Park scheduling method and device considering photovoltaic reasonable consumption interval, and storage medium

Abstract

The invention discloses a park scheduling method and equipment considering a photovoltaic reasonable consumption interval, and a storage medium, and relates to a power system scheduling method, and equipment and a storage medium required by the method. The method comprises the following steps: S.1, acquiring historical operation data of a park and preprocessing the historical operation data; and S.2, constructing a nonlinear regression model for fitting the unit operation cost of the park with the photovoltaic load ratio and the photovoltaic consumption rate. And S.3, after the equation of the fitted nonlinear regression model is obtained, solving the photovoltaic consumption rate when the unit operation cost of the park is the lowest under different photovoltaic load ratios. And S.4, performing day-ahead prediction on the photovoltaic power generation amount and the energy consumption load of the next day based on the photovoltaic power generation amount and the energy consumption load data of the park in the past one year in combination with different weather conditions. And S.5, constructing a park optimization scheduling model considering the reasonable photovoltaic consumption interval. And S.6, according to the park optimization scheduling model, controlling a park photovoltaic device, an energy storage device and user energy consumption equipment.

Description

Technical Field

[0001] The present invention discloses a park dispatching method, equipment and storage medium taking into account the reasonable photovoltaic consumption range, and relates to a method for dispatching a power system, and equipment and storage medium required by the method. Background Art

[0002] In recent years, new energy has developed rapidly, and the high penetration of new energy has become an important feature of the future new power system. Among them, photovoltaics, as a clean and renewable energy, has gradually become one of the important sources of electricity in the park.

[0003] At present, most park energy management and dispatching methods are mainly aimed at improving the photovoltaic absorption rate. The corresponding dispatching model lacks consideration of the reasonable photovoltaic absorption range, and cannot maximize the economic benefits of the park while ensuring the safety and stable operation of the power grid. For example, some park dispatching systems unilaterally pursue full photovoltaic absorption, ignoring the load limit of power grid equipment and the impact of power fluctuations on system stability. Although this "maximum absorption" model reduces the phenomenon of abandoned light, it does not take into account the comprehensive operating costs of the park, which may result in low utilization of power grid equipment such as lines and transformers. From the perspective of the whole society, it is not the best choice; therefore, it is necessary to design a new dispatching method to better meet social needs. Summary of the invention

[0004] In view of this, the present invention proposes a park scheduling method, equipment and storage medium that take into account the reasonable photovoltaic consumption range; it solves the reasonable economic consumption of distributed photovoltaics in the park under the background of high proportion of new energy penetration, constructs a park optimization scheduling model that takes into account the reasonable photovoltaic consumption range, effectively improves the utilization rate of park electrical equipment and the economic benefits of park energy use, reduces resource waste, and has good social promotion value.

[0005] A park scheduling method considering a reasonable photovoltaic consumption range of the present invention includes:

[0006] Step S.1, obtain the historical operation data of the park and pre-process it; the operation data includes: park load, photovoltaic consumption rate and corresponding unit operation cost of the park.

[0007] Step S.2: construct a nonlinear regression model to fit the unit operating cost of the park to the photovoltaic load ratio and the photovoltaic absorption rate.

[0008] Step S.3, after obtaining the equation of the fitted nonlinear regression model, solve the photovoltaic absorption rate when the unit operating cost of the park is the lowest under different photovoltaic load ratios, that is, the optimal photovoltaic absorption rate, and then take a margin of 3% above and below as the reasonable photovoltaic absorption range.

[0009] Step S.4: Based on the photovoltaic power generation and energy load data of the park in the past year and combined with different weather conditions, a day-ahead forecast is made for the photovoltaic power generation and energy load of the next day.

[0010] Step S.5: Construct a park optimization scheduling model that takes into account the reasonable photovoltaic consumption range, and use the genetic-differential algorithm (GA-DE) to solve the scheduling model.

[0011] Step S.6: Control the park's photovoltaic, energy storage devices and user energy-using equipment based on the solution of the park's optimal scheduling model.

