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Journal articles on the topic 'Energy modeling and optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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1

Tovar-Facio, Javier, Mariano Martín, and José María Ponce-Ortega. "Sustainable energy transition: modeling and optimization." Current Opinion in Chemical Engineering 31 (March 2021): 100661. http://dx.doi.org/10.1016/j.coche.2020.100661.

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Maleki, Akbar. "Modeling and optimization of energy systems." Journal of Thermal Analysis and Calorimetry 144, no. 5 (April 15, 2021): 1635–38. http://dx.doi.org/10.1007/s10973-021-10782-7.

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3

Xiao, Dong, Xiao-li Pan, Yong Yuan, Zhi-zhong Mao, and Fu-li Wang. "Modeling and optimization for piercing energy consumption." Journal of Iron and Steel Research International 16, no. 2 (February 2009): 40–44. http://dx.doi.org/10.1016/s1006-706x(09)60025-x.

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4

Vera, J., and F. Urbina. "Modeling the decentralized optimization of communicative energy." EPL (Europhysics Letters) 131, no. 6 (October 13, 2020): 68002. http://dx.doi.org/10.1209/0295-5075/131/68002.

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5

Evans, R. J., and P. D. Franzon. "Energy consumption modeling and optimization for SRAM's." IEEE Journal of Solid-State Circuits 30, no. 5 (May 1995): 571–79. http://dx.doi.org/10.1109/4.384170.

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6

Liu, Pei, Dimitrios I. Gerogiorgis, and Efstratios N. Pistikopoulos. "Modeling and optimization of polygeneration energy systems." Catalysis Today 127, no. 1-4 (September 30, 2007): 347–59. http://dx.doi.org/10.1016/j.cattod.2007.05.024.

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7

Kusiak, Andrew, Mingyang Li, and Fan Tang. "Modeling and optimization of HVAC energy consumption." Applied Energy 87, no. 10 (October 2010): 3092–102. http://dx.doi.org/10.1016/j.apenergy.2010.04.008.

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8

Yin, Yonghua. "OPTIMUM ENERGY FOR ENERGY PACKET NETWORKS." Probability in the Engineering and Informational Sciences 31, no. 4 (April 9, 2017): 516–39. http://dx.doi.org/10.1017/s0269964817000067.

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The concept of Energy Packet Network (EPN) proposed by Gelenbe, is a new framework for modeling power grids that takes distributed energy generation such as renewable energy sources into consideration, and which contributes to modeling the smart grid. Based on G-network theory, this paper presents a simplified model of EPN and formulates energy-distribution as an optimization problem. We analyze it theoretically, and detail its optimal solutions. In addition to using existing optimization algorithms, a heuristic algorithm is proposed to solve for EPN optimization. The optimal solutions and efficacy of the algorithm are illustrated with numerical experiments. Further, we present an EPN with disconnections and a similar optimization problem is investigated. Optimal solutions are presented, and numerical results using the analytic optimal solutions, random solutions, a cooperative particle swarm optimizer and a heuristic algorithm illustrate the power of different approaches for solving energy-distribution problems using the EPN formalism.

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Bansal, Manoj. "Optimization Modelling for Renewable Energy Resources based Distribution Generation." Revista Gestão Inovação e Tecnologias 11, no. 3 (June 30, 2021): 1510–19. http://dx.doi.org/10.47059/revistageintec.v11i3.2027.

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Bryan, Lisk, John Collett, and Robert Walters. "Smart Modeling for Water Distribution System Energy Optimization." Proceedings of the Water Environment Federation 2016, no. 10 (January 1, 2016): 3174–81. http://dx.doi.org/10.2175/193864716819707788.

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11

Liu, Tianhao, Dongdong Zhang, Hang Dai, and Thomas Wu. "Intelligent Modeling and Optimization for Smart Energy Hub." IEEE Transactions on Industrial Electronics 66, no. 12 (December 2019): 9898–908. http://dx.doi.org/10.1109/tie.2019.2903766.

