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1
Bassam, Fadel, and Andrey Kryukov. "DIGITAL MODELS OF DISTRIBUTION ELECTRIC NETWORK OF IRAQ." Modern Technologies and Scientific and Technological Progress 2020, no. 1 (June 16, 2020): 187–88. http://dx.doi.org/10.36629/2686-9896-2020-1-187-188.
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The restoration and development of Iraq's electric power industry is planned to be carried out on the basis of the concept of intelligent electric networks (smart grid). To implement this concept, it is necessary to create integrated digital models of electrical networks. Such models can be implemented on the basis of methods and tools for modeling electric power systems in phase coordinates. The report presents the results of modeling normal and emergency modes of distribution electric networks of 11 kV.
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Gu, Huaying, and Chaoqun Han. "Analysis of China’s Pure Electric Vehicle Sales Based on Spatial Econometric Models." International Journal of Economics and Finance 13, no. 1 (December 5, 2020): 12. http://dx.doi.org/10.5539/ijef.v13n1p12.
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This paper empirically investigates the spatial dependence and serial correlation structures among different China’s brands of pure electric vehicle (EV) sales using spatial econometric models. Based on the newly proposed economic distance spatial weight matrix, the empirical results show that EV endurance mileage, power battery capacity, charging time, government subsidy, retail price, and each brand market share have important impacts on EV sales. The per capita disposable income of urban households, gasoline price, loan rate and the number of charging pile are statistically significant determinants of EV sales. In particular, the improvements of the number of charging pile and the rise of gasoline price can increase EV sales, while the rise of loan rate or tight monetary policy may increase the consumers’ cost of purchasing EVs and then decrease EV sales. Another interesting finding is that though the per capita disposable income of urban households increases the EV sales decreases. A plausible explanation would seem to be that the impact of the per capita disposable income of urban households on the EV sales is offset by the decline in government subsidies or the incomplete infrastructures such as the inconvenient of charging stations. Besides, the significantly positive spatial dependence and serial correlation exist among EV manufactures indicates that when developing EV sales strategies, EV manufacturers must consider not only the properties of the EVs they produce, but also the properties of similar types of EVs produced by other brands in the EV market.
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Slushaienko, Nataliia. "OPTIMIZATION MODELS OF THE UKRAINIAN POWER INDUSTRY." Ekonomika 92, no. 4 (January 1, 2013): 114–26. http://dx.doi.org/10.15388/ekon.2013.0.2339.
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Abstract. The paper deals with the environmental aspect of power industry development and uses unilateral and bilateral auction and games theory optimization modeling to simulate competitive market introduction to the Ukrainian electric power industry. The main goal of the research is to investigate how the transition will influence prices’, volumes’ dynamics and benefit distribution among market players while also addressing the problem of possible market fluctuations due to price sensitivity.Key words: electric power market, environment, competitive market introduction, auctions, games theory
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Hua, Y. P., G. W. Lan, and Y. L. Du. "MULTI-SCALE SPATIAL MODELLING OF ELECTRIC POWER DISTRIBUTION NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1121–25. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1121-2020.
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Abstract. The research proposes a multi-scale spatial data model of electric power distribution networks (EPDNs) to address the problem that the single-scale EPDN data cannot meet the needs of data representation and spatial analysis of multiple levels of detail (LODs). This study comprehensively analyses the equipment used in the EPDN, summarizes the detailed information of EPDN elements and constructs a reasonable EPDN structure system. Based on the analysis of a large number of use cases in the operation and maintenance field of EPDN, this research identifies the elements of the graphic data and attribute data relating to the EPDN. According to the needs of different users and different application modes of EPDN data in multi-scale data representation, the EPDN data models are divided into four LODs, and the simplification principle of constructing different LODs is put forward and the elements contained in each LOD are carefully modelled. This study divides the EPDN elements information into graphic attribute and functional attribute, and then the attribute information of the EPDN data models in different LODs is described in detail. In addition, the EPDN data of Yan Shan campus of Guilin University of Technology is modelled with the proposed method, which has achieved good visualization results.
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Meinecke, Steffen, Leon Thurner, and Martin Braun. "Review of Steady-State Electric Power Distribution System Datasets." Energies 13, no. 18 (September 15, 2020): 4826. http://dx.doi.org/10.3390/en13184826.
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Publicly available grid datasets with electric steady-state equivalent circuit models are crucial for the development and comparison of a variety of power system simulation tools and algorithms. Such algorithms are essential to analyze and improve the integration of distributed energy resources (DERs) in electrical power systems. Increased penetration of DERs, new technologies, and changing regulatory frameworks require the continuous development of the grid infrastructure. As a result, the number and versatility of grid datasets, which are required in power system research, increases. Furthermore, the used grids are created by different methods and intentions. This paper gives orientation within these developments: First, a concise overview of well-known, publicly available grid datasets is provided. Second, background information on the compilation of the grid datasets, including different methods, intentions and data origins, is reviewed and characterized. Third, common terms to describe electric steady-state distribution grids, such as representative grid or benchmark grid, are assembled and reviewed. Recommendations for the use of these grid terms are made.
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Sun, Yiguo, Zongwu Cai, and Qi Li. "SEMIPARAMETRIC FUNCTIONAL COEFFICIENT MODELS WITH INTEGRATED COVARIATES." Econometric Theory 29, no. 3 (January 8, 2013): 659–72. http://dx.doi.org/10.1017/s0266466612000710.
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AbstractCai, Li, and Park (Journal of Econometrics, 2009) and Xiao (Journal of Econometrics, 2009) developed asymptotic theories for estimators of semiparametric varying coefficient models when regressors are integrated processes but the smooth coefficients are functionals of stationary processes. Using a recent result from Phillips (Econometric Theory, 2009), we extend this line of research by allowing for both the regressors and the covariates entering the smooth functionals to be integrated variables. We derive the asymptotic distribution for the proposed semiparametric estimator. An empirical application is presented to examine the purchasing power parity hypothesis between U.S. and Canadian dollars.
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Okeniyi, Joshua Olusegun, Olayinka Soledayo Ohunakin, and Elizabeth Toyin Okeniyi. "Assessments of Wind-Energy Potential in Selected Sites from Three Geopolitical Zones in Nigeria: Implications for Renewable/Sustainable Rural Electrification." Scientific World Journal 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/581679.
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Electricity generation in rural communities is an acute problem militating against socioeconomic well-being of the populace in these communities in developing countries, including Nigeria. In this paper, assessments of wind-energy potential in selected sites from three major geopolitical zones of Nigeria were investigated. For this, daily wind-speed data from Katsina in northern, Warri in southwestern and Calabar in southeastern Nigeria were analysed using the Gumbel and the Weibull probability distributions for assessing wind-energy potential as a renewable/sustainable solution for the country’s rural-electrification problems. Results showed that the wind-speed models identified Katsina with higher wind-speed class than both Warri and Calabar that were otherwise identified as low wind-speed sites. However, econometrics of electricity power simulation at different hub heights of low wind-speed turbine systems showed that the cost of electric-power generation in the three study sites was converging to affordable cost per kWh of electric energy from the wind resource at each site. These power simulations identified cost/kWh of electricity generation at Kaduna as €0.0507, at Warri as €0.0774, and at Calabar as €0.0819. These bare positive implications on renewable/sustainable rural electrification in the study sites even as requisite options for promoting utilization of this viable wind-resource energy in the remote communities in the environs of the study sites were suggested.
