Authors:S. Jaisiva, K. Prabaakaran, C. Kumar, M. Lakshmanan, Abdullah Alwabli, Amar Jaffar, Ayman Alharbi, Abdulaziz Miyajan Abstract: This article applies a novel intelligence technique to solve power system issues faced daily. Compensation for reactive power is a significant issue faced by power system operators in research. The solution can be obtained by handling a multi-objective task and multiconstraints by reducing the active power loss and minimizing the voltage deviation at the load end. The novelty of the research focuses on integrating artificial neural network techniques with the firefly algorithm, a novel optimization algorithm for attaining an objective function. The Levenberg–Marquardt back-propagation algorithm is most suited for proper tuning of the control variables. The objective of this research can be attained by appropriately tuning the control variables connected with the IEEE test bus systems, which helps to maximally improve the voltage profile. Existing research studies have focused on reactive power management, which is attained by solving optimal reactive power flow problems employing nature-inspired approach techniques such as the symbiotic organism search algorithm, the cuckoo search algorithm, the black hole algorithm, the krill herd algorithm, and whale optimization. The evolving strategy, the firefly algorithm (FFA), minimizes the multiconstraint functions more competently and effectively than any conventional algorithm. To showcase the strength of the firefly algorithm incorporating AI, it is examined on standard IEEE test bus systems, namely, the 14-, 30-, and 58-bus networks. The obtained results quantify the effectiveness of the proposed methodology, that is, the artificial intelligence technique implementing the firefly algorithm gives better results than conventional methods. PubDate: 2023-12-01T00:00:00Z
Authors:Zeyuan Shen, Chao Wang, Yao Wang, Haibo Zhao, Zhong Wu, Ende Hu Abstract: Introduction: The urban distribution system plays a crucial role in efficient power distribution within urban areas. The increasing frequency and intensity of extreme events in recent years pose significant challenges to the reliable operation of urban distribution systems. While extensive research focuses on emergency frequency control strategies for large-scale power grids, there is a need for targeted attention to address the emergency frequency control challenges arising when the urban distribution system becomes isolated from the superior power grid due to extreme events.Methods: This paper aims to enhance the system's resilience to extreme events by investigating the coordinated regulation of various resources within the urban distribution system. The studied resources include synchronous generators, wind farms, battery energy storage systems, temperature control loads, and conventional load resources. A reduced-order model for the multi-resource system’s frequency response is established. Analytical expressions for key parameters, including the lowest system frequency, lowest point time, and quasi-steady state frequency, are derived.Results: To address the challenge of multi-resource coordinated regulation, an emergency frequency control strategy is proposed. This strategy takes into account the system safety frequency constraint, resource control amount constraint, and line power flow constraint. Simulations are conducted using the MATLAB/Simulink platform, considering IEEE 13 bus and IEEE 33 bus distribution systems as test cases.Discussion: Simulation results demonstrate the effectiveness of the proposed method in regulating the distribution system's resources, ensuring that the lowest frequency remains within the safety threshold of 49.8 Hz. Moreover, the proposed method minimizes control costs and limits load shedding, thereby fully leveraging the capabilities of diverse resources in the urban distribution system. This research contributes valuable insights into addressing emergency frequency control challenges in urban distribution systems during extreme events. PubDate: 2023-12-01T00:00:00Z
Authors:Nan Wei, Luomeng Zhang, Siwei Liu, Hongxing Ye Abstract: In recent years, the deployment of high-voltage direct current (HVDC) tie-lines in power grids has become a prevalent solution in some countries to transmit renewable energy from remote locations to load centers. However, the variability and uncertainty associated with renewable energy generation pose challenges to effectively utilizing this technology. This work proposes a novel multistage planning-operation model, aiming to unlock the potential flexibility in the HVDC transmission system and increase the renewable penetration. By incorporating flexibility, which is essential for accommodating the uncertainty in renewable generation, our model optimally shares the inter-regional flexibility between the sending- and receiving-end grids. One of the key features of our proposed model is its robustness and non-anticipativity, meaning it can account for different levels of uncertainty and make decisions that are suitable for multiple scenarios. This work develops two solution approaches to solve this challenging multistage model with variable uncertainty sets. We validate the proposed approach through a case study conducted on a real-world inter-regional grid. The numerical results demonstrate that our approach effectively unlocks more inter-regional flexibility and assists in increasing the renewable hosting capacity. PubDate: 2023-11-30T00:00:00Z
