Wind, light and water storage and charging operation scheduling method and device

Abstract

The invention provides a wind-light-water storage and charging operation scheduling method and device, and relates to the technical field of new energy power generation. The method comprises the steps of firstly obtaining a new energy power station power generation model and a pumped storage station operation model; constructing an electric vehicle information matrix according to an electric vehicle rated capacity probability distribution function, a charge state probability distribution function before the electric vehicle travels, an electric vehicle driving distance distribution function and a charging station arrival time distribution function of the electric vehicle; constructing a carbon emission objective function according to a power source of the power grid system; and finally, according to the new energy power station power generation model, the pumped storage station operation model and the first electric vehicle information matrix, solving the carbon emission target function by taking the minimum carbon emission as a target, and scheduling a power grid system according to the obtained solution. The power grid electric energy comprehensive utilization rate is low, and energy conservation and emission reduction are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of renewable energy power generation technology, and in particular to a method and device for dispatching wind, solar and water storage and charging operations. Background Art

[0002] As the proportion of wind and solar power continues to grow, the power grid needs to pay more and more attention to absorbing all grid-connected renewable energy power generation. In this context, the integrated design of wind and solar renewable energy systems and energy storage devices has the advantage of stabilizing the fluctuation characteristics of wind and solar power.

[0003] On the other hand, as the number of electric vehicles increases, the potential of electric vehicles as an invisible energy storage device cannot be ignored. Some areas have rich terrain and water resources, and pumped storage construction is close to cities. The joint dispatch of wind power, photovoltaics, pumped storage, and electric vehicles is of great significance to improving the participation of electric vehicles in the power regulation of new energy systems and the integrated supply of clean energy power.

[0004] At present, the pumped storage system is mainly regulated by the control targets of the power grid, and has less linkage with electric vehicles. When the pumped storage system cannot be effectively dispatched, the pumped storage may frequently fluctuate between pumping and releasing water, resulting in a low comprehensive utilization rate of power grid electricity, which is not conducive to energy conservation and emission reduction. Summary of the invention

[0005] The embodiments of the present invention provide a method and device for scheduling the operation of a wind-solar-water storage and charging system to solve the problem of low power utilization rate caused by the failure of optimizing the scheduling of the wind-solar-water storage and charging system.

[0006] In a first aspect, an embodiment of the present invention provides a wind, solar, water storage and charging operation scheduling method, which is applied to a power grid system provided with a new energy power station, a pumped storage station and a charging station. The wind, solar, water storage and charging operation scheduling method includes:

[0007] Obtain the power generation model of the renewable energy power station and the operation model of the pumped storage station;

[0008] The electric vehicle information matrix is ​​constructed according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station;

[0009] Constructing a carbon emission objective function based on the power source of the grid system;

[0010] According to the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, the carbon emission objective function is solved with the goal of minimizing carbon emissions, and the power grid system is dispatched according to the obtained solution.

[0011] In a possible implementation, the wind power station power generation model is:

[0012]

[0013] Where P nom is the rated power of the wind turbine, v(t) is the actual local wind speed, v ci and v co are the cut-in wind speed and cut-out wind speed of the wind turbine respectively;

[0014] The photovoltaic power station power generation model is:

[0015]

[0016] Where P PV (t) is the power generation of a single photovoltaic cell, Rad(t) is the light intensity, A PV is the photovoltaic area, f PV (t) is the photovoltaic power generation efficiency, T PV (r) is the actual working temperature of photovoltaic, T env (r) is the ambient temperature, T nom is the photovoltaic standard temperature.

[0017] In a possible implementation, the pumped storage station operation model is:

[0018]

[0019] In the formula, Q PS (t) and Q PS (t-1) is the amount of water stored in the pumped storage power station at time r and r-1, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and releasing amounts of the pumped storage power station, C char and C disc are the pumping and discharge coefficients, η char and η disc are the pump efficiency and turbine efficiency respectively, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc They are the power consumed by the water pump and the power generated by the turbine generator respectively.

[0020] In a possible implementation, the electric vehicle information matrix is ​​constructed according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station, including:

[0021] Obtaining a probability distribution function of the rated capacity of the electric vehicle, a probability distribution function of the state of charge of the electric vehicle before travel, a distribution function of the travel distance of the electric vehicle, and a distribution function of the time when the electric vehicle arrives at the charging station;

[0022] Constructing an electric vehicle information basic matrix representing the states of a plurality of electric vehicles, wherein each row of the electric vehicle information basic matrix corresponds to the state information of one vehicle;

[0023] According to the probability distribution function of the electric vehicle's rated capacity, the probability distribution function of the electric vehicle's state of charge before travel, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station, each column in the electric vehicle information basic matrix is ​​solved to obtain the electric vehicle information matrix.

