CN110175865A - Electric car charging real time pricing method based on ubiquitous cognition technology - Google Patents
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Abstract
The invention belongs to the ubiquitous cognition technology application fields of electric power Internet of Things, a kind of electric car charging real time pricing method based on ubiquitous cognition technology is provided, it include: based on ubiquitous electric power technology of Internet of things application, using big data cluster analysis method, the acquisition running condition informations such as electric car geographical location and state-of-charge in real time form cluster sample point set；Cluster group variety is carried out to sample point using clustering algorithm to divide, and determines the real time charging demand of each charging station at a distance from charging station according to cluster centre；Based on real-time grid running state information, the node voltage of each access point after electric car access power distribution network is calculated using Forward and backward substitution method；Judge whether node voltage meets rack constraint condition, realizes Real-Time Pricing.The invention proposes a kind of methods based on ubiquitous electric power technology of Internet of things analysis electric car real time charging demand, and the constraint of power distribution network real-time running state is combined to carry out the real-time adjustment of charging electricity price, have a good application prospect.
Description
Technical field
The invention belongs to the ubiquitous cognition technology application fields of electric power Internet of Things, are based on ubiquitous sense more particularly, to one kind
The electric car charging real time pricing method for knowing technology, wherein ubiquitous cognition technology includes according to electric car real time running state
It determines real time charging demand and two sides of influence to power distribution network is accessed according to real-time grid running state analysis electric car
Face.
Background technique
The unordered charging behavior of scale automobile user may further increase load peak and load peak-valley difference, give
The safe operation of power distribution network brings huge challenge.Spot Price in electricity market is as a kind of instantaneous supply and demand for being conceived to electric power
The pricing method for balancing, taking into account safe operation of power system, orderly charging to electric car has better effect of optimization.
Ubiquitous electric power Internet of Things framework system towards distribution network construction includes sensing layer, network layer, podium level, application layer
Four-layer structure.The traveling such as geographical location, battery charge state of electric car can be obtained in real time by sensing layer and network layer
Status information and the running state information of power distribution network and real-time Transmission；In podium level and application layer using big data method to reality
When shared information be analyzed and processed to realizing advanced application.In this context, real-time according to the perception of ubiquitous electric power Internet of Things
Charge requirement, and then propose that electric automobile charging station real time pricing method is run to optimize power distribution network with before wide application
Scape.
Summary of the invention
The electric car that the purpose of the present invention is to provide a kind of based on ubiquitous cognition technology charges real time pricing method, should
Method be based on big data technology establish pricing model, solve current pricing method can not to electric car real time charging demand into
The problem of row quantitative analysis.
To achieve the above object, the electric car that the present invention provides a kind of based on ubiquitous cognition technology charges Real-Time Pricing
Method, method includes the following steps:
(1) acquisition transport condition data forms cluster sample point set in real time.Based on ubiquitous electric power technology of Internet of things application
The transport condition datas such as the geographical location of acquisition electric car and state-of-charge in real time form cluster sample point set.
(2) clustering determines electric car real time charging demand.Sample point is gathered using CFSFDP clustering algorithm
Class group variety divides, and determines the attaching relation of group variety and charging station at a distance from charging station according to cluster centre, calculates each charging
The real time charging demand stood.
(3) influence of the analysis charge requirement to power distribution network real-time running state.By the real time charging demand of each charging station
Power distribution network is accessed, real-time grid running state information is based on, after calculating electric car access power distribution network using Forward and backward substitution method
Trend distribution, obtain the node voltage of each access point.
(4) electricity price of adjustment charging in real time.Judge whether electric car access posterior nodal point voltage meets rack constraint condition, from
And electricity price is adjusted in real time.
(5) each electric car real time running status data and real-time grid running state data are acquired simultaneously in subsequent time
Repeat the above steps (1)~(4), realizes the charging Real-Time Pricing of electric car at any one time.
