Wind-solar water storage and charging operation scheduling method and device

Abstract

The present invention provides a wind, solar, and water storage and charging operation scheduling method and device, which relates to the field of new energy power generation technology. The method first obtains the power generation model of the new energy power station and the operation model of the pumped storage station; then, an electric vehicle information matrix is constructed based on the probability distribution function of the electric vehicle rated capacity, the probability distribution function of the electric vehicle's state of charge before travel, the electric vehicle's driving distance distribution function, and the electric vehicle's arrival time at the charging station distribution function; then, a carbon emission target function is constructed based on the power source of the power grid system; finally, 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 target function is solved with the goal of minimizing carbon emissions, and the power grid system is dispatched based on the obtained solution. In the embodiment of the present invention, the comprehensive utilization rate of the power grid is low, which is conducive to energy conservation and emission reduction.

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 share of wind and solar power continues to grow, the power grid needs to pay increasing attention to accommodating all renewable energy generation connected to the grid. In this context, the integrated design of wind and solar power systems and energy storage devices has the advantage of stabilizing the fluctuating characteristics of wind and solar power.

[0003] On the other hand, as the number of electric vehicles increases, their potential as a hidden energy storage device cannot be ignored. In some areas with abundant terrain and water resources, pumped storage projects are located close to cities. The combined deployment of wind power, photovoltaic power, pumped storage, and electric vehicles is crucial for increasing electric vehicle participation in new energy system power regulation and integrating clean energy supply.

[0004] At present, the pumped storage system is mainly regulated by the control targets of the power grid, and has little 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 wind, solar, and water storage and charging operations to solve the problem of low power utilization caused by the failure of wind, solar, and water storage and charging systems to optimize scheduling.

[0006] In a first aspect, an embodiment of the present invention provides a method for dispatching wind, solar, and water storage and charging operations, which is applied to a power grid system including a new energy power station, a pumped storage station, and a charging station. The method comprises:

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

[0008] An electric vehicle information matrix is constructed based on the probability distribution function of the electric vehicle's 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 power 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 power generation model of the photovoltaic power station is:

[0015]

[0016] Where, P PV (t) is the power generation of a single photovoltaic, 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] Where 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 discharge 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, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc are the power consumed by the water pump and the power generated by the turbine generator respectively.

[0020] In one possible implementation, the electric vehicle information matrix is constructed based on 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 electric vehicle's travel distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, including:

[0021] Obtaining a probability distribution function of the electric vehicle's rated capacity, a probability distribution function of the electric vehicle's state of charge before travel, a distribution function of the electric vehicle's travel distance, and a distribution function of the electric vehicle's arrival time at a 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 electric vehicle's driving distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, 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] Where, f d (x) is the distribution function of 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 times, 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 the trip, ISOC0 is the state of charge of the electric vehicle before the trip, 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 one possible implementation, solving each column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function, the electric vehicle pre-trip 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] Where, ISOC i,t is the initial state of charge of the ith electric vehicle at time t, RC i is the rated capacity of the i-th electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling the probability distribution function of the state of charge of the electric vehicle before the trip, 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] Where, 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 are shown in Table 2. i,0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

[0050] In one 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 based on 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 grid export power 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 solving multiple parameters in the carbon emission objective function using a genetic algorithm;

[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 based on a probability distribution function of an electric vehicle's rated capacity, a probability distribution function of an electric vehicle's state of charge before travel, a distribution function of an electric vehicle's travel distance, and a distribution function of an electric vehicle's arrival time at a charging station;

[0059] An objective function construction module is used to construct a carbon emission objective function based on the power source of the power grid system;

[0060] as well as,

[0061] An 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 to the prior art are as follows:

[0063] The present invention provides a wind, solar, and water storage and charging operation scheduling method and device. The method first obtains a new energy power station generation model and a pumped storage station operation model; then constructs an electric vehicle information matrix based on the electric vehicle rated capacity probability distribution function, the electric vehicle pre-trip charge state probability distribution function, the electric vehicle driving distance distribution function, and the electric vehicle arrival time distribution function; then constructs a carbon emission target function based on the power source of the power grid system; finally, based on the new energy power station generation model, the pumped storage station operation model, and the first electric vehicle information matrix, the carbon emission target function is solved with the goal of minimizing carbon emissions, and the power grid system is scheduled based on the obtained solution. In an embodiment of the present invention, the maximum residence time of the electric vehicle is used as the step size for rolling optimization scheduling, and various constraints of the energy system are comprehensively considered. A genetic algorithm is used for optimization, with the system's carbon emissions as the optimization target, to achieve output control optimization of the system's actively controllable devices and charge and discharge adjustment of the electric vehicles. The comprehensive utilization rate of the power grid electricity is low, which is conducive to energy conservation 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 following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. 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 any creative work.

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

[0067] Figure 3 Schematic diagram of rolling optimization scheduling of optimization scheduling provided by an embodiment of the present invention;

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

[0069] Figure 5 It is a structural diagram of the 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 particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in alternative 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 detail.

[0071] In order to make the objectives, technical solutions and advantages of the present invention more clear, the following will be described through specific implementation methods 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 power generation model of the photovoltaic power station is:

[0078]

[0079] Where, P PV (t) is the power generation of a single photovoltaic, 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 embodiments, the pumped storage station operation model is:

[0081]

[0082] Where Q PS (t) and Q PS (t-1) is the amount of water stored in the pumped storage power station at time t and time t-1, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and discharge 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, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc 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 by the embodiment of the present invention. Figure 2 As shown, this wind, solar, hydro, and storage new energy system primarily consists of wind power, photovoltaics, a pumped-storage energy storage system, charging stations with charging piles, a grid-interactive substation, and urban area loads. Wind power and photovoltaics provide clean, green electricity, while electric vehicles in the pumped-storage system and charging stations smooth out fluctuations in wind and solar output and shift peaks and valleys. This operation and scheduling method uses the maximum dwell time of electric vehicles as the step size for rolling optimization scheduling, comprehensively considers various constraints of the energy system, and employs a genetic algorithm for optimization, taking the system's carbon emissions as the optimization objective. This optimizes the output control of the system's actively controllable devices and adjusts the charging and discharging of electric vehicles. Furthermore, given the energy system's significant regulatory capacity, this capacity is reported to the grid to participate in peak-shaving operations.

[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, 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] Where Q PS (t) and Q PS (t-1) is the amount of water stored in the pumped storage power station at time t and time t-1, σ PS is the reservoir leakage loss coefficient, Q char (t) and Q disc (t) are the pumping and discharge 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, ρ water is the density of water, h is the head of water, P PS,char and P PS,disc 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 based on 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.

[0094] In some embodiments, constructing an electric vehicle information matrix based on a probability distribution function of an electric vehicle's rated capacity, a probability distribution function of an electric vehicle's state of charge before travel, a distribution function of an electric vehicle's travel distance, and a distribution function of an electric vehicle's arrival time at a charging station includes:

[0095] Obtaining a probability distribution function of the electric vehicle's rated capacity, a probability distribution function of the electric vehicle's state of charge before travel, a distribution function of the electric vehicle's travel distance, and a distribution function of the electric vehicle's arrival time at a 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 electric vehicle's driving distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, 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] Where, f d (x) is the distribution function of 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 times, respectively.

[0107] In some embodiments, the electric vehicle rated capacity probability distribution function 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 the trip, ISOC0 is the state of charge of the electric vehicle before the trip, 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, solving each column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function, the electric vehicle pre-trip 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:

[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] Where, ISOC i,t is the initial state of charge of the ith electric vehicle at time t, RC i is the rated capacity of the i-th electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling the probability distribution function of the state of charge of the electric vehicle before the trip, 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] Where, 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 are shown in Table 2. 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, they are 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 the trip, 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] Where, f d (x) is the distribution function of 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 times, 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. 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 the trip, ISOC0 is the state of charge of the electric vehicle before the trip, 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 matrix solution, the embodiment of the present invention solves the first column of the electric vehicle information basic matrix according to the electric vehicle rated capacity probability distribution function;

[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] Where, ISOC i,t is the initial state of charge of the ith electric vehicle at time t, RC i is the rated capacity of the i-th electric vehicle, ISOC0 is the state of charge before the trip calculated by sampling the probability distribution function of the state of charge of the electric vehicle before the trip, 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] Where, 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 are shown in Table 2. i,0 The time required to charge an electric vehicle at maximum power, P c is the charging power.

