Optimal dispatching method for electric vehicles considering market competitiveness of multiple types of power suppliers

Abstract

The present invention proposes an electric vehicle optimization scheduling method that takes into account the market competitiveness of multiple types of power suppliers, including: establishing a non-cooperative game model of multiple types of energy power suppliers (such as photovoltaic, wind power and traditional power suppliers) under multiple time and space scenario factors; constructing an electric vehicle user evolution game model based on the travel needs of electric vehicle users; with the goal of maximizing the target benefits of power suppliers and maximizing user satisfaction, the two-layer game model is solved through a distributed iterative algorithm to achieve the overall optimization of the system bidding scheduling. The method proposed in the present invention applies game theory to the power market, explores the potential of various entities to participate in the power market competition, improves the operating efficiency of the power grid, makes full use of the power generation capacity of distributed energy, reduces energy waste, promotes the development and application of new energy technologies, and promotes the low-carbon development of energy, providing an effective method for the management and optimization of the low-carbonization of the power market.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle optimization dispatching in the power market, and in particular to an electric vehicle optimization dispatching method taking into account the market competitiveness of multiple types of power suppliers. Background Art

[0002] Energy decarbonization is the future development direction of the power system. With the rapid growth of electric vehicles and the continuous promotion of renewable energy, the trend of diversification of power demand and power suppliers is becoming increasingly obvious. Multiple types of power suppliers (such as photovoltaic, wind power and traditional power suppliers) participate in the power market competition and interact with electric vehicle users to optimize resource allocation and improve resource utilization. However, how to balance the interests of multiple types of power suppliers and electric vehicle users under the bidding mechanism faces the following difficulties:

[0003] (1) The interactive relationships among multiple entities in the electricity market are complex and difficult to characterize;

[0004] (2) The uncertainty and volatility of renewable resources pose challenges to the competitiveness of power suppliers;

[0005] (3) Market interactions are complex, and it is difficult to balance the interests of all participants. Summary of the invention

[0006] In view of the shortcomings of the existing technology, the present invention provides an electric vehicle optimization scheduling method that takes into account the market competitiveness of multiple types of power suppliers, which solves the technical problem that the interaction strategies of multiple types of entities in the current power market are insufficient, resulting in many difficulties in realizing the interaction between power suppliers and users.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] An electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers, the method comprising the following steps:

[0009] S1. Construct a market environment and interactive strategy for transactions between multiple types of power suppliers and electric vehicle users that takes into account uncertainty and multiple time and space scales; the multiple types of power suppliers include: photovoltaic power suppliers, wind power power suppliers and traditional power suppliers;

[0010] S2. Construct constraints for shared charging areas based on the spatiotemporal characteristics of regional charging stations and the operating characteristics of each power supplier;

[0011] S3. Based on the interactive strategy and target requirements, and taking into account the output uncertainty of photovoltaic and wind power suppliers and the output characteristics of traditional power suppliers, a non-cooperative game model and constraint conditions for multi-type energy power suppliers are constructed;

[0012] S4. According to the user travel behavior and demand preferences and the characteristics of the electricity market, the lower-level electric vehicle user selection game model and its constraints are constructed based on the logit dynamic evolution equation;

[0013] S5. With the goal of maximizing the target benefits of power suppliers and maximizing user satisfaction, the two-layer game model is solved through a distributed iterative algorithm.

[0014] Furthermore, the interactive strategy in step S1 is as follows: each power supplier competes for the reserve power strategy through non-cooperative game, and sends the reserve power and electricity price strategy to the power market at the same time. Electric vehicle users select power suppliers for transactions through evolutionary games based on price signals and report their own needs to power suppliers. Power suppliers integrate the total demand of users in each time period, formulate power plans, and arrange charging for electric vehicle users according to user needs.

[0015] Furthermore, step S2 specifically includes:

[0016] S21. Design the output model of photovoltaic generators and analyze the photovoltaic output characteristics under multiple time and space scenarios. The formula is expressed as:

[0017] ;

[0018] in, yes t The output of photovoltaic generators at all times; is the total number of photovoltaic arrays; It is the rated output power of the photovoltaic array under standard test conditions; is the solar irradiance under standard test conditions; is the actual solar irradiance at the working point; is the temperature coefficient; is the temperature of the working point at time t; is the temperature under standard test conditions;

[0019] S22. Design a wind turbine generator output model and analyze the wind power output characteristics under multiple time and space scenarios. The formula is expressed as:

[0020] ;

[0021] in, is the output of the wind turbine generator set at time t; is the rated power of the wind turbine; is the cut-in wind speed; is the minimum cut-in wind speed; is the maximum cut-in wind speed;

[0022] S23. Design a model for energy storage devices for photovoltaic and wind power suppliers to balance the volatility of renewable energy power market bidding. The formula is:

[0023] ;

[0024] in, The amount of energy stored after the energy storage device is charged / discharged; It is the energy storage before charging / discharging; is the energy loss rate of the energy storage system; is the charging power at time t; is the energy released at time t; and They are charging and discharging efficiency respectively; is the charge / discharge time;

[0025] S24. Design system operation constraints under the condition of shared charging area by multiple types of power suppliers, including:

[0026] Number of charging piles occupied by power suppliers:

[0027] ;

[0028] Qualification constraints for power suppliers to occupy charging piles:

[0029] ;

[0030] Restrictions on the number of charging piles occupied by power suppliers:

[0031] ;

[0032] in, is the number of charging piles occupied by power supplier j; is the output of power supplier j at time t, i.e., the reserve power; is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required by the power supplier.

