CN113971484A - Planning method of electric vehicle charging station based on CRITIC method and non-cooperative game - Google Patents
Planning method of electric vehicle charging station based on CRITIC method and non-cooperative game Download PDFInfo
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Abstract
The invention discloses a planning method of an electric vehicle charging station based on a CRITIC method and a non-cooperative game, which comprises the following steps: step one, calculating the space-time charging requirement of an EV (electric vehicle) in a planned area; step two, constructing each subentry index of the EV charging station site selection, weighting the subentry indexes by adopting a CRITIC method, and further establishing a charging station site selection comprehensive index capable of reflecting the EV charging demand; analyzing benefits of three participants of the EV charging station, and establishing a multi-main-body game model of the EV charging station through a non-cooperative game theory; and step four, finally, establishing an upper layer addressing model taking the maximum EV charging station addressing comprehensive index as a target and a lower layer constant volume model taking multi-body game balance as a target, and performing iterative solution on the upper layer and the lower layer of the models by adopting a branch-and-bound method and an iterative search method of embedded auxiliary objective functions. The method plans the electric vehicle charging station through the relevance of the indexes and the game, and is high in accuracy and reliability.
Description
Technical Field
The invention belongs to the technical field of automobile charging stations, and particularly relates to a planning method of an electric automobile charging station based on a CRITIC method and a non-cooperative game.
Background
In recent years, with the increasing environmental pollution and the gradual depletion of petroleum resources, the great development and popularization of Electric Vehicles (EVs) become one of effective countermeasures, and the planning and construction of EV charging stations are key links for realizing the countermeasures. The reasonable planning of the EV charging station not only can reduce investment and save land, but also can ensure the efficiency of a traffic network and reduce the operation burden of a power distribution system. At present, with the continuous increase of the EV permeability, the establishment of a complete EV charging system is urgent.
In order to make the layout of the stations of the EV charging station more reasonable, in 2016, electric power automation equipment, analysis and evaluation of an infrastructure configuration scheme of an electric vehicle charging and battery replacement service network considering the admission capacity of a power distribution network, published in the paragraph 36 and 6, a charging station location model is established by an analytic hierarchy process, wherein multiple factors such as road blockage, service radius, admission capacity of the power distribution network and the like are considered. The method provides four criteria of a rapid charging network operation level, a user experience degree, a traffic network operation influence degree and a power distribution network operation influence degree in an electric vehicle rapid charging network comprehensive evaluation index system and method, and combines an analytic hierarchy process and an entropy weight process to calculate the weight of each criterion, so that a charging station site selection index system with a target layer, a criterion layer and an index layer is formed. Meanwhile, the data mining method is adopted abroad to evaluate the optimal position of the electric vehicle charging station in the Ankara region of Turkish, and then the optimal charging station site is selected. There is a document that proposes a location-aware-based electric vehicle charging station address selection method. Some people determine the weight of the location index of the EV charging station based on an entropy weight method or a hierarchy analysis method, however, the conflict of the charging demand index is not considered in the process of index weighting of the entropy weight method, and the hierarchy analysis method can consider the correlation and the conflict among indexes, but the index weighting is too subjective, and the weighting result is lack of stability.
In order to provide a reasonable EV charging station capacity configuration scheme, the method for planning the charging station of the electric vehicle based on the characteristics of the user and the traffic information is provided from the perspective of convenient charging of the EV user in a charging station planning text considering the travel characteristics of the user and the available margin of a power distribution network line, and in a charging station planning text considering traffic information and power distribution network capacity constraints, such as Lizhong, Qiang, Gaoyu and the like. And the planning of the centralized charging station is combined with the dispatching of the power distribution network, and a centralized charging station double-layer planning model considering the peak clipping and valley filling functions is established. Or the influence of the user factors and the power distribution network factors on the charging station planning is considered at the same time, and the multi-target planning model of the electric vehicle charging station is established.
However, although the above-mentioned prior art considers the influence of user factors or distribution network factors on the charging station planning, they are considered from the charging station side, and are not considered from the government level and the whole society, so that the multi-benefit agent cannot achieve a win-win situation in the face of the benefit conflict problem among the charging station, the EV user and the power supply company.
Disclosure of Invention
In view of the above, the invention provides a method for planning an electric vehicle charging station based on a CRITIC (CRITIC) method and a Non-cooperative Game (NG).
In order to achieve the above object, the technical solution of the present invention is as follows.
A method for planning an electric vehicle charging station based on a CRITIC method and a non-cooperative game comprises the following steps:
step one, calculating the space-time charging requirement of an EV (electric vehicle) in a planned area;
step two, constructing each subentry index of the EV charging station site selection, weighting the subentry indexes by adopting a CRITIC method, and further establishing a charging station site selection comprehensive index capable of reflecting the EV charging demand;
analyzing benefits of three participants of the EV charging station, and establishing a multi-main-body game model of the EV charging station through a non-cooperative game theory;
and step four, finally, establishing an upper layer addressing model taking the maximum EV charging station addressing comprehensive index as a target and a lower layer constant volume model taking multi-body game balance as a target, and performing iterative solution on the upper layer and the lower layer of the models by adopting a branch-and-bound method and an iterative search method of embedded auxiliary objective functions.
In particular, the first and second (c) substrates,
in the first step, analyzing EV charging demand; counting the charging probability of the EV in each planned time within the day to obtain the time sequence charging requirement; and analyzing various factors influencing EV charging on different partitions to obtain the EV space charging demand.
