CN117621897A - Multi-pile networking negotiation charging management system and method - Google Patents
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Abstract
The invention relates to the technical field of charging piles, and provides a multi-pile networking negotiation charging management system and method, wherein the system comprises the following steps: the historical state data acquisition module acquires historical state data of each group of charging piles in the target area; the charging pile state classification module classifies states of the charging piles according to the historical state data; the distributed negotiation and decision module utilizes a particle swarm optimization algorithm and a negotiation cooperative mechanism according to the state of the charging pile; solving to obtain negotiation and decision results; and the charging execution module is used for generating a charging strategy for controlling each group of charging piles to execute charging actions. According to the invention, by collecting historical state data of the charging pile, and utilizing a particle swarm optimization algorithm and a negotiation cooperative mechanism, the power in the power grid is reasonably distributed by considering the power grid load, the user demand and the charging pile state, and early warning and discrimination are timely carried out when the charging pile fails, so that the effects of optimizing the power utilization of the power grid, improving the stability of the power system and reducing the cost are achieved.
Description
Technical Field
The invention relates to the technical field of charging piles, in particular to a multi-pile networking negotiation charging management system and method.
Background
The centralized distribution or the uneven distribution of the charging piles can cause different degrees of load impact on the power grid, thereby affecting the stability and the power supply capacity of the power grid. When a large number of electric vehicles are intensively charged in a specific area, the grid load of the area can be instantaneously increased. If the grid does not have sufficient capacity to meet this demand, it may result in a grid under-power. This causes problems such as voltage drop, current overload, and device damage.
Because of uneven distribution of charging piles, some areas can face the problem of insufficient power supply capacity of a power grid, and other areas have excessive power supply capacity. This uneven distribution results in wasted resources and grid instability. To meet the high power requirements of the charging pile, grid operators need to perform grid upgrades, including increasing substation capacity, improving cabling, transformers, and other facilities. This entails high costs, especially in case of a rapid increase in the demand for the charging piles. Likewise, uneven distribution of charging piles results in uneven load distribution of the power grid, such that the power grid of some areas is in an overload state, while the power grid of other areas is in a low load state. Such unbalance may affect the stability and efficiency of the grid. Accordingly, the existing charging pile arrangement has the following problems:
1. dynamic power allocation problem: the charging pile network needs to achieve highly dynamic power distribution to cope with changes in the charging requirements of the electric vehicle and fluctuations in the grid load. Conventional static power distribution methods have been difficult to meet this demand because they have failed to effectively cope with peak grid load pressures. The prior art does not fully take into account how to distribute the charging power rapidly and accurately in case of a sudden increase in instantaneous load, so as to ensure the stability and reliability of the grid.
2. Charging period intelligent scheduling: intelligent scheduling of charging periods requires more advanced considerations. Not only is charging encouraged in low-load time periods, but also the electricity consumption condition of users, different use conditions of charging piles in the area and charging pile charging demand characteristics of different time periods in the same area are considered. The prior art generally does not fully account for these factors, especially uncertainty in user behavior, e.g., 8-9 pm is the peak of charge for cell charging piles, but for first line cities, 8-9 pm charging demand during large holidays drops significantly.
3. Charging pile fault and network stability: the failure of the charging stake results in an instability of the entire charging stake network, which relates to how quickly to detect and respond to the failure of the charging stake. The prior art still has limitations in the aspects of health monitoring and automatic isolation of the charging pile, and cannot sufficiently cope with various fault conditions, especially in a large-scale charging pile network. At the same time, how to ensure that the impact on the user is minimized when a fault occurs, and how to maximize the fault tolerance of the network.
In summary, the prior art has the problems that the stability and reliability of the power grid cannot be maintained under the condition of rapid increase of instantaneous load, the electricity consumption condition of a user cannot be considered, and monitoring and adjustment cannot be performed on the fault of the charging pile.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a multi-pile networking negotiation charging management system and method, which aims to solve the problems that the stability and reliability of a power grid cannot be maintained under the condition of rapid increase of instantaneous load, the electricity consumption condition of a user cannot be considered, and monitoring and adjustment cannot be performed aiming at the fault of a charging pile.
In a first aspect of the present invention, a multi-pile networking negotiation charging management system is provided, including:
the historical state data acquisition module is configured to acquire historical state data of each group of charging piles in the target area;
the charging pile state classification module is configured to perform state classification on the charging piles according to historical state data of each group of charging piles to obtain charging pile states of each group of charging piles;
the distributed negotiation and decision module is configured to obtain negotiation and decision results by solving the conditions of a minimum objective function by utilizing a particle swarm optimization algorithm and a negotiation coordination mechanism according to the state of the charging piles of each group of charging piles; wherein the objective function is a function of grid load, user demand and charging pile status;
and the charging execution module is configured to generate a charging strategy for controlling each group of charging piles to execute charging action according to the negotiation and decision result.