[0024] In a possible implementation, the electric vehicle information basic matrix is:

[0025]

[0026] Where, EVI t is the basic information matrix of electric vehicles at time t, RC n is the rated capacity of the nth electric vehicle, ISOC n,t is the initial state of charge of the nth electric vehicle, TSOC n is the target state of charge of the nth electric vehicle, RT n is the residence time of the nth electric vehicle.

[0027] In a possible implementation, the electric vehicle driving distance distribution function is:

[0028]

[0029] In the formula, f d (x) is the distribution function of the electric vehicle driving distance, x is the driving distance, u d and δ d are the mean and standard deviation of commuting distance, respectively;

[0030] The time distribution function of the electric vehicle arriving at the charging station is:

[0031]

[0032] Where t is the arrival time, and are the mean and standard deviation of the arrival time, respectively.

[0033] In a possible implementation, the probability distribution function of the electric vehicle rated capacity is:

[0034]

[0035] Where f(RC) is the probability distribution function of the rated capacity of the electric vehicle, RC min and RC max are the minimum and maximum capacities of electric vehicles, respectively, f0(RC) is the Gamma distribution function, α and β are the shape and rate parameters of the Gamma distribution, respectively, and Γ is the Gamma function;

[0036] The probability distribution function of the state of charge of the electric vehicle before traveling is:

[0037]

[0038] Where f(ISOC0) is the probability distribution function of the state of charge of the electric vehicle before traveling, ISOC0 is the state of charge of the electric vehicle before traveling, ISOC 0,min ISOC is the minimum state of charge for electric vehicles to meet their travel conditions. 0,max is the maximum state of charge of the electric vehicle, f0(ISOC0) is the normal distribution function, σ and μ are the standard deviation and mean of ISOC0 distribution, respectively.

[0039] In a possible implementation, solving each column in the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function, the electric vehicle pre-travel state of charge probability distribution function, the electric vehicle driving distance distribution function, and the electric vehicle arrival time distribution function to obtain the electric vehicle information matrix includes:

[0040] Solving the first column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function;

[0041] According to the second formula, the second column of the electric vehicle information basic matrix is ​​solved, wherein the second formula is:

[0042]

[0043] In the formula, ISOC i,t is the initial charge state of the ith electric vehicle at time t, RC i is the rated capacity of the ith electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling according to the probability distribution function of the state of charge before the trip of the electric vehicle, λ i is the power consumption per unit mileage of the i-th electric vehicle, D i is the driving distance of the i-th electric vehicle;

[0044] According to the third formula, the third column of the electric vehicle information basic matrix is ​​solved, wherein the third formula is:

[0045] TSOC i =ISOC i,t

[0046] In the formula, ISOC i,t is the state of charge of the i-th electric vehicle before its trip at time t;

[0047] According to the fourth formula, the fourth column of the electric vehicle information basic matrix is ​​solved, wherein the fourth formula is:

[0048]

[0049] Where f(RT) is the probability distribution function of the extra stay time of electric vehicles, C t The results of sampling based on the additional residence time of electric vehicles, RT i,0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

[0050] In a possible implementation, solving the carbon emission objective function based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix with the goal of minimizing carbon emissions, and scheduling the power grid system according to the obtained solution includes:

[0051] The carbon emission objective function is:

[0052]

[0053] Among them, C is the target of rolling optimization scheduling at time t, c grid,t Carbon emission coefficient of the power grid, P grid,buy (t) is the grid inlet power of the system at time t, P grid,sell (t) is the power grid export quantity of the system;

[0054] Substituting the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix into the carbon emission objective function, and using a genetic algorithm to solve multiple parameters in the carbon emission objective function;

[0055] The power grid system is dispatched according to the obtained solution.

[0056] In a second aspect, an embodiment of the present invention provides a wind-solar water storage and charging operation scheduling device, comprising:

[0057] Model acquisition module, used to obtain the power generation model of the new energy power station and the operation model of the pumped storage station;

[0058] An electric vehicle information construction module is used to construct an electric vehicle information matrix according to a probability distribution function of an electric vehicle's rated capacity, a probability distribution function of an electric vehicle's state of charge before traveling, a distribution function of an electric vehicle's driving distance, and a distribution function of an electric vehicle's arrival time at a charging station;

[0059] An objective function building module, used to build a carbon emission objective function according to the power source of the power grid system;

[0060] as well as,

[0061] The objective function optimization module is used to solve the carbon emission objective function based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, with the goal of minimizing carbon emissions, and to dispatch the power grid system according to the obtained solution.