[0033] Furthermore, step S3 specifically includes:

[0034] S31. Design the power supply costs of various types of power suppliers and set power prices. The power supply costs take into account the power supply costs, profit benefits and power error costs of the power suppliers, and the formula is expressed as follows:

[0035] ;

[0036] in, is the total power supply cost of power supplier j at time t; is the power generation cost of power supplier j at time t; is the profit benefit of power supplier j at time t; is the electricity error cost of power supplier j at time t;

[0037] S32. Design the objective function of the non-cooperative game model within the power supplier's dispatch cycle, i.e., the cost function , the solution goal of the model is to minimize the cost function, and the specific formula is expressed as:

[0038] ;

[0039] ;

[0040] S33. Design the operation constraints of the non-cooperative game model of each power supplier during the dispatch cycle, considering two operation conditions. The specific constraints are:

[0041] The first case: When the number of charging piles meets the power supply demand of the power supplier, the reserve power of each power supplier at each time should not exceed its maximum output, which can be expressed as:

[0042] ;

[0043] The second situation: When the number of charging piles cannot meet the total demand for reserve power of each power supplier, the reserve power of each power supplier at each time should not exceed the maximum allowable charging power of the charging pile, which is expressed as:

[0044] ;

[0045] in, is the output of power supplier j at time t, i.e., the reserve power; is the maximum output of power supplier j at time t; is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required for power suppliers;

[0046] S34. According to the cost function and constraints, a non-cooperative game model of power suppliers is established, and the equilibrium solution is obtained, which is specifically expressed as:

[0047] Participant Set W j , that is, power supplier j∈ W j , expressed as:

[0048] ;

[0049] Policy Set , describes the reserve power set of the power supplier in each period, which is specifically expressed as:

[0050] ;

[0051] Cost function:

[0052] ;

[0053] in, The policy set generated for electricity supplier j; The strategic reserve power for power supplier j at time t; is the strategy selection except for power supplier j; the above strategy set Linearly bounded and continuous, the cost function is a strictly convex function, then the unique equilibrium solution is obtained , so that .

[0054] More specifically, the calculation process of the power supplier's power generation cost, profit benefit and power error cost in step S31 is respectively:

[0055] The power generation cost of power supplier j at time t It can be expressed as:

[0056] ;

[0057] The profit of power supplier j at time t It can be expressed as:

[0058] ;

[0059] The power error cost of power supplier j at time t It can be expressed as:

[0060] ;

[0061] in, and are the secondary cost coefficient and primary cost coefficient of power supplier j respectively; Prepare power for power supplier j in time period t; is the profit coefficient of power supplier j at time t; is the electricity price of power supplier j at time t; is the electricity planning error cost coefficient, and all coefficients are greater than 0;

[0062] is the total user demand of power supplier j at time t, and its formula is expressed as:

[0063] ;

[0064] in, is the electricity demand of type i user selecting power supplier j at time t.

[0065] Furthermore, step S4 specifically includes:

[0066] S41. Establish an electric vehicle user benefit model that takes into account user electricity preferences, specifically:

[0067] The electric vehicle user benefit function model is established based on the user's travel habits, and the formula is expressed as follows:

[0068] ;

[0069] in, 、 , is the user weight coefficient, all greater than 0; is the distance coefficient from type i user to power supplier j; The service benefits obtained by type i user from power supplier j; Scheduling time cost function for type i user; Scheduling cost satisfaction for type i users;

[0070] S42, establishing constraints for generating an electric vehicle user strategy set, specifically including the following constraints:

[0071] 1) When an electric vehicle user is charging, the charging power at any time cannot be less than the system's minimum charging power and cannot be greater than the maximum rated charging power;

[0072] Establishing charging power The constraints are:

[0073] ;

[0074] in, is the minimum rated charging power; is the maximum rated charging power;

[0075] 2) Establish battery charging constraints as:

[0076] ;

[0077] in, is the state of charge at time t+1, is the state of charge at time t, For battery charging efficiency; is the rated capacity of the battery, For charging time;

[0078] 3) Considering battery life, safety, battery capacity, and dynamic constraints on battery state of charge:

[0079] ;

[0080] in, is the minimum state of charge limit of the battery, It is the maximum state of charge limit of the battery;

[0081] 4) The state of charge of electric vehicle users after charging cannot be lower than the preset expected value and cannot exceed the battery capacity. Establish the charging demand constraint, the expression is:

[0082] ;

[0083] in, The expected state of charge of the electric vehicle user; It is the state of charge of the electric vehicle user at the end of charging;

[0084] 5) Electric vehicle users only charge during the response period and not during other periods. Establish scheduling period constraints for electric vehicle users:

[0085] ;

[0086] in, is the charging power at time t; Access moments for electric vehicle users; Departure moments for electric car users;

[0087] S43. Establish an electric vehicle user population evolution game model, which is specifically expressed as follows:

[0088] Participants W i , describing different types of user groups, each of which contains multiple individual users with consistent travel behaviors; specifically expressed as:

[0089] ;

[0090] Policy Set , describes the choice and demand of type i users for power suppliers during the dispatch period, which can be specifically expressed as:

[0091] ;

[0092] Benefit function:

[0093] ;

[0094] In the above formula, is the strategy selection vector when type i user selects a power supplier j, satisfying ;Benefit function It is used to describe the payment situation when type i user chooses power supplier j;

[0095] S44. Dynamically simulate the decision-making behavior of the EV population based on Logit, specifically:

[0096] During the dynamic evolution of the user's choice of power supplier transaction, each time there is an opportunity to modify the strategy, the user will compare the benefits of choosing the current strategy with the benefits of choosing other strategies and use the conditional conversion rate to modify the strategy. Modify its own strategy; when the Logit protocol is used for conditional conversion, the conditional conversion probability of type i user from strategy m to strategy j is:

[0097] ;

[0098] The dynamic evolution equation of the user group is obtained as follows:

[0099] ;

[0100] Discretize the dynamic evolution equation again and get the following formula:

[0101] ;

[0102] in, is the noise level; is the number of evolutionary game iterations; is the iteration step of the evolutionary game; exp[ ] refers to the exponential function with the natural number e as the base;

[0103] when , that is, when all individuals in the population do not change their decisions, evolution reaches a state of equilibrium.

[0104] More specifically, in step S41, the calculation process of each parameter in the user benefit function model is:

[0105] S411, distance coefficient from type i user to power supplier j , its formula is expressed as:

[0106] ;

[0107] in, is the distance from type i user to power supplier j, which is related to the distance coefficient Inversely proportional;

[0108] S412, service benefits obtained by type i user from power supplier j , its formula is expressed as:

[0109] ;

[0110] in, is the ancillary service level coefficient of power supplier j at time t, which is related to the power supply reliability and ancillary service setting level of the power supplier;

[0111] S413, according to the starting time of the electric vehicle user scheduling time, establish the electric vehicle user scheduling time cost function and the charging cost satisfaction function, specifically:

[0112] The power supply ratio of power suppliers is defined as follows:

[0113] ;

[0114] in, is the reserve power of charging station j at time t; hour, Take the maximum value ; hour, Take the minimum value ; ;

[0115] The electric vehicle user dispatch time satisfaction coefficient is defined as:

[0116] ;

[0117] The electric vehicle user charging cost satisfaction coefficient is defined as:

[0118] ;

[0119] The above two coefficients are inversely proportional to each other; based on the above two coefficients, combined with the electricity price of power supplier j at time t , get the scheduling time cost function of type i user and dispatch cost satisfaction The formulas are expressed as:

[0120] ;

[0121] ;

[0122] in, is the electricity demand of type i user selecting power supplier j at time t.

[0123] Furthermore, step S5 specifically includes:

[0124] S51. Establish the process of solving the evolutionary game of electric vehicle users, which is expressed as:

[0125] 1) Randomly generate the initial electricity sales price for each power supplier ;

[0126] 2) According to the constraints obtained in steps S2, S3, and S4, the initial strategy of the electric vehicle user population is randomly generated;

[0127] 3) Calculate the benefit function ;

[0128] 4) Calculate the probability of strategy switching ;

[0129] 5) Update the user strategy adoption ratio;

[0130] 6) Repeat 3)-5) until evolutionary equilibrium is reached;

[0131] 7) Output user population stabilization strategy;

[0132] S52. Establish the non-cooperative bidding process for power suppliers, expressed as:

[0133] 1) Each power supplier calculates the total demand of users within its jurisdiction ;

[0134] 2) Each power supplier shall Update the respective battery reserve power Electricity sales price Strategy;

[0135] 3) Until the convergence condition is reached ;

[0136] 4) Each power supplier reports its final power reserve Electricity sales price to the electricity market;

[0137] S53, repeat steps S51 and S52, iterate multiple times until the strategy converges and stabilizes, solve the equilibrium solution, and achieve the overall optimization of the system bidding-scheduling.

[0138] By means of the above technical solution, the method proposed by the present invention has at least the following beneficial effects:

[0139] 1. By constructing a dispatching model based on a two-layer game, the present invention models the market competition among multiple types of power suppliers and their interactive relationship with electric vehicle users, ensuring the balance of supply and demand under different time scales and market conditions, taking into account the benefits of power suppliers and users and the overall stability of the power system, improving the efficiency of renewable energy utilization, and providing a more promising trading strategy for the existing power market.

[0140] 2. The present invention takes into account the multi-dimensional travel needs of users, and by optimizing the charging behavior of electric vehicle users, improves the flexibility of users in choosing prices, charging time and locations, taps the potential of electric vehicle users to participate in electricity market regulation, and promotes the popularization of green energy and environmentally friendly travel. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0142] Figure 1 A schematic diagram of the interaction between multiple types of power suppliers, power markets, and users of the present invention;

[0143] Figure 2 It is a schematic diagram of the solution process of the double-layer game model of the present invention. DETAILED DESCRIPTION

[0144] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0145] Although the steps in the present invention are arranged with numbers, they are not used to limit the order of the steps. Unless the order of the steps is clearly stated or the execution of a certain step requires other steps as a basis, the relative order of the steps can be adjusted. It is understood that the term "and / or" used in this article involves and covers any and all possible combinations of one or more of the associated listed items.