[0150] Step 103: construct a carbon emission objective function based on 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, with the goal of minimizing carbon emissions, solve the carbon emission objective function, and dispatch the power grid system 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 grid export power 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 solving multiple parameters in the carbon emission objective function using a genetic algorithm;

[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, such as 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 electricity load, is the charging capacity of all electric vehicles at the charging station, For pumped storage energy, 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] Where 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 are constructed using the M method as follows:

[0167]

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

[0169] like Figure 3 As shown, Figure 3It is a rolling optimization scheduling diagram of the optimization scheduling provided by the 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 the time, as well as the load and wind power and photovoltaic output information predicted from time t+1 to time T in the future, to obtain the scheduling parameters from time t to time T, and use the scheduling parameters at time t as the control information at the current time to regulate the operation of the system.

[0170] At the next time, t+1, the system optimizes scheduling based on the wind and PV output information at t+1 and the predicted wind, PV, and regional load information from t+2 to T+1. The scheduling parameters at this time are based on the optimized scheduling results at t+. Repeating these steps achieves rolling optimized scheduling for the wind, solar, hydro, and storage / 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 scheduling time t. The population is composed of multiple randomly generated optimization variable combinations that satisfy 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 capacity of each electric vehicle in the charging station. T is the maximum residence time of all electric vehicles at 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 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, using each individual in the population as the system scheduling parameter, calculate the carbon emissions of the system from t to t+T, and complete the calculation of the fitness value 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. 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 optimized scheduling obtains the scheduling parameters for all times from t to t+T, and the optimized 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 moment, 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 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 equipped with a renewable energy power station, a pumped storage station, and a charging station. The embodiment first obtains a power generation model for the renewable energy power station and an operating model for the pumped storage station. An electric vehicle information matrix is then constructed based on 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 travel distance, and the distribution function of the time it takes the electric vehicle to arrive at the charging station. A carbon emission target function is then constructed based on the power source of the power grid system. Finally, based on the power generation model of the renewable energy power station, the operating model of the pumped storage station, and the first electric vehicle information matrix, the carbon emission target function is solved with the goal of minimizing carbon emissions, and the power grid system is dispatched based on the obtained solution. The embodiment of the present invention uses the maximum residence time of electric vehicles as the step size for rolling optimization scheduling, comprehensively considers various constraints of the energy system, and employs a genetic algorithm for optimization, taking the system's carbon emissions as the optimization target. This optimizes the output control of the system's actively controllable devices and adjusts the charging and discharging of electric vehicles, achieving a low comprehensive utilization rate of grid power, which is beneficial for energy conservation and emission reduction.

[0179] It should be understood that the size of the serial numbers of each step in the above embodiment 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 embodiment of the present invention.

[0180] The following is an embodiment of the device of the present invention. For details not described in detail, please refer to the corresponding method embodiment described above.

[0181] Figure 5 The following is a schematic diagram showing the structure of a wind-solar-water storage and charging operation scheduling device provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:

[0182] like Figure 5As shown, the wind, solar, and 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 obtain 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 based on 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 travel 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 based on 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 embodiments, the description of each embodiment has its own focus. 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. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0189] If the modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the processes in the above-mentioned embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-mentioned wind, solar, hydropower, storage and charging operation scheduling methods.