Further, the step (1) includes:
Based on ubiquitous electric power technology of Internet of things application, the geography of each electric car in t moment survey region is acquired in real time
Coordinate (xtn, ytn) and state-of-charge SOCtnData, n are positive integer, represent n-th electric car.
Wherein, what state-of-charge SOC was represented is the residual capacity of battery and the ratio of its rated capacity, value range 0
~1, which represents the situation more than needed of electric car remaining capacity, directly demand journey of the reflection automobile user to charging
Degree.
By collecting the geographical location of each electric car and state-of-charge in t moment survey region, building cluster sample point
Set Wt。
Further, the step (2) includes:
(2.1) CFSFDP algorithm cluster depends on two elements of local density and distance of cluster sample.Wherein, part is close
Charge requirement concentration of the element ρ reflection centered on candidate samples point in certain geographic range is spent, apart from element δ for protecting
Demonstrate,prove cluster centre between distance as far as possible.
Local density element ρ:
In formula, ρtnIndicate the local density of n-th of sample point of t moment, WtFor the set of t moment sample point, SOCtnWhen for t
The state-of-charge of n-th of sample point is carved, (1-SOC is selectedtn) coefficient as local density, if the SOC of n-th of sample point compared with
Low, according to the definition of local density, local density values are larger, then have a possibility that larger to be chosen as workload demand concentration
Cluster centre；dmnFor the space length (being calculated by coordinate) of n-th and m-th sample point, dcFor distance is truncated, for limiting
The service range of charging station.
Apart from element δ:
Wt n={ k ∈ Wt:ρtk> ρtn}
In formula, δtnIndicate n-th of sample point of t moment apart from element, Wt nIt is greater than n-th of sample point for local density
Sample set.If the local density of sample point is maximum, the maximum distance of other points in the point and region is taken as apart from element；
Otherwise, local density is taken as greater than the sample point of the point and the minimum range of the point apart from element.
(2.2) the threshold value ρ of local density and distance is given0、δ0Variation range [ρmin, ρmax]、[δmin, δmax], changing
Change ρ in range0、δ0, the sample point of local density and distance both greater than threshold value is chosen as cluster centre, obtains multiple groups cluster
Centralization { Ω0、Ω1…Ωj, calculate cluster centre number N in each setj；Remaining sample is drawn based on k arest neighbors thought
Corresponding group variety is assigned to, and calculates the silhouette coefficient S of cluster resultj.Select silhouette coefficient SjMaximum cluster result.Cluster obtains N
A cluster centre (xti, yti) and group variety wti, i indicates the number of N number of cluster centre, 1≤i≤N.
Wherein silhouette coefficient is the quality for evaluating cluster result from algorithm angle, by calculating all cluster sample profiles
The average value of coefficient obtains the total silhouette coefficient of cluster result and is compared.The silhouette coefficient formula of sample n is as follows:
Wherein, anFor sample n to the average distance of same other samples of cluster, anIt is smaller, illustrate that sample n should be more clustered
The cluster；bnFor the average distance of sample n all samples into other clusters, bnIt is bigger, illustrate that sample n is more not belonging to other clusters.sn∈
[- 1,1] illustrates that sample n cluster is reasonable close to 1；Close to -1, illustrate that sample n should more be categorized into other cluster；If being approximately
0, then illustrate sample n on the boundary of two clusters.Therefore, total silhouette coefficient S illustrates that Clustering Effect is better closer to 1.
(2.3) judge cluster centre (xti, yti) whether it is greater than the service half of the charging station to the distance of nearest charging station
Diameter dc, if more than illustrating that the corresponding group variety of the central point is relatively small in the charge requirement at the moment, then ignoring filling for the group variety
Electricity demanding；Otherwise the group variety belongs to the charging station, and group variety w is calculated as followstiCharge requirement:
The total charge requirement of group variety=group variety sample point number × charging station public alternating-current charging pile charge power
According to the attaching relation of group variety and charging station, by the group variety w in charging station service rangetiCharging station is accessed, in turn
The summation of the group variety for adhering to each charging station separately total charge requirement can be obtained the real time charging demand of each charging station of t moment.
