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Research on the assessment of the capacity of urban distribution networks to accept electric vehicles based on the improved TOPSIS method

2021; Institution of Engineering and Technology; Volume: 15; Issue: 19 Linguagem: Inglês

10.1049/gtd2.12216

1751-8695

Meixia Zhang, Quanjie Sun, Xiu Yang,

Electric and Hybrid Vehicle Technologies

IET Generation, Transmission & DistributionVolume 15, Issue 19 p. 2804-2818 ORIGINAL RESEARCH PAPEROpen Access Research on the assessment of the capacity of urban distribution networks to accept electric vehicles based on the improved TOPSIS method Meixia Zhang, Meixia Zhang College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of ChinaSearch for more papers by this authorQuanjie Sun, Quanjie Sun orcid.org/0000-0002-0169-8362 College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of ChinaSearch for more papers by this authorXiu Yang, Corresponding Author Xiu Yang [email protected] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of China Correspondence Xiu Yang, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, People's Republic of China. Email: [email protected]Search for more papers by this author Meixia Zhang, Meixia Zhang College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of ChinaSearch for more papers by this authorQuanjie Sun, Quanjie Sun orcid.org/0000-0002-0169-8362 College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of ChinaSearch for more papers by this authorXiu Yang, Corresponding Author Xiu Yang [email protected] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, 200090 People's Republic of China Correspondence Xiu Yang, College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, People's Republic of China. Email: [email protected]Search for more papers by this author First published: 08 June 2021 https://doi.org/10.1049/gtd2.12216Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract This study proposes a TOPSIS-based method for assessing the ability of distribution networks to accept electric vehicles. This method establishes an assessment index system in terms of the rationality, safety, and economy of the distribution network operation, and assesses the capacity of the distribution network in all aspects. Firstly, a fuzzy theory-based model of users' charging psychology under the influence of time-of-use electricity price was constructed, and the spatio-temporal distribution of EV charging loads in the target area was predicted using travel chain theory and Monte Carlo methods. Secondly, considering the rationality, safety and economy of the distribution network operation, a comprehensive evaluation index system for acceptability has been constructed. Then, a comprehensive weighting method for evaluation indexes based on AHP and entropy weight method is proposed, and the improved TOPSIS is used to evaluate the acceptance capacity of the distribution network when EV charging loads are connected in different ways. Finally, a typical IEEE33 distribution network is used to simulate the time and space distribution of the charging load, and taking the charging load access schemes proposed in this paper to verify the effectiveness of the evaluation method. 1 INTRODUCTION With the growing energy and environmental problems, electric vehicles, which have the advantages of being efficient and clean, are being promoted by governments around the world. Global EV ownership exceeds 10 million for the first time in 2020. Among them, the growth trend of new energy EV ownership in China is more obvious, with 4.92 million new energy vehicles nationwide by the end of 2020, accounting for 1.75% of the total number of vehicles, an increase of 1.11 million vehicles or 29.18% over 2019. With further requirements for the construction of new energy vehicles and charging facilities in the 14th Five-Year Plan, EV access is expected to exceed 20 million by the end of the 14th Five-Year Plan. According to the National Development and Reform Commission's Energy Research Institute and National Renewable Energy Centre projections (2017), as of 2025, EVs across society would ideally be able to provide 250 GW of energy storage capacity, equivalent to eight times the total installed energy storage in China in 2018 [1]. The randomness and aggregation of the large-scale EV charging load in the temporal and spatial distribution will become more prominent, which will inevitably bring more severe tests to the safe and economic operation of the urban distribution network and power quality. Therefore, it is necessary to study the evaluation of the urban distribution network's acceptance capacity of EVs, which has reference significance for the planning of charging stations and the upgrading of the distribution network. The main steps to evaluate the ability of the distribution network to accept EVs include two points, the first step is EV charging load forecasting, the second step is to select an evaluation method to evaluate the acceptance capacity of the distribution network. EV charging load forecasting has been studied by scholars, and common methods commonly rely on mathematical statistical models to analyse the temporal and spatial characteristics of EV user trips and take into account factors such as the traffic road network to predict the spatial and temporal distribution of EV charging loads. In [2, 3], the trip chain theory and Monte Carlo method were used to build the EV charging load prediction model. LI et al. [4] considered the constraints of the transportation network to predict the urban EV charging load. LI et al. [5] predicted the charging load based on the parking generation rate and the traffic flow of the road network. All