Authors:Feng You, Xiuli Si, Rong Dong, Dong Lin, Yien Xu, Yiming Xu Abstract: Power systems would face issues in system frequency stability when high scales of variable renewable energy generation are integrated in them. Battery energy storage systems (BESSs) with advanced control capability and rapid control response have become a countermeasure to solve the issues in system frequency stability. This research addresses a flexible synthetic inertial control strategy of the BESS to enhance the dynamic system frequency indices including the frequency nadir, settling frequency, and rate of change of the system frequency. To this end, the control loops based on the frequency excursion and rate of change of the system frequency are implemented into the d-axis controller of the BESS. The adaptive control coefficient of both control loops could be adjusted according to the instantaneous state of charge (SOC) so that it can inject more power to the grid at a higher SOC. The benefits of the proposed combined inertial control strategy are investigated with various sizes of disturbance and SOCs of the BESSs. Results successfully illustrate that the proposed combined inertial control strategy of the BESS is capable of enhancing the system frequency stability so as to promote variable renewable energy accommodation. PubDate: 2022-05-25T00:00:00Z
Authors:Jian Wang, Xihai Zhang, Fangfang Zhang, Junhe Wan, Lei Kou, Wende Ke Abstract: Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed. PubDate: 2022-05-25T00:00:00Z
Authors:Guowei Cai, Shujia Guo, Cheng Liu Abstract: With the increase in the power system scale, the identification of electromechanical oscillation mode parameters by traditional numerical methods can no longer meet the requirements of complete real-time analysis. Therefore, a method based on machine learning (multilayer artificial neural networks) is proposed to identify the electromechanical oscillation mode parameters of the power system. This method can take the monitorable variables of the WAMS as the input of the model and the key characteristic information such as frequency and damping ratio as the output. After processing the input and output data with randomized dynamic mode decomposition (randomized-DMD), their mapping relationship can be analyzed by using the multilayer neuron architecture. The simulation results of the 4-generator 2-area system and the IEEE 16-generator 5-area system show that this method can accurately calculate the key characteristic parameters of the system without considering the change in the control parameters and after the offline training of historical data, which shows higher accuracy, stronger robustness, and sensitive online tracking ability. PubDate: 2022-05-25T00:00:00Z
Authors:Xiaozhuo Xu, Cheng Xing, Qi Wu, Wei Qian, Yunji Zhao, Xiangwei Guo Abstract: To reduce the impact of series battery pack inconsistency on energy utilization, an active state of charge (SOC) balancing method based on an inductor and capacitor is proposed. Only one inductor and one capacitor can achieve a direct transfer of balanced energy between the highest power cell and the lowest power cell. This method has the characteristics of a simple structure, small size, simple control, fast balancing speed, and low topology cost. First, the topology, working principle, parameter design, and control strategy of the proposed balancing method are explained. Second, the characteristics of the speed and efficiency are analyzed through the simulation models, and the advantages of its low topology cost and simple control are explained. Finally, by building an experimental platform for a four-cell series battery pack, the effectiveness of the new balancing method in the charging/discharging process and the dynamic process of the battery pack is verified. After the end of the balancing, the SOC difference is less than 4%. PubDate: 2022-05-25T00:00:00Z
Authors:Lin Zhu, Shiyu Huang, Zhigang Wu, Yonghao Hu, Mengjun Liao, Min Xu Abstract: As the number of wind farms (WFs) in urban power grids gradually increases, their dynamic equivalence is needed for stability analysis. This paper proposes a dynamic equivalence method for distributed multiple wind farms in an urban power grid. The key idea is to characterize wind farms dynamics and then use them for coherence by considering the differences between doubly-fed induction generators (DFIG) and permanent magnet direct-drive synchronous generators (PMSG). The mathematical-physical models and operational control characteristics are firstly analyzed to find the essential attributes representing the dynamic characteristics. Then we introduce the similarity based on the dynamic timing warping (DTW) distances, which help construct the clustering index for coherency wind farms. Meanwhile, comprehensive constraints, which ensure the consistency of the urban power grid topology, are adequately considered. Finally, the parameters of coherent wind farms are aggregated based on the clustering groups. The proposed method is validated in an urban grid by the time-domain simulation. PubDate: 2022-05-25T00:00:00Z