If the electric car real time charging demand to charging station charging being calculated is greater than the specified appearance of the charging station
Amount, then the real time charging demand of the charging station is replaced with the charging station capacity.
Further, the step (3) includes:
Based on the power distribution network running state information acquired in real time, power distribution network running state information includes that power distribution network network is opened up
Flutter, each node load distribution etc., using Forward and backward substitution method calculate electric car real time charging demand access after power distribution network tide
Flow distribution obtains the node voltage V of each access pointit’And variationWherein Vi0For the volume of access point
Constant voltage.
Further, the step (4) includes:
Judge whether the node voltage offset of electric car access point meets rack constraint condition, thus adjustment electricity in real time
Valence.Specifically, ifThen the electricity price of charging station remains unchanged at the t moment access point, i.e. M(t+1)i=Mti；IfThen illustrate that network load kicks the beam at this time, the electricity price of charging station at the t moment access point should be lowered, i.e. M(t+1)i
=MtiΔ M, wherein Δ M is the adjusting step of electricity price；OtherwiseIllustrate that network load is overweight at this time, Ying Jiang t
The electricity price up-regulation of charging station, i.e. M at the moment access point(t+1)i=Mti+ΔM.During adjusting electricity price, if adjusted
Electricity price meets or exceeds limit value, then takes limit value Mmin、Mmax。
In general, technical solution of the present invention compared with prior art, can achieve the following beneficial effects:
On the one hand, the invention belongs to ubiquitous cognition technology application, the real time running state for acquiring electric car passes through cluster
Analysis quantitatively calculates the real time charging demand of automobile user, and it is real-time to overcome existing pricing method quantitative analysis electric car
The difficulty of charge requirement；On the other hand, the present invention has fully considered the constraint of power distribution network real-time running state, realizes to charging
The real-time adjustment for electricity price of standing has a good application prospect so that correct guidance electric car orderly charges.
Detailed description of the invention
Fig. 1 is the stream of the electric car charging real time pricing method provided in an embodiment of the present invention based on ubiquitous cognition technology
Cheng Tu.
Fig. 2 is the flow chart of cluster process provided in an embodiment of the present invention.
Fig. 3 is electric car the probability distribution of samples points and IEEE33 meshed network in planning region provided in an embodiment of the present invention
The schematic diagram of wiring.
Fig. 4 is the cluster result of electric car real time charging demand in planning region provided in an embodiment of the present invention.
Fig. 5 is that have the electric car access charging station front and back of charge requirement to match in planning region provided in an embodiment of the present invention
Grid nodes variation schematic diagram.
Specific embodiment
To make the objectives, technical solutions, and advantages of the present invention more comprehensible, with reference to the accompanying drawings and embodiments, to this
Invention is further elaborated.It should be appreciated that specific embodiment described herein is not used to only to explain invention
Limit the present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below each other it
Between do not constitute conflict and can be combined with each other.
The real time pricing method as shown in Figure 1, electric car based on ubiquitous cognition technology charges, comprising:
(1) acquisition transport condition data forms cluster sample point set in real time.Based on ubiquitous electric power technology of Internet of things application
The transport condition datas such as the geographical location of acquisition electric car and state-of-charge in real time form cluster sample point set.
(2) clustering determines electric car real time charging demand.Sample point is gathered using CFSFDP clustering algorithm
Class group variety divides, and cluster detailed process is as shown in Figure 2.Group variety and charging station are determined at a distance from charging station according to cluster centre
Attaching relation calculates the real time charging demand of each charging station.
(3) influence of the analysis charge requirement to power distribution network real-time running state.By the real time charging demand of each charging station
Power distribution network is accessed, real-time grid running state information is based on, after calculating electric car access power distribution network using Forward and backward substitution method
Trend distribution, obtain the node voltage of each access point.
