A distribution network multi-agent collaborative operation optimization method, system, terminal and medium considering price incentive vehicle-grid interaction

Abstract

The application discloses a kind of considering price incentive vehicle-network interaction distribution network multi-agent collaborative operation optimization method, system, terminal and medium, it is related to vehicle-network interaction technical field, including: construct based on user response willingness result-oriented V2G parameter configuration strategy, V2G parameter includes charging price, discharge incentive price, current rate limit and discharge depth limit value;The result determined according to V2G parameter configuration strategy is applied to distribution network real-time optimization scheduling, establishes real-time optimization scheduling model, and real-time calculates V2G response capacity;Consider distribution network usage fee, with market interest benefit profit maximization as target to build power transaction clearing model, obtain power transaction clearing result;Real-time optimization scheduling model and power transaction clearing model are combined to establish distribution network multi-agent collaborative operation optimization model, and optimization solution is carried out, and optimal operation scheme is obtained.The method can effectively reduce grid operation cost, improve user income, promote new energy consumption.

Description

Technical Field

[0001] This invention relates to the field of vehicle-to-grid (V2G) interaction technology, and specifically to a method, system, terminal, and medium for optimizing the collaborative operation of a distribution network involving multiple stakeholders, taking into account price incentives for V2G interaction. Background Technology

[0002] In recent years, electric vehicles (EVs) have been connected to the power grid, utilizing vehicle-to-grid (V2G) technology to build a two-way interactive system, effectively leveraging the flexible adjustment capabilities of batteries. V2G provides crucial support for the efficient and economical operation of new power systems. However, the widespread application of V2G technology targets a massive number of individual EV owners. If V2G parameters such as current rate, depth of discharge, and charging / discharging prices are set improperly during dispatching, it can lead to adverse behaviors such as deep discharge, high-rate charging / discharging, and low user benefits. Although operators can obtain higher response capacity, these adverse behaviors exacerbate additional battery wear, causing anxiety among EV owners regarding battery life and operating costs, and reducing users' willingness to participate in V2G management.

[0003] Furthermore, during EV charging, vehicles typically charge immediately after completing a trip; this charging method is known as unordered charging. While this is a common practice among users under normal circumstances, it can impact the power grid, especially in areas with fixed electricity prices, because the charging period overlaps with peak grid load periods.

[0004] Orderly charging control methods can be divided into two types: direct control and indirect control. Direct control, under the premise of meeting grid load and user charging demand, involves the distribution network dispatching directly managing the switching and charging power of charging piles. The goal is to improve the grid's activity and stability, achieving unified scheduling of EV charging. Indirect control, on the other hand, uses a price-guided mechanism. EV dealers set time-of-use (TOU) prices based on system status, and users charge in an orderly manner based on their own needs and price fluctuations. Price guidance plays a crucial role in orderly charging strategies. Currently, common EV charging price strategies include fixed prices, peak-valley TOU pricing, dynamic TOU pricing, and real-time pricing. Fixed prices do not consider grid load fluctuations, which may lead to users charging during peak load periods, increasing grid pressure. Therefore, they are generally less effective than TOU pricing in regulating grid load. Peak-valley TOU pricing encourages users to charge during low-load periods through price levers, thereby achieving grid load balance, optimized resource utilization, and reduced overall power system costs. Dynamic time-of-use pricing offers greater flexibility, adjusting prices in real time based on grid load, supply and demand, and weather changes. However, this places higher demands on users, particularly regarding the ability of smart devices to support and monitor price changes in real time. Balancing user needs with technological feasibility is a major challenge in promoting dynamic pricing. Real-time pricing, on the other hand, adjusts prices instantly based on changes in grid load, market demand, and supply, incentivizing users to adjust their charging behavior according to price fluctuations. However, it also faces challenges such as large price volatility, high technical requirements, and poor user adaptability.

[0005] Moreover, with the further advancement of current power sector reforms and the electricity market, regional interactive management and grid operation are considered key aspects of the reform and the main concentration of complex factors in market transactions. In a decentralized market trading environment, market stakeholders, including electric vehicles, and grid company operations and control entities all pursue revenue as a goal and formulate appropriate operational plans. These market stakeholders not only need to consider the output correlation, spatiotemporal complementarity, and cross-influence relationships of internal users (such as clean energy, energy storage devices, flexible loads, and electric vehicles), but also need to fully absorb the competition, cooperation, and alliance interactions of other prosumers. Simultaneously, they also increase the flexibility of the electricity sales side and the demand side, characterized by strong transaction initiative and supply-demand uncertainty, which also brings significant challenges to the operation and control of the distribution network. Therefore, prosumers in the electricity market need a practical method and trading model to protect their information security.

[0006] Furthermore, the interactive operation management and control of regional distributed resources and users can reduce system losses and improve power supply reliability, resulting in significant economic and social benefits. This is of great significance to power system research. Ensuring the quality of operation plays a crucial role in the efficient operation and healthy development of the power grid. Traditional distribution network operation management and control mainly focus on regulating on-load tap-changing transformers, switched capacitor banks, and switch reconfiguration to achieve the goal of energy conservation and emission reduction. However, due to the formation of market stakeholders and the integration of many distributed resources (including distributed generation, distributed energy storage, and controllable loads) in market transactions, the optimization of system operation becomes extremely complex. The decentralization of these network elements will bring enormous technical challenges to the centralized control of regional interactive operation management. Summary of the Invention

[0007] The purpose of this invention is to provide a method, system, terminal, and medium for optimizing the collaborative operation of distribution networks by multiple stakeholders, taking into account price incentives and vehicle-grid interaction. This method can effectively reduce grid operating costs, increase user benefits, and promote the consumption of new energy sources.

[0008] This invention is achieved through the following technical solution:

[0009] In a first aspect, the first embodiment of the present invention provides a method for optimizing the collaborative operation of a distribution network involving multiple stakeholders, taking into account price incentives and vehicle-network interaction, comprising:

[0010] A V2G parameter configuration strategy based on user response intentions and results is constructed, wherein the V2G parameters include charging price, discharging incentive price, current rate limit and discharge depth limit;

[0011] The results determined according to the V2G parameter configuration strategy are applied to the real-time optimization scheduling of the distribution network to establish a real-time optimization scheduling model with the goal of minimizing operating costs and to calculate the V2G response capacity in real time.