Optionally, the charging pile state classification module specifically includes:
a historical state data processing unit configured to extract state features of the historical state data and calculate a confidence level of the charging pile according to the state features;
the charging pile state classification unit is configured to calculate confidence degree of the charging pile and fuzzy membership degree of a threshold value by using a fuzzy C-means method, and classify the states of each group of charging piles according to the fuzzy membership degree to obtain the charging pile state of each group of charging piles.
Optionally, the charging pile state classification unit specifically includes:
a classification definition subunit configured to define a feature vector of a data point as x= (X) 1 ,x 2 ,…,x N ) The feature vector defining the cluster center is v= (V) 1 ,v 2 ,…,v C ) The method comprises the steps of carrying out a first treatment on the surface of the N is the number of data points, C is the number of clustering centers, and each clustering center represents the state of one charging pile;
a fuzzy membership calculation subunit configured to determine a fuzzy membership matrix U = [ U ] ij ] N×C ]By Euclidean distance d ij :[d ij ＝||X i -V j || 2 ]Calculating blurMembership value Wherein X is i Is the eigenvector of the ith data point, V j Is the feature vector of the jth cluster center, m is an ambiguity parameter, generally takes an integer greater than 1, and t represents the iteration number;
and the charging pile state classification subunit is configured to take the clustering center with the largest fuzzy membership value as the charging pile state of the charging pile.
Optionally, the historical state data includes charging power and charging period, and the charging pile state includes an idle state, a charging state, a fault state, and a deactivated state.
Optionally, the distributed negotiation and decision module specifically includes:
the objective function building unit is configured to build an objective function based on the power grid load, the user demand and the charging pile state;
a minimized objective function solving unit configured to find a set of particles that minimize the objective function using a particle swarm optimization algorithm, on the condition that the objective function is minimized;
and the negotiation and decision unit is configured to generate and control the state of the charging pile corresponding to each particle by using a negotiation collaboration mechanism based on the solved group of particles minimizing the objective function, so as to obtain a negotiation and decision result.
Optionally, in the particle swarm optimization algorithm, the expression of the objective function is specifically:
wherein F (X) is a fitness function, and the particles are measuredEach particle X in the population i Expressed as an objective function; x is X i The ith particle in the particle swarm represents the state of the charging pile; a, a i A target state or target value representing a target or desired value of the state of the charging pile; alpha is a weight related to the user demand and represents the influence degree of the user on the charging demand; u (t) is a user demand, and represents the charging demand of the user at time t; beta is a weight related to the state of the charging pile and represents the influence degree of the state of the charging pile on the charging requirement; s (t) is a charging pile state, and represents the charging pile state at the time t; delta is the weight related to the power grid load at the previous moment and represents the influence degree of the power grid load at the previous moment on the charging demand; p (t-1) is the grid load at the previous moment, and represents the grid load condition at the moment t-1; the E is the weight related to the state of the charging pile at the previous moment, and represents the influence degree of the state of the charging pile at the previous moment on the charging requirement; c (t-1) is the state of the charging pile at the previous time, and represents the state of the charging pile at time t-1.
Optionally, in the negotiation coordination mechanism, an expression for controlling the state of the charging pile corresponding to each particle is generated by using the negotiation coordination mechanism, and specifically includes:
wherein, neighborBest i Representing the best position in the neighborhood of particle i;the speed of the particle i at the next time step determines the change of the charging pile in the charging behavior; ω is the inertial weight that affects how far the charging pile maintains the current speed; c 1 And c 2 Are learning factors which help the charging pile learn and adapt to different situations; r is (r) 1 And r 2 Is a random number for introducing randomness; pbest i Is the individual optimal position of the single charging pile charging power reached in the past schedule; gbes is the global most of the charging power in the whole charging pile systemA good position; />The position of particle i at the current time step represents the current state of the charging pile.
Optionally, the system further comprises: fill electric pile fault monitoring module, fill electric pile fault monitoring module specifically includes:
the charging pile real-time state acquisition unit is configured to acquire the charging pile real-time state of each group of charging piles;
the system comprises a charging pile real-time state updating unit, a clustering center updating unit and a clustering center updating unit, wherein the clustering center updating unit is configured to update a fuzzy membership value according to the charging pile real-time state and update a clustering center according to the updated fuzzy membership value; the updating expression of the clustering center is specifically as follows:
wherein V is j Representing the new position of the jth cluster center, u ij Is the fuzzy membership value of the data point i to the clustering center j, X i Is the eigenvector of the data point i, m is the ambiguity parameter;
and the iteration updating unit is configured to obtain negotiation and decision results through iteration updating according to the updated clustering center.