[0062] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0063] The present invention provides a wind, solar, water storage and charging operation scheduling method and device, which first obtains the power generation model of the new energy power station and the operation model of the pumped storage station; then constructs the electric vehicle information matrix according to the probability distribution function of the rated capacity of the electric vehicle, the probability distribution function of the state of charge before the electric vehicle travels, the distribution function of the electric vehicle driving distance and the distribution function of the time when the electric vehicle arrives at the charging station; then constructs the carbon emission target function according to the power source of the power grid system; finally, according to the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, the carbon emission target function is solved with the minimum carbon emission as the goal, and the power grid system is scheduled according to the obtained solution. The implementation method of the present invention uses the maximum residence time of the electric vehicle as the step size of the rolling optimization scheduling, and comprehensively considers the constraints of various aspects of the energy system, adopts the genetic algorithm for optimization, takes the carbon emissions of the system as the optimization target, realizes the output control optimization of the active control device in the system, and the charging and discharging adjustment of the electric vehicle, and the comprehensive utilization rate of the power grid is low, which is conducive to energy saving and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0065] Figure 1 It is a flow chart for realizing the wind-solar-water storage and charging operation scheduling method provided in an embodiment of the present invention;

[0066] Figure 2 This is an application scenario diagram of the wind-solar-water storage and charging operation scheduling method provided in an embodiment of the present invention;

[0067] Figure 3 It is a schematic diagram of rolling optimization scheduling of optimization scheduling provided by an embodiment of the present invention;

[0068] Figure 4 is a schematic diagram of system operation scheduling parameter optimization provided by an embodiment of the present invention;

[0069] Figure 5 It is a structural schematic diagram of a wind-solar water storage and charging operation scheduling device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In the following description, specific details such as specific system structures and techniques are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0071] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0072] Figure 1 The implementation flow chart of the wind-solar-water storage and charging operation scheduling method provided in the embodiment of the present invention is detailed as follows:

[0073] In step 101, a power generation model of a new energy power station and an operation model of a pumped storage station are obtained.

[0074] In some embodiments, the wind power station power generation model is:

[0075]

[0076] Where P nom is the rated power of the wind turbine, v(t) is the actual local wind speed, v ci and v co are the cut-in wind speed and cut-out wind speed of the wind turbine respectively;

[0077] The photovoltaic power station power generation model is:

[0078]

[0079] Where P PV (t) is the power generation of a single photovoltaic cell, Rad(t) is the light intensity, APV is the photovoltaic area, f PV (t) is the photovoltaic power generation efficiency, T PV (t) is the actual working temperature of photovoltaic, T env (t) is the ambient temperature, T nom is the photovoltaic standard temperature.

[0080] In some implementations, the pumped storage station operation model is:

[0081]

[0082] In the formula, Q PS (t) and Q PS (t-1) are the water volume stored in the pumped storage power station at time t and t-1, respectively, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and releasing amounts of the pumped storage power station, C char and t disc are the pumping and discharge coefficients, η char and η disc are the pump efficiency and turbine efficiency respectively, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc They are the power consumed by the water pump and the power generated by the turbine generator respectively.

[0083] For example, Figure 2 This is an application scenario diagram of the wind-solar water storage and charging operation scheduling method provided in the embodiment of the present invention. Figure 2 As shown in the figure, the wind, photovoltaic, water storage and charging new energy system is mainly composed of wind power, photovoltaic, pumped storage energy storage system, charging station with charging piles, grid interactive substation, and urban area load. It provides clean and green electricity through wind power and photovoltaic, and realizes the smoothing of wind and solar output fluctuations and peak shaving and valley filling through pumped storage system and electric vehicles at charging station. The operation scheduling method uses the maximum residence time of electric vehicles as the step size of rolling optimization scheduling, and comprehensively considers the constraints of various aspects of the energy system. It uses genetic algorithm for optimization and takes the carbon emissions of the system as the optimization target to achieve the output control optimization of the active control device in the system and the charging and discharging adjustment of electric vehicles. In addition, considering that the energy system has a large adjustment capacity, this capacity is reported to the power grid to participate in the peak regulation of the power grid.

