Photovoltaic power distribution network vehicle network cooperative control method and system based on cooperative game model

Abstract

The invention discloses a photovoltaic power distribution network vehicle-network cooperative control method and system based on a cooperative game model, and the method comprises the steps: determining the distribution weight according to the contribution degree of vehicle-network both parties to the cooperation alliance income, calculating the income of the vehicle-network both parties through employing a Shapley value, adjusting the weight through iteration until the distribution is stable, and obtaining the cooperative alliance income. Generating a current time-of-use electricity price for guiding charging and discharging of the EV; calculating a user charging and discharging strategy and an income redistribution result within the adjustable capacity range under the guidance of the current time-of-use electricity price; and judging whether a cycle termination condition is met or not, if the termination condition is met, exiting the cycle, and outputting the optimal income value of each subject and the current optimal electricity price. Through the cooperative game model, the photovoltaic consumption efficiency is improved, the power grid load fluctuation and the node voltage stability are improved, a fairness income distribution mechanism based on the contribution degree motivates the two parties to continuously participate, algorithm regulation and control are achieved depending on an existing monitoring device, extra hardware investment is not needed, and the method has both economical efficiency and engineering practicability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of power distribution, and particularly relates to a photovoltaic power distribution network vehicle network collaborative control method and system based on a cooperative game model. BACKGROUND

[0002] In the scenario of high proportion of distributed photovoltaic access to the power distribution network, the volatility of photovoltaic output leads to problems such as power flow reverse sending, distribution transformer overload, and voltage rise, which seriously affect the power quality of the power distribution network. At the same time, after a large number of electric vehicles (EV) are connected to the power grid, if their charging and discharging behaviors lack effective guidance, they will exacerbate the load fluctuation of the power grid, and even the phenomenon of "peak on peak" will occur.

[0003] The existing vehicle network interaction technology has obvious deficiencies. Traditional strategies focus on power grid dispatching or single user interests, and do not build a coalition model of the interests of both the vehicle network. It is difficult to form a sustainable interaction mechanism. The mainstream dispatching algorithm is not well adapted to the dynamic characteristics of photovoltaic output, leading to prominent problems of reverse sending of photovoltaic surplus power, and unable to realize efficient local consumption of photovoltaic resources. The existing revenue distribution model does not reasonably quantify the contribution difference of both the vehicle network to the coalition, and lacks distribution fairness, which cannot effectively encourage both sides to coordinate and control, resulting in long-term problems such as power grid voltage fluctuation. SUMMARY

[0004] The application provides a photovoltaic power distribution network vehicle network collaborative control method and system based on a cooperative game model, which is used to solve the technical problems of lack of vehicle network interaction coalition mechanism, low efficiency of photovoltaic consumption, and unfair revenue distribution in the prior art.

[0005] In a first aspect, the application provides a photovoltaic power distribution network vehicle network collaborative control method based on a cooperative game model, comprising:

[0006] According to the driving demand of the electric vehicle and the battery capacity constraint, the up-regulatable capacity and the down-regulatable capacity of the EV in each period are determined, and the regulatable capacity range is obtained;

[0007] A cooperative coalition of interaction between the vehicle network is constructed, and a vehicle network collaborative interaction game model is constructed under a preset constraint condition with the maximum revenue of the cooperative coalition as the target;

[0008] The allocation weight is determined according to the contribution degree of both the vehicle network to the revenue of the cooperative coalition, the revenue of both the vehicle network is calculated by using the Shapley value, the weight is adjusted by iteration until the allocation is stable, and the current time-of-use electricity price guiding the charging and discharging of the EV is generated;

[0009] The user charging and discharging strategy and the revenue redistribution result in the regulatable capacity range under the current time-of-use electricity price are calculated;

[0010] The judgment module is configured to judge whether a loop termination condition is met, and if the termination condition is met, the loop is exited, and optimal income values of each subject and a current optimal electricity price are output.

[0011] In a second aspect, the application provides a photovoltaic power distribution network vehicle network collaborative control system based on a cooperative game model, comprising:

[0012] The determination module is configured to determine the up-regulatable capacity and the down-regulatable capacity of the EV in each time period according to the driving demand of the electric vehicle and the battery capacity constraint, and obtain a regulatable capacity range.

[0013] The construction module is configured to construct a cooperative alliance of interaction between the vehicle network, and construct a vehicle network collaborative interaction game model with the maximum income of the cooperative alliance as the target under a preset constraint condition.

[0014] The generation module is configured to determine a distribution weight according to the contribution degrees of the vehicle network to the income of the cooperative alliance, calculate the incomes of the vehicle network by using a Shapley value, and generate a current time-of-use electricity price for guiding the charging and discharging of the EV by iteratively adjusting the weight until the distribution is stable.

[0015] The calculation module is configured to calculate a user charging and discharging strategy and a re-distribution result of income in the regulatable capacity range under the current time-of-use electricity price.

