CN111047119B - Electric vehicle charging station dynamic pricing method for regulating and controlling power quality - Google Patents
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Abstract
The invention discloses a dynamic pricing method for an electric vehicle charging station for regulating and controlling electric energy quality, which mainly aims at the condition that a plurality of national power grid electric vehicle charging stations exist in a region, simulates the demand response of a user according to the comprehensive cost of the electric vehicle user for charging to different charging stations on the basis of historical operating data of the charging stations, and achieves the effect of regulating and controlling the electric energy quality by formulating reasonable service charge prices, so that the network loss and the voltage deviation influence caused by the connection of an electric vehicle load to a power grid are minimum. According to the invention, user demand response is simulated through specific influence on user decision factors, and under the condition that the same operator manages the operation charging station, the influence of electric vehicle load on the power quality of a power grid can be reduced on the premise that the total operation and the charge are not changed by formulating reasonable service fee price.
Description
Technical Field
The invention relates to a dynamic pricing method for electric vehicle charging stations for regulating and controlling electric energy quality, which is used for making service charge prices of a plurality of electric vehicle charging stations so as to influence the selection of electric vehicle users on the charging stations, namely the electric vehicle load is connected into different states of a power distribution network, and the electric energy quality of the power grid caused by the electric vehicle load is regulated and controlled.
Background
The number of electric vehicles is rapidly increasing, the charging demand is also greatly increased, and the charging power is rapidly increased. If the electric automobile is used as a heavy load and is connected to a power grid in a large scale in the same time period, the safe and stable operation of the power grid can be influenced. Therefore, the electric automobile needs to be guided to be charged orderly, the user can be guided to charge at different time periods and different sites by adjusting the price of the charging station, the power flow of the power grid is changed by changing the load state, and the influence of the load of the electric automobile on the power grid is reduced.
Aiming at interaction between an electric automobile and a power grid and a price setting strategy of the electric automobile, the method is mainly realized by using a convex optimization theory, data load prediction and other modes, and reasonable price is made through demand response of a user. However, the current research does not pay attention to how to adjust the service fee price to intervene in the selection of the charging station by the electric vehicle user in the scenario of multiple charging stations of the same operator, so that the influence of the electric energy quality caused by the electric vehicle accessing to the power grid is minimized.
Disclosure of Invention
The invention mainly aims at the condition that a plurality of national power grid electric vehicle charging stations exist in an area, on the basis of historical operation data of the charging stations, the demand response of users is simulated according to the comprehensive cost of the electric vehicle users for charging to different charging stations, and the effect of regulating and controlling the quality of electric energy is achieved by formulating reasonable service fee prices, so that the network loss and the voltage deviation influence caused by the fact that electric vehicle loads are connected into a power grid are minimum.
The purpose of the invention is realized by the following technical scheme: an electric vehicle charging station dynamic pricing method for regulating and controlling power quality comprises the following steps:
And 2, forming the comprehensive cost selected by the electric vehicle user for the charging station according to the distance between the electric vehicle and the charging station, the amount to be charged and the service fee pricing of the charging station.
Step 3, selecting the optimal charging station of the electric vehicle user according to the comprehensive cost of the electric vehicle user to the charging station in the step 2; the electric vehicle goes to the charging station with the minimum comprehensive cost for charging, namely the charging station with the minimum comprehensive cost is the corresponding optimal charging station:
wherein N represents an electric vehicle set, M represents a charging station set, SiCharging station number S indicating electric vehicle i selectionijIs composed of SiConverted binary matrix form, TiIndicating that the electric vehicle has arrived at the charging station SiThe overall cost during charging; and for any electric automobile i, selecting the charging station j with the minimum comprehensive cost for charging.
