PLOS ONE: [sortOrder=DATE_NEWEST_FIRST, sort=Date, newest first, q=subject:"Energy and power"]PLOShttps://journals.plos.org/plosone/webmaster@plos.orgaccelerating the publication of peer-reviewed sciencehttps://journals.plos.org/plosone/search/feed/atom?sortOrder=DATE_NEWEST_FIRST&unformattedQuery=subject:%22Energy+and+power%22&sort=Date,+newest+firstAll PLOS articles are Open Access.https://journals.plos.org/plosone/resource/img/favicon.icohttps://journals.plos.org/plosone/resource/img/favicon.ico2024-03-29T14:42:45ZA modified particle swarm optimization rat search algorithm and its engineering applicationManish Kumar SinglaJyoti GuptaMohammed H. AlsharifMun-Kyeom Kim10.1371/journal.pone.02968002024-03-28T14:00:00Z2024-03-28T14:00:00Z<p>by Manish Kumar Singla, Jyoti Gupta, Mohammed H. Alsharif, Mun-Kyeom Kim</p>
Solar energy generation requires photovoltaic (PV) systems to be optimised, regulated, and simulated with efficiency. The performance of PV systems is greatly impacted by the fluctuation and occasionally restricted accessibility of model parameters, which makes it difficult to identify these characteristics over time. To extract the features of solar modules and build highly accurate models for PV system modelling, control, and optimisation, current-voltage data collecting is essential. To overcome these difficulties, the modified particle swarm optimization rat search algorithm is presented in this manuscript. The modified rat search algorithm is incorporated to increase the PSO algorithm’s accuracy and efficiency, which leads to better outcomes. The RSA mechanism increases both the population’s diversity and the quality of exploration. For triple diode model of both monocrystalline and polycrystalline, PSORSA has showed exceptional performance in comparison to other algorithm i.e. RMSE for monocrystalline is 3.21E-11 and for polycrystalline is 1.86E-11. Similar performance can be observed from the PSORSA for four diode model i.e. RMSE for monocrystalline is 4.14E-09 and for polycrystalline is 4.72E-09. The findings show that PSORSA outperforms the most advanced techniques in terms of output, accuracy, and dependability. As a result, PSORSA proves to be a trustworthy instrument for assessing solar cell and PV module data.Reflections from COP28: Resisting healthwashing in climate change negotiationsAmiteshwar SinghTarek EzzineRenzo R. GuintoSophie GeppRobbie M. ParksMeelan ThondooPoorvaprabha PatilKim R. van Daalen10.1371/journal.pgph.00030762024-03-28T14:00:00Z2024-03-28T14:00:00Z<p>by Amiteshwar Singh, Tarek Ezzine, Renzo R. Guinto, Sophie Gepp, Robbie M. Parks, Meelan Thondoo, Poorvaprabha Patil, Kim R. van Daalen</p>Advancing the climate change and health nexus: The 2024 AgendaVanessa KerrySadath Sayeed10.1371/journal.pgph.00030082024-03-28T14:00:00Z2024-03-28T14:00:00Z<p>by Vanessa Kerry, Sadath Sayeed</p>The influence of environmental diplomacy, economic determinants and renewable energy consumption on environmental degradation: Empirical evidence of G20 countriesMuhammad RizwanullahJian ShiMuhammad NasrullahXue Zhou10.1371/journal.pone.03009212024-03-25T14:00:00Z2024-03-25T14:00:00Z<p>by Muhammad Rizwanullah, Jian Shi, Muhammad Nasrullah, Xue Zhou</p>
This study examines how various environmental and economic variables contribute to environmental degradation. Industrialization, trade openness, and foreign direct investment are among the variables, as are environmental diplomacy, environmental diplomacy secure, and renewable energy consumption. Therefore, the data covers the years 1991–2020, and our sample includes all 19 countries and two groups (the European Union and the African Union). The research used the Pesaran CD test to determine cross-section dependency, CIPS and CADF test to determine stationarity, the Wald test for hetrodcedasasticity and the Wooldridge test for autocorrelation; therefore, VIF for multicollinearity, Durbin and Hausman to analyze the endogeneity. It also employed Westerlund’s cointegration test to ensure cross-sectional dependence, Wald test for group-wise heteroscedasticity, Wooldridge test for autocorrelation, VIF for multicollinearity, and Durbin and Hausman for endogeneity. The two-step system generalized method of moments (GMM) is used to estimate the results and confirm the relationship between independent variables (Industrialization, trade openness, FDI, environmental diplomacy, secure environmental diplomacy, and renewable energy) and dependent variables (Environmental Degradation) in G20 countries. Therefore, Industrialization, trade openness, foreign direct investment, ecological diplomacy, and renewable energy consumption significantly impact ecological degradation. Environmental diplomacy is crucial to combat degradation and stimulate global collaboration. G20 nations enact strict environmental restrictions to tackle climate change and encourage economic growth.Smart distribution network voltage estimation using PMU technology considering zero injection constraintsSwathi TangiD. N. GaonkarRamakrishna S. S. NuvvulaPolamarasetty P. KumarIlhami ColakAhmad F. TazayMohamed I. Mosaad10.1371/journal.pone.02936162024-03-25T14:00:00Z2024-03-25T14:00:00Z<p>by Swathi Tangi, D. N. Gaonkar, Ramakrishna S. S. Nuvvula, Polamarasetty P. Kumar, Ilhami Colak, Ahmad F. Tazay, Mohamed I. Mosaad</p>