(4) electricity price of adjustment charging in real time.Judge whether electric car access posterior nodal point voltage meets rack constraint condition, from
And electricity price is adjusted in real time.
(5) each electric car real time running status data and real-time grid running state data are acquired simultaneously in subsequent time
Repeat the above steps (1)~(4), realizes the charging Real-Time Pricing of electric car at any one time.
For the realizability and validity for further verifying the method for the present invention, below with electric in planning region shown in Fig. 3
It is illustrated for electrical automobile the probability distribution of samples points and grid structure.
(1) example scene
Assuming that there is a 50km2Urban area, the electric car in planning region has 36, on the ground of any t moment
Position distribution situation is managed as shown in Fig. 3 black dot.The grid structure in the region shares 33 nodes as shown in Fig. 3 heavy line,
The Large Electric vehicle charging station that a rated capacity is 1MW is wherein had at No. 20 nodes of network.According to selected areas
The limit value M of the operating condition initialization electricity price M of electricity marketmax、MminAnd adjusting step ΔM；It is electronic according to current each big city
The current situation and Related literature analysis of vehicle charging station charging service radius will charge for city electric car charging station
Stand service radius dcIt is taken as 6km；According to the investigation to the practical charge power of city alternating-current charging pile, each electric car charging is selected
Stand public alternating-current charging pile charge power be 40kW.
(2) real-time data collection forms cluster sample point set
Geographical coordinate (the x of each electric car in t moment survey region is acquired in real timetn, ytn) and state-of-charge SOCtnNumber
According to building cluster sample point set Wt。
(3) clustering determines electric car real time charging demand
The charge requirement position of charge requirement urgency level and space coordinate reflection based on state-of-charge reflection, calculates
The local density of each sample point and range index.Calculation formula is respectively as follows:
Wt n={ k ∈ Wt:ρtk> ρtn}
The threshold value ρ of given local density and distance0、δ0Variation range [ρmin, ρmax]、[δmin, δmax], in variation range
Interior change ρ0、δ0, the sample point of local density and distance both greater than threshold value is chosen as cluster centre, obtains multiple groups cluster centre
Gather { Ω0、Ω1…Ωj, calculate cluster centre number N in each setj；Remaining sample is divided into based on k arest neighbors thought
Corresponding group variety, and calculate the silhouette coefficient S of cluster resultj.Silhouette coefficient SjCalculation formula is as follows:
Select the maximum cluster result of silhouette coefficient Sj.Final result as shown in figure 4, obtain 5 cluster centres and group variety,
It is the electric car that number 1,10,28,4,36 indicates respectively, it is relatively large illustrates that this 5 cluster centre points have at the moment
Charge requirement.
By space coordinate calculate separately the space of charging station of 5 cluster centre points at No. 20 nodes of distribution network away from
From according to the service radius d at public AC charging stationcIt may determine that in addition to No. 4 central points, remaining central point exists for 6km
In the service range of charging station.Thus illustrate, the corresponding group variety of No. 4 central points is relatively small in the charge requirement at the moment, nothing
Charging station need to be accessed.
The charge power for providing public alternating-current charging pile is 40kW.It is total by the group variety electric car quantity for accessing charging station
It is 30 × 40=1200kW with the real time charging demand for calculating the charging station, more than the rated capacity 1MW of charging station, therefore actually
Real time charging demand of the obtained charging station at the moment is 1000kW.
(4) influence of the analysis charge requirement to power distribution network real-time running state
The real time charging demand of charging station is accessed into power distribution network, is reset in planning region according to distribution network planning standard
The parameter of IEEE33 meshed network is based on real-time grid running state information, calculates electric car using Forward and backward substitution method and connects
The trend distribution for entering rear power distribution network, obtains the node voltage of each access point.
