Green Energy Management Research Articles

Innovative hybrid energy storage systems with sustainable integration of green hydrogen and energy management solutions for standalone PV microgrids based on reduced fractional gradient descent algorithm

This paper investigates innovative solutions to enhance the performance and lifespan of standalone photovoltaic (PV)-based microgrids, with a particular emphasis on off-grid communities. A major challenge in these systems is the limited lifespan of batteries. To overcome this issue, researchers have created hybrid energy storage systems (HESS) along with advanced power management strategies. This study introduces innovative multi-level HESS approaches and a related energy management strategy designed to alleviate the charge/discharge stress on batteries. Comprehensive Matlab Simulink models of various HESS topologies within standalone PV microgrids are utilized to evaluate system performance under diverse weather conditions and load profiles for rural site. The findings reveal that the proposed HESS significantly extends battery life expectancy compared to existing solutions. Furthermore, the paper presents a novel energy management strategy based on the Reduced Fractional Gradient Descent (RFGD) algorithm optimization, tailored for hybrid systems that include photovoltaic, fuel cell, battery, and supercapacitor components. This strategy aims to minimize hydrogen consumption of Fuel Cells (FCs), thereby supporting the production of green ammonia for local industrial use. The RFGD algorithm is selected for its minimal user-defined parameters and high convergence efficiency. The proposed method is compared with other algorithms, such as the Lyrebird Optimization Algorithm (LOA) and Osprey Optimization Algorithm (OOA). The RFGD algorithm exhibits superior accuracy in optimizing energy management, achieving a 15 % reduction in hydrogen consumption. Its efficiency is evident from the reduced computational time compared to conventional algorithms. Although minor losses in computational resources were observed, they were substantially lower than those associated with traditional optimization techniques. Overall, the RFGD algorithm offers a robust and efficient solution for enhancing the performance of hybrid energy systems.

A Bibliometric Analysis of Green Shipping: Research Progress and Challenges for Sustainable Maritime Transport

This paper aims to systematically review and analyze the literature on green shipping, focusing on research trends, key areas, and future directions. A bibliometric analysis is conducted on 1339 articles published between 2000 and 2023, using data from the SCI-EXPANDED and SSCI core collections on the Web of Science platform. Co-occurrence analyses are performed for authors, countries, institutions, and keywords. The analysis reveals exponential growth in green shipping research over the past decade, with a significant increase since 2019. China leads in publication output and international collaboration. Key research areas identified include sustainable development, green energy, green ports, and green supply chain management. Green shipping research is rapidly expanding, emphasizing sustainable practices and technological advancements. Future research is expected to integrate artificial intelligence, big data analytics, and other advanced technologies to further promote sustainability. Strengthening international cooperation and interdisciplinary integration will be crucial for advancing the global green shipping agenda.

Does fintech really matter for energy, economy and environment? From the lenses of SDG-7, SDG-8, SDG-13, COP27 and COP28

This study investigates the impact of fintech on economic development, energy transition and environmental well-being in OECD nations from 2005 to 2018 by utilizing MM-QR and CS-ARDL methods. Results reveal the different degree of positive impact of fintech on the said variables. In this regard, fintech plays a key role in planning, development and management of financial institutions and enhancing economic growth and development in the region across all the quantiles. Moreover, energy transition and green energy management get influence positively through technological involvement in the financial system. Lastly, the relationship between fintech and environment is moderately positive in the beginning and gets stronger in higher quantiles. Fintech facilitates environmental policy stringency, green finance and channelization of various financial sources towards environment well-being. It also effectively integrates economy, energy and environment towards attainment of sustainable development goal-7 (ensure affordable and clean energy), goal-8 (economic growth and development) and goal −13 (Climate challenges, concerns and action) and objectives of COP27 and 28. In light with the findings, several policy implications are suggested.

