Energy Strategy Reviews最新文献

{"title":"Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions","authors":"M. Gandomzadeh ,&nbsp;A.A. Yaghoubi ,&nbsp;A. Hoorsun ,&nbsp;A. Parsay ,&nbsp;A. Gholami ,&nbsp;M. Zandi ,&nbsp;R. Gavagsaz-Ghoachani ,&nbsp;H.A. Kazem","doi":"10.1016/j.esr.2024.101629","DOIUrl":"10.1016/j.esr.2024.101629","url":null,"abstract":"<div><div>This review consolidates four decades of research (1983–2024) on dust mitigation for photovoltaic systems, categorizing strategies into four key areas: preventive measures, dust monitoring systems, active cleaning methods, and multi-criteria decision-making strategies for cleaning schedules. A systematic content analysis was employed to critically evaluate methodologies, findings, and emerging trends. Preventive measures, notably super-hydrophobic coatings, can reduce dust accumulation by up to 50 %. However, integrating monitoring systems remains complex. The review highlights the potential of hybrid cleaning methods combining manual, mechanical, electromechanical, and electrostatic approaches to balance their benefits and limitations for higher efficiency and practicality. It also emphasizes a shift from fixed-interval cleaning schedules to dynamic, AI-driven decisions based on real-time data, potentially reducing the levelized cost of energy by 8 % for large-scale photovoltaic plants and 4.3 % for smaller systems. Unmanned aerial vehicle-based cleaning methods are recognized as a promising future solution for large-scale photovoltaic systems. The review identifies critical research gaps and provides recommendations for advancing dust mitigation technologies and optimizing photovoltaic cleaning and maintenance strategies to minimize soiling effects. This comprehensive analysis aims to guide future research toward more sustainable and economically viable soil detection and mitigation systems as well as photovoltaic system management.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101629"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Capturing complex electricity load patterns: A hybrid deep learning approach with proposed external-convolution attention","authors":"Mohammad Sadegh Zare ,&nbsp;Mohammad Reza Nikoo ,&nbsp;Mingjie Chen ,&nbsp;Amir H. Gandomi","doi":"10.1016/j.esr.2025.101638","DOIUrl":"10.1016/j.esr.2025.101638","url":null,"abstract":"<div><div>Short-term electricity load forecasting is a critical factor in optimizing power systems, minimizing operating costs, and securing reliable energy resources. There are various approaches for short-term electricity load forecasting, but handling complex dependencies and sudden changes in load data remains challenging. This study introduces a hybrid deep learning model to improve load forecasting accuracy. The model combines the strengths of various deep learning architectures such as Convolutional Neural Network, Temporal Convolutional Network, and Bidirectional Long Short-Term Memory with a proposed attention mechanism. This approach helps to extract temporal relations and learn long-term patterns. Furthermore, the proposed External-Convolution Attention technique effectively captures global and temporal patterns within the input sequences. Three data sets are used to conduct experiments and validate the proposed model. The proposed model is compared against several machine learning and deep learning models across five evaluation metrics. The findings show the strength of the proposed model by outperforming other models. Our load forecasting method achieves improvements ranging from 2% to 21% across different evaluation metrics. The study also evaluates the effects of datasets, features, and prediction horizons. The presented hybrid deep learning model and a novel attention mechanism improve load forecasting accuracy, contributing to the advancement of artificial intelligence in energy optimization techniques. Our model demonstrates superior performance through extensive experimentation and diverse scenarios by identifying complex load patterns and adjusting to various datasets. This indicates its practical applicability in engineering for optimizing power systems and minimizing operational costs.