[0084] The power generation model of the wind power station in the figure is:

[0085]

[0086] Where P nomis the rated power of the wind turbine, v(t) is the actual local wind speed, v ci and v co are the cut-in wind speed and cut-out wind speed of the wind turbine respectively;

[0087] The power generation model of the photovoltaic power station in the figure is:

[0088]

[0089] Where P PV (t) is the power generation of a single photovoltaic cell, Rad(t) is the light intensity, A PV is the photovoltaic area, f PV (t) is the photovoltaic power generation efficiency, T PV (t) is the actual working temperature of photovoltaic, T env (t) is the ambient temperature, T nom is the photovoltaic standard temperature.

[0090] The operation model of the pumped storage station in the figure is:

[0091]

[0092] In the formula, Q PS (t) and Q PS (t-1) are the water volume stored in the pumped storage power station at time t and t-1, respectively, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and releasing amounts of the pumped storage power station, C char and C disc are the pumping and discharge coefficients, η char and η disc are the pump efficiency and turbine efficiency respectively, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc They are the power consumed by the water pump and the power generated by the turbine generator respectively.

[0093] Step 102, constructing an electric vehicle information matrix according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station.

[0094] In some embodiments, the electric vehicle information matrix is ​​constructed according to the electric vehicle rated capacity probability distribution function, the electric vehicle pre-travel state of charge probability distribution function, the electric vehicle driving distance distribution function, and the electric vehicle arrival time distribution function, including:

[0095] Obtaining a probability distribution function of the rated capacity of the electric vehicle, a probability distribution function of the state of charge of the electric vehicle before travel, a distribution function of the travel distance of the electric vehicle, and a distribution function of the time when the electric vehicle arrives at the charging station;

[0096] Constructing an electric vehicle information basic matrix representing the states of a plurality of electric vehicles, wherein each row of the electric vehicle information basic matrix corresponds to the state information of one vehicle;

[0097] According to the probability distribution function of the electric vehicle's rated capacity, the probability distribution function of the electric vehicle's state of charge before travel, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station, each column in the electric vehicle information basic matrix is ​​solved to obtain the electric vehicle information matrix.

[0098] In some embodiments, the electric vehicle information basic matrix is:

[0099]

[0100] Where, EVI t is the basic information matrix of electric vehicles at time t, RC n is the rated capacity of the nth electric vehicle, ISOC n,t is the initial state of charge of the nth electric vehicle, TSOC n is the target state of charge of the nth electric vehicle, RT n is the residence time of the nth electric vehicle.

[0101] In some embodiments, the electric vehicle driving distance distribution function is:

[0102]

[0103] In the formula, f d (x) is the distribution function of the electric vehicle driving distance, x is the driving distance, u d and δ d are the mean and standard deviation of commuting distance, respectively;

[0104] The time distribution function of the electric vehicle arriving at the charging station is:

[0105]

[0106] Where t is the arrival time, and are the mean and standard deviation of the arrival time, respectively.

[0107] In some embodiments, the probability distribution function of the electric vehicle rated capacity is:

[0108]

[0109] Where f(RC) is the probability distribution function of the rated capacity of the electric vehicle, RC min and RC max are the minimum and maximum capacities of electric vehicles, respectively, f0(RC) is the Gamma distribution function, α and β are the shape and rate parameters of the Gamma distribution, respectively, and Γ is the Gamma function;

[0110] The probability distribution function of the state of charge of the electric vehicle before traveling is:

[0111]

[0112] Where f(ISOC0) is the probability distribution function of the state of charge of the electric vehicle before traveling, ISOC0 is the state of charge of the electric vehicle before traveling, ISOC 0,min ISOC is the minimum state of charge for electric vehicles to meet their travel conditions. 0,max is the maximum state of charge of the electric vehicle, f0(ISOC0) is the normal distribution function, σ and μ are the standard deviation and mean of ISOC0 distribution, respectively.

[0113] In some embodiments, the electric vehicle information matrix is ​​obtained by solving each column in the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function, the electric vehicle pre-travel state of charge probability distribution function, the electric vehicle driving distance distribution function, and the electric vehicle arrival time distribution function, including:

[0114] Solving the first column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function;

[0115] According to the second formula, the second column of the electric vehicle information basic matrix is ​​solved, wherein the second formula is:

[0116]

[0117] In the formula, ISOC i,t is the initial charge state of the ith electric vehicle at time t, RC i is the rated capacity of the ith electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling according to the probability distribution function of the state of charge before the trip of the electric vehicle, λ i is the power consumption per unit mileage of the i-th electric vehicle, D i is the driving distance of the i-th electric vehicle;

[0118] According to the third formula, the third column of the electric vehicle information basic matrix is ​​solved, wherein the third formula is:

[0119] TSOC i =ISOC i,t

[0120] In the formula, ISOC i,t is the state of charge of the i-th electric vehicle before its trip at time t;

[0121] According to the fourth formula, the fourth column of the electric vehicle information basic matrix is ​​solved, wherein the fourth formula is:

[0122]

[0123] Where f(RT) is the probability distribution function of the extra stay time of electric vehicles, C t The results of sampling based on the additional residence time of electric vehicles, RT i,0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

[0124] For example, in terms of constructing an electric vehicle information model, an embodiment of the present invention constructs an electric vehicle information matrix. In one application scenario, the basic electric vehicle information matrix is:

[0125]

[0126] Where, EVI t is the basic information matrix of electric vehicles at time t, RC n is the rated capacity of the nth electric vehicle, ISOC n,t is the initial state of charge of the nth electric vehicle, TSOC n is the target state of charge of the nth electric vehicle, RT n is the residence time of the nth electric vehicle.

[0127] In this matrix, each row corresponds to a car, and each column corresponds to a piece of information about the car. In fact, this information is constructed through multiple functions, specifically, the probability distribution function of the rated capacity of the electric vehicle, the probability distribution function of the charge state of the electric vehicle before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the time when the electric vehicle arrives at the charging station.

[0128] The distribution function of electric vehicle driving distance is:

[0129]

[0130] In the formula, f d (x) is the distribution function of the electric vehicle driving distance, x is the driving distance, u d and δ dare the mean and standard deviation of commuting distance, respectively.

[0131] The time distribution function of electric vehicles arriving at the charging station is:

[0132]

[0133] Where t is the arrival time, and are the mean and standard deviation of the arrival time, respectively.

[0134] The probability distribution function of electric vehicle rated capacity is:

[0135]

[0136] Where f(RC) is the probability distribution function of the rated capacity of the electric vehicle, RC min and RC max are the minimum and maximum capacities of electric vehicles respectively, f0(RC) is the Gamma distribution function, α and β are the shape and rate parameters of the Gamma distribution respectively, and Γ is the Gamma function.

[0137] The probability distribution function of the state of charge of an electric vehicle before traveling is:

[0138]

[0139] Where f(ISOC0) is the probability distribution function of the state of charge of the electric vehicle before traveling, ISOC0 is the state of charge of the electric vehicle before traveling, ISOC 0,min ISOC is the minimum state of charge for electric vehicles to meet their travel conditions. 0,max is the maximum state of charge of the electric vehicle, f0(ISOC0) is the normal distribution function, σ and μ are the standard deviation and mean of ISOC0 distribution, respectively.

[0140] In terms of solving the matrix, the embodiment of the present invention solves the first column of the basic matrix of electric vehicle information according to the probability distribution function of the rated capacity of the electric vehicle;

[0141] According to the second formula, the second column of the electric vehicle information basic matrix is ​​solved, wherein the second formula is:

[0142]

[0143] In the formula, ISOC i,t is the initial charge state of the ith electric vehicle at time t, RC i is the rated capacity of the ith electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling according to the probability distribution function of the state of charge before the trip of the electric vehicle, λ iis the power consumption per unit mileage of the i-th electric vehicle, D i is the driving distance of the i-th electric vehicle;

[0144] According to the third formula, the third column of the electric vehicle information basic matrix is ​​solved, wherein the third formula is:

[0145] TSOC i =ISOC i,t

[0146] In the formula, ISOC i,t is the state of charge of the i-th electric vehicle before its trip at time t;

[0147] According to the fourth formula, the fourth column of the electric vehicle information basic matrix is ​​solved, wherein the fourth formula is:

[0148]

[0149] Where f(RT) is the probability distribution function of the extra stay time of electric vehicles, C t The results of sampling based on the additional residence time of electric vehicles, RT i,0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

[0150] Step 103, constructing a carbon emission objective function according to the power source of the power grid system.

[0151] Step 104, based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, the carbon emission objective function is solved with the goal of minimizing carbon emissions, and the power grid system is dispatched according to the obtained solution.

[0152] In some embodiments, the carbon emission objective function is:

[0153]

[0154] Among them, C is the target of rolling optimization scheduling at time t, c grid,t Carbon emission coefficient of the power grid, P grid,buy (t) is the grid inlet power of the system at time t, P grid,sell (t) is the power grid export quantity of the system;

[0155] Substituting the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix into the carbon emission objective function, and using a genetic algorithm to solve multiple parameters in the carbon emission objective function;

[0156] The power grid system is dispatched according to the obtained solution.