[0146] Please refer to Figure 1-2 This embodiment proposes an electric vehicle optimization scheduling method that takes into account the market competitiveness of multiple types of power suppliers based on the traditional power supplier trading strategy and electric vehicle optimization scheduling. It models the operating conditions of each trading entity to achieve efficient use of renewable energy and orderly guidance of electric vehicles, meet the pursuit of green new power systems, and provide a more diversified market bidding strategy that is more in line with the realization of low-carbon energy for the existing power bidding market. It also designs an electric vehicle user interaction strategy to achieve resource allocation optimization. The method includes the following steps:

[0147] S1. Construct a market environment and interactive strategy for transactions between multiple types of power suppliers and electric vehicle users that takes into account uncertainty and multiple time and space scales; the multiple types of power suppliers include: photovoltaic power suppliers, wind power power suppliers and traditional power suppliers;

[0148] More specifically, the interactive strategy in step S1 is as follows: each power supplier competes for the reserve power strategy through non-cooperative game, and sends the reserve power and electricity price strategy to the power market at the same time. Electric vehicle users select power suppliers for transactions through evolutionary games based on price signals and report their own needs to the power suppliers. The power suppliers integrate the total demand of users in each time period, formulate power plans, and arrange charging for electric vehicle users according to user needs.

[0149] In this embodiment, Figure 1 As shown, renewable energy power suppliers (including photovoltaic power suppliers and wind power power suppliers) and power grids (i.e. traditional power suppliers) can sell electricity. Power suppliers compete for reserved electricity through non-cooperative games based on their own operating conditions, and set charging electricity prices at the same time. The electricity and electricity prices prepared for sale at each time are sent to the power market. Electric vehicle users choose a power supplier to trade based on their travel needs and price information, and at the same time feed back their own travel information to the power market and power suppliers.

[0150] S2. Construct constraints for shared charging areas based on the spatiotemporal characteristics of regional charging stations and the operating characteristics of each power supplier;

[0151] As a preferred implementation of step S2, the following steps are specifically included:

[0152] S21. Design the output model of photovoltaic generators and analyze the photovoltaic output characteristics under multiple time and space scenarios. The formula is expressed as:

[0153] ;

[0154] in, yes t The output of photovoltaic generators at all times; is the total number of photovoltaic arrays; It is the rated output power of the photovoltaic array under standard test conditions; is the solar irradiance under standard test conditions; is the actual solar irradiance at the working point; is the temperature coefficient; is the temperature of the working point at time t; is the temperature under standard test conditions;

[0155] S22. Design a wind turbine generator output model and analyze the wind power output characteristics under multiple time and space scenarios. The formula is expressed as:

[0156] ;

[0157] in, is the output of the wind turbine generator set at time t; is the rated power of the wind turbine; is the cut-in wind speed; is the minimum cut-in wind speed; is the maximum cut-in wind speed;

[0158] S23. Design a model for energy storage devices for photovoltaic and wind power suppliers to balance the volatility of renewable energy power market bidding. The formula is:

[0159] ;

[0160] in, The amount of energy stored after the energy storage device is charged / discharged; It is the energy storage before charging / discharging; is the energy loss rate of the energy storage system; is the charging power at time t; is the energy released at time t; and They are charging and discharging efficiency respectively; is the charge / discharge time;

[0161] S24. Design system operation constraints under the condition of shared charging area by multiple types of power suppliers, including:

[0162] Number of charging piles occupied by power suppliers:

[0163] ;

[0164] Qualification constraints for power suppliers to occupy charging piles:

[0165] ;

[0166] Restrictions on the number of charging piles occupied by power suppliers:

[0167] ;

[0168] Among the above three formulas, is the number of charging piles occupied by power supplier j; is the output of power supplier j at time t, i.e., the reserve power; is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required by the power supplier.

[0169] In this embodiment, the size of the reserve power is determined based on the actual power generated by the supplier at the same time, that is, the output. The supplier may not necessarily put all the generated power at time t into the market game at that moment.

[0170] S3. Based on the interactive strategy and target requirements, and taking into account the output uncertainty of photovoltaic and wind power suppliers and the output characteristics of traditional power suppliers, a non-cooperative game model and constraint conditions for multi-type energy power suppliers are constructed;

[0171] As a preferred implementation of step S3, the following steps are specifically included:

[0172] S31. Design the power supply costs of various types of power suppliers and set power prices. The power supply costs take into account the power supply costs, profit benefits and power error costs of the power suppliers, and the formula is expressed as follows:

[0173] ;

[0174] in, is the total power supply cost of power supplier j at time t; is the power generation cost of power supplier j at time t; is the profit benefit of power supplier j at time t; is the electricity error cost of power supplier j at time t;

[0175] More specifically, the calculation process of the above power generation cost, profit benefit and power error cost in this embodiment is as follows:

[0176] The power generation cost of power supplier j at time t It can be expressed as:

[0177] ;

[0178] The profit of power supplier j at time t It can be expressed as:

[0179] ;

[0180] The power error cost of power supplier j at time t It can be expressed as:

[0181] ;

[0182] Among the above three formulas, and are the secondary cost coefficient and primary cost coefficient of power supplier j respectively; Prepare power for power supplier j in time period t; is the profit coefficient of power supplier j at time t; is the electricity price of power supplier j at time t; is the electricity planning error cost coefficient, and all coefficients are greater than 0;

[0183] is the total user demand of power supplier j at time t, and its formula is expressed as:

[0184] ;

[0185] in, The electricity demand of type i user to select power supplier j at time t;

[0186] S32. Design the objective function of the non-cooperative game model within the power supplier's dispatch cycle, i.e., the cost function , the solution goal of the model is to minimize the cost function, and the specific formula is expressed as:

[0187] ;

[0188] ;

[0189] S33. Design the operation constraints of the non-cooperative game model of each power supplier during the dispatch cycle, considering two operation conditions. The specific constraints are:

[0190] The first case: When the number of charging piles meets the power supply demand of the power supplier, the reserve power of each power supplier at each time should not exceed its maximum output, which can be expressed as:

[0191] ;

[0192] The second situation: When the number of charging piles cannot meet the total demand for reserve power of each power supplier, the reserve power of each power supplier at each time should not exceed the maximum allowable charging power of the charging pile, which is expressed as:

[0193] ;

[0194] In the above two formulas, is the output of power supplier j at time t, i.e., the reserve power; is the maximum output of power supplier j at time t; is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required for power suppliers;

[0195] S34. According to the cost function and constraints, a non-cooperative game model of power suppliers is established, and the equilibrium solution is obtained, which is specifically expressed as:

[0196] Participant Set W j , that is, power supplier j∈ W j , expressed as:

[0197] ;

[0198] Policy Set , describes the reserve power set of the power supplier in each period, which is specifically expressed as:

[0199] ;

[0200] Cost function:

[0201] ;

[0202] in, The policy set generated for electricity supplier j; The strategic reserve power for power supplier j at time t; is the strategy selection except for power supplier j; the above strategy set Linearly bounded and continuous, the cost function is a strictly convex function, then the unique equilibrium solution is obtained , so that .

[0203] S4. According to the user travel behavior and demand preferences and the characteristics of the electricity market, the lower-level electric vehicle user selection game model and its constraints are constructed based on the logit dynamic evolution equation improved by the imitation protocol;

[0204] As a preferred implementation of step S4, the following steps are specifically included:

[0205] S41. Establish an electric vehicle user benefit model that takes into account user electricity preferences, specifically:

[0206] The electric vehicle user benefit function model is established based on the user's travel habits, and the formula is expressed as follows:

[0207] ;

[0208] in, 、 , is the user weight coefficient, all greater than 0; is the distance coefficient from type i user to power supplier j; The service benefits obtained by type i user from power supplier j; Scheduling time cost function for type i user; Scheduling cost satisfaction for type i users;

[0209] More specifically, the above benefit function The calculation process of each parameter in this embodiment is:

[0210] S411, distance coefficient from type i user to power supplier j , its formula is expressed as:

[0211] ;

[0212] in, is the distance from type i user to power supplier j, which is related to the distance coefficient Inversely proportional;

[0213] S412, service benefits obtained by type i user from power supplier j , its formula is expressed as:

[0214] ;

[0215] in, is the ancillary service level coefficient of power supplier j at time t, which is related to the power supply reliability and ancillary service setting level of the power supplier;

[0216] S413, according to the starting time of the electric vehicle user scheduling time, establish the electric vehicle user scheduling time cost function and the charging cost satisfaction function, specifically:

[0217] The power supply ratio of power suppliers is defined as follows:

[0218] ;

[0219] in, is the reserve power of charging station j at time t; hour, Take the maximum value ; hour, Take the minimum value ; ;

[0220] The electric vehicle user dispatch time satisfaction coefficient is defined as:

[0221] ;

[0222] The electric vehicle user charging cost satisfaction coefficient is defined as:

[0223] ;

[0224] The above two coefficients are inversely proportional to each other; based on the above two coefficients, combined with the electricity price of power supplier j at time t , get the scheduling time cost function of type i user and dispatch cost satisfaction The formulas are expressed as:

[0225] ;

[0226] ;

[0227] in, is the electricity demand of type i user selecting power supplier j at time t.

[0228] S42, establishing constraints for generating an electric vehicle user strategy set, specifically including the following constraints:

[0229] 1) When an electric vehicle user is charging, the charging power at any time cannot be less than the system's minimum charging power and cannot be greater than the maximum rated charging power;

[0230] Establishing charging power The constraints are:

[0231] ;

[0232] in, is the minimum rated charging power; is the maximum rated charging power;

[0233] 2) Establish battery charging constraints as:

[0234] ;

[0235] in, is the state of charge at time t+1, is the state of charge at time t, For battery charging efficiency; is the rated capacity of the battery, For charging time;

[0236] 3) Considering battery life, safety, battery capacity, and dynamic constraints on battery state of charge:

[0237] ;

[0238] in, is the minimum state of charge limit of the battery, It is the maximum state of charge limit of the battery;

[0239] 4) The state of charge of electric vehicle users after charging cannot be lower than the preset expected value and cannot exceed the battery capacity. Establish the charging demand constraint, the expression is:

[0240] ;

[0241] in, The expected state of charge of the electric vehicle user; It is the state of charge of the electric vehicle user at the end of charging;

[0242] 5) Electric vehicle users only charge during the response period and not during other periods. Establish scheduling period constraints for electric vehicle users:

[0243] ;

[0244] in, is the charging power at time t; Access moments for electric vehicle users; Departure moments for electric car users;

[0245] S43. Establish an electric vehicle user population evolution game model, which is specifically expressed as follows:

[0246] Participants W i , describing different types of user groups, each of which contains multiple individual users with consistent travel behaviors; specifically expressed as:

[0247] ;

[0248] Policy Set , describes the choice and demand of type i users for power suppliers during the dispatch period, which can be specifically expressed as:

[0249] ;

[0250] Benefit function:

[0251] ;

[0252] In the above formula, is the strategy selection vector when type i user selects a power supplier j, satisfying ;Benefit function It is used to describe the payment situation when type i user chooses power supplier j;

[0253] S44. Dynamically simulate the decision-making behavior of the EV population based on Logit, specifically:

[0254] During the dynamic evolution of the user's choice of power supplier transaction, each time there is an opportunity to modify the strategy, the user will compare the benefits of choosing the current strategy with the benefits of choosing other strategies and use the conditional conversion rate to modify the strategy. Modify its own strategy; when the Logit protocol is used for conditional conversion, the conditional conversion probability of type i user from strategy m to strategy j is:

[0255] ;

[0256] The dynamic evolution equation of the user group is obtained as follows:

[0257] ;

[0258] Discretize the dynamic evolution equation again and get the following formula:

[0259] ;

[0260] In the above two formulas, is the noise level; is the number of evolutionary game iterations; is the iteration step of the evolutionary game; exp[ ] refers to the exponential function with the natural number e as the base;

[0261] when , that is, when all individuals in the population do not change their decisions, evolution reaches a state of equilibrium.

[0262] S5. With the goal of maximizing the target benefits of power suppliers and maximizing user satisfaction, the two-layer game model is solved through a distributed iterative algorithm;

[0263] As a preferred implementation of step S5, Figure 2 As shown, the specific steps include:

[0264] S51. Establish the process of solving the evolutionary game of electric vehicle users, which is expressed as:

[0265] 1) Randomly generate the initial electricity sales price for each power supplier ;

[0266] 2) According to the constraints obtained in steps S2, S3, and S4, the initial strategy of the electric vehicle user population is randomly generated;

[0267] 3) Calculate the benefit function of electric vehicle users’ payment ;

[0268] 4) Calculate the probability of strategy switching ;

[0269] 5) Update the user strategy adoption ratio;

[0270] 6) Repeat steps 3)-5) until the evolution reaches equilibrium;

[0271] 7) Output user population stabilization strategy;

[0272] S52. Establish the non-cooperative bidding process for power suppliers, expressed as:

[0273] 1) Each power supplier calculates the total demand of users within its jurisdiction ;

[0274] 2) Each power supplier shall Update the respective battery reserve power Electricity sales price Strategy;

[0275] 3) Until the convergence condition is reached ;

[0276] 4) Each power supplier reports its final power reserve Electricity sales price to the electricity market;

[0277] S53, repeat steps S51 and S52, iterate multiple times until the strategy converges and stabilizes, solve the equilibrium solution, and achieve the overall optimization of the system bidding-scheduling.

[0278] In summary, the electric vehicle optimization scheduling method proposed in the present invention taking into account the market competitiveness of multiple types of power suppliers provides a new operating solution for optimizing bidding in the existing electric vehicle market. It can consider the market interaction of photovoltaic power suppliers, wind power power suppliers, traditional power suppliers and users on the basis of multi-stakeholder transactions. Through two-layer bidding optimization, on the basis of achieving supply and demand balance, it takes into account the benefits of power suppliers and users and the overall stability of the power system, improves the utilization efficiency of renewable energy, and realizes the low carbonization of the power system. At the same time, it takes into account the multi-dimensional travel needs of the user side. By optimizing the charging behavior of electric vehicle users, it improves the flexibility of users in price, charging time and location selection, and reflects the flexibility of the overall system.

[0279] In this specification, the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" refer to at least one embodiment or example described in combination with specific features, structures, materials or characteristics. These described specific features, structures, materials or characteristics can be combined in an appropriate manner in one or more embodiments or examples. In addition, the technician can combine and combine different embodiments or examples and their features described in this specification without contradiction.

[0280] The logic and / or steps shown in the flowchart or described in other ways can be regarded as a sequence of executable instructions for implementing logical functions. These instructions can be implemented in any computer-readable medium for use by instruction execution systems, devices or equipment. These systems, devices or equipment include processor systems or other systems capable of receiving and executing instructions.

[0281] The above embodiments have described the principles and implementations of the present invention in detail, and have used specific examples to illustrate the working principles thereof. These examples are only used to help understand the method of the present invention and its core concept. Meanwhile, according to the concept of the present invention, the actual implementation methods and application scope may vary. Therefore, the contents of this specification should not be construed as limiting the present invention.