Optionally, the iterative updating unit specifically includes:
the target function updating subunit is configured to recalculate the target function based on the updated clustering center to obtain an updated target function;
and the negotiation and decision result updating subunit is configured to update the individual optimal position, the global optimal position and the speed and position of each particle for a group of particles which are solved by using the updated objective function and minimize the objective function, and regenerate and control the charging pile state of each particle corresponding to the charging pile according to the updating result to obtain the updated negotiation and decision result.
The second aspect of the present invention provides a multi-pile networking negotiation charging management method, comprising:
collecting historical state data of each group of charging piles in a target area;
carrying out state classification on the charging piles according to the historical state data of each group of charging piles to obtain the charging pile state of each group of charging piles;
according to the state of the charging piles of each group of charging piles, a particle swarm optimization algorithm and a negotiation cooperative mechanism are utilized, and a negotiation and decision result is obtained by solving under the condition of minimizing an objective function; wherein the objective function is a function of grid load, user demand and charging pile status;
and generating a charging strategy for controlling each group of charging piles to execute charging action according to the negotiation and decision result.
The invention has the beneficial effects that: the multi-pile networking negotiation charging management system and method are provided, the historical state data of the charging piles are collected, the particle swarm optimization algorithm and the negotiation cooperative mechanism are utilized, the power in the power grid is reasonably distributed under the condition of minimizing an objective function, and early warning and discrimination are timely carried out when the charging piles fail, so that the effects of optimizing the power utilization of the power grid, improving the stability of the power system and reducing the cost are achieved.
Drawings
Fig. 1 is a schematic structural diagram of a multi-pile networking negotiation charging management system provided by the invention;
fig. 2 is a flow chart of a multi-pile networking negotiation charging management method provided by the invention.
Reference numerals:
10-a historical state data acquisition module; 20-a charging pile state classification module; 30-a distributed negotiation and decision module; 40-a charge execution module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1:
referring to fig. 1, fig. 1 is a schematic structural diagram of a multi-pile networking negotiation charging management system according to an embodiment of the present invention.
As shown in fig. 1, a multi-pile networking negotiation charging management system includes: a historical state data collection module 10, the historical state data collection module 10 configured to collect historical state data for each set of charging piles within a target area; a charging pile state classification module 20, wherein the charging pile state classification module 20 is configured to perform state classification on the charging piles according to historical state data of each group of charging piles to obtain charging pile states of each group of charging piles; the distributed negotiation and decision module 30 is configured to obtain a negotiation and decision result by solving the distributed negotiation and decision module 30 according to the state of the charging piles of each group of charging piles and by using a particle swarm optimization algorithm and a negotiation cooperation mechanism on the condition of minimizing an objective function; wherein the objective function is a function of grid load, user demand and charging pile status; the charging execution module 40 is configured to generate a charging policy for controlling each group of charging piles to execute a charging action according to the negotiation and decision result.
It should be noted that, in the prior art, a static power distribution manner is generally adopted by the charging pile network, so that the charging pile network cannot effectively cope with the peak power grid load pressure, and when the transient load increases suddenly, the charging power cannot be distributed rapidly and accurately, so as to ensure the stability and reliability of the power grid; meanwhile, the existing charging pile networking does not consider the electricity utilization condition of users of different charging piles, and a large amount of power resource waste exists; moreover, the existing charging pile networking cannot consider the influence of the charging pile fault on the instability of the whole charging pile networking, and the fault tolerance is not high in a large-scale charging pile network.
Therefore, the embodiment provides a multi-pile networking negotiation charging management system, which classifies the states of each charging pile (including an idle state, a charging state, a fault state and a disabling state) by collecting historical state data of the charging pile, and reasonably distributes power in a power grid by using a particle swarm optimization algorithm and a negotiation coordination mechanism under the premise of considering the load, the user requirement and the charging state of the power grid and taking a minimum objective function as a condition. Therefore, by analyzing and predicting historical data and real-time data, the rapid and accurate charging power distribution is realized according to the power distribution influence of the power grid load, the user demand and the charging state on the charging pile network, the stability and the reliability of the power grid are improved, the power consumption demand of the user is met, and the effects of optimizing the power utilization of the power grid, improving the stability of a power system, reducing the cost and the like can be realized.
In a preferred embodiment, the charging pile state classification module 20 specifically includes: a historical state data processing unit configured to extract state features of the historical state data and calculate a confidence level of the charging pile according to the state features; the charging pile state classification unit is configured to calculate confidence degree of the charging pile and fuzzy membership degree of a threshold value by using a fuzzy C-means method, and classify the states of each group of charging piles according to the fuzzy membership degree to obtain the charging pile state of each group of charging piles.