[0012] Furthermore, in step S.2, the specific steps of constructing a nonlinear regression model for fitting the unit operating cost of the park with the photovoltaic load ratio and the photovoltaic consumption rate are as follows:

[0013] Y=β0+β1X1+β2X1 2 +β3X2+β4X2 2 +β5X1X2+ε

[0014] Among them, Y is the unit operating cost of the park, X1 and X2 are the photovoltaic load ratio and photovoltaic absorption rate respectively, β0 is the intercept, β1, β2, β3, β4, β5 are regression coefficients, and ε is the error term;

[0015] The least square method is used to estimate the regression coefficients β0, β1, β2, β3, β4, and β5, and the mean square error is selected as the error term ε. After the goodness of fit test, the nonlinear model is obtained;

[0016] The coefficient of determination for the goodness-of-fit test is as follows:

[0017]

[0018] Among them, R 2 is the coefficient of determination, SSR is the regression sum of squares, SSE is the residual sum of squares, and SST is the total sum of squares. 2 The closer it is to 1, the better the model fits the data.

[0019] Furthermore, in step S.3, the specific steps of the reasonable photovoltaic consumption area are as follows:

[0020]

[0021] In the formula, λ pv (t) is the photovoltaic absorption rate of the park power grid system at time t; are the minimum and maximum values ​​of the reasonable photovoltaic consumption range, The maximum value can be 100%.

[0022] Furthermore, in step S.3.1, the objective function is constructed with the lowest comprehensive operation cost of the park as the goal:

[0023] minF1=C1+C2+C3-C4

[0024] In the formula, F1 is the total cost of park operation; C1 is the electricity purchase cost; C2 is the photovoltaic absorption cost; C3 is the equipment operation and maintenance cost; C4 is the electricity sales income.

[0025] The electricity purchase cost is:

[0026] In the formula, C e (t) is the unit electricity purchase cost at time t; P e (t) is the power purchased by the park from the power grid at time t; T is the dispatching period, which is 24 hours.

[0027] Furthermore, in step S.3.1.1, the photovoltaic consumption cost is:

[0028]

[0029] Where k1 and k2 are the unit power penalty cost coefficients for photovoltaic consumption outside the reasonable range; P pv (t) is the photovoltaic output of the park at time t.

[0030] Furthermore, in step S.3.1.2, the equipment operation and maintenance cost mainly includes the operation loss of photovoltaic and energy storage equipment, and the formula is:

[0031] C3=C pv +C ess

[0032] Among them, the operating cost of the park's photovoltaic power generation device is C pv The calculation method is as follows:

[0033]

[0034] Where: c pv The electricity cost of photovoltaic power generation equipment taking into account operation, maintenance and depreciation factors.

[0035] Furthermore, in step S.3.1.2.1, the energy storage system charging and discharging loss cost C ess The calculation method is as follows:

[0036]

[0037] Where: c ess is the cost coefficient of charging and discharging loss of the energy storage system; P cha (t) is the charging power of the energy storage system at time t; Pdis (t) is the discharge power of the energy storage system at time t.

[0038] Furthermore, in step S.5.1, the park optimization scheduling model considering the reasonable photovoltaic consumption range is considered, and its constraints include: electric power balance constraint, photovoltaic output power constraint, photovoltaic consumption rate constraint and energy storage equipment constraint;

[0039] Specifically, the electric power balance constraint is:

[0040] P e (t)+P pv (t)+P dis (t) = P sell (t)+P load (t)+P cha (t)

[0041] Where P load (t) is the power load of the park at time t.

[0042] Specifically, the photovoltaic output power constraint is:

[0043]

[0044] In the formula, is the maximum output power of the distributed photovoltaic power station during period t.

[0045] The most ideal state for the absorption of new energy is full absorption, but this will aggravate the fluctuation of the power system and affect the safe and stable operation of the power system. Therefore, the photovoltaic absorption rate is constrained as follows:

[0046]

[0047] In the formula, It is the minimum photovoltaic power consumption rate allowed by the park power grid.

[0048] Specifically, energy storage equipment constraints:

[0049]

[0050] In the formula, are the charging and discharging power of energy storage device m at time t respectively; are the maximum charging and discharging rates of energy storage device m; C m is the maximum capacity of the energy storage device; E m (t) is the storage energy of energy storage device m at time t; are the maximum and minimum storage capacities of the energy storage device respectively.

