Vehicle-station-network interaction method considering response willingness of electric vehicle user

Abstract

The invention relates to a vehicle-station-network interaction method considering the response willingness of an electric vehicle user, which fully excavates the flexible adjustment capability of a light storage charging station and the electric vehicle user, and comprises the following steps: step 1, establishing a station-network interaction optimization model considering electric energy-standby coupling, deriving to obtain a node electric energy marginal electricity price DLMP reflecting the electric energy marginal cost of each node of the power distribution network and a node standby marginal electricity price RDLMP reflecting the standby marginal cost; step 2, then, according to the willingness of the electric vehicle user, establishing an excitation-response mechanism between the optical storage charging station and the electric vehicle user; step 3, establishing an electric vehicle user willingness-incentive joint optimization model on the basis of the above steps, and forming vehicle-station-network multi-agent interaction optimization; the method has the advantages that the excitation-response mechanism is effectively utilized, the flexible adjustment capability is fully excavated, and the economical efficiency and the scheduling flexibility are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle-station-network interaction, and in particular relates to a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users. Background Art

[0002] In recent years, the problem of global warming and the energy crisis have become increasingly severe, and the concept of "green, low-carbon, energy-saving first" has been deeply rooted in the hearts of the people. As a clean energy means of transportation, the number of electric vehicles has grown rapidly with the support of policies of various countries. At the same time, the scale of distributed photovoltaic and other new energy power generation has developed rapidly, which has brought huge pressure on the distribution network. In this context, the integrated photovoltaic and storage charging station that couples new energy power generation, energy storage and electric vehicle charging has received widespread attention. It can not only promote the local consumption of photovoltaic power generation, but also use the flexible adjustment ability of energy storage to alleviate the regulation pressure of the distribution network and improve the operation economy and reliability of the distribution network. At present, in terms of vehicle-station-network interaction methods, existing studies mostly use the interaction cost of electric energy as the basis. The core of establishing an interactive electricity price mechanism ignores the backup costs caused by system uncertainty, makes it difficult to effectively guide the uncertainty management of photovoltaic and storage charging stations, and fails to give full play to the flexible adjustment capabilities of on-site energy storage. At the same time, existing research does not consider the willingness of electric vehicle users to participate in demand response, nor does it consider the diversified incentive measures set by photovoltaic and storage charging stations to improve user response willingness. Therefore, it is difficult to maximize the demand response capabilities of electric vehicle users and achieve friendly interaction between vehicles, stations, and networks. Therefore, it is very necessary to provide a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users, which effectively utilizes the incentive-response mechanism, fully taps the flexible adjustment capabilities, and improves economy and scheduling flexibility. Summary of the invention

[0003] 1. Technical issues

[0004] In view of the above-mentioned existing technical status, this application mainly addresses the following technical problems:

[0005] 1. Existing studies mostly establish interactive electricity price mechanisms based on the interactive cost of electric energy, ignoring the backup costs caused by system uncertainty. It is difficult to effectively guide the uncertainty management of photovoltaic storage charging stations and fail to give full play to the flexible adjustment capabilities of energy storage in the station;

[0006] 2. Existing research has not considered the willingness of electric vehicle users to participate in demand, nor has it considered the diversified incentives set up by solar-storage charging stations to improve user response willingness. Therefore, it is difficult to maximize the demand response capabilities of electric vehicle users and achieve friendly interaction between vehicle, station and network.

[0007] (II) Technical solution

[0008] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a vehicle-station-network interaction method that effectively utilizes the incentive-response mechanism, fully taps the flexible adjustment capability, improves economy and scheduling flexibility, and takes into account the response willingness of electric vehicle users.

[0009] The object of the present invention is achieved by: a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users, fully tapping the flexible adjustment capabilities of photovoltaic charging stations and electric vehicle users, the method comprising the following steps:

[0010] Step 1: A station-grid interaction optimization model considering the coupling of energy and reserve is established, and the node energy marginal price DLMP reflecting the energy marginal cost of each node in the distribution network and the node reserve marginal price RDLMP reflecting the reserve marginal cost are derived;

[0011] Step 2: Then, based on the willingness of electric vehicle users, an incentive-response mechanism between the solar-storage charging station and electric vehicle users is established;

[0012] Step 3: Based on the above steps, a joint optimization model of electric vehicle user willingness and incentives is established to form a multi-agent interactive optimization of vehicle-station-network.

[0013] Furthermore, the step 1 specifically includes the following steps:

[0014] Step 1.1: The distribution network initializes DLMP and RDLMP and publishes them to each PV-storage charging station;

[0015] Step 1.2: The solar-storage charging station issues incentive signals to electric vehicle users based on the electricity price information released by the distribution network, and influences the willingness of electric vehicle users to participate in demand response through incentive signals, guiding them to provide services;

[0016] Step 1.3: After receiving the incentive signal, the electric vehicle user determines the route selection plan based on his / her willingness and takes the minimum total energy consumption as the goal, and reports the demand response plan to the solar storage charging station;

[0017] Step 1.4: The photovoltaic charging station comprehensively considers the internal distributed photovoltaic output, the uncertainty of the load within the station, and the demand response plan reported by the electric vehicle users, reduces the operating cost by optimizing the charging and discharging power and backup plan of the energy storage within the station, and updates the incentive signal for electric vehicle users;

[0018] Step 1.5: Return to step 1.3 until the percentage difference between the incentive values ​​of the two iterations is less than the convergence threshold; then, the photovoltaic storage charging station reports its overall power interaction plan and backup demand with the distribution network in each period;

[0019] Step 1.6: The distribution network determines the robust economic dispatch scheme based on the reported information of each photovoltaic storage charging station, combined with its own backup demand, distribution network operation constraints and operation constraints of each power generation unit, and derives the DLMP and RDLMP based on the robust economic dispatch model;

[0020] Step 1.7: Return to step 1.2 until the difference in electricity prices between the previous and next two iterations is less than the convergence threshold.