To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network’s operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method’s viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.Branch error reduction criterion-based signal recursive decomposition and its application to wind power generation forecastingFen XiaoSiyu YangXiao LiJunhong Ni10.1371/journal.pone.02999552024-03-22T14:00:00Z2024-03-22T14:00:00Z<p>by Fen Xiao, Siyu Yang, Xiao Li, Junhong Ni</p>
Due to the ability of sidestepping mode aliasing and endpoint effects, variational mode decomposition (VMD) is usually used as the forecasting module of a hybrid model in time-series forecasting. However, the forecast accuracy of the hybrid model is sensitive to the manually set mode number of VMD; neither underdecomposition (the mode number is too small) nor over-decomposition (the mode number is too large) improves forecasting accuracy. To address this issue, a branch error reduction (BER) criterion is proposed in this study that is based on which a mode number adaptive VMD-based recursive decomposition method is used. This decomposition method is combined with commonly used single forecasting models and applied to the wind power generation forecasting task. Experimental results validate the effectiveness of the proposed combination.Demand side management using optimization strategies for efficient electric vehicle load management in modern power gridsManoj Kumar V.Bharatiraja ChokkalingamDevakirubakaran S.10.1371/journal.pone.03008032024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Manoj Kumar V., Bharatiraja Chokkalingam, Devakirubakaran S.</p>
The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid’s balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.Analysis of the quality of tunnel roof topography by automatic cutting control under the coupling of multiple factorsJinnan LuBo LiYun ZhuMiao XieQingshuang MengZhixiang LiuYufeng Dong10.1371/journal.pone.02998052024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Jinnan Lu, Bo Li, Yun Zhu, Miao Xie, Qingshuang Meng, Zhixiang Liu, Yufeng Dong</p>
The automatic cutting of coal and rock surface morphology modeling based on the actual geological environment of coal mine underground excavation and mining is of great significance for improving the surface quality of coal and rock after cutting and enhancing the safety and stability of advanced support. To this end, using the principle of coordinate transformation, the kinematic trajectory of the cutting head of the tunneling machine is established, and the contour morphology of the cutting head under variable cutting technology is obtained. Then, based on the regenerative vibration theory of the cutting head, a dynamic model of the cutting head coal wall is established, and the coordinate relationship of the cutting head in the tunnel coordinate system under vibration induction is analyzed. Based on fractal theory and Z-MAP method, a simulation method for the surface morphology of coal and rock after cutting is proposed, which is driven by the cutting trajectory Under the coupling effect of cutting vibration induction and random fragmentation of coal and rock, simulation of the surface morphology of comprehensive excavation tunnels was conducted, and relevant experiments were conducted to verify the results. A 1:3 similarity experimental model of EBZ160 tunneling machine was used to build a cutting head coal and rock system cutting experimental platform for comparative experiments of cutting morphology. Furthermore, statistical methods were used to compare and evaluate the simulated roof with the actual roof. The results show that the relative errors between the maximum range of peaks and valleys, the peak skewness coefficient of height standard deviation, and the kurtosis coefficient of the actual roof are 1.3%, 24.5%, 16%, and 2.9%, respectively. Overall, this indicates that the surface morphology distribution characteristics of the simulated roof and the actual roof are similar, verifying the effectiveness of the modeling and simulation method proposed in this paper, and providing theoretical support for the design and optimization of advanced support in the future.The impact of carbon emissions from lag fertilization on wheat productionAtif RahimQianrui PengHuashuai ChenYuxi Liu10.1371/journal.pone.02992992024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Atif Rahim, Qianrui Peng, Huashuai Chen, Yuxi Liu</p>
This study examines the influence of lag fertilization techniques on Pakistani wheat production, highlighting the need to understand and mitigate the environmental impacts of farming methods. The basic purpose of this study is to investigate the impact of CO2 emission from fertilization and other factors on wheat production in Pakistan, using a time series of data from 1990 to 2020. CO2 emission from fertilization (CO2EF) is estimated using the default values provided by the IPCC guidelines. The ARDL approach analyses the short-run and long-run effects of CO2EF, technology level, energy use, agricultural land, and agricultural labor on wheat production. The results show that all factors have significantly impacted wheat production in Pakistan at levels of 1% and 5% significance, both in the short and long run. These findings suggest that reducing CO2EF, technology level, energy use, agricultural land, and agricultural labor on wheat production can help to increase wheat production in Pakistan. The study also highlights the importance of adopting sustainable and efficient fertilization practices, exploring alternative fertilizers, and using crop rotation systems to mitigate the adverse effects of carbon emissions from nitrogen fertilization, energy use, and the use of technology. These measures can contribute to a more sustainable and climate-resilient agriculture sector in Pakistan.Encirclement of productive capacities and institutions in context of sustainable developmentRenhan GuoGhulam Rasool Madni10.1371/journal.pone.02973502024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Renhan Guo, Ghulam Rasool Madni</p>