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12

Zhang, Shirong, and Xiaohua Xia. "Modeling and energy efficiency optimization of belt conveyors." Applied Energy 88, no. 9 (September 2011): 3061–71. http://dx.doi.org/10.1016/j.apenergy.2011.03.015.

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13

AIYOSHI, Eitaro, Takashi OKAMOTO, and Yoko KOBAYASHI. "Heuristic Optimization and Modeling（１）." Journal of the Atomic Energy Society of Japan 53, no. 11 (2011): 776. http://dx.doi.org/10.3327/jaesjb.53.11_776.

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AIYOSHI, Eitaro, Takashi OKAMOTO, and Yoko KOBAYASHI. "Heuristic Optimization and Modeling (1)." Journal of the Atomic Energy Society of Japan 53, no. 11 (2011): 777–81. http://dx.doi.org/10.3327/jaesjb.53.11_777.

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AIYOSHI, Eitaro, Takashi OKAMOTO, and Yoko KOBAYASHI. "Heuristic Optimization and Modeling (2)." Journal of the Atomic Energy Society of Japan 54, no. 1 (2012): 57–60. http://dx.doi.org/10.3327/jaesjb.54.1_57.

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AIYOSHI, Eitaro, Takashi OKAMOTO, and Yoko KOBAYASHI. "Heuristic Optimization and Modeling (3)." Journal of the Atomic Energy Society of Japan 54, no. 2 (2012): 133–36. http://dx.doi.org/10.3327/jaesjb.54.2_133.

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AIYOSHI, Eitaro, Takashi OKAMOTO, and Yoko KOBAYASHI. "Heuristic Optimization and Modeling (4)." Journal of the Atomic Energy Society of Japan 54, no. 3 (2012): 201–4. http://dx.doi.org/10.3327/jaesjb.54.3_201.

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18

Liu, Yang, Fu Ting Bao, and Jia Jia Du. "Research on Multi-Domain Unified Modeling and Optimization of a Micro Mirror." Advanced Materials Research 154-155 (October 2010): 246–50. http://dx.doi.org/10.4028/www.scientific.net/amr.154-155.246.

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Referring to the natures of nonlinear dynamics and coupled energy domains presented by Micro-Opto-Electro-Mechanical Systems (MOEMS), a multi energy domain unified simulation language (modelica) based coupled modeling, simulation and optimization methodology is put forward. Then by choosing a micro mirror as the example, corresponding multi energy domain unified modeling and simulation processes of such a coupled micro device show the efficiency and convenience of this advanced modeling method. The dynamic input/output curves comparison also shows that the simulation result is accurate enough for system level design. At last, by using genetic optimization algorithm, the characteristics input/output curves of the micro mirror are converged to meet the ideal design requirement curves after 700 generations evolution. It proves the accuracy and efficiency of this system level simulation and optimization method.

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19

Li, Jingchao, Yulong Ying, Xingdan Lou, Juanjuan Fan, Yunlongyu Chen, and Dongyuan Bi. "Integrated Energy System Optimization Based on Standardized Matrix Modeling Method." Applied Sciences 8, no. 12 (November 23, 2018): 2372. http://dx.doi.org/10.3390/app8122372.

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Aiming at the optimization of an integrated energy system, a standardized matrix modeling method and optimization method for an integrated energy system is proposed. Firstly, from the perspective of system engineering, the energy flow between energy conversion devices is used as a state variable to deal with nonlinear problems caused by the introduction of scheduling factors, and a standardized matrix model of the integrated energy system is constructed. Secondly, based on the proposed model, the structural optimization (i.e., energy flow structure and equipment type), design optimization (i.e., equipment capacity and quantity), and operation optimization for the integrated energy system can be achieved. The simulation case studies have shown that the proposed integrated energy system standardized matrix modeling method and optimization method are both simple and efficient, and can be effectively used to decide the system components and their interconnections, and the technical characteristics and daily operating strategy of the system components.

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20

Weng, Shilie, Chenghong Gu, and Yiwu Weng. "Energy internet technology: modeling, optimization and dispatch of integrated energy systems." Frontiers in Energy 12, no. 4 (December 2018): 481–83. http://dx.doi.org/10.1007/s11708-018-0604-9.