[0016] The output module is configured to judge whether a loop termination condition is met, and if the termination condition is met, the loop is exited, and optimal income values of each subject and a current optimal electricity price are output.

[0017] In a third aspect, an electronic device is provided, comprising at least one processor, and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the photovoltaic power distribution network vehicle network collaborative control method based on the cooperative game model of any embodiment of the application.

[0018] In a fourth aspect, the application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the photovoltaic power distribution network vehicle network collaborative control method based on the cooperative game model of any embodiment of the application.

[0019] The photovoltaic power distribution network vehicle network collaborative control method and system based on the cooperative game model of the application, through the photovoltaic monitoring device and the power grid monitoring system, real-time collection of photovoltaic output, power distribution network node voltage, power distribution network node current and load data, based on the driving characteristic model of the electric vehicle, calculation of the EV adjustable charging and discharging power range in each period, determination of the adjustable capacity; the power grid and the EV group are constructed as a cooperative alliance, and an optimization model is established with the goal of maximizing the alliance revenue, and the constraint conditions are checked and the EV charging and discharging strategy is solved by means of CPLEX / YALMIP solver; according to the contribution degree of both parties to the alliance revenue, the distribution weight is determined, the improved Shapley value is used to calculate the revenue and the weight is adjusted to the distribution stability, and the time-of-use electricity price guiding the EV charging and discharging is generated; the solver optimizes the EV charging and discharging strategy, guides the charging to consume surplus power in the photovoltaic peak period, discharges to support the power grid in the load peak period, judges the convergence based on the iterative difference of the alliance revenue, and outputs the optimal electricity price and the charging and discharging scheme, realizes the vehicle network collaborative interaction and the maximization of the alliance revenue. The scheme improves the photovoltaic consumption efficiency, improves the power grid load fluctuation and node voltage stability through the cooperative game model, and the fair revenue distribution mechanism based on the contribution degree encourages both parties to participate continuously, relies on the existing monitoring device to realize the algorithm regulation and control, does not need additional hardware investment, and has economic efficiency and engineering practicability. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creating laborious work.

[0021] Figure 1 A flow chart of a photovoltaic power distribution network vehicle network collaborative control method based on a cooperative game model provided by an embodiment of the application;

[0022] Figure 2 A 21-node low-voltage power distribution network topology provided by an embodiment of the application;

[0023] Figure 3 An adjustable capacity diagram of an electric vehicle provided by an embodiment of the application;

[0024] Figure 4 A schematic diagram of the power grid load before the electric vehicle participates in interaction provided by an embodiment of the application;

[0025] Figure 5 A schematic diagram of the power grid load after the electric vehicle participates in interaction provided by an embodiment of the application;

[0026] Figure 6A schematic diagram of voltage variation of a front and rear node 16 in cooperative interaction is provided in an embodiment of the present application.

[0027] Figure 7 A structural block diagram of a photovoltaic distribution network vehicle network cooperative control system based on a cooperative game model is provided in an embodiment of the present application.

[0028] Figure 8 A structural schematic diagram of an electronic device is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0030] Please refer to Figure 1 , which shows a flowchart of a photovoltaic distribution network vehicle network cooperative control method based on a cooperative game model.

[0031] As shown in Figure 1 , the photovoltaic distribution network vehicle network cooperative control method based on the cooperative game model specifically includes the following steps:

[0032] Step S101, determining the up-regulatable capacity and the down-regulatable capacity of the EV in each period according to the driving demand of the electric vehicle and the battery capacity constraint, and obtaining the regulatable capacity range.

[0033] In this step, the photovoltaic array monitoring device is used to obtain the real-time output data of the distributed photovoltaic in real time with a sampling interval of 15 minutes or 30 minutes, and the collection accuracy meets the power fluctuation analysis requirement of the distribution network. The three-phase voltage (AB, BC phase) and current (A, C phase) data of each node of the distribution network are collected through the distribution network SCADA system, the voltage transformer adopts V / V connection method, the current transformer adopts incomplete star connection method, the node load data is obtained synchronously, and the sampling interval is consistent with the photovoltaic data.

[0034] Vehicle network interaction mainly utilizes the charging and discharging of the electric vehicle to provide power support for the power grid, and the available capacity of the electric vehicle is an important variable of the vehicle network interaction. Determining the accurate regulatable capacity is a prerequisite for the analysis of the vehicle network interaction strategy. The factors affecting the charging and discharging capacity of the electric vehicle include the charging and discharging time and the charging and discharging power. The charging and discharging time is jointly determined by the starting charging and discharging time and the charging and discharging time length, and the starting available time of the electric vehicle satisfies the normal distribution:

[0035] ,

[0036] where, is the end time of the last trip, i.e., the start available time; is the average expected value of the trip end time; is the standard deviation of the statistical trip end time. and varies with the different driving characteristics of different types of vehicles.

