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Block diagram of the proposed system. Station IDs are the numbers determined by the electric charger operating organizations to discriminate each station.
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An increase in the number of electrical vehicles has resulted in an increase in the number of electrical vehicle charging stations. As a result, the electricity load consumed by charging stations has become large enough to de-stabilize the electricity supply system. Therefore, real-time monitoring of how much electricity each charging station is co...
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... proposed system aims at extracting charging time information from real-time operating data of EV charging station and using it to estimate the energy consumption. Figure 1 shows the block diagram of the proposed system. The system consists of a training phase which generates an electricity consumption estimation model for each charging station, and an operating phase, which estimates the electricity consumption from the real-time operating data of charging stations based on the learned model. ...
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... proposed system aims at extracting charging time information from real-time operating data of EV charging station and using it to estimate the energy consumption. Figure 1 shows the block diagram of the proposed system. The system consists of a training phase which generates an electricity consumption estimation model for each charging station, and an operating phase, which estimates the electricity consumption from the real-time operating data of charging stations based on the learned model. ...
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... the corresponding data includes invalid data, a filtering process is required. In addition, the data selection process (minimum electricity consumption limitation process and near-term data selection) Figure 1. Block diagram of the proposed system. ...
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... 3 shows the distributions of estimation errors of stations with and without the minimum electricity consumption constraint. Figure 10 shows the graphs of the distributions with and without minimum electricity consumption constraint. From the results, it is evident that we have improved the distributions of estimation errors using minimum electricity consumption. ...
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... 3 shows the distributions of estimation errors of stations with and without the minimum electricity consumption constraint. Figure 10 shows the graphs of the distributions with and without minimum electricity consumption constraint. From the results, it is evident that we have improved the distributions of estimation errors using minimum electricity consumption. ...
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... represents the case without the constraints and 'Constraint' represents the case with the constraint. Figure 10. Graphs of energy consumption estimation error distribution (a) without constraint and (b) with constraint. ...
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... the other hand, if the term was longer than 3-months, the strength of recent data became too weak to generate proper curve. Figure 11 shows electricity consumption distribution according to charging time for a sample charging station during a month and 3 months. We can see that there are too few data acquired during a month in Figure 11b, which makes it difficult to generate appropriate regression curve. ...
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... 11 shows electricity consumption distribution according to charging time for a sample charging station during a month and 3 months. We can see that there are too few data acquired during a month in Figure 11b, which makes it difficult to generate appropriate regression curve. On the other hand, if the system used data collected during over 3 months, there are too many widespread data, which generates a messy curve. ...
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... 4 shows the distributions of estimation errors of stations with and without the recent data selection step. Figure 12 shows the graphs of the Figure 10. Graphs of energy consumption estimation error distribution (a) without constraint and (b) with constraint. ...
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... 4 shows the distributions of estimation errors of stations with and without the recent data selection step. Figure 12 shows the graphs of the Figure 10. Graphs of energy consumption estimation error distribution (a) without constraint and (b) with constraint. ...
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... the other hand, if the term was longer than 3-months, the strength of recent data became too weak to generate proper curve. Figure 11 shows electricity consumption distribution according to charging time for a sample charging station during a month and 3 months. We can see that there are too few data acquired during a month in Figure 11b, which makes it difficult to generate appropriate regression curve. ...
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... 11 shows electricity consumption distribution according to charging time for a sample charging station during a month and 3 months. We can see that there are too few data acquired during a month in Figure 11b, which makes it difficult to generate appropriate regression curve. On the other hand, if the system used data collected during over 3 months, there are too many widespread data, which generates a messy curve. ...
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... 4 shows the distributions of estimation errors of stations with and without the recent data selection step. Figure 12 shows the graphs of the distributions with and without the recent data selection step. It is evident that the distributions of estimation errors have improved with the recent data selection step. ...
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... is evident that the distributions of estimation errors have improved with the recent data selection step. Figure 12. Graphs of energy consumption estimation error distribution (a) without recent data selection step and (b) with recent data selection step. ...
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... of energy consumption estimation error distribution (a) without recent data selection step and (b) with recent data selection step. Figure 12. Graphs of energy consumption estimation error distribution (a) without recent data selection step and (b) with recent data selection step. ...