In this embodiment, after the historical state data is obtained, the data is preprocessed first, including data cleaning, outlier detection and missing value filling, so as to ensure accuracy and integrity of the data, and then the collected historical state data (charging power and charging period) is extracted by the historical state data processing unit, for example: and extracting frequency domain features such as frequency spectrum, frequency components, power spectrum density and other state features by utilizing Fourier transformation or wavelet transformation, and analyzing the state features to obtain the confidence of the charging pile. After that, the state of the charging pile is classified by calculating the confidence level of the charging pile and the fuzzy membership degree of the threshold value by using a fuzzy C-means algorithm (FCM) through the state classification unit of the charging pile. The charging pile state classification unit calculates the confidence level of the charging pile and the fuzzy membership degree of the threshold value by using a fuzzy C-means algorithm (FCM), and specifically comprises the following implementation steps:
(1) Classification definition:
1. assume that there are N data points and C cluster centers, each representing the state of one charging stake.
2. Defining a feature vector of data points: x= (X 1 ,x 2 ,…,x N )
3. Feature vector v= (V) defining cluster center 1 ,v 2 ,…,v C )
(2) Calculating fuzzy membership degree:
4. fuzzy membership matrix U: [ U= [ U ] ij ] N×C ]Wherein (u) ij ) Representing the membership value of the ith data point to the jth cluster center.
5. Distance metric formulas, typically using euclidean distance: [ d ] ij ＝||X i -V j || 2 ]Wherein (X) i ) Is the eigenvector of the ith data point, (V) j ) Is the feature vector of the j-th cluster center.
6. By calculating the U matrix, membership between each charging pile state category and each data point in the charging pile history data can be determined. This means that each data point is assigned to a different state class whose membership value describes the extent to which it belongs to each state. By analyzing the U matrix, it can be determined which state class each data point is most likely to be in. Specifically, for each data point, a cluster center with the largest membership value in the U matrix can be found, and the cluster center is the most probable state category. Further, the charging pile management system can be assisted in identifying the current state of the charging pile, such as an idle state, a charging state, a fault state, a disabled state and the like, so as to take corresponding measures and decisions. Thus, the fuzzy membership update formula:
where (m) is the ambiguity parameter (typically taking an integer greater than 1) and (t) represents the number of iterations.
(3) Classification of charging pile states:
7. cluster centers typically represent different status categories in the charge pile management system, such as idle status, charge status, fault status, and inactive status. Each cluster center of each state class represents a charging pile state, and the membership value u ij Indicating the extent to which data point i belongs to the charging pile state j.
Therefore, the embodiment judges the most likely charging pile state of the charging pile corresponding to each data point by establishing the fuzzy membership matrix, so as to classify each charging pile, further help a charging pile management system to identify the current state of the charging pile, provide data support of the charging pile state for corresponding measures and decisions, realize an intelligent charging method based on data driving, optimize power of a power grid and improve stability of a power system.
In a preferred embodiment, the distributed negotiation and decision module 30 specifically includes: the objective function building unit is configured to build an objective function based on the power grid load, the user demand and the charging pile state; a minimized objective function solving unit configured to find a set of particles that minimize the objective function using a particle swarm optimization algorithm, on the condition that the objective function is minimized; and the negotiation and decision unit is configured to generate and control the state of the charging pile corresponding to each particle by using a negotiation collaboration mechanism based on the solved group of particles minimizing the objective function, so as to obtain a negotiation and decision result.
In this embodiment, a particle swarm optimization algorithm is adopted to find a group of particles minimizing an objective function so as to realize intelligent management and coordination of charging piles, and balance between different targets can be balanced by adjusting weight parameter sums so as to meet user requirements, optimize power grid load and improve system benefit, so that the particle swarm optimization algorithm specifically comprises:
wherein F (X) is an fitness function, and each particle X in the particle swarm is measured i Expressed as an objective function; x is X i The ith particle in the particle swarm represents the state of the charging pile; the state of the charging pile can be divided into: idle state: the charging pile is not used, and can provide charging service for the electric automobile. State of charge: the charging stake is charging the electric automobile, and charging power and charging speed may be different. Fault state: when the charging pile fails or needs maintenance, the charging pile is in a failure state and is temporarily unavailable. And (3) a deactivated state: the charging stake is deactivated for some reason and cannot provide charging service. a, a i A target state or target value representing a target or desired value of the state of the charging pile; alpha is a weight related to the user's demand; u (t) is a user demand, and represents the charging power demand of the user at the time t, wherein the charging power demand is obtained by inquiring charging power demand data of the user at different time periods in the charging reservation information; beta is a weight related to the state of the charging pile and represents the influence degree of the state of the charging pile on the charging requirement; s (t) is a charging pile state, and represents the charging pile state at the time t; delta is the weight related to the power grid load at the previous moment and represents the influence degree of the power grid load at the previous moment on the charging demand; p (t-1) is the total power of the power grid load at the previous moment, and represents the total power condition of the power grid load at the moment t-1; the E is the weight related to the state of the charging pile at the previous moment, and represents the influence degree of the state of the charging pile at the previous moment on the charging requirement; c (t-1) is the state of the charging pile at the previous time, and represents the state of the charging pile at time t-1.