[0037] The daily driving distance of an electric vehicle satisfies a normal distribution:

[0038] ,

[0039] where, is the daily driving distance (km); is the average expected value of the daily driving distance of a vehicle; is the standard deviation of the statistical daily driving distance. and is related to the type of vehicle and the demand for use.

[0040] The charging and discharging duration is related to the expected state of charge of the battery after charging and discharging is completed:

[0041] ,

[0042] where, is the expected state of charge; is the initial state of charge; is the battery capacity (kWh); is the charging and discharging power (kW); is the charging and discharging efficiency; is the initial charging and discharging time; is the end charging and discharging time; is the charging and discharging duration (h).

[0043] The charging and discharging schedulable threshold of an individual electric vehicle participating in the vehicle-grid interaction is divided into up-regulatable capacity and down-regulatable capacity. The expression for calculating the up-regulatable capacity is:

[0044] ,

[0045] where, is the up-regulatable capacity, is the maximum state of charge, is the current state of charge of the electric vehicle, is the battery capacity, is the charging and discharging power, is the charging and discharging efficiency, is the initial charging and discharging time, To end the charging and discharging process, In charging state;

[0046] The expression for calculating the adjustable capacity is:

[0047]

[0048] In the formula, To allow for capacity reduction, At minimum state of charge, It is in the discharge state.

[0049] After an electric vehicle has completed its individual charging and discharging cycle, it must meet both travel needs and battery capacity constraints.

[0050] ,

[0051] In the formula, For the desired state of charge, This is the initial state of charge. To allow for capacity reduction, To allow for capacity reduction, This refers to the battery capacity.

[0052] Step S102: Construct a cooperative alliance for interaction between the vehicle and the network, and build a vehicle-network collaborative interaction game model with the goal of maximizing the benefits of the cooperative alliance under preset constraints.

[0053] In this step, the interactive alliance formed by the vehicle and the grid includes the power grid. and electric vehicles Two members, together forming a set .alliance Represents all possible combinations of members, i.e. , , and Four scenarios.

[0054] In cooperative game theory, marginal contribution is used to represent the members. The addition of either member to the alliance will increase the overall contribution of the alliance. Interactive Alliance The marginal contribution made is:

[0055] ,

[0056] In the formula, the characteristic function Indicates alliance The combined benefits, that is, the entire alliance Total revenue; Member The alliance's revenue when not participating in interactive cooperation.

[0057] To eliminate the impact of the joining order on the revenue distribution, all possible orders of joining the alliance are traversed, and the revenue of the i th member from the joint revenue of the alliance is denoted as

[0058]

[0059] is the number of participants in the alliance; is the number of members in the set

[0060] The revenue of the power grid from the joint revenue of the vehicle-grid interaction alliance is

[0061]

[0062] The revenue of the electric vehicle group from the joint revenue of the vehicle-grid interaction alliance is

[0063]

[0064] are the joint revenues of the vehicle-grid cooperation alliance, i.e., the vehicle-grid interaction alliance, respectively.

[0065] It should be noted that the constraint conditions include:

[0066] The cooperation revenue constraint of the vehicle-grid interaction alliance is expressed as

[0067]

[0068] is the joint revenue of the cooperation alliance is the joint revenue of the cooperation alliance when the member does not participate in the alliance. The vehicle-grid power constraint, including the photovoltaic power generation output constraint and the electric vehicle charging and discharging power constraint, is expressed as

[0069]

[0070]

[0071]

[0072] ​​​​​​​​​​​​​​​​​​​​​​wherein, is the photovoltaic power generation power; , is the upper and lower limits of the photovoltaic power generation power; , is the upper and lower limits of the electric vehicle charging and discharging power.

[0073] The electric vehicle battery capacity constraint is expressed as:

[0074] ,

[0075] wherein, is the state of charge of the battery of the electric vehicle; , is the upper and lower limits of the state of charge of the battery.

[0076] In step S103, the allocation weight is determined according to the contribution degrees of the vehicle and the network to the cooperative alliance benefits, the Shapley value is used to calculate the benefits of the vehicle and the network, the weight is adjusted iteratively until the allocation is stable, and the current time-of-use electricity price for guiding the charging and discharging of EVs is generated.

[0077] In this step, the vehicle and the network have significant differences in the influence factors of their strategy selection in the interactive process, and the benefits obtained from the interaction are also different. In the field of new energy consumption, the contributions of the power grid and the electric vehicle group to the cooperative alliance show differentiated characteristics: the electric vehicle provides power support through charging and discharging behavior, and the power grid improves the new energy consumption capacity through interactive regulation.

[0078] To quantify the contributions of the two parties to the alliance, the above influence factors are summarized into a joint benefit allocation weight system, and a benefit allocation model based on improved Shapley value is constructed, which specifically includes:

[0079] The vehicle and network alliance benefit allocation weight is defined as , including the power grid contribution weight and the electric vehicle contribution weight , which is calculated as follows:

[0080] ,

[0081] ,

[0082] wherein, is the total distributed photovoltaic power consumed by the power grid due to the participation of the electric vehicle in the interaction; is the power of the electric vehicle discharging into the grid.