Step two, establishing an EV charging station address selection index system; and (3) considering traffic factors, functional area factors and power supply reliability factors which influence EV charging, establishing site selection subentry indexes respectively corresponding to the traffic factors, the functional area factors and the power supply reliability factors, determining the weight of each index by a CRITIC method, and further establishing a site selection comprehensive index of the EV charging station.
Step three, establishing a multi-interest main body NG model; considering the planning of the EV charging station from the government level and the whole society, analyzing the influence of interest conflicts of the charging station, EV users and a power supply company on the planning decision of the charging station, and establishing a corresponding NG model.
Step four, establishing a double-layer planning model of the EV charging station; and establishing an upper layer addressing model taking addressing comprehensive indexes as maximum targets and a lower layer constant volume model taking multi-interest subject game balance as targets. And the upper layer model transmits the initial addressing scheme to the lower layer, the lower layer optimizes the capacity of the charging pile according to the charging load and other variables which need to be met by the charging station after addressing, the calculation result is returned to the upper layer, then the upper layer optimizes the positions and the number of the charging stations again according to the result of the lower layer, and iteration is carried out until the addressing and constant volume scheme of the charging station is optimal.
The upper layer model is a problem of mixed integer programming, and the method adopts a branch-and-bound method to solve; the lower layer model is a game model, and a plurality of equilibrium solutions may exist in the model, so that the optimal Nash equilibrium solution is determined by adopting an iterative search method of embedded auxiliary objective functions.
The invention has the following beneficial effects:
1) by analyzing various factors influencing the EV charging station site selection, a corresponding charging station site selection subentry index system is established, a CRITIC method is further adopted to establish a charging station site selection comprehensive index, and adverse effects on reasonable site selection of the EV charging station caused by conflict among multiple charging demand indexes and contrast intensity difference are overcome.
2) By adding a non-cooperative game link of a multi-interest main body, the optimal configuration of the capacity of the EV charging station is realized under the condition that the win-win situation of the charging station, a power supply company and EV users can be guaranteed.
Drawings
Figure 1 is a basic schematic diagram of CRITIC-NG planning implemented by the present invention.
FIG. 2 is a plot of land use property information as implemented by the present invention.
Fig. 3 is a diagram of the parking probability of EVs in various functional areas, which is implemented by the present invention.
Fig. 4 is a diagram of the distribution of nodes at the intersection implemented by the present invention.
Fig. 5 is a grid structure and load point distribution diagram implemented by the present invention.
Fig. 6 is a diagram of an iteration result of the site selection comprehensive index implemented by the present invention.
Fig. 7 is a comprehensive index diagram for addressing planning areas implemented by the present invention.
Fig. 8 is an EV charging station site landing map implemented by the present invention.
FIG. 9 is a position diagram of an EV charging station under a satellite map implemented by the present invention
Fig. 10 is a diagram of the access positions of EV charging stations implemented by the present invention in the grid.
Fig. 11 is a schematic view of the charging load of the charging station 1 and the charging station 2 implemented by the present invention.
Fig. 12 is a schematic view of the charging load of the charging station 3 and the charging station 4 implemented by the present invention.
Fig. 13 is a schematic view of the charging loads of the charging station 5 and the charging station 6 implemented in the present invention.
Fig. 14 is a schematic view of the charging load of the charging station 7 and the charging station 8 implemented by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Fig. 1 shows a basic schematic diagram of CRITIC-NG planning implemented by the present invention, which includes the following steps:
step one, calculating the space-time charging requirement of an EV (electric vehicle) in a planned area;
step two, constructing each subentry index of the EV charging station site selection, weighting the subentry indexes by adopting a CRITIC method, and further establishing a charging station site selection comprehensive index capable of reflecting the EV charging demand;
analyzing benefits of three participants of the EV charging station, and establishing a multi-main-body game model of the EV charging station through a non-cooperative game theory;
and step four, finally, establishing an upper layer addressing model taking the maximum EV charging station addressing comprehensive index as a target and a lower layer constant volume model taking multi-body game balance as a target, and performing iterative solution on the upper layer and the lower layer of the models by adopting a branch-and-bound method and an iterative search method of embedded auxiliary objective functions.
For the analysis of the EV charging demand, spatially, the spatial distribution of the EV charging demand is influenced by various factors such as traffic factors, functional area factors, and power supply reliability factors in a planned area. The traffic of the EV is different when the grades of the roads are different, and the charging requirement of the EV is larger when the traffic is larger; from the functional zoning perspective of cities, residential areas and business areas which can attract more people are undoubtedly larger in EV flow, and more EV charging can be caused; the charging satisfaction of EV users is influenced by the power supply reliability of the power grid, and the EV users can select different positions to charge according to the difference of the power supply reliability of the regional power grid. Therefore, the charging requirements of the EV can be influenced by the people flow and the traffic flow of the planning area, and the site layout of the EV charging station can be directly influenced.
Thus, a probability function f of the spatial distribution of the EV charging demand is constructedsSee formula (1).
fs＝g[Z(x，y)，IPE，IEN，ICE] (1)
In the formula: z (x, y) is a block at the x row and y column position; i isPEThe index of the charging requirement influenced by traffic factors; i isCEThe index of the charging requirement influenced by the functional area factors; i isENThe index of charging requirement is influenced by the reliable power supply factor.
In terms of time, the traveling habit of the user determines the probability of starting charging at each time EV during a day. Assuming that an EV battery is in a full state before the EV user departs on the same day, charging on the same day after the EV user departs on the same day, after normalization processing is carried out on statistical data of all EV charging moments in a region to be planned, the charging probability of each moment of the EV is approximated to normal distribution by using a maximum likelihood estimation method, and a probability density function f of the charging probability istSee formula (2).