of these studies have not considered the impact of time-of-use electricity price on EV users' charging decisions in their modelling, and the changes in users' psychology when they generate charging demand need further analysis. The access of large-scale EVs will bring major challenges to the security, stability, and economic operation of the distribution network, and will cause problems such as line overload, increased network loss, transformer overload, harmonic pollution, and three-phase imbalance [6]. Scholars have conducted research on the ability of the distribution network to accept EVs, mainly considering reliability, economy, security, coordination, efficiency, and quality [7]. There are three categories of evaluation methods. The first category is the quantitative evaluation method, which directly obtains the maximum number of EVs that can be charged simultaneously in the distribution network through power flow calculation. Liao [8] obtained the maximum number of EVs in the distribution network based on the maximum network loss and maximum node voltage deviation. Liu et al [9] analysed the node voltage deviation based on probabilistic power flow, and analysed the number of EVs that the distribution network can accept. The second category is the evaluation of individual operating index such as the operating economy or reliability of the distribution network. Xu et al. [10] evaluated the reliability of large-scale EVs connected to the distribution network. Yang [11] evaluated the economic impact of the intelligent charging system of EVs on the distribution network from the perspective of charging costs and benefits. Although this kind of method can quantitatively evaluate the individual operation index of the distribution network, its evaluation index is relatively single and lacks a comprehensive evaluation of the distribution network. The third evaluation method is to use multiple indexes to comprehensively evaluate the ability of the distribution network to accept EVs. According to the construction and operation requirements of the distribution network, the comprehensive evaluation method can combine the planning of future EV charging stations or the optimal scheduling of charging loads, and incorporate the technical rationality, safety and economic indexes into the comprehensive assessment framework and the comprehensive evaluation method of the distribution network's ability to accept EVs, combining the essential characteristics of the indexes and the spatial and temporal distribution characteristics of EV charging loads to make the assessment framework more comprehensive and objective, so as to reflect more scientifically the comprehensive state of the distribution network after accessing large-scale EVs, and provide strong technical support and guarantee for the overall improvement of the safe, stable and economic operation of the urban distribution network. This method first builds a comprehensive evaluation index system that can reflect the construction and development of the distribution network, and then uses the evaluation calculation method for analysis and research. At present, studies mostly use the Analytic Hierarchy Process (AHP) method to evaluate the capacity of the distribution network to accept EVs [12, 13]. However, this method is highly subjective and may lead to a lack of objectivity in the evaluation results. The TOPSIS has been widely used in the evaluation of smart grids and integrated energy systems, it has high applicability, and the accuracy of the evaluation results is not affected by the number of research objects' index or the number of evaluation objects. Zhou et al. [14] used the TOPSIS to evaluate the smart grid. Ju [15] used a comprehensive evaluation method based on TOPSIS to evaluate the economic benefits of the integrated energy system. However, few studies have used TOPSIS to evaluate the ability of EVs to be accepted in the distribution network. Although this method has low requirements on the original data and strong adaptability, the traditional method cannot objectively and effectively reflect the difference between the evaluation scheme and the optimal ideal solution, which may lead to deviations in the evaluation results, need to use other methods to improve so as to achieve comprehensive evaluation. Therefore, this paper proposes an evaluation method of the urban distribution network's acceptance of EVs based on the improved TOPSIS method. Firstly, the impact of time-of-use price on users' charging demand is considered, a user charging decision model is constructed based on fuzzy theory, and the spatio-temporal distribution of charging loads is simulated using travel chains and Monte Carlo method. Secondly, considering the rationality, safety and economy of the distribution network operation, an assessment index system is established to comprehensively assess the acceptance capacity of the distribution network. Then, using the entropy method to modify the AHP method, this comprehensive assignment method is used to assign weights to each assessment index, and on this basis, an assessment method combining the TOPSIS method and the grey correlation degree is constructed. Finally, the IEEE33 standard distribution network model is used to simulate and analyse the acceptance capacity of EVs when they are connected to the distribution network in different ways. The major contributions of this paper are summarized as follows: The charging decision of EV users determines the spatial and temporal distribution of the charging load. In this paper, the charging decision process of electric private vehicles is modelled in detail, and users are classified into random type users and demand type users