[0012] Considering the usage fees of the power distribution network, a power trading clearing model is constructed with the goal of maximizing the profits of market stakeholders, and the power trading clearing results are obtained.

[0013] A multi-entity collaborative operation optimization model for the distribution network is established by combining a real-time optimization scheduling model and a power trading clearing model. The optimal operation scheme is obtained by optimizing the solution.

[0014] Furthermore, the V2G parameter configuration strategy includes: response willingness assessment and parameter setting. The response willingness assessment is based on a fuzzy logic system with V2G parameters as input variables and user response willingness values ​​as output results. It quantifies user response willingness through membership function construction, fuzzy rule base operation, and defuzzification. The parameter setting is used to filter out users with high response willingness based on response willingness thresholds, and to reversely determine the user's psychological expectation range for V2G parameters based on the input variables corresponding to high response willingness, thus completing the V2G parameter setting.

[0015] Furthermore, the method for constructing the membership function includes:

[0016] Based on the Weber-Fechner law, the user's perception of charging and discharging prices is characterized, and a membership function of charging and discharging prices is constructed.

[0017] Based on the battery dynamic aging model and the requirements for power battery life in national standards, and combined with changes in user acceptance, a membership function of current rate and depth of discharge is constructed.

[0018] Furthermore, the optimization objective of the real-time optimized scheduling model is to minimize the sum of the main grid purchase cost, grid loss cost, renewable energy purchase cost, and V2G price compensation in a single time period.

[0019] Furthermore, the electricity trading clearing model is a P2P electricity trading model, which uses a long short-term memory neural network. It takes lighting, temperature, wind speed, trading willingness and market share as input features and outputs the initial trading price and trading volume.

[0020] Furthermore, the method for calculating the network usage fee includes: calculating the electrical distance between nodes, and calculating the network usage fee based on the electrical distance and the network usage fee unit price.

[0021] Secondly, another embodiment of the present invention provides a multi-entity collaborative operation optimization system for distribution networks that considers price incentives and vehicle-to-grid interaction, comprising:

[0022] The module is used to build a V2G parameter configuration strategy based on user response intentions and results. The V2G parameters include charging price, discharge incentive price, current rate limit and discharge depth limit.

[0023] The real-time scheduling module is used to apply the results determined according to the V2G parameter configuration strategy to the real-time optimization scheduling of the distribution network, establish a real-time optimization scheduling model with the goal of minimizing operating costs, and calculate the V2G response capacity in real time.

[0024] The transaction clearing module is used to consider the distribution network usage fee and construct an electricity transaction clearing model with the goal of maximizing the profits of market stakeholders, so as to obtain the electricity transaction clearing result.

[0025] The collaborative optimization module combines the real-time optimization scheduling model and the power trading clearing model to establish a multi-entity collaborative operation optimization model for the distribution network, and performs optimization to obtain the optimal operation scheme.

[0026] Furthermore, the V2G parameter configuration strategy includes: response willingness assessment and parameter setting. The response willingness assessment is based on a fuzzy logic system with V2G parameters as input variables and user response willingness values ​​as output results. It quantifies user response willingness through membership function construction, fuzzy rule base operation, and defuzzification. The parameter setting is used to filter out users with high response willingness based on response willingness thresholds, and to reversely determine the user's psychological expectation range for V2G parameters based on the input variables corresponding to high response willingness, thus completing the V2G parameter setting.

[0027] Thirdly, another embodiment of the present invention provides a smart terminal, including a processor, an input device, an output device, and a memory, wherein the processor, the input device, the output device, and the memory are interconnected, the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method described in the above embodiments.

[0028] Fourthly, another embodiment of the present invention provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor, cause the processor to perform the methods described in the above embodiments.

[0029] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0030] This invention provides a method, system, terminal, and medium for optimizing the collaborative operation of a distribution network by considering price incentives for vehicle-to-grid (V2G) interaction. It constructs a V2G parameter configuration strategy to avoid adverse behaviors during V2G scheduling, alleviate additional battery losses, and enhance user willingness to respond. For real-time optimized scheduling, it ensures safe V2G response while evaluating V2G response capacity under multiple factors and proposes a collaborative scheduling strategy for EV grid access peak shaving that takes into account V2G price incentives. Simultaneously, considering the network usage fees collected by the distribution network, it constructs a power trading clearing model with market stakeholders participating in market transactions, aiming to maximize their own profits. Finally, it establishes a multi-stakeholder collaborative operation optimization model for the distribution network, which can effectively reduce grid operating costs, increase user benefits, and promote the consumption of renewable energy. Attached Figure Description

[0031] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be considered as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort. In the drawings:

[0032] Figure 1 A flowchart of a multi-entity collaborative operation optimization method for distribution networks that considers price incentives and vehicle-network interaction, provided in the first embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram of the actual 152-node system topology in the first embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram comparing the consumption of decentralized resources with and without P2P transactions in the first embodiment of the present invention;

[0035] Figure 4 This is a schematic diagram illustrating the total number of transactions when network usage fees change in the first embodiment of the present invention;

[0036] Figure 5 This is a schematic diagram of the structure of a multi-entity collaborative operation optimization system for distribution networks that considers price incentives for vehicle-network interaction, provided as another embodiment of the present invention. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0038] like Figure 1 As shown, the first embodiment of the present invention provides a method for optimizing the collaborative operation of a distribution network by multiple stakeholders, considering price incentives and vehicle-to-grid interaction, comprising:

[0039] A V2G parameter configuration strategy based on user response intentions and results is constructed. The V2G parameters include charging price, discharging incentive price, current rate limit, and depth of discharge limit.

[0040] The results determined by the V2G parameter configuration strategy are applied to the real-time optimization scheduling of the distribution network to establish a real-time optimization scheduling model with the goal of minimizing operating costs and to calculate the V2G response capacity in real time.

[0041] Considering the usage fees of the power distribution network, a power trading clearing model is constructed with the goal of maximizing the profits of market stakeholders, and the power trading clearing results are obtained.