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Baraniak, Joanna, and Jacek Starzyński. "Modeling the Impact of Electric Vehicle Charging Systems on Electric Power Quality." Energies 13, no. 15 (August 1, 2020): 3951. http://dx.doi.org/10.3390/en13153951.
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Recently, there has been dynamic development of electromobility. This trend is predicted to grow exponentially, which will contribute to the creation of an extensive infrastructure of electric car charging points. For distribution network operators, this implies that in addition to the increase in demand for power and electricity, the number of converter systems connected to their networks will also increase. It is therefore necessary to determine the potential impact of electric vehicle charging systems on electricity quality. To this end, the authors of the present study developed simulation models based on measurements of the actual charger and the data provided by the manufacturers of the equipment and using simulation models widely described in the literature. On the basis of the study results, the impact of electric car chargers on electric power quality was assessed, and attention was given to the opportunities offered by the development of charging systems with vehicle-to-grid (V2G) functionality. Changes in technical standards aimed at selecting power supply cables by considering their heating under the influence of higher current harmonics generated by converter systems were proposed.
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Yang, Zhangang, Junchao Qu, Yingchuan Ma, and Xudong Shi. "Modeling and Simulation of Power Distribution System in More Electric Aircraft." Journal of Electrical and Computer Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/847624.
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The More Electric Aircraft concept is a fast-developing trend in modern aircraft industry. With this new concept, the performance of the aircraft can be further optimized and meanwhile the operating and maintenance cost will be decreased effectively. In order to optimize the power system integrity and have the ability to investigate the performance of the overall system in any possible situations, one accurate simulation model of the aircraft power system will be very helpful and necessary. This paper mainly introduces a method to build a simulation model for the power distribution system, which is based on detailed component models. The power distribution system model consists of power generation unit, transformer rectifier unit, DC-DC converter unit, and DC-AC inverter unit. In order to optimize the performance of the power distribution system and improve the quality of the distributed power, a feedback control network is designed based on the characteristics of the power distribution system. The simulation result indicates that this new simulation model is well designed and it works accurately. Moreover, steady state performance and transient state performance of the model can fulfill the requirements of aircraft power distribution system in the realistic application.
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Pirouzi, Sasan, Jamshid Aghaei, Taher Niknam, Miadreza Shafie-khah, Vahid Vahidinasab, and João P. S. Catalão. "Two alternative robust optimization models for flexible power management of electric vehicles in distribution networks." Energy 141 (December 2017): 635–51. http://dx.doi.org/10.1016/j.energy.2017.09.109.
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Zhang, Ke, Yi Bo Zheng, and Hang Yang Yu. "The Study on Electric Power Equipment Reliability Analysis Using Statistical Method." Applied Mechanics and Materials 203 (October 2012): 340–44. http://dx.doi.org/10.4028/www.scientific.net/amm.203.340.
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This paper gives an overview of selected statistical methods and models commonly used in reliability engineering. The purpose of this paper is to discuss several concepts commonly applied in the analysis of equipment performance and, in particular, in predicting the remaining life of power equipment. Parametric methods are well suited for the prediction of the end of life of electric power equipment. However, care must be taken in selecting appropriate model since vastly different results can be obtained with different models fitted into the same data. Nonparametric methods are useful only when no suitable probability distribution can be fitted into the data set.
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Garcia-Garrido, Eduardo, Montserrat Mendoza-Villena, Pedro M. Lara-Santillan, Enrique Zorzano-Alba, and Alberto Falces. "Net demand short-term forecasting in a distribution substation with PV power generation." E3S Web of Conferences 152 (2020): 01001. http://dx.doi.org/10.1051/e3sconf/202015201001.
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The integration of renewable energies, specifically solar energy, in electric distribution systems is increasingly common. For an optimal operation, it is very important to forecast the final net demand of the power distribution network, considering the variability of solar energy combined with the variability of the electric energy consumption habits of population. This paper presents the methodology followed to forecast the net demand in a power distribution substation. Two approaches are considered, the net demand direct prediction, and the indirect prediction with the forecasts of PV power generation and load demand. Artificial Neural Network (ANN) based models and autoregressive models with exogenous variables (ARX) are used to predict the net demand, directly and indirectly, for the 24 hours of the day-ahead. The methodology is applied to a medium voltage distribution substation and the direct and indirect forecasts are compared.
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Latinopoulos, Charilaos, Aruna Sivakumar, and John W. Polak. "Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models." Energies 14, no. 4 (February 19, 2021): 1090. http://dx.doi.org/10.3390/en14041090.
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The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments.
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Vivas, Eliana, Héctor Allende-Cid, and Rodrigo Salas. "A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score." Entropy 22, no. 12 (December 15, 2020): 1412. http://dx.doi.org/10.3390/e22121412.
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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.
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Das, Sanjoy, Padmavathy Kankanala, and Anil Pahwa. "Outage Estimation in Electric Power Distribution Systems Using a Neural Network Ensemble." Energies 14, no. 16 (August 6, 2021): 4797. http://dx.doi.org/10.3390/en14164797.
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Outages in an overhead power distribution system are caused by multiple environmental factors, such as weather, trees, and animal activity. Since they form a major portion of the outages, the ability to accurately estimate these outages is a significant step towards enhancing the reliability of power distribution systems. Earlier research with statistical models, neural networks, and committee machines to estimate weather-related and animal-related outages has reported some success. In this paper, a deep neural network ensemble model for outage estimation is proposed. The entire input space is partitioned with a distinct neural network in the ensemble performing outage estimate in each partition. A novel algorithm is proposed to train the neural networks in the ensemble, while simultaneously partitioning the input space in a suitable manner. The proposed approach has been compared with the earlier approaches for outage estimation for four U.S. cities. The results suggest that the proposed method significantly improves the estimates of outages caused by wind and lightning in power distribution systems. A comparative analysis with a previously published model for animal-related outages further establishes the overall effectiveness of the deep neural network ensemble.
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Jones, C. Birk, Matthew Lave, William Vining, and Brooke Marshall Garcia. "Uncontrolled Electric Vehicle Charging Impacts on Distribution Electric Power Systems with Primarily Residential, Commercial or Industrial Loads." Energies 14, no. 6 (March 18, 2021): 1688. http://dx.doi.org/10.3390/en14061688.