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Energy efficiency in production systems and processes is a key global research topic, especially in light of the Green Deal, Industry 4.0/5.0 paradigms, and rising energy prices. Research on improving the energy efficiency of production based on artificial intelligence (AI) analysis brings promising solutions, and the digital transformation of industry towards green energy is slowly becoming a reality. New production planning rules, the optimization of the use of the Industrial Internet of Things (IIoT), industrial cyber-physical systems (ICPSs), and the effective use of production data and their optimization with AI bring further opportunities for sustainable, energy-efficient production. The aim of this study is to systematically evaluate and quantify the research results, trends, and research impact on energy management in production based on AI-based demand forecasting. The value of the research includes the broader use of AI which will reduce the impact of the observed environmental and economic problems in the areas of reducing energy consumption, forecasting accuracy, and production efficiency. In addition, the demand for Green AI technologies in creating sustainable solutions, reducing the impact of AI on the environment, and improving the accuracy of forecasts, including in the area of optimization of electricity storage, will increase. A key emerging research trend in green energy management in manufacturing is the use of AI-based demand forecasting to optimize energy consumption, reduce waste, and increase sustainability. An innovative perspective that leverages AI’s ability to accurately forecast energy demand allows manufacturers to align energy consumption with production schedules, minimizing excess energy consumption and emissions. Advanced machine learning (ML) algorithms can integrate real-time data from various sources, such as weather patterns and market demand, to improve forecast accuracy. This supports both sustainability and economic efficiency. In addition, AI-based demand forecasting can enable more dynamic and responsive energy management systems, paving the way for smarter, more resilient manufacturing processes. The paper’s contribution goes beyond mere description, making analyses, comparisons, and generalizations based on the leading current literature, logical conclusions from the state-of-the-art, and the authors’ knowledge and experience in renewable energy, AI, and mechatronics.

Green Energy Harvesting and Management Systems in Intelligent Buildings for Cost-Effective Operation

Nowadays, the rise of Internet of Things (IoT) devices is driving technological upgrades and transformations in the construction industry, the integration of IoT devices in buildings is crucial for both the buildings themselves and the intelligent cities. However, large-scale IoT devices increase energy consumption and bring higher operating costs to buildings. Therefore, harvesting the ambient cost-effective and clean energy sources is essential for the future development of intelligent buildings. In this work, we investigate the feasibility of integrating a typical triboelectric droplet energy harvester (DEH) into buildings. We demonstrate the energy harvesting capabilities of DEH on different sloped roof surfaces and complex curved building surfaces by simulating rainy weather with various rainfall intensities. The results indicate energy harvesting efficiency increases with larger tilt angles, which guides future smart architectural designs. This work is significant for the future integration of diversified, all-weather green energy collection and management systems, including raindrop energy, wind power generation, and solar energy, which will contribute to energy conservation and cost control in the next generation of smart buildings.

SUSTAINABLE SOLUTIONS: GREEN ENERGY MANAGEMENT AS A CATALYST FOR ECONOMIC DEVELOPMENT IN UZBEKISTAN

This article delves into the pivotal role of effective green energymanagement in fostering sustainable economic development. Analyzing various strategies and best practices sheds light on how the integration of renewable energy sources can drive long-term economic growth while simultaneously mitigating environmental degradation. Through a detailed examination of case studies and empirical evidence, this article offers insights into the potential benefits and challenges associated with green energy management in the context of economic development, providing a comprehensive framework for policymakers, businesses, and stakeholders to navigate the transition towards a more sustainable and prosperous future.

Research on the Analysis of Influencing Factors of Energy Consumption and Energy Saving Countermeasures of Green Buildings Based on DEMATEL-ISM

Abstract In the quest for sustainability, managing and optimizing energy consumption within green buildings has emerged as a critical area of focus. The complexity of factors affecting energy use demands more sophisticated analysis than what traditional methods offer. This study leverages the DEMATEL method to isolate crucial influencing factors. It employs the ISM method to map their logical interconnections, forming a comprehensive model of energy consumption determinants in green buildings. We identify building design, energy efficiency, and green technology as critical elements, with building design exerting the most influence (weight: 0.35). Through an in-depth fuzzy DEMATEL-ISM analysis, we develop specific energy-saving measures—such as design improvement and technology enhancement—that promise up to a 20% reduction in energy consumption. Our research introduces a groundbreaking green building energy management tool and provides a solid foundation for policy development and practical applications.

