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Multi-period design optimization for a 5th generation district heating and cooling network

ENERGY AND BUILDINGS 2023年第1期284卷

作者： Wirtz, Marco Heleno, Miguel Romberg, Hannah Schreiber, Thomas Mueller, Dirk Rhein Westfal TH Aachen Inst Energy Efficient Bldg & Indoor Climate E ON Energy Res Ctr Mathieustr 10 Aachen Germany Univ Calif Berkeley Lawrence Berkeley Natl Lab 1 Cyclotron Rd Berkeley CA 94720 USA

In the planning phase of district energy systems, optimization models based on mathematical program-ming are a widely used approach. Most optimization models determine the optimal energy system design based on a single representative year. However, this approach is unable to consider changing economic and technological boundary conditions over the system's lifetime. As a result, these one-period models fall short for districts with 5th generation district heating and cooling (5GDHC) networks, which are typ-ically developed over long time periods and are built in multiple construction phases. In this paper, we present two multi-period optimization approaches for designing 5GDHC districts: The first approach is a forward-looking model which determines the optimal investment pathway by using a perfect foresight of future parameter developments. In the second approach, a one-period model is solved repeatedly for every investment period without knowledge of future parameter developments. Both approaches are compared to a one-period model for a real-world 5GDHC district in Germany. In comparison with a one-period design approach, the forward-looking method leads to total cost savings of up to 17% and the sequential method of up to 11%. By using a forward-looking model, gas-fired technologies are sized smaller while the capacity of electricity-driven technologies in the energy hub as well as photovoltaic modules and thermal energy storages in buildings increases compared to a one-period model. The case study shows that a multi-period modeling approach is an important addition to design optimization models for 5GDHC networks and can have a significant impact on the optimal design.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Multi-period optimization Multi-year optimization Design optimization 5GDHC linear programming District heating

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Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services

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SENSORS 2023年第6期23卷 3207-3207页

作者： Yang, Zijian Tian, Feng Jin, Jifeng Liu, Huijie Chinese Acad Sci Innovat Acad Microsatellites Shanghai 201203 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

In the realm of providing space-based internet access services, utilizing large-scale low Earth orbit (LEO) satellite networks have emerged as a promising solution for bridging the digital divide and connecting previously unconnected regions. The deployment of LEO satellites can augment terrestrial networks, with increased efficiency and reduced costs. However, as the size of LEO constellations continues to grow, the routing algorithm design of such networks faces numerous challenges. In this study, we present a novel routing algorithm, designated as Internet Fast Access Routing (IFAR), aimed at facilitating faster internet access for users. The algorithm consists of two main components. Firstly, we develop a formal model that calculates the minimum number of hops between any two satellites in the Walker-Delta constellation, along with the corresponding forwarding direction from source to destination. Then, a linear programming is formulated, to match each satellite to the visible satellite on the ground. Upon receipt of user data, each satellite then forwards the data only to the set of visible satellites that correspond to its own satellite. To validate the efficacy of IFAR, we conduct extensive simulation work, and the experimental results showcase the potential of IFAR to enhance the routing capabilities of LEO satellite networks and improve the overall quality of space-based internet access services.

关键词： LEO mega-constellation routing algorithm space communications satellite neworks linear programming

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L Control of Positive Semi-Markov Jump Systems with State Delay

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021年第12期51卷 7569-7578页

作者： Zong, Guangdeng Qi, Wenhai Karimi, Hamid Reza School of Engineering Qufu Normal University Rizhao276826 China Department of Mechanical Engineering Politecnico di Milano Milan20133 Italy

In this article, the issue of L_1 control design is addressed for a class of delayed stochastic jump systems subject to semi-Markov jump parameters. The stochastic jump systems in the presence of positivity constraints are described by positive semi-Markov jump systems (S-MJSs). By constructing new linear Lyapunov functional dependent double integral, some sojourn-time-dependent sufficient conditions are established to realize the corresponding stochastic stability with a prescribed L_1 -gain performance index. Then, a switching controller via gain matrix decomposition is designed to achieve positivity and stochastic stabilization with a prescribed L_1 -gain performance, which can be solved with the help of linear programming approach. Finally, the virus mutation treatment model verifies the effectiveness of the theoretical results. © 2013 IEEE.

