Electric Vehicle Charging Scheduling Method Based on Time Cost and Microgrid Load Balancing

Abstract

The present invention discloses an electric vehicle charging scheduling method based on time cost and microgrid load balancing, comprising the following steps: Step 1, calculating the user time cost; Step 2, updating the load of the MG system during the period from the start of charging to the end of charging of the electric vehicle, and then obtaining the average load of the MG system according to the updated load of the MG system and the charging duration; Step 3, obtaining the total index of the i-th vehicle at the j-th charging station through normalization and weighting, and using the total index as an input to give an optimal charging scheduling strategy through the MTC-SLBMS algorithm. This method effectively reduces the charging time cost of EV users and optimizes the user charging experience. At the same time, it realizes the spatial load balancing among MMGs and improves the operation safety of the charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to an electric vehicle charging scheduling method based on time cost and microgrid load balancing. Background Art

[0002] In recent years, the global electric vehicle (EV) industry has shown a rapid development trend, and EV has become an important choice to replace traditional fuel vehicles. The rapid development of EV has brought significant impacts on the power system, especially in the load management of multi-microgrids (MMGs). Electric vehicles will generate higher power demand when charging in a centralized manner, which causes MMGs to bear greater load pressure in a short period of time. Especially in some urban areas with a high EV penetration rate, the problem of uneven load faced by MMGs is becoming increasingly prominent. Uneven load may cause local MG overload and affect power supply reliability. Therefore, effective solutions are needed to manage and balance the load of MMGs. If the charging behavior of EV users can be guided by effective strategies, EVs can become a tool for balancing the load between MMGs, which will greatly reduce the impact of centralized EV charging on MMGs. By formulating a new charging scheduling algorithm that meets the needs of EV users and balances the load differences between MMGs, the efficiency and reliability of the EV charging process can be ensured.

[0003] Specifically, the existing technology [1] Y. Xiang, J. Yang, X. Li, C. Gu, S. Zhang, Routing

[0004] optimization of electric vehicles for charging with event-driven pricing strategy, IEEE Transactions on Automation Science and Engineering 19(1) (2021) 7– 20. The optimal charging path planning for a single vehicle based on a road network model is disclosed. In addition, [2] X. Li, Y. Xiang, L. Lyu, C. Ji, Q. Zhang,

[0005] F. Teng, Y. Liu, "Price incentive-based charging navigation strategy for electric vehicles", IEEE Transactions on Industry Applications 56 (5) (2020) 5762–5774 proposed a large-scale vehicle scheduling algorithm to minimize the user's charging time cost based on Reference [1]. However, neither of them considered the impact of large-scale vehicles on the MMG load.

[0006] In addition, [3] X. Chen, H. Wang, F. Wu, Y. Wu, M. C. González, J. Zhang, "Multi-microgrid load balancing through ev charging networks", IEEE Internet of Things Journal 9 (7) (2021) 5019–5026 was also disclosed. A spatial load balancing algorithm for large-scale vehicle charging scheduling was proposed, but it did not consider the user's time cost, resulting in a high queuing time cost for users at local charging stations. Therefore, a new scheduling algorithm is proposed from both the user's perspective and the MMG's perspective to jointly optimize the user's time cost and the degree of MMG spatial load balancing. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention proposes an electric vehicle charging scheduling method based on time cost and microgrid load balancing. By integrating real-time traffic information, charging station information, and the load information of the MMG, a charging navigation strategy is accurately formulated for users, and the number of EVs accepted by each charging station is reasonably regulated. It effectively reduces the charging time cost of EV users and optimizes the user's charging experience. At the same time, the spatial load balancing between MMGs is achieved, and the operation safety of the charging station is improved.

[0008] To solve the above technical problems, the technical solution of the present invention is as follows:

[0009] An electric vehicle charging scheduling method based on time cost and microgrid load balancing, comprising the following steps:

[0010] Step 1, calculating the user's time cost, which is obtained by summing the on-road driving time cost of the electric vehicle to each charging station, the waiting time cost of the corresponding charging station, and the charging time cost;

[0011] Step 2: Determine the minimum load and maximum load of the MMG system at a certain moment. First, let the electric vehicles go to the charging stations with lower loads for charging. Assume that the th electric vehicle is going to the th charging station for charging. Update the load of the MG system during the charging period of the electric vehicle from the start of charging to the end of charging, and then obtain the average load of the MG system based on the updated load of the MG system and the charging duration.