according to the electric quantity to be charged of the electric vehicles served by each charging station, the electric vehicle load of the charging station can be determined; because the actual power of the electric automobile connected to the power grid is related to the battery capacity of the electric automobile, the electric quantity to be charged and the type of charging pile (alternating current, direct current and the like), the electric quantity to be charged is approximate to the power of the electric automobile; obtaining the network loss P of the power distribution network at the moment according to the power distribution network trend modelLossAnd each node voltage offset VD：
Wherein N isdRepresenting a set of nodes of the distribution network, PSUBIndicating the injection power, P, of the distribution networkDk、QDkRepresenting the k basic active and reactive loads, P, of the distribution network nodei、QiRepresenting i charging active and reactive powers, V, of the electric vehiclekRepresenting the voltage, V, of node k of the distribution network0Indicating the standard voltage of node k of the distribution network, GkhAnd BkhRepresenting the conductance and susceptance, theta, between node k and node h of the distribution networkkhRepresenting the voltage phase angle deviation between node k and node h of the distribution network.
Minimize PLOSS+αVD
wherein alpha is a weight coefficient in the multi-objective optimization,
represents an upper limit of electric vehicles that can be serviced by charging station j within a unit time period,
representing the upper limit of active power which can be borne by a power distribution network node k in a unit time period; the meaning of the objective function is that the network loss and the voltage deviation of the power distribution network are minimized, wherein the optimization variable isprice, the optimization model also needs to satisfy the relevant calculation constraints in steps 2, 3 and 4.
Further, in step 1, a probability distribution of the electric vehicle with the charging requirement in the region is obtained by using a distribution fitting method, an SVR method, a neural network method or the like.
Further, the historical operation data of the charging station in the step 1 comprises the charging electric quantity demand of the electric vehicle, a charging station, charging time and charging station charging fee; the fitted probability distribution should be the distribution over the domain of definition.
Further, the comprehensive cost selected by the electric vehicle user for the charging station in the step 2 is composed of the cost of the distance from the electric vehicle to the charging station and the payment of the service fee:
wij＝β*dij+SOCneedi*pricej
wherein, wijRepresents the integrated cost of charging the electric vehicle i to the charging station j, dijRepresents the distance from the electric vehicle i to the charging station j, SOCnetiRepresents the waiting charge amount of the electric vehicle i, pricejRepresents the price of the service fee charged by the charging station j per degree of electricity, beta is the weight coefficient of the distance, and the value of beta is enabled to be beta x dijAnd SOCneti*pricejIn the same order of magnitude.
The invention has the beneficial effects that: the charging station service charge price assignment strategy solved by the minimized electric energy quality influence optimization model can ensure that when a plurality of charging stations of the same operator exist in an area, the electric energy quality influence on the power grid is minimized on the premise of keeping the operation and the income of the charging stations unchanged. Different service fee pricing of each charging bundle is introduced, so that for a power grid, the influence of the electric automobile as a random large load is reduced, and the charging cost is reduced for a user as a whole.
Brief description of the drawings
FIG. 1 is a flow chart of an electric vehicle charging station dynamic pricing method for regulating power quality according to the invention;
FIG. 2 is a graph fitted with a probability distribution of historical load data;
FIG. 3 is a distribution diagram of an electric vehicle and a charging station in a region;
FIG. 4 shows an electric vehicle selection site map only subject to the cost of distance to the charging site;
FIG. 5 is a plot of electric vehicle selection sites at an optimized price;
FIG. 6 is a graph comparing voltage deviation of each node of a distribution network with the same price and the optimal price.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, embodiments accompanying figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, the dynamic pricing method for the electric vehicle charging station for regulating and controlling the quality of electric energy provided by the invention comprises the following steps:
wij＝β*dij+SOCneedi*pricej
wherein, wijRepresents the integrated cost of charging the electric vehicle i to the charging station j, dijRepresents the distance from the electric vehicle i to the charging station j, SOCnetiRepresents the waiting charge amount of the electric vehicle i, pricejRepresents the price of the service fee charged by the charging station j per degree, beta is a weight coefficient of the distance, and the value of the weight coefficient is enabled to be beta x dijAnd SOCneti*pricejIn the same order of magnitude.