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101638"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pollution havens in high-income emerging nations: Can green energy, financial development and environmental rules change this?","authors":"Xiaoqian Zhang ,&nbsp;Linglu Linjin","doi":"10.1016/j.esr.2024.101635","DOIUrl":"10.1016/j.esr.2024.101635","url":null,"abstract":"<div><div>This study investigates the role of green energy, financial development, and environmental regulations in addressing pollution havens within high-income emerging economies. While these nations benefit from rapid industrialization, they simultaneously face significant environmental challenges due to lax environmental standards and a tendency to attract pollution-intensive industries. This paper explores whether green energy adoption, financial sector advancements, and stringent environmental policies can mitigate such adverse impacts. Using data spanning from 1995 to 2023 and employing the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, we analyze the experiences of BRICS countries—Brazil, Russia, India, China, and South Africa. The findings reveal that green energy adoption consistently correlates with a reduction in CO2 emissions across these nations, with China showing the most substantial improvement. Financial development positively contributes to environmental outcomes, particularly in South Africa, where access to green finance has spurred investments in sustainable projects. Moreover, environmental regulations have the most profound impact in Russia, where stringent policies have led to a measurable decline in pollution. Countries with higher levels of financial development and regulatory rigor also show stronger resilience against the inflow of pollution-intensive industries. The study highlights the critical role of a balanced approach that integrates economic growth with sustainability, suggesting that high-income emerging economies can leverage green energy and financial development, supported by robust policies, to transition away from pollution havens and toward a sustainable growth trajectory. A policy implication from these findings is that governments should prioritize investments in renewable energy and strengthen regulatory frameworks to attract cleaner industries.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101635"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Iran's Petroleum Contract “IPC” in comparison with Buy-back and Production Sharing Agreement","authors":"Mohammad Ali Bahmaei ,&nbsp;Ehsan Afshar","doi":"10.1016/j.esr.2024.101599","DOIUrl":"10.1016/j.esr.2024.101599","url":null,"abstract":"<div><div>Iran's Petroleum Contract “IPC” as a risk service contract, contains some important improvements and features in order to attract foreign investments and know-how to the long-standing oil industry of Iran. However, the views of international oil companies (“IOC”) towards contractual regimes in the oil sector show that they are often not eager to enter into the risk service-prone contracts and therefore, IPC is another alternative and a new opportunity for IOCs to invest in the oil industry in Iran, as compared with Production Sharing Agreement (PSA) and modern concessions.</div><div>According to the current laws and regulations, adopting PSA in Iran's oil industry is not strictly forbidden from legal point of view. Therefore, using a well-designed PSA, at least for some high risk and cost regions, may ensure the balance of interests between National Iranian Oil Company (“NIOC”) and foreign oil companies, without disrupting the existing IPC's framework. Thus, through JCPOA and removal of the sanctions, the Iranian authorities in the oil industry should be prepared to take more flexible and favorable attitude towards the petroleum contracts in Iran for the benefit of the country in compliance with the applicable laws and regulations.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101599"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic nexus among energy security, energy equity and net electricity regions: Optimizing renewable energy integration and demand response for electrical supply systems","authors":"Jiang Wu ,&nbsp;Yang Zhang ,&nbsp;Akhtar Khan","doi":"10.1016/j.esr.2024.101611","DOIUrl":"10.1016/j.esr.2024.101611","url":null,"abstract":"<div><div>This study optimizes a local renewable electricity supply system to integrate demand response tactics for power-intensive industry activities. We use a multi-phase optimization framework to design demand response scheduling for continuous production and identify the best configuration for a local energy generating and storage system. Our main goals are to reduce overall expenses and lessen the impact of global warming. Important elements, including electric batteries, local solar and wind power generation, and intraday and day-ahead electricity trading, are all thoroughly examined in the study. The results show that, although at a significant increase in cost, adding a battery system has the potential to lower emissions and enable large-scale market electricity trading. To attain ecological and economic sustainability within the environment, this research emphasizes optimizing industrial processes. It also highlights the value of demand response methods and the integration of renewable energy in reducing the effects of climate change. The results show that the systems of demand response, integrating the usage of some renewable energy types, can significantly contribute to improved ecological and economic performance in manufacturing plants. Such research also points to the need to guard policy environments that facilitate the deployment of RE and efficient AES infrastructures to support organizations’ sustainable industrial transformation.