An assessment of the aggregated and disaggregated effects of natural resources rents on environmental sustainability in BRICS economies

ABSTRACT This study seeks to investigate how natural resources rents impact environmental sustainability measured by CO2 emissions and PM2.5 air pollution in Brazil, Russia, India, China, and South Africa (BRICS) economies from 1995 to 2019. To enhance robust policy implications, natural resources are decomposed into disaggregated (coal, gas, oil, forest, and mineral rents) and aggregated (total natural resources rents) indicators. Besides, the intervening roles of affluence, urbanization, and green policies vectoring green technological innovation, green finance, green energy, and waste management within the theoretical underpinning of STIRPAT model are considered. The empirical verification is anchored on second-generation estimators entailing Common Correlated Effects Mean Group, Augmented Mean Group, and Panel Quantile Regression. Similarly, Fully Modified OLS is employed to gauge the country-specific effects amidst varying robustness analyses. The fallouts from the analyses reveal that natural resources rents inhibit the attainments of environmental sustainability in BRICS economies by positively driving CO2 emissions and PM2.5 air pollution. Conversely, green policies are observed to substantially drive environmental sustainability by mitigating both pollutants whereas affluence and urbanization escalate them. The distributional effects from Panel Quantile Regression and Fully Modified OLS corroborate the main findings divergently. Additionally, two channels of causality, including unidirectional and bidirectional nexuses, are apparent from the estimated models. Policy measures are suggested based on the empirical findings.

Green energy management in DC microgrids enhanced with robust model predictive control and muddled tuna swarm MPPT

Green energy management in DC microgrids enhanced with robust model predictive control and muddled tuna swarm MPPT

Sustainable Green Energy Management: Optimizing Scheduling of Multi-Energy Systems Considered Energy Cost and Emission Using Attractive Repulsive Shuffled Frog-Leaping

As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA.

Testing the LCC hypothesis by considering environmental sustainability and economic development: Role of green energy and resource management

This study aims to demystify the role of green energy and green technology in establishing the nexus between behavioural intentions of tourists, technologies, and digital payments by using Perceived value (PV), Compatibility (CO), Perceived Enjoyment (PE), and Social Influence (SI) as a predictor variables, Trust (TR) and Satisfaction (SA) as a mediating variables and Behavioural Intentions (BI) as an outcome Variable. For the empirical estimation, we employ smart PLS-SEM, TAM (Technology Acceptance Model) and SPSS and Tested the LCC hypothesis. Key findings suggest that green energy and perceived value have the highest positive impact on tourists' trust towards digital payments followed by compatibility, social influence and perceived enjoyment. Similarly, tourists’ satisfaction and green technology is one of the important determinants of choosing any digital mode of payment, is mostly influence by perceived value, perceived enjoyment, compatibility and social influence. Moreover, if we choose between trust and satisfaction, trust plays a significant role in exploring the behavioural intentions of tourists about green energy and green technology followed by tourists’ satisfaction. In addition, Tourists’ trust and satisfaction are highly correlated and influence each other. The study offers novel policy implications in terms of use of green technology and green energy in enhancing trust and satisfaction of tourists in order to deeper understanding of different dimensions of digital payments and M−wallets, and allowing them to explore the long-term value inherent of digital payments and M−wallets.