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An increase in Electric Vehicles (EV) will result in higher demands on the distribution electric power systems (EPS) which may result in thermal line overloading and low voltage violations. To understand the impact, this work simulates two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on 10 actual distribution feeders that support residential, commercial, and industrial loads. The simulations include actual driving patterns of existing (non-EV) vehicles taken from global positioning system (GPS) data. The GPS driving behaviors, which explain the spatial and temporal EV charging demands, provide information on each vehicles travel distance, dwell locations, and dwell durations. Then, the EPS simulations incorporate the EV charging demands to calculate the power flow across the feeder. Simulation results show that voltage impacts are modest (less than 0.01 p.u.), likely due to robust feeder designs and the models only represent the high-voltage (“primary”) system components. Line loading impacts are more noticeable, with a maximum increase of about 15%. Additionally, the feeder peak load times experience a slight shift for residential and mixed feeders (≈1 h), not at all for the industrial, and 8 h for the commercial feeder.
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De Oliveira-De Jesus, Paulo, Mario Rios, and Gustavo Ramos. "Energy Loss Allocation in Smart Distribution Systems with Electric Vehicle Integration." Energies 11, no. 8 (July 28, 2018): 1962. http://dx.doi.org/10.3390/en11081962.
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This paper presents a three-phase loss allocation procedure for distribution networks. The key contribution of the paper is the computation of specific marginal loss coefficients (MLCs) per bus and per phase expressly considering non-linear load models for Electric Vehicles (EV). The method was applied in a unbalanced 12.47 kV feeder with 12,780 households and 1000 EVs under peak and off-peak load conditions. Results obtained were also compared with the traditional roll-in embedded allocation procedure (pro rata) using non-linear and standard constant power models. Results show the influence of the non-linear load model in the energy losses allocated. This result highlights the importance of considering an appropriate EV load model to appraise the overall losses encouraging the use and further development of the methodology
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Wei, Congying, Jian Xu, Siyang Liao, and Yuanzhang Sun. "Aggregation and Scheduling Models for Electric Vehicles in Distribution Networks Considering Power Fluctuations and Load Rebound." IEEE Transactions on Sustainable Energy 11, no. 4 (October 2020): 2755–64. http://dx.doi.org/10.1109/tste.2020.2975040.
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Marchenko, Andrey, and Alexander Fishov. "The Impact of Distributed Generation on Power Quality of the Electric Network." Applied Mechanics and Materials 792 (September 2015): 248–54. http://dx.doi.org/10.4028/www.scientific.net/amm.792.248.
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The paper describes the investigation results of distributed generation connection to the electricity distribution network, and its impact on the quality of electric energy at the load nodes of the surrounding area. The studies were performed with the use of the electro-dynamic model of electric power systems. Technological and functional models were created to provide accounts on low generation services to improve electricity quality.
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Aibinu, Ajibade A., Dharma Dassanayake, Toong-Khuan Chan, and Ram Thangaraj. "Cost estimation for electric light and power elements during building design." Engineering, Construction and Architectural Management 22, no. 2 (March 16, 2015): 190–213. http://dx.doi.org/10.1108/ecam-01-2014-0010.
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Purpose – The study reported in this paper proposed the use of artificial neural networks (ANN) as viable alternative to regression for predicting the cost of building services elements at the early stage of design. The purpose of this paper is to develop, test and validate ANN models for predicting the costs of electrical services components. Design/Methodology/Approach – The research is based on data mining of over 200 building projects in the office of a medium size electrical contractor. Of the over 200 projects examined, 71 usable data were found and used for the ANN modeling. Regression models were also explored using IBM Statistical Package for Social Sciences Statistics Software 21, for the purpose of comparison with the ANN models. Findings – The findings show that the cost forecasting models based on ANN algorithm are more viable alternative to regression models for predicting the costs of light wiring, power wiring and cable pathways. The ANN prediction errors achieved are 6.4, 4.5 and 4.5 per cent for the three models developed whereas the regression models were insignificant. They did not fit any of the known regression distributions. Practical implications – The validated ANN models were converted to a desktop application (user interface) package – “Intelligent Estimator.” The application is important because it can be used by construction professionals to reliably and quickly forecast the costs of power wiring, light wiring and cable pathways using building variables that are readily available or measurable during design stage, i.e. fully enclosed covered area, unenclosed covered area, internal perimeter length and number of floors. Originality/value – Previous studies have concluded that the methods of estimating the budget for building structure and fabric work are inappropriate for use with mechanical and electrical services. Thus, this study is unique because it applied the ANN modeling technique, for the first time, to cost modeling of electrical services components for building using real world data. The analysis shows that ANN is a better alternative to regression models for predicting cost of services elements because the relationship between cost and the cost drivers are non-linear and distribution types are unknown.
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Buyakova, Natalia V., Andrey V. Kryukov, Dmitriy A. Seredkin, and Le Van Thao. "Computer models in problems of ensuring electromagnetic safety." E3S Web of Conferences 216 (2020): 01062. http://dx.doi.org/10.1051/e3sconf/202021601062.
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In the process of the smart grids formation a special attention is started to be paid to the issues of power engineering facilities safe functioning and reduction in their negative impact on personnel and environment. One of the important factors of such an impact is the generation of significant levels of strength of electromagnetic fields (EMF) by high-voltage overhead power transmission lines (OPL). In the context of the electric power industry digitalization, in order to improve the electromagnetic safety of service personnel, development of methods and means for digital modeling of EMF created by power lines of various designs is required, including traction networks of electrified railways. Computer models providing an adequate determination of the OPL EMF can be implemented on the basis of methods and tools designed for determining the parameters of the modes of electric energy systems in phase coordinates, developed at the Irkutsk State University for Railroads Communications. As an example of using these methods, the solution to the problem of constructing pictures of the distribution of EMF in the space surrounding the intersection of a flexible symmetrical conductor and a 25 kV traction network is given.
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Guarderas, Galo, Airan Frances, Dionisio Ramirez, Rafael Asensi, and Javier Uceda. "Blackbox Large-Signal Modeling of Grid-Connected DC-AC Electronic Power Converters." Energies 12, no. 6 (March 14, 2019): 989. http://dx.doi.org/10.3390/en12060989.
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Modern electric power distribution systems are progressively integrating electronic power converters. However, the design of electronic-power-converter-based systems is not a straightforward task, as the interactions among the different converters can lead to dynamic degradation or instabilities. In addition, electric power distribution systems are expected to consist of commercial-off-the-shelf converters, which implies limited information about the dynamic behavior of the devices. Large-signal blackbox modeling approaches have been proposed in order to obtain accurate dynamic models of commercial converters that can be used for system-level analyses. However, most of the works are focused on DC-DC converters. In this work, a large-signal blackbox model is proposed to model grid-connected three-phase DC-AC converters. An experimental setup has been used to demonstrate the limitations of small-signal models and the capability of the proposed modeling approach to capture the dynamic behavior of the converter when large perturbations are applied. Finally, the automation of the model identification process is discussed.
