As more non-dispatchable renewable resources are integrated into the grid, it will become increasingly difficult to predict the transfer capabilities and the network congestion. At the same time, ...renewable resources require operators to acquire more operating reserves. With today's deterministic reserve requirements unable to ensure optimal reserve locations, improvements to reserve policies are needed to ensure reserve deliverability while maintaining a reliable system at least cost. This paper proposes a daily reserve zone determination procedure, which is able to reflect system operating conditions by utilizing probabilistic power flows. A statistical clustering algorithm is used to cluster buses together to produce the zones; the algorithm uses a centrality measurement, which is based on weighted power transfer distribution factors. The proposed method is validated by testing it on a modified IEEE 118-bus system for multiple days; the proposed method is compared against existing reserve zone partitioning procedures. While the proposed reserve zone determination method is a heuristic, it is shown to be effective and it is a computationally tractable method. The proposed method can be used on its own and can be used along with stochastic programming techniques that implicitly determine reserves.
The income gap between regions and its expansion are the main manifestations of the imbalanced and inadequate economic development in China. High-speed railway (HSR) construction is regarded as an ...important method to drive domestic demand, drive the pulse of the economy, and promote the coordinated development of regions. Based on the opening of HSR and the acceleration of ordinary railways, we used the weighted average travel time model and accessibility coefficient to estimate the changes on accessibility in 286 cities at prefecture-level and above from 2000 to 2018. Then, the influence mechanisms of improving regional accessibility on urban residents' income were estimated by using the bidirectional-fixed effects panel model and the recursive model respectively. We found that: (1) The accessibility of urban areas has been greatly improved due to the opening of HSR and the acceleration of ordinary railway, among which the improvement of HSR cities is greater. (2) The improvement of regional accessibility significantly promoted the income growth of urban residents, and the increase of the regional accessibility coefficient by 1 unit led to an average increase of 2140 yuan in the per capita disposable income of urban residents. (3) There is regional heterogeneity in the impact of improving regional accessibility on urban residents' income, and it has a significant effect on the eastern and northeastern regions. It has a greater positive effect on improving the income of residents in central cities compared with peripheral cities. (4) Regional accessibility can promote urban income growth through regional employment and fixed asset investment. In the future, the transportation network should be further improved to facilitate the regional economic cycle, strengthen the coordination and complementarity of regional economies, and promote regional economic integration so as to promote the improvement of resident income level and the common prosperity of the people.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This open access book provides integrated treatment technology of rural domestic sewage, and highlights ten typical cases in China. The integrated sewage treatment system (ISTE) combine sewage ...pretreatment, biological treatment, sedimentation, and disinfection, which particularly suitable for decentralized domestic wastewater treatment in rural areas without pipe networks. The main advantages of ISTE include the compact structure, small footprint, short construction period, high treatment efficiency, and economical rationality. First applied in Japan, ISTE has recently achieved rapid growth in China in the last decade. The relevant technological R&D, practice, and promotion experience accumulated by Chinese enterprises can offer significant reference to other developing countries and regions. The book consists of five chapters. The first chapter introduces characteristics, environmental risks and management styles of rural domestic sewage. The second chapter illustrates the collection and treatment approaches for rural domestic sewage.The third chapter presents the integrated sewage treatment process of high efficiency. The fourth chapter involves the design concept, manufacturing requirements, automatic control, and the cloud management system for ISTE. Chapter five covers ten typical application cases of ISTE in multiple regions of China. This book is suitable for graduate students, engineers, university teachers and government administrators engaged in decentralized sewage treatment.
The air-ground cooperative emergency networks can assist with the rapid reconstruction of communication in the disaster area, where unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) ...are deployed as base stations. The trajectory optimization of emergency base stations is of vital importance to the communication performance, which is related to the timeliness and effectiveness of rescue. In this paper, federated multi-agent deep deterministic policy gradient (F-MADDPG) based trajectory optimization algorithm is proposed to maximize the average spectrum efficiency. Specifically, the property of MADDPG is inherited to jointly control of multiple vehicles and federated averaging (FA) is utilized to eliminate the isolation of data to accelerate the convergence. Distributed F-MADDPG (DF-MADDPG) is further designed to reduce the communication overhead with a distributed architecture. The simulation results indicate that the proposed F-MADDPG and DF-MADDPG based algorithms significantly outperform the existing trajectory optimization algorithms, in terms of the average spectrum efficiency and the speed of convergence.
As part of the day-ahead market clearing process, MISO solves one of the most challenging Security Constrained Unit Commitment (SCUC) models. With footprint expansions and increased activity from ...financial traders, the day-ahead market unit commitment encounters significant performance challenges from time to time. This paper reviews MISO's day-ahead market clearing process and identifies the factors contributing to these performance challenges. It then shares techniques that have been developed to overcome these challenges. Three alternative backup solving approaches were developed. An incremental solving algorithm is proposed to better handle the iterative market clearing process and to perform solution polishing and commitment reason identification. Numerical results based on MISO data are presented.