Authors:Zheng-Chuang Wang, Jin-Cai Niu Abstract: This study aims to propose a wind power prediction method that achieves high accuracy in order to minimize the impact of wind power on the power system and reduce scheduling difficulties in systems incorporating wind power. The importance of developing renewable energy has been recognized by society due to the increasing severity of the energy crisis. Wind energy offers advantages such as efficiency, cleanliness, and ease of development. However, the random nature of wind energy poses challenges to power systems and complicates the scheduling process. Therefore, accurate wind power prediction is of utmost importance. A wind power prediction model was constructed based on an improved tunicate swarm algorithm–extreme learning machine (ITSA-ELM). The improved tunicate swarm algorithm (ITSA) optimizes the random parameters of extreme learning machine (ELM), resulting in the best prediction performance. ITSA is an enhancement of the tunicate swarm algorithm (TSA), which introduces a reverse learning mechanism, a non-linear self-learning factor, and a Cauchy mutation strategy to address the drawbacks of poor convergence and susceptibility to local optima in TSA. Two different scenarios were used to verify the effectiveness of ITSA-ELM. The results showed that ITSA-ELM has a decrease of 1.20% and 21.67% in MAPE, compared with TSA-ELM, in May and December, respectively. This study has significant implications for promoting the development of renewable energy and reducing scheduling difficulties in power systems. PubDate: 2023-11-30T00:00:00Z
Authors:Weikai Yi, Qihang Li, Xiangyang Zhao, Wei Liu, Jinwu Du Abstract: The Sanshui salt mine is the sole location in the Guangdong province of South China with the potential to construct a salt cavern gas storage (SCGS) facility. Nevertheless, the gas storage construction of this mine faces significant challenges due to the presence of low‒grade salt deposits and numerous interlayers. To demonstrate the feasibility and calculate the gas storage capacity in this specific mining area, two representative salt caverns within this salt mine were simulated using a self-developed cavern-building simulation program, enabling us to accurately determine their respective volumes and shapes. Herein, the findings indicate that the combined caverns possess a total mining space volume of 1,157,000 m3, with the brine space accounting for merely 291,800 m3 (representing 25.22% of the overall mining space), and an extensive sedimentary volume of 865,200 m3 is also observed (constituting approximately 74.78% of the total mining capacity). Fortunately, this study has revealed that the sediments exhibit a porosity exceeding 40% and possess favorable permeability; consequently, countermeasures have been proposed to enhance the gas storage capacity within the pore space of these caverns, and we also utilized FLAC3D software for numerical simulation calculations to compare the stability of the cavern under different conditions of sediment pore utilization by calculating the volume loss rate, cavern wall displacement deformation, and plastic zone distribution. Moreover, the proposed method is anticipated to double the caverns’ working gas volume, increasing it from 40 million m3 to nearly 80 million m3. On the other hand, the long-term stability of caverns is numerically assessed under different pore space utilization rates of the sediments. The results also indicate that the caverns’ volume shrinkage, plastic zones, and surrounding rock displacement remain within allowable limits during 30 years of gas storage operation. The primary problem in the subsequent phase lies in effectively achieving gas injection and brine removal from the pore space of sediments while devising a methodology to extend this method to other salt caverns within similar salt mine areas. Thus, this study provides theoretical and technical guidance for the establishment of gas storage in existing salt caverns in the Sanshui salt mine and in salt mines worldwide that share similar geological conditions. PubDate: 2023-11-30T00:00:00Z