(5) charging station electricity price is adjusted in real time
According to the calculation of tidal current of step (4), the variation situation that can obtain each node of power distribution network is as shown in Figure 5.By
Figure is it is found that the variation of each node all meets rack constraint conditionTherefore the electricity of the t moment charging station
Valence remains unchanged.To realize electric car in the charging Real-Time Pricing at the moment.
Ubiquitous electric power technology of Internet of things application can be based on using the method for the present invention, quantitatively divided using big data clustering method
The real time charging demand of electric car is analysed, and the constraint of combination power distribution network real-time running state carries out the real-time tune of charging electricity price
It is whole, it has a good application prospect.
The content being not described in detail in this specification belongs to the prior art well known to those skilled in the art.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, any modifications, equivalent replacements, and improvements done within the spirit and principles of the present invention should be included in
Within protection scope of the present invention.
Claims (5)
- The real time pricing method 1. electric car based on ubiquitous cognition technology charges, it is characterised in that this method includes following step It is rapid:(1) acquisition transport condition data forms cluster sample point set in real timeAcquire the transport condition data of electric car, including geographical location and lotus in real time based on the application of ubiquitous electric power technology of Internet of things Electricity condition forms cluster sample point set；(2) clustering determines electric car real time charging demandCluster group variety is carried out to sample point using CFSFDP clustering algorithm to divide, and is determined at a distance from charging station according to cluster centre The attaching relation of group variety and charging station calculates the real time charging demand of each charging station；(3) influence of the analysis charge requirement to power distribution network real-time running stateThe real time charging demand of each charging station is accessed into power distribution network, is based on real-time grid running state information, using before pushing back Trend distribution after calculating electric car access power distribution network for algorithm, obtains the node voltage of each access point；(4) electricity price of adjustment charging in real timeJudge whether electric car access posterior nodal point voltage meets rack constraint condition, thus the electricity price of adjustment charging in real time；(5) each electric car real time running status data and real-time grid running state data and repetition are acquired in subsequent time The charging Real-Time Pricing of electric car at any one time is realized in above-mentioned steps (1)~(4).
- The real time pricing method 2. electric car according to claim 1 based on ubiquitous cognition technology charges, feature exist Include: in " acquisition transport condition data forms cluster sample point set in real time "Based on ubiquitous electric power technology of Internet of things application, the geographical coordinate of each electric car in t moment survey region is acquired in real time (xtn, ytn) and state-of-charge SOCtnData, n are positive integer, represent n-th electric car, and what state-of-charge SOC was represented is electricity The residual capacity in pond and the ratio of its rated capacity, value range are 0~1, which represents the richness of electric car remaining capacity Remaining situation, directly reflection automobile user pass through each electric car in collection t moment survey region to the desirability of charging Geographical location and state-of-charge, building cluster sample point set Wt。
- The real time pricing method 3. electric car according to claim 1 based on ubiquitous cognition technology charges, feature exist Include: in " clustering determines electric car real time charging demand "Step 2.1, CFSFDP algorithm cluster depends on two elements of local density and distance of cluster sample, wherein part is close Charge requirement concentration of the element ρ reflection centered on candidate samples point in certain geographic range is spent, apart from element δ for protecting Demonstrate,prove cluster centre between distance as far as possible；Local density element ρ:In formula, ρtnIndicate the local density of n-th of sample point of t moment, WtFor the set of t moment sample point, SOCtnFor t moment The state-of-charge of n sample point selectes (1-SOCtn) coefficient as local density, if the SOC of n-th of sample point is lower, root According to the definition of local density, local density values are larger, then have a possibility that larger to be chosen as the cluster of workload demand concentration Center；dmnFor the space length of n-th and m-th sample point, which is calculated by coordinate, dcFor distance is truncated, for limiting Determine the service range of charging