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101611"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Policy pathways through FinTech and green finance for low-carbon energy transition in BRICS nations","authors":"Bo Dai ,&nbsp;Jian Zhang ,&nbsp;Nasir Hussain","doi":"10.1016/j.esr.2024.101603","DOIUrl":"10.1016/j.esr.2024.101603","url":null,"abstract":"<div><div>This research examines the challenges faced by the BRICS countries, with a specific focus on issues surrounding energy production and environmental degradation. It investigates the potential of green financing and knowledge-for-money exchange to accelerate the transition to sustainable energy sources. This study employs sophisticated statistical methodologies to analyze the impact of economic variables, technological advancements, and ecological banking practices on the adoption of sustainable electricity utilization. The findings reveal a stable and positive relationship between the increase in the share of renewable energy sources, advancement in financial and technological sectors, the preservation of biological diversity, and the rational use of resources. The use of environmentally friendly energy solutions is particular sensitive to improvement in financial technology (FinTech) and sustainable funding sources. However, reliance on exploiting natural resources poses challenges to fully adopting low-carbon energy policies. This research underscores the critical importantance of environmental conservation and adocates for the use of sustainable funding mechanisms and financial innovation to implement effective policies for transitioning to low-carbon energy systems.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101603"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy harvesting techniques for wireless sensor networks: A systematic literature review","authors":"Bernardo Yaser León Ávila ,&nbsp;Carlos Alberto García Vázquez ,&nbsp;Osmel Pérez Baluja ,&nbsp;Daniel Tudor Cotfas ,&nbsp;Petru Adrian Cotfas","doi":"10.1016/j.esr.2024.101617","DOIUrl":"10.1016/j.esr.2024.101617","url":null,"abstract":"<div><div>Energy harvesting has emerged as a promising avenue for addressing the constraints imposed by battery lifespan in wireless sensor networks (WSNs), paving the way for more sustainable and autonomous operations. This paper presents a comprehensive and systematic literature review (SLR) that critically examines the latest advancements and methodologies in energy harvesting for wireless sensor networks (WSNs). The review encompasses the entire system architecture, including energy storage and power management systems. The review is based on bibliometric analysis and a detailed examination of an extensive collection of 196 peer-reviewed studies published between 2014 and 2023. The text provides a comprehensive assessment of diverse technologies, techniques, and mechanisms for extracting energy from environmental sources, including thermal, light, mechanical, radio frequency, chemical, and biological, to power a wireless sensor node device. Furthermore, the concepts of transducers, energy sources, and energy types are elucidated and defined, thus establishing a lucid framework for classifying and analyzing these techniques and technologies. Additionally, key trends, challenges, and future research directions are identified, emphasizing areas that require further exploration to advance the field, particularly in battery-less systems employing capacitors and supercapacitors.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101617"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating the energy transition: Interplay of geopolitics, economic complexity, and environmental governance in OECD countries","authors":"Linhui Wu ,&nbsp;Saddam Hussain","doi":"10.1016/j.esr.2024.101624","DOIUrl":"10.1016/j.esr.2024.101624","url":null,"abstract":"<div><div>This research aims to determine, within this context, the influence of environmental governance, economic complexity, and geopolitical risks on energy transition in 20 OECD countries between 1990 and 2021. The current study is anchored on the Paris Agreement and COP27, which promotes clean energy (SDG 7) and environmental conservation (SDG 13). In this analysis, the new Mixed Multi-Quantile Regression (MMQR) model is used to mitigate slope heterogeneity and cross-sectional dependence because it accommodates heterogeneity in distinct phases of the energy transition. In addition, an asymmetric approach is employed to investigate the moderating role of geopolitical risk on the link between environmental governance, economic complexity, and energy transition. The main results of this research show that environmental