[0083] Based on the Shapley value theory, the marginal contributions of the two parties are calculated by traversing the joining order of the alliance members, the interaction benefits are redistributed in combination with the weight coefficients, a dynamic adjustment mechanism is formed, and the specific process is as follows:

[0084] The adjustment amount is calculated according to the contribution deviation formula after each allocation, and the income allocation result is iteratively updated until the deviation is less than the set threshold, thereby ensuring that the income allocation matches the actual contributions of both parties, and encouraging the vehicle network to continuously participate in cooperative interaction. The income of the current power grid subject and the electric vehicle group is calculated as follows:

[0085]

[0086]

[0087] In the formula, is the charging and discharging price of the electric vehicle; is the charging and discharging power of the electric vehicle; is the battery loss price; is the cost required for the power grid to transfer peak load; is the peak load transfer power; is the power grid peak shaving cost.

[0088] The weight adjustment amount is obtained by optimizing the weight according to the income brought by the alliance multi-consumption of new energy, and the calculation formula is as follows:

[0089]

[0090]

[0091] In the formula, is the allocation weight of the th member; is the initial allocation weight; is the income of the th member before adjustment; is the new income of the th member after adjustment. If , the member can get more income from the alliance, otherwise, the member gets less income. According to the income allocation result, a new time-of-use electricity price is formulated, so that each participant has the willingness to continue to participate in cooperation. The user charging and discharging strategy and income redistribution result under the current time-of-use electricity price are calculated.

[0092] Step S104, the user charging and discharging strategy and income redistribution result within the adjustable capacity range under the current time-of-use electricity price are calculated.

[0093]

[0094] ​​​​​​​Step S105, it is judged whether the loop termination condition is met, if the termination condition is reached, the loop is exited, and the optimal income value of each subject and the current optimal electricity price are output.

[0095] In this step, the loop termination condition is as follows:

[0096]

[0097]

[0098]

[0099] In the formula, is the allocation weight of the nth member, is the initial allocation weight, is the total power of the distributed photovoltaic power participating in the interaction of the electric vehicle to make the power grid consume, is the electric vehicle power, is the power grid contribution weight, is the electric vehicle contribution weight.

[0100] In summary, the method of the application, through the photovoltaic monitoring device and the power grid monitoring system, real-time collects photovoltaic output, distribution network node voltage, distribution network node current and load data, calculates the EV adjustable charging and discharging power range in each period based on the electric vehicle driving characteristic model, determines the up and down adjustable capacity; the power grid and the EV group are constructed as a cooperative alliance, an optimization model is established with the goal of maximizing the alliance income, the constraint conditions are checked and the EV charging and discharging strategy is solved by means of CPLEX / YALMIP solver; the allocation weight is determined according to the contribution of both parties to the alliance income, the improved Shapley value is used to calculate the income and adjust the weight to the stable distribution, and the time-of-use electricity price guiding EV charging and discharging is generated; the solver optimizes the EV charging and discharging strategy, guides charging to consume surplus power in the photovoltaic peak period, discharges to support the power grid in the load peak period, judges the convergence based on the iterative difference of the alliance income, outputs the optimal electricity price and the charging and discharging scheme, realizes the vehicle-grid collaborative interaction and maximization of alliance income. The scheme improves the photovoltaic consumption efficiency, improves the power grid load fluctuation and node voltage stability through the cooperative game model, and encourages both parties to continuously participate based on the fair income distribution mechanism of contribution, relies on the existing monitoring device to realize algorithm regulation, does not need additional hardware investment, and has economic efficiency and engineering practicability.

[0101] In one specific embodiment, referring to Figure 2 , a certain residential area in a 21-node distribution network is used for example verification, the number of electric vehicles in the area is set to 150, and they are divided into private cars and taxis according to their purpose, of which the taxis account for about 10% of the total number of electric vehicles. The parameters and available charging and discharging time of the two types of electric vehicles are shown in Table 1.​​​​

[0102] Table 1 Electric vehicle load parameters

[0103] ,

[0104] In order to verify the beneficial effects of the present application, scientific demonstration is carried out through experiments.

[0105] According to the automobile travel survey data, the initial state of charge (SOC) of taxis and private cars presents differentiated distribution characteristics: the initial SOC of taxis is mainly concentrated in the interval of 20%~40%, and the initial SOC of private cars is mainly distributed in the interval of 70%~95%. Based on the above distribution characteristics, the adjustable capacity of the two types of vehicles presents significant differences, as shown in FIG. 1. The results show that the taxi has a small down-regulation capacity due to the low initial SOC and short charging time, and the up-regulation capacity is limited by travel demand and is also difficult to be greatly improved; the private car has a large up-regulation capacity under the premise of meeting the travel power demand due to the high initial SOC, long time in the network and flexible charging period. Figure 3

[0106] From the analysis of the charging and discharging scheduling participation probability, the taxi has a low willingness to participate in the grid scheduling due to the limited down-regulation capacity and urgent charging demand; the private car has a high charging and discharging scheduling participation probability under the premise of ensuring full power travel due to the sufficient up-regulation capacity and large charging time flexibility. This characteristic provides data support for the design of differentiated incentive mechanism in vehicle-grid collaborative scheduling strategy, and can guide the private car to preferentially charge in the photovoltaic peak period through time-of-use price to consume surplus power and improve distributed energy consumption efficiency.