[0042] A multi-entity collaborative operation optimization model for the distribution network is established by combining a real-time optimization scheduling model and a power trading clearing model. The optimal operation scheme is obtained by optimizing the solution.

[0043] For grid-connected electric vehicles (GEVs) participating in V2G dispatch, the V2G parameter settings affect user willingness to respond from two aspects: battery degradation and user benefits. The main factors affecting battery degradation are battery operating temperature, current rate, and depth of discharge. In actual operation, the EV's battery temperature control system monitors and controls the battery operating temperature within a suitable range in real time. Therefore, operators need to pay attention to the limit settings for current rate and depth of discharge, while setting appropriate charging and discharging incentive prices for user benefits. To ensure that the set V2G parameters maximize user willingness to respond, operators need to comprehensively consider the range of user expectations for the parameters, because V2G parameters set based on user psychological expectations align with user preferences.

[0044] Based on the above problem analysis, this invention proposes a V2G parameter configuration strategy based on user response intention, including response intention assessment and parameter setting. Response intention assessment uses the Mamdani fuzzy logic system to quantitatively represent user response intention, with V2G parameters as input variables and user response intention values ​​as output results. The impact of V2G parameters on user response intention is uncertain and non-linear; the fuzzy logic system can effectively simulate the thinking and operation of humans making decisions in the face of uncertain and fuzzy data. The input variables are fuzzified using membership functions, and then defuzzified after processing with a fuzzy rule base to obtain a clear output result, thus completing the quantitative representation of user response intention. Parameter setting uses response intention thresholds to filter out high and low response intentions. Since low response intentions do not participate in the subsequent parameter setting process, the range of user psychological expectations for V2G parameters is determined inversely based on the input variables corresponding to high response intentions. The operator's decision-making level then completes the V2G parameter setting based on this range. The resulting V2G parameter configuration takes into account user psychological expectations and can promote improved user response intentions in scheduling applications. To implement a complete V2G parameter configuration strategy, it is necessary to determine the parameter set, membership function, fuzzy rule base, and defuzzification method involved in the fuzzy logic system.

[0045] Determining the parameter set: Using V2G parameters such as current ratio, depth of discharge, charging price, and discharge incentive price as input variables, the following set of factors influencing user willingness to respond is formed:

[0046] V = {P c , P d (1)

[0047] In the formula: Pc For charging prices, P d C represents the discharge incentive price, reflecting the user's perception of the economic benefits of the V2G process; D represents the current rate and the depth of discharge, reflecting the user's perception of battery safety during the V2G process. The evaluation set for the influencing factors is defined as shown in Table 1.

[0048] Table 1. Evaluation set of influencing factors

[0049] Influencing factors Judgment Collection Meaning of content Charging price <![CDATA[{P cL ,P cM ,P cH }]]> <![CDATA[P cL P cM P cH The prices are represented in order as low, moderate, and high for charging. Discharge excitation price <![CDATA[{P dL ,P dM ,P dH }]]> <![CDATA[P dL P dM P dH The prices of discharge excitation are listed in order: low, moderate, and high. Current ratio <![CDATA[{C ad ,C inad }]]> <![CDATA[C ad C inad This indicates whether the charge / discharge rate is suitable or unsuitable. Depth of discharge <![CDATA[{D ad ,D inad }]]> <![CDATA[D ad D inad This indicates whether the discharge depth is suitable or unsuitable.

[0050] Construction of Membership Functions: Membership describes the true degree of membership of an element to a fuzzy set in the universe of discourse, and the membership function serves as a mathematical tool to characterize membership. The implementation of response willingness assessment requires calculating the membership degree of each input variable to each fuzzy subset within the evaluation set by constructing membership functions. Due to a lack of effective data and statistics, an assignment method is used to determine the membership functions of fuzzy subsets. To reduce the error risk of the simple assignment method and provide theoretical support, a method for constructing membership functions for each input parameter is developed based on the relationship analysis between user characteristics and V2G parameters. First, the relationship between changes in user psychology and changes in charging and discharging prices is characterized based on the Weber-Fechner law, determining the price distribution under different levels of user perception, thus completing the construction of the membership function for charging and discharging incentive prices. Second, based on the battery dynamic aging model and the characterization of changes in user acceptance, the distribution of acceptable discharge depth limits and current rate limits is determined, thus completing the construction of the membership functions for discharge depth and current rate.

[0051] Among them, the membership function of the charging and discharging price incentive is constructed as follows:

[0052] 1) Application of the Weber-Fechner Law:

[0053] According to the Weber-Fechner law, the intensity of human perception of external stimuli can be described as a logarithmic proportional relationship, as shown in the following mathematical model:

[0054] K = αlgR (2)

[0055] In the formula: K is the perceived intensity; α is the sensory coefficient; R is the intensity of the external stimulus. According to formula (2), the psychological effects of charging price and discharging incentive price on EV users can be quantified by formula (3):

[0056] (3)

[0057] In the formula: To assess the user's perception of charging prices. The smaller the value, the more affordable the current charging price is perceived by the user; To assess the user's perceived strength regarding the price of discharge excitation. The larger the value, the higher the user perceives the current discharge excitation price; , These are the corresponding perception coefficients; For charging price, The price is for discharge excitation. To make... and To better reflect the changing trends in users' perception of charging and discharging prices, , The normalization process is as follows:

[0058] (4)

[0059] In the formula: This is the upper limit for charging prices. This represents the lower limit of the charging price. This represents the upper limit of the price for discharge excitation. This represents the lower limit of the charging and discharging excitation price. However, different users within the same group do not have entirely consistent perceptions of the charging and discharging price. and The value is affected by uncertainty caused by inter-individual perceptual bias. Based on the changing pattern of user perceptual bias, a perceptual bias term ΔK is introduced to address the impact of uncertainty. and Make corrections.

[0060] (5)

[0061] 2) Membership function of charging price:

[0062] The perceived intensity Ksc is divided into 3 segments, with the dividing point being K. sc1 With K sc2 Define when K sc ∈[0,K sc1 This manifests as users perceiving charging prices as low; K sc ∈[K sc1 ,K sc2 This is reflected in the user's perception that the charging price is moderate; K sc ∈[K sc2 [1] This manifests as users perceiving charging prices as high. Due to the existence of perception bias among individual users, each perceived intensity value corresponds to a price range, with the range boundary values ​​being the charging prices corresponding to the two dashed lines. Then, based on the above division definition, the charging price distribution caused by user group perception bias is described as shown in Equation (6):

[0063] (6)

[0064] Construction of the membership function for depth of discharge and current ratio:

[0065] 1) Battery cycle life degradation.