关键词： linear programming

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Probabilistic estimation of spinning reserves in smart grids with Bayesian-driven reserve allocation adjustment algorithm

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2021年第3期15卷 433-455页

作者： Bapin, Yerzhigit Zarikas, Vasilios Nazarbayev Univ Sch Engn & Digital Sci Astana Kazakhstan

Purpose This study aims to introduce a methodology for optimal allocation of spinning reserves taking into account load, wind and solar generation by application of the univariate and bivariate parametric models, conventional intra and inter-zonal spinning reserve capacity as well as demand response through utilization of capacity outage probability tables and the equivalent assisting unit approach. Design/methodology/approach The method uses a novel approach to model wind power generation using the bivariate Farlie-Gumbel-Morgenstern probability density function (PDF). The study also uses the Bayesian network (BN) algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours. Findings The results show that the utilization of bivariate wind prediction model along with reserve allocation adjustment algorithm improve reliability of the power grid by 2.66% and reduce the total system operating costs by 1.12%. Originality/value The method uses a novel approach to model wind power generation using the bivariate Farlie-Gumbel-Morgenstern PDF. The study also uses the BN algorithm to perform the adjustment of spinning reserve allocation, based on the actual unit commitment of the previous hours.

关键词： Energy sector Renewable energies Solar linear programming Monte Carlo simulation Mixed integer programming Wind Wind-PV Markov model Bivariate probability density function Spinning reserves Interconnected power system Demand response Bayesian networks Reserve allocation adjustment algorithm

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Application of a robust data envelopment analysis model for performance evaluation of electricity distribution companies

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INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT 2021年第4期15卷 724-742页

作者： Fallahi, Alireza Fallahi, Fatemeh Sarhadi, Hassan Ghaderi, S. F. Ebrahimi, Reza Univ Texas Arlington Dept Ind Mfg & Syst Engn Arlington TX USA Univ Tehran Sch Ind Engn Coll Engn Tehran Iran Acad Univ FC Manning Sch Business Wolfville NS Canada Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

Purpose This study evaluates the efficiency and productivity change of 39 electricity distribution companies in Iran over the period 2005-2014. For purposes of electricity management and utilization of scarce resources, Iran's 33 provinces have been classified into five regions by the Ministry of the Interior. Analyzing the efficiency of distribution companies across these regions yields significant understanding of these resources and helps policymakers to generate more informed decisions. Design/methodology/approach The proposed method of this study develops nonparametric data envelopment analysis (DEA) with the consideration of geographic classification, size and type of company. At the first stage, a DEA model is used to estimate the relative technical efficiency and productivity change of these companies. At the second stage, distributions of efficiency improvements are examined based on geographic classification, size and type of the company type. A stability test is also conducted to verify the proposed model's robustness. Findings The results demonstrate that the average technical efficiency of the companies increased during the years 2006-2009, but decreased during 2010-2014. The productivity measurement reveals that low efficiency change was the largest contributor to the small increase in productivity change rather than technology change. In addition, testing the hypothesis that the large and small companies have statistically the same efficiency scores revealed no statistical difference among them. Moreover, another test did not detect a difference among companies at the urban and provincial levels. Practical implications By applying this approach, policymakers and practitioners in the power industry at the country and corporate level can effectively compare the efficiency and productivity changes among electricity distribution companies, and therefore generate more informed decisions. Originality/value The paper's novel concept applies DEA to Iran's ele

关键词： Efficiency Productivity Energy sector linear programming Resource management Optimization DEA Electricity distribution Input-output tables Correlation analysis Energy balance

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FCSN 3-D PU: Fully Connected Spatiotemporal Network Based 3-D Phase Unwrapping