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Xu, Yongming, Xi Ding, Jianxun Xu, Yunqian Li, Xueyu Ma, and Chuangxin Guo. "Coordinated Planning of Electricity/gas/storage Distribution Network Based on LSTM and Demand Response." E3S Web of Conferences 256 (2021): 02026. http://dx.doi.org/10.1051/e3sconf/202125602026.
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This paper presents a collaborative planning method of an electricity-gas-storage regional integrated energy system based on LSTM neural network and demand response. First, the LSTM Neural network is used for load forecasting, and the energy hub structure of the electric gas storage system is established. Then, the mathematical models of power storage, gas storage, electric network topology, gas network topology, and P2G are established to minimize the expansion cost of the electricity-gas-storage system, and the collaborative planning of energy storage, power lines, and natural gas pipelines is proposed based on the existing electric gas coupling integrated energy system. The original model which is difficult to solve is transformed into a mixed-integer linear programming model by introducing auxiliary variables, and the CPLEX solver is called to solve it. Finally, the economic advantages of collaborative planning of electricity-gas-storage system are verified by an example, and the connection of power storage and gas storage can reduce system pressure and optimize equipment selection.
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Ódor, Géza, and Bálint Hartmann. "Power-Law Distributions of Dynamic Cascade Failures in Power-Grid Models." Entropy 22, no. 6 (June 16, 2020): 666. http://dx.doi.org/10.3390/e22060666.
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Power-law distributed cascade failures are well known in power-grid systems. Understanding this phenomena has been done by various DC threshold models, self-tuned at their critical point. Here, we attempt to describe it using an AC threshold model, with a second-order Kuramoto type equation of motion of the power-flow. We have focused on the exploration of network heterogeneity effects, starting from homogeneous two-dimensional (2D) square lattices to the US power-grid, possessing identical nodes and links, to a realistic electric power-grid obtained from the Hungarian electrical database. The last one exhibits node dependent parameters, topologically marginally on the verge of robust networks. We show that too weak quenched heterogeneity, coming solely from the probabilistic self-frequencies of nodes (2D square lattice), is not sufficient for finding power-law distributed cascades. On the other hand, too strong heterogeneity destroys the synchronization of the system. We found agreement with the empirically observed power-law failure size distributions on the US grid, as well as on the Hungarian networks near the synchronization transition point. We have also investigated the consequence of replacing the usual Gaussian self-frequencies to exponential distributed ones, describing renewable energy sources. We found a drop in the steady state synchronization averages, but the cascade size distribution, both for the US and Hungarian systems, remained insensitive and have kept the universal tails, being characterized by the exponent τ ≃ 1.8 . We have also investigated the effect of an instantaneous feedback mechanism in case of the Hungarian power-grid.
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Kim, Myunghoi. "Power Integrity Analysis of Power Distribution Network Segmented Using DGS–Electromagnetic Bandgap Structure in Mixed-Signal PCBs." Electronics 9, no. 12 (December 1, 2020): 2036. http://dx.doi.org/10.3390/electronics9122036.
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In this paper, we present the power integrity analysis of a power distribution network (PDN) employing a segmentation technique based on the electromagnetic bandgap (EBG) structure with a defected ground structure (DGS). For efficient analysis of power integrity, a domain decomposition method (DDM) with a novel modeling of the DGS–EBG-based PDN is presented. In the DDM, analytical models for the partitioned parts of the PDN are developed, and their impedance parameters are analytically extracted. The resonant modes for the power integrity analysis are rigorously examined using the DDM and electric-field distribution. The effect of the DGS–EBG stopband on the resonant modes are analyzed. The proposed DDM and power integrity analysis are verified using full-wave simulation and measurements. The DDM result shows good agreement with the full-wave simulation and measurements.
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Jadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model." Energies 12, no. 10 (May 17, 2019): 1891. http://dx.doi.org/10.3390/en12101891.
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Load forecasting is of crucial importance for smart grids and the electricity market in terms of the meeting the demand for and distribution of electrical energy. This research proposes a hybrid algorithm for improving the forecasting accuracy where a non-dominated sorting genetic algorithm II (NSGA II) is employed for selecting the input vector, where its fitness function is a multi-layer perceptron neural network (MLPNN). Thus, the output of the NSGA II is the output of the best-trained MLPNN which has the best combination of inputs. The result of NSGA II is fed to the Adaptive Neuro-Fuzzy Inference System (ANFIS) as its input and the results demonstrate an improved forecasting accuracy of the MLPNN-ANFIS compared to the MLPNN and ANFIS models. In addition, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), differential evolution (DE), and imperialistic competitive algorithm (ICA) are used for optimized design of the ANFIS. Electricity demand data for Bonneville, Oregon are used to test the model and among the different tested models, NSGA II-ANFIS-GA provides better accuracy. Obtained values of error indicators for one-hour-ahead demand forecasting are 107.2644, 1.5063, 65.4250, 1.0570, and 0.9940 for RMSE, RMSE%, MAE, MAPE, and R, respectively.
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Zhang, Bo, Bicheng Huang, and Zhongxian Wang. "Study on Capacity Distribution of Optimal Configuration Ratio of Urban Replenishment Station of Electric Vehicles." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 1 (February 20, 2020): 119–25. http://dx.doi.org/10.2174/1874476105666190830111228.
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Background: This paper deeply studies the influence of the capacity distribution of urban charging and replacing power stations on the distribution network and gives the optimal solution. Objective: The optimal configuration scheme of the charging and replacing station has been given to reduce the impact of the charging and replacing station on the distribution network. Methods: First, the Monte Carlo simulation method is used to extract the influencing factors. The probability load models of the battery replacement station, the slow charging station and the fast charging station are established respectively. The capacity distribution is represented by three different types of charging and switching station configuration ratios. Furthermore, the impact of the charging and replacing power station on the economics and safety of the distribution network is analyzed. Different from other literature, the “peak-filling” model with “minimum peak load”, “maximum valley load” and “minimum peak-to-valley difference” is established, and then PSO is adopted. Results: Finally, the actual configuration is used to compare and analyze the four configuration schemes to prove the superiority of the optimization scheme. Conclusion: Three models have different key influencing factors, so that the electric vehicle load forecasting is more targeted and accurate. The results show that more battery replacement stations can reduce the impact of EV charging on the distribution network, and the construction of the supply replenishment should be based on the construction of the power station.
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Valenzuela, Alex, Carlos Barrera, and Esteban Inga. "Electrical assessment of georeferenced distribution network due to electric vehicles deployment." I+D Tecnológico 16, no. 1 (January 28, 2020): 61–68. http://dx.doi.org/10.33412/idt.v16.1.2441.