Claims

1. An electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers, characterized in that: The following steps are involved: S1. Construct a market environment and interactive strategy for transactions between multiple types of power suppliers and electric vehicle users that takes into account uncertainty and multiple spatial and temporal scales; The multiple types of power suppliers include: photovoltaic power suppliers, wind power power suppliers and traditional power suppliers; S2. Construct constraints for shared charging areas based on the spatiotemporal characteristics of regional charging stations and the operating characteristics of each power supplier; S3. Based on the interactive strategy and target requirements, and taking into account the output uncertainty of photovoltaic and wind power suppliers and the output characteristics of traditional power suppliers, a non-cooperative game model and constraint conditions for multiple types of energy power suppliers are constructed; Step S3 specifically includes: S31. Design the power supply costs of various types of power suppliers and set power prices. The power supply costs take into account the power supply costs, profit benefits and power error costs of the power suppliers, and the formula is: in, is the total power supply cost of power supplier j at time t; is the power generation cost of power supplier j at time t; is the profit benefit of power supplier j at time t; is the power error cost of power supplier j at time t; Step S31 specifically includes: The power generation cost of power supplier j at time t It can be expressed as: The profit of power supplier j at time t It can be expressed as: The power error cost of power supplier j at time t It can be expressed as: Among them, α j With β j are the secondary cost coefficient and primary cost coefficient of power supplier j respectively; Prepare power for power supplier j in time period t; is the profit coefficient of power supplier j at time t; is the electricity price of power supplier j at time t; ∈ is the electricity planning error cost coefficient, and all coefficients are greater than 0; is the total user demand of power supplier j at time t, and its formula is expressed as: in, The electricity demand of type i user to select power supplier j at time t; S32. Design the objective function of the non-cooperative game model within the power supplier's dispatch cycle, i.e., the cost function The solution goal of the model is to minimize the cost function, and the specific formula is expressed as: S33. Design the operation constraints of the non-cooperative game model of each power supplier during the dispatch cycle, considering two operation conditions. The specific constraints are: The first case: When the number of charging piles meets the power supply demand of the power supplier, the reserve power of each power supplier at each time should not exceed its maximum output, which can be expressed as: The second situation: When the number of charging piles cannot meet the total demand for reserve power of each power supplier, the reserve power of each power supplier at each time should not exceed the maximum allowable charging power of the charging pile, which is expressed as: in, is the output of power supplier j at time t, i.e., the reserve power; P is the maximum output of power supplier j at time t; c,N is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required for power suppliers; S34. According to the cost function and constraints, a non-cooperative game model of power suppliers is established, and the equilibrium solution is obtained, which is specifically expressed as: Participant Set W j , that is, power supplier j∈W j , expressed as: W j ={1,2,…,J}; Strategy set q j , describes the reserve power set of the power supplier in each period, which is specifically expressed as: Cost function: Among them, q j The policy set generated for electricity supplier j; The strategic reserve power for power supplier j at time t; q -j is the strategy selection except for power supplier j; the above strategy set q j Linearly bounded and continuous, the cost function is a strictly convex function, then the unique equilibrium solution is obtained Make S4. According to the user travel behavior and demand preferences and the characteristics of the electricity market, the lower-level electric vehicle user selection game model and its constraints are constructed based on the logit dynamic evolution equation; S5. With the goal of maximizing the target benefits of power suppliers and maximizing user satisfaction, the two-layer game model is solved through a distributed iterative algorithm.

2. The electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers according to claim 1 is characterized in that: In step S1, the interactive strategy is specifically as follows: each power supplier competes for the reserve power strategy through non-cooperative game, and sends the reserve power and electricity price strategy to the power market at the same time. Electric vehicle users select power suppliers for transactions through evolutionary games based on price signals and report their own needs to power suppliers. Power suppliers integrate the total demand of users in each time period, formulate power plans, and arrange charging for electric vehicle users according to user needs.

3. The electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers according to claim 1 is characterized in that: Step S2 specifically includes: S21. Design the output model of photovoltaic generators and analyze the photovoltaic output characteristics under multiple time and space scenarios. The formula is expressed as: Among them, P PV(t) is the output of the photovoltaic generator set at time t; N PV is the total number of photovoltaic arrays; P SC is the rated output power of the photovoltaic array under standard test conditions; G SC G is the solar irradiance under standard test conditions; C (t) is the actual solar irradiance at the working point; k is the temperature coefficient; T C (t) is the temperature of the working point at time t; T SC is the temperature under standard test conditions; S22. Design a wind turbine generator output model and analyze the wind power output characteristics under multiple time and space scenarios. The formula is expressed as: Among them, P wind (t) is the output of the wind turbine generator set at time t; P r is the rated power of the wind turbine; v is the cut-in wind speed; v min is the minimum cut-in wind speed; v max is the maximum cut-in wind speed; S23. Design a model for energy storage devices for photovoltaic and wind power suppliers to balance the volatility of renewable energy power market bidding. The formula is: in, The amount of energy stored after the energy storage device is charged / discharged; is the energy storage capacity before charging / discharging; δ1 is the energy loss rate of the energy storage system; is the charging power at time t; is the energy released at time t; η ch and η dis are the charging and discharging efficiencies respectively; Δt is the charging / discharging time; S24. Design system operation constraints under the condition of shared charging area by multiple types of power suppliers, including: Number of charging piles occupied by power suppliers: Qualification constraints for power suppliers to occupy charging piles: Restrictions on the number of charging piles occupied by power suppliers: in, is the number of charging piles occupied by power supplier j; is the output of power supplier j at time t, i.e., the reserve power; P c,N is the rated charging power of the charging pile; The minimum required number of charging piles for power suppliers; The maximum number of charging piles required by the power supplier.