Authors:Xinxin Zhang, Kaili Xu, Maogang He, Jingfu Wang Abstract: Rural energy is related to the domestic energy supply, consumption, and improvement of living standards of more than one-third of the population in China. In the “14th Five-Year Plan,” it has been clearly pointed out that it is necessary to strengthen the clean utilization of coal and implement the construction of rural clean energy projects. At present, the energy consumption structure of rural areas in China is transiting from traditional solid energy to commercial energy and clean energy. Based on this background, this paper reviews the transition trend, influencing factors, and regional differences of China’s rural household energy consumption structure from the 1990s. Taking into account China’s goal of carbon peaking by 2030 and carbon neutrality by 2060, carbon dioxide and pollutant emissions generated in the process of energy consumption and the energy-saving potential of rural households are analyzed and discussed. Moreover, the evolution of rural energy policies in China is presented and related proposals are also made. This review aims to provide reference for relevant researchers and policy makers. PubDate: 2022-05-25T00:00:00Z
Authors:Stavros Michailos, Jon Gibbins Abstract: The principal purpose of this study is to examine the changes in process conditions that might be needed to achieve up to 99% capture levels in amine post-combustion capture (PCC) plants for combined cycle gas turbine (CCGT) flue gases. This information is of interest since, while 95% capture is adequate for current market and regulatory conditions, net zero fossil emissions (99% capture for a CCGT plant) will be required to deliver global climate mitigation targets and is increasingly a target for national climate policies. The conventionally-configured plant in the study is based on FEED studies carried out by Bechtel Corporation and uses MEA at 35% w/w. Performance modelling is undertaken using the Aspen Plus CCSI MEA Steady State Model. The results show that efficient operation at higher capture levels appears to be feasible with minimal adjustments to the plant configuration, provided that the absorber has a sufficient packing height and the stripper is capable of operation at pressures above 2 bar. The study primarily focuses on operation at low lean loadings (0.09–0.15 molCO2/molMEA) and correspondingly low L/G ratios ( PubDate: 2022-05-24T00:00:00Z
Authors:Muhammad Aslam, Ali Hussein Al-Marshadi Abstract: The statistical tests under classical statistics can be only applied when the data is linear and has certain observations. The existing statistical tests cannot be applied for circular/angles data. In this paper, the Watson-Williams test under neutrosophic is introduced to analyze having uncertain, imprecise, and indeterminate circular/angles data. The neutrosophic test statistic is introduced and applied to wind direction data. From the real example and simulation study, it can be concluded the proposed neutrosophic Watson-Williams test performs better than the Watson-Williams test under classical statistics. PubDate: 2022-05-24T00:00:00Z
Authors:Zhenling Chen, Xiaoyan Niu, Xiaofang Gao, Huihui Chen Abstract: Green (technical) innovation is expected to be an effective tool for addressing environmental crises. However, the effect of environmental regulations on green innovation may depend on the type of environmental regulation. To that end, this study utilizes panel data covering 30 Chinese provinces to explore the mechanism underlying the relationship between these two variables in light of the heterogeneity in environmental regulations and pollutants. The direct effects of three types of environmental regulations and four pollutants are verified, as are the thresholds in the effects of environmental regulations on green innovation. The results show that 1) both market-incentive and public participation-based environmental regulations have positive effects on green innovation, while command-and-control regulations do not. Unlike the effects of the market-incentive tool, which has a single threshold, the effect of public participation-based environmental regulations has two thresholds, which indicates that there is too little public participation for such regulations to be effective and too much for them to be sensitive to environmental protection. 2) Three of the four pollutants (industrial wastewater, waste gas, and carbon emissions) have a significantly positive impact on green innovation only when they exceed the first threshold value, whereas an increase in industrial solid waste has little effect on green innovation until it exceeds the second threshold value. 3) In the eastern region, all three kinds of environmental regulations play significant roles in promoting green innovation, and their effects are greater than those in the western region. However, the effect of environmental regulations in the central region is not different from that in the western region. PubDate: 2022-05-24T00:00:00Z
Authors:Xuxiang Feng, Lu Shi, Yumeng Zhang Abstract: This study presents a command filtered control scheme for multi-input multi-output (MIMO) strict feedback nonlinear unmodeled dynamical systems with its applications to power systems. To deal with dynamic uncertainties, a dynamic signal is introduced, together with radial basis function neural networks (RBFNNs) to overcome the influences of the dynamic uncertainties. Command filters (CFs) are used to prevent the explosion of complexity, where the compensating signals can eliminate the effect of filter errors. Compared with single-input single-output strict feedback nonlinear systems, the method proposed in this study has more suitability. In the end, the simulation experiments are carried out by applying the developed algorithm to power systems, where the simulation results verify the efficacy of the approach proposed. PubDate: 2022-05-24T00:00:00Z