Therefore, the embodiment establishes the objective function related to the power grid load, the user demand and the charging pile state based on the fitness function, balances the balance among different targets by considering the total power of the power grid load at the previous moment, the charging pile state at the previous moment, the user demand at the current moment and the charging pile state at the current moment, so as to meet the user demand, optimize the power grid load and improve the system benefit according to the control of the charging pile state.
In this embodiment, a collaborative negotiation mechanism is further adopted, so that charging behavior can be coordinated between charging piles, and the coordination can be achieved by modifying a speed update formula, so as to consider the states and demands of neighboring charging piles. In the negotiation coordination mechanism, an expression for controlling the state of the charging pile corresponding to each particle is generated by using the negotiation coordination mechanism, specifically:
wherein, neighborBest i Representing the best position in the neighborhood of particle i;the speed of the particle i at the next time step determines the change of the charging pile in the charging behavior; ω is the inertial weight that affects how far the charging pile maintains the current speed; c 1 And c 2 Are learning factors which help the charging pile learn and adapt to different situations, such as charging pile power update situations when one charging pile fails; r is (r) 1 And r 2 Is a random number for introducing randomness; pbest i Is the individual optimal position of the single charging pile charging power reached in the past schedule; gbes is the global optimal position of charging power in the whole charging pile system; />The position of particle i at the current time step represents the current state of the charging pile.
Therefore, the embodiment negotiates the charging strategy of each charging pile by utilizing a cooperative negotiation mechanism based on the particle swarm optimization algorithm of the intelligent decentralized node, and finally obtains the charging state of each charging pile in the control area, so that the power grid load is balanced while the charging requirement of a user is considered. The negotiation mechanism negotiates charging behaviors among a plurality of groups of charging piles in the area, and controls different use states among different charging piles in the same period to distribute power of the power grid so as to meet user requirements and power grid load balance and solve the problem that the stability and reliability of the power grid are affected by instantaneous load sharp increase of the power grid. In the decision process, the objective function is optimized through the change of real-time data, the user demand and the change of the state of the charging pile, the stability of the power grid load is considered, the peak load is reduced, the power grid benefit is improved, the power grid load and the user demand are considered as factors of the stability, and the user demand can be met, the power grid load is optimized and the system benefit is improved.
In a preferred embodiment, the system further comprises: fill electric pile fault monitoring module, fill electric pile fault monitoring module specifically includes: the charging pile real-time state acquisition unit is configured to acquire the charging pile real-time state of each group of charging piles; the system comprises a charging pile real-time state updating unit, a clustering center updating unit and a clustering center updating unit, wherein the clustering center updating unit is configured to update a fuzzy membership value according to the charging pile real-time state and update a clustering center according to the updated fuzzy membership value; and the iteration updating unit is configured to obtain negotiation and decision results through iteration updating according to the updated clustering center.
In this embodiment, considering that a fault, a communication interruption or other abnormal conditions of a charging pile may occur in the charging pile network, the situation that the whole charging pile network is unstable occurs, the fuzzy membership value is updated by acquiring the real-time state of the charging pile of each group of charging piles, and then the clustering center is updated, and the system can gradually adjust the boundary and the characteristic of each state by continuously and iteratively updating the position of the clustering center, so that the system better matches with the actual data distribution. Finally, the state represented by the clustering center can reflect the state of the charging pile more accurately, so that the system is facilitated to judge the current state of the charging pile.
The cluster center position is updated according to the membership value of the data point in the iterative process of the algorithm. The clustering center updates the formula:
the cluster center update formula is to redefine the location of each cluster center by considering the membership of the data points to the cluster center to better fit the data distribution. Wherein V is j Representing the new position of the jth cluster center, u ij Is the membership value of the data point i to the cluster center j, X i Is the eigenvector of data point i and m is the ambiguity parameter. The clustering center updating formula is a key step for adjusting the position of the state class of the charging pile, and by updating the clustering center, the system can judge the state of the charging pile more accurately. For the iteration stop condition, in this embodiment, the iteration is ended according to a certain stop condition, for example, the maximum iteration number is reached or the change of the cluster center is smaller than a certain threshold.