In the formula: μ and σ are the expected value and standard deviation, respectively, of a normal distribution.
Based on the analysis of the EV space charging requirement, the space influence factors of EV charging are quantized into site selection item indexes capable of reflecting the EV charging requirement, and then the CRITIC method is adopted to carry out weighted summation on all indexes to establish site selection comprehensive indexes of the EV charging station, namely step two.
Specifically, first, the factors affecting EV charging demand are quantified:
1) traffic factor
The traffic factor mainly considers the traffic flow of different intersection nodes, is used for reflecting the actual traffic capacity of the road and uses the traffic EV capacity index IPETo measure. Let the number of segments connected to the intersection node R be nLAnd if so, the traffic flow of the intersection node R is shown in the formula (3).
In the formula: rrIs the R-th intersection node connected with the intersection node R, R is 1,2, …, nL；
The traffic flow of the R-th road section connected with the intersection node R in the t-th time period is vehicle/h.
2) Functional area factors
The functional area factor mainly considers the probability of EV parking in each functional area in different time periods, is used for reflecting the EV capturing capacity of different functional areas, and is used for capturing EV capacity index ICEAnd (4) showing. Recording the residential area in the planning area as KHAnd the industrial sector is marked as KICommercial zone, noted as KCAnd other regions are denoted as KOThe ability to capture an EV I is expressed by the probability that the EV will park in a different functional areaCE，KSee formula (4).
In the formula: k is each functional region, K ═ KH,KI,KC,KO}；
The number of EVs parked in zone K for period t; n is a radical ofEVAnd reserving the amount of the EV in the planning area.
3) Power supply reliability factor
The power supply reliability of the power grid will affect the charging satisfaction of the EV users, and the EV users can select different positions for charging according to the difference of the power supply reliability of the regional power grid. In the charging process of a charging pile of an EV user using a certain charging station, the charging station has the higher frequency and longer interruption time of EV charging interruption caused by power failure, and the charging satisfaction of the user is lower. When the power supply reliability of a load node is greater than or equal to 99.90%, the load node is considered as the number of nodes meeting the power supply reliability requirement. Reliable power supplyIndex of sexual activity IENThe expression is shown in formula (5).
In the formula: n is a radical ofENThe number of load nodes for meeting the power supply reliability requirement; n is a radical ofNIs the total number of load nodes.
Next, the CRITIC method is used to assign an index weight to the index.
In order to establish a reasonable EV charging station site selection index system, the invention needs to perform weighting summation on the passing EV capacity index, the captured EV capacity index and the power supply reliability index. However, different degrees of correlation and conflict exist among the site selection item indexes, and the degree of correlation among the indexes determines the accuracy and objectivity of weighting and is not ignored, so that the invention adopts a CRITIC method to determine the weight of the index by judging the size of the information content in the site selection item indexes. The measurement of the amount of information contained is carried out in two ways: firstly, the contrast strength among the site selection subentry indexes is expressed by standard deviation, and the larger the standard deviation value is, the larger the contained information amount is; and secondly, the conflict between the address sub-item indexes is expressed by a correlation coefficient between the indexes, and the greater the correlation between the two address sub-item indexes is, the lower the conflict between the two indexes is. Then the contrast intensity of the address item-selecting index is multiplied by the conflict quantization value to represent the information content contained in the index, thus not only considering the contrast intensity of the data, but also considering the correlation among the address item-selecting indexes.
The method divides the size of the area to be planned into n blocks, each block has m address selection itemizing indexes, establishes an n multiplied by m index matrix A for the whole area, and then assigns the weight of each index by using a CRITIC method. The CRITIC method is as follows:
step 1: and standardizing site selection item indexes. The index matrix a is shown in equation (6).
In the formula: a isijThe size of the jth addressing subentry index of the ith block; i is 1,2, …, n, n is the number of blocks; j is 1,2, …, m, m is the number of indexes.
The index matrix A is standardized by a polarization method, and the standardized matrix A' is shown in formula (7).
Step 2: and calculating a correlation coefficient matrix K of the index based on the matrix A'. See formula (8).
In the formula: k is a radical ofhjThe correlation coefficient between the h address item index and the j address item index after standardization; h is 1,2, …, m; j is 1,2, …, m. k is a radical ofhjSee formula (9).
In the formula: cov (A)h'，Aj') is the h-th column vector A in the matrix Ah' and j column vector Aj' covariance between; sigma (A)h') is a column vector Ah' standard deviation; sigma (A)j') is a column vector AjStandard deviation of'.
And step 3: and quantifying the conflict between the index of the address division items. Conflict quantization value b between jth addressing subentry index and other indexesjSee formula (10).
And 4, step 4: the amount of information contained in the index is calculated. The information amount is calculated as shown in equation (11).
In the formula: b isjFor the information content contained in the jth address subentry index, BjThe larger the index is, the larger the information content contained in the jth address division index is, and the greater the relative importance of the index is.
And 5: calculating the objective weight of the index. Objective weight W of jth addressing subentry indexjSee formula (12).
And then, establishing an address selection comprehensive index.
After the weight of each site selection item index is obtained, the size of the single item numerical value of each index is multiplied by the weight of the corresponding index to carry out weighted summation to obtain a site selection comprehensive index, which is shown in a formula (13).
In the formula:
the size of the address comprehensive index of the ith block is 1,2, …, n;
converting the block i and the block Z (x, y) according to a sequence counting method to enable the blocks to be in corresponding relation, wherein the position of the block is the size of an address selection comprehensive index of the block in the x row and the y column;
the size of the jth address sub-index is the location of the xth row and yth column block.