Authors:Peng Li, Yuanfeng Chen, Kang Yang, Ping Yang, Jingyi Yu, Senjing Yao, Zhuoli Zhao, Chun Sing Lai, Ahmed F. Zobaa, Loi Lei Lai Abstract: To achieve the national carbon-peak and carbon-neutral strategic development goals, it is necessary to build power systems dominated by renewable and sustainable energy. The future power system with a high proportion of renewable and sustainable energy is required to have large-scale, low-cost, flexible, and adjustable resources. To this end, this article aggregates user-side distributed energy storage and electric vehicles into a virtual power plant, considering the uncertainty of wind power fluctuations and the uncertainty of electric vehicle charging and discharging to establish a day-ahead and intra-day peak regulation model for combined peak regulation of virtual and thermal power plants. The bounding algorithm seeks the optimal strategy for the two-stage model of joint peak regulation and obtains the day-ahead and intra-day two-stage optimal peak regulation strategy. The simulation example shows that the virtual power plant and its day-ahead and intra-day optimal peak regulation strategy can reduce the peak regulation cost of the power system, as compared with the deep peak regulation of thermal power plants with a special supporting energy storage power station. This work provides a global perspective for virtual power plants to participate in the formulation of power system peak regulation rules. PubDate: 2022-05-24T00:00:00Z
Authors:Donghua Mao, Jinyi Qiu Abstract: With the construction of new power systems, distributed power sources are connected in large numbers and the possibility of faults increases. The optimal allocation of repair resources is important to improve the fault management efficiency and the quality of power supply services in the producer–consumer community. Using a large number of historical fault resources accumulated in the producer–consumer community, we first preprocess the fault information by the rough set theory, then establish an optimal allocation model that minimizes the total fault loss, consider fault risk classification and repair response capability, and finally use the improved gray wolf optimization algorithm to perform the optimal calculation. To address the problems of the traditional gray wolf algorithm, tent mapping is introduced in the generation of the initial population to enhance the uniformity of the initial population. The cooperative competition mechanism is introduced to improve the utilization of effective information among individuals. Finally, the feasibility and superiority of the algorithm are verified through the analysis of calculation cases. Finally, the feasibility of this configuration method is verified through the analysis of arithmetic cases. PubDate: 2022-05-24T00:00:00Z
Authors:Pengfei Chen, Honggang Chang, Yongqiang Fu, Yongfan Tang, Xuesong Huang, Weichu Yu Abstract: Drag reduction (DR) is critical to the success of hydraulic fracturing operations with slickwater, and it is a challenge to accurately predict DR due to the problem of high injection rates. Although a practical pipe diameter model is frequently used to predict the field DR based on laboratory experimental data, there exist many limitations. This study, on account of dynamic similarity, shows two novel general models for the prediction of field DR, and such two models can give reliable predictions when the laboratory and field Reynolds numbers (Re) are the same. For general model 1, the DR can be predicted by using the laboratory volumetric flow rate, pipe diameter and pressure drop, and the field volumetric flow rate, with a deviation ranging from −10 to 10%. For general model 2, it is simpler than general model 1, and the DR can be predicted by using the laboratory pipe diameter and the field volumetric flow rate, with a deviation ranging from −6 to 6%. The two novel general models can be used for more scenarios than the existing reported ones. PubDate: 2022-05-24T00:00:00Z
Authors:Tianxiang Li, Qian Xiao, Hongjie Jia, Yunfei Mu, Xinying Wang, Wenbiao Lu, Tianjiao Pu Abstract: Regional energy internet (REI) contains massive market agents, whose interests and objectives vary from each other. In consequence, it is challenging to stimulate the energy conservation and emissions reduction participation of each agent by the conventional schedule optimization method. This paper proposes a multi-agent schedule optimization method for REI considering the improved tiered reward and punishment carbon trading model. Firstly, the energy flow constraints and device constraints of REI are established. Secondly, to tighten restrictions on carbon emissions, the relative carbon emission is used as the criterion to establish the improved tied reward and punishment carbon trading model. Next, to analyze the real multi-agent game situation in the market, different agents are classified, and the objective functions are defined based on their revenue. Finally, a two-layer algorithm is used to solve the above multi-agent model. Simulation results verify that the proposed method can effectively reduce carbon emissions and significantly enhance the revenue of the region. PubDate: 2022-05-24T00:00:00Z