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Gao, Zhuang He, Xiufeng Ma, Zhangfeng Shi, Guoqiang Hohai Univ Sch Earth Sci & Engn Nanjing Peoples R China Nanyang Technol Univ Earth Observ Singapore Singapore City 639798 Singapore Nanyang Technol Univ Asian Sch Environm Singapore City 639798 Singapore Hong Kong Polytech Univ Dept Land Surveying & GeoInformat Hong Kong Peoples R China

Phase unwrapping (PU) based on spatial networks is a key procedure in time series synthetic aperture radar interferometry (TS-InSAR). Although the state-of-the-art techniques have shown good success in common cases, their performance remained uncertain in some challenging cases where the reliability of spatial networks is difficult, e.g., reservoir areas with sparse points. In this context, this letter presents a new 3-D PU method based on the fully connected spatiotemporal network (FCSN) to improve both the accuracy and robustness of PU. The rationale behind this is that we first implement a spatiotemporal network refinement including temporal interferogram pair selection and spatial network optimization. Based on the generated spatiotemporal network, we then establish a 3-D PU mathematical framework by elaborating the 2-D edgelist PU theory into the 3-D domain. In this framework, all interferograms are unwrapped using the integer linear programming (ILP) method under the minimum L-1-Norm criterion. The new feature of the proposed method is that after a single solution search, all interferograms are unwrapped with relatively high accuracy. The experimental results on two real datasets confirm its effectiveness.

关键词： Three-dimensional displays Spatiotemporal phenomena Reliability Geoscience and remote sensing linear programming Image edge detection Mathematical models 3-D phase unwrapping (PU) edgelist constraints phase triangularity spatiotemporal network time series synthetic aperture radar interferometry (TS-InSAR)

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Using Data-Driven Prediction of Downstream 1D River Flow to Overcome the Challenges of Hydrologic River Modeling

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WATER 2023年第21期15卷 3843页

作者： Feinstein, Jeremy Ploussard, Quentin Veselka, Thomas Yan, Eugene Argonne Natl Lab Environm Sci Div 9700 S Cass Ave Lemont IL 60439 USA Argonne Natl Lab Energy Syst & Infrastructure Anal Div 9700 S Cass Ave Lemont IL 60439 USA

Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system's interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.

关键词： linear programming unit hydrograph deep learning convolutional neural network convolution methods

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Structure Guided Multiparameter Waveform Inversion With Attenuation Compensation in Viscoacoustic Medium

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1页

作者： Deng, Yong Liu, Guochang Du, Jing Li, Chao Wu, Qibin China Univ Petro Beijing State Key Lab Petro Resources & Prospecting Beijing 102249 Peoples R China China Natl Offshore Oil Corp Ltd Zhanjiang 524000 Guangdong Peoples R China China Univ Petro Beijing State Key Lab Petro Resources & Prospecting Beijing 102249 Peoples R China ZKCC Marine Informat Syst Co Ltd Beijing 100190 Peoples R China

Yong Deng, Guochang Jing Abstract- Full waveform inversion (FWI) has been proven as an effective method for subsurface parameter estimation by iteratively reducing the residual between the predictions and the observations. In recent years, FWI has been widely used due to the increasing computing power. However, seismic waves usually suffer from energy dissipation and phase distortion during their propagation in the anelastic medium, which will decelerate the convergence rate of FWI and make the inversion processing even more time-consuming. In this letter, we propose a structure guided FWI method with attenuation compensation to estimate velocity model and Q model simultaneously. We refer to the proposed method as structure guided Q-compensated FWI (SGQFWI). With the help of a fractional decoupled viscoacoustic equation, we introduce the attenuation compensation mechanism into FWI iterative algorithm, which can effectively compensate the deep weak amplitude, so as to obtain a more accurate inversion gradient and improve the accuracy and efficiency of inversion. Additionally, the structure regularization is added to model update, which helps to retrieve local detailed information more efficiently. Benefit from the incorporation of the priori structure constraint and attenuation compensation, this method has the ability to invert velocity and Q model with improved efficiency and resolution. Synthetic and field data examples are adopted to further verify the effectiveness of the proposed method.