according to their charging demand urgency, and fuzzy theory is introduced to simulate the charging decision of users under the time-of-use electricity price. This paper proposes a comprehensive assessment method for the capacity of distribution networks to accept electric vehicles based on an improved TOPSIS method for the first time. Firstly, a framework for evaluating the ability of the distribution network to accept EVs is constructed, and a comprehensive evaluation index system is proposed that integrates the destination layer, the criterion layer and the index layer, in which the rationality, safety and economy of the distribution network operation are taken into account. Secondly, the entropy weighting method is used to improve the AHP method, and grey correlation analysis and TOPSIS are combined, which makes the weighting of each assessment index more objective and the assessment method more scientific. The proposed method is of good theoretical guidance and practical value. 2 GENERAL FRAMEWORK Figure 1 illustrates the flowchart for the assessment of the capacity of the urban distribution network to accept the charging load of EVs. Firstly, multiple sources of data such as typical distribution networks, EV parameters, charging station charging equipment configurations and types of functional areas are analysed. Then, the spatial and temporal distribution of EV charging load in the target region is simulated based on trip chain theory and Monte Carlo method to obtain the charging demand of each region under the current EV ownership. Finally, based on the access location and number of EV charging loads in a typical distribution network, a variety of EV charging load access solutions are set up, and the optimal access solution is evaluated using the distribution network acceptance capacity assessment method based on the TOPSIS, and the safety and economic impact of EV charging load access on the distribution network under this solution is then analysed. FIGURE 1Open in figure viewerPowerPoint The flow chart of urban distribution network's capacity assessment of EV charging load 3 URBAN ELECTRIC PRIVATE CAR CHARGING LOAD MODELING BASED ON TRIP CHAIN AND MONTE CARLO METHOD Trip chain theory The trip chain theory is to connect different trip purposes in a specific temporal order to form a trip chain for EVs, which contains information on different types of trip characteristics and can better describe the user's travel process while reflecting the coherence between different trips [16].This paper takes electric private cars as the object of study and uses trip chain theory to investigate their spatio-temporal travel trajectories and travel characteristics. G TC is the set of spatio-temporal characteristic quantities of EVs trips, which can be described as follows: G TC = { s i , d i , t 0 , t s i → d i d , t d i p , s s i → d i d } (1) i ∈ { 1 , 2 , 3 , 4 , 5 } ; s i , d i ∈ { H , W , C , R , O } where i is the number of trips taken by the user on that day; s i and d i are the start and end points of the user's ith trip, respectively. The starting and ending points in the proposed trip chains in this paper mainly include residential, work, commercial, recreational and other areas [17], denoted by H, W, C, R and O respectively. Assuming that the user's first trip starts in a residential area, t 0 is the moment of the first trip; t s i → d i d is the travel time from the starting point s i to the end point d i ; t d i p is the residence time at the destination d i ; and s s i → d i d is the distance travelled on the ith trip. The spatio-temporal trajectory of the trip chain varies as shown in Figure 2. FIGURE 2Open in figure viewerPowerPoint Spatio-temporal trajectory changes of trip chain Electric vehicle power consumption This paper simplifies the EV power consumption, ignoring the actual driving process of the user driving habits and the influence of external factors on the vehicle battery power consumption, that the battery power consumption and vehicle mileage is a linear relationship, the vehicle driving process of its battery power consumption and the battery power when reaching the destination can be can be expressed as follows: Δ E s i → d i = s s i → d i d · e 0 (2) E d i = E d i − 1 − Δ E s i → d i (3) SO C d i = ( E d i − 1 − Δ E s i → d i ) / B ev (4)where e 0 is the electricity consumption per unit mile of the EV, in kWh/km, Δ E s i → d i is the total power consumption of the vehicle from s i to d i , in kWh; B ev is the vehicle battery capacity, in kWh. EV user charging decision model EV users are classified into demand-based and random users based on the amount of battery power SOC remaining at their current location. The remaining SOC of the former is not sufficient for the next leg of the journey and should be recharged in time. The latter has a relatively adequate SOC, allowing charging schedules to be arranged according to the cost of charging at the current moment. The specific division principles are: Demand type S O C d i + 1 ≤ 20 % Random type S O C d i + 1 > 20 % (5) SO C d i − s s i + 1 → d i + 1 d · e 0 B ev = SO C d i + 1 (6)where SO C d i and SO C d i + 1 are the battery power of the vehicle at the current position d i and at the end of the next trip d i + 1 , respectively. s s i + 1 → d i + 1 d is the estimated distance travelled. Assume that the battery margin is 20% and if SO C d i + 1 ≤ 20 % , then the user is a demand-based user and set his charging decision factor r to 1. If SO C d i + 1 > 20 % , then the user is a random user and r is unknown under the current judgement condition. The choice of charging mode and charging power P d i at d i for a demand-based user with a rigid charging requirement can be expressed