[0157] Exemplarily, the carbon emission objective function of the present invention is:

[0158]

[0159] Before optimizing according to the objective function, it is generally necessary to formulate constraints for each part and supply and demand balance constraints:

[0160]

[0161] Among them, P wd To generate power for wind turbines, To generate power for the photovoltaic array, is the discharge amount of all electric vehicles at the charging station, is the discharge capacity of pumped storage, P grid,in is the power input to the grid, P load For user power load, is the charging capacity of all electric vehicles at the charging station, For pumped storage energy storage, P grid,out The amount of electricity supplied to the grid for the system.

[0162] The upper and lower limits of each device’s output are as follows:

[0163]

[0164] Among them, C represents the capacity parameters related to each device.

[0165] Energy storage device constraints:

[0166] The constraint of the energy storage device is that the energy storage device cannot store and release energy at the same time. Therefore, the constraints of each energy storage device constructed using the M method are as follows:

[0167]

[0168] Where M is a sufficiently large value, typically set to 108. and They are the charging and discharging state parameters of the pumped storage power station respectively.

[0169] like Figure 3 As shown, Figure 3It is a schematic diagram of rolling optimization scheduling of the optimization scheduling provided by an embodiment of the present invention. The present invention proposes a wind, solar, water storage and charging new energy system and its low-carbon operation optimization scheduling method. This scheduling method is a rolling optimization scheduling method. For the scheduling time t, the system is optimized and scheduled through the load and renewable energy output information at this time, and the load and wind power and photovoltaic output information predicted from time t+1 to time T in the future, and the scheduling parameters from time t to time T are obtained. The scheduling parameters at time t are used as the control information at the current time to regulate the operation of the system.

[0170] When the next time t+1 is reached, the wind power and photovoltaic output information at time t+1, as well as the predicted wind power, photovoltaic, and regional load information from time t+2 to time T+1 are used as the dispatch basis for optimization scheduling, and the dispatch parameters at this time are based on the optimization scheduling results at time t+. Repeating the above operations will achieve rolling optimization scheduling of the wind, solar, water storage and charging new energy system.

[0171] like Figure 4 As shown, Figure 4 Schematic diagram of system operation scheduling parameter optimization provided by an embodiment of the present invention. The process of the optimization scheduling process is as follows:

[0172] (a) Generate a randomly initialized population at the scheduling time t. The population is a combination of multiple randomly generated optimization variables that meet the constraints. First, based on the electric vehicle information at the scheduling time, generate optimization variables. The optimization variables are the pumped storage / discharge energy from t to t+T, the grid interaction, and the charge / discharge amount of each electric vehicle in the charging station. T is the maximum residence time of all electric vehicles in the charging station at the scheduling time.

[0173] (b) Calculate the fitness value of each individual in the population. The calculation process first reads the predicted information of the load and wind and solar output from the scheduling time t+1 to t+T and the load and wind and solar output information determined at time t. Then, each individual in the population is used as the scheduling parameter of the system to calculate the carbon emissions of the system from t to t+T, and complete the fitness value calculation of all individuals in the population.

[0174] (c) According to the fitness value of each individual in the population, excellent individuals are selected and inherited to the next generation of the population.

[0175] (d) Perform crossover and mutation operations to generate the next generation of population. The crossover operation in this process is to exchange the chromosomes of excellent individuals according to probability to generate new individuals, and the mutation operation is to randomly replace part of the chromosome values ​​of the selected individuals with other values ​​with a certain probability;

[0176] (e) When the optimization process meets the optimization convergence criterion, the system optimization scheduling at the scheduling time t is completed. The optimization scheduling obtains the scheduling parameters of all times from t to t+T, and the optimization scheduling result at time t is used as the scheduling parameter of the system at that scheduling time.

[0177] (4) Based on the optimized dispatching parameters at the dispatching time, the upper and lower adjustment spaces of the system are calculated. The upper and lower adjustment spaces are the amount of electricity that can be received and supplied to the power grid under the premise of meeting the charging needs of each electric vehicle in the system and the load needs of regional users at that moment. If the system can provide a larger space for sending and receiving electricity and there is demand from the power grid, it can participate in the peak load regulation of the power grid and reduce the total carbon emissions of the system.