Step 3, selecting the optimal charging station of the electric vehicle user according to the comprehensive cost of the electric vehicle user to the charging station in the step 2; the electric vehicle goes to the charging station with the minimum comprehensive cost for charging, namely the charging station with the minimum comprehensive cost is the corresponding optimal charging station:
wherein N represents an electric vehicle set, M represents a charging station set, SiCharging station number S indicating electric vehicle i selectionijIs composed of SiConverted binary matrix form, TiIndicating that the electric vehicle has arrived at the charging station SiThe overall cost during charging; and for any electric vehicle i, selecting the charging station j with the minimum comprehensive cost for charging.
according to the electric quantity to be charged of the electric vehicles served by each charging station, the electric vehicle load of the charging station can be determined; because the actual power of the electric automobile connected to the power grid is related to the battery capacity of the electric automobile, the electric quantity to be charged and the type of charging pile (alternating current, direct current and the like), the electric quantity to be charged is close to the electric quantity to be chargedThe power of the electric automobile is similar to that of the electric automobile; obtaining the network loss P of the power distribution network at the moment according to the power distribution network trend modelLossAnd each node voltage offset VD：
Wherein N isdRepresenting a set of nodes, P, of the distribution networkSUBIndicating the injection power, P, of the distribution networkDk、QDkRepresenting the k basic active and reactive loads, P, of the distribution network nodei、QiRepresenting i charging active and reactive powers, V, of the electric vehiclekRepresenting the voltage, V, of node k of the distribution network0Indicating the standard voltage of node k of the distribution network, GkhAnd BkhRepresenting the conductance and susceptance, theta, between node k and node h of the distribution networkkhRepresenting the voltage phase angle deviation between node k and node h of the distribution network.
Minimize PLOSS+αVD
wherein alpha is a weight coefficient in the multi-objective optimization,
represents an upper limit of electric vehicles that can be serviced by charging station j within a unit time period,
represents the upper limit of the active power that the node k of the power distribution network can bear in a unit time period,
price jthe upper and lower bounds of the service charge price; the meaning of the objective function is to minimize the network loss and the voltage offset of the power distribution network, wherein the optimization variable is price, and the optimization model also needs to meet the relevant calculation constraints in the steps 2, 3 and 4.
In this embodiment, the optimal service fee pricing for each station is shown in the following table:
the pricing of each station is the same, and the electric vehicle user selects each charging station as shown in FIG. 4; under optimal pricing, the electric vehicle selection site map is shown in fig. 5. At optimal pricing, the voltage offset of each node is reduced, as shown in FIG. 6.
The power distribution network loss and the evaluation voltage offset are compared with the situation that the service cost price of each station is equal, namely the situation is not optimized, as follows:
| electric network loss (MW) | Mean voltage offset (p.u.) | |
| Optimizing prices | 0.0042 | 0.9138 |
| The same price | 0.0043 | 0.9133 |
The foregoing is merely a preferred embodiment of the present invention, and although the present invention has been disclosed in the context of preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (3)
1. An electric vehicle charging station dynamic pricing method for regulating and controlling power quality is characterized by comprising the following steps:
step 1, obtaining the probability distribution of the electric vehicle with the charging requirement in an area according to the historical operation data of the charging station, and randomly generating the charging power of the electric vehicle according with the probability distribution;
step 2, forming the comprehensive cost of the electric vehicle user for the charging station selection according to the distance between the electric vehicle and the charging station, the amount to be charged and the service fee pricing of the charging station;
wij＝β*dij+SOCneedi*pricej