[0066] To describe the combined effect of discharge depth D and current rate C on battery cycle life during the scheduling process, a semi-empirical battery dynamic aging model based on the Arrhenius degradation model is adopted:

[0067] (7)

[0068] In the formula: Q loss The percentage of battery capacity loss; A is the pre-exponential factor; E a The activation energy is T; R is the gas constant. bat Absolute temperature of the battery; A h Let z be the battery throughput; z be the time coefficient; C be the current rate; and B be the compensation coefficient for the current rate. Based on equation (7), the discrete form of the battery dynamic aging model is as follows:

[0069] (8)

[0070] In the formula: , These represent the cumulative battery capacity loss during the i-th and (i+1)-th cycles, respectively. The battery throughput for the i-th cycle is calculated using equation (9).

[0071] (9)

[0072] In the formula: The maximum capacity of the battery during the i-th cycle; Let be the depth of discharge in the i-th cycle.

[0073] 2) User-expected limits for depth of discharge and current ratio.

[0074] To determine appropriate and user-acceptable limits for the depth of discharge (D) and current rate (C), it is assumed that when the impact of D and C on battery cycle life meets regulatory requirements, D and C are within the user-acceptable range and meet user expectations. Therefore, according to the Chinese national standard GB / T31484—2015 regarding the life requirements of power batteries, when the cycle life of an electric vehicle power battery reaches 1000 cycles, the battery capacity should not be less than 80% of the initial capacity.

[0075] 3) Membership function of discharge depth and current ratio.

[0076] Select D b Cb When applied to actual response processes, D b C b This is the boundary value between D and C. Some users may believe that a value lower than D is appropriate. b C b The limits for V2G responses would be safer, leading to D... b C b The value of is affected by the uncertainty of user perception and acceptance. Therefore, an acceptance coefficient r is introduced to construct a transition interval to represent the change in user acceptance caused by user uncertainty. The specific settings are shown in Table 2, where r C With r D This represents the corresponding acceptance coefficient.

[0077] Table 2 User Acceptance of Current Ratio C and Discharge Depth D

[0078] User acceptance Current ratio C range Discharge depth D range Acceptable <![CDATA[0≤C<r C C b ]]> <![CDATA[0≤D<r D D b ]]> Transitional range of acceptance changes <![CDATA[r C C b ≤C<C b ]]> <![CDATA[r D D b ≤D<D b ]]> Unacceptable <![CDATA[C b ≤C]]> <![CDATA[D b ≤D]]>

[0079] Based on the results in Table 2, the range of results corresponding to "acceptable" for the user is defined as the suitable current rate C and depth of discharge D range for the battery, while the range of results corresponding to "unacceptable" for the user is defined as the unsuitable current rate C and depth of discharge D range. In practical applications, by determining D... b C b r C r D These four values ​​determine the boundary values ​​of the shaded area, and then construct the membership function of current ratio and discharge depth.

[0080] Defuzzification: In order to evaluate the performance of the fuzzy system model, the output results are clearly estimated by defuzzification methods (such as centroid, weighted average, maximum membership principle, etc.). In this embodiment, the centroid method is used for defuzzification, and the corresponding defuzzified output result is calculated by equation (10):

[0081] (10)

[0082] In the formula: w is the user's willingness to respond; x is the input parameter; M is the membership function.

[0083] V2G parameter configuration results: During real-time scheduling, for different vehicle users in the current time period, the parameter setting module adjusts the response willingness value w and the response willingness threshold w. y By comparing and selecting the range of input variables corresponding to high willingness to respond, the expected range of each V2G parameter under the high willingness to respond state was determined. The results are shown in Table 3, where P cy P dy C y D yThese correspond to the response willingness threshold w. y Input values ​​for charging price, discharging excitation price, current ratio, and depth of discharge:

[0084] Table 3 Input variables under high willingness to respond

[0085] High willingness to respond <![CDATA[P c Scope <![CDATA[P d Scope C range D range <![CDATA[w≥w y ]]> <![CDATA[[P c,min ,P cy ]]]> <![CDATA[[P dy ,P d,max ]]]> <![CDATA[[0,C y ]]]> <![CDATA[[0,D y ]]]>

[0086] Based on the results in Table 3, to facilitate unified price settlement, consider user response intentions, and obtain more adjustable capacity, the minimum charging price input value P among different vehicle users is selected for the charging and discharging incentive price. cy Value and the maximum discharge excitation price input value P dy This completes the unified configuration of charging and discharging incentive prices. The input value C is based on the current ratio of different vehicles. y With the depth of discharge input value D y Complete the current rate and depth of discharge configuration for the corresponding vehicle. Then, determine the charging price P under V2G mode for the current scheduling period. c,V2G Discharge excitation price P d,V2G Current ratio limit C V2G With the depth of discharge limit D V2G The settings.

[0087] The following is an example of optimized scheduling application of V2G parameter configuration strategies:

[0088] 1) Real-time optimized scheduling under V2G parameter application

[0089] Based on the foregoing analysis, the results of the V2G parameter configuration strategy are applied to the real-time optimization scheduling of the distribution network to correct the electric vehicle-related constraints in the optimization scheduling model. The scheduling interval Δt is set to 15 minutes, and continuous rolling optimization is performed within a single time period. The results of the parameter configuration strategy form V2G constraints, avoiding unfavorable behaviors during the scheduling process, reducing additional battery losses, and improving user willingness to respond. A power aggregation model based on classification criteria avoids risky situations while simultaneously calculating the V2G response capacity in real time.