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

[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, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These 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 scope of protection 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 equipped with a renewable energy power station, a pumped storage station, and a charging station, the wind, solar, and 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 based on 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 travel distance, and the distribution function of the electric vehicle's arrival time at the charging station. The basic electric vehicle information matrix is: Where, for The basic matrix of electric vehicle information at all times, For the The rated capacity of an electric vehicle, For the The initial state of charge of an electric vehicle, For the The target state of charge of an electric vehicle, For the The dwell time of an electric vehicle; Constructing a carbon emission objective function based on the power source of the power grid system; Based on the power generation model of the new energy power station, the operation model of the pumped storage station, and the 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; Each column in the basic electric vehicle information matrix is solved 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 travel, the electric vehicle's travel distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, including: 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: Where, for Moment The initial state of charge of an electric vehicle, For the The rated capacity of an electric vehicle, is the first value calculated by sampling the probability distribution function of the state of charge of the electric vehicle before traveling. The state of charge of an electric vehicle before traveling. For the The power consumption per unit mileage of an electric vehicle, For the distance travelled by electric vehicles; According to the third formula, the third column of the electric vehicle information basic matrix is solved, wherein the third formula is: ； According to the fourth formula, the fourth column of the electric vehicle information basic matrix is solved, wherein the fourth formula is: Where, is the probability distribution function of the extra stay time of electric vehicles, The results of sampling based on the extra stay time of electric vehicles are: The time required to charge an electric vehicle at maximum power, is the charging power.

2. The wind-solar-water storage and charging operation scheduling method according to claim 1 is characterized in that: The power generation model of a wind power station is: Where, is the rated power of the wind turbine, is the actual local wind speed, and are the cut-in wind speed and cut-out wind speed of the wind turbine respectively; The power generation model of a photovoltaic power station is: Where, is the power generation of a single photovoltaic cell, is the light intensity, is the photovoltaic area, is the photovoltaic power generation efficiency, is the actual working temperature of photovoltaic, is the ambient temperature, 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: Where, and Pumped storage power stations are located in and The amount of water available at any time, is the reservoir leakage loss coefficient, and are the pumping and discharge volumes of the pumped storage power station, and are the pumping and discharge coefficients, and are the pump efficiency and turbine efficiency, is the density of water, For the lift of water, and 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 travel, the electric vehicle's travel distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, including: Obtaining a probability distribution function of the electric vehicle's rated capacity, a probability distribution function of the electric vehicle's state of charge before travel, a distribution function of the electric vehicle's travel distance, and a distribution function of the electric vehicle's arrival time at a 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 electric vehicle's driving distance distribution function, and the electric vehicle's arrival time at the charging station distribution function, 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 electric vehicle driving distance distribution function is: Where, is the electric vehicle driving distance distribution function, is the driving distance, and 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, is the arrival time, and are the mean and standard deviation of the arrival times, respectively.

6. 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, is the probability distribution function of the rated capacity of electric vehicles, and are the minimum and maximum capacities of electric vehicles, is the Gamma distribution function, and are the shape and rate parameters of the Gamma distribution, is the Gamma function; The probability distribution function of the state of charge of the electric vehicle before traveling is: Where, is the probability distribution function of the state of charge of the electric vehicle before the trip, is the state of charge of the electric vehicle before traveling, is the minimum state of charge for electric vehicles to meet their travel conditions, is the maximum state of charge of the electric vehicle, Normal distribution function, and They are The standard deviation and mean of the distribution.

7. The wind-solar-water storage and charging operation scheduling method according to any one of claims 1 to 6, characterized in that: 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 electric vehicle information matrix with the goal of minimizing carbon emissions, and dispatching the power grid system based on the obtained solution includes: The carbon emission objective function is: in, for The goal of constant rolling optimization scheduling, The carbon emission coefficient of the power grid, for The grid inlet power of the system at all times, is the grid export power of the system; Substituting the power generation model of the new energy power station, the operation model of the pumped storage station, and the 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.

8. A wind-solar-water storage and charging operation scheduling device, characterized in that: For implementing the wind-solar water storage and charging operation scheduling method according to any one of claims 1 to 7, 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 based on a probability distribution function of an electric vehicle's rated capacity, a probability distribution function of an electric vehicle's state of charge before travel, a distribution function of an electric vehicle's travel distance, and a distribution function of an electric vehicle's arrival time at a charging station; An objective function construction module is used to construct a carbon emission objective function based on 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 electric vehicle information matrix, with the goal of minimizing carbon emissions, and dispatch the power grid system according to the obtained solution.

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