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Several cities worldwide are focused to reduce the environmental degradation based on the deployment and integration of renewable energies and electric vehicles (EV) into the distribution network. The first one replaces electricity produced by fossil fuels with solar, wind or hydro power plants, whilst the second one is a feasible alternative to substitute internal combustion engine (ICE) vehicles with eco-friendly vehicles. Set in this context, this paper proposes an examination about the main effects in a georeferenced distribution system when non-linear loads are connected to the grid. The distribution network model contemplates georeferenced data from customers, where unbalanced currents due to the customer’s consumption in each distribution transformer is evaluated using a variety of coefficient for commercial and residential load models. Voltage unbalance, harmonics and load flow analysis is performed in PowerFactory to determine the impacts of EVs to the grid.
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Battapothula, Gurappa, Chandrasekhar Yammani, and Sydulu Maheswarapu. "Multi-objective optimal planning of fast charging stations by considering various load models in distribution system." International Journal of Emerging Electric Power Systems 22, no. 4 (June 7, 2021): 439–50. http://dx.doi.org/10.1515/ijeeps-2020-0252.
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Abstract Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.
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Guan, Jun, Wei Du, Xiuli Wang, Xianjue Luo, Xingyang Liu, and Xue Li. "A Reliability Evaluation Method for Independent Small Offshore Electric Systems." Energies 14, no. 11 (May 24, 2021): 3035. http://dx.doi.org/10.3390/en14113035.
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As an independent power system, the reliability of offshore electric system is closely related to the smooth progress of offshore oil production. There are two major characteristics of this type of power system. One is that it includes a generation system, transmission system, as well as a distribution system, and the other is that the load shedding measures in the event of a fault are different from that of the onshore power grid. Therefore, traditional reliability assessment models and algorithms cannot be used directly. Based on the theory of overall reliability evaluation, a reliability evaluation method suitable for offshore electric systems and the corresponding reliability indicators are proposed in this paper. In state sampling, the overall system sampling is divided into generation system sampling, transmission system sampling, and distribution system sampling based on the hybrid sampling theory. In state assessment, the priority decoupling load shedding model and the cascade fault model are established considering the actual production. At the end of this paper, the power system of an offshore oil platform is taken as an actual example to calculate the reliability index. Based on the failure analysis, relevant measures to improve the reliability of the system are proposed.
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Xu, Hui, Zhong Fu Tan, Huan Huan Li, and Zhi Hong Chen. "Profits Distribution Optimizing Model of Multi-Type Generation Resources Joint Scheduling." Applied Mechanics and Materials 441 (December 2013): 1081–84. http://dx.doi.org/10.4028/www.scientific.net/amm.441.1081.
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Power generation resource joint scheduling optimizing is of great significance for electric power system run economically and to achieve energy saving targets. To compare multi-types power generation resource scheduling models economic and environment benefits under different scheduling objectives and constraints, this paper took the unit output, generation resources, pollution and other aspects of constraints, respectively, took the lowest coal consumption and the smallest pollutant emissions as the target established the contract power, the energy-saving generation dispatching optimization model. Then introduced Shapley Value method, and based on it, established a generating profit distribution model. The simulation results show that energy-saving generation scheduling model can significantly reduce pollutant emissions, and Shapley Value method is better to take the profits of each generation resources into account.
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Baharum, Adam, Faris Mahdi Alwan, and Saad Talib Hasson. "A Case Study of Reliability and Performance of the Electric Power Distribution Station Based on Time between Failures." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/583683.
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This paper presents an algorithm for estimating the performance of high-power station systems connected in series, parallel, and mixed series-parallel with collective factor failures caused by any part of the system equipment. Failures that occur frequently can induce a selective effect, which means that the failures generated from different equipment parts can cause failures in various subsets of the system elements. The objectives of this study are to increase the lifetime of the station and reduce sudden station failures. The case study data was collected from an electricity distribution company in Baghdad, Iraq. Data analysis was performed using the most valid distribution of the Weibull distribution with scale parameterα= 1.3137 and shape parameterβ= 94.618. Our analysis revealed that the reliability value decreased by 2.82% in 30 days. The highest critical value was obtained for components T1, CBF5, CBF7, CBF8, CBF9, and CBF10and must be changed by a new item as soon as possible. We believe that the results of this research can be used for the maintenance of power systems models and preventive maintenance models for power systems.
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Mollaei, Nader, and Seyyed Hadi Mousavi. "Application of a Hadoop-based Distributed System for Offline Processing of Power Quality Disturbances." International Journal of Power Electronics and Drive Systems (IJPEDS) 8, no. 2 (June 1, 2017): 695. http://dx.doi.org/10.11591/ijpeds.v8.i2.pp695-704.
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Electric power quality is a critical issue for electric utilities and their customers and identification of the power quality disturbances is an important task in power system monitoring and protection. Offline processing of power quality disturbances provides an economic alternative for electric distribution companies, not capable of buying enough number of power quality analyzers for monitoring the disturbances online. Due to the wide frequency range of the disturbances which may happen in a power system, a high sampling rate is necessary for digital processing of the disturbances. Therefore, a large volume of data must be processed for this purpose for each node of an electric distribution network and such a processing has not yet been practical. However, thanks to the rapid developments of digital processors and computer networks, processing big databases is not so hard today. Apache Hadoop is an open-source software framework that allows for the distributed processing of large datasets using simple programming models. In this paper, application of Hadoop distributed computing software for offline processing of power quality disturbances is proposed and it is shown that this application makes such a processing possible and leads to a very cheaper system with widespread usage, compared to the power quality analyzers.
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Fursanov, M. I., and A. A. Zalotoy. "Improvement of the Method of Calculation of Steady-State Modes of Urban Electric Networks Taking into Account Consumer Energy Sources." ENERGETIKA. Proceedings of CIS higher education institutions and power engineering associations 62, no. 6 (November 29, 2019): 514–27. http://dx.doi.org/10.21122/1029-7448-2019-62-6-514-527.
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The method of calculation of the steady-state mode of the open city distribution electric network with small generation sources is offered. It is shown that the city electric network consists of passive and active physical elements. Passive elements include power transformers and linear regulators, overhead and cable power lines, current-limiting reactors, etc. Active elements of networks are power sources (transformer inputs of low voltage of feeding substations and distributed small generation sources) and loads, mathematical models of which can be various. Power sources in urban distribution networks of 10 kV and below are classified by type and power and can be of two kinds, viz. power feeding centers of the network and small generation sources. Consumer loads are modeled by current sources connected to network nodes. The parameters of the current sources are usually graphs of the change of the module of the actual current value and the power factor in time. The values of these parameters in single-line substitution schemes of 6–10 kV distribution networks with isolated neutral are assumed to be average for three phases. The power centers are transformer inputs of low voltage power substations of the main electric networks of 35 kV and above, equipped with digital devices of the account of the electric power connected to the automated system of control and accounting of power resources. The developed technique allows receiving the refined balanced calculation model of the steady mode of the open city network for the set time of the daily schedule. It includes the sequential distribution of the specified power of each network power supply between its loads, followed by clarification of flows and power losses in the sections, as well as voltages and actual loads in the nodes of the scheme by the overlay method.