[0021] Furthermore, the willingness of electric vehicle users to participate in demand response in step 1.2 is specifically as follows: the psychology of electric vehicle users in each period can be divided into two willingness states, namely, willingness to participate in demand response and unwillingness to participate in demand response. The value of the willingness of electric vehicle users is defined as the probability that the user is willing to participate in demand response. The willingness state of electric vehicle users is a random variable and obeys the following binomial distribution: p i ~B(1,σ i ),i=1,2,...,N EV (1), where N EV is the total number of electric vehicle users; p i represents the willingness state of user i; σ i represents the willingness of user i.

[0022] Furthermore, the incentive-response mechanism between the solar-storage charging station and the electric vehicle user established in step 2 specifically includes three types of incentives: charging discount incentive, discharging discount incentive and path compensation incentive.

[0023] Furthermore, the charging discount incentive is specifically: after considering the charging discount incentive, the charging cost of the electric vehicle is as follows: In the formula, represents the charging cost of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the charging discount incentive provided by solar-storage charging station n at time t; is the charging cost coefficient of the photovoltaic storage charging station n; is the charging power of the i-th electric vehicle at the photovoltaic charging station n at time t; Δt is the duration of the electric vehicle participating in the demand response; That is, the charging amount of the i-th electric vehicle at the photovoltaic charging station n; is the DLMP at the node to which the solar-storage charging station n is connected at the moment.

[0024] Furthermore, the discharge discount incentive is specifically: after considering the discharge discount incentive, the discharge benefits of the electric vehicle are as follows: In the formula, represents the discharge benefit of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the discharge discount incentive provided by the solar-storage charging station n at time t; is the discharge cost coefficient of the photovoltaic storage charging station n; is the discharge power of the i-th electric vehicle at the photovoltaic charging station n at time t; That is, the discharge amount of the i-th electric vehicle at the solar storage charging station n.

[0025] Furthermore, the path compensation incentive is specifically: after considering the path compensation incentive, the travel energy consumption cost of the electric vehicle is as follows: In the formula, represents the energy consumption cost of the trip of the i-th electric vehicle to the photovoltaic charging station n at time t; represents the path compensation incentive provided by the solar-storage charging station n at time t; It is the total energy consumption of the i-th electric vehicle traveling to the photovoltaic charging station n to participate in demand response at time t-1.

[0026] Furthermore, the vehicle-station-network multi-agent interactive optimization model in step 3 specifically includes: an electric vehicle user demand response model, a photovoltaic storage charging station operation optimization model considering user response, and a distribution network robust economic dispatch model.

[0027] Furthermore, the electric vehicle user demand response model is specifically as follows: assuming that each electric vehicle user can participate in demand response at most once a day, the number of electric vehicles willing to go to the solar storage charging station to participate in demand response at each moment can be calculated, as shown in the following formula: Where N CS Indicates the number of solar-storage charging stations; Indicates t 1 The number of electric vehicles participating in the demand response of the photovoltaic and storage charging station n at all times; To characterize t 1 At the moment, the solar energy storage charging station n releases the state variable of the incentive type; and They represent the situation after considering the path compensation excitation, t 1 The willingness of electric vehicles to participate in the demand response of the photovoltaic storage charging station at n at the moment; It represents the number of electric vehicles participating in the demand response of photovoltaic charging station n at time t.

[0028] Furthermore, the operation optimization model of the photovoltaic charging station considering user response is specifically as follows: the photovoltaic charging station considers the uncertainty of the net load demand in the station, and based on DLMP and RDLMP, as well as the demand response of electric vehicle users, optimizes the charging and discharging power, backup power and three incentive signals of the energy storage in the photovoltaic charging station to obtain a solution with the minimum expected cost. Its objective function is as follows: In the formula, and They represent the total operating cost and optimization decision variables of the photovoltaic storage charging station n after considering the demand response of electric vehicles; and They represent the energy storage dispatching cost of the photovoltaic storage charging station and its interaction cost with the power and backup of the distribution network; represents the cost of providing incentives to electric vehicle users by the solar-storage charging station; S n represents the number of scenes; s is the scene number; ρ n,s is the scenario probability; T represents the optimization period; t represents the time period; and They represent the downward reserve and upward reserve of energy storage under scenario s respectively; γ represents the unit charging and discharging cost of energy storage; and They represent the charging power and discharging power of energy storage in scenario s respectively; η cha and η dis Respectively represent the charging and discharging efficiency of energy storage; and They represent the active DLMP and RDLMP at the node connected to the photovoltaic charging station n at time t respectively; and They respectively represent the active power and standby power purchased by the solar-storage charging station from the distribution network at time t.

[0029] (III) Beneficial effects

[0030] 1. The method of the present invention can effectively use the incentive-response mechanism to guide the PV charging station and electric vehicle users to interact flexibly, and fully tap the potential of electric vehicles in assisting the PV charging station to store energy for peak load shifting and standby regulation; compared with the station-grid interaction scenario, the vehicle-station-grid multi-agent interaction optimization improves the economy and scheduling flexibility of the distribution network and PV charging station;

[0031] 2. In the method of the present invention, the photovoltaic charging station influences the willingness of electric vehicle users through a variety of incentive signals, so that electric vehicle users can obtain certain benefits while participating in demand response, providing certain experience for flexible resources such as electric vehicles to participate in demand response, which is conducive to further exerting the dispatchable potential of demand-side resources and supporting the economic operation of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a diagram of the vehicle-station-network multi-agent flexible interaction architecture of the present invention.