The comprehensive site selection index integrates a traffic factor index reflecting the charging requirement, a functional area factor index and a power supply reliability index, so that the charging requirement of the EV is reflected more comprehensively. When the address selection comprehensive index value of a block is larger, the charging requirement of the EV space of the block is higher, and the establishment of the EV charging station is more reasonable.
In EV charging station operation, it is often desirable in terms of EV charging stations to configure charging post capacity to be reasonable to achieve minimized construction and operation costs and maximized revenue. And EV charging users often want charging piles constructed by charging stations to have as large capacity as possible so as to ensure that the charging waiting cost of the users is minimum. From the perspective of power supply companies, the more the charging piles are, the more electric vehicles which are connected to the power grid for disordered charging at the same time period are, the higher the charging probability at the load peak is, the further aggravation of the load peak-valley difference of the power grid is, and the further aggravation of the burden of a power system is. Therefore, the EV charging station, the EV users and the power supply company have benefit conflicts, three participants are different benefit subjects, but the three participants have respective targets and requirements, the three participants are closely related and have certain benefit relation, the target benefit of one party is influenced by the strategies of the other two parties, and therefore the planning decision of the EV charging station is a typical game problem.
The game theory is a decision theory for solving the problem that a plurality of participating subjects pursue self interest maximization. The game theory model mainly comprises three elements: participants, game strategies, utility functions. When each game participant has no motivation to change the own strategy or cannot independently change the own strategy, the game reaches balance, which is also called Nash balance. In the planning decision game of the EV charging station, game strategies of a charging station party, EV users and a power supply company are limited, all participants are independent of one another, have full rationality, and have mutual knowledge of information. The strategy of the EV user is expressed by considering the charging queuing time, and a plurality of strategies of the EV user are expressed by the acceptance degree of the EV user to different charging queuing times; the strategy of the charging station side is the selection of the charging station side on the number and the capacity of the charging stations; the strategy of the power supply company is the degree of acceptance of the influence of charging stations with different numbers and capacities on the power grid after the charging stations are connected into the power grid. Therefore, in the third step of the invention, the non-cooperative game theory in the complete information environment is adopted to establish an EV charging station game theory model, which is shown in a formula (14).
MG＝{NG；UA,UB,UC；fA,fB,fC} (14)
In the formula: n is a radical ofGThe number of game participants; u is a strategy set; f is a utility function; A. b, C represent participating three parties, namely a charging station party, an EV user, and a power supply company.
The utility function of the EV charging station side is the main body of investment construction and maintenance operation of the charging station, and the annual cost of the EV charging station is guaranteed to be the minimum for maximizing the benefits of the EV charging station side. Therefore, utility function f of EV charging stationAThe annual payment cost is the minimum, and the annual payment cost is composed of annual investment cost, annual operation and maintenance cost and annual profit, which is shown in the formula (15).
fA＝Cqp+Cinvs-Cprs (15)
In the formula: cprsFor the charging station annual revenue, calculated by equation (16); cinvsCalculating the annual investment cost of the charging station by using an equation (17); copFor the charging station annual operating cost, it is calculated by equation (18).
In the formula: n is a radical ofstaThe number of charging stations;
charging capacity of the EV in the t period of the ith charging station;
charging the electricity price for the EV charging station in the t-th time period; r is0The current rate is the current rate; t is a planning period;
the number of charging piles in the ith charging station; pc is the rated charging power of the charging pile; ccIs the price per unit capacity of the charging pile;
the floor area of the ith charging station;
a price per unit area for the ith charging station;
other investment costs for the ith charging station; ct bThe price of the power sold by the power grid at the moment t.
For the utility function of the EV charging user, in order to capture the charging behavior of the EV user for self-benefit in charging the charging station, the utility function of the electric vehicle user is the charging queuing cost. The EV arrival service at the charging station is independent and meets the characteristics of stationarity, no aftereffect and universality. Therefore, the charging waiting model of the electric automobile is classified into a queuing theory M/M/s model, and the utility function f of the EV userBThe expression is shown in formula (19).
fB＝365CBTtPNEV (19)
In the formula: cBIs the unit time waiting cost; n is a radical ofEVKeeping the quantity of the electric automobiles in the station service range; p is daily charging probability and is obtained by formula (2); t istFor the average charge queue time, the calculation is performed by equation (20) to equation (23).
In the formula: t is tcAverage single-vehicle charging time; p0The probability of all the charging piles being idle is obtained; rho is the service strength of the charging pile; gamma is the number of EVs reaching the charging station in unit time satisfying the Poisson flow; ν is the average service rate of charging piles.
For the utility function of the power supply company, besides the need of constructing lines and transformers for the EV charging stations, the disordered charging of the EV charging and the charging behavior during the peak load period will burden the power distribution network, causing losses to the power supply company. Therefore, utility function f of the power supply companyCSee formula (24) for line investment, distribution transformer investment and network loss cost.
fC＝Closs+CDT+Cline (24)
In the formula: clineCalculating a line construction cost for connecting the charging station and the substation by using equation (25); cDTThe cost of configuring a dedicated transformer for an EV charging station, calculated by equation (26); clossFor the network loss cost, it is calculated by equation (27).
In the formula: cpIs the unit loss cost; n is a radical ofLThe number of feeder lines required to be configured for building a charging station;
the network loss of the ith feeder line in the t period is measured;
the capacity of the transformer allocated to the ith charging station; cDTIs the investment cost of a unit capacity transformer; n is a radical ofBThe number of the transformer substations; sigmaijIf a line needs to be built, sigma is the connection condition between the charging station i and the transformer substation j ij1, otherwise σij＝0；LijThe length of a line to be built between the transformer substation j and the charging station i; cFLIs the construction cost per unit length of line.