The question of whether productive capacities and institutional quality facilitate or impede progress towards sustainable development is a significant issue that has not been extensively explored in prior literature. Despite their importance, these variables are often overlooked in the literature on sustainable development, yet they play a crucial role in enabling efforts to achieve sustainable development. In this study, we examined how productive capacities affect sustainable development, with a moderating impact of institutional quality. The sample was comprised of 44 Belt and Road Initiative (BRI) economies, covering the period from 2000 to 2018. Using a two-step system GMM, we found that the relation between productive capacities and sustainable development is dynamic, positive, and significant. Additionally, institutional quality played a moderating role in achieving sustainable development, especially among regionally connected countries. Our findings suggest that sustainable development is strongly linked to a country’s productive capacities. Therefore, improving productive capacities and institutional quality may lead to long-term development and sustainability. These results are valuable to academia as they provide new thought regarding the influence of productive capacities and institutional quality on sustainable development, and policymakers may benefit from the suggestions presented regarding productive capacities and institutional quality.Understanding the factors that affect households’ investment decisions required by the energy transitionArmando Aguayo-MendozaAne Irizar-ArrietaDiego Casado-MansillaCruz E. Borges10.1371/journal.pone.02972222024-03-21T14:00:00Z2024-03-21T14:00:00Z<p>by Armando Aguayo-Mendoza, Ane Irizar-Arrieta, Diego Casado-Mansilla, Cruz E. Borges</p>
In energy systems’ economic models, people’s behaviour is often underestimated, and they are generally unaware of how habits impact energy efficiency. Improving efficiency is challenging, and recommendations alone may not be sufficient. Changing behaviour requires understanding the direct impact of needs and habits on energy efficiency. This research introduces a methodology that retrieves human expert knowledge from four key aspects of the current energy transition: everyday appliances, buildings, mobility, flexibility, and energy efficiency. The aim is to examine the causal relationship between energy consumption and human behaviour, gaining a deeper understanding of the links among the factors that drive final energy consumers to change habits through the adoption of energy-saving measures. Working in collaboration with expert panels, this study provides a methodology for extracting expert human knowledge based on a set of future energy transition scenarios aligned with the achievement of the Paris Agreement, a taxonomy of 32 factors that have a strong influence on households’ investment decisions, and the results of a survey that characterises the European population through the 32-factor taxonomy and some socioeconomic conditions. In addition, the survey included a sample of the Latin American population to analyse how socioeconomic conditions (region, education, gender, etc.) influence the prioritisation of these factors. We discuss the high priority given to competence and autonomy over financial factors by inhabitants of the European Union residential sector. We provide an analysis of the factors through which other similar projects are focused and on which we converge. In addition, we contribute by presenting the hierarchy of priorities assigned by people. This highlights the importance for policymakers to take these aspects seriously when implementing energy policy interventions that go beyond purely financial measures and fiscal incentives.Does financial inclusion and information communication technology affect environmental degradation in oil-producing countries?Isbat AlamLu ShichangSaqib MuneerKhalid Mahsan AlshammaryMuhammad Zia ur Rehman10.1371/journal.pone.02985452024-03-20T14:00:00Z2024-03-20T14:00:00Z<p>by Isbat Alam, Lu Shichang, Saqib Muneer, Khalid Mahsan Alshammary, Muhammad Zia ur Rehman</p>