[0012] Step 3: Combine the user time cost obtained in Step 1 and the average load of the MG system obtained in Step 2, and through normalization and weighting, obtain the total index of the th vehicle at the th charging station, and use the total index as the input to give the optimal charging scheduling strategy through the MTC - SLBMS algorithm.

[0013] Preferably, in Step 1, the estimation method of the driving time cost of the electric vehicle on the way to each charging station:

[0014] First, model the traffic network, and the traffic network model is expressed as , where is the set of all road nodes, is the set of all connected road sections, is the set of road weights,

[0015] When the th electric vehicle sends a charging request at moment, with the initial power state of , the electric vehicle dispatching center will record the location of the th electric vehicle. The driving speed of the electric vehicle on the road section at moment can be expressed as:

[0016]

[0017]

[0018] Among them, is the free - flow speed of the road section , represents the traffic capacity of the road section , is the traffic flow of the road section at moment, The ratio of to is the road saturation of the road section , ​ is the adaptive coefficient for different road levels, and the driving speed of the electric vehicle on different road sections is calculated using the speed-flow model , and the driving time of the electric vehicle on each road section is calculated through the following formula:

[0019]

[0020] where represents the distance from node to node , thus a time-weighted matrix can be constructed,

[0021]

[0022] where represents the estimated driving time from node to node .

[0023] Preferably, in step 1, the estimation method for the driving time cost of the electric vehicle on the way to each charging station further includes:

[0024] Using the Floyd algorithm, substituting the time-weighted matrix , and finally generating the node sequence included in the shortest path:

[0025]

[0026] wherein, each node in represents a specific road node on the driving path of the electric vehicle, is the starting node, i.e., the current position of the electric vehicle, while is the end node, i.e., the position of the charging station;

[0027] Thus, the distances to the charging stations in each MMG system are obtained. The distance between the th electric vehicle and each charging station can be represented by a vector R, and its definition is as follows:

[0028]

[0029] In the formula the distance from the th electric vehicle to the th charging station, N represents the number of electric vehicles in the MMG system, represents the number of charging stations in the MMG system. Therefore represents the total length of the road sections in the planned path:

[0030]

[0031] The cited speed - energy consumption model reflects the relationship between energy consumption and driving speed:

[0032]

[0033] In the formula 、 、 are the energy consumption per unit mileage of urban expressways, arterial roads, and secondary arterial roads respectively;

[0034] Furthermore, the driving energy consumption of the th electric vehicle to the th charging station is obtained :

[0035]

[0036] According to the driving energy consumption, the reachable range of the electric vehicle is determined, whether the remaining SoC of the electric vehicle supports reaching the target charging station is judged, and a candidate set is generated , finally, the starting point of each electric vehicle user is determined, and the updated time - weighted matrix is used to traverse the shortest - time path to each charging station, and a suitable charging station is recommended for the electric vehicle in the candidate set .

[0037] Preferably, in the traffic network model, the road section length is used as the weight of the traffic network, and then a set of road weights is obtained. The elements in the set of road weights are expressed as:

[0038]

[0039] Among them, represents the distance from the th point to the th point. Assuming that the roads are two - way connected, then .

[0040] Preferably, the candidate set is set with constraint conditions. The constraint conditions of the candidate set are expressed as: .

[0041] Preferably, in step 1, the calculation method of the queuing time cost is:

[0042] When the th electric vehicle is at the moment Submit a charging application and send the current location of the electric vehicle to the dispatching center. Assume that the electric vehicle is going to the th charging station for charging. Calculate the driving time on the way to the th charging station through the traffic network model. The arrival time of the electric vehicle at the station can be calculated as:

[0043]

[0044] The arrival time of the electric vehicle at the charging station and the start charging time satisfy:

[0045]

[0046]

[0047] In the formula, represents the waiting time for the electric vehicle to reach the charging station from node to the charging station . represents the estimated charging end time of the th charging pile at the th charging station. is the number of charging piles at the th charging station. represents the earliest charging end time of all charging piles at the th charging station. Determine the waiting time by calculating the difference between the earliest charging end time and the arrival time of the electric vehicle.

[0048] Preferably, in step 1, the charging time cost calculation method is:

[0049] Given that the initial power state of the st electric vehicle at time is , then the remaining SoC of the electric vehicle when it reaches the th charging station satisfies:

[0050]

[0051] Where is the st electric vehicle's energy consumption when going to the th charging station. After calculating the remaining SoC of the electric vehicle when it arrives at the station, substitute into the charging curve to calculate the charging duration .