And establishing a double-layer planning model of the EV charging station and solving.
Firstly, establishing an EV charging station double-layer planning model. The planning of the EV charging station comprises two aspects of site selection and volume fixing, which have differences but cannot be divided, so the modeling of the planning of the EV charging station is carried out by a double-layer planning method. The upper layer model is the addressing problem with the maximum addressing comprehensive index as the target, and the lower layer is the constant volume problem with the multi-benefit subject game balance as the target. And the upper layer plans to transmit the site selection scheme to the lower layer, the lower layer optimizes the capacity of the charging pile according to the service range of the charging station after site selection and the variables such as the charging load required to be met, the calculation result is transmitted to the upper layer, and then the upper layer further optimizes the site selection scheme according to the power and the quantity of the charging pile obtained by the lower layer. The mathematical model is shown in formula (28).
In the formula: f1And F2Respectively an objective function of the upper layer plan and the lower layer plan; sstaFor charging stationCapacity of charging pile, from rated power p of charging pilecAnd the number N of charging pilescDetermining; g1And G2Constraints for upper and lower layer plans, respectively.
For the upper model:
1. objective function
The upper layer model is an address selection model of the EV charging station, and the influence of traffic factors, functional area factors and power grid factors should be comprehensively considered, so that from the spatial distribution characteristic of the charging demand of the EV, the model selects the position of the EV charging station by taking the address selection comprehensive index as the maximum target, and the position is specifically expressed in a formula (29).
In the formula: wPEThe weight is the weight of the passing EV capacity index; wENThe weight is the power supply reliability index; wCEWeight size for capturing EV capability index;
the data acquisition method comprises the following steps of respectively obtaining the traffic EV capacity index size, the power supply reliability index size and the acquisition EV index size of the xth column and the yth row block.
2. Constraint conditions
1) The number of charging stations constrains:
in the formula:
maximum number of EV charging stations to build.
2) Service area coverage constraints for a group of charging stations:
ηc≥1 (31)
in the formula: etacIs the service area coverage of the charging station group.
Charging station construction area constraints
In the formula: ssta[Z(x，y)]Charging station area for building in block Z (x, y);
to build the maximum available area of the charging station in zone Z (x, y).
For the lower layer model:
1. objective function
The lower layer model is a volumetric model of the EV charging station, which is based on game theory. In the capacity planning of the EV charging station, three participants play games for pursuing the maximization of own benefits, but the optimal solution of the model is determined by Nash balance of the games, and under the balance strategy, no game participant can obtain larger benefits by independently changing own strategy. Therefore, it is reasonable to determine the optimal charging pile capacity through game Nash balance, and the objective function of the lower model is shown in the formula (33).
minF2＝{minfA，minfB，minfC} (33)
2. Constraint conditions
1) The queuing latency constraint:
in the formula: t ist maxIs the maximum value of the queuing waiting time
2) The single pile charging power range of the charging pile is restricted:
in the formula:
respectively the maximum value and the minimum value of the single pile charging power.
3) The quantity of charging piles of the EV charging station is restricted:
in the formula:
the maximum number of charging piles is allowed to be built for the charging station.
4) And (3) restricting upper and lower limits of the voltage amplitude of the node of the power distribution network:
in the formula: vi′The voltage amplitude of the node i' of the power distribution network;
and
the upper limit and the lower limit of the voltage amplitude of the node of the power distribution network are respectively set; and N' is a load node set of the power distribution network in the planned area.
5) Capacity constraint of the distribution line:
PEVC S+∑Pl、Load，≤Pmax (38)
in the formula: pWVCSTotal active power, SIGMA R, for intra-day EV charging station consumptionl、LoadThe real power consumed by other loads on the line during the day,
the maximum power that the feeder can access is determined by the load on the feeder and the transmission capability of the feeder.
And solving the EV charging station double-layer planning model.
The upper model is a problem of mixed integer programming, the model is simple, and a branch-and-bound method can be adopted for solving.
The lower layer model is an EV charging station operation game model with complete rationality of participants and limited game strategy, and the Nash equilibrium existence theorem shows that the model has Nash equilibrium solution, but the model is not a uniform optimization problem of full system optimization but a problem that each participant pursues own optimization, so that a solution method for searching global optimization is not suitable for solution of the lower layer model.
In addition, the lower-layer game model does not constrain the upper and lower benefit limits of the participants, a plurality of Nash equilibrium solutions are likely to occur in the model solving process, but the current iterative search method for solving the game model cannot optimize the plurality of equilibrium solutions, so that an auxiliary objective function for optimizing is necessary. Under the scene that the game process is not interfered and only the game result is simply evaluated and regulated, an auxiliary objective function is constructed with the minimum social loss, so that the optimal Nash equilibrium solution is obtained, and the optimization of benefit distribution in the true sense can be achieved. In view of the above, the invention improves the iterative search solution method, embeds the auxiliary objective function optimized by equilibrium solution into the iterative search method, finds the equilibrium solution of the game model by comparing the interests of the participants under different strategy sets, and then evaluates and selects the result of the game through the auxiliary objective function, thereby determining the optimal equilibrium point of the game model.
The solving method is as follows:
1) constructing an auxiliary objective function
Considering that the constructed charging station can achieve the effect of benefiting the people, the auxiliary objective function is to maximize the benefits of the whole society, namely the loss of the benefits of the whole society in the game process of the charging station is minimum. See formula (39) for the auxiliary objective function.
minF3＝min(fA+fB+fC) (39)
In the formula: f3Is an auxiliary objective function.