[0090] 2) Real-time optimization scheduling model under V2G parameter constraints

[0091] (11)

[0092] In the formula: Real-time dispatch decisions are formulated with the optimization objective of minimizing the sum of the costs of purchasing electricity from the main grid, grid loss costs, renewable energy purchase costs, and V2G price compensation in a single time period. C g Main grid purchase price per unit of electricity; P g,t Let C be the power transmitted from the main network to the distribution network at time t; lossE represents the unit cost of network loss. Line I is the set of all branches in the power distribution network; ij,t Let r be the current in branch ij at time t; ij Let ij be the resistance of branch ij; , These represent the total charging and discharging power of all GEVs actually responding to node j during the scheduling process; The unit cost of electricity purchase for new energy sources; The cost of penalty for power curtailment per unit The number of new energy sources connected; and These are the predicted and actual power values ​​of the i-th new energy electric field at time t, respectively.

[0093] 3) Multi-stakeholder collaborative optimization model for distribution networks considering vehicle-to-grid interaction

[0094] The optimization objective for each market stakeholder: In this embodiment of the invention, the revenue of each stakeholder is calculated using the following formula:

[0095] (12)

[0096] (13)

[0097] (14)

[0098] (15)

[0099] Among them, c G This represents the unit production cost of renewable energy. This represents the predicted power output per unit of electricity generated during time period t. and These represent the unit electricity prices for electricity purchased from and sold from the grid during time period t, respectively. and These represent the unit electricity prices at which entity f buys and sells electricity to entity j during time period t. and These represent the total amount of electricity that entity f needs to sell and purchase from entity j during time period t, respectively.

[0100] In this P2P model, each transaction begins with an initial bid, which can be determined by an external feature model. This model encapsulates parameters such as lighting, temperature, wind speed, trading willingness, and market share, and combines historical feature data with the price curves of prosumers and consumers. It utilizes Long Short-Term Memory (LSTM) to achieve feature packaging modeling, as follows:

[0101] 1. Light intensity time series sample set

[0102] (16)

[0103] In the formula, L g Let H represent g groups of different illumination time series samples, H represent the continuous time length of the time series, g represent different sequence numbers, and l represent the number of samples. h、d This represents the illumination intensity in segment d of the H time series. Sample set L g It contains g distinct illumination sequences, each of length H, where the g-th sequence is... t The illumination intensity of the H portion of the sequence is l h，d .

[0104] 2. Temperature time series sample set

[0105] (17)

[0106] In the formula, U g There are g groups of time series samples at different temperatures, u h，d It is the temperature of segment d in the hth time series.

[0107] 3. Wind speed time series sample set

[0108] (18)

[0109] In the formula, W g Let g represent the time series sample set of wind speeds, while w h、d This represents the wind speed in segment d of the h-time series.

[0110] 4. Time series sample set of market composition ratio

[0111] (19)

[0112] In the formula, F i，g f is a time series sample set of g different market components of the i-th prosumer, h，d It is the market component of the d-cross section in the h-time series.

[0113] 5. Time series sample set of trading intentions

[0114] (20)

[0115] In the formula, S i、g Let s be a time series sample set of g different transaction intentions of the i-th prosumer. h、d These are the d cross-sectional trading intentions in the h-time series.

[0116] 6. Initial electricity price output sample set for each producer-seller transaction

[0117] (twenty one)

[0118] In the formula, C i，g Let c be a time series sample of g different initial electricity prices for the i-th producer-consumer. h，d It is the initial electricity price for segment d in the h-time series.

[0119] 7. Initial electricity output sample set for each producer-seller transaction

[0120] (twenty two)

[0121] Among them, P i，g Let p be the time series sample set of the lighting of the i-th generator. h，d The initial trading power for part d in each time series.

[0122] The initial electricity price and quantity in the electricity market are influenced by external factors such as lighting, temperature, and wind speed, as well as internal implicit information and self-optimization objectives. This information is not used as input features, but rather as the content learned by the LSTM model. Therefore, the input features for this training are lighting, temperature, wind speed, trading willingness, and market share, while the output features are the initial electricity price (i.e., initial pricing) and the initial quantity (i.e., initial quantity).

[0123] Currently, recurrent neural networks (RNNs) and LSTM models are effective techniques for improving training accuracy when handling large amounts of time-series samples. RNNs can only remember a portion of the sequence, while LSTM models add three logical control units: an input gate, an output gate, and a forget gate. Furthermore, it uses the backpropagation algorithm, which effectively addresses the problems of excessive weight influence and vanishing gradients in RNNs, thus significantly improving training accuracy.

[0124] For newly established market participants, a sample set can be generated through simulated transactions for offline learning. For existing prosumers, actual generated samples can be added to the simulated sample set to improve the sample set, and the model can be continuously updated through online learning to improve its accuracy.

[0125] In the process of electricity trading, the actual network usage fees are taken into account. By establishing a network usage fee model, electricity trading clearing becomes more feasible, as follows:

[0126] To determine network usage fees, electrical distance needs to be calculated, which is done in the following way:

[0127] (twenty three)

[0128] (twenty four)

[0129] (25)

[0130] (26)

[0131] (27)

[0132] (28)

[0133] (29)

[0134] Therefore, the network usage fee is:

[0135] (30)

[0136] Where J, B, Q, G, and P represent the abbreviations of the elements in the Jacobian matrix, J P and J Q Let a and d represent the active and reactive variables in the Jacobian matrix, respectively. a and d are defined process variables. C ed ij，t d represents the network usage fee between nodes ji. P ij and d Q ij p represents active electrical distance and reactive electrical distance. td0 This indicates the unit price for network usage fees.

[0137] Distribution network operators can manage the changes in power flow caused by peer-to-peer (P2P) power transactions. Based on this, they optimize network operation and management, establishing a multi-entity collaborative operation optimization model for the distribution network, as follows:

[0138] The model aims to optimize network usage costs through appropriate measures while minimizing network and equipment losses.

