilized the “Chivas” tourist ground transportation service forms the basis of the analysis. The findings reveal significant negative gaps across all dimensions of service quality, indicating a shortfall in meeting tourists’ expectations. Notably, the reliability dimension exhibits the most pronounced gap, highlighting the importance of fulfilling service commitments to cultivate trust. The study underscores the crucial role of service quality in the tourism sector and proposes targeted improvements, including enhancing facility modernity, providing staff training, and enhancing service responsiveness and reliability. Addressing these gaps has the potential to enrich the tourist experience, bolster the positive image of transportation services, and enhance the city’s appeal. Full article
(This article belongs to the Special Issue Sense of Place, Place Attachment and Sustainability in Culture, Tourism, Hospitality and Events)
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26 pages, 4914 KiB  
Article
Capacity Optimization of Pumped–Hydro–Wind–Photovoltaic Hybrid System Based on Normal Boundary Intersection Method
by Hailun Wang, Yang Li, Feng Wu, Shengming He and Renshan Ding
Sustainability 2024, 16(17), 7244; https://doi.org/10.3390/su16177244 (registering DOI) - 23 Aug 2024
Viewed by 188
Abstract
Introducing pumped storage to retrofit existing cascade hydropower plants into hybrid pumped storage hydropower plants (HPSPs) could increase the regulating capacity of hydropower. From this perspective, a capacity configuration optimization method for a multi-energy complementary power generation system comprising hydro, wind, and photovoltaic [...] Read more.
Introducing pumped storage to retrofit existing cascade hydropower plants into hybrid pumped storage hydropower plants (HPSPs) could increase the regulating capacity of hydropower. From this perspective, a capacity configuration optimization method for a multi-energy complementary power generation system comprising hydro, wind, and photovoltaic power is developed. Firstly, to address the uncertainty of wind and photovoltaic power outputs, the K-means clustering algorithm is applied to deal with historical data on load and photovoltaic, wind, and water inflow within a specific region over the past year. This process helps reduce the number of scenarios, resulting in 12 representative scenarios and their corresponding probabilities. Secondly, with the aim of enhancing outbound transmission channel utilization and decreasing the peak–valley difference for the receiving-end power grid’s load curve, a multi-objective optimization model based on the normal boundary intersection (NBI) algorithm is developed for the capacity optimization of the multi-energy complementary power generation system. The result shows that retrofitting cascade hydropower plants with pumped storage units to construct HPSPs enhances their ability to accommodate wind and photovoltaic power. The optimal capacity of wind and photovoltaic power is increased, the utilization rate of the system’s transmission channel is improved, and the peak-to-valley difference for the residual load of the receiving-end power grid is reduced. Full article
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16 pages, 5691 KiB  
Article
Advancing the Sustainability of Geopolymer Technology through the Development of Rice Husk Ash Based Solid Activators
by Olga Andriana Panitsa, Dimitrios Kioupis and Glikeria Kakali
Sustainability 2024, 16(17), 7243; https://doi.org/10.3390/su16177243 (registering DOI) - 23 Aug 2024
Viewed by 187
Abstract
Rice husk ash (RHA), an agricultural waste byproduct, has already been tested as a component in geopolymeric binders, typically as part of the precursor solid mix, alongside materials like fly ash (FA), slag, and cement. This study presents a novel approach where RHA [...] Read more.
Rice husk ash (RHA), an agricultural waste byproduct, has already been tested as a component in geopolymeric binders, typically as part of the precursor solid mix, alongside materials like fly ash (FA), slag, and cement. This study presents a novel approach where RHA is employed to create a solid activator, aimed at entirely replacing commercial sodium silicates. The synthesis process involves mixing RHA, NaOH (NH), and water by applying a SiO2/Na2O molar ratio equal to 1, followed by mild thermal treatment at 150 °C for 1 h, resulting in the production of a solid powder characterized by high Na2SiO3 content (60–76%). Additionally, microwave treatment (SiO2/Na2O = 1, 460 W for 5 min) increases the environmental and economical sustainability of alkali silicates production from RHA since this processing is 12 times faster than conventional thermal treatment reducing at the same time the final product’s embodied energy. The efficacy of this new material as a sole solid activator for the geopolymerization of Greek FA is investigated through various techniques (XRD, FTIR, SEM). One-part geopolymers prepared with RHA-based solid activators demonstrated mechanical performance comparable to those prepared with commercial products (~62 MPa at 7 days). This research contributes to the advancement of sustainable construction practices emphasizing the importance of local materials and reduced environmental impact in achieving long-term sustainability goals. Full article
(This article belongs to the Special Issue Sustainability in Construction Materials)
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