[0051] Further, in step S.5.2, the scheduling model is solved using the genetic-difference algorithm (GA-DE);

[0052] The specific steps are as follows:

[0053] Initializing the population means randomly generating a set of strategies as the initial exploration space of the algorithm. After determining the lowest park operation cost as the objective function, the initial parameters of the population size, crossover rate, and mutation rate are determined. The individuals in the population are represented by different scheduling schemes, such as park energy storage configuration and charging strategies for charging piles.

[0054] The fitness function is the core of the genetic algorithm. The higher the fitness, the lower the cost of the solution. When calculating the fitness of individuals in the population, the algorithm evaluates the fitness of each solution to determine which solutions have a greater chance of survival and reproduction in the next generation;

[0055] Furthermore, each iteration will generate a new population P k In each iteration, the algorithm evaluates the individuals of the current population according to the fitness function and generates the next generation population P through operations such as selection, crossover, and mutation. k+1 ;

[0056] When the configuration scheme with the lowest cost is found or the number of iterations reaches the upper limit, the algorithm outputs the scheme with the lowest operation cost of the park as the final result. If the termination condition is not met, the algorithm continues to iterate and optimize.

[0057] Step S.6: Control the park's photovoltaic power generation, energy storage devices, and user energy-consuming equipment according to the park's optimal scheduling model.

[0058] In the present invention, the collaborative energy intelligent control terminal can realize flexible access to metering and sensing devices on the user side and the distribution side, and has functions such as data collection, precise metering, energy management, and fault reporting. In addition, the collaborative energy intelligent control terminal includes an optical fiber communication interface, a remote communication module, an analog quantity acquisition module, etc., which can quickly transmit and process park data. In order to facilitate staff to monitor the park, it is also equipped with an LCD display screen, which can automatically display information such as communication mode, signal strength, abnormal alarm, and operating status, which can well meet the application scenarios of the park.

[0059] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that the memory stores the computer program, and when the processor executes the computer program, a park scheduling method that considers a reasonable photovoltaic consumption range is implemented.

[0060] A computer-readable storage medium stores a computer program thereon, wherein the storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the park scheduling method that takes into account the reasonable photovoltaic consumption range.

[0061] In view of the problem that the full absorption of photovoltaic power in the prior art leads to the reduction of the economic performance and stability of the park, the present invention proposes a park scheduling method that takes into account the reasonable absorption range of photovoltaic power. By analyzing the historical operation data of the park, a nonlinear model between the unit operation cost of the park and the photovoltaic load ratio and the photovoltaic absorption rate is established, and the optimal photovoltaic absorption rate under different photovoltaic load ratios is calculated, thereby constructing a reasonable absorption range of photovoltaic power, and using this range as a constraint condition for the optimization scheduling model. By introducing a penalty function to limit the excess absorption of photovoltaic power, the output and load distribution of the park equipment are optimized, and a balance between the economic benefits and stability of the park is achieved.

[0062] 1. The present invention proposes a specific solution process for the reasonable photovoltaic absorption range. Through the analysis of the data, a nonlinear regression model is constructed with the unit operating cost of the park as the dependent variable, and the photovoltaic load ratio and photovoltaic absorption rate as independent variables. The core of the model is to estimate the regression coefficient in the model using the least squares method, and determine the reliability of the model through the goodness of fit test. Finally, using the fitted nonlinear model, after a given photovoltaic load ratio, the optimal photovoltaic absorption rate that minimizes the operating cost of the park is found by the derivative method, and a margin of 3% is taken as the reasonable photovoltaic absorption range.

[0063] 2. The present invention constructs an optimal dispatching model for a park that takes into account the reasonable photovoltaic consumption range. The long short-term memory network (LSTM) is used to predict the photovoltaic power generation and load demand of the park on the day before, and the corresponding reasonable photovoltaic consumption range is matched according to the prediction results. The optimization dispatching model is constructed with the goal of minimizing the comprehensive operation cost of the park. The cost penalty is imposed on the part that exceeds the reasonable photovoltaic consumption range through the penalty function, and the genetic-differential algorithm (GA-DE) is selected for solution, which can effectively calculate the optimal output and load distribution of each device, ensuring the economy and stability of the system.