station；Apart from element δ:Wt n={ k ∈ Wt:ρtk> ρtn}In formula, δtnIndicate n-th of sample point of t moment apart from element, Wt nIt is greater than the sample set of n-th of sample point for local density It closes, if the local density of sample point is maximum, the maximum distance of other points in the point and region is taken as apart from element；Otherwise, Local density is taken as greater than the sample point of the point and the minimum range of the point apart from element；Step 2.2, the threshold value ρ of local density and distance is given0、δ0Variation range [ρmin, ρmax]、[δmin, δmax], changing Change ρ in range0、δ0, the sample point of local density and distance both greater than threshold value is chosen as cluster centre, obtains multiple groups cluster Centralization { Ω0、Ω1…Ωj, calculate cluster centre number N in each setj；Remaining sample is drawn based on k arest neighbors thought Corresponding group variety is assigned to, and calculates the silhouette coefficient S of cluster resultj, select silhouette coefficient SjMaximum cluster result, cluster obtain N A cluster centre (xti, yti) and group variety wti, i indicates the number of N number of cluster centre, 1≤i≤N；Wherein silhouette coefficient is the quality for evaluating cluster result from algorithm angle, by calculating all cluster sample silhouette coefficients Average value obtain the total silhouette coefficient of cluster result and be compared, the silhouette coefficient formula of sample n is as follows:Wherein, anFor sample n to the average distance of same other samples of cluster, anIt is smaller, illustrate that sample n should more be clustered the cluster； bnFor the average distance of sample n all samples into other clusters, bnIt is bigger, illustrate that sample n is more not belonging to other clusters, sn∈ [- 1, 1], close to 1, illustrate sample n cluster rationally, close to -1, illustrate that sample n should more be categorized into other cluster, if being approximately 0, Illustrate sample n on the boundary of two clusters, total silhouette coefficient S illustrates that Clustering Effect is better closer to 1；Step 2.3, judge cluster centre (xti, yti) whether it is greater than the service radius of the charging station to the distance of nearest charging station dc, if more than illustrating that the corresponding group variety of the central point is relatively small in the charge requirement at the moment, then ignoring the charging of the group variety Demand；Otherwise the group variety belongs to the charging station, and group variety w is calculated as followstiCharge requirement:The total charge requirement of group variety=group variety sample point number × charging station public alternating-current charging pile charge powerAccording to the attaching relation of group variety and charging station, by the group variety w in charging station service rangetiAccess charging station, and then to point The real time charging demand of each charging station of t moment can be obtained in the total charge requirement summation of group variety for belonging to each charging station；If the electric car real time charging demand to charging station charging being calculated is greater than the rated capacity of the charging station, The real time charging demand of the charging station is replaced with the charging station capacity.
- The real time pricing method 4. electric car according to claim 1 based on ubiquitous cognition technology charges, feature exist It include: that shape is run based on the power distribution network acquired in real time in " influence of the analysis charge requirement to power distribution network real-time running state " State information, power distribution network running state information includes distribution network topology, the distribution of each node load, using Forward and backward substitution method meter The trend distribution for calculating power distribution network after electric car real time charging demand accesses, obtains the node voltage Vit ' and electricity of each access point Pressure offsetWherein Vi0For the voltage rating of access point.
- The real time pricing method 5. electric car according to claim 1 based on ubiquitous cognition technology charges, feature exist It include: to judge whether the node voltage offset of electric car access point meets rack constraint in " electricity price of adjustment charging in real time " Condition, so that electricity price is adjusted in real time, specifically, ifThen the electricity price of charging station maintains not at the t moment access point Become, i.e. M(t+1)i=Mti；IfThen illustrate that network load kicks the beam at this time, it should be by charging station at the t moment access point Electricity price lower, i.e. M(t+1)i=MtiΔ M, wherein Δ M is the adjusting step of electricity price；OtherwiseIllustrate at this time Network load is overweight, should raise the electricity price of charging station at the t moment access point, i.e. M(t+1)i=Mti+ Δ M, in adjustment electricity price In the process, if electricity price adjusted meets or exceeds limit value, limit value M is takenmin、Mmax。
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