关键词： Mathematical models Attenuation Petroleum Phase distortion linear programming Extrapolation Convergence Attenuation compensation multiparameter waveform inversion structure guidance constraints

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Improving the Intelligibility of Speech for Simulated Electric and Acoustic Stimulation Using Fully Convolutional Neural Networks

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 184-195页

作者： Wang, Natalie Yu-Hsien Wang, Hsiao-Lan Sharon Wang, Tao-Wei Fu, Szu-Wei Lu, Xugan Wang, Hsin-Min Tsao, Yu Asia Univ Dept Audiol & Speech Language Pathol Taichung 41354 Taiwan Natl Taiwan Normal Univ Dept Special Educ Taipei 10610 Taiwan Acad Sinica Res Ctr Informat Technol Innovat Taipei 11529 Taiwan Natl Inst Informat & Commun Technol Tokyo 1840015 Japan Acad Sinica Inst Informat Sci Taipei 11529 Taiwan

Combined electric and acoustic stimulation (EAS) has demonstrated better speech recognition than conventional cochlear implant (CI) and yielded satisfactory performance under quiet conditions. However, when noise signals are involved, both the electric signal and the acoustic signal may be distorted, thereby resulting in poor recognition performance. To suppress noise effects, speech enhancement (SE) is a necessary unit in EAS devices. Recently, a time-domain speech enhancement algorithm based on the fully convolutional neural networks (FCN) with a short-time objective intelligibility (STOI)-based objective function (termed FCN(S) in short) has received increasing attention due to its simple structure and effectiveness of restoring clean speech signals from noisy counterparts. With evidence showing the benefits of FCN(S) for normal speech, this study sets out to assess its ability to improve the intelligibility of EAS simulated speech. Objective evaluations and listening tests were conducted to examine the performance of FCN(S) in improving the speech intelligibility of normal and vocoded speech in noisy environments. The experimental results show that, compared with the traditional minimum-mean square-error SE method and the deep denoising autoencoder SE method, FCN(S) can obtain better gain in the speech intelligibility for normal as well as vocoded speech. This study, being the first to evaluate deep learning SE approaches for EAS, confirms that FCN(S) is an effective SE approach that may potentially be integrated into an EAS processor to benefit users in noisy environments.

关键词： Noise measurement Speech enhancement Speech recognition Acoustics Noise reduction linear programming Convolutional neural networks Electric and acoustic stimulation (EAS) cochlear implant fully convolutional neural network speech enhancement

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A General Quality-Related Nonlinear Process Monitoring Approach Based on Input-Output Kernel PLS

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Kong, Xiangyu Luo, Jiayu Feng, Xiaowei Liu, Meizhi High Tech Inst Xian Xian 710025 Peoples R China Shanxi Datong Univ Sch Phys & Elect Sci Datong 037009 Peoples R China

Projection to latent structure (PLS) is a well-known data-based approach widely used in industrial process monitoring. Kernel PLS (KPLS) was proposed in prior studies to apply the PLS in the nonlinear process. However, KPLS-based methods only consider the nonlinear variation of the input and ignore that of the input and output simultaneously. Once the nonlinearity lies in inputs and outputs, the KPLS-based methods cannot accurately describe the nonlinear feature, and result in missing alarms. To provide a common monitoring approach for various nonlinear cases, an input-output KPLS (IO-KPLS) model is proposed. The proposed IO-KPLS maps both the original input and output variables into a high-dimensional space. A new nonlinear objective function is then established to extract latent variables. In addition, a nonlinear regression is designed to construct the IO-KPLS model. By constructing statistics, a complete quality-related process monitoring strategy is designed. Driven by the proposed strategy, the nonlinear feature between input and output can be efficiently extracted, and a comprehensive monitoring performance is provided. A numerical example and two industrial benchmarks are performed to demonstrate the efficiency of the proposed method.

关键词： Feature extraction Kernel Process monitoring linear programming Data mining Principal component analysis Loading Data-driven fault detection kernel projection to latent structure (KPLS) nonlinear process monitoring quality-related

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