Authors:Muhammad Yousaf Raza, Boqiang Lin Abstract: Pakistan is moving toward the large-scale use of coal. Coal plays a dominant role in Pakistan’s energy mix and is estimated to reach 30% by 2030. The purpose of this study is to analyze coal imports and indigenous reserves in relation to CO2 emissions. In particular, this study constructs the logarithmic mean Divisia index (LMDI) method to see the impact of the factors, decoupling index for the economic relationship, and pollution from coal-fired power plants from 1986 to 2019. The empirical results show that 1) coal consumption and imports are interrelated, while coal production has had the lowest production level since 1986; 2) the energy intensity impact plays a medium role in decreasing coal utilization, followed by the coal share effect; however, the aggregated impact accounts for 0.023% of the total coal use; 3) the economic and population activity effects progressively increase with coal consumption by 0.25% and 0.35%, respectively, with the annual average growth; 4) only “three” decoupling states were observed: expansive coupling, expansive negative decoupling, and weak decoupling. Expansive negative decoupling occurred due to high energy share and energy intensity. Expansive coupling occurred only in 2001 due to rapid growth in coal proportion and a sluggish decrease in energy intensity, and weak decoupling showed a decoupling association between economic growth and coal utilization; and 5) the various coal compositions, such as moisture, volatile matter, fixed carbon, ash, and sulfur, can be evaded by 1.82, 4.83, 5.16, 1.43, and 0.39 Mt currently. Finally, environmental analysis recognized that implementing clean coal technologies significantly saves fuel and, consequently, reduces emissions. This study also discusses further policies. PubDate: 2023-11-30T00:00:00Z
Authors:Qing Yang, Ziyi Yang, Wanyang Zhao, Shaohui Zou, Delu Wang, Ruixin Ma Abstract: The identification of factors that drive enterprise carbon efficiency and the assessment of current emission reduction policies from the perspective of enterprise heterogeneity are essential for designing more effective emission reduction policies that optimize the allocation of inter-enterprise resources. Using a panel of 602,470 observations of Chinese industrial enterprises over multiple years, we quantified the factors driving carbon efficiency and their contributions. We also examined the heterogeneity of these effects at the industry and regional levels. This was done by constructing multiple fixed effect models and the Shapley value decomposition model. Additionally, we evaluated carbon reduction policies related to the key driving factors, taking into account the heterogeneity of the enterprise. The study discovered significant variations in inter-enterprise carbon efficiency, with a trend of local leadership and overall trailing. Based on the results of Shapley value decomposition, industry differences, enterprise scale, and regional differences are the most critical factors affecting enterprise carbon efficiency. Based on the results of the t-test and robustness test, enterprise scale has a significant inverted “U”-shaped impact on that. The degree of impact is greater in the eastern region and related industries in China with higher levels of market development, environmental regulation, and carbon market penetration. Previous policies have overlooked the heterogeneity of carbon efficiency among enterprises, resulting in firms and regions with large production scales and high carbon efficiency shouldering a greater burden of emission reduction tasks. It is likely to encourage the flow of output away from enterprises or areas with high carbon efficiency and towards those with low carbon emissions, which hinders the improvement of overall carbon efficiency. The conclusions have valuable policy implications. These include the recommendation to reduce direct control of total emission reduction targets in carbon reduction policies. Instead, based on the inter-enterprise heterogeneity of carbon efficiency, the policies should focus on strengthening economic measures and developing specific carbon emission reduction policies at the provincial, industry, and enterprise levels. PubDate: 2023-11-30T00:00:00Z