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Zhang, Yechi, Jianzhou Wang, and Haiyan Lu. "Research and Application of a Novel Combined Model Based on Multiobjective Optimization for Multistep-Ahead Electric Load Forecasting." Energies 12, no. 10 (May 20, 2019): 1931. http://dx.doi.org/10.3390/en12101931.
Full textAbstract:
Accurate forecasting of electric loads has a great impact on actual power generation, power distribution, and tariff pricing. Therefore, in recent years, scholars all over the world have been proposing more forecasting models aimed at improving forecasting performance; however, many of them are conventional forecasting models which do not take the limitations of individual predicting models or data preprocessing into account, leading to poor forecasting accuracy. In this study, to overcome these drawbacks, a novel model combining a data preprocessing technique, forecasting algorithms and an advanced optimization algorithm is developed. Thirty-minute electrical load data from power stations in New South Wales and Queensland, Australia, are used as the testing data to estimate our proposed model’s effectiveness. From experimental results, our proposed combined model shows absolute superiority in both forecasting accuracy and forecasting stability compared with other conventional forecasting models.
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Wenge, Christoph, Hui Guo, and Christian Roehrig. "Measurement-based harmonic current modeling of mobile storage for power quality study in the distribution system." Archives of Electrical Engineering 66, no. 4 (December 20, 2017): 801–14. http://dx.doi.org/10.1515/aee-2017-0061.
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Abstract Electric vehicles (EVs) can be utilized as mobile storages in a power system. The use of battery chargers can cause current harmonics in the supplied AC system. In order to analyze the impact of different EVs with regardto their number and their emission of current harmonics, a generic harmonic current model of EV types was built and implemented in the power system simulation tool PSS®NETOMAC. Based on the measurement data for different types of EVs three standardized harmonic EV models were developed and parametrized. Further, the identified harmonic models are used by the computation of load flow in a modeled, German power distribution system. As a benchmark, a case scenario was studied regarding a high market penetration of EVs in the year 2030 for Germany. The impact of the EV charging on the power distribution system was analyzed and evaluated with valid power quality standards.
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Klyuev, Roman V., Igor I. Bosikov, Oksana A. Gavrina, and Vladimir Ch Revazov. "System analysis of power consumption by nonferrous metallurgy enterprises on the basis of rank modeling of individual technocenosis castes." MATEC Web of Conferences 226 (2018): 04018. http://dx.doi.org/10.1051/matecconf/201822604018.
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To increase energy efficiency at non-ferrous metallurgy enterprises, an integrated system approach for estimation of electricity consumption is needed. The paper presents the results of a rank analysis of the power consumption of individual castes of process equipment on the basis of an integrated energy survey of the enterprise. A methodology for constructing mathematical models for calculating and predicting electric power consumption for all castes of the ranked H-distribution of technocenosis has been developed. For the first time, according to the established regularity of the H-distribution, a mathematical model for predicting power consumption has been developed, including a quantitative analysis of the energy characteristics of consumers by individual castes of technocenosis. A retrospective check of the relative error in the prediction of electricity consumption showed that for the model it does not exceed 2%, which is significantly lower than the relative error of the prediction for a number of models of other types. The received model is recommended for use in the automated system of dispatching control of power consumption for the purposes of short-term forecasting of electric power consumption at industrial enterprises of non-ferrous metallurgy.
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Wu, Jun Yong, Hong Jun Fu, Jian Hua Sun, Xi Lu Zhang, and Lu Yu Ji. "Distribution Calculation of Harmonics and Unbalance in Power System Including Railway Traction Substations." Advanced Materials Research 433-440 (January 2012): 3470–78. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3470.
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Harmonics and unbalance in Henan power system become more and more serious while a large number of traction substations of high-speed railway are put into operation and traction power increases. In this paper, the distributions of harmonics and unbalance in Henan electric power system are studied. Firstly, three-phase fundamental and harmonic models of the whole system including railway traction substations are built, and a decoupled method of power flow is put forward. Then, the power system harmonic penetration (PSHP) software package is developed, and the power quality of two railway traction substations has been measured. At last, the distributions of harmonics and unbalance of Zhengzhou power network are calculated and compared with the measured results under different traction powers. Some rules of the distributions have been summarized and some advices have been put forward.
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Mariño, Perfecto, Francisco Poza, Santiago Otero, and Fernando Machado. "Multidisciplinary Software Developments in a Power Transformers Scenario." Key Engineering Materials 293-294 (September 2005): 635–42. http://dx.doi.org/10.4028/www.scientific.net/kem.293-294.635.
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Power transformers’ failures carry great costs to electric companies. To diminish this problem in four working 40 MVA transformers, the authors have implemented the measurement system of a failure prediction tool, which is the basis of a predictive maintenance infrastructure. The prediction models obtain their inputs from sensors, whose values must be conditioned, sampled and filtered before feeding the forecasting algorithms. Applying Data Warehouse tech- niques, the models have been provided with an abstraction of sensors the authors have called Virtual Cards. By means of these virtual devices, models have access to clean data, both fresh and historic, from the set of sensors they need. Besides, several characteristics of the data flow coming from the Virtual Cards, such as the sample rate or the set of sensors itself, can be dynamically reconfigured. A replication scheme was implemented to allow the distribution of demanding processing tasks and the remote management of the prediction applications.
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Han, Xue, Claes Sandels, Kun Zhu, and Lars Nordström. "Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks." International Journal of Emerging Electric Power Systems 14, no. 5 (August 7, 2013): 421–31. http://dx.doi.org/10.1515/ijeeps-2013-0007.
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Abstract There has been a large body of statements claiming that the large-scale deployment of Distributed Energy Resources (DERs) could eventually reshape the future distribution grid operation in numerous ways. Thus, it is necessary to introduce a framework to measure to what extent the power system operation will be changed by various parameters of DERs. This article proposed a modelling framework for an overview analysis on the correlation between DERs. Furthermore, to validate the framework, the authors described the reference models of different categories of DERs with their unique characteristics, comprising distributed generation, active demand and electric vehicles. Subsequently, quantitative analysis was made on the basis of the current and envisioned DER deployment scenarios proposed for Sweden. Simulations are performed in two typical distribution network models for four seasons. The simulation results show that in general the DER deployment brings in the possibilities to reduce the power losses and voltage drops by compensating power from the local generation and optimizing the local load profiles.
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Bulatov, Yuri, Andrey Kryukov, and Aleksandr Cherepanov. "Mathematical models for determining limit operating modes in electrical networks with distributed generation plants." Science Bulletin of the Novosibirsk State Technical University, no. 4 (December 18, 2020): 17–36. http://dx.doi.org/10.17212/1814-1196-2020-4-17-36.