2) Solving a game model;
step 1: and (4) initializing game strategies of a charging station party, EV users and a power supply company, and randomly setting balance points.
Step 2: and the three game participants sequentially and independently perform decision optimization. And the participants obtain the optimal combination of the next group of strategies through a genetic algorithm according to the optimization result of the previous round.
And step 3: and (4) carrying out information sharing on the respective strategies of the game participants, judging whether the optimal combination meets the constraint condition, if so, continuing the step (4), and if not, returning to the step (1).
And 4, step 4: and judging whether the Nash equilibrium point can be found by the game model under the strategy, and if the optimal solutions obtained by the game participants in adjacent times are the same, determining that the game is balanced at the moment. And if the Nash balance requirement is not met, returning to the step 1 and continuing searching.
And 5: and recording a new equilibrium solution of the game model. Comparing this equalization solution with the previous equalization solution according to equation (39), the equalization solution that can make the social utilization loss smaller is retained.
Step 6: and (5) carrying out multiple iterations on the steps 1-5 until the model cannot solve a new Nash equilibrium solution, and finally outputting the final Nash equilibrium solution.
Examples are given.
19.48km in an economic technology development area of a certain northeast city by using the proposed CRITIC-NG planning method2The area (d) is planned for an EV charging station. The area to be planned is an area enclosed by the lines shown in fig. 7, wherein there are 2 business areas, 5 residential areas, 23 industrial areas and 6 other areas, the land property information corresponding to the functional areas is shown in fig. 2, the parking probability of the EV in each functional area is shown in fig. 3, 51 intersection nodes are in the area, the distribution of the intersection nodes is shown in fig. 4, and there are 38 load nodes, and the distribution of the grid network frame and the load points in the area is shown in fig. 5. The electric car holding capacity in the region to be planned is 2000 cars, wherein 400 cars and 1600 cars are normalized, statistical data of EV charging moments are normalized, and charging probability of each moment of EV is approximated to normal distribution and positive distribution by a maximum likelihood estimation methodParameters of the state distribution: mu is 17.3; σ ═ 3.3, and vehicle parameters are given in table 1.
Table 1EV parameters assign the index weights of the address sub-items by using the CRITIC method, and the weighting results are shown in table 2.
| Weight of | 0.3585 | 0.3429 | 0.2986 |
TABLE 2CRITIC determined index weights
The branch-and-bound method is used to solve the upper-layer planning model, and the result after 150 iterations is shown in fig. 6. The area to be planned is divided into 80 equal-size square blocks (see fig. 7) with the side length of 500m, the charging demand degrees of the blocks are different due to different functional properties, traffic flows and grid racks of different blocks, the address selection comprehensive index size of each block is calculated by using a formula (13), and the specific numerical value size is shown in table 3 as shown in fig. 7.
TABLE 3 numerical value of site selection comprehensive index
As can be seen from fig. 7, the result of the site selection comprehensive index shows that the spatial distribution characteristics of the EV charging demands of each block are clear, and in a unit area, residential areas and commercial areas can attract more traffic and people, which causes a larger charging demand of the EV; national roads and provincial roads with strong circulation capability can attract more EVs, and conversely, EV traffic flow of rural roads with narrow roads is smaller; meanwhile, the charging drop point of the EV is also influenced by the power supply reliability of the power grid, and the land with high power supply reliability can attract more EV users to come and charge.
6.2.2EV charging station location results
According to the size of the EV site selection comprehensive index, the sites of 8 EV charging stations in the planning area are determined through the solution of an EV charging station double-layer planning model, the site falling positions under a street map are shown in figure 8, and the positions of the EV charging stations under a satellite map are shown in figure 9.
Capacity configuration results for 6.2.3EV charging stations
In the planned game of the EV charging station, three participants play the game on the configured capacity required by the charging station at each location, and the annual total cost results of the three participants after the game are shown in table 4.
TABLE 4 annual combined costs of the three participants
By analyzing the table 4, in the game process, the construction of the quick charging pile can enable one side of the EV charging station to obtain more profits, the charging waiting time of EV users is reduced, but the load of a power grid is aggravated, and the loss of a power supply company is aggravated; when the EV users are applied to the residential area and the industrial area and are charged through the slow charging pile, although the charging time is long, the charging waiting cost of the EV users in unit time is low, and therefore the charging queuing cost of the EV users cannot be too high.
Considering the charging environments of different functional areas and the parking probability of the EV, namely the commercial area has short land use and high land price, the scale of the configurable EV charging station is not suitable to be overlarge, and the parking time of the EV in the commercial area is not long, so that the EV charging station close to the commercial area needs to be provided with a quick charging pile; on the contrary, the land occupation of the industrial area and the residential area is not tight, a large-scale charging station can be built, the EV can be parked in the industrial area and the residential area for a long time, and a slow charging pile is preferably built. The EV charging station capacity planning results are shown in table 5.
TABLE 5 planning results for EV charging station capacities
The rated charging power of the rapid charging pile is 30kW, an electric private car needs to be charged for 30 minutes, and an electric taxi needs to be charged for 90 minutes, so that the electric energy can be completely supplemented; the rated charging power of the slow charging pile is 7kW, an electric private car needs to be charged for 120 minutes, and an electric taxi needs to be charged for 400 minutes to completely supplement electric energy.
The mid-grid access position of the EV charging station is shown in fig. 10. The charging process of 2000 EVs in the planned area was simulated by monte carlo, and the daily charging demand of 8 EV charging stations after simulation is shown in fig. 11-14.