Therefore, the charging pile fault monitoring module provided by the embodiment continuously monitors the changes of the behavior of the charging pile, the load of the power grid and the demands of users so as to dynamically adjust, generate a real-time charging strategy of each group of charging piles, and timely perform early warning and judgment when the charging piles are in fault, thereby achieving the effects of optimizing the power utilization of the power grid, improving the stability of the power system and reducing the cost.
The iteration updating unit specifically comprises: the target function updating subunit is configured to recalculate the target function based on the updated clustering center to obtain an updated target function; and the negotiation and decision result updating subunit is configured to update the individual optimal position, the global optimal position and the speed and position of each particle for a group of particles which are solved by using the updated objective function and minimize the objective function, and regenerate and control the charging pile state of each particle corresponding to the charging pile according to the updating result to obtain the updated negotiation and decision result.
In practical application, the embodiment obtains the real-time state of the charging piles of each group of charging piles to update the fuzzy membership value, and further updates the clustering center, which specifically includes:
1. initializing: a set of particles is randomly generated, each particle representing a charging pile state of a charging pile. Meanwhile, initializing a fitness function, wherein the fitness function comprises an objective function improved part and a charging pile fault module and is used for measuring the quality of charging behaviors.
2. The iteration starts: and entering an iteration loop, and setting iteration times or stopping conditions.
3. Collaborative negotiation mechanism: in each iteration, a co-negotiation mechanism is performed between charging piles, which can be achieved by modifying the speed update formula. The state and demand of the neighbor charging piles are considered to coordinate the charging behavior.
4. Objective function improvement: in the fitness function, parts of objective function improvement are included, such as considering grid load balance, user demand response, etc. The fitness function will take into account the charging behavior of the charging stake and calculate the fitness value from the improved part of the objective function.
5. And a charging pile fault module: and introducing a charging pile fault module, and detecting the fault state of the charging pile in each iteration. If a fault is found, the charging strategy needs to be adjusted accordingly to isolate the faulty charging pile and maintain the stability of the network.
6. Evaluation: for each particle, its fitness value is calculated, which measures the quality of the charging behaviour, while taking into account the effect of the fault state.
7. Updating individual best: for each particle, the fitness value of its current location is compared with its individual best fitness value (Pbest). If the current location is better, it is updated to the new individual's best location.
8. Updating the global best: the individual best fitness values of all particles are compared and the global best position (Gbest) is selected as the best solution in the whole population.
9. Update speed and location: the speed and position of each particle are updated according to a speed update formula, taking into account individual experience (Pbest), group synergy (Gbest), and charging pile fault status.
10. Convergence detection: it is checked whether the condition for stopping the iteration is fulfilled, e.g. the maximum number of iterations is reached or the fitness value is sufficiently close to the optimal solution.
11. Iteration: if the stopping condition is not met, continuing iteration, and repeating the steps 3 to 10.
12. Outputting a result: after stopping the iteration, outputting the found optimal solution or near optimal solution and the corresponding fitness value.
Therefore, the charging behavior of the charging pile is continuously optimized through the whole iteration process, so that the user requirements are met, the power grid load is optimized, the influence of the charging pile faults on the network is considered, and the system benefit and stability are improved.
Therefore, the multi-pile networking negotiation charging management system provided by the embodiment utilizes the particle swarm optimization algorithm and the negotiation cooperative mechanism to reasonably distribute the power in the power grid by collecting the historical state data of the charging piles and taking the minimized objective function as a condition, so that the stability and the reliability of the power grid can be improved, the problem of insufficient power can be solved, enough power supply can be ensured, the requirements of electric vehicle users can be met, and the usability and the convenience of the electric vehicle can be improved; meanwhile, the intelligent charging strategy is adopted, so that the power grid upgrading requirement can be reduced, the operation and maintenance cost is saved, the charging efficiency can be improved, the energy waste is reduced, and the environmental impact is reduced.
Referring to fig. 2, fig. 2 is a flow chart of a multi-pile networking negotiation charging management method according to an embodiment of the present invention.
As shown in fig. 2, a multi-pile networking negotiation charging management method includes the steps of:
s1: collecting historical state data of each group of charging piles in a target area;
s2: carrying out state classification on the charging piles according to the historical state data of each group of charging piles to obtain the charging pile state of each group of charging piles;
s3: according to the state of the charging piles of each group of charging piles, a particle swarm optimization algorithm and a negotiation cooperative mechanism are utilized, and a negotiation and decision result is obtained by solving under the condition of minimizing an objective function; wherein the objective function is a function of grid load, user demand and charging pile status;
s4: and generating a charging strategy for controlling each group of charging piles to execute charging action according to the negotiation and decision result.