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... of energy consumption estimation error distribution (a) without recent data selection step and (b) with recent data selection step. Method NRMSE Figure 12. Graphs of energy consumption estimation error distribution (a) without recent data selection step and (b) with recent data selection step. ...
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... first, our system is able to show the graphs of estimated electricity consumption value in a relation to time (hour) for each station. Figure 13 shows the example graphs for several sample stations. The black lines of the graphs represent actual electricity consumption value in relation to time (hour), and the orange lines of the graphs represents the estimated electricity consumption value in relation to time (hour). ...
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... showing the information in a map, we can check the electricity load of EV charging stations in a specific location. Figure 14 shows the example maps of estimated electricity consumption of charging stations on Jeju island. Each map represents the estimated electricity load of each charging station for sample times. ...
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Citations
... A user-written DLL in Python regulates charging and discharging based on load demand and State of Charge (SOC) of BESSs through the storage controller in OpenDSS. A comprehensive method is employed to assess the effects on distribution network parameters [4], providing valuable insights for the installation of EF charging stations in portside distribution networks [49]. ...
The maritime industry is a significant emitter of greenhouse gases in marine ecosystems, prompting a global shift towards renewable‐powered electric vessels, where energy storage is pivotal. The authors examine the potential ramifications of coordinating the charging of Electric Ferries (EFs) on local distribution networks, with Gladstone Marina in Queensland, Australia, serving as a case study. Employing OpenDSS software for power flow analysis, the authors utilise actual load data and simulate a network with four Battery Energy Storage Systems (BESSs) representing proposed charging stations. The authors discuss the impact on bus voltage, load current, and power flow by integrating a storage controller to optimise BESS charging and discharging dynamics. The Dynamic Link Library (DLL) of MATLAB Simulink‐based BESS's dynamic model is linked with OpenDSS environment to replicate the actual electric ferry storage. Additionally, a user‐written DLL in Python regulates BESS charging and discharging by the storage controller according to load demand and BESS State of Charge for ensuring efficient operation within the network. The power flow results without inclusion of BESSs to the network, referred to as the base case, are used for relative comparison with the results in the coordinated mode. The power flow analysis suggests that bus voltages rise by approximately 1%–1.5%, while load current consumption decreases by around 2%–2.5% compared to the base case with variable load. Selected lines and transformers maintain consistent power flows. Notably, a reduction in total power consumption and losses is observed, particularly under an 80% load demand increase. These findings indicate that the coordinated mode with a storage controller effectively manages BESS charging and discharging according to demand. Moreover, the storage controller ensures system parameters remain within permissible limits. The support of real and reactive power by BESSs during peak hours validates their role as peak shavers for the test network, suggesting that EFs can operate in either Grid to Ferry mode during charging and Ferry to Grid mode during discharging.
... Bus charging stations are generally set up in bus stops to meet the charging needs of new energy electric buses. Based on the evaluation indicators for the construction needs of charging piles, the optimization plan for the site selection of charging piles can be determined, and corresponding conclusions can be drawn Ahn et al., 2019;Fredriksson et al., 2019). For the optimization problem of charging station location, multiple methods such as fuzzy comprehensive evaluation, analytic hierarchy process, and expert consultation can be combined to obtain the optimal solution (Li et al., 2017;Cui et al., 2018;Erbas et al., 2018). ...
Energy conservation and emission reduction are important policies vigorously promoted in China. With the continuous popularization of the concept of green transportation, electric vehicles have become a green transportation tool with good development prospects, greatly reducing the pressure on the environment and resources caused by rapid economic growth. The development status of electric vehicles has a significant impact on urban energy security, environmental protection, and sustainable development in China. With the widespread application of new energy vehicles, charging piles have become an important auxiliary infrastructure necessary for the development of electric vehicles. They have significant social and economic benefits, so it is imperative to build electric vehicle charging piles. There are many factors to consider in the scientific layout of electric vehicle charging stations, and the location selection problem of electric vehicle charging stations is a multiple-attribute group decision-making (MAGDM) problem. Recently, the Combined Compromise Solution (CoCoSo) technique and CRITIC technique have been utilized to deal with MAGDM issues. Spherical fuzzy sets (SFSs) can uncover the uncertainty and fuzziness in MAGDM more effectively and deeply. In this paper, on basis of CoCoSo technique, a novel spherical fuzzy number CoCoSo (SFN-CoCoSo) technique based on spherical fuzzy number cosine similarity measure (SFNCSM) and spherical fuzzy number Euclidean distance (SFNED) is conducted for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the CRITIC technique is extended to SFSs to acquire the attribute weights based on the SFNCSM and SFNED. Finally, the SFN-CoCoSo technique is utilized for location selection problem of electric vehicle charging stations to prove practicability of the developed technique and compare the SFN-CoCoSo technique with existing techniques to further demonstrate its superiority.