As can be seen from fig. 7 and 11-14, the charging load is mainly concentrated at 12 near the EV charging station in the commercial district; 00-20: 00; EV charging stations close to residential areas, the charging load is mainly concentrated in two time periods of 0:00-5:00 and 20:00-24: 00; near the EV charging stations in the industrial area, the charging load is mainly concentrated at 10:00-18: 00.
In summary, the electric vehicle charging station double-layer planning method based on the CRITIC method and the non-cooperative game provided by the invention has the following two advantages:
1) by analyzing various factors influencing the EV charging station site selection, a corresponding charging station site selection subentry index system is established, a CRITIC method is further adopted to establish a charging station site selection comprehensive index, and adverse effects on reasonable site selection of the EV charging station caused by conflict among multiple charging demand indexes and contrast intensity difference are overcome.
2) By adding a non-cooperative game link of a multi-interest main body, the optimal configuration of the capacity of the EV charging station is realized under the condition that the win-win situation of the charging station, a power supply company and EV users can be guaranteed.
The present invention is not limited to the above preferred embodiments, and any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for planning an electric vehicle charging station based on a CRITIC method and a non-cooperative game comprises the following steps:
step one, calculating the space-time charging requirement of an EV (electric vehicle) in a planned area;
step two, constructing each subentry index of the EV charging station site selection, weighting the subentry indexes by adopting a CRITIC method, and further establishing a charging station site selection comprehensive index capable of reflecting the EV charging demand;
analyzing benefits of three participants of the EV charging station, and establishing a multi-main-body game model of the EV charging station through a non-cooperative game theory;
and step four, establishing an upper layer addressing model taking the maximum EV charging station addressing comprehensive index as a target and a lower layer constant volume model taking multi-body game balance as a target, and performing iterative solution on the upper layer and the lower layer of the models by adopting a branch-and-bound method and an iterative search method of embedded auxiliary objective functions.
2. The method for planning an electric vehicle charging station based on the CRITIC method and the non-cooperative game as claimed in claim 1, wherein in the first step, analysis of EV charging demand is performed; counting the charging probability of the EV in each planned time within the day to obtain the time sequence charging requirement; and analyzing various factors influencing EV charging on different partitions to obtain the EV space charging demand.
3. The method for planning an electric vehicle charging station based on the CRITIC method and the non-cooperative game as claimed in claim 2, wherein in the second step, an EV charging station addressing index system is established; and (3) considering traffic factors, functional area factors and power supply reliability factors which influence EV charging, establishing site selection subentry indexes respectively corresponding to the traffic factors, the functional area factors and the power supply reliability factors, determining the weight of each index by a CRITIC method, and further establishing a site selection comprehensive index of the EV charging station.
4. The method for planning an electric vehicle charging station based on the CRITIC method and the non-cooperative game as claimed in claim 3, wherein in step three, a multi-interest subject NG model is established; considering the planning of the EV charging station from the government level and the whole society, analyzing the influence of interest conflicts of the charging station, EV users and a power supply company on the planning decision of the charging station, and establishing a corresponding NG model.
5. The method for planning an electric vehicle charging station based on the CRITIC method and the non-cooperative game as claimed in claim 4, wherein the fourth step is to establish a double-layer planning model of the EV charging station; and establishing an upper layer addressing model taking addressing comprehensive indexes as maximum targets and a lower layer constant volume model taking multi-interest subject game balance as targets. And the upper layer model transmits the initial addressing scheme to the lower layer, the lower layer optimizes the capacity of the charging pile according to the charging load and other variables which need to be met by the charging station after addressing, the calculation result is returned to the upper layer, then the upper layer optimizes the positions and the number of the charging stations again according to the result of the lower layer, and iteration is carried out until the addressing and constant volume scheme of the charging station is optimal.
6. The method for planning electric vehicle charging stations based on the CRITIC method and the non-cooperative game as claimed in claim 5, wherein for the analysis of the EV charging demand, a probability function f of the spatial distribution of the EV charging demand needs to be constructed3See the following formula:
fs＝g[Z(x，y)，IPE，IEN，ICE]
in the formula: z (x, y) is a block at the x row and y column position; i isPEThe index of the charging requirement influenced by traffic factors; i isCEThe index of the charging requirement influenced by the functional area factors; i isENThe index of the charging requirement is influenced by the power supply reliability factor;
where statistical data for all EV charging moments in the area to be planned are normalizedThen, the charging probability of each time of the EV is approximated to normal distribution by a maximum likelihood estimation method, and the probability density function f of the charging probability istSee the following formula:
in the formula: μ and σ are the expected value and standard deviation, respectively, of a normal distribution.