Advances in financial inclusions have contributed to economic growth and poverty alleviation, addressing environmental implications and implementing measures to mitigate climate change. Financial inclusions force advanced countries to progress their policies in a manner that does not hinder developing countries’ current and future development. Consequently, this research examined the asymmetric effects of information and communication technology (ICT), financial inclusion, consumption of primary energy, employment to population ratio, and human development index on CO<sub>2</sub> emissions in oil-producing countries (UAE, Nigeria, Russia, Saudi Arabia, Norway, Kazakhstan, Kuwait, Iraq, USA, and Canada). The study utilizes annual panel data spanning from 1990 to 2021. In addition, this study investigates the validity of the Environmental Kuznets Curve (EKC) trend on the entire sample, taking into account the effects of energy consumption and population to investigate the impact of financial inclusion on environmental degradation. The study used quantile regression, FMOLS, and FE-OLS techniques. Preliminary outcomes revealed that the data did not follow a normal distribution, emphasizing the need to use quantile regression (QR). This technique can effectively detect outliers, data non-normality, and structural changes. The outcomes from the quantile regression analysis indicate that ICT consistently reduces CO<sub>2</sub> emissions in all quantiles (ranging from the 1st to the 9th quantile). In the same way, financial inclusion, and employment to population ratio constrains CO<sub>2</sub> emissions across each quantile. On the other side, primary energy consumption and Human development index were found to increase CO<sub>2</sub> emissions in each quantile (1st to 9th). The findings of this research have implications for both the academic and policy domains. By unraveling the intricate interplay between financial inclusion, ICT, and environmental degradation in oil-producing nations, the study contributes to a nuanced understanding of sustainable development challenges. Ultimately, the research aims to guide the formulation of targeted policies that leverage financial inclusion and technology to foster environmentally responsible economic growth in oil-dependent economies.Research on distributionally robust energy storage capacity allocation for output fluctuations in high permeability wind and solar distribution networksXin WangBo SunCheng GeQian LiuZhiwei LiMengqi Huang10.1371/journal.pone.02992262024-03-19T14:00:00Z2024-03-19T14:00:00Z<p>by Xin Wang, Bo Sun, Cheng Ge, Qian Liu, Zhiwei Li, Mengqi Huang</p>
This paper presents a novel approach to addressing the challenges associated with energy storage capacity allocation in high-permeability wind and solar distribution networks. The proposed method is a two-phase distributed robust energy storage capacity allocation method, which aims to regulate the stochasticity and volatility of net energy output. Firstly, an energy storage capacity allocation model is established, which considers energy storage’s investment and operation costs to minimize the total cost. Then, a two-stage distributed robust energy storage capacity allocation model is established with the confidence set of uncertainty probability distribution constrained by 1-norm and ∞-norm. Finally, a Column and Constraint Generation (C&CG) algorithm is used to solve the problem. The validity of the proposed energy storage capacity allocation model is confirmed by examining different wind and solar penetration levels. Furthermore, the model’s superiority is demonstrated by comparing it with deterministic and robust models.New energy vehicles’ technology innovation coordination strategy based on alliance negotiation under dual credit policyMiaomiao MaWeidong MengBo HuangYuyu Li10.1371/journal.pone.02999152024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Miaomiao Ma, Weidong Meng, Bo Huang, Yuyu Li</p>
The development of new energy vehicles (NEVs) is one of the effective ways to alleviate carbon emissions, environmental pollution, and energy scarcity in the transportation sector. The Chinese government has innovatively proposed the “dual credit policy,” but it is still a hot topic whether it can promote the NEVs’ technological innovation. In this study, we construct game models and obtain the technological innovation strategies for NEVs under the dual credit policy, considering that the NEV supply chain contains one manufacturer and N suppliers. Further, we construct bargaining game models and study how to encourage manufacturers and suppliers to enhance technological innovation, realize supply chain coordination, and give the alliance strategy to maximize suppliers’ profit. We found that the dual credit policy can effectively stimulate technological innovation, and the higher the credit price or technological innovation credit factor, the higher the technical level of NEVs. The findings could guide the government to adjust and revise the policy. Second, we found that the bargaining games could coordinate the NEV supply chain so that decentralized enterprises can achieve optimal technological innovation under centralized decision-making. Third, we found that suppliers can improve their profits by choosing a suitable alliance strategy under the manufacturer’s different negotiating power.A dynamic traffic signal scheduling system based on improved greedy algorithmGuangling SunRui QiYulong LiuFeng Xu10.1371/journal.pone.02984172024-03-15T14:00:00Z2024-03-15T14:00:00Z<p>by Guangling Sun, Rui Qi, Yulong Liu, Feng Xu</p>
Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.