[0139] (31)

[0140] (32)

[0141] (33)

[0142] (34)

[0143] (35)

[0144] (36)

[0145] (37)

[0146] (38)

[0147] (39)

[0148] (40)

[0149] (41)

[0150] (42)

[0151] (43)

[0152] (44)

[0153] (45)

[0154] (46)

[0155] (47)

[0156] (48)

[0157] (49)

[0158] (50)

[0159] The aforementioned power flow constraints and equations have been widely applied in power flow models; ℓ represents an invariant value. In the equation, T represents the total number of time periods; branch ij represents the positive direction of power flow from node i to node j; E represents the set of all branches in the network; B represents the set of all nodes in the network; r ij I represents the resistance value on branch ij. ij,t V represents the current in branch ij during time period t; i,t Let represent the voltage at node i during time period t. δ(j) represents the set of terminal nodes of the branch with node j as the starting node; π(j) represents the set of starting nodes of the branch with node j as the ending node; x ij Indicates the reactance value on branch ij; g j b j Let P represent the conductance and susceptance of node j, respectively; j,t Q j,t P represents the active and reactive power injected into node j during time period t, respectively. sub j,t Q represents the active power injection at node j of the substation during time period t, while Q...sub j,t Q represents the reactive power injection at node j of the substation during time period t; SVC j,t Q represents the reactive power compensation amount of the continuous reactive power compensation device at node j during time period t. CB j,t P represents the reactive power compensation of capacitor bank j at time period t; L j,t Q L j,t Then, P represents the active and reactive loads of node j in time period t, respectively; ij,t Q ij,t These represent the active and reactive power on branch ij during time period t, respectively. SVC This represents the set of nodes containing continuous reactive power compensation devices. (B) CB This represents the set of nodes containing grouped switching capacitor banks; σ CB j,t Q represents the number of operational groups and is a discrete variable. CB,step j δ represents the compensation power for each CB group; CB,IN j,t and δ CB,DE j,t It is a 0-1 identifier representing the switching operation. If δ CB,IN j,t =1 indicates that an additional CB is put into operation at node j in time period t, δ CB,DE j,t Similarly; N CB j Y represents the maximum number of CB (Card Strikers) throws and cuts during time period T. CB j This represents the upper limit of the number of CB groups that node j can switch to each time. ESS,c j,t Let P represent the active charging amount of energy storage at node j during time period t, and P represent the active charging amount of energy storage. ESS,d j,t E represents the active power discharge of energy storage at node j during time period t; ESS j,t u represents the total energy storage capacity of node j during time period t. c j,t and u d j,t These are 0-1 symbols indicating charging and discharging. If u c j,t =1, which means that node j is charged during time period t. d j,t Similarly; η cj,t and η d j,t These represent the charging and discharging efficiencies of node j during time period t, respectively.

[0160] At the P2P transaction level, the operational constraints of the distribution network are often overlooked, resulting in no feasible solution to the power flow problem. To address this challenge, a slight penalty is implemented, reducing the transaction volume for a specific time period when OPF (Optical Power Flow) becomes infeasible. For transaction purposes, the load considered in the equations should be adjusted as follows:

[0161] (51)

[0162] (52)

[0163] (53)

[0164] (54)

[0165] (55)

[0166] Finally, the distributed operation management model was optimized and solved using the Lagrange double multiplier fast solution algorithm to obtain the optimal result.

[0167] Therefore, the specific methods and steps for coordinating market participants and distribution networks include:

[0168] Step a: Set initial limits for network usage fees and initial price p per unit distance. tdo .

[0169] Step b: Determine the network usage fee p using a consensus blockchain method. tdo Electricity sales / purchase amounts of all producers and consumers participating in the P2P market within the scope and corresponding prices Specifically, adjustable producer-consumers should send their transaction information to agent entities to optimize the operation of proactive distribution networks.

[0170] Step c: Use expressions (31)-(55) in the multi-entity collaborative operation optimization model of the distribution network to adjust the transaction power after the operation check. Then, update the network usage limits and the initial price per unit distance. .

[0171] Step d: Calculate the P2P market model to obtain updated transaction power. and price And update the unit distance network usage fee limit. Repeat steps b and c until the operation limit is met.

[0172] Step e: Adjust the initial price p in the network usage fee limit. td0 Repeat steps b through d until the maximum profit from network usage fees is reached.

[0173] A case study was conducted on an improved 152-node distribution network using the multi-stakeholder collaborative operation optimization method for considering price incentives and vehicle-to-grid interaction, as provided in this invention, to demonstrate the effectiveness of the proposed method. A schematic diagram of the node system is shown below. Figure 2 As shown, the parameters of this node system are shown in Table 4:

[0174] Table 4 Node System Parameters

[0175] CB Node SVC node ESS node {28,39,48,70,82,95,115,134,147} {30,43,85,89,124,150} {16,38,72,91,107,128}

[0176] In this study, different EV models with high ownership rates were selected for optimized scheduling to verify the applicability of the proposed method to different types of vehicles. The vehicle parameters are shown in Table 5. Based on the example data, the V2G parameter settings for different types of vehicle users under high response intention were completed through the V2G parameter configuration strategy, as shown in Table 6. Among them, model A is based on BYD Dolphin, model B is based on Tesla Model Y, and model C is based on BYD Han EV.

[0177] Table 5 Vehicle Parameters

[0178] parameter Model A Model B Model C <![CDATA[Rated capacity of battery R ev / (A·h)]]> 135.0 172.5 150.0 <![CDATA[Rated voltage U of the battery n / V]]> 332.8 347.8 480.0 <![CDATA[Battery cost Q b / yuan]]> 60000 80000 86000

[0179] Table 6 V2G parameter settings

[0180] V2G parameters Model A Model B Model C <![CDATA[Charging price P c,V2G (yuan / (kW⋅h))]]> 0.76 0.79 0.77 <![CDATA[Discharge excitation price P d,V2G (yuan / (kW⋅h))]]> 0.88 0.89 0.88 <![CDATA[Current magnification limit C V2G > 1.00 0.85 0.90

[0181] To verify the feasibility and effectiveness of the proposed V2G parameter configuration strategy and real-time optimization scheduling model, the following calculation example was set based on the parameter settings:

[0182] S1: The V2G parameters are set using the V2G parameter configuration strategy in this paper, and the response capacity assessment adopts a real-time optimization scheduling model;

[0183] S2: Charging price (0.8 yuan / (kW⋅h)) and discharge excitation price (0.85 yuan / (kW⋅h)). The current ratio and discharge depth limits are the same as those of S1. The response capacity assessment adopts a real-time optimized scheduling model.

[0184] S3: The charging price and discharge incentive price are the same as S1, the current rate and discharge depth have no limit requirements, and the response capacity assessment adopts a real-time optimization scheduling model.