[0052] Preferably, the charging duration and The relationship satisfies the following conditions:

[0053]

[0054]

[0055]

[0056] Traverse to find the expected waiting time of each power station , where For j, then sum the on-road time cost of the electric vehicle to each charging station, the corresponding waiting time cost, and the charging time cost, and record it as the total time cost :

[0057] .

[0058] Preferably, the load update method of the MG system is:

[0059]

[0060] where is the load value of the MG system where the th charging station is located at the th moment, is the initial load at the th moment, represents the moment when the th electric vehicle starts to connect to charge.

[0061] Preferably, in step 2, the average load is introduced to describe the load change of the MG system during the period from the start of charging of the electric vehicle connecting to the grid to the end of charging. Assume that when the th electric vehicle arrives at the th charging station, the average load expression of the MG system is as follows:

[0062] .

[0063] Preferably, in step 3, after normalizing the user time cost and the average load of the MG system, the weights are calculated by the entropy weight method, and then the total index of the th vehicle at the th charging station is obtained, so as to select the optimal charging station according to the total index :

[0064]

[0065]

[0066]

[0067] Among them, and are the weight coefficients calculated by the entropy weight method respectively.

[0068] The present invention has the following characteristics and beneficial effects:

[0069] Adopting the above technical solution, the present invention establishes an EV charging network model coupling MMG and traffic network. From the dual perspectives of users and MMG, it comprehensively considers the user's charging time cost and the load fluctuation of MMG during the charging process. This embodiment proposes a minimum time cost - load balancing strategy (MTC - SLBMS). This strategy aims to optimize the user experience and MMG stability simultaneously. Through effective scheduling, it realizes a win - win situation for users and the power grid. In the simulation study, in this embodiment, the MTC - SLBMS algorithm is compared with the traditional shortest path strategy (SDMS), minimum time cost strategy (MTMS), load balancing strategy (LBMS), and improved load balancing strategy (ILBMS). The results show that MTC - SLBMS can not only significantly reduce the user's charging time cost but also effectively balance the load fluctuation of MMG. This strategy retains the respective advantages of MTMS and ILBMS while overcoming their deficiencies, providing a more comprehensive solution. Specifically, MTC - SLBMS saves the user's time cost by dynamically adjusting the charging demand distribution and avoids the problem of grid overload during peak hours. Such a design not only improves the charging efficiency of EV users but also enhances the stability and reliability of MMG. The present invention provides a more comprehensive and efficient solution for large - scale electric vehicle charging scheduling and optimizes the overall performance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0071] Figure 1 It is a flowchart of the electric vehicle charging scheduling method based on time cost and micro - grid load balancing according to the embodiment of the present invention.

[0072] Figure 2 It is a schematic diagram for verifying the feasibility of the allocation strategy according to the embodiment of the present invention.

[0073] Figure 3 It is a comparison schematic diagram of the change of user time cost with the increase in the number of vehicles under different strategies.

[0074] Figure 4 It is a comparative schematic diagram of the change of the average valley-to-peak ratio with the increase in the number of vehicles under different strategies. Specific implementation manners

[0075] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments may be combined with each other.

[0076] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be understood as a limitation to the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present invention, unless otherwise specified, the meaning of "plurality" is two or more.

[0077] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected, or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood through specific situations.

[0078] The present invention provides an electric vehicle charging scheduling method based on time cost and microgrid load balancing. By establishing an EV charging network model that couples an MMG and a transportation network, from the dual perspectives of users and the MMG, comprehensively considering the user's charging time cost and the load fluctuation of the MMG, a new scheduling algorithm MTC-SLBMS is proposed, considering the influence of real factors such as traffic flow, queuing waiting time, and charging duration on large-scale EV charging scheduling. At the same time, the spatial load balancing degree between MMGs is also considered, and the two indicators of user time cost and MMG power are fused to achieve the purpose of common optimization.

[0079] Specifically, it is achieved through the following methods:

[0080] Step 1. User time cost

[0081] (1) Road travel time cost

[0082] First, a traffic network is modeled, and the traffic network model is expressed as , where is the set of all road nodes, is the set of all connected road segments, is the set of road weights. In this embodiment, the length of the road segment is used as the weight of the traffic network in the constructed traffic network model, so that the generated set of road weighted matrices The element in is expressed as:

[0083]

[0084] Among them, represents the distance from the point to the point. Assuming that the roads are all two-way connected, then . When the st electric vehicle issues a charging request at time, the initial battery state is , and the electric vehicle dispatching center will record the position of the th electric vehicle.