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21

Maroufmashat, Taqvi, Miragha, Fowler, and Elkamel. "Modeling and Optimization of Energy Hubs: A Comprehensive Review." Inventions 4, no. 3 (August 23, 2019): 50. http://dx.doi.org/10.3390/inventions4030050.

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: The concept of energy hubs has grown in prominence as a part of future energy systems, driven by the spread of Distributed Energy Resources (DERs) and the inception of the smart grid. This paper systematically reviews 200 articles about energy hubs, published from 2007 to 2017, and summarizes them based on their modeling approach, planning and operation, economic and environmental considerations, and energy hub applications. The common applications of energy hubs are considered, such as distributed energy resources, the consideration of Plug-in Hybrid Electric Vehicles (PHEVs), and the hydrogen economy. This paper examines modeling approaches towards energy hubs, including storage and its network models; it mentions some of the optimization strategies used to tackle the efficient operation and control of energy hubs. The novelty of this work lies in the classification of research papers related to energy hubs, the development of a generic framework for modeling these multiple energy flow carriers with storage and network considerations, and the provision of solution techniques in line with energy hub optimization.

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22

Ma, Tieju, and Yoshiteru Nakamori. "Modeling technological change in energy systems – From optimization to agent-based modeling." Energy 34, no. 7 (July 2009): 873–79. http://dx.doi.org/10.1016/j.energy.2009.03.005.

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23

Abdullah, Arif, Mohd Fadzil Faisae Ab Rashid, S. G. Ponnambalam, and Zakri Ghazalli. "Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization." Assembly Automation 39, no. 2 (April 1, 2019): 356–68. http://dx.doi.org/10.1108/aa-06-2018-091.

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Purpose Environmental problems in manufacturing industries are a global issue owing to severe lack fossil resources. In assembly sequence planning (ASP), the research effort mainly aims to improve profit and human-related factors, but it still lacks in the consideration of the environmental issue. This paper aims to present an energy-efficient model for the ASP problem. Design/methodology/approach The proposed model considered energy utilization during the assembly process, particularly idle energy utilization. The problem was then optimized using moth flame optimization (MFO) and compared with well-established algorithms such as genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). A computational test was conducted using five assembly problems ranging from 12 to 40 components. Findings The results of the computational experiments indicated that the proposed model was capable of generating an energy-efficient assembly sequence. At the same time, the results also showed that MFO consistently performed better in terms of the best and mean fitness, with acceptable computational time. Originality/value This paper proposed a new energy-efficient ASP model that can be a guideline to design assembly station. Furthermore, this is the first attempt to implement MFO for the ASP problem.

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24

Huang, Jin Xin, Jie Zhan, and Lin Niu. "Modeling and Optimization for Capacitive Converter of Energy Harvester." Advanced Materials Research 791-793 (September 2013): 1815–18. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.1815.

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A new converter with spherical cap for energy harvesting is proposed. Based on the method of separated variables within the toridal coordinate system, a corresponding analytical model for spherical cap converter is further established so as to obtain the analytic expressions of the topology capacitance and the output voltage. The concept of energy increment factor is specifically defined to denote the improvement of energy storage efficiency.

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25

Bonavolontà, Antonella, Davide Mesturini, Cesare Dolcin, Pietro Marani, Emma Frosina, and Adolfo Senatore. "Downstream Compensator: Innovative Systems, Modeling Analysis and Energy Optimization." E3S Web of Conferences 197 (2020): 07003. http://dx.doi.org/10.1051/e3sconf/202019707003.