Authors:Chaoyang Xu, Shixuan Huang, Hu Luo, Guoneng Li, Yinwei Fan, Shutian Wei, Cheng Xu, Wenwen Guo Abstract: Thermoelectric generator (TEG) with improved performance is a promising technology in power supply and energy harvesting. Existing studies primarily adopt constant material properties to investigate TEG performance. However, thermoelectric (TE) material properties are subjected to considerable variations with temperature. Thus, reasonable doubts have risen concerning the influence level of temperature-dependent material properties on TEG performance. To solve this problem, an efficient and a comprehensive one-dimensional numerical model is developed to fully consider the third-order polynomial temperature-dependent thermal conductivity, Seebeck coefficient, and electrical resistivity. Control volume and finite difference algorithms are compared, and experiments are conducted to verify the developed numerical model. The temperature distribution along the TE leg obviously differs from the parabolic shape, which is a classic temperature distribution under the assumption of constant material properties. Insights find that the local change rate of thermal conductivity and Thomson effect are the essential reasons for the abovementioned phenomenon. It has been found that Thomson heat is released in the part of the leg near the cold-end, whereas it is absorbed in the remaining parts of the leg near the hot-end. The electric power on the basis of constant material properties is confirmed to be accurate enough by the developed numerical model, but the parabolic shape of the TE efficiency can be only obtained when temperature-dependent material properties are considered. Furthermore, it is wise to improve the TE efficiency by structural optimization. The present work provides an efficient and a comprehensive one-dimensional numerical model to include temperature-dependent material properties. New insights into the temperature and heat flux distribution, Thomson influence, and structural optimization potential are also presented for the in-depth understanding of the TE conversion process. PubDate: 2023-11-30T00:00:00Z
Authors:Tao Lang, Chen Ni, Keqiang Chen, Enxiang Xu, Jia Yin, Xi Shen, Xing Wu, Desheng Zhang Abstract: The performance and operational stability of non-clogging pumps can be affected by cavitation. To accurately identify the cavitation state of the non-clogging pump and provide technical references for monitoring its operation, a study was conducted on the optimization of Elman neural networks for cavitation monitoring and identification using the Improved Lévy Flight Bat Algorithm (ILBA) on the basis of the traditional Bat Algorithm (BA). The ILBA employs multiple bats to interact and search for targets and utilizes the local search strategy of Lévy flight, effectively avoiding local minima by taking advantage of the non-uniform random walk characteristics of large jumps. The ILBA algorithm demonstrates superior performance compared to other traditional algorithms through simulation testing and comparative calculations with eight benchmark test functions. On this basis, the optimization of the weights and thresholds of the Elman neural network was carried out by the improved bat algorithm. This leads to an enhancement in the accuracy of the neural network for identifying and classifying cavitation data, and the establishment of the ILBA-Elman cavitation diagnosis model was achieved. Collect pressure pulsation signals at the tongue of the non-clogging pump volute through cavitation tests. Through the cavitation feature extraction method based on Variational Mode Decomposition (VMD) and Multi-scale Dispersion Entropy (MDE), the interference signal can be effectively suppressed and the complexity of the time series can be measured from multiple angles, thereby creating a cavitation feature data set. The improved cavitation diagnosis model (ILBA-Elman) can realize the effective identification of the cavitation characteristics of non-clogging pumps through a variety of algorithm comparison experiments. PubDate: 2023-11-30T00:00:00Z
Authors:Bo Li, Yangyang Zhou, Zhengguang Wu, Aihong Kang, Bangwei Wu, Chufan Luo Abstract: Because of the fast deterioration speed of the surface function of conventional asphalt pavement, thin overlayer with many advantages has been applied to the pavement surface. However, due to problems such as insufficient stripping resistance and cracking resistance, the performance of the thin overlayer needs to be further improved. To achieve this target, basalt fiber was introduced into two types of thin overlayer asphalt mixtures (Open graded friction course, OGFC-5, and Stone matrix asphalt mixture, SMA-5). The wheel tracking test and uniaxial penetration test for high temperature deformation resistance, low temperature bending beam test and indirect tensile asphalt (IDEAL) cracking test for cracking resistance, cantabro test for stripping resistance, and friction coefficient test for skid resistance were conducted to evaluate various performance of thin overlayer asphalt mixtures, along with the dynamic modulus test for dynamic mechanical response. The results showed that adding basalt fiber could enhance the high temperature deformation resistance, low temperature cracking resistance, intermediate temperature cracking resistance and stripping resistance of the thin overlayer, while having no significant impact on skid resistance. Furthermore, adding basalt fiber could increase the modulus in the high temperature region and decrease the modulus in the low temperature region of the thin overlayer asphalt mixtures, indicating thin overlayer with basalt fiber presenting superior both high temperature and low temperature performance. In addition, the evaluation indexes of SLT and SHT proposed from dynamic modulus test exhibited good consistency with the results of the performance tests. PubDate: 2023-11-30T00:00:00Z