[0085] It can be understood that in the urban traffic network, the driving speed of electric vehicles is mainly affected by road capacity and traffic flow. Therefore, the driving speed of the electric vehicle on the road segment at time can be expressed as:

[0086]

[0087]

[0088] Among them, the free flow speed of the road segment , represents the traffic capacity of the road segment , the traffic flow of the road segment at time, the ratio of to is the road saturation at , , are adaptive coefficients for different road levels.

[0089] It should be noted that in the speed-flow model, roads are divided into urban expressways, arterial roads, and secondary roads. In this embodiment, the speed-flow model is used to calculate the driving speed of electric vehicles on different sections. , and the driving time of electric vehicles on each section is calculated through the following formula:

[0090]

[0091] where represents the distance from node to node , thus a time-weighted matrix can be constructed:

[0092]

[0093] where represents the estimated driving time from node to node .

[0094] Then, the Floyd algorithm is used, substituting the time-weighted matrix , to find the shortest driving time path between any two points, and finally generating the node sequence included in the shortest path:

[0095]

[0096] where each node in the sequence represents a specific road node on the driving path of the electric vehicle. The path passes through these nodes in sequence from the starting point to the ending point. is the starting node, that is, the current position of the electric vehicle, and is the ending node, that is, the location of the charging station.

[0097] Thus, the distance to the charging stations in each MMG system is obtained. The distance between the th electric vehicle and each charging station can be represented by a vector R, and its definition is as follows:

[0098]

[0099] In the formula the distance from the th electric vehicle to the th charging station, N represents the number of electric vehicles in the MMG system, represents the number of charging stations in the MMG system. Therefore represents the total length of the sections in the planned path:

[0100]

[0101] It is understandable that in the urban traffic network, the energy consumption per unit mileage of electric vehicles varies greatly under different traffic conditions. Therefore, in this embodiment, the speed - energy consumption model is cited to reflect the relationship between energy consumption and driving speed:

[0102]

[0103] In the formula , , are the energy consumption per unit mileage of the urban expressway, arterial road, and secondary arterial road respectively;

[0104] Furthermore, the driving energy consumption of the th electric vehicle to the th charging station is calculated :

[0105]

[0106] The reachable range of the electric vehicle is determined according to the driving energy consumption, and it is judged whether the remaining SoC of the electric vehicle supports reaching the target charging station, and a candidate set is generated, where the constraints of the candidate set can be expressed as:

[0107]

[0108] Finally, the starting point of each electric vehicle user is determined, and the updated time - weighted matrix is used to traverse the shortest - time path to each charging station, and a suitable charging station is recommended for the electric vehicle in the candidate set .

[0109] It is understandable that after each vehicle calculates the most suitable target charging station using the algorithm, it simultaneously generates the path with the shortest driving time of the vehicle. In the case of a large number of vehicles, there are already many vehicles driving towards their respective target charging stations before the vehicle being allocated at the current moment, so the traffic flow on the road will change, thus affecting the vehicle driving time. Therefore, each vehicle will use the updated time - weighted matrix at the current moment during the calculation.

[0110] (2)Queuing time cost

[0111] It is understandable that when allocating a suitable charging station for the user, the queuing waiting time and charging time at the station are also considered from the user's perspective. Considering the uncertainty of the arrival and departure times of EVs at the charging station, an estimated queuing queue is generated to calculate the expected waiting time after the vehicle arrives at the station.

[0112] When the th electric vehicle is at the moment Submit a charging application and send the current location of the electric vehicle to the dispatching center. Assume that the electric vehicle is going to the th charging station for charging. Calculate the driving time on the way to the th charging station through the traffic network model. The arrival time of the electric vehicle at the station can be calculated as:

[0113]

[0114] The arrival time and start charging time of the electric vehicle at the charging station satisfy:

[0115]

[0116]

[0117] In the formula, represents the waiting time for the electric vehicle to reach the charging station from node to the charging station . represents the estimated charging end time of the th charging pile at the th charging station. is the number of charging piles at the th charging station. represents the earliest charging end time of all charging piles at the th charging station. Determine the waiting time by calculating the difference between the earliest charging end time and the arrival time of the electric vehicle.