Authors:Lukas Fridolin Pfeiffer, Nicola Jobst, Cornelius Gauckler, Mika Lindén, Mario Marinaro, Stefano Passerini, Margret Wohlfahrt-Mehrens, Peter Axmann Abstract: Sodium-ion batteries promise efficient, affordable and sustainable electrical energy storage that avoids critical raw materials such as lithium, cobalt and copper. In this work, a manganese-based, cobalt-free, layered NaxMn3/4Ni1/4O2 cathode active material for sodium-ion batteries is developed. A synthesis phase diagram was developed by varying the sodium content x and the calcination temperature. The calcination process towards a phase pure P2-Na2/3Mn3/4Ni1/4O2 material was investigated in detail using in-situ XRD and TGA-DSC-MS. The resulting material was characterized with ICP-OES, XRD and SEM. A stacking fault model to account for anisotropic broadening of (10l) reflexes in XRD is presented and discussed with respect to the synthesis process. In electrochemical half-cells, P2-Na2/3Mn3/4Ni1/4O2 delivers an attractive initial specific discharge capacity beyond 200 mAh g−1, when cycled between 4.3 and 1.5 V. The structural transformation during cycling was studied using operando XRD to gain deeper insights into the reaction mechanism. The influence of storage under humid conditions on the crystal structure, particle surface and electrochemistry was investigated using model experiments. Due to the broad scope of this work, raw material questions, fundamental investigations and industrially relevant production processes are addressed. PubDate: 2022-05-24T00:00:00Z
Authors:Qing Shi Abstract: With accelerating automotive electrification process, quantitative analysis of cobalt demand becomes a critical issue in China. How much cobalt is expected to be needed from 2021 to 2030 to support a smooth automotive electrification in China' This study aims to answer this question comprehensively by examining the responses of annual cobalt demand to variations in electric vehicle sales, battery capacity factors, and cobalt substitution effects, which has not been fully explored in previous literature. Scenario analysis based on the Bass model is adopted and historical data from 2012 to 2020 are used for this study. The results show that 1) the peak annual cobalt demand will reach 35.58–126.97 kt/year during 2021–2030; 2) cobalt demand is expected to decline by 14.29% if the market share of ternary lithium-ion battery decreases by 10%; 3) while cobalt substitution can reduce the demand substantially, it cannot offset the growth of cobalt demand driven by the increasing EV sales and battery capacity. These results provide a knowledge base for policy suggestions to manage the cobalt demand—supply balance in China better. PubDate: 2022-05-24T00:00:00Z
Authors:Benxin Li, Xuan Zhang, Yumin Zhang, Yixiao Yu, Ying Zang, Xueqing Zhang Abstract: The penetration of a high proportion of renewable energy sources (RES) into the power grid intensifies the source–load imbalance, which greatly weakens the network transmission performance and power supply quality, and the effect of relying only on individual regulation within the region is negligible. To enhance the capacity of interconnection and coordination among different areas of power systems and improve the accommodation level of RES and low-carbon efficiency, an optimal transmission switching model based on the bus tearing method is proposed in this article. Firstly, the complex power system is decomposed based on the bus tearing method, and thus, the interconnected power grid structure of the multiarea system is constructed. Secondly, the optimal model of interconnected power grid decomposition and coordination structure considering renewable energy generation is constructed, based on exquisite modeling, to reduce the difficulty of unified analysis and decision-making of the multiarea interconnected power system, and the expression of the model is simplified in the form of the matrix. Then, the analytical target cascading (ATC) method is used to decouple the complex model from the main problem and subproblem and solve the distributed parallel problem, to understand the optimization of the decomposition and coordination structure of the interconnected power grid with source–load coordination. Finally, based on the case studies of the IEEE 14-bus system and IEEE 118-bus system, the effectiveness of the proposed model and method is verified, the coordinated operation of the interconnected power grid and the optimal allocation of network resources are achieved, and the economy of power system operation is improved. PubDate: 2022-05-23T00:00:00Z
Authors:Lifei Ma, Jizhen Liu, Qinghua Wang Abstract: With the application of advanced information and communication technology in building cluster energy system (BCES), energy management based on two-way interaction has become an effective method to improve its operation efficiency. BCES can quickly respond to the mismatch between supply and demand by adjusting flexible load and system operation strategy, which can improve operation reliability and reduce energy cost. This paper proposes an energy management and pricing framework of BCES based on two-Stage optimization method. First, on the basis of profit-seeking modeling of energy service provider (ESP) and building clusters (BCs), a dynamic pricing decision-making framework for energy management in a hierarchical energy market is proposed, which considers both ESP’s energy supply income and BCs’ comprehensive benefit. The dynamic pricing problem is formulated as a discrete finite Markov decision process (MDP), and Q-learning algorithm is adopted to solve the MDP problem. Moreover, an operation optimization model of the BCES based on the obtained optimal price decision is established, and the established model is solved by the alternating direction multiplier method algorithm (ADMM). Through numerical simulation case studies, it is demonstrated that the proposed method can achieve the optimal pricing decision-making closer to the psychological needs of ESP and BCs, and can significantly reduce the cost of BCs and improve the operational efficiency of BCES. PubDate: 2022-05-23T00:00:00Z
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