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The Energy optimization is becoming fundamental in the Fluid Power world. University and industries are working hard to promote innovative and efficient ideas to optimize components that are the main cause of energy dissipation of ICE and recently Electric Off-Road vehicles. A new hydraulic layout based on the concept of “Downstream compensation” is introduced and then validated using real test data. Three architectures of this innovative Directional Control Valve are presented in this paper. The first idea of layout includes a compensator controlled by two pressure signals taken before and after the main spool of the hydraulic circuit. Thanks to its controlled stroke, this compensator diverts to a highpressure accumulator part of flow that otherwise would be delivered to the tank. Moreover, two different layouts able to satisfy the Flow Sharing characteristic were developed. In any configuration, the compensator, thanks to its downstream position, allows to control the return flow, realizing a remarkable energy recovery from the overrunning loads and the simultaneous use of multiple actuators at different pressure levels. For all the analyzed hydraulic circuit, lumped parameter models were realized, using a commercial software. These models, validated with experimental tests, have allowed to calculate the energy recovery achieved by the system. Moreover, an optimization of the most important system’s parameters and components were realized to improve the system efficiency. In every tested configuration, this compensator ensures great advantages for both the energy recovery and the economic point of view. Finally, an outlook is drawn of the reuse of recovered flow through the application of an electrohydraulic motor.

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26

Crovetto, Andrea, Fei Wang, and Ole Hansen. "Modeling and Optimization of an Electrostatic Energy Harvesting Device." Journal of Microelectromechanical Systems 23, no. 5 (October 2014): 1141–55. http://dx.doi.org/10.1109/jmems.2014.2306963.

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27

Bornatico, Raffaele, Jonathan Hüssy, Andreas Witzig, and Lino Guzzella. "Surrogate modeling for the fast optimization of energy systems." Energy 57 (August 2013): 653–62. http://dx.doi.org/10.1016/j.energy.2013.05.044.

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28

Trautman, Neal, Ali Razban, and Jie Chen. "Overall chilled water system energy consumption modeling and optimization." Applied Energy 299 (October 2021): 117166. http://dx.doi.org/10.1016/j.apenergy.2021.117166.

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29

Li, Qian, Siyuan Wang, Xiaoyan Zhou, Anan Zhang, and Roksana Zaman. "Modeling and Optimization of RIES Based on Composite Energy Pipeline Energy Supply." IEEE Transactions on Applied Superconductivity 31, no. 8 (November 2021): 1–5. http://dx.doi.org/10.1109/tasc.2021.3090340.

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30

Li, Ming, and Zhe Wang. "Modeling and Optimization of Integrated Energy System Based on Energy Circuit Theory." IEEJ Transactions on Electrical and Electronic Engineering 16, no. 5 (April 4, 2021): 696–703. http://dx.doi.org/10.1002/tee.23349.

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31

Dai, Rui-Cheng, Bi Zhao, Xiao-Di Zhang, Jun-Wei Yu, Bo Fan, and Biao Liu. "Joint Virtual Energy Storage Modeling with Electric Vehicle Participation in Energy Local Area Smart Grid." Complexity 2020 (October 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/3102729.

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In this research, the joint virtual energy storage modeling with electric vehicle participation in energy local area Smart Grid is considered. This article first constructs a virtual energy storage model and a joint virtual energy storage model for air conditioning and electric vehicles. Therefore, for the optimization problem of virtual energy storage power, a continuous rolling optimization algorithm to determine the feasible solution of the high-dimensional complex constraint optimization problem is proposed to solve the optimization problem. Finally, the analysis, for example, illustrates the economics of joint virtual energy storage in the Smart Grid. The results prove that air conditioning and electric vehicles have the ability to jointly participate in virtual energy storage, and the comparison proves that joint virtual energy storage can effectively improve the economics of electricity consumption.

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32

Zhou, Jia, Hany Abdel-Khalik, Paul Talbot, and Cristian Rabiti. "A Hybrid Energy System Workflow for Energy Portfolio Optimization." Energies 14, no. 15 (July 21, 2021): 4392. http://dx.doi.org/10.3390/en14154392.

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This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

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33

Wei, Xiupeng, Guanglin Xu, and Andrew Kusiak. "Modeling and optimization of a chiller plant." Energy 73 (August 2014): 898–907. http://dx.doi.org/10.1016/j.energy.2014.06.102.

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34

Raji, Atanda K., and Doudou N. Luta. "Modeling and Optimization of a Community Microgrid Components." Energy Procedia 156 (January 2019): 406–11. http://dx.doi.org/10.1016/j.egypro.2018.11.103.