[0118] (3) Charging time cost

[0119] Given that the initial power state of the th electric vehicle at time is , then the remaining SoC of the electric vehicle when it reaches the th charging station satisfies:

[0120]

[0121] Among them is the th electric vehicle's energy consumption when going to the th charging station. After calculating the remaining SoC of the electric vehicle after arriving at the station, substitute it into the charging curve to calculate the charging duration .

[0122] Charging duration and satisfy the following conditions:

[0123]

[0124]

[0125]

[0126] Traverse to find the estimated waiting time for each power station , where For j, then sum the on-road time cost of the electric vehicle to each charging station, the corresponding waiting time cost, and the charging time cost, and denote it as the total time cost:

[0127] .

[0128] Step 2. Spatial load balancing

[0129] The minimum load and the maximum load existing in the MMG system at time are respectively denoted as and . It can be understood that vehicles are preferentially directed to charging stations with lower loads for charging, so as to achieve the purpose of "peak shaving and valley filling", make the MMG power tend to be stable, and ensure the safe operation of MMG.

[0130] Assume that the th electric vehicle goes to the th charging station for charging. The load update of MG during the period from the start of vehicle charging to the end of charging can be expressed as:

[0131]

[0132] Among them, is the load value of the MG system where the th charging station is located at time, is the initial load at time, represents the moment when the th electric vehicle starts to connect to the charging. represents the charging duration of the th EV at the th charging station.

[0133] Among them, the MG system refers to the single MG where the vehicle charges. In this embodiment, a charging station is used as an MG system.

[0134] It should be noted that when formulating the charging distribution strategy, the future load changes need to be comprehensively considered, rather than only based on the current or past instantaneous data. Therefore, the average load is introduced to describe the load change of MG during the period from the start of vehicle connection to the grid for charging to the end of charging. Assume that the When an electric vehicle arrives at the th charging station, the average load of the MG system is defined as:

[0135]

[0136] Average load The smaller it is, the lower the average load of the th MG during the charging of the th EV. In this way, the MG load change during the period from when the EV is connected to the grid for charging to the end of charging is fully estimated, rather than only considering the instantaneous power of the MG at the moment when the charging application is submitted.

[0137] Step 3. Selection of the optimal charging station

[0138] In order to balance the dual requirements of user cost and MMG stability, in this embodiment, the entropy weight method is used to integrate the user's time cost and the impact of EV charging on the MMG to achieve the purpose of joint optimization.

[0139] Specifically, in this embodiment, an optimization model is defined, which contains multiple weighting coefficients, and these coefficients reflect the relative importance between user cost and MMG impact. By adjusting these weighting coefficients, the relationship between user cost and MMG load can be flexibly balanced to ensure that while reducing the user's waiting time, the stability of the MMG is also improved. Among them, the entropy weight method is an objective weighting method based on information entropy, which is used to determine the weights of various indicators in multi-index evaluation. Assume that the user time cost of the th vehicle at the th charging station and the average load of the th MG are respectively denoted as and and after normalization. Then, through the entropy weight method, the weights and of and are calculated, so as to synthesize the two into a parameter, and the formula is as follows

[0140]

[0141] where is the total index of the th vehicle at the th charging station, and :

[0142]

[0143]

[0144]

[0145] MTC - SLBMS combines the dual considerations of time cost and spatial load balancing, which is beneficial to taking into account both the charging experience of EV users and the stability of MMG. Through the weighting factors and , a balance can be found between meeting the time requirements of EV users and the load balancing of MMG.

[0146] Furthermore, the effects of the above - mentioned technical solutions are verified through simulation:

[0147] The initial load used in the simulation (i.e., the load consumption when no EV is charging) is sourced from the actual data of California's power demand

[33] . After being scaled down, the data is randomly assigned to charging stations. The sampling interval of the data is 5 minutes. In the simulation, the total number of time samples = 288, and the sampling time window is 0:00 - 23:55. All N EVs send charging requests in chronological order within this time window. We explore the average time cost of users and the load - balancing effect under different matching strategies. The number of charging piles C in each charging station is set to 50, and the total number of vehicles N ranges from 150 to 1500.

[0148] Specifically, in this embodiment, taking a 34 - node traffic road network as an example, the feasibility of the new allocation strategy is verified. As Figure 1 shown, in an actual traffic network within a 20 km × 20 km city, this area contains 34 road network nodes and 54 road segments, and this area is divided into 7 MGs, each of which contains a charging station.