Connectedness and spillovers in the innovation network of green transportation

Greener alternatives for fuelling automobiles, such as hydrogen transport and electric vehicles, have shown considerable promise in transportation. Many others are sceptical of the growing enthusiasm for these new technologies, believing that energy storage technologies and management are insufficient for a complete shift. Such a network of variables and smart grid technologies that can help with the transition may reveal some systemic hazards linked with financial institutions, company risk and failure, and so on. This study attempts to characterise spillovers and connections between the indices of green transportation, smart grid, innovative materials, energy storage, and energy management globally. To do this, we employ a novel strategy developed by Balcilar et al. (2021) as well as a robustness check using the well-known Diebold and Yilmaz (2012) method. The study highlights the sub-systemic sector's connections, giving policymakers insights into instruments to support financial market sustainability and stability. It would be critical to separate the impact of these indicators, but given the intrinsic relationship, this would be nearly impossible. The transportation innovation network is not rigid and established in its interconnection. The role of indicators shifts from transmitting to absorbing shocks regularly, and policymakers who want to encourage long-term solutions must be aware of this.

Short term multi-steps wind speed forecasting for carbon neutral microgrid by decomposition based hybrid model

Combining the benefits of an empirical mode decomposition (EMD), a convolutional neural network (CNN), and a long-short term memory (LSTM) neural network, this research proposes a hybrid model for forecasting short-term multistep ahead wind speed that can assist microgrids in achieving their low generation cost and carbon neutrality goals. Prior to EMD, historical wind speed time series were pre-processed, and then the resulting time series of wind speeds were decomposed into intrinsic mode functions (IMFs). Following that, a CNN-LSTM model that had been subjected to optimization was employed to carry out the training and subsequent evaluation (validation and implementation of the model). While CNN is able to directly extract the time series' intrinsic features, LSTM networks can fully leverage the data for improved prediction. In order to demonstrate the superiority of the proposed model, its results were compared to those of six other models (including hybrids and standalones), and the findings of the experiments provide empirical evidence that the hybrid EMD-CLSTM model proposed is superior. The strategy that has been proposed has also been utilized in industrial settings, where it has been found to be successful. The green energy management system (GEMS) of Leonics Co. Ltd. has incorporated the proposed model into their short-term decision-making and scheduling procedures in order to achieve their goals of a better energy mix, low-cost power generation, and carbon neutrality. Last but not least, the energy management system of the modified GEMS microgrid was upgraded to enable a greater penetration of wind energy sources without decreasing the microgrid's stability or reliability, making it possible for the system to eventually become carbon neutral.

Development of Green Supermarket Evaluation Model Based on Green Process and Green Output—Case of Bangkok City

The study aimed to develop a green supermarket evaluation model based on a green process and green output to provide empirical evidence on the model’s relationship with the green process, green output, and green supermarket. To this end, the study combined data from a survey of one store manager, one facility manager, and three assistant store managers from each of the 190 supermarket branches in Bangkok, Thailand with data from an in-depth interview of a sample group of green supermarket management specialists. The green supermarket evaluation model is divided into three levels, two elements (green process and green output), nine major indicators for green supermarket evaluation, and one hundred one sub-indicators for green supermarket evaluation, according to the findings. The five major indicators of the green process are green procurement, green storage, green service, green transportation and green environmental and energy management system, while the four major indicators of green output are social performance, environmental performance, economic performance, and organization image performance. In addition, the sub-indicator details were also identified for this evaluation model.

Green energy aware and cluster based communication for future load prediction in IoT

Green energy management has become a critical economic solution for efficient energy consumption; however, the available literature is deficient in emphasising the importance of edge intelligence in a controllable Internet of Things (IoT). Green energy aware IoT networks are well-suited for a variety of real-time applications such as smart cities, smart homes, smart grids, and industries. Due to the fact that IoT nodes are energy constrained and function on a small internal battery, the development of energy-efficient solutions becomes important. Simultaneously, it is necessary to forecast energy consumption in energy aware IoT networks in order to meet upcoming load. With this in mind, this research proposes a novel green energy-aware cluster communication and future load prediction technique for IoT networks called GEQCC-FLP. The proposed GEQCC-FLP technique’s objective is to determine the effective set of cluster heads (CHs) and forecast the network’s incoming load. The GEQCC-FLP technique consists of two primary stages: clustering using a satin bowerbird optimizer (SBO) and load prediction using a deep random vector functional link network (DRVFLN). Additionally, the constructs a fitness function from three parameters: energy, distance, and delay. Additionally, the Adam optimizer can be used to optimise the DRVFLN model’s hyperparameters, hence improving the prediction outcomes. A DRVFLN-based Adam optimizer with deep random vector functional link networks is used to forecast the network’s incoming load (DRVFLN). This clustering technique is based on the SBO algorithm, which generates three fitness function parameters: energy input, distance travelled, and delay. To demonstrate the enhanced outcome of the GEQCC-FLP technique, a variety of simulations are run and the results examined from a variety of angles. The GEQCC-FLP methodology outperformed the prior methods by 0.563 and 0.687, respectively, in terms of root mean square error (RMSE) and mean absolute percent error (MAPE). The extensive results and discussion support the conclusion that the GEQCC-FLP strategy is an extremely effective method for regulating the usage of renewable energy in Internet of Things networks. The experimental results indicated that the GEQCC-FLP technique outperformed the other techniques.