Authors:Weilong Xu, Chenjiu Jiang, Kaiwei Jia, Xiaoyi Yu Abstract: Based on the data of listed companies in China’s iron and steel industry from 2007 to 2020, the article investigates the impact mechanism and the path of action of China’s carbon emissions trading pilot on the green total factor productivity of iron and steel enterprises by constructing a multi-period difference-in-difference model difference-in-differences. The study finds that: 1) China’s iron and steel enterprises significantly improve their green total factor productivity driven by the carbon trading pilot, and the findings pass the corresponding robustness tests. 2) the mechanism analysis indicates that the carbon trading pilot promotes the green total factor productivity of iron and steel enterprises by forcing the technological progress of enterprises. 3) The heterogeneity analysis shows that the positive effect is more significant for large iron and steel enterprises with high social responsibility rating and high local government competition intensity, but not for small enterprises with low social responsibility rating and low local government competition intensity. 4) the dynamic effect shows that there is a certain lag in the promotion effect of the carbon emission trading pilot on the green total factor productivity of iron and steel enterprises, but its long-term effect is more obvious. This paper puts forward corresponding suggestions for accelerating the construction of a national unified green and low-carbon market system and actively promoting the deepening of the “dual-carbon” goal. PubDate: 2023-11-30T00:00:00Z
Authors:Ding Xiaoyan, Guo Guihong, Cong Bori, Chen Zheng, Ding Longting Abstract: The new generation of pavement technology with the goal of longevity is an important supporting technology that can promote the achievement of sustainable development of high-speed roadways. To further investigate the evolution trend of long-life pavement performance, this paper paved experimental sections to explore the correlations between pavement structure combinations and pavement performances. This paper presented four experimental sections with different pavement structures, asphalt concrete layer thicknesses, and pavement materials. Then, this paper analyzed the effects of the seasonal factors, pavement structure, and lanes on the deflection value and rut depth from three dimensions by the Pearson correlation coefficient (PCC). Finally, this paper used the analysis of variance (ANOVA) to analyze the relationships between the layer thickness of various materials in the pavement structure and the pavement performances, including the deflection value, international roughness index (IRI), texture depth (TD), British Pendulum Number (BPN), sideway force coefficient (SFC), rut depth, and disease area. The results showed that the seasonal factors significantly affected the deflection values of pavement structures with PCCs of 0.61, 0.72, 0.53, and 0.78. The high temperatures increased the average deflection values by 22.85%, 72.88%, 77.61%, and 88.13%, respectively. Under the influence of high temperature in summer and traffic loads, the increased ranges of average rut depth were −0.2%, 4.89%, 9.56%, and 7.31%, respectively. The results of ANOVA showed that the pavement structure type and thickness of each structural layer significantly affected the deflection value, and there also was a strong correlation between the pavement structure type, thickness, BPN, and SFC with p-values less than 0.05. Increasing the thickness of the asphalt surface was beneficial for reducing the area of defects, while laying the semi-rigid base layer was beneficial for maintaining the deflection value and rut depth at a lower level. PubDate: 2023-11-29T00:00:00Z