[0107] Based on the analysis of the adjustable capacity of electric vehicles, typical daily photovoltaic power generation data is collected, a cooperative game model is constructed to guide the private car and part of the taxi to participate in the vehicle-grid collaborative charging and discharging scheduling, and the genetic algorithm combined with CPLEX / YALMIP solver is used to optimize the time-of-use price and the charging and discharging strategy of electric vehicles, to obtain the vehicle-grid interaction alliance revenue distribution results and verify the effectiveness of the strategy, as shown in FIG. 2. Figure 4 、 Figure 5

[0108] ​​Before optimization, the unordered charging and discharging of electric vehicles results in the superposition of power consumption peak after 17:00-23:00 working period and EV charging load, and the peak load of the power grid reaches 1470.05kW, while there is 1672.78kW surplus power feeding back to the power grid during 11:00-13:00 photovoltaic peak period; after optimization, the EV increases charging power in response to the increase of photovoltaic output during 10:00-17:00, and the photovoltaic surplus is reduced to 733.68kW, and the power grid peak load is reduced to 1019.80kW during the peak period of 17:00-23:00. The strategy realizes peak load shifting of the power grid through the response of EV charging and discharging, and the power fluctuation is reduced by 13%, and at the same time, the distributed photovoltaic in-situ consumption capacity is significantly improved, and the amount of power flow feeding back is reduced by 11%, which verifies the effectiveness of the high proportion photovoltaic distribution network vehicle network collaborative strategy and system based on the cooperative game model in optimizing the operation of the power grid and new energy consumption.

[0109] Under the foregoing district vehicle network collaborative interaction strategy, the voltage variation characteristics of the end node 16 are analyzed, as shown in Figure 6 When the electric vehicle does not participate in the collaborative interaction, the node voltage presents significant fluctuation during the photovoltaic power generation peak period of 10:00-13:00, and there is voltage out-of-limit phenomenon, in which the node voltage rises to 243.86V at the maximum photovoltaic surplus power time of 11:00, exceeding the nominal voltage 220V by +10.85%, and the voltage rise leads to the photovoltaic surplus power feeding back to the power grid, affecting the system operation stability. After the implementation of the collaborative interaction strategy of the application, the charging and discharging behavior of the electric vehicle is dynamically adjusted with the photovoltaic output, and the node voltage fluctuation amplitude is significantly reduced, and is stabilized around 220V during the photovoltaic power generation period of 6:00-18:00, and there is no voltage out-of-limit situation, and the node voltage is 233.77V at the maximum photovoltaic surplus power time. The measured data show that the node voltage fluctuation rate is reduced from 15.34% to 9.24% by the strategy, which effectively improves the voltage fluctuation problem caused by photovoltaic feeding back, guarantees the voltage stability of the grid-connected point, and verifies the significant effectiveness of the high proportion photovoltaic distribution network vehicle network collaborative strategy and system based on the cooperative game model in inhibiting the voltage out-of-limit caused by the access of distributed photovoltaic and improving the power quality of the distribution network.

[0110] The economic benefits of the application are quantitatively allocated by improving the Shapley value model through the collaborative interaction of electric vehicles to realize the in-situ consumption of distributed photovoltaic and node voltage optimization, and the specific method is as follows: according to the benefit allocation weight calculation method, when the electric vehicle participates in the interaction and makes the total power consumption of distributed photovoltaic 3709.3kW and the total power of grid-connected discharging 1409.4kW, the benefit allocation proportion of the power grid and the electric vehicle is determined by weight calculation as follows:

[0111]

[0112]

[0113] The benefit comparison based on the time-of-use price mechanism is shown in Table 2. As can be seen from the table, in the non-cooperative state, the daily charging cost of the electric vehicle is 1886.98 yuan, and the power grid benefit is only the charging cost; after implementing the cooperative interaction, the actual charging cost of the electric vehicle is reduced to 1793.82 yuan, the benefit is increased by 93.16 yuan, including the charging expenditure of 2403.50 yuan, the night discharging benefit of 562.29 yuan and the alliance distribution benefit of 47.39 yuan, and the total benefit of the power grid is 2400.73 yuan, which is increased by 513.75 yuan than before optimization, including the peak shaving cost reduction and the load transfer benefit of 437.67 yuan, and the alliance distribution benefit of 121.85 yuan. The distribution mechanism significantly improves the total benefit of the vehicle-grid interaction alliance, the electric vehicle improves the participation enthusiasm by reducing the actual charging cost, and the power grid realizes the benefit growth through new energy consumption and load regulation, forming a virtuous interaction mechanism to encourage both parties to continuously participate, verifying the significant advantages of the application in optimizing resource allocation and improving system economy.