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35

Tgarguifa, Ahmed, Souad Abderafi, and Tijani Bounahmidi. "Modeling and optimization of distillation to produce bioethanol." Energy Procedia 139 (December 2017): 43–48. http://dx.doi.org/10.1016/j.egypro.2017.11.170.

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36

Muhammed, Hardi A., and Soorkeu A. Atrooshi. "Modeling solar chimney for geometry optimization." Renewable Energy 138 (August 2019): 212–23. http://dx.doi.org/10.1016/j.renene.2019.01.068.

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37

Ouassou, Jabir Ali, Julian Straus, Marte Fodstad, Gunhild Reigstad, and Ove Wolfgang. "Applying Endogenous Learning Models in Energy System Optimization." Energies 14, no. 16 (August 7, 2021): 4819. http://dx.doi.org/10.3390/en14164819.

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Conventional energy production based on fossil fuels causes emissions that contribute to global warming. Accurate energy system models are required for a cost-optimal transition to a zero-emission energy system, which is an endeavor that requires a methodical modeling of cost reductions due to technological learning effects. In this review, we summarize common methodologies for modeling technological learning and associated cost reductions via learning curves. This is followed by a literature survey to uncover learning rates for relevant low-carbon technologies required to model future energy systems. The focus is on (i) learning effects in hydrogen production technologies and (ii) the application of endogenous learning in energy system models. Finally, we discuss methodological shortcomings of typical learning curves and possible remedies. One of our main results is an up-to-date overview of learning rates that can be applied in energy system models.

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38

El Khaili, Mohamed, Redouane Marhoum, Chaimaa Fouhad, and Hassan Ouajji. "Contribution to Multi-Energy Flow Management for Building Energy Hub." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 01 (March 1, 2021): 27–34. http://dx.doi.org/10.5383/juspn.15.01.004.

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Global demand for primary fossil energy continues to increase. However, the production of energy from fossil fuels, in addition to depleting available reserves, releases millions of tons of Greenhouse Gas (GHG) into the atmosphere. Thus, it is obvious that the high concentration of GHGs in the air disrupts the natural greenhouse effect and consequently causes global warming. The implementation of action plans aimed at reducing greenhouse gas emissions has led all countries to use clean energy sources (sun, earth, wind) called renewable energies and also to rationalize the use of energies whether based on fossil fuels or renewable. Our paper presents a modeling of the demand and its management to ensure an optimization of the energy consumption and the reduction of its bill

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39

Harutyunyan, Robert. "Computer Modeling and Optimization of Soil Thawing Using Microwave Energy." Известия высших учебных заведений. Электромеханика 63, no. 6 (2020): 37–43. http://dx.doi.org/10.17213/0136-3360-2020-6-37-43.

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A mathematical model is developed to calculate the electromagnetic field A mathematical model is formu-lated, and a finite-difference method and computer programs are developed that allow effective computer mod-eling and optimization of high-speed soil thawing processes in various ways, including using magnetrons. As the main modeling method, a variant of the end-to-end "enthalpy" method without smoothing the concentrated heat capacity is used. A series of calculations was performed for the informative case of Sandstone and sandy loam during thawing by magnetrons of standard structures. The significant influence of nonlinearities of ther-mal parameters, phase transitions of melting and evaporation, and the type of boundary conditions on the val-ues of temperature and electric fields is established. The effect of losses on convective cooling and evaporation is negligible. The results of the work can be applied in the practice of research and design of earthmoving op-erations in the cryolithozone.

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40

Huang, Jinxin, Nannan Gao, and Hongbo Li. "Modeling and Optimization of Capacitive Converter for Energy Scavenging System." Energy and Power Engineering 05, no. 04 (2013): 116–20. http://dx.doi.org/10.4236/epe.2013.54b022.

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41

Cremaschi, Lorenzo. "Modeling, case studies, and optimization methods for building energy systems." Science and Technology for the Built Environment 24, no. 4 (March 27, 2018): 325–26. http://dx.doi.org/10.1080/23744731.2018.1444304.