7. The method as claimed in claim 6, wherein the CRITIC method and the non-cooperative game based planning method for the electric vehicle charging station are characterized in that the CRITIC method is used for weighting, that is, weighting and summing a passing EV capacity index, a captured EV capacity index and a power supply reliability index, however, different degrees of correlation and conflict exist among the address subentry indexes, the size of the area to be planned is divided into n blocks, each block has m address subentry indexes, an n × m index matrix a is established for the whole area, then the CRITIC method is used for assigning weights to the indexes, and the CRITIC method weighting steps are as follows:
step 1: the site selection itemized indexes are standardized, and an index matrix A is shown as the following formula:
in the formula: a isijThe size of the jth addressing subentry index of the ith block; n, n is the number of blocks; j is 1,2, and m is the number of indexes;
the index matrix A is standardized by a polar differentiation method, and the standardized matrix A' is shown as the following formula:
step 2: calculating a correlation coefficient matrix K of the index based on the matrix a', as follows:
in the formula: k is a radical ofhjThe correlation coefficient between the h address item index and the j address item index after standardization; h 1,2,. said, m; j is 1,2, 1, m, khjThe formula is shown in the following formula:
in the formula: cov (A)h′，Aj') is the h-th column vector A in the matrix Ah' and j column vector Aj' covariance between; sigma (A)h') is a column vector Ah' standard deviation; sigma (A)j') is a column vector Aj' standard deviation;
and step 3: quantifying the conflict between the address sub-indexes and the conflict quantization value b between the jth address sub-index and other indexesjSee the following formula:
and 4, step 4: and calculating the information quantity contained in the index, wherein the information quantity is calculated according to the following formula:
in the formula: b isjFor the information content contained in the jth address subentry index, BjThe larger the index is, the larger the information content contained in the jth address subentry index is, the larger the relative importance of the index is;
and 5: calculating the objective weight W of the jth site-selection subentry indexjSee the following formula:
8. the method for planning an electric vehicle charging station based on CRITIC method and non-cooperative game as claimed in claim 7, wherein the establishment of the site selection comprehensive index:
after the weight of each address subentry index is obtained, the size of the single numerical value of each index is multiplied by the weight of the corresponding index for weighted summation to obtain an address comprehensive index, which is shown as the following formula:
in the formula:
the size of an address comprehensive index of the ith block is 1, 2., n;
converting the block i and the block Z (x, y) according to a sequence counting method to enable the blocks to be in corresponding relation, wherein the position of the block is the size of an address selection comprehensive index of the block in the x row and the y column;
the size of the jth address sub-index is the location of the xth row and yth column block.
9. The method for planning an electric vehicle charging station based on the CRITIC method and the non-cooperative game as claimed in claim 8, wherein in the third step, the non-cooperative game theory in the complete information environment is adopted to establish an EV charging station game theory model, which is as follows:
MG＝{NG；UA，UB，UC；fA，fB，fC}
in the formula: n is a radical ofGThe number of game participants; u is a strategy set; f is a utility function; A. b, C, which are three participating parties, namely a charging station party, an EV user and a power supply company;
the utility function of the EV charging station side is shown as the following formula:
fA＝Cop+Cinvs-Cprs
in the formula: cprsFor annual revenue of charging stations, CinvsFor charging station annual investment costs, CopAnnual operating costs for charging stations;
for the utility function of an EV charging user, the expression is given by:
fB＝365CBTtPNEV
in the formula: cBIs the unit time waiting cost; n is a radical ofEVKeeping the quantity of the electric automobiles in the station service range; p is daily charging probability; t istQueuing up the average charge time;
for utility functions of the power supply company, see the following equation:
fc＝Closs+CDT+Cline
in the formula: clineLine construction costs for connecting charging stations to substations, CDTCost of configuring dedicated transformers for EV charging stations, ClossIs the network loss cost.
10. The method of claim 9, wherein a two-level planning model and solution for EV charging stations is established,
firstly, establishing an EV charging station double-layer planning model, wherein the upper layer model is the addressing problem with the maximum addressing comprehensive index as a target, and the lower layer is the constant volume problem with the multi-benefit subject game balance as a target; the upper layer plans to transmit the site selection scheme to the lower layer, the lower layer optimizes the capacity of the charging pile according to the service range of the charging station after site selection and the charging load and other variables needing to be met, the calculation result is transmitted to the upper layer, then the upper layer further optimizes the site selection scheme according to the power and the quantity of the charging pile obtained by the lower layer, and the mathematical model is shown as the following formula:
in the formula: f1And F2Respectively an objective function of the upper layer plan and the lower layer plan; sstaThe capacity of the charging pile is configured for the charging station, and the rated power p of the charging pilecAnd the number N of charging pilesGDetermining; g1And G2Constraint conditions for upper layer planning and lower layer planning respectively;
the method for solving the double-layer planning model of the EV charging station comprises the following steps:
1) constructing an auxiliary objective function
The auxiliary objective function is given by:
minF3＝min(fA+fB+fC)
in the formula: f3Is an auxiliary objective function;
2) solving a game model;
step 1: initializing game strategies of a charging station party, EV users and a power supply company, and randomly setting balance points;
step 2: and the three game participants sequentially and independently perform decision optimization. The participants obtain the optimal combination of the next group of strategies through a genetic algorithm according to the optimization result of the previous round;
and step 3: carrying out information sharing on respective strategies of game participants, judging whether the optimal combination meets constraint conditions, continuing the step 4 if the optimal combination meets the constraint conditions, and returning to the step 1 if the optimal combination cannot meet the constraint conditions;
and 4, step 4: and judging whether the Nash equilibrium point can be found by the game model under the strategy, and if the optimal solutions obtained by the game participants in adjacent times are the same, determining that the game is balanced at the moment. If the Nash balance requirement is not met, returning to the step 1 and continuing searching;
and 5: and recording a new equilibrium solution of the game model. Comparing the equilibrium solution with the previous equilibrium solution according to an auxiliary objective function formula, and reserving the equilibrium solution which can make the social utilization loss smaller;
step 6: and (5) carrying out multiple iterations on the steps 1-5 until the model cannot solve a new Nash equilibrium solution, and finally outputting the final Nash equilibrium solution.
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| CN117973817A (en) * | 2024-04-01 | 2024-05-03 | 交通运输部规划研究院 | Public charging infrastructure layout method for trunk highway network |
| CN117973817B (en) * | 2024-04-01 | 2024-06-28 | 交通运输部规划研究院 | Public charging infrastructure layout method for trunk highway network |
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