[0033] Figure 2 It is a schematic diagram of the user demand response characteristics of an electric vehicle according to the present invention.

[0034] Figure 3 Schematic diagram of the relationship between user willingness and incentives in the present invention.

[0035] Figure 4 This is a road network topology diagram of the present invention.

[0036] Figure 5 This is a schematic diagram of the excitation signal of the 2-node photovoltaic charging station of the present invention.

[0037] Figure 6 This is a schematic diagram of the excitation signal of the 12-node photovoltaic storage charging station of the present invention.

[0038] Figure 7 This is a schematic diagram of the excitation signal of the 34-node photovoltaic storage charging station of the present invention.

[0039] Figure 8 It is a schematic diagram of the marginal electricity price of the node where the photovoltaic storage charging station 3 of the present invention is located. DETAILED DESCRIPTION

[0040] The present invention is further described below in conjunction with embodiments and / or drawings.

[0041] Example 1

[0042] like Figure 1-8 As shown, a vehicle-station-network interaction method considering the response willingness of electric vehicle users is provided to fully exploit the flexible adjustment capabilities of photovoltaic charging stations and electric vehicle users. The method comprises the following steps:

[0043] Step 1: A station-grid interaction optimization model considering the coupling of energy and reserve is established, and the node energy marginal price DLMP reflecting the energy marginal cost of each node in the distribution network and the node reserve marginal price RDLMP reflecting the reserve marginal cost are derived;

[0044] In the present invention, the step 1 specifically includes the following steps:

[0045] Step 1.1: The distribution network initializes DLMP and RDLMP and publishes them to each PV-storage charging station;

[0046] Step 1.2: The solar-storage charging station issues incentive signals to electric vehicle users based on the electricity price information released by the distribution network, and influences the willingness of electric vehicle users to participate in demand response through incentive signals, guiding them to provide services;

[0047] Step 1.3: After receiving the incentive signal, the electric vehicle user determines the route selection plan based on his / her willingness and takes the minimum total energy consumption as the goal, and reports the demand response plan to the solar storage charging station;

[0048] Step 1.4: The photovoltaic charging station comprehensively considers the internal distributed photovoltaic output, the uncertainty of the load within the station, and the demand response plan reported by the electric vehicle users, reduces the operating cost by optimizing the charging and discharging power and backup plan of the energy storage within the station, and updates the incentive signal for electric vehicle users;

[0049] Step 1.5: Return to step 1.3 until the percentage difference between the incentive values ​​of the two iterations is less than the convergence threshold; then, the photovoltaic storage charging station reports its overall power interaction plan and backup demand with the distribution network in each period;

[0050] Step 1.6: The distribution network determines the robust economic dispatch scheme based on the reported information of each photovoltaic storage charging station, combined with its own backup demand, distribution network operation constraints and operation constraints of each power generation unit, and derives the DLMP and RDLMP based on the robust economic dispatch model;

[0051] Step 1.7: Return to step 1.2 until the difference in electricity prices between the previous and next two iterations is less than the convergence threshold.

[0052] In the present invention, the willingness of electric vehicle users to participate in demand response in step 1.2 is specifically: the psychology of electric vehicle users in each period can be divided into two willingness states, namely, willingness to participate in demand response and unwillingness to participate in demand response. The value of the willingness of electric vehicle users is defined as the probability that the user is willing to participate in demand response. The willingness state of electric vehicle users is a random variable and obeys the following binomial distribution: p i ~B(1,σ i ),i=1,2,...,N EV (1), where N EV is the total number of electric vehicle users; p i represents the willingness state of user i, 1 means participating in demand response, and 0 means not participating in demand response; σ i represents the willingness of user i, σ i ∈[0,1].

[0053] The demand response characteristics of electric vehicle users are as follows: Figure 2 As shown in the figure, a piecewise linear function is used to characterize the relationship between electric vehicle user willingness and incentives, which is divided into three stages.

[0054] ① Phase 1: When the incentive level is lower than the dead zone, the user’s willingness is 0 and they are unwilling to participate in demand response.

[0055] ② Stage 2: When the incentive level is higher than the dead zone and lower than the saturation incentive value, as the incentive level increases, the willingness increases linearly from 0, and the number of electric vehicle users participating in demand response gradually increases.

[0056] ③ Stage 3: When the incentive level is higher than the saturation incentive value, the user's willingness also reaches saturation. At this time, even if the incentive level continues to increase, the willingness of electric vehicle users will not increase accordingly.

[0057] Figure 2 In max Indicates the maximum value of electric vehicle user willingness; and Represent the dead zone excitation value and saturation excitation value respectively.

[0058] Step 2: Then, based on the willingness of electric vehicle users, an incentive-response mechanism between the solar-storage charging station and electric vehicle users is established;

[0059] Step 3: Based on the above steps, a joint optimization model of electric vehicle user willingness and incentives is established to form a multi-agent interactive optimization of vehicle-station-network.

[0060] The present invention is a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users. In use, the method of the present invention can effectively use the incentive-response mechanism to guide the photovoltaic charging station and the electric vehicle users to interact flexibly, and fully tap the potential of electric vehicles in assisting the photovoltaic charging station's on-site energy storage to perform peak shaving and valley filling and standby regulation; compared with the station-network interaction scenario, the vehicle-station-network multi-subject interaction optimization improves the economy of the distribution network and the photovoltaic charging station and the flexibility of scheduling; in the method of the present invention, the photovoltaic charging station affects the willingness of electric vehicle users through a variety of incentive signals, so that electric vehicle users can obtain certain benefits while participating in demand response, and provide certain experience for flexible resources such as electric vehicles to participate in demand response, which is conducive to further exerting the dispatchable potential of demand-side resources and supporting the economic operation of the distribution network; the present invention has the advantages of effectively utilizing the incentive-response mechanism, fully tapping the flexible adjustment capability, and improving the economy and scheduling flexibility.