Online agricultural product trading has the characteristics of rapid and diversified transaction data; there is a fuzzy correspondence between sales volume influencing factors and sales volume ...levels. Based on this, this paper combines the data preprocessing technology of fuzzy membership and optimized deep learning algorithm, adding a self-encoding method with sparseness restriction, and proposes a deep learning sales forecasting model based on transformative computing with fuzzy membership-the super crown model (Super Imperial Crown Model, referred to as SICM). The model uses fuzzy membership to process the weighted relationship between sales influencing factors and sales rank, and uses a sparse autoencoder network to adaptively extract sample features; sales rank classification prediction uses Softmax classifier; BP fine-tuning is used to Achieve parameter optimization. Finally, use the collected transaction data to apply R software to simulate the optimized model and compare and analyze the comprehensive prediction performance. The results show that the super crown model can realize real-time and accurate dynamic sales classification prediction according to the characteristics of current online agricultural product transaction data, effectively overcome the imbalance of supply and demand caused by information imbalance, and promote the study of deep learning in the field of e-commerce transactions effect. Presented algorithm based on transformative computing techniques can be used in optimization of sales processes, management and analysis of sales markets.
•The SICM model has strong self-learning ability, which can realize the adaptive extraction of characteristic information of online agricultural product transaction data, effectively avoiding the traditional shallow classifier in the process of artificial cross-validation optimization through preset parameter values. The repetitive forecasting evaluation operation not only reduces the time for model training to the optimal level, but also can truly achieve real-time dynamic demand forecasting by inputting data at any time and producing results at any time, and achieves high forecasting accuracy.•In view of the “fuzzy mapping” relationship between the factors affecting the online sales of agricultural products and the sales classification level, the concept of fuzzy membership is introduced in the data preprocessing stage to reduce the sales forecast error.•The SICM model has good incremental self-learning update capability and generalization performance for agricultural product online transaction data with large capacity, diversity and high rate growth characteristics, and can be used as a long-term model for online agricultural products Real-time intelligent prediction of sales volume can also be extended to the entire e-commerce field.
In this paper, to enhance the spectrum utilization in cognitive unmanned aerial vehicle networks (CUAVNs), we propose a cooperative spectrum sensing scheme based on a continuous hidden Markov model ...(CHMM) with a novel signal-to-noise ratio (SNR) estimation method. First, to exploit the Markov property in the spectrum state, we model the spectrum states and the corresponding fusion values as a hidden Markov model. A spectrum prediction is obtained by combining the parameters of CHMM and a preliminary sensing result (obtained from a clustered heterogeneous two-stage-fusion scheme), and this prediction can further guide the sensing detection procedure. Then, we analyze the detection performance of the proposed scheme by deriving its closed-formed expressions. Furthermore, considering imperfect SNR estimation in practical applications, we design a novel SNR estimation scheme which is inspired by the reconstruction of the signal on graphs to enhance the proposed CHMM-based sensing scheme with practical SNR estimation. Simulation results demonstrate the proposed CHMM-based cooperative spectrum sensing scheme outperforms the ones without CHMM, and the CHMM-based sensing scheme with the proposed SNR estimator can outperform the existing algorithm considerably.
Abstract
Deep Reinforcement Learning (DRL) has shown promise for voltage control in power systems due to its speed and model‐free nature. However, learning optimal control policies through trial and ...error on a real grid is infeasible due to the mission‐critical nature of power systems. Instead, DRL agents are typically trained on a simulator, which may not accurately represent the real grid. This discrepancy can lead to suboptimal control policies and raises concerns for power system operators. In this paper, we revisit the problem of RL‐based voltage control and investigate how model inaccuracies affect the performance of the DRL agent. Extensive numerical experiments are conducted to quantify the impact of model inaccuracies on learning outcomes. Specifically, techniques that enable the DRL agent are focused on learning robust policies that can still perform well in the presence of model errors. Furthermore, the impact of the agent's decisions on the overall system loss are analyzed to provide additional insight into the control problem. This work aims to address the concerns of power system operators and make DRL‐based voltage control more practical and reliable.
Ammonoids are key fossil indexes for Triassic biochronology, as all Triassic stages and substages were initially defined on ammonoid faunas. In recent decades, the temporal resolution of ammonoid ...biostratigraphical scales for the Early Triassic has been greatly improved. However, many uncertainties in zones correlation and superpositions remain, mainly due to sampling heterogeneities, preservation biases, and faunal endemicity. In this work, we present the first comprehensive Early Triassic ammonoid zonation from South Tibet, China, a previously poorly investigated region. Ammonoids were sampled from the Kangshare Formation at four sections (Selong, Paizi, Qubu and Xialong), representing a total of 140 species, ranging from the Griesbachian to the Smithian. These new robust data allow the construction of a high-resolution biostratigraphy using the Unitary Association (UA) method. A total of 22 Unitary Association zones (UAZs) were recognized, including two UAZs for the Griesbachian, nine for the Dienerian, and 11 for the Smithian. Then, we integrated data from neighboring basins, i.e., Spiti (India) and the Salt Range (Pakistan), and the new data from South Tibet to construct synthetic, laterally reproducible Dienerian-Smithian ammonoid UAZs, which include 12 UAZs for the Dienerian and 16 UAZs for the Smithian. Based on the newly obtained data and high-resolution biostratigraphic scales, we revised global correlations known for the ammonoid biostratigraphy in the Griesbachian, Dienerian and Smithian. Finally, the high-resolution ammonoid zones are generally in agreement with conodont zones in defining stage/substage boundaries. They also provide a robust and accurate time calibration for Early Triassic carbon isotope trends and temperature changes.
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