In the embodiment, by collecting the historical state data of the charging pile, and utilizing a particle swarm optimization algorithm and a negotiation cooperative mechanism, the power in the power grid is reasonably distributed under the condition of minimizing an objective function, and early warning and discrimination are timely carried out when the charging pile fails, so that the effects of optimizing the power utilization of the power grid, improving the stability of a power system and reducing the cost are achieved.
The specific implementation of the multi-pile networking negotiation charging management method is basically the same as the embodiments of the multi-pile networking negotiation charging management system, and is not repeated here.
In describing embodiments of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "center", "top", "bottom", "inner", "outer", "inside", "outside", etc. indicate orientations or positional relationships based on the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention. Wherein "inside" refers to an interior or enclosed area or space. "peripheral" refers to the area surrounding a particular component or region.
In the description of embodiments of the present invention, the terms "first," "second," "third," "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first", "a second", "a third" and a fourth "may explicitly or implicitly include one or more such feature. In the description of the present invention, unless otherwise indicated, the meaning of "a plurality" is two or more.
In describing embodiments of the present invention, it should be noted that the terms "mounted," "connected," and "assembled" are to be construed broadly, as they may be fixedly connected, detachably connected, or integrally connected, unless otherwise specifically indicated and defined; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In the description of embodiments of the invention, a particular feature, structure, material, or characteristic may be combined in any suitable manner in one or more embodiments or examples.
In the description of the embodiments of the present invention, it is to be understood that "-" and "-" denote the same ranges of the two values, and the ranges include the endpoints. For example, "A-B" means a range greater than or equal to A and less than or equal to B. "A-B" means a range of greater than or equal to A and less than or equal to B.
In the description of embodiments of the present invention, the term "and/or" is merely an association relationship describing an association object, meaning that three relationships may exist, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A multi-stake networking negotiation charging management system, comprising:
the historical state data acquisition module is configured to acquire historical state data of each group of charging piles in the target area;
the charging pile state classification module is configured to perform state classification on the charging piles according to historical state data of each group of charging piles to obtain charging pile states of each group of charging piles;
the distributed negotiation and decision module is configured to obtain negotiation and decision results by solving the conditions of a minimum objective function by utilizing a particle swarm optimization algorithm and a negotiation coordination mechanism according to the state of the charging piles of each group of charging piles; wherein the objective function is a function of grid load, user demand and charging pile status;
and the charging execution module is configured to generate a charging strategy for controlling each group of charging piles to execute charging action according to the negotiation and decision result.
2. The multi-stake networking negotiation charging management system of claim 1, wherein the charging stake state classifying module comprises:
a historical state data processing unit configured to extract state features of the historical state data and calculate a confidence level of the charging pile according to the state features;
the charging pile state classification unit is configured to calculate confidence degree of the charging pile and fuzzy membership degree of a threshold value by using a fuzzy C-means method, and classify the states of each group of charging piles according to the fuzzy membership degree to obtain the charging pile state of each group of charging piles.
3. The multi-stake networking negotiation charging management system of claim 2, wherein the charging stake state classifying unit specifically comprises:
a classification definition subunit configured to define a feature vector of a data point as x= (X) 1 ,x 2 ,…,x N ) Defining the feature vector of the cluster center asV＝(v 1 ,v 2 ,…,v C ) The method comprises the steps of carrying out a first treatment on the surface of the N is the number of data points, C is the number of clustering centers, and each clustering center represents the state of one charging pile;
a fuzzy membership calculation subunit configured to determine a fuzzy membership matrix U = [ U ] ij ] N×C ]By Euclidean distance d ij :[d ij ＝||X i -V j || 2 ]Calculating fuzzy membership valueWherein X is i Is the eigenvector of the ith data point, V j Is the feature vector of the jth cluster center, m is an ambiguity parameter, generally takes an integer greater than 1, and t represents the iteration number;
and the charging pile state classification subunit is configured to take the clustering center with the largest fuzzy membership value as the charging pile state of the charging pile.
4. A multi-stake networking negotiation charging management system according to claim 2 or claim 3, wherein the historical state data comprises charging power and charging periods, the charging stake states comprising an idle state, a charging state, a fault state and a disabled state.
5. The multi-stake networking negotiation charging management system of claim 4, wherein the distributed negotiation and decision module comprises:
the objective function building unit is configured to build an objective function based on the power grid load, the user demand and the charging pile state;
a minimized objective function solving unit configured to find a set of particles that minimize the objective function using a particle swarm optimization algorithm, on the condition that the objective function is minimized;
and the negotiation and decision unit is configured to generate and control the state of the charging pile corresponding to each particle by using a negotiation collaboration mechanism based on the solved group of particles minimizing the objective function, so as to obtain a negotiation and decision result.