[0061] Example 2

[0062] like Figure 1-8 As shown, a vehicle-station-network interaction method considering the response willingness of electric vehicle users fully exploits the flexible adjustment capabilities of photovoltaic charging stations and electric vehicle users, characterized in that the method comprises the following steps:

[0063] Step 1: A station-grid interaction optimization model considering the coupling of energy and reserve is established, and the node energy marginal price DLMP reflecting the energy marginal cost of each node in the distribution network and the node reserve marginal price RDLMP reflecting the reserve marginal cost are derived;

[0064] Step 2: Then, based on the willingness of electric vehicle users, an incentive-response mechanism between the solar-storage charging station and electric vehicle users is established;

[0065] In the present invention, an incentive-response mechanism is established between a photovoltaic storage charging station and an electric vehicle user, which specifically includes three incentives: a charging discount incentive, a discharging discount incentive, and a path compensation incentive.

[0066] ① Charging discount incentives: When the photovoltaic output level in the photovoltaic storage charging station is higher than the load demand in the station, charging discount incentives are issued to guide electric vehicle users to charge, assist the photovoltaic storage charging station to realize on-site photovoltaic consumption, and reduce the standby demand to a certain extent.

[0067] After taking into account the charging discount incentive, the charging cost of an electric vehicle is as follows: In the formula, represents the charging cost of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the charging discount incentive provided by the solar-storage charging station n at time t, satisfying is the charging cost coefficient of the photovoltaic storage charging station n; is the charging power of the i-th electric vehicle at the photovoltaic charging station n at time t; Δt is the duration of the electric vehicle participating in the demand response; That is, the charging amount of the i-th electric vehicle at the photovoltaic charging station n; is the DLMP at the node to which the solar-storage charging station n is connected at the moment.

[0068] ②Discharge discount incentive: When the load demand in the photovoltaic storage charging station is higher than the photovoltaic output, a discharge discount incentive is issued to guide electric vehicle users to discharge.

[0069] After considering the discharge discount incentive, the discharge benefits of electric vehicles are as follows: In the formula, represents the discharge benefit of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the discharge discount incentive provided by the solar-storage charging station n at time t, satisfying is the discharge cost coefficient of the photovoltaic storage charging station n; is the discharge power of the i-th electric vehicle at the photovoltaic charging station n at time t; That is, the discharge amount of the i-th electric vehicle at the solar storage charging station n.

[0070] ③ Path compensation incentive: In order to further increase the willingness of electric vehicle users, the solar-storage charging station provides additional path compensation incentives, so that the energy consumption of electric vehicles participating in demand response can be compensated to a certain extent.

[0071] After considering the path compensation incentive, the energy consumption cost of an electric vehicle is as follows: In the formula, represents the energy consumption cost of the trip of the i-th electric vehicle to the photovoltaic charging station n at time t; represents the path compensation incentive provided by the photovoltaic charging station n at time t, satisfying It is the total energy consumption of the i-th electric vehicle traveling to the photovoltaic charging station n to participate in demand response at time t-1.

[0072] Electric vehicle user benefits: Compared with no incentive signal, the total cost that can be saved by the i-th electric vehicle participating in demand response at the photovoltaic storage charging station n at time t is: In the formula, They respectively represent the user cost savings brought about by charging discount incentives, discharging discount incentives and path compensation incentives.

[0073] Step 3: Based on the above steps, a joint optimization model of electric vehicle user willingness and incentives is established to form a multi-agent interactive optimization of vehicle-station-network.

[0074] The present invention is a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users. In use, the method of the present invention can effectively use the incentive-response mechanism to guide the photovoltaic charging station and the electric vehicle users to interact flexibly, and fully tap the potential of electric vehicles in assisting the photovoltaic charging station's on-site energy storage to perform peak shaving and valley filling and standby regulation; compared with the station-network interaction scenario, the vehicle-station-network multi-subject interaction optimization improves the economy of the distribution network and the photovoltaic charging station and the flexibility of scheduling; in the method of the present invention, the photovoltaic charging station affects the willingness of electric vehicle users through a variety of incentive signals, so that electric vehicle users can obtain certain benefits while participating in demand response, and provide certain experience for flexible resources such as electric vehicles to participate in demand response, which is conducive to further exerting the dispatchable potential of demand-side resources and supporting the economic operation of the distribution network; the present invention has the advantages of effectively utilizing the incentive-response mechanism, fully tapping the flexible adjustment capability, and improving the economy and scheduling flexibility.

[0075] Example 3

[0076] like Figure 1-8 As shown, a vehicle-station-network interaction method considering the response willingness of electric vehicle users fully exploits the flexible adjustment capabilities of photovoltaic charging stations and electric vehicle users, characterized in that the method comprises the following steps:

[0077] Step 1: A station-grid interaction optimization model considering the coupling of energy and reserve is established, and the node energy marginal price DLMP reflecting the energy marginal cost of each node in the distribution network and the node reserve marginal price RDLMP reflecting the reserve marginal cost are derived;

[0078] Step 2: Then, based on the willingness of electric vehicle users, an incentive-response mechanism between the solar-storage charging station and electric vehicle users is established;

[0079] Step 3: Based on the above steps, a joint optimization model of electric vehicle user willingness and incentives is established to form a multi-agent interactive optimization of vehicle-station-network.