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42

Boughaleb, J., A. Arnaud, P. J. Cottinet, S. Monfray, P. Gelenne, P. Kermel, S. Quenard, F. Boeuf, D. Guyomar, and T. Skotnicki. "Thermal modeling and optimization of a thermally matched energy harvester." Smart Materials and Structures 24, no. 8 (July 15, 2015): 085025. http://dx.doi.org/10.1088/0964-1726/24/8/085025.

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43

Bi, Z. M., and Lihui Wang. "Energy Modeling of Machine Tools for Optimization of Machine Setups." IEEE Transactions on Automation Science and Engineering 9, no. 3 (July 2012): 607–13. http://dx.doi.org/10.1109/tase.2012.2195173.

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44

Kuznetsov, Oleg, Viktor Chepurnov, Albina Gurskaya, Mikhail Dolgopolov, and Sali Radzhapov. "C-beta energy converter efficiency modeling." EPJ Web of Conferences 222 (2019): 02012. http://dx.doi.org/10.1051/epjconf/201922202012.

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To construct beta converters with maximum efficiency it is necessary to carry out the theoretical calculation in order to determine their optimal parameters - the geometry of the structure, the thickness of the deposition of the radioisotope layer, the depth and the width of the p-n junction, and others. To date, many different theoretical models and calculations methods had been proposed. There are fairly simple theoretical models based on the Bethe-Bloch formula and the calculation of the rate of generation of electron-hole pairs, and on calculations by equivalent circuits. Also, the Monte-Carlo method is used for theoretical modeling of beta converters. This paper explores beta converter optimization using the Monte-Carlo method. The purpose of the study is to conduct Monte-Carlo simulation of the beta converter to determine its optimal parameters.

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45

cardoso, M. A. A., and L. J. J. Durlofsky. "Use of Reduced-Order Modeling Procedures for Production Optimization." SPE Journal 15, no. 02 (December 3, 2009): 426–35. http://dx.doi.org/10.2118/119057-pa.

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Summary The determination of optimal well settings is very demanding computationally because the simulation model must be run many times during the course of the optimization. For this reason, reduced-order modeling procedures, which are a family of techniques that enable highly efficient simulations, may be very useful for optimization problems. In this paper, we describe a recently developed reduced-order modeling (ROM) technique that has been used in other application areas, the trajectory piecewise linearization (TPWL) procedure, and incorporate it in production-optimization computations. The TPWL methodology represents solutions encountered during the optimization runs in terms of Taylor-series expansions around previously simulated states. This requires a small number of preprocessing (training) simulations using the full (high-fidelity) model, during which pressure and saturation states and Jacobian matrices are saved. These states and matrices are then projected into a low-dimensional space using proper orthogonal decomposition (POD). Simulations in this reduced space can be performed very efficiently; in this work, we observe runtime speedups of a factor of 450. Overall speedups are, however, less because of the preprocessing overhead. We assess the TPWL representation for simulations of waterflood in a heterogeneous 3D model containing more than 20,000 gridblocks and six wells. The high degree of accuracy of the TPWL model is first demonstrated for several testing simulations in which producer- and injector-well settings differ from those used in the training runs. The TPWL representations are then used in optimizations involving the determination of optimal bottomhole pressures (BHPs) for a reservoir model with four production wells and two injection wells. A gradient-based algorithm is applied for the optimizations. In the first case, the BHPs of the producers and injectors are optimized at six different times (36 control variables) and in the second case at 15 different times (90 control variables). Results for optimized net present value (NPV) using TPWL are shown to be in consistently close agreement with those computed using high-fidelity simulations. Most significantly, when the optimal well settings obtained using the TPWL procedure are applied in high-fidelity models, the resulting NPVs are within approximately 0.5% of the values determined using the high-fidelity simulations. Our overall conclusion is that the TPWL representation may be quite useful in production-optimization problems.