[0114] Table 2 Benefit distribution of vehicle-grid interaction

[0115]

[0116] In the embodiment, the photovoltaic monitoring device is used to obtain photovoltaic output data in real time, the power grid monitoring system is used to collect the voltage, current and load information of the distribution network node, the EV adjustable charging and discharging power range in each period is calculated based on the EV driving characteristic model to determine the adjustable capacity and the adjustable capacity; the second step is to build a cooperative alliance of vehicle-grid interaction, and the CPLEX / YALMIP is used to solve the charging and discharging strategy of the electric vehicle group under the current electricity price, and the constraint conditions are tested to determine whether the current alliance is established; the third step is to determine the distribution weight according to the contribution of both parties to the alliance benefit, calculate the benefits of both parties by using the Shapley value, adjust the weight through iteration until the distribution is stable, and generate the time-of-use price for guiding the EV charging and discharging; the fourth step is to determine whether the loop termination condition is met, if the termination condition is met, the loop is exited, and the optimal benefit value of each subject and the current optimal electricity price are output.

[0117] The technical effects achieved are:

[0118] ​(1) To solve the problems of power flow reverse, voltage rise and excessive load peak-valley difference caused by high proportion of photovoltaic access, a car-grid cooperation alliance is built and the improved Shapley value is used to quantify the contribution of both sides, so as to guide electric vehicles to charge surplus power at photovoltaic peak period and discharge at load peak period. The measured data show that this strategy makes the distributed photovoltaic power consumption efficiency increase by 56.1%, the surplus power decrease from 1672.78 kW to 733.68 kW, the load peak-valley difference decrease by 30.6%, and the node voltage fluctuation rate decrease by 40.1%, effectively solving the power quality problem caused by photovoltaic output fluctuation and improving the operation stability of distribution network.

[0119] (2) To solve the problem of insufficient fairness in traditional car-grid interaction, a dynamic income distribution mechanism is established based on cooperative game model, which quantifies the infrastructure support of power grid in photovoltaic consumption and the power regulation contribution of electric vehicles, so that the total income of the alliance increases by 606.91 yuan compared with the non-cooperative mode, of which the power grid income increases by 513.75 yuan and the electric vehicle income increases by 93.16 yuan.

[0120] (3) Through the coordinated regulation of electric vehicle charging and discharging behavior and photovoltaic output, the node voltage out-of-limit problem caused by distributed photovoltaic access is significantly inhibited. Simulation data shows that the voltage fluctuation rate of terminal node 16 at photovoltaic peak period decreases from 15.34% to 9.24%, and the voltage decreases from 243.86V to 233.77V, reducing the input cost of voltage regulation and ensuring the voltage of grid-connected point within the range of ±7% of the nominal value, effectively improving the power quality of distribution network.

[0121] (4) Based on the data collected by existing photovoltaic monitoring devices and power grid SCADA system, without additional hardware investment, the iterative optimization of electric vehicle charging and discharging strategy is realized through CPLEX / YALMIP solver, which can converge in an average of 4-6 iterations with a single solving time of less than 15 seconds, meeting the real-time demand of engineering. The example verification shows that this system can directly use the control signal issued by the distribution network automation system to realize closed-loop regulation of car-grid interaction, with outstanding economic efficiency and engineering adaptability.

[0122] Please refer to Figure 7 , which shows a structure block diagram of a photovoltaic distribution network car-grid collaborative control system based on a cooperative game model.

[0123] As shown in Figure 7 , the photovoltaic distribution network car-grid collaborative control system 200 includes a determination module 210, a construction module 220, a generation module 230, a calculation module 240, and an output module 250.

[0124] The determining module 210 is configured to determine the up-regulatable capacity and the down-regulatable capacity of the EV at each time period according to the driving demand of the electric vehicle and the battery capacity constraint, and obtain a regulatable capacity range; the constructing module 220 is configured to construct a cooperation alliance of interaction between the vehicle and the network, and construct a vehicle-network collaborative interaction game model under a preset constraint condition with the maximum revenue of the cooperation alliance as the target; the generating module 230 is configured to determine a distribution weight according to the contribution degrees of the vehicle and the network to the revenue of the cooperation alliance, calculate the revenues of the vehicle and the network by using the Shapley value, and generate a current time-of-use electricity price for guiding the charging and discharging of the EV by iteratively adjusting the weight until the distribution is stable; the calculating module 240 is configured to calculate a user charging and discharging strategy and a revenue redistribution result in the regulatable capacity range under the current time-of-use electricity price; and the outputting module 250 is configured to judge whether a loop termination condition is met, and output the optimal revenue value of each subject and the current optimal electricity price if the termination condition is met.