[0080] In the present invention, 1. User willingness model: based on Figure 2 The incentive-response mechanism shown in Figure 2 shows that after the solar-storage charging station issues the above three incentives, the willingness of electric vehicle users is as follows: Figure 3 shown.

[0081] Figure 3 middle, and Respectively represent the dead zone excitation values ​​of charging excitation and discharging excitation; and Respectively represent the saturation excitation values ​​of charging excitation and discharging excitation; and They represent the maximum willingness of electric vehicle users to participate in the charging demand response and discharging demand response of the photovoltaic storage charging station n without path compensation incentives; and They respectively represent the maximum willingness of electric vehicle users to participate in the charging demand response and discharging demand response of the solar-storage charging station n after considering the path compensation incentive.

[0082] Curves 1 and 2 represent the relationship between the user's willingness to participate in charging demand response and charging incentives when the path compensation incentive is not considered and is considered, respectively; curves 3 and 4 represent the relationship between the user's willingness to participate in discharging demand response and discharging incentives when the path compensation incentive is not considered and is considered, respectively.

[0083] By comparing curves 1, 3 or 2, 4, it can be seen that electric vehicle users are more willing to respond to charging demand than to discharging demand because they are concerned about the discharge life of the vehicle battery; by comparing curves 1, 2 or 3, 4, it can be seen that path compensation incentives will not change the dead zone incentive value and saturation incentive value of charging and discharging incentives, but can increase the upper limit of users' willingness to participate in demand response.

[0084] According to the above parameters, the second slope values ​​of the four function curves can be expressed as follows: According to formula (6), the four piecewise function curves can be expressed as:

[0085] In the formula, and They represent the willingness of electric vehicles to participate in the demand response of the photovoltaic storage charging station n at time t without path compensation incentive; and They respectively represent the willingness of electric vehicles to participate in the demand response at the photovoltaic charging station n at time t after considering the path compensation incentive.

[0086] 2. Vehicle-station-network interaction optimization model, specifically including: electric vehicle user demand response model, photovoltaic storage charging station operation optimization model considering user response, and distribution network robust economic dispatch model.

[0087] ① Electric vehicle user demand response model: Since frequent participation in demand response will increase the number of times electric vehicle batteries are charged and discharged, affecting the service life of the on-board batteries, it is set that each electric vehicle user can only participate in demand response once a day; Figure 3 Curves 2 and 4 can be used to calculate the number of electric vehicles willing to go to the photovoltaic charging station to participate in demand response at each moment, as shown in the following formula: Where N CS Indicates the number of solar-storage charging stations; Indicates t 1 The number of electric vehicles participating in the demand response of the photovoltaic and storage charging station n at all times; To characterize t 1 The state variable of the type of incentive issued by the photovoltaic charging station n at time instant, a value of 1 indicates that the photovoltaic charging station issues a discharge incentive, and a value of 0 indicates that the photovoltaic charging station issues a charging incentive; and They represent the situation after considering the path compensation excitation, t 1 The willingness of electric vehicles to participate in the demand response of the photovoltaic storage charging station at n at the moment; It represents the number of electric vehicles participating in the demand response of photovoltaic charging station n at time t.

[0088] Formula (11) represents t 1 The total number of electric vehicles that are willing to participate in the demand response of all solar-energy storage charging stations at all times; electric vehicle users decide to go to the solar-energy storage charging station with the least energy consumption to participate in demand response based on the distance from the location of the solar-energy storage charging station at that moment, in order to maximize economic efficiency.

[0089] Electric vehicle users need to consider the following constraints: 1) Charging and discharging power constraints: In the formula, To represent the charging and discharging state variable of the i-th electric vehicle at the photovoltaic charging station n at time t, a value of 1 indicates that the electric vehicle is discharging, and a value of 0 indicates charging; and The maximum discharge and charge power of electric vehicle batteries.

[0090] 2) Power Constraints: In the formula, and are the charging and discharging amounts of the i-th electric vehicle at the photovoltaic charging station n at time t respectively; represents the remaining power of the i-th electric vehicle at the photovoltaic charging station n at time t; η EV,cha and η EV,dis The charging and discharging efficiency of electric vehicle batteries; E EV Indicates the battery capacity of an electric vehicle; and They represent the minimum and maximum SOC values ​​of the electric vehicle batteries participating in demand response.

[0091] In addition, to ensure that the electric vehicle has sufficient power to support it to reach the target node, the electric vehicle demand response dead zone SOC constant is set If the initial SOC of the battery of an electric vehicle at a certain moment is higher than Electric vehicle users can ignore the impact of the battery's real-time power on participating in demand response; if the battery's initial SOC is lower than The electric vehicle user will give up participating in the demand response of the photovoltaic charging station at that moment; similarly, after the electric vehicle completes the demand response at a certain moment, in order to ensure its subsequent journey, the battery SOC cannot be lower than

[0092] According to the above model, the total amount of electric vehicle charging and discharging and the total amount of path loss at the photovoltaic charging station n at each moment can be obtained as follows:

[0093] ②Optimization model of photovoltaic charging station operation considering user response: The photovoltaic charging station considers the uncertainty of net load demand in the station, and optimizes the charging and discharging power, backup power and three incentive signals of the energy storage in the photovoltaic charging station based on the DLMP and RDLMP of this node and the demand response of electric vehicle users to obtain the solution with the minimum expected cost. The objective function is as follows: In the formula, and They represent the total operating cost and optimization decision variables of the photovoltaic storage charging station n after considering the demand response of electric vehicles; and They represent the energy storage dispatching cost of the photovoltaic storage charging station and its interaction cost with the power and backup of the distribution network; represents the cost of providing incentives to electric vehicle users by the solar-storage charging station; S n represents the number of scenes; s is the scene number; ρ n,s is the scenario probability; T represents the optimization period; t represents the time period; and They represent the downward reserve and upward reserve of energy storage under scenario s respectively; γ represents the unit charging and discharging cost of energy storage; and They represent the charging power and discharging power of energy storage in scenario s respectively; η cha and ηdis Respectively represent the charging and discharging efficiency of energy storage; and They represent the active DLMP and RDLMP at the node connected to the photovoltaic charging station n at time t respectively; and They respectively represent the active power and standby power purchased by the solar-storage charging station from the distribution network at time t.