as follows: P d i = P d i sc t d i p ≥ t d i sc P d i fc t d i p < t d i sc (7)where t d i sc is the length of time the user needs to charge at d i for slow charging. P d i sc and P d i fc are the slow-charging and fast-charging power of the charging station at d i , respectively, in kW When the parking time is longer than the slow charging time, users tend to choose the slow charging to minimise damage to the battery; conversely, users choose the fast charging method to meet the rigid charging demand. For random-type users with free charging demand, in this paper, referring to the fuzzy inference-based charging decision method for random-type demand users proposed by Zhang [18], we take the time-of-use electricity price and parking time adequacy as the inputs of the fuzzy algorithm and obtain the charging probability of random-type users by fuzzy calculation. Charging load calculation The Monte Carlo method is used to simulate all EVs in the target area. The basic idea of Monte Carlo (MC) simulation is that by building a probabilistic statistical model of a random process, obtaining a probability density function, generating a large number of random numbers that obey a probability distribution, extracting values from them, simulating the random process and repeating the above process over and over again, calculating the approximation of the problem to be solved from the experimental data obtained from the simulation, the accuracy of the approximate solution is expressed in terms of the standard error of the estimate. The Monte Carlo stochastic simulation method, based on probabilistic statistics, has been the modelling tool of more researchers due to its suitability for analysing the stochastic charging behaviour of a large number of EVs, and has achieved a wealth of research results [19, 20]. The charging time and the charging load are counted separately for different demand-based users and random users to obtain the total spatial and temporal distribution of charging demand. The charging load calculation flow chart is shown in Figure A1 in the Appendices. 4 INDEX SYSTEM FOR ASSESSING THE ABILITY OF DISTRIBUTION NETWORK TO ACCEPT EVS Currently, in the field of distribution network capacity assessment, there is a lack of a more objective, reasonable and comprehensive assessment method. This paper proposes a comprehensive assessment method based on the improved TOPSIS method, which is necessary for a comprehensive and objective analysis of the capacity of distribution networks to accommodate EVs. Based on the modelling of EV charging load and the evaluation of traditional distribution network operation, this paper considers the impact of EV access on the distribution network and establishes an index system in terms of rationality, safety and economy to assess the acceptance capacity of the distribution network in all aspects. In order to reflect the objectivity and rationality of the method, a combination of AHP and entropy weighting method is used to assign weights to a variety of indicators under different EV charging load access schemes. Finally, TOPSIS is used to assess the capacity of the distribution network when charging loads are connected in different ways. The acceptability assessment framework is shown in Figure 3. FIGURE 3Open in figure viewerPowerPoint Evaluation framework for the acceptability of EVs by distribution network The assessment process has been applied in a number of research areas such as smart grids and active distribution grids [14, 21], all of which have achieved more objective and comprehensive assessment results, with good theoretical guidance and practical value, and the method has strong stability as well as good generalisability. Electric vehicle charging load is a new type of electrical load. The proposed comprehensive assessment framework can effectively assess the capacity of the distribution network to accept EVs, and studies have used this assessment process to effectively analyse the capacity of the distribution network to accept EVs when planning charging stations [22]. On the basis of the traditional distribution network capacity assessment, six assessment indexes were selected based on the three criteria of rationality, safety and economy respectively [10], as shown in Figure 4. FIGURE 4Open in figure viewerPowerPoint Evaluation index system of distribution network acceptability Voltage deviation non-out of limit rate T 1 : The ratio of the number of nodes whose voltage does not cross the limit after the distribution network is connected to the EV charging load to the total number of nodes. This index is used to assess whether the voltage excursions at each node meet the relevant technical standards after the electric vehicle charging load has been connected. Here, 0.9–1.1 is considered as the effective level range for node voltages. T 1 = N v N × 100 % (8)where N v and N are the number of nodes in the distribution network that meet the voltage offset criteria and the total number of nodes in the system, respectively. (2) Node reactive power non-compliance rate T 2 : The ratio of the number of nodes whose power factor cannot meet the required standard for reactive power configuration to the total number of nodes after the distribution network is connected to the EV charging load. This index is used to assess whether the reactive power at each node is up to standard after the EV charging load has been connected. Here, the standard range of nodal power factors is set to 0.85–1. T 2 = 1 − N q N × 100 % (9)where N and N q are the total number of nodes and the number of nodes in the distribution network that meet the reactive power criteria, respectively. (3) Network security operational index S 1 : The