Green Energy and Water Resource Management: A Case Study of Fishery and Solar Power Symbiosis in Taiwan

Renewable energy development is a key pathway for mitigating climate change. The Taiwan government has been actively developing low carbon green energy with solar photovoltaic technology and wind power as their primary development projects. Cigu Taiwan provides an ideal research site to examine tradeoffs between ecological conservation, marine fisheries, and green power development, and the factors affect commitments to ecological conservation in the face of these tradeoffs. This research investigates the fishery and electricity symbiosis project in Cigu through a novel combination of the theory of planned behavior and the contingent valuation method to analyze the factors influencing the local residents’ behavioral intentions to safeguard ecological achievements in ecologically fragile areas through conservation trust funds. Analysis of survey responses from a convenience sample of 715 residents and resource users in the Cigu area reports that attitudes (ATT), subjective norms (SN), perceived behavioral control (PBC), environmental concern (EC), and environmental risk (ER) significantly influence the behavioral intention to pay eco-compensation fees; the local residents’ willingness to pay for the conservation trust funds was NTD 621.4/year (USD 21.9/year), and decreased to NTD 545.9/year (USD 19.2/year) after the implementation of fishery and electricity symbiosis. The discussion section argues that the drivers of ATT, SN, PBC, EC, and ER can be used by policy makers to direct local residents’ intentions and behavior toward conserving ecological achievements in fragile eco-environmental areas through payments for ecosystem services. Thus, this strategy can improve the sustainability of ecological and environmental restoration programs.

Deep Learning Based Purchase Forecasting for Food Producer-Retailer Team Merchandising

Expired foods turning into waste has always been an important issue. In Taiwan, more than 36,000 metric tons of unopened expired food, worth more than $130 million, are thrown away from retail stores as waste each year. Insufficient inventory results in the loss of business prospects for retailers, whilst excessive inventory results in abandoned merchandise. Foods with a short shelf life are particularly vulnerable. Typically, food producer and retailer would form team merchandising (MD). The team MD mechanism is responsible for ensuring safety and quality, not for forecasting demand. This study uses artificial neural networks (ANNs) to analyze sales data to forecasting purchase volume in response to changes in store environment, weather, events, and consumer attributes. The study object is a sort of cream puff with a short shelf life cobranded by a retailer. According to the experimental results, the adopted proposed model in this study effectively reduces the error in purchasing; the mean-square percentage error (MSPE) of the forecast values is less than 6%. The importance of this study is on promoting the team MD’s green energy management capabilities in food production and verifiably achieving the goal of environmental sustainability.