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Decentralization of electricity generation based on distributed generation plants is an important segment of the new technology platform for the power industry. On the basis of this approach, significant positive effects can be obtained, which consist in reducing financial costs of energy supply, increasing the uninterrupted power supply, improving the quality of electricity and stimulating the use of renewable energy sources. Effective use of distributed generation in electric power systems requires the development of methods and tools that provide coordinated management of normal, emergency and post-emergency modes. Of particular relevance is the problem of determining the limit operating modes of networks, at the nodal points of which relatively low power generators are connected. In some situations, for example, when using small hydraulic stations, groups of such generators can be located at significant distances for 6-10-20 kV distribution networks from consumption centers. In this case there will be a noticeable limitation of the regions of static aperiodic stability. The article presents the results of developments aimed at implementing methods for determining the limit operating modes by static aperiodic stability in networks with distributed generation plants. The proposed approach is based on the limit modes equations which provide the formation of effective algorithms for the operational finding of points belonging to the boundaries of stability regions. The results of the construction of the indicated areas for a 6 kV electric network with distributed generation plants based on low-power hydraulic stations are presented. Additionally, the transient processes in the studied electric power system were simulated in the Matlab system for various space points of the controlled mode parameters.
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Kajanova, Martina, and Peter Bracinik. "Definition of Discrete Choice Models of EV Owners Based on Different Socio-Demographic Aspects." Applied Sciences 11, no. 8 (April 19, 2021): 3679. http://dx.doi.org/10.3390/app11083679.
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With an increasing number of electric vehicles (EVs), their owners’ involvement in the control of electric power systems and their market seems to be the only option for stable operation of future power networks. However, these people usually have little knowledge about power systems’ operation and follow just their interests. Therefore, this paper deals with the decision-making process of EV drivers at the charging station. The paper presents the stated preference survey used to collect the responses to hypothetical scenarios, where respondents chose between three alternatives, namely slow charging, fast charging, and vehicle-to-grid services. The survey also contained questions about respondents’ socio-demographic characteristics, as gender, age, etc. The decision-making prediction models for each socio-demographic characteristic were created using the acquired data. The paper presents the estimated parameters of the attributes affecting the respondents’ choices for the models that allow models’ simple implementation. Knowing these models and the customers’ composition, the operators of the charging stations or the distribution networks could better estimate EV owners’ behavior and so their expected power demand. Moreover, operators could more effectively implement incentives for their customers and affect the customers’ behavior in a way that is suitable for better operation of their power systems.
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Fu, Zhumu, Bin Wang, Xiaona Song, Leipo Liu, and Xiaohong Wang. "Power-Split Hybrid Electric Vehicle Energy Management Based on Improved Logic Threshold Approach." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/840648.
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We design an improved logic threshold approach of energy management for a power-split HEV assisted by an integrated starter generator (ISG). By combining the efficiency map and the optimum torque curve of internal combustion engine (ICE) with the state of charge (SOC) of batteries, the improved logic threshold controller manages the ICE within its peak efficiency region at first. Then the electrical power demand is established based on the ICE energy output. On that premise, a variable logic threshold valueKis defined to achieve the power distribution between the ISG and the electric motor/generator (EMG). Finally, simulation models for the power-split HEV with improved logic threshold controller are established in ADVISOR. Compared to the equally power-split HEV with the logic threshold controller, when using the improved logic threshold controller, the battery power consumption, the ICE efficiency, the fuel consumption, and the motor driving system efficiency are improved.
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Yang, Yunchu, Jiangrui Qian, and Yang Chen. "Multi-scale modeling and thermal transfer properties of electric heating fabrics system." International Journal of Clothing Science and Technology 31, no. 6 (November 4, 2019): 825–38. http://dx.doi.org/10.1108/ijcst-03-2019-0026.
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Purpose The purpose of this paper is to investigate the thermal transfer properties of electric heating fabric system which contains heating units or conductive yarns by numerical simulation, in order to optimize and evaluate the thermal performance of heating clothing. Design/methodology/approach Two kinds of FEM models are created by ANSYS system: macro-scale models of the fabrics system with heating units and air layer; and meso-scale models of the plain-woven fabrics were established embedded with the stainless yarns. In the macro-scale model, the interior and surface temperature field distribution were simulated and analyzed based on different heating unit size, heating power, heating region, air layer thickness and ambient temperature. For meso-scale models, the effects of the conductive yarns temperature, covering fabrics and pore-filling material on the temperature field distribution were simulated and analyzed. Findings With the increasing of the air layer thickness or the effective conductivity, the heat transfer along the direction of fabric thickness decreases gradually. The heat transfer along the fabric plane can be increased by dispersing the heating region. With the increasing of the conductive yarns’ temperature or the covering fabrics’ conductivity, the heat transfer distance along the fabric warp direction can be increased. Filling the internal pores of the fabric with 10 wt% SiC/TPU hybrid materials can effectively increase the in-plane heat transfer and improve the temperature uniformity on the surface of heated fabrics. Originality/value The finite element method was used to establish the simulation models of the heating fabric systems. The influence of several parameters on the thermal performance was analyzed and discussed, as well as the internal and external temperature distribution in the macro and micro scales models.
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Cao, Li Xin, Yan Song Li, and Jun Liu. "Research on the Modeling of the Faraday Effect Based on the OCT Used in Power System." Applied Mechanics and Materials 551 (May 2014): 275–79. http://dx.doi.org/10.4028/www.scientific.net/amm.551.275.
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Smart grid puts forward higher requirement for measurement which the traditional electromagnetic CTs cannot meet for its disadvantages of saturation and low transient accuracy. For its good performances, OCT is very suitable for power system, especially the system with high voltage level to improve equipment safety and to reduce costs. In this paper, magneto-optical sensing system based on Faraday effect is applied in the measurement of the electric current and the principles and simulation models of Faraday effect based on COMSOL Multiphysics are discussed in detail. It can be seen that Faraday magneto-optical effect could be simulated to calculate the electric current distribution , which is a new research direction.
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Sowinski, Janusz. "The Impact of the Selection of Exogenous Variables in the ANFIS Model on the Results of the Daily Load Forecast in the Power Company." Energies 14, no. 2 (January 9, 2021): 345. http://dx.doi.org/10.3390/en14020345.
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Forecasting of daily loads is crucial for the Distribution System Operators (DSO). Contemporary short-term load forecasting models (STLF) are very well recognized and described in numerous articles. One of such models is the Adaptive Neuro-Fuzzy Inference System (ANFIS), which requires a large set of historical data. A well-recognized issue both for the ANFIS and other daily load forecasting models is the selection of exogenous variables. This article attempts to verify the statement that an appropriate selection of exogenous variables of the ANFIS model affects the accuracy of the forecasts obtained ex post. This proposal seems to be a return to the roots of the Polish econometrics school and the use of the Hellwig method to select exogenous variables of the ANFIS model. In this context, it is also worth asking whether the use of the Hellwig method in conjunction with the ANFIS model makes it possible to investigate the significance of weather variables on the profile of the daily load in an energy company. The functioning of the ANFIS model was tested for some consumers exhibiting high load randomness located within the area under supervision of the examined power company. The load curves featuring seasonal variability and weekly similarity are suitable for forecasting with the ANFIS model. The Hellwig method has been used to select exogenous variables in the ANFIS model. The optimal set of variables has been determined on the basis of integral indicators of information capacity H. Including an additional variable, i.e., air temperature, has also been taken into consideration. Some results of ex post daily load forecast are presented.
