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It takes electric vehicles (EVs) a long time to charge, which is bound to influence the charging experience of vehicle owners. At the same time, large-scale charging behavior also brings about large load pressure on, and elevates the overload risk of, the power distribution network. To solve these problems, we proposed a high-efficiency charging se...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... is selected to verify the effect of the CSECA The allowable range of the SoC of the ESB for simulation is 10% to 90%, the MidSoC is 30%, and the control interval ∆ is 1 min. Some 1500 EVs are involved in the simulation. Figure 10 shows the simulation results of CS9 using the CSECA algorithm. It can be seen from Figure 10a that the actual charging load of the EVs is higher than the day-ahead charging power constraint in some time periods and the SoC of the ESB is always within the allowable range in a 24 h period. These results indicate that, under the control of the CSEC, CS9 cannot only meet the charging demand of more EVs, but also guarantees that the ESB works in the allowable range of SoC while regulating the ESB. Meanwhile, the ESB is charged after the SoC reaches approximately 10%, and the charging state does not end before the SoC recovers to MidSoC. Figure 10b shows that the CSEC timeously adjusts the output of the ESB to fill the power shortage when the charging load of the EVs exceeds the power constraint of the distribution network, so as to meet the charging demand of EVs as far as possible. As shown in Figure 10c, the CSECA algorithm adjusts the output of the ESB to turn in the charging state when the SoC of the ESB reaches the minimum value of its allowable range, thus protecting the ESB. Moreover, the actual charging power of the ESB is always based on the precondition of maximally meeting the charging demand of the EVs. When the charging demand of EVs is larger than the day-ahead charging power constraint, the ESB also stops charging; when the charging demand of EVs is lower than the day-ahead charging power constraint, the ESB charges the EVs with the difference between the charging demand and the power constraint. The above results prove that the CSECA is able to achieve Algorithm 2. Figure 11 shows the actual distribution network load and the day-ahead charging power constraint of the CS9 within 24 h under the effect of the CSECA algorithm. It can be seen from the figure that under the control effect of the algorithm, the actual distribution network load is always within the allowable range, thus verifying that the CSECA algorithm is able to realize Algorithm 1. Figure 11. Curves of actual distribution network load and the day-ahead charging power constraint under the effect of the ...
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... research focused on high-efficiency charging of EVs in cities. Differing from previous research, we designed a high-efficiency charging system for EVs in cities by combining the orderly guidance and control of EV charging: the system framework is shown in Figure 1. The system is composed of four parts: (1) charging demand transfer platform (CDTP); (2) smart grid OMS (SGOMS); (3) charging management platform (CMP); and (4) CS energy controller (CSEC). The information exchange among these four parts is realized over the Internet. The system provides real-time charging reservation, CS selection, and charging pile binding services. By using the system, an EV owner only need apply for charging reservation by using mobile networks while driving. Afterwards, the system helps the owner to search for an optimal CS for selection by the owner and binds a charging pile at the CS before the owner makes the selection decision, so as to avoid the charging pile being occupied by another EV before the owner makes a decision, and to guarantee that the EV applying for charging reservation can be charged upon arrival. The optimal CS is determined by using the three-level CS selection model proposed in the research, and the model is applicable to conventional grid-connected CSs and grid-connected CSs that contain new energy sources and energy storage. Differing from the orderly guidance model in terms of the time, the CS selection model is designed to realize the orderly guidance of EV charging in a spatial domain and is based on the assumption that the charging price at a CS is not determined at the discretion of single CS operators but by a unified pricing agency. In addition, the CSs within the same service area adopt the same charging price, which is known as the charging price of a service area, and different service areas have disparate charging prices. It was proposed in [25] that the CO 2 emissions of an EV are higher than those of an equivalent oil-fueled vehicle if the electricity used by the EV mainly comes from the thermal power plant that applies coal as its primary energy. Obviously, if as much electricity as is used by the EV derives from new energy sources, it can reduce the possibility, and even avoid the occurrence, of the above situation. Therefore, improving the average immediate utilization rate of new energies in service areas of the system is also taken as an aim of the model. Moreover, to improve the charging efficiency and reduce anxiety among EV owners, we also apply the reduction of the average waiting time for EV charging as an aim of the model. As to the EV charging control, we assume that the EVs in service areas of the proposed high-efficiency EV charging service system have achieved scale development. In addition, it is assumed that the capacity of the distribution network in an area is limited, so the charging of EVs needs to be controlled to some extent. Considering this, we also came up with a CSECA. It is worth noting that the algorithm uses the first-in/first-out (FIFO) charging rule (EVs arriving earlier at a CS preferentially obtain the optimal charging power) as the premise from which obtain maximum utility. By using the algorithm, the distribution network load of CSs can be maintained within the allowable range, thus avoiding the overloading of the distribution network caused by EV charging. Given this precondition, the EV charging demand at a CS can be met to maximum effect. For the CSs containing energy storage battery (ESB), the algorithm guarantees that the state of charge (SoC) and output of the ESB are within the allowable working range. The remainder of the paper is arranged as follows: Section 2 introduces the design and communication frameworks of the proposed high-efficiency EV charging service system; Section 3 explains the service rules of the system and then describes the three-level CS selection model, as well as the aims and meanings of each level. Finally, the section elaborates the CSECA algorithm, including the control aims and means of the algorithm; Section 4 verifies the proposed model and algorithm through simulation; and Section 5 draws conclusions from the research. ...
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