Power Quality Enhancement and Power Flow Analysis of a PV Integrated UPQC System in a Distribution Network

Increasing awareness for green energy and sustainable energy management has accelerated the popularity for the incorporation of distributed energy resources and distributed energy storage into the distribution network and microgrid. This has proliferated the use of power electronic-based devices giving rise to a serious issue of deteriorating power quality (PQ) in the distribution system. In this context, this article presents a photovoltaic (PV) integrated unified power quality conditioner (UPQC) operating with an adaptive compensating technique based on variable leaky least mean square (VLLMS) algorithm. It is a soft computing-oriented method that offers quicker convergence to the desired condition in an iterative approach keeping the weight of the updating parameters within the specified limit. The VLLMS-based algorithm eliminates the use of low pass or moving average filter for the extraction of fundamental components from polluted source voltage and load current to generate reference signal for the switching of shunt as well as series voltage source converters (VSC) of the UPQC. Due to the involvement of feed-forward component of PV in the compensating technique of shunt VSC, it efficiently and smoothly manages power balance between grid, load, and PV besides resolving the PQ issues of current harmonics and poor power factor at PCC. It also ensures the regulation of dc-link voltage. The series converter maintains pure sinusoidal voltage at the load terminal irrespective of sag/swell and harmonics present in the grid voltage. The effectiveness of the proposed system is verified through simulation as well as hardware implementation under different static and dynamic operating conditions.

Green Energy Management through the Implementation of RES in the EU. Analysis of the Opinions of Poland and Germany

The EU’s energy policy is geared towards introducing changes in order to stop or avoid negative climate change. The determinants of the energy policies of Poland and Germany are presented along with the priorities until 2050. The possibilities of reducing the impact of energy on the environment were interpreted, emphasizing its common goal. Activities supporting the EU and maintaining the level of energy security are indicated. Possible negative effects in the social and health sphere are presented as a response to the failure to implement these demands. The literature was analyzed and proprietary questionnaires were carried out, and the results realized in the EU by the independent FBI Economic Department were implemented. On the basis of earlier pilot studies, variants of a successful transformation were implemented, assuming different roles performed by a citizen (entrepreneur). Opportunities to tackle climate change and global challenges have been identified based on innovation and motivation. The opinion on the way of thinking in Poland and Germany was compared, and a descriptive model was prepared to increase the achievement of the assumed goals, together with a model based on innovation. Citizens should be active in efforts to promote renewable energy. The EU has a strong base to work to avoid climate change. What is needed is innovative thinking and a motivation to introduce changes with the involvement of every citizen through solidarity, efficiency, and justice. The existing foundations of legal solutions create adaptation possibilities and the ability to achieve goals through self-discipline, comparing with the assumption of EU civic awareness, significantly affecting the successful implementation of RES. Analyzing the attitudes of Poland and Germany, it seems that the distant social consequences are not sufficient. It is necessary not only to perform selected management functions (planning, organizing), but also partly to motivate. There is a strong need to increase motivation and implement the control process required under the threat of negative consequences in the current period as a key strategic preventive action. The analysis of the opinions of Poland and Germany against the background of the European Union allowed for no significant differences, subject to Germany’s readiness to take greater risk. Most of Poland is less inclined to take risks. The lack of an unambiguous opinion may indicate incomplete awareness of both the negative effects of climate change. Both countries are concerned about the unplanned costs of transformation. Germany, unlike Poland, has dedicated employees to handle the climate, and conducts an energy audit and is of interest to investors, thanks to investment plans.

Biogenic architectures for green, cheap, and efficient thermal energy storage and management

A key challenge for efficient thermal management of civil buildings is the development of shape-stabilized phase change materials (PCM) for thermal energy storage and release. Nevertheless, some issues related to the disposal of such devices are arising as they are generally not biodegradable and recyclable. In this work, we developed two new renewable and biodegradable thermal energy storage composites obtained from renewable resources. These are based on the use of bio-derived alcohol as PCM and on porous biogenic structures, namely cuttlebone and pomelo peel, as shape stabilizers, which are currently waste materials. The results point out that both cuttlebone and pomelo peel can spontaneously absorb huge amounts of the considered PCM and retain it in the liquid state. The thermal energy storage capacity of the composites is about 70% that of neat PCM, whereas the volumetric efficiency (i.e., the ratio between the thermal energy storage capacity of the composite and the neat PCM in J cm−3) approaches 90% and 70% in cuttlebone and pomelo peel composites, respectively. The properties appear stable over at least 100 melting/solidification cycles.