[0149] The time spent by users under different strategies is measured by the average user time cost. The definition is as follows:

[0150]

[0151] Among them, represents the average user time cost, represents the on - road driving time of the th vehicle, represents the queuing waiting time of the th vehicle, represents the The charging duration of a vehicle, and N represents the total number of vehicles. To measure the load balance degree among MMGs at time t, the valley-to-peak ratio is used as a performance indicator.

[0152] Among them, is defined as follows:

[0153]

[0154] The larger it is, the smaller the valley-to-peak gap among MMGs at time t, which is beneficial to the safe operation of the power grid. On this basis, the average valley-to-peak ratio is defined to measure the load balance degree among MMGs within the entire sampling window, and the definition is as follows:

[0155]

[0156] It should be noted that in this embodiment, the average time cost of users and the load balancing effect under different matching strategies are explored, and the number C of charging piles in each charging station is set to 50. Figure 2 The variation of the user time cost with the increase in the number of vehicles under different strategies is compared, Figure 3 and the variation of the average valley-to-peak ratio with the increase in the number of vehicles under different strategies is compared.

[0157] From Figure 2 and Figure 3 's analysis, it is observed that the MTC-SLBMS algorithm shows significant effects in reducing the average time cost of users and improving the average valley-to-peak ratio. Specifically, the TMMS algorithm does provide the lowest average time cost of users in an ideal environment, but its main defect lies in the uneven load distribution. In addition, although the ILBMS algorithm performs optimally in balancing the power grid load, it sacrifices the time cost of users, resulting in a significant increase in the average time cost of users. And the experimental data show that the MTC-SLBMS algorithm not only reduces the total waiting time of users in the system but also effectively improves the spatial load balance of the power grid. This balancing strategy makes the MTC-SLBMS algorithm more flexible and adaptable in practical applications, and can dynamically adjust resource allocation according to user needs and the actual operating conditions of the power grid. The implementation of this algorithm helps to relieve the pressure during peak hours of the power grid and reduce energy waste through more reasonable load distribution, thus supporting the sustainable development of the power grid.

[0158] The embodiments of the present invention have been described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the described embodiments. For those skilled in the art, various changes, modifications, substitutions, and variations can be made to these embodiments including components without departing from the principles and spirit of the present invention, and still fall within the protection scope of the present invention.

Claims

1. A method for scheduling electric vehicle charging based on time cost and microgrid load balancing, characterized in that: The steps include: Step 1: Calculate the user time cost. The user time cost is the sum of the travel time cost of the electric vehicle to each charging station, the waiting time cost of the corresponding charging station, and the charging time cost. The estimation method of the travel time cost of the electric vehicle to each charging station is as follows: First, a traffic network model is constructed. The traffic network model is represented by G=(V, E, D), where V is the set of all road nodes, E is the set of all connected road segments, and D is the set of road weights. When the nth electric car sends a charging request at time t, the initial power state is The electric vehicle dispatch center will record the location of the nth electric vehicle. At time t, the electric vehicle is on section e. ij Driving speed It can be expressed as: in, Section e ij The free flow speed, C ij Indicates road segment e ij The traffic capacity, q ij (t) Section e ij The traffic flow at time t, q ij (t) With C ij The ratio is the road saturation of the road section at time t, a, b, and m are the adaptive coefficients of different road levels, and the speed-flow model is used to calculate the driving speed v of electric vehicles on different road sections. ij , the driving time of electric vehicles on each road section is calculated by the following formula: where d ij Represents the distance from node i to node j, so that a time-weighted matrix T can be constructed, and the expression is as follows: Where T ij represents the estimated travel time from node i to node j; Step 2: Determine the minimum load and maximum load of the MMG system at a certain moment, and give priority to letting electric vehicles go to charging stations with lower loads for charging. Assuming that the nth electric vehicle is going to the jth charging station for charging, update the load of the MG system from the start to the end of charging, and then obtain the average load of the MG system based on the updated load and charging time of the MG system, where the MMG is composed of several single MG systems. Step 3: Combine the user time cost obtained in step 1 and the average load of the MG system obtained in step 2, obtain the total index of the nth vehicle at the jth charging station through normalization and weighting, and use the total index as input to give the optimal charging scheduling strategy through a minimum time cost-load balancing strategy. In step 3, after the user time cost and the average load of the MG system are normalized, the weight is calculated by the entropy weight method, and then the total index C of the nth vehicle at the jth charging station is obtained. n,j , thus selecting the optimal charging station j according to the total index: j=argmin(C n,1 ,…,C n,j ,…),j∈Sol n w1+w2=1 Among them, w1 and w2 are weight coefficients calculated by entropy weight method.

2. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 1 is characterized in that: In step 1, the method for estimating the travel time cost of the electric vehicle to each charging station further includes: Using the Floyd algorithm, substituting the time-weighted matrix T, we can finally generate the node sequence contained in the shortest path: L ij =(k1,k2,…k m ) Among them, L ij Each node in represents a specific road node on the driving path of the electric vehicle, k1 is the starting node, that is, the current position of the electric vehicle, and k m is the terminal node, i.e. the location of the charging station; Thus, the distance to the charging station in each MMG system can be calculated. The distance between the nth electric vehicle and each charging station can be represented by a vector R, which is defined as follows: Where R n,j The distance from the nth electric vehicle to the jth charging station, N represents the number of electric vehicles in the MMG system, N G represents the number of charging stations in the MMG system, so R n,j Expressed as the sum of the lengths of the segments in the planned path: The speed-energy consumption model used reflects the relationship between energy consumption and driving speed: Where ΔE f , ΔE m , ΔE se They are the energy consumption per unit mileage of urban expressways, main roads, and secondary roads; Then calculate the energy consumption ΔE of the nth electric car to the jth charging station n,j : Determine the reach of the electric vehicle based on the driving energy consumption, determine whether the remaining SoC of the electric vehicle can support reaching the target charging station, and generate the candidate set Sol n Finally, the starting point of each electric vehicle user is determined, and the updated time weighted matrix T is used to traverse the shortest time path to each charging station in the candidate set Sol n Recommend suitable charging stations for electric vehicles.

3. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 1 is characterized in that: In the traffic network model, the length of the road section is used as the weight of the traffic network, and then a set of road weights D is obtained. The element D in the set of road weights D is ij It is expressed as: Among them, d ij represents the distance from point i to point j. Assuming that the roads are bidirectionally connected, then d ij =d ji .

4. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 2 is characterized in that: The candidate set Sol n Set constraints, candidate set Sol n The constraints are expressed as:

5. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 1 is characterized in that: In step 1, the calculation method of the queuing time cost is: When the nth electric car applies for charging at time t, it sends the current location of the electric car to the dispatch center. Assuming that it needs to go to the jth charging station for charging, the travel time to reach the jth charging station is calculated through the traffic network model. The arrival time of electric vehicles can be calculated as: The arrival time of electric vehicles at the charging station and the start time of charging meet the following requirements: In the formula, represents the waiting time for an electric vehicle to reach charging station j from node n, represents the estimated charging end time of the pth charging pile at the jth charging station, where p is the number of charging piles at the jth charging station. It represents the earliest charging end time of all charging piles in the jth charging station. The waiting time is determined by calculating the difference between the earliest charging end time and the arrival time of the electric vehicle.

6. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 5 is characterized in that: In step 1, the charging time cost calculation method is: It is known that the initial power state of the nth electric car at time t is The remaining SoC of the electric vehicle when it arrives at the jth charging station is satisfy: where ΔE n,j is the energy consumption of n electric vehicles going to the jth charging station. After calculating the remaining SoC of the electric vehicle after arriving at the station, Substitute into the charging curve to calculate the charging time 7. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 6 is characterized in that: The charging time and The relationship satisfies the following conditions: SOC(t)=1.0+t -mt -(1+k)e -zt Traverse and find the estimated waiting time for each power station where j∈Sol n j, then add the time cost of the electric vehicle on the way to each charging station, the corresponding waiting time cost and the charging time cost, and record it as the total time cost 8. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 7 is characterized in that: The load update method of the MG system is: Among them, P j (t) is the load value of the MG system where the jth charging station is located at time t, is the initial load at time t, Indicates the time when the nth electric car starts to connect to charge.

9. The electric vehicle charging scheduling method based on time cost and microgrid load balancing according to claim 8 is characterized in that: In step 2, the average load is introduced to describe the load change of the MG system from the time when the electric vehicle is connected to the grid and starts charging to the time when the charging ends. Assuming that when the nth electric vehicle arrives at the jth charging station, the average load expression of the MG system is as follows:

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