6. The multi-pile networking negotiation charging management system according to claim 5, wherein in the particle swarm optimization algorithm, the expression of the objective function is specifically:
wherein F (X) is an fitness function, and each particle X in the particle swarm is measured i Expressed as an objective function; x is X i The ith particle in the particle swarm represents the state of the charging pile; a, a i A target state or target value representing a target or desired value of the state of the charging pile; alpha is a weight related to the user demand and represents the influence degree of the user on the charging demand; u (t) is a user demand, and represents the charging demand of the user at time t; beta is a weight related to the state of the charging pile and represents the influence degree of the state of the charging pile on the charging requirement; s (t) is a charging pile state, and represents the charging pile state at the time t; delta is the weight related to the power grid load at the previous moment and represents the influence degree of the power grid load at the previous moment on the charging demand; p (t-1) is the grid load at the previous moment, and represents the grid load condition at the moment t-1; the E is the weight related to the state of the charging pile at the previous moment, and represents the influence degree of the state of the charging pile at the previous moment on the charging requirement; c (t-1) is the state of the charging pile at the previous time, and represents the state of the charging pile at time t-1.
7. The multi-pile networking negotiation charging management system according to claim 6, wherein the negotiation collaboration mechanism is utilized to generate an expression for controlling a charging pile state of each particle corresponding to the charging pile, specifically:
wherein, neighborBest i Representing the best position in the neighborhood of particle i;the speed of the particle i at the next time step determines the change of the charging pile in the charging behavior; ω is the inertial weight that affects how far the charging pile maintains the current speed; c 1 And c 2 Are learning factors which help the charging pile learn and adapt to different situations; r is (r) 1 And r 2 Is a random number for introducing randomness; pbest i Is the individual optimal position of the single charging pile charging power reached in the past schedule; gbes is the global optimal position of charging power in the whole charging pile system; />The position of particle i at the current time step represents the current state of the charging pile.
8. The multi-stake networking negotiation charging management system of claim 7, the system further comprising: fill electric pile fault monitoring module, fill electric pile fault monitoring module specifically includes:
the charging pile real-time state acquisition unit is configured to acquire the charging pile real-time state of each group of charging piles;
the system comprises a charging pile real-time state updating unit, a clustering center updating unit and a clustering center updating unit, wherein the clustering center updating unit is configured to update a fuzzy membership value according to the charging pile real-time state and update a clustering center according to the updated fuzzy membership value; the updating expression of the clustering center is specifically as follows:
wherein V is j Representing the new position of the jth cluster center, u ij Is the fuzzy membership value of the data point i to the clustering center j, X i Is the eigenvector of the data point i, m is the ambiguity parameter;
and the iteration updating unit is configured to obtain negotiation and decision results through iteration updating according to the updated clustering center.
9. The multi-stake networking negotiation charging management system of claim 8, wherein the iterative updating unit comprises:
the target function updating subunit is configured to recalculate the target function based on the updated clustering center to obtain an updated target function;
and the negotiation and decision result updating subunit is configured to update the individual optimal position, the global optimal position and the speed and position of each particle for a group of particles which are solved by using the updated objective function and minimize the objective function, and regenerate and control the charging pile state of each particle corresponding to the charging pile according to the updating result to obtain the updated negotiation and decision result.
10. The multi-pile networking negotiation charging management method is characterized by comprising the following steps of:
collecting historical state data of each group of charging piles in a target area;
carrying out state classification on the charging piles according to the historical state data of each group of charging piles to obtain the charging pile state of each group of charging piles;
according to the state of the charging piles of each group of charging piles, a particle swarm optimization algorithm and a negotiation cooperative mechanism are utilized, and a negotiation and decision result is obtained by solving under the condition of minimizing an objective function; wherein the objective function is a function of grid load, user demand and charging pile status;
and generating a charging strategy for controlling each group of charging piles to execute charging action according to the negotiation and decision result.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117875672A (en) * | 2024-03-11 | 2024-04-12 | 云南山高新能源有限公司 | Electric vehicle charging station management system |
| CN117885593A (en) * | 2024-03-14 | 2024-04-16 | 江苏智融能源科技有限公司 | Charging station data management and control method and system based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117875672A (en) * | 2024-03-11 | 2024-04-12 | 云南山高新能源有限公司 | Electric vehicle charging station management system |
| CN117875672B (en) * | 2024-03-11 | 2024-06-04 | 云南山高新能源有限公司 | Electric vehicle charging station management system |
| CN117885593A (en) * | 2024-03-14 | 2024-04-16 | 江苏智融能源科技有限公司 | Charging station data management and control method and system based on big data |
| CN117885593B (en) * | 2024-03-14 | 2024-05-24 | 江苏智融能源科技有限公司 | Charging station data management and control method and system based on big data |
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