... 14 Estimation of electricity consumption was also carried out for different areas such as polymer material production 15 and electric vehicle charging stations. 16 Wang et al. proposed a model called ESN-DE, which uses an improved echo state network (ESN) to perform the estimation of electrical energy consumption. In the proposed model, the differential evolution (DE) algorithm is used to find the optimal values of three important parameters of the ESN. ...
The increase in energy consumption is affected by the developments in technology as well as the global population growth. Increasing energy consumption makes it difficult to ensure electrical energy supply security. Meeting the energy demand can be achieved with the right planning. Proper planning is critical for both economical use of resources and low cost for the end consumer. On the other hand, erroneous estimation of demand may cause waste of resources and energy crisis. Accurate estimation is possible by accurately modeling the factors affecting electricity consumption. Apart from known factors such as seasonal conditions, days of the week and hours, modeling in extreme events such as pandemics that affect all our behaviors increases the success in modeling the future projection. This ensures that the security of electrical energy supply is carried out effectively with limited resources. For this purpose, in this study, a hybrid multiple linear regression‐feedforward artificial neural network (MLR‐FFANN) based algorithm model was proposed, taking into account the estimated impact of the COVID‐19 pandemic on the energy consumption values of Bursa, an industrial city in Turkey. The aim of the hybrid MLR‐FFANN approach was to simultaneously optimize the β polynomial for multiple linear regression and the weight and bias coefficients for the forward propagation neural network using the adaptive guided differential evolution, equilibrium optimizer, slime mold algorithm, and stochastic fractal search with fitness distance balance (SFSFDB) optimization algorithms. The success of the model whose parameters were optimized using the optimization algorithms was determined according to mean absolute error, mean absolute percentage error, and root mean square error evaluation criteria and statistical analysis of these results. According to the results of the analysis, the MLR‐FFANN approach whose parameters were optimized with the SFSFDB algorithm was more successful in the training of the dataset containing the COVID‐19 precautions.
... Romania, according to European Commission (EC) Directives 2016/30 November 2016 [11][12][13], has as strategic objective the clean energy [14,15]. Recently, the interest in producing electrical energy by using renewable sources is growing up. ...
As fuel consumption in the transport sector has increased at a faster pace than in other sectors, the use of electromobility represents the main strategy adopted by the automotive industry. In this context, as the number of electrical vehicles (EVs) will increase, it will also be necessary to increase the number of charging stations. The present paper presents a complete solution for charging stations that can be located in the office or mall parking area. This solution includes a mode 3 AC charging stations of International Electrotechnical Commission (IEC) 61851-1 Standard, an EV simulator for testing the good functionality of the charging stations (i.e., communications, residual-current device (RCD) protection) and a software application used for controlling the charging process by the programmable logic controller (PLC).
With the widespread use of electric vehicles, their charging power demand has increased and become a significant burden on power grids. The uncoordinated deployment of electric vehicle charging stations and the uncertainty surrounding charging behaviors can cause harmful impacts on power grids. The charging power demand during the fast charging process especially is severely fluctuating, because its charging duration is short and the rated power of the fast chargers is high. This paper presents a methodology to analyze and forecast the aggregated charging power demand from multiple fast-charging stations. Then, pattern of fast-charging power demand is analyzed to identify its irregular trend with the distribution of peak time and values. The forecasting model, based on long short-term memory neural network, is proposed in this paper to address the fluctuating of fast-charging power demand. The forecasting performance of the proposed model is validated in comparison with other deep learning approaches, using real-world datasets measured from fast-charging stations in Jeju Island, South Korea. The results show that the proposed model outperforms forecasting fast-charging power demand aggregated by multiple charging stations.