[0178] The embodiment of the present invention is applied to a power grid system with a new energy power station, a pumped storage station and a charging station. First, the power generation model of the new energy power station and the operation model of the pumped storage station are obtained; then, an electric vehicle information matrix is ​​constructed according to the probability distribution function of the rated capacity of the electric vehicle, the probability distribution function of the state of charge before the electric vehicle travels, the distribution function of the electric vehicle driving distance and the distribution function of the time when the electric vehicle arrives at the charging station; then, a carbon emission target function is constructed according to the power source of the power grid system; finally, according to the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, the carbon emission target function is solved with the minimum carbon emission as the goal, and the power grid system is dispatched according to the obtained solution. The embodiment of the present invention uses the maximum residence time of the electric vehicle as the step size of the rolling optimization scheduling, and comprehensively considers the constraints of various aspects of the energy system, adopts a genetic algorithm for optimization, takes the carbon emissions of the system as the optimization target, realizes the output control optimization of the active control device in the system, and the charging and discharging adjustment of the electric vehicle, and the comprehensive utilization rate of the power grid is low, which is conducive to energy saving and emission reduction.

[0179] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0180] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0181] Figure 5 The structural schematic diagram of the wind-solar-water storage and charging operation scheduling device provided in the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0182] like Figure 5As shown, the wind-solar-water storage and charging operation scheduling device 5 includes: a model acquisition module 501, an electric vehicle information construction module 502, an objective function construction module 503 and an objective function optimization module 504, wherein:

[0183] Model acquisition module 501, used to acquire the power generation model of the new energy power station and the operation model of the pumped storage station;

[0184] The electric vehicle information construction module 502 is used to construct an electric vehicle information matrix according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station;

[0185] An objective function construction module 503 is used to construct a carbon emission objective function according to the power source of the power grid system;

[0186] The objective function optimization module 504 is used to solve the carbon emission objective function based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, with the goal of minimizing carbon emissions, and to dispatch the power grid system according to the obtained solution.

[0187] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0188] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0189] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned wind, solar, water storage and charging operation scheduling method implementation methods can be implemented.

[0190] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal and software distribution medium, etc.

[0191] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A wind-solar water storage and charging operation scheduling method, characterized in that: Applied to a power grid system with a new energy power station, a pumped storage station and a charging station, the wind, solar, water storage and charging operation scheduling method includes: Obtain the power generation model of the renewable energy power station and the operation model of the pumped storage station; The electric vehicle information matrix is ​​constructed according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station; Constructing a carbon emission objective function based on the power source of the grid system; According to the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, the carbon emission objective function is solved with the goal of minimizing carbon emissions, and the power grid system is dispatched according to the obtained solution.

2. The wind-solar water storage and charging operation scheduling method according to claim 1 is characterized in that: The wind power station power generation model is: Where P nom is the rated power of the wind turbine, v(t) is the actual local wind speed, v ci and v co are the cut-in wind speed and cut-out wind speed of the wind turbine respectively; The photovoltaic power station power generation model is: Where P PV (t) is the power generation of a single photovoltaic cell, Rad(t) is the light intensity, A PV is the photovoltaic area, f PV (t) is the photovoltaic power generation efficiency, T PV (t) is the actual working temperature of photovoltaic, T env (t) is the ambient temperature, T nom is the photovoltaic standard temperature.

3. The wind-solar water storage and charging operation scheduling method according to claim 1 is characterized in that: The pumped storage station operation model is: In the formula, Q PS (t) and Q PS (t-1) are the water volume stored in the pumped storage power station at time t and t-1, respectively, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and releasing amounts of the pumped storage power station, C char and C disc are the pumping and discharge coefficients, η char and η disc are the pump efficiency and turbine efficiency respectively, ρ water is the density of water, h is the head of water, P PS，char and P PS，disc They are the power consumed by the water pump and the power generated by the turbine generator respectively.

4. The wind-solar water storage and charging operation scheduling method according to claim 1 is characterized in that: The electric vehicle information matrix is ​​constructed according to the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before traveling, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station, including: Obtaining a probability distribution function of the rated capacity of the electric vehicle, a probability distribution function of the state of charge of the electric vehicle before travel, a distribution function of the travel distance of the electric vehicle, and a distribution function of the time when the electric vehicle arrives at the charging station; Constructing an electric vehicle information basic matrix representing the states of a plurality of electric vehicles, wherein each row of the electric vehicle information basic matrix corresponds to the state information of one vehicle; According to the probability distribution function of the electric vehicle's rated capacity, the probability distribution function of the electric vehicle's state of charge before travel, the distribution function of the electric vehicle's driving distance, and the distribution function of the electric vehicle's arrival time at the charging station, each column in the electric vehicle information basic matrix is ​​solved to obtain the electric vehicle information matrix.