4. The electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers according to claim 1 is characterized in that: Step S4 specifically includes: S41. Establish an electric vehicle user benefit model that takes into account user electricity preferences, specifically: The electric vehicle user benefit function model is established based on the user's travel habits, and the formula is expressed as follows: Among them, a i , b i ,k i is the user weight coefficient, all greater than 0; is the distance coefficient from type i user to power supplier j; The service benefits obtained by type i user from power supplier j; Scheduling time cost function for type i user; Scheduling cost satisfaction for type i users; S42, establishing constraints for generating an electric vehicle user strategy set, specifically including the following constraints: 1) When an electric vehicle user is charging, the charging power at any time cannot be less than the system's minimum charging power and cannot be greater than the maximum rated charging power; Establish charging power p t The constraints are: p min ≤p t ≤p max ; Among them, p min is the minimum rated charging power; p max is the maximum rated charging power; 2) Establish battery charging constraints as: Among them, SOC t+1 is the state of charge at time t+1, SOC t is the state of charge at time t, η c is the battery charging efficiency; C N is the rated capacity of the battery, Δt is the charging time; 3) Considering battery life, safety, battery capacity, and dynamic constraints on battery state of charge: SOC min ≤SOC t ≤SOC max ; Among them, SOC min SOC is the minimum state of charge limit of the battery. max It is the maximum state of charge limit of the battery; 4) The state of charge of the electric vehicle user after charging cannot be lower than the preset expected value and cannot exceed the battery capacity; establish the charging demand constraint, the expression is: SOC n ≤SOC out ≤1; Among them, SOC n SOC is the expected state of charge of electric vehicle users; out It is the state of charge of the electric vehicle user at the end of charging; 5) Electric vehicle users only charge during the response period and do not charge during other periods; establish scheduling period constraints for electric vehicle users: p t =0,t <t in or t>t out ; Among them, p t is the charging power at time t; t in Access time for electric vehicle users; t out Departure moments for electric car users; S43. Establish an electric vehicle user population evolution game model, which is specifically expressed as follows: Participant W i , describing different types of user groups, each of which contains multiple individual users with consistent travel behaviors; specifically expressed as: IN i ={1,2,…,I},i∈W i Strategy Set R i , describes the choice and demand of type i users for power suppliers during the dispatch period, which can be specifically expressed as: Benefit function: In the above formula, is the strategy selection vector when type i user selects a power supplier j, satisfying Benefit Function It is used to describe the payment situation when type i user chooses power supplier j; S44. Dynamically simulate the decision-making behavior of the EV population based on Logit, specifically: In the dynamic evolution of user selection of power supplier transactions, every time there is an opportunity to modify the strategy, the user will compare the benefits of choosing the current strategy with the benefits of choosing other strategies, and modify its own strategy at the conditional conversion rate σ; when the Logit protocol is used for conditional conversion, the conditional conversion probability of type i user from strategy m to strategy j is: The dynamic evolution equation of the user group is obtained as follows: Discretize the dynamic evolution equation again and get the following formula: In the above two formulas, η is the noise level; ω is the number of evolutionary game iterations; z is the evolutionary game iteration step; exp[] refers to the exponential function with the natural number e as the base; when That is, when all individuals in the population do not change their decisions, evolution reaches a state of equilibrium.

5. The electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers according to claim 4 is characterized in that: Step S41 specifically includes: S411, distance coefficient from type i user to power supplier j Its formula is: Among them, d i,j is the distance from type i user to power supplier j, which is related to the distance coefficient Inversely proportional; S412, service benefits obtained by type i user from power supplier j Its formula is: Among them, z t,j is the ancillary service level coefficient of power supplier j at time t, which is related to the power supply reliability and ancillary service setting level of the power supplier; S413, according to the starting time of the electric vehicle user scheduling time, establish the electric vehicle user scheduling time cost function and the charging cost satisfaction function, specifically: The power supply ratio of power suppliers is defined as follows: in, is the reserve power of charging station j at time t; When θ t Take the maximum value When θ t Take the minimum value 0≤θ t ≤1; The electric vehicle user dispatch time satisfaction coefficient is defined as: The electric vehicle user charging cost satisfaction coefficient is defined as: The above two coefficients are inversely proportional to each other; based on the above two coefficients, combined with the electricity price of power supplier j at time t Get the scheduling time cost function of type i user and dispatch cost satisfaction The formulas are expressed as: in, is the electricity demand of type i user selecting power supplier j at time t.

6. The electric vehicle optimization scheduling method considering the market competitiveness of multiple types of power suppliers according to claim 1 is characterized in that: Step S5 specifically includes: S51. Establish the process of solving the evolutionary game of electric vehicle users, which is expressed as: 1) Randomly generate the initial electricity selling price γ0 of each power supplier; 2) According to the constraints obtained in steps S2, S3, and S4, the initial strategy of the electric vehicle user population is randomly generated; 3) Calculate the benefit function 4) Calculate the probability of strategy switching 5) Update the user strategy adoption ratio; 6) Repeat steps 3)-5) until the evolution reaches equilibrium; 7) Output user population stabilization strategy; S52. Establish the non-cooperative bidding process for power suppliers, expressed as: 1) Each power supplier calculates the total demand of users within its jurisdiction 2) Each power supplier shall Update the reserve power of each battery Electricity sales price Strategy; 3) Until the convergence condition is reached 4) Each power supplier reports its final power reserve Electricity sales price to the electricity market; S53, repeat steps S51 and S52, iterate multiple times until the strategy converges and stabilizes, solve the equilibrium solution, and achieve the overall optimization of the system bidding-scheduling.

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