governance significantly and positively affects energy transition at all quantiles, while economic complexity has a positive impact at some quantiles. However, their impacts tend to differ during the energy transition process. On the one hand, the interface between environmental governance and geopolitics presents potential support for the transition process. It creates difficulties for it, on the other hand. (3) Economic development positively correlates with energy transition because nations from the developed economy possess the ability and the resources to impact energy transition effectively. Nevertheless, when considering geopolitical risks, economic complexity turns out to be negative and contributes to the problem of energy transition. This work establishes that economic diversification and good ecological policies enable energy transition. The results are also validated by a panel Granger causality test that shows that environmental governance and economic complexity can improve the environment for accepting clean energy and its financial health. The research offers a policy guide for policymakers, managers, and investors operating in the global climate, geopolitical risk, conflicts, and renewable energy.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101624"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Volatility spillovers and conditional correlations between oil, renewables and stock markets: A multivariate GARCH-in-mean analysis","authors":"Wenxue Wang ,&nbsp;Peter G. Moffatt ,&nbsp;Zheng Zhang ,&nbsp;Muhammad Yousaf Raza","doi":"10.1016/j.esr.2025.101639","DOIUrl":"10.1016/j.esr.2025.101639","url":null,"abstract":"<div><div>We investigate linkages between three different markets: renewable energy (represented by a range of renewable energy ETFs); traditional energy (represented by crude oil ETF); and common stocks (represented by the S&amp;P 500 Index ETF). We use daily data from 2008 to 2021. The econometric framework adopted is the VARMA-DCC-GARCH-in-mean model. We find that this framework is ideal because it allows us to identify the impact of uncertainty in one market on returns in another market, and also volatility spillovers, that is, the phenomenon of high uncertainty in one market spreading to other markets. Our key findings are as follows. Stock-market uncertainty influences traditional energy (negatively) and renewable energy (positively) at the mean level. Stock market volatility has a positive spillover effect on both conventional and renewable energies in the short-run, but these spillover effects are negative in the long-run. Our estimates of the time-paths of dynamic conditional correlations provide evidence that the renewable market is more heavily “financialized” than the traditional energy market, and moreover that the strong financialization of renewables is robust to financial crises.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101639"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and managing residential energy demand for a low-carbon future","authors":"Chang Zhang ,&nbsp;Mirzat Ullah ,&nbsp;Hind Alofaysan ,&nbsp;Hakimjon Hakimov ,&nbsp;Sophia Audrey","doi":"10.1016/j.esr.2024.101610","DOIUrl":"10.1016/j.esr.2024.101610","url":null,"abstract":"<div><div>Achieving a decarbonized society requires balancing two critical and seemingly conflicting objectives: reducing energy consumption and ensuring energy demand flexibility to adapt to the variability of renewable energy production. This study introduces \"energy demand science\" as a multidisciplinary field to address these challenges, focusing on the residential sector, which significantly impacts energy use due to occupant behavior and lifestyle. Using a comprehensive review of literature and advanced modeling techniques, this research explores mechanisms driving energy demand. Key results show that energy demand can be reduced by up to <strong>40 % by 2050</strong> through lifestyle adjustments, urbanization, and innovative technologies, aligning with global warming targets below 1.5 °C. Advanced modeling techniques and high-resolution data analyses were employed to explore mechanisms driving energy demand, supported by historical data from 1970 to present, incorporating advancements in IoT and smart metering technologies. The results highlight the importance of integrating technological, human, natural, and socio-economic factors to achieve a sustainable reduction in energy use while maintaining flexibility. Policy implications emphasize the need for holistic, interdisciplinary strategies to enable efficient demand-side management, enhance renewable energy integration, and align energy consumption with decarbonization goals.</div></div>","PeriodicalId":11546,"journal":{"name":"Energy Strategy Reviews","volume":"57 ","pages":"Article 101610"},"PeriodicalIF":7.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}