5. The wind-solar water storage and charging operation scheduling method according to claim 4 is characterized in that: The basic matrix of electric vehicle information is: Where, EVI t is the basic information matrix of electric vehicles at time t, RC n is the rated capacity of the nth electric vehicle, ISOC n，t is the initial state of charge of the nth electric vehicle, TSOC n is the target state of charge of the nth electric vehicle, RT n is the residence time of the nth electric vehicle.

6. The wind-solar water storage and charging operation scheduling method according to claim 4 is characterized in that: The electric vehicle driving distance distribution function is: In the formula, f d (x) is the distribution function of the electric vehicle driving distance, x is the driving distance, u d and δ d are the mean and standard deviation of commuting distance, respectively; The time distribution function of the electric vehicle arriving at the charging station is: Where t is the arrival time, and are the mean and standard deviation of the arrival time, respectively.

7. The wind-solar water storage and charging operation scheduling method according to claim 4 is characterized in that: The probability distribution function of the electric vehicle rated capacity is: Where f(RC) is the probability distribution function of the rated capacity of the electric vehicle, RC min and RC max are the minimum and maximum capacities of electric vehicles, respectively, f0(RC) is the Gamma distribution function, α and β are the shape and rate parameters of the Gamma distribution, respectively, and Γ is the Gamma function; The probability distribution function of the state of charge of the electric vehicle before traveling is: Where f(ISOC0) is the probability distribution function of the state of charge of the electric vehicle before traveling, ISOC0 is the state of charge of the electric vehicle before traveling, ISOC 0，min ISOC is the minimum state of charge for electric vehicles to meet their travel conditions. 0，max is the maximum state of charge of the electric vehicle, f0(ISOC0) is the normal distribution function, σ and μ are the standard deviation and mean of ISOC0 distribution, respectively.

8. The wind-solar water storage and charging operation scheduling method according to claim 5 is characterized in that: The method of solving each column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function, the electric vehicle state of charge probability distribution function before travel, the electric vehicle driving distance distribution function and the electric vehicle arrival time distribution function of the charging station to obtain the electric vehicle information matrix includes: Solving the first column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function; According to the second formula, the second column of the electric vehicle information basic matrix is ​​solved, wherein the second formula is: In the formula, ISOC i，t is the initial charge state of the ith electric vehicle at time t, RC i is the rated capacity of the ith electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling according to the probability distribution function of the state of charge before the trip of the electric vehicle, λ i is the power consumption per unit mileage of the i-th electric vehicle, D i is the driving distance of the i-th electric vehicle; According to the third formula, the third column of the electric vehicle information basic matrix is ​​solved, wherein the third formula is: TSOC i =ISOC i，t In the formula, ISOC i，t is the state of charge of the i-th electric vehicle before its trip at time t; According to the fourth formula, the fourth column of the electric vehicle information basic matrix is ​​solved, wherein the fourth formula is: Where f(RT) is the probability distribution function of the extra stay time of electric vehicles, C t The results of sampling based on the additional residence time of electric vehicles, RT i，0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

9. The wind-solar water storage and charging operation scheduling method according to any one of claims 1 to 8, characterized in that: The step of solving the carbon emission objective function based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix with the goal of minimizing carbon emissions, and dispatching the power grid system according to the obtained solution includes: The carbon emission objective function is: Among them, C is the target of rolling optimization scheduling at time t, c grid，t Carbon emission coefficient of the power grid, P grid，buy (t) is the grid inlet power of the system at time t, P grid，sell (t) is the power grid export quantity of the system; Substituting the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix into the carbon emission objective function, and using a genetic algorithm to solve multiple parameters in the carbon emission objective function; The power grid system is dispatched according to the obtained solution.

10. A wind-solar water storage and charging operation scheduling device, characterized in that: Used to implement the wind-solar water storage and charging operation scheduling method according to any one of claims 1 to 9, the wind-solar water storage and charging operation scheduling device comprises: Model acquisition module, used to obtain the power generation model of the new energy power station and the operation model of the pumped storage station; An electric vehicle information construction module is used to construct an electric vehicle information matrix according to a probability distribution function of an electric vehicle's rated capacity, a probability distribution function of an electric vehicle's state of charge before traveling, a distribution function of an electric vehicle's driving distance, and a distribution function of an electric vehicle's arrival time at a charging station; An objective function building module, used to build a carbon emission objective function according to the power source of the power grid system; as well as, The objective function optimization module is used to solve the carbon emission objective function based on the power generation model of the new energy power station, the operation model of the pumped storage station and the first electric vehicle information matrix, with the goal of minimizing carbon emissions, and to dispatch the power grid system according to the obtained solution.

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