[0094] The constraints of the photovoltaic charging station operation optimization model include: 1) Energy storage charging and discharging power constraints: In the formula, and Respectively represent the maximum discharge power and charging power of energy storage; To characterize the charging and discharging state variable of the energy storage at the photovoltaic charging station n at time t, a value of 1 indicates energy storage discharging, and a value of 0 indicates charging; and They respectively represent the discharge and charging power of the energy storage at the solar-storage charging station n at time t in scenario s.

[0095] 2) Energy storage backup power constraints: In the formula, and They represent the downward reserve and upward reserve of energy storage in scenario s respectively; To characterize the reserve state variable of energy storage in scenario s, a value of 1 indicates that the energy storage provides upward reserve, and a value of 0 indicates that the energy storage provides downward reserve.

[0096] 3) Energy storage active power-reserve joint constraints:

[0097] Equations (24) and (25) represent the active power and standby coupling power constraints of energy storage; Equations (26) and (27) represent the remaining power constraints of energy storage; E n,s,t represents the remaining energy storage capacity at time t in scenario s; Formula (28) indicates that the energy storage capacity is equal at the beginning and end of the optimization cycle; E n Indicates the rated capacity of energy storage; and Respectively represent the minimum and maximum value of energy storage SOC; E n,s,t＝T and E n,s,t＝0 They respectively represent the remaining energy storage capacity at the end of scheduling and the beginning of scheduling.

[0098] 4) Interaction power constraints between photovoltaic charging stations and distribution networks:

[0099] Equations (29)-(31) represent the active power, reserve power, and active-reserve coupling power interaction constraints between the photovoltaic storage charging station and the distribution network, respectively; represents the maximum exchange power between the photovoltaic storage charging station and the distribution network; Equations (32) and (33) are the opportunity constraints for the photovoltaic storage charging station to purchase active power and standby power from the distribution network; and They represent the load demand and photovoltaic output in the photovoltaic charging station at time t in scenario s; and They represent the total amount of electric vehicle charging and discharging at the photovoltaic storage charging station n at time t respectively; and Respectively represent the fluctuation value of station load and photovoltaic active power; α n For confidence.

[0100] ③ Robust economic dispatch model of distribution network: The distribution network comprehensively considers the power / backup demand reported by each photovoltaic storage charging station and the net load demand of other nodes, and establishes a robust economic dispatch model for the distribution network to achieve economic dispatch under the premise of ensuring the system power demand and backup demand; on this basis, the active marginal price DLMP of the distribution node and the backup marginal price RDLMP of the distribution node can be defined as the partial derivatives of the Lagrangian function with respect to the active load forecast value and the active net load forecast deviation at the node.

[0101] The present invention is a vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users. In use, the method of the present invention can effectively use the incentive-response mechanism to guide the photovoltaic charging station and the electric vehicle users to interact flexibly, and fully tap the potential of electric vehicles in assisting the photovoltaic charging station's on-site energy storage to perform peak shaving and valley filling and standby regulation; compared with the station-network interaction scenario, the vehicle-station-network multi-subject interaction optimization improves the economy of the distribution network and the photovoltaic charging station and the flexibility of scheduling; in the method of the present invention, the photovoltaic charging station affects the willingness of electric vehicle users through a variety of incentive signals, so that electric vehicle users can obtain certain benefits while participating in demand response, and provide certain experience for flexible resources such as electric vehicles to participate in demand response, which is conducive to further exerting the dispatchable potential of demand-side resources and supporting the economic operation of the distribution network; the present invention has the advantages of effectively utilizing the incentive-response mechanism, fully tapping the flexible adjustment capability, and improving the economy and scheduling flexibility.

[0102] Example Analysis

[0103] Use Figure 4The road network structure shown in the figure includes 45 road network nodes in total, of which blue represents residential areas (represented as H areas), yellow represents work areas dominated by production units, enterprises and institutions (represented as W areas), and red represents other types of areas (represented as O areas); the roads between the road network nodes are all two-way lanes, and the two-way distances of the same road section are approximately the same; road network nodes 2, 12, and 34 are connected to three photovoltaic storage charging stations, with photovoltaic installed capacities of 380kW, 840kW, and 780kW, respectively; the photovoltaic storage charging stations are numbered 1, 2, and 3, and their basic parameters are shown in Table 1.

[0104] Table 1 Basic parameters of solar-storage charging station

[0105]

[0106] In Table 1, E n Indicates the rated capacity of energy storage in the photovoltaic charging station; and Respectively represent the maximum charging power and discharging power of energy storage; η cha and η dis Respectively represent the charging and discharging efficiency of energy storage; and Respectively represent the minimum and maximum values ​​of energy storage SOC; represents the maximum exchange power between the photovoltaic storage charging station and the distribution network; γ is the unit charging and discharging cost of energy storage; and They represent the charging and discharging cost coefficients of the photovoltaic storage charging station respectively.

[0107] The number of electric vehicles simulated in this case is 1000, and their basic parameters are shown in Table 2.