[0125] It should be understood that Figure 7 each step in the method described with reference to Figure 1 corresponds to a respective module in the system described in Figure 7 the foregoing description of operations and features for the method and the corresponding technical effects apply equally to the modules in

[0126] In some embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program, the program instructing a processor to execute the method of any of the above embodiments when executed by the processor.

[0127] As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, and the computer executable instructions are configured to:

[0128] determine the up-regulatable capacity and the down-regulatable capacity of the EV at each time period according to the driving demand of the electric vehicle and the battery capacity constraint, and obtain a regulatable capacity range;

[0129] construct a cooperation alliance of interaction between the vehicle and the network, and construct a vehicle-network collaborative interaction game model under a preset constraint condition with the maximum revenue of the cooperation alliance as the target;

[0130] determine a distribution weight according to the contribution degrees of the vehicle and the network to the revenue of the cooperation alliance, calculate the revenues of the vehicle and the network by using the Shapley value, and generate a current time-of-use electricity price for guiding the charging and discharging of the EV by iteratively adjusting the weight until the distribution is stable;

[0131] calculate a user charging and discharging strategy and a revenue redistribution result in the regulatable capacity range under the current time-of-use electricity price;

[0132] If the loop termination condition is met, the loop is exited, and the optimal income value of each subject and the current optimal electricity price are output.

[0133] The computer readable storage medium can include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required by at least one function; and the data storage area can store data created according to the use of the photovoltaic power distribution network and vehicle network collaborative control system based on the cooperative game model. In addition, the computer readable storage medium can include a high-speed random access memory, and can also include a memory such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state memory device. In some embodiments, the computer readable storage medium can optionally include a memory remotely arranged relative to the processor, and these remote memories can be connected to the photovoltaic power distribution network and vehicle network collaborative control system based on the cooperative game model through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0134] Figure 8 is a structural schematic diagram of an electronic device provided by an embodiment of the application, as shown in Figure 8 The device includes a processor 310 and a memory 320. The electronic device can also include an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 can be connected by a bus or other means, Figure 8 for example, by a bus connection. The memory 320 is the computer readable storage medium described above. The processor 310 executes various function applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 320, that is, implements the photovoltaic power distribution network and vehicle network collaborative control method based on the cooperative game model of the above method embodiment. The input device 330 can receive input digital or character information, and generate key signal inputs related to user settings and function control of the photovoltaic power distribution network and vehicle network collaborative control system based on the cooperative game model. The output device 340 can include a display device such as a display screen.

[0135] The above electronic device can execute the method provided by the embodiments of the application, and has the corresponding function modules and beneficial effects of executing the method. Technical details not described in detail in the present embodiment can be referred to the method provided by the embodiments of the application.

[0136] As an implementation manner, the above electronic device is applied to the photovoltaic power distribution network and vehicle network collaborative control system based on the cooperative game model, and is used for a client, and includes at least one processor, and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to

[0137] determining the up-regulation capacity and the down-regulation capacity of the EV in each time period according to the driving demand of the electric vehicle and the battery capacity constraint, and obtaining the adjustable capacity range;

[0138] constructing a cooperative alliance of interaction between the vehicle and the power grid, and constructing a game model of the cooperative interaction between the vehicle and the power grid under the preset constraint condition and with the maximum income of the cooperative alliance as the target;

[0139] determining the distribution weight according to the contribution degrees of the vehicle and the power grid to the income of the cooperative alliance, calculating the income of the vehicle and the power grid by using the Shapley value, adjusting the weight by iteration until the distribution is stable, and generating the current time-of-use electricity price for guiding the charging and discharging of the EV;

[0140] calculating the charging and discharging strategy and the income redistribution result of the user in the adjustable capacity range under the current time-of-use electricity price;

[0141] judging whether the loop termination condition is met, and if the termination condition is met, exiting the loop and outputting the optimal income value of each subject and the current optimal electricity price.

[0142] From the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be realized by means of software and the necessary general hardware platform, and of course, it can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiments.

[0143] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model, characterized in that, include: Based on the driving needs of electric vehicles and battery capacity constraints, the adjustable capacity and adjustable capacity of EVs at different times are determined to obtain the adjustable capacity range. Construct a cooperative alliance between the vehicle and the network, and build a collaborative interactive game model between the vehicle and the network with the goal of maximizing the benefits of the cooperative alliance under preset constraints. The allocation weights are determined based on the contribution of both the vehicle and the network to the revenue of the cooperative alliance. The revenue of both the vehicle and the network is calculated using the Shapley value. The weights are iteratively adjusted until the allocation is stable, and the current time-of-use electricity price that guides EV charging and discharging is generated. Calculate the user charging and discharging strategies and revenue redistribution results within the adjustable capacity range under the current time-of-use electricity price; Determine if the loop termination condition is met. If the termination condition is met, exit the loop and output the optimal revenue value of each entity and the current optimal electricity price.