ratio of the number of lines with current values which exceed the safe load capacity to the total number of lines after the distribution network has been connected to the EV charging load. This index is used to assess whether a single circuit in the network meets the criteria for safe operation after the charging load has been connected. S 1 = L o u t L × 100 % (10)where L and L o u t are the total number of lines and the number of lines in the distribution network that exceed the safe operating interval of the maximum current in the network, respectively. (4) Load rate S 2 : The ratio of the average load of a distribution transformer or line to the maximum load over a short period of time after the distribution network has been connected to the EV charging load. This index is used to assess the impact on the safe operation of the distribution network for a short period of time after the charging load has been connected. S 2 = P a v P max × 100 % (11)where P a v and P max are the short term average load and the maximum load value generated in the distribution network respectively. (5) Network loss value E 1 : The sum of the active losses of each line after the distribution network is connected to the EV charging load. These indexes used to assess the impact of the access of charging loads on the operational economy of the distribution network. E 1 = ∑ ( P i 2 + Q i 2 ) · R i U 2 (12)where P i and Q i are the active and reactive power of line i, respectively. R i is the resistance of line i and the connected equipment. U i is the voltage of the line i. (6) Additional reactive power consumption fee E 2 : The additional cost of reactive power compensation to ensure that the power factor is at a relatively reasonable value after the distribution network is connected to the electric vehicle charging load. This index is used to assess the additional investment required for reactive power compensation at each node in the distribution network due to insufficient power factor. E 2 = η · Q n e e d (13)where η is the investment necessary to compensate for the unit capacity of reactive power compensation. Q n e e d is the reactive power compensation capacity required after the electric vehicle charging load is connected, set to 0.01 million/kVar in this paper. 5 RESEARCH ON THE TOPSIS-BASED ACCEPTANCE CAPACITY ASSESSMENT METHOD TOPSIS (Technique for order preference by similarity to an ideal solution) The basic idea of TOPSIS is to standardise the indexes in the original multi-attribute decision matrix, select the best index value to form a positive ideal solution and the worst index value to form a negative ideal solution according to the order of the indicators, and then measure the closeness to the ideal value by the closeness of each scheme to the positive and negative ideal solutions, and rank the schemes according to the closeness [23]. Although the TOPSIS method is less commonly used in comprehensive assessments of distribution networks, the method has a high degree of applicability and the accuracy of the assessment results is not affected by the number of indicators studied or by the number of objects assessed. It has a wide range of applications and can work well in large-scale evaluations of large and complex systems, as well as helping to perform horizontal analysis and longitudinal comparisons between multiple evaluation objects [4]. Comprehensive weighting method On the basis of the system of indicators for assessing the acceptance capacity of the distribution network in Section 4, the weights assigned to each index are determined. The AHP is widely used to determine the weights, but as it is a subjective method, it relies too much on the experience and opinions of experts in determining the time relationship of each indicator, and has the disadvantage of being subjective and arbitrary. In order to make up for this shortcoming, we use the entropy method for objective weighting, and eventually the subjective and objective weights are comprehensively weighted, and this method is currently a more scientific and general assignment method. 5.2.1 AHP for subjective empowerment AHP is a subjective empowerment method that can combine qualitative concepts with quantitative data. Through the system of indicators that has been established, the degree of influence of each index on the previous layer of indicators is compared to form a judgement matrix for that layer, followed by a descending layer to carry out the same process of judgement on the degree of influence, up to the bottom layer. Calculating the weight of each index by taking the maximum eigenvalue of the judgement matrix and its corresponding eigenvector. Finally, the consistency test of the judgment matrix is carried out. Here, a judgement matrix is created by means of the scaling criteria shown in Table 1 [24]. TABLE 1. Scaling principle and meaning of the judgment matrix of AHP Scale Means 1 Indicates that the first two factors are of equal importance 3 Indicates that the former is slightly more important than the latter 5 Indicates that the former is more important than the latter 7 Indicates that compared with the first two factors, the former is extremely important than the latter 9 Indicates that the former is absolutely more important than the latter 2,4,6,8 Indicates the intermediate value of the degree of adjacent comparison above the reciprocal of 1,2,…,9 Indicates the importance of the latter to the former in comparison to the former Referring to the scaling method in Table 1, we first judge the three criteria of the criterion layer to obtain the judgment matrix of the criterion layer, which can be expressed as follows. A = 1 1 3 1 1 2 1 / 3 1 / 2 1 (14) Secondly, the maximum eigenva