Authors:Peng Xu, Beibei Wang Abstract: In 2022, China faced unusually high temperatures, leading to a lack of hydropower in the southwest and increased power demand in the east. This incongruity exerted substantial strain on the power system. To tackle this, a structured method called orderly power utilization (OPU) is suggested as an effective approach to manage short-term power shortages and prevent recurring blackouts. However, typical OPU strategies tend to overlook the principles of fairness, openness, and justice (OEJ), potentially causing problems for various users, especially major industries. Herein, we introduce a comprehensive OPU framework. According to the demand difference in OPU plans in different periods, the optimization cycle is divided into several intervals to achieve computational enhancement. Furthermore, in the interest of judiciously managing the manifold OPU resources characterized by heterogeneous parameters, we introduce an aggregated operational model underpinned by the formalism of zonotopic sets. Numerical simulation results indicate the great potential of the proposed method to solve power shortage problems while upholding the imperatives of OEJ. PubDate: 2023-11-29T00:00:00Z
Authors:Abdul Hafeez, Aamir Ali, M. U. Keerio, Noor Hussain Mugheri, Ghulam Abbas, Aamir Khan, Sohrab Mirsaeidi, Amr Yousef, Ezzeddine Touti, Mounir Bouzguenda Abstract: To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss. PubDate: 2023-11-29T00:00:00Z
Authors:Nada Mosaad, Omar Abdel-Rahim, Tamer F. Megahed, Wesam Rohouma, Tanemasa Asano, Sobhy M. Abdelkader Abstract: A centralized secondary control is utilized in a DC islanded microgrid to fine-tune voltage levels following the implementation of droop control. This is done to avoid conflicts between current allocation and voltage adjustments. However, because it introduces a single point of failure, a distributed secondary control is preferred. This paper introduces a consensus-based secondary distributed control approach to restore critical bus voltages to their nominal values and properly distribute current among converters. The critical bus takes the lead in voltage adjustments, with only connected energy resources contributing to regulation. The microgrid is represented as an undirected graph to facilitate consensus building. Two adjustment terms, δv and δi, are generated to assist in returning voltage to its nominal level and correctly allocating current among energy resources. To enhance consistency and improve controller performance compared to those reported in existing literature, all buses are connected to a leader node. In the event of the failure of all converters except one, voltage can still be effectively restored. MATLAB-Simulink simulations are conducted on two medium-voltage DC (MVDC) microgrids to validate the efficiency of the proposed control method. The results confirmed that the proposed control method can effectively maintain voltage stability and enhance the precise distribution of current among agents by 8%. PubDate: 2023-11-29T00:00:00Z
Authors:Bingrui Gao, Xuze Han, Fangbing Ye, Yuankang Li Abstract: This paper investigates the impact of the popularization and usage of the Internet on household electricity consumption in China, as well as the mediating role of sleep duration. By employing data from the China Family Panel Studies (CFPS) and employing the basic ordinary least squares (OLS) model, the mediation model, and the instrumental variable (IV) approach, we derive the following conclusions. The results from the basic OLS regression indicate a positive relationship between internet usage and household electricity expenditure, implying that households that use the Internet tend to have higher electricity bills. Subsequently, by introducing sleep duration as a mediating variable, we find that internet usage leads to shorter sleep duration, indirectly resulting in increased household electricity costs. To address potential endogeneity concerns, we employ the instrumental variable approach to correct for the impact of internet usage on household electricity consumption. In addition, through heterogeneity analysis, we found that internet usage impacts households with different characteristics. PubDate: 2023-11-29T00:00:00Z
Authors:Liang Dan, Zhang Jian, Zhou Wensheng, Hua Zhao, Zhang Qichen, Wang Liqi Abstract: Introduction: Through the combination of hot water and chemical agent, the low-heat synergistic chemical profile control and flooding technology can not only reduce the risks of sand production and string damage caused by high-temperature thermal recovery technology, but also further improve the recovery factor on the basis of hot water flooding.Methods: Based on laboratory testing, the synergism mechanism of thermal energy and chemical agent is studied.Results: The research results showed that in the near wellbore zone of the injection well, crude oil viscosity reduction mainly relies on thermal energy, and chemical agents assist in improving the water oil mobility ratio and reducing the risk of cross flow. After entering the deep formation, due to the decrease in temperature, the thermal energy effect is weakened, but the chemical agents still aggregate at the oil-water interface and continue to act on heavy oil to reduce viscosity. The