[0185] Based on the three examples above, the economic benefit results are compared as shown in Table 7:

[0186] Table 7 Economic Benefit Results

[0187] Calculation example Distribution network operating cost / yuan EV users save on charging costs / yuan Network loss cost / yuan S1 37415.62 1191.62 1681.12 S2 36921.16 342.27 1731.42 S3 37241.22 1025.56 1651.18

[0188] Based on the results in Table 7, applying V2G technology reduces the amount of electricity purchased from the main grid and network losses by charging during off-peak hours and discharging during peak hours, thereby reducing the operating costs of the distribution network. Due to the increased charging price and decreased discharging price, S2 significantly reduces distribution network operating costs compared to S1. However, because user revenue is reduced, the cost savings for users are lower, resulting in low user willingness to respond in practical applications. Although S3 offers higher response capacity for operators compared to S1, the increased additional battery losses lead to low user willingness to respond.

[0189] Then, using the TensorFlow 2.0 framework in Python 3.7, an LSTM neural network was trained on the sample time series. Each layer consisted of three stacked units, with 32 neurons in each hidden layer. Each batch contained 128 samples, and the learning rate was 0.006. An early termination training strategy was used, and each feature model was trained independently for 50,000 iterations. Experimental results show that the root mean square error (RMSE) of the four feature training set samples after LSTM training is less than the RMSE of the corresponding test set samples, and no overfitting is observed (Table 8).

[0190] Table 8 Comparison of RMSE fits between LSTM training and test sets

[0191] Sample training set 0.0636 0.0723 0.0726 0.0619 Sample test set 0.0854 0.0886 0.0874 0.0914

[0192] This invention uses deep learning tools in MATLAB to construct a DNN model and compares it with an LSTM model. Based on the input and output feature dimensions, the input layer has 4 neurons, the output layer has 1 neuron, and the hidden layers are set to 2 layers with 30 and 20 neurons respectively. The learning rate is 0.001, and the activation function is tanh. After training set normalization, the RMSE of the DNN fit on the training set samples is smaller than that on the corresponding test set samples; no overfitting is observed in Table 9.

[0193] Table 9 Comparison of RMSE fits between DNN training and test sets

[0194] Sample training set 0.1724 0.1739 0.1654 0.1674 Sample test set 0.2163 0.2078 0.1961 0.1939

[0195] like Figure 3As shown, the integration of the P2P electricity trading model may lead to a significant increase in power consumption for both WTG and PVG. When P2P trading is not implemented, distributed renewable resources with equal total capacity are placed on the nodes of the trading broker. This highlights the potential of P2P trading in improving energy consumption flexibility, promoting the use of renewable energy, and minimizing the need for long-distance electricity trading. Therefore, the application of this P2P electricity trading model in ADN operation and management is of great significance.

[0196] Figure 4 This displays the overall electricity trading in the actual system, accommodating different network usage fee unit prices. For example... Figure 4 As shown in Table 10, reducing the network usage fee unit leads to a simultaneous increase in the number of transactions. However, it is crucial to limit electricity transactions to optimize network usage fee profitability and prevent any negative impact on branch traffic. Using this method, the optimal network usage fee (US$0.3264) can be obtained.

[0197] Table 10 Comparison of Profit and Loss under Different Business Management Models (×10) 5 $)

[0198] Model Total loss income The operation management model of the present invention 30.6 269.4 Ignoring network usage fees 89.8 - P2P transactions not considered 21.7 -

[0199] This invention provides a method for optimizing the collaborative operation of a distribution network by multiple stakeholders, taking into account price incentives for vehicle-to-grid (V2G) interaction. It constructs a V2G parameter configuration strategy to avoid adverse behaviors during V2G scheduling, alleviate additional battery losses, and enhance user willingness to respond. For real-time optimized scheduling, it ensures safe V2G response while evaluating V2G response capacity under multiple factors and proposes a collaborative scheduling strategy for EV grid access peak shaving that takes into account V2G price incentives. Simultaneously, considering the network usage fees collected by the distribution network, it constructs a power trading clearing model with market stakeholders participating in market transactions, aiming to maximize their own profits. Finally, it establishes a multi-stakeholder collaborative operation optimization model for the distribution network, which can effectively reduce grid operating costs, increase user benefits, and promote the consumption of renewable energy.

[0200] like Figure 5As shown, another embodiment of the present invention provides a distribution network multi-entity collaborative operation optimization system considering price incentives for vehicle-to-grid interaction, comprising: a construction module for constructing a V2G parameter configuration strategy based on user response willingness, wherein the V2G parameters include charging price, discharging incentive price, current rate limit, and discharge depth limit; a real-time scheduling module for applying the results determined according to the V2G parameter configuration strategy to the real-time optimization scheduling of the distribution network, establishing a real-time optimization scheduling model with the goal of minimizing operating costs, and calculating the V2G response capacity in real time; a transaction clearing module for considering the distribution network usage fee situation, constructing a power transaction clearing model with the goal of maximizing the profits of market stakeholders, and obtaining the power transaction clearing result; and a collaborative optimization module for combining the real-time optimization scheduling model and the power transaction clearing model to establish a distribution network multi-entity collaborative operation optimization model, performing optimization solutions, and obtaining the optimal operation scheme.

[0201] The V2G parameter configuration strategy includes: response willingness assessment and parameter setting. The response willingness assessment is based on a fuzzy logic system with V2G parameters as input variables and user response willingness values ​​as output results. It quantifies user response willingness through membership function construction, fuzzy rule base operation and defuzzification. The parameter setting is used to filter out users with high response willingness based on response willingness thresholds, and to reversely determine the user's psychological expectation range of V2G parameters based on the input variables corresponding to high response willingness, thus completing the V2G parameter setting.

[0202] The execution process of each module can be carried out according to the steps of the multi-entity collaborative operation optimization method for power distribution network considering price incentive vehicle-network interaction in the first embodiment, and will not be described in detail in this embodiment.

[0203] The third embodiment of the present invention provides a smart terminal, which includes a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the multi-entity collaborative operation optimization method for distribution networks that considers price incentives and vehicle-to-grid interaction, as described in the first embodiment above.