[0108] Table 2 Basic parameters of electric vehicles

[0109]

[0110] In Table 2, E EV Indicates the battery capacity of an electric vehicle; and is the maximum charging and discharging power of the electric vehicle battery; η EV,cha and η EV,dis charging and discharging efficiency of electric vehicle batteries; and They represent the minimum and maximum SOC values ​​of the electric vehicle batteries participating in demand response, respectively; is the demand response dead zone SOC constant.

[0111] The demand response willingness parameters of electric vehicle users are shown in Table 3.

[0112] Table 3 Electric vehicle response willingness parameters

[0113]

[0114] In Table 3, and Respectively represent the dead zone excitation values ​​of charge and discharge excitation; and Respectively represent the saturation excitation value of charge and discharge excitation; and It represents the maximum willingness of electric vehicle users to participate in the n-charging and discharging demand response of the photovoltaic storage charging station without path compensation incentive; and It represents the maximum willingness of electric vehicle users to participate in the charging and discharging demand response of the photovoltaic storage charging station after considering the path compensation incentive.

[0115] The photovoltaic charging station is optimized and dispatched to obtain the incentive signal with the minimum total dispatch cost. The results are as follows: Figure 5-7 Taking the node where the photovoltaic charging station 3 is located as an example, the DLMP and RDLMP conditions in each period are as follows: Figure 8 shown.

[0116] Depend on Figure 5-7 It can be seen that photovoltaic storage charging station 1 faces charging demand at 8:00, and photovoltaic storage charging station 2 faces charging demand at both 8:00 and 14:00, so charging discount incentives are issued to electric vehicles during the corresponding time periods; at most other times, the charging loads in the three photovoltaic storage charging stations are higher than the photovoltaic power generation level, so the photovoltaic storage charging stations issue discharge discount incentives to guide electric vehicles to discharge.

[0117] Combination Figure 6 It can be seen that at times 1-7 when the DLMP level is relatively low, the three photovoltaic storage charging stations all purchase electricity from the distribution network to meet the charging needs within the station; at times 8-12 and 16-23 when the DLMP level is relatively high, in order to reduce the total operating cost, the photovoltaic storage charging station chooses to issue discharge discount incentives to electric vehicles to reduce the cost of purchasing electricity through electric vehicle discharge. Since the discharge cost coefficient of the photovoltaic storage charging station is 0.8, setting the discharge discount incentive level too high will increase the operating cost of the photovoltaic storage charging station, so the maximum level of the discharge discount incentive is 0.25; at times 13-15, the photovoltaic power generation level within the station is close to the load level, and the discharge demand of the photovoltaic storage charging station is relatively small. Since there is a dead zone in the demand response willingness of electric vehicles, the cost of the photovoltaic storage charging station issuing discharge discount incentives to electric vehicles at this time is higher than the cost of purchasing electricity directly from the distribution network. Therefore, the photovoltaic storage charging station chooses not to issue discharge discount incentives during this period; at time 24, the DLMP level is relatively low, but due to the power constraint of the photovoltaic storage charging station's energy storage, the photovoltaic storage charging station needs to charge the energy storage, so at this moment the photovoltaic storage charging station still issues discharge discount incentives to guide electric vehicles to discharge.

[0118] The solar-storage charging station provides path compensation incentives for electric vehicles, which will further increase the willingness of electric vehicles. Therefore, the solar-storage charging station reasonably sets the path compensation incentive level according to the power consumption of the electric vehicle. Figure 5-7 The incentives set ultimately minimize the total operating cost.

[0119] In order to verify the advantages of this method, the following two models are set for comparison:

[0120] Case 1: Without considering the participation of electric vehicles in demand response, only the distribution network and the photovoltaic storage charging station are interactively optimized.

[0121] Case 2: The proposed vehicle-station-network multi-agent interactive optimization model is used to determine the scheduling plan.

[0122] The comparison of vehicle-station-network scheduling costs for Case 1 and Case 2 is shown in Table 4.

[0123] Table 4 Comparison of dispatching costs between station-network interaction and vehicle-station-network interaction

[0124]

[0125] It can be seen from Table 4 that the dispatching costs of the three photovoltaic charging stations in Case 2 are lower than those in Case 1. At the same time, the dispatching cost of the distribution network is also reduced by 773.7 yuan, and the total dispatching cost of the photovoltaic charging station and the distribution network is reduced by 6033.66 yuan; therefore, the proposed method can reduce the dispatching cost of the entire system by about 9.54%.

Claims

1. A vehicle-station-network interaction method that takes into account the response willingness of electric vehicle users, fully tapping the flexible adjustment capabilities of photovoltaic charging stations and electric vehicle users, characterized by: The method comprises the following steps: Step 1: A station-grid interaction optimization model considering the coupling of energy and reserve is established, and the node energy marginal price DLMP reflecting the energy marginal cost of each node in the distribution network and the node reserve marginal price RDLMP reflecting the reserve marginal cost are derived; Step 2: Then, based on the willingness of electric vehicle users, an incentive-response mechanism between the solar-storage charging station and electric vehicle users is established; Step 3: Based on the above steps, a joint optimization model of electric vehicle user willingness and incentives is established to form a multi-agent interactive optimization of vehicle-station-network.

2. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 1, characterized in that: The step 1 specifically comprises the following steps: Step 1.1: The distribution network initializes DLMP and RDLMP and publishes them to each PV-storage charging station; Step 1.2: The solar-storage charging station issues incentive signals to electric vehicle users based on the electricity price information released by the distribution network, and influences the willingness of electric vehicle users to participate in demand response through incentive signals, guiding them to provide services; Step 1.3: After receiving the incentive signal, the electric vehicle user determines the route selection plan based on his / her willingness and takes the minimum total energy consumption as the goal, and reports the demand response plan to the solar storage charging station; Step 1.4: The photovoltaic charging station comprehensively considers the internal distributed photovoltaic output, the uncertainty of the load within the station, and the demand response plan reported by the electric vehicle users, reduces the operating cost by optimizing the charging and discharging power and backup plan of the energy storage within the station, and updates the incentive signal for electric vehicle users; Step 1.5: Return to step 1.3 until the percentage difference between the incentive values ​​of the two iterations is less than the convergence threshold; then, the photovoltaic storage charging station reports its overall power interaction plan and backup demand with the distribution network in each period; Step 1.6: The distribution network determines the robust economic dispatch scheme based on the reported information of each photovoltaic storage charging station, combined with its own backup demand, distribution network operation constraints and operation constraints of each power generation unit, and derives the DLMP and RDLMP based on the robust economic dispatch model; Step 1.7: Return to step 1.2 until the difference in electricity prices between the previous and next two iterations is less than the convergence threshold.

3. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 2, characterized in that: The willingness of electric vehicle users to participate in demand response in step 1.2 is specifically: the psychology of electric vehicle users in each period can be divided into two willingness states, namely, willingness to participate in demand response and unwillingness to participate in demand response. The value of the willingness of electric vehicle users is defined as the probability that the user is willing to participate in demand response. The willingness state of electric vehicle users is a random variable and obeys the following binomial distribution: p i ~B(1,σ i ),i=1,2,...,N EV (1), where N EV is the total number of electric vehicle users; p i represents the willingness state of user i; σ i represents the willingness of user i.

4. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 1, characterized in that: The incentive-response mechanism between the solar-storage charging station and the electric vehicle user established in step 2 specifically includes three types of incentives: charging discount incentive, discharging discount incentive and path compensation incentive.

5. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 4, characterized in that: The charging discount incentive is specifically: after considering the charging discount incentive, the charging cost of the electric vehicle is as follows: In the formula, represents the charging cost of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the charging discount incentive provided by solar-storage charging station n at time t; is the charging cost coefficient of the photovoltaic storage charging station n; is the charging power of the i-th electric vehicle at the photovoltaic charging station n at time t; Δt is the duration of the electric vehicle participating in the demand response; That is, the charging amount of the i-th electric vehicle at the photovoltaic charging station n; is the DLMP at the node to which the solar-storage charging station n is connected at the moment.

6. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 4, characterized in that: The discharge discount incentive is specifically: after considering the discharge discount incentive, the discharge benefits of the electric vehicle are as follows: In the formula, represents the discharge benefit of the i-th electric vehicle at the photovoltaic charging station n at time t; represents the discharge discount incentive provided by the solar-storage charging station n at time t; is the discharge cost coefficient of the photovoltaic storage charging station n; is the discharge power of the i-th electric vehicle at the photovoltaic charging station n at time t; That is, the discharge amount of the i-th electric vehicle at the solar storage charging station n.

7. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 4, characterized in that: The path compensation incentive is specifically: after considering the path compensation incentive, the travel energy consumption cost of the electric vehicle is as follows: In the formula, represents the energy consumption cost of the trip of the i-th electric vehicle to the photovoltaic charging station n at time t; represents the path compensation incentive provided by the solar-storage charging station n at time t; It is the total energy consumption of the i-th electric vehicle traveling to the photovoltaic charging station n to participate in demand response at time t-1.

8. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 1, characterized in that: The vehicle-station-network multi-agent interactive optimization model in step 3 specifically includes: an electric vehicle user demand response model, a photovoltaic storage charging station operation optimization model considering user response, and a distribution network robust economic dispatch model.

9. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 8, characterized in that: The electric vehicle user demand response model is specifically as follows: assuming that each electric vehicle user can participate in demand response at most once a day, the number of electric vehicles willing to go to the solar storage charging station to participate in demand response at each moment can be calculated, as shown in the following formula: Where N CS Indicates the number of solar-storage charging stations; represents the number of electric vehicles participating in the demand response of photovoltaic charging station n at time t1; A state variable representing the type of incentive issued by the photovoltaic charging station n at time t1; and They represent the willingness of electric vehicles to participate in the demand response at the photovoltaic charging station n at time t1 after considering the path compensation incentive; It represents the number of electric vehicles participating in the demand response of photovoltaic charging station n at time t.

10. A vehicle-station-network interaction method considering the response willingness of electric vehicle users as claimed in claim 8, characterized in that: The operation optimization model of the photovoltaic charging station considering user response is specifically as follows: the photovoltaic charging station considers the uncertainty of the net load demand in the station, and based on DLMP and RDLMP, as well as the demand response of electric vehicle users, optimizes the charging and discharging power, standby power and three incentive signals of the energy storage in the photovoltaic charging station to obtain a solution with the minimum expected cost. The objective function is as follows: In the formula, and They represent the total operating cost and optimization decision variables of the photovoltaic storage charging station n after considering the demand response of electric vehicles; and They represent the energy storage dispatching cost of the photovoltaic storage charging station and its interaction cost with the power and backup of the distribution network; represents the cost of providing incentives to electric vehicle users by the solar-storage charging station; S n represents the number of scenes; s is the scene number; ρ n,s is the scenario probability; T represents the optimization period; t represents the time period; and They represent the downward reserve and upward reserve of energy storage under scenario s respectively; γ represents the unit charging and discharging cost of energy storage; and They represent the charging power and discharging power of energy storage in scenario s respectively; η cha and η dis They represent the charging and discharging efficiency of energy storage respectively; and They represent the active DLMP and RDLMP at the node connected to the photovoltaic charging station n at time t respectively; and They respectively represent the active power and standby power purchased by the solar-storage charging station from the distribution network at time t.

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