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Koshevyi, Oleksandr, Dmytro Levkivskyi, Victoria Kosheva, and Andrii Mozharovskyi. "Сomputer modeling and optimization of energy efficiency potentials in civil engineering." Strength of Materials and Theory of Structures, no. 106 (May 24, 2021): 274–81. http://dx.doi.org/10.32347/2410-2547.2021.106.274-281.

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The paper presents the results of creating a software package for optimizing the calculation of potentials of alternative energy sources in the regions of Ukraine based on BIM technologies (AutoCAD, ArchiCAD, Revit), which are combined using the IFC format. The software package uses mathematical and graph-analytical models of climate and energy zoning in the regions of Ukraine, and with the help of MS Excel visualizes the research process and automates, accelerates optimal decision in design, reconstruction and construction. The process of forming a database for traditional energy sources (electricity, oil products, natural gas, coal, firewood) and a database of energy potentials of alternative energy sources (solar energy, wind energy, geothermal energy, hydropower of small rivers, potentials of livestock and crop biomass potential of excess pressure of natural gas, potentials of heat of soil, ground and sewage, potentials of energy of peat and forest waste) for all regions of Ukraine. The structure of the software package and a block diagram has been developed, all indicators are reduced to a single unit of measurement (MW*h / year per 1000 people). To analyze and make optimal decisions, informative-illustrative bar and sector pie charts are built in MS Excel on five main areas of energy consumption, taking into account alternative energy sources for each region of Ukraine. The general analysis of energy consumption and optimization calculations are carried out with the help of informative-illustrative diagram SANKEY, which is created with the help of "SankeyDiagramGenerator", and visualizes the whole process of graph-analytical modeling of energy consumption in Ukraine.

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47

��, Ekaterina Frolkina, ��, and Igor Tsarkov. "Developing An Optimization Model For Managing Energy Efficiency Programs." Russian Journal of Project Management 5, no. 2 (June 17, 2016): 41–54. http://dx.doi.org/10.12737/20514.

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Energy saving in all economic sectors is one of the most important strategic objectives of the 21st century. Energy efficiency programs are usually implemented in parallel with core activities of the company and thereby create an additional burden on limited budget. Cost-effectiveness of such a program is not always obvious so the decision to start can be delayed, and actual result may not be the expected one. In order to ensure economic efficiency the authors offer to use the tool of mathematical modeling to optimize energy efficiency program. The paper also presents the results of existing research analysis on the different approaches towards mathematical modeling of programs, the authors identify four main goals of the simulation. The authors� approach is aimed at optimizing energy efficiency program for one or more criteria within certain restrictions.

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Powell, Kody M., and Kasra Mohammadi. "Modeling, Control, and Optimization of Multi-Generation and Hybrid Energy Systems." Processes 9, no. 7 (June 29, 2021): 1125. http://dx.doi.org/10.3390/pr9071125.

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Rhodes, John H. "Industrial Fuel Optimization through Dynamic Cost Modeling." Energy Engineering 101, no. 5 (September 2004): 70–77. http://dx.doi.org/10.1080/01998590409509280.

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Safari, Fatemeh, and Arjomand Mehrabani-Zeinabad. "Modeling, Simulation, and Thermally Optimization of Thermally-Coupled Distillation Columns." International Journal of Chemoinformatics and Chemical Engineering 5, no. 1 (January 2016): 1–11. http://dx.doi.org/10.4018/ijcce.2016010101.

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Distillation is one of the most widely used separation units that consumes the largest amount of energy in chemical and petrochemical industries. Heat integration of thermally coupled distillation column is one of the methods to reduce energy-consumption. This paper provides a comparison between two simple columns with direct configuration and thermally coupled distillation column with direct sequence backward integration arrangement for separation of a ternary mixture based on energy-consumption. The influence of changing numbers of first and second column trays on heating and cooling rate of each column are investigated based on a developed mathematical model using conservation law of mass and energy and bubble-point method. The average relative error between calculated and industrial temperatures in some trays is about 0.74%. The condenser duty of high pressure column is about 9.73×109 kJ/h to provide heating of low pressure column. According to the simulation results, the thermally coupled construction saves energy about 50% more.

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