[0204] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0205] Input devices may include touchpads, microphones, etc., and output devices may include displays (LCDs, etc.), speakers, etc.

[0206] The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store information about the device type.

[0207] In specific implementations, the processor, input device, and output device described in the embodiments of the present invention can execute the implementation methods described in the method embodiments of the present invention, or they can execute the implementation methods described in the system embodiments of the present invention, which will not be repeated here.

[0208] The present invention also provides an embodiment of a computer-readable storage medium storing a computer program, the computer program including program instructions, which, when executed by a processor, cause the processor to perform the multi-entity collaborative operation optimization method for distribution network considering price incentives and vehicle-to-grid interaction described in the above embodiments.

[0209] The computer-readable storage medium can be an internal storage unit of the terminal described in the foregoing embodiments, such as the terminal's hard drive or memory. The computer-readable storage medium can also be an external storage device of the terminal, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal. Further, the computer-readable storage medium can include both internal storage units and external storage devices of the terminal. The computer-readable storage medium is used to store the computer program and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0210] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0211] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the terminals and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0212] In the several embodiments provided in this application, it should be understood that the disclosed terminals and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices or units, or may be electrical, mechanical or other forms of connection.

[0213] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for optimizing the collaborative operation of a distribution network involving multiple stakeholders, considering price incentives and vehicle-network interaction, characterized in that: include: A V2G parameter configuration strategy based on user response intentions and results is constructed, wherein the V2G parameters include charging price, discharging incentive price, current rate limit and discharge depth limit; The results determined according to the V2G parameter configuration strategy are applied to the real-time optimization scheduling of the distribution network to establish a real-time optimization scheduling model with the goal of minimizing operating costs and to calculate the V2G response capacity in real time. Considering the usage fees of the power distribution network, a power trading clearing model is constructed with the goal of maximizing the profits of market stakeholders, and the power trading clearing results are obtained. A multi-entity collaborative operation optimization model for the distribution network is established by combining a real-time optimization scheduling model and a power trading clearing model. The optimal operation scheme is obtained by optimizing the solution.

2. The method for optimizing the multi-entity collaborative operation of a distribution network considering price incentives and vehicle-network interaction as described in claim 1, characterized in that, The V2G parameter configuration strategy includes: response willingness assessment and parameter setting. The response willingness assessment is based on a fuzzy logic system with V2G parameters as input variables and user response willingness values ​​as output results. It quantifies user response willingness through membership function construction, fuzzy rule base operation, and defuzzification. The parameter setting is used to filter out users with high response willingness based on response willingness thresholds, and to reversely determine the user's psychological expectation range of V2G parameters based on the input variables corresponding to high response willingness, thus completing the V2G parameter setting.

3. The method for optimizing the multi-entity collaborative operation of a distribution network considering price incentives and vehicle-network interaction as described in claim 2, characterized in that, The method for constructing the membership function includes: Based on the Weber-Fechner law, the user's perception of charging and discharging prices is characterized, and a membership function of charging and discharging prices is constructed. Based on the battery dynamic aging model and the requirements for power battery life in national standards, and combined with changes in user acceptance, a membership function of current rate and depth of discharge is constructed.

4. The method for optimizing the multi-entity collaborative operation of a distribution network considering price incentives and vehicle-network interaction as described in claim 1, characterized in that, The optimization objective of the real-time optimized scheduling model is to minimize the sum of the costs of purchasing electricity from the main grid, grid loss costs, renewable energy purchase costs, and V2G price compensation in a single time period.

5. The method for optimizing the multi-entity collaborative operation of a distribution network considering price incentives and vehicle-network interaction as described in claim 1, characterized in that, The electricity trading clearing model is a P2P electricity trading model that uses a long short-term memory neural network. It takes lighting, temperature, wind speed, trading willingness and market share as input features and outputs the initial trading price and trading volume.

6. The method for optimizing the multi-entity collaborative operation of a distribution network considering price incentives and vehicle-network interaction as described in claim 1, characterized in that, The method for calculating network usage fees includes: calculating the electrical distance between nodes, and calculating the network usage fee based on the electrical distance and the unit price of network usage fees.

7. A multi-entity collaborative operation optimization system for distribution networks that considers price incentives and vehicle-network interaction, characterized in that, include: The module is used to build a V2G parameter configuration strategy based on user response intentions and results. The V2G parameters include charging price, discharge incentive price, current rate limit and discharge depth limit. The real-time scheduling module is used to apply the results determined according to the V2G parameter configuration strategy to the real-time optimization scheduling of the distribution network, establish a real-time optimization scheduling model with the goal of minimizing operating costs, and calculate the V2G response capacity in real time. The transaction clearing module is used to consider the distribution network usage fee and construct an electricity transaction clearing model with the goal of maximizing the profits of market stakeholders, so as to obtain the electricity transaction clearing result. The collaborative optimization module combines the real-time optimization scheduling model and the power trading clearing model to establish a multi-entity collaborative operation optimization model for the distribution network, and performs optimization to obtain the optimal operation scheme.

8. The power distribution network multi-entity collaborative operation optimization system considering price incentives and vehicle-grid interaction as described in claim 7, characterized in that, The V2G parameter configuration strategy includes: response willingness assessment and parameter setting. The response willingness assessment is based on a fuzzy logic system with V2G parameters as input variables and user response willingness values ​​as output results. It quantifies user response willingness through membership function construction, fuzzy rule base operation, and defuzzification. The parameter setting is used to filter out users with high response willingness based on response willingness thresholds, and to reversely determine the user's psychological expectation range of V2G parameters based on the input variables corresponding to high response willingness, thus completing the V2G parameter setting.

9. A smart terminal, comprising a processor, an input device, an output device, and a memory, wherein the processor, input device, output device, and memory are interconnected, and the memory is used to store a computer program, the computer program comprising program instructions, characterized in that, The processor is configured to invoke the program instructions and execute the computer program to implement the multi-entity collaborative operation optimization method for power distribution networks that considers price incentives and vehicle-to-grid interaction, as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions cause the processor to perform the multi-entity collaborative operation optimization method for distribution networks that considers price incentives and vehicle-to-grid interaction as described in any one of claims 1 to 6.

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