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Shibl, Mostafa, Loay Ismail, and Ahmed Massoud. "Machine Learning-Based Management of Electric Vehicles Charging: Towards Highly-Dispersed Fast Chargers." Energies 13, no. 20 (October 17, 2020): 5429. http://dx.doi.org/10.3390/en13205429.
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Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the coordinated charging supports flattening the load profile. Therefore, an effective coordination technique is crucial for the protection of the distribution grid and its components. The substantial power used through charging EVs has undeniable negative impacts on the power grid. Additionally, with the increasing use of EVs, an effective solution for the coordination of EVs charging, particularly when considering the anticipated proliferation of EV fast chargers, is imminently required. In this paper, different machine learning (ML) approaches are compared for the coordination of EVs charging. The ML models can predict the power to be used in EVs charging stations (EVCS). Due to its ability to use historical data to learn and identify patterns for making future decisions with minimal user intervention, ML has been utilized. ML models used in this paper are (1) Decision Tree (DT), (2) Random Forest (RF), (3) Support Vector Machine (SVM), (4) Naïve Bayes (NB), (5) K-Nearest Neighbors (KNN), (6) Deep Neural Networks (DNN), and (7) Long Short-Term Memory (LSTM). These approaches are chosen as they are classifiers known to have the leading results for multiclass classification problems. The results found shed insight on the importance of the techniques used and their high potential in providing a reliable solution for the coordinated charging of EVs, thus improving the performance of the power grid, and reducing power losses and voltage fluctuations. The use of ML provides a less complex method to coordinate EVs, in comparison with conventional optimization techniques such as quadratic programming, and the use of ML is faster as it requires less computational power. LSTM provided the best results with an accuracy of 95% for predicting the most appropriate power rating (PR) for EVCS, followed by RF, DT, DNN, SVM, KNN, and NB. Additionally, LSTM was also the model with the smallest error rate, at a value of ±0.7%, followed by RF, DT, KNN, SVM, DNN, and NB. The results obtained from the LSTM model were similar to the results obtained from past literature using quadratic programming, with the increased speed and simplicity of ML.
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Jörgens, Christoph, and Markus Clemens. "A Review about the Modeling and Simulation of Electro-Quasistatic Fields in HVDC Cable Systems." Energies 13, no. 19 (October 5, 2020): 5189. http://dx.doi.org/10.3390/en13195189.
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In comparison to high-voltage alternating current (HVAC) cable systems, high-voltage direct current (HVDC) systems have several advantages, e.g., the transmitted power or long-distance transmission. The insulating materials feature a non-linear dependency on the electric field and the temperature. Applying a constant voltage, space charges accumulate in the insulation and yield a slowly time-varying electric field. As a complement to measurements, numerical simulations are used to obtain the electric field distribution inside the insulation. The simulation results can be used to design HVDC cable components such that possible failure can be avoided. This work is a review about the simulation of the time-varying electric field in HVDC cable components, using conductivity-based cable models. The effective mechanisms and descriptions of charge movement result in different conductivity models. The corresponding simulation results of the models are compared against measurements and analytic approximations. Different numerical techniques show variations of the accuracy and the computation time that are compared. Coupled electro-thermal field simulations are applied to consider the environment and its effect on the resulting electric field distribution. A special case of an electro-quasistatic field describes the drying process of soil, resulting from the temperature and electric field. The effect of electro-osmosis at HVDC ground electrodes is considered within this model.
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Cazzato, Fabio, Marco Di Clerico, Maria Carmen Falvo, Simone Ferrero, and Marco Vivian. "New Dispatching Paradigm in Power Systems Including EV Charging Stations and Dispersed Generation: A Real Test Case." Energies 13, no. 4 (February 20, 2020): 944. http://dx.doi.org/10.3390/en13040944.
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Electric Vehicles (EVs) are becoming one of the main answers to the decarbonization of the transport sector and Renewable Energy Sources (RES) to the decarbonization of the electricity production sector. Nevertheless, their impact on the electric grids cannot be neglected. New paradigms for the management of the grids where they are connected, which are typically distribution grids in Medium Voltage (MV) and Low Voltage (LV), are necessary. A reform of dispatching rules, including the management of distribution grids and the resources there connected, is in progress in Europe. In this paper, a new paradigm linked to the design of reform is proposed and then tested, in reference to a real distribution grid, operated by the main Italian Distribution System Operator (DSO), e-distribuzione. First, in reference to suitable future scenarios of spread of RES-based power plants and EVs charging stations (EVCS), using Power Flow (PF) models, a check of the operation of the distribution grid, in reference to the usual rules of management, is made. Second, a new dispatching model, involving DSO and the resources connected to its grids, is tested, using an Optimal Power Flow (OPF) algorithm. Results show that the new paradigm of dispatching can effectively be useful for preventing some operation problems of the distribution grids.
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Gracheva, E. I., and O. V. Naumov. "Application of fuzzy regression analysis method for determination of electric power losses in intrafactory power supply networks." Safety and Reliability of Power Industry 11, no. 4 (January 21, 2019): 325–31. http://dx.doi.org/10.24223/1999-5555-2018-11-4-325-331.
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One of the main objectives of the development of modern industry in Russia, along with an increase in the absolute volumes of electric power (EP) production, is to strengthen control over its more rational use. Saving EP and reducing the cost of its transmission along power distribution networks is of great importance for the country's energy sector. In terms of their physical nature, in terms of production, transmission and consumption, EP losses are no different from EP served to consumers. Therefore, the assessment of power losses in electrical networks is based on the same economic principles as the assessment of energy served to consumers. EP losses have a significant impact on the technical and economic parameters of the network, since the cost of losses is included in the estimated cost (reduced costs) and cost price (annual operating costs) of EP transmission. The cost component of losses in the cost of EP transmission has a large proportion. The article presents the results of research on the possibility of application of fuzzy regression analysis for problems of assessment and prediction of electric power losses in intrafactory networks. Initial information on the network is uncertain to some extent, which complicates application of traditional methods. The calculation is presented for conventional and fuzzy regression models, along with estimation of error of these models. The relevance of application of fuzzy regression analysis methods is determined by the difficulty of obtaining reliable information about the circuit and regime parameters of intrafactory networks, the probabilistic nature of change of the modes, as well as a whole complex of affecting factors, which are generally challenging for quantitative assessment. Advantages of application of fuzzy regression analysis consist in obtaining confidence intervals of required variables (value of electric power losses) for schemes of networks with uncertain initial information on their parameters, which is characteristic of intrafactory power supply systems, and enables to consider dynamics of their variation.