wherein, wijRepresents the integrated cost of charging the electric vehicle i to the charging station j, dijRepresents the distance from the electric vehicle i to the charging station j, SOCnetiRepresents the waiting charge amount of the electric vehicle i, pricejRepresents the price of the service fee charged by the charging station j per degree of electricity, beta is the weight coefficient of the distance, and the value of beta is enabled to be beta x dijAnd SOCneti*pricejIn the same order of magnitude;
step 3, selecting the optimal charging station of the electric vehicle user according to the comprehensive cost of the electric vehicle user to the charging station in the step 2, and charging the electric vehicle to the charging station with the minimum comprehensive cost:
wherein N represents an electric vehicle set, M represents a charging station set, SiCharging station number S indicating electric vehicle i selectionijIs composed of SiConverted binary matrix form, TiIndicating that the electric vehicle has arrived at the charging station SiThe overall cost during charging; w is aijRepresenting the comprehensive cost of charging the electric vehicle i to the charging station j;
step 4, determining electric vehicle loads of all charging stations, namely electric vehicle loads accessed into a power distribution network, according to the optimal charging stations corresponding to each electric vehicle user obtained in the step 3; obtaining the network loss P of the power distribution network at the moment according to the power distribution network flow modelLossAnd each node voltage offset VD：
Wherein N isdRepresenting a set of nodes of the distribution network, PSUBIndicating the injection power, P, of the distribution networkDk、QDkRepresenting the k basic active and reactive loads, P, of the distribution network nodei、QiRepresenting i charging active and reactive powers, V, of the electric vehiclekRepresenting the voltage, V, of node k of the distribution network0Indicating the standard voltage of node k of the distribution network, GkhAnd BkhRepresenting the conductance and susceptance, theta, between node k and node h of the distribution networkkhRepresenting the voltage phase angle deviation between a node k and a node h of the power distribution network;
step 5, setting up a minimized electric energy quality influence model by taking the upper limit of power load of each node, the load flow balance of the power distribution network, the demand response rule of electric vehicle users and the set price interval of service charge as constraint conditions, taking the service charge price of each charging station as an optimization variable and taking the network loss and the voltage average deviation of the power distribution network as objective functions, and optimally pricing the service charge of each charging station in the region; the optimization model for minimizing the influence of the electric energy quality comprises the following steps:
Minimize PLOSS+αVD
wherein alpha is a weight coefficient in the multi-objective optimization,
represents an upper limit of electric vehicles that can be serviced by charging station j within a unit time period,
indicating node k of the distribution network toThe upper limit of active power which can be born in a unit time period; the meaning of the objective function is to minimize the network loss and the voltage offset of the power distribution network, wherein the optimization variable is pricejAnd further obtaining the dynamic pricing of each electric vehicle charging station.
2. The dynamic pricing method for electric vehicle charging stations for regulating and controlling power quality as claimed in claim 1, wherein in step 1, the probability distribution of the electric vehicles with charging demands in the region is obtained by using a distribution fitting, SVR or neural network method.
3. The dynamic pricing method for electric vehicle charging stations for regulating and controlling electric energy quality as claimed in claim 1, wherein the historical operation data of the charging stations in step 1 includes electric vehicle charging capacity demand, charging stations, charging time, charging station charging fee; the fitted probability distribution should be the distribution over the domain of definition.
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| CN111660861B (en) * | 2020-06-03 | 2021-10-26 | 国网重庆市电力公司营销服务中心 | Charging control method for electric vehicle in charging station |
| CN112348547A (en) * | 2020-06-05 | 2021-02-09 | 浙江图盛输变电工程有限公司温州科技分公司 | Electric vehicle charging station dynamic pricing method based on minimized electric energy quality model |
| CN112277714A (en) * | 2020-09-18 | 2021-01-29 | 国网浙江省电力有限公司杭州供电公司 | Charging pile distribution method and device based on electric vehicle charging station profits |
| CN117207818A (en) * | 2023-09-15 | 2023-12-12 | 国网安徽省电力有限公司经济技术研究院 | Electric automobile charging station electric energy quality monitoring analysis system |
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