2. The photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model according to claim 1, characterized in that, in, The expression for calculating the adjustable capacity is: In the formula, W up For adjustable capacity, SOC max At maximum state of charge, SOC i The electric vehicle's current state of charge, E is the battery capacity, and P is the current state of charge. EV Let η be the charge / discharge power, η be the charge / discharge efficiency, τ be the initial charge / discharge time, and t be the discharge time. l To end the charging / discharging process, P EV >0 indicates a charging state; The expression for calculating the adjustable capacity is: In the formula, W down For adjustable capacity, SOC min For the minimum state of charge, P EV <0 indicates a discharge state.

3. The photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model according to claim 1, characterized in that, The expression for the battery capacity constraint is: SOC l ·E≤W dbwn +W up +SOC0·E≤E, In the formula, SOC l The desired state of charge, SOC0 is the initial state of charge, and W... down For adjustable capacity, W down E represents the battery capacity, which is adjustable.

4. The photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model according to claim 1, characterized in that, The preset constraints include: The revenue constraint for the CarNet Interactive Alliance cooperation is expressed as follows: V(S j )≥V(S j -{i}), In the formula, V(S) j ) for cooperative alliance S j The combined revenue of the alliance, V(S) j -{i}) represents the alliance S when member i does not participate. j -{i}'s combined alliance benefits; The vehicle-to-grid power constraint is expressed as: P PV，min ≤P PV ≤P PV，max , P EV，min ≤P EV ≤P EV,max , In the formula, P PV P represents the power generated by photovoltaic power generation. PV,max P PV,min P represents the upper and lower limits of photovoltaic power generation. EV,max P EV,min The upper and lower limits for the charging and discharging power of electric vehicles. The battery capacity constraint for electric vehicles is expressed as follows: SOC min ≤SOC i ≤SOC max , In the formula, SOC max At maximum state of charge, SOC i The current state of charge (SOC) of an electric vehicle. min This is the minimum charged state.

5. The photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model according to claim 1, characterized in that, The expressions for calculating the benefits to the current power grid main body and the electric vehicle group are as follows: φ1(V)=c c P c -(c f +c d )P f -c t P dc -F+Δφ1(V), φ2(V)=(c f +c d )P f -c c P c +c t P dc +Δφ2(V), In the formula, φ1(V) represents the current revenue of the main power grid entity, φ2(V) represents the revenue of the electric vehicle group, and c c c f For the charging and discharging price of electric vehicles, P c P f For the charging and discharging power of electric vehicles, c d For battery wear and tear price, c t The cost required for the power grid to transfer peak load, P dc F represents the peak load transfer power, and F represents the grid peak shaving cost. The weight adjustment amount is obtained by optimizing the weights based on the benefits brought by the alliance's increased consumption of new energy sources. The expression is as follows: Df i (V)=(λ i -λ0)·V(S), In the formula, λ i Assign weights to the i-th member, where λ0 is the initial weight assignment, and P PV The total distributed photovoltaic power absorbed by the grid due to the interaction of electric vehicles; P EV λ is the discharge power of electric vehicles connected to the grid. g λ is the weight for the contribution of the power grid. v Weighting of contribution to electric vehicles.

6. The photovoltaic distribution network vehicle-grid coordinated control method based on a cooperative game model according to claim 1, characterized in that, The expression for the loop termination condition is: V (a+1) (S)≥V (a) (S), P l (t)+P c (t)-P f (t)>P PV (t), In the formula, P l (t) represents the initial load of the regional power grid during time period t; V (a+1) (S) represents the alliance's profit in the (a+1)th cycle, and P c P f For the charging and discharging power of electric vehicles, c d This refers to the price of battery wear and tear.

7. A photovoltaic distribution network vehicle-grid coordinated control system based on a cooperative game theory model, characterized in that, include: The module is configured to determine the adjustable capacity and adjustable capacity of the EV at different times based on the driving needs of the electric vehicle and the battery capacity constraints, so as to obtain the adjustable capacity range. The module is configured to build a cooperative alliance for interaction between the vehicle and the network, and to construct a vehicle-network collaborative interaction game model with the goal of maximizing the benefits of the cooperative alliance under preset constraints. The generation module determines the allocation weights based on the contribution of both the vehicle and the network to the revenue of the cooperative alliance, calculates the revenue of both the vehicle and the network using the Shapley value, and iteratively adjusts the weights until the allocation is stable, generating the current time-of-use electricity price to guide EV charging and discharging. The calculation module is configured to calculate the user charging and discharging strategies and revenue redistribution results within the adjustable capacity range under the guidance of the current time-of-use electricity price; The output module is configured to determine whether the loop termination condition is met. If the termination condition is met, the loop exits and outputs the optimal revenue value of each entity and the current optimal electricity price.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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