higher the temperature, the greater the role of heat energy, and the weaker the role of chemical agents. The higher the injection concentration of chemical agent, the more obvious the leading role of chemistry. Based on the comprehensive evaluation index, the optimized temperature is 80°C and the injection concentration is 1500 mg/L. The scheme study of Bohai L Oilfield shows that compared with cold water flooding, hot water flooding can only improve oil recovery by 1.2 percentage points, and low heat collaborative chemical profile control flooding technology can further improve oil recovery by 4.7 percentage points on the basis of hot water flooding, with obvious oil increase effect.Conclusion: It is suggested that Bohai L Oilfield should apply low-heat coordinated chemical profile control and flooding technology to improve the development effect of the oilfield, and provide new technical ideas for safe and economic development of offshore heavy oil. PubDate: 2023-11-29T00:00:00Z
Authors:Xiaoxia Yan, Yan Zhang Abstract: With the proposal of “Carbon Peak and Carbon Neutrality” goals, China is facing a more serious carbon emissions reduction situation, and how the booming digital economy effectively helps China’s carbon emissions reduction is one of the most urgent things that should be solved. To study the impact of the digital economy on carbon emission intensity, this paper is based on the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2011 to 2021, and applies the double-fixed effect model and the threshold effect model to study the impact of the digital economy on carbon emission intensity and the mechanism of its action, as well as to analyze the mechanism of the digital economy’s action on carbon emission intensity from the perspective of technological innovation. The results of the study show that: i) The digital economy can reduce the intensity of regional carbon emissions; ii) The carbon emission reduction effect of the digital economy is non-linear, and its carbon emission reduction effect gradually increases with the level of development of the digital economy; iii) In addition to the direct impact of the digital economy on carbon emission intensity, it also has an indirect impact on carbon emissions through technological innovation; iv) There is regional heterogeneity in the carbon emission reduction effect of the digital economy, and the carbon emission reduction effect is more significant in the central and western parts of the country and regions with a high level of human capital development. Based on the conclusions obtained, this paper suggests: i) The rational integration of the digital economy and regional development should be strengthened; ii) Strong provinces in the digital economy should be encouraged to help weaker provinces, to narrow the “digital divide” between provinces; iii) Differentiated development strategies should be formulated in accordance with local conditions, to give full play to the optimal effect of the digital economy in carbon emission reduction. PubDate: 2023-11-29T00:00:00Z
Authors:Yingchun Feng, Jie Fan, Bo Gao, Yu Jiang, Jianrun Chen, Rui Zhang, Min Chen Abstract: To maximize the expected profits and manage the risks of renewable energy system under electricity market environment, scenario-based- stochastic optimization model can be established to generate energy bidding strategies, in which the probabilistic scenarios of risk parameters are usually obtained by using statistical or machine learning methods. This paper proposes a practical multivariate statistical method for risk parameter scenario generation, which is used by a wind energy system faced with uncertain electricity prices and wind power productions, and it considers the correlation between dependent risk parameters by using historical data directly. The probabilities of scenarios containing correlated risk parameters are calculated by using multivariate histograms, in which the asymmetric correlation between different parameters existing in the historical data are preserved. Additionally, in order to make the stochastic optimization problem with large numbers of scenarios tractable, a multivariate scenario reduction method is used to trim down the scenario number. By solving the stochastic optimization problem, optimal day-ahead bidding curves for the wind energy system are generated, and Douglas–Peucker algorithm is used to fit the bidding curves according to market requirements. Case studies based on real world data in electricity markets are performed to prove the effectiveness of the proposed risk parameter scenario generation method and energy bidding strategies. Finally, conclusions and practical suggestions on future research works are provided. PubDate: 2023-11-29T00:00:00Z
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