[0064] 3. The dispatching strategy in the present invention is sent to various energy devices in the park through the coordinated energy intelligent control terminal, realizing intelligent regulation of photovoltaic, energy storage equipment, and energy-consuming equipment. The control terminal not only has data collection and management functions, but also can monitor the operating status of the equipment in real time to ensure the execution effect of the dispatching strategy.

[0065] The present invention constructs an optimization model for the reasonable photovoltaic absorption range, effectively solving the problem of the increase in system standby costs and the capacity limit of new energy access to the park caused by the one-sided pursuit of full photovoltaic absorption in the prior art. The waste of over-reliance on energy storage and grid equipment utilization is avoided through a reasonable photovoltaic absorption strategy, effectively reducing the overall energy cost of the park and promoting the development of green energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1This is a work flow chart of a park scheduling method considering a reasonable photovoltaic consumption range of the present invention;

[0067] Figure 2 The present invention is a flow chart of solving the genetic-differential algorithm (GA-DE) of a park scheduling model taking into account the reasonable photovoltaic consumption range. DETAILED DESCRIPTION

[0068] The following is attached to the instruction manual Figure 1-2 The present invention is further described in detail:

[0069] The purpose of this embodiment is to comprehensively consider the reasonable consumption range of photovoltaic power in the park and optimize the dispatching strategy of the park energy system. In this embodiment, the electric energy mainly comes from the power grid and new energy generators, and the new energy generators are mainly photovoltaic generators; the energy storage equipment only includes equipment that stores electricity; the park load includes rigid load and flexible load. In this embodiment, office electrical loads such as computers, printers and projectors, lighting loads and elevator loads in the park are all rigid loads to maintain the normal operation of the park; flexible loads mainly include electric vehicles and energy storage equipment.

[0070] Combine the following Figure 1 , a flow chart of a park scheduling method considering a reasonable photovoltaic consumption range provided in this embodiment, the method comprises the following steps:

[0071] Step S.1, obtain the historical operation data of the park and pre-process it; the operation data includes: park load, photovoltaic consumption rate and corresponding unit operation cost of the park.

[0072] Step S.2: construct a nonlinear regression model to fit the unit operating cost of the park to the photovoltaic load ratio and the photovoltaic absorption rate.

[0073] In this embodiment, the specific steps are as follows:

[0074] Y=β0+β1X1+β2X1 2 +β3X2+β4X2 2 +β5X1X2+ε

[0075] Among them, Y is the unit operating cost of the park, X1 and X2 are the photovoltaic load ratio and photovoltaic absorption rate respectively, β0 is the intercept, β1, β2, β3, β4, β5 are regression coefficients, and ε is the error term.

[0076] Then, the least squares method is used to estimate the regression coefficients β0, β1, β2, β3, β4, and β5, and the mean square error is selected as the error term ε. After the goodness of fit test, the nonlinear model is obtained.

[0077] The coefficient of determination for the goodness-of-fit test is as follows:

[0078]

[0079] Among them, R 2 is the coefficient of determination, SSR is the regression sum of squares, SSE is the residual sum of squares, and SST is the total sum of squares. 2 The closer it is to 1, the better the model fits the data.

[0080] Step S.3, after obtaining the equation of the fitted nonlinear regression model, solve the photovoltaic absorption rate when the unit operating cost of the park is the lowest under different photovoltaic load ratios, that is, the optimal photovoltaic absorption rate, and then take a margin of 3% above and below as the reasonable photovoltaic absorption range.

[0081] The reasonable consumption range of photovoltaic power is as follows:

[0082]

[0083] In the formula, λ pv (t) is the photovoltaic absorption rate of the park power grid system at time t; are the minimum and maximum values ​​of the reasonable photovoltaic consumption range, The maximum value can be 100%.

[0084] Step S.4: Based on the photovoltaic power generation and energy load data of the park in the past year and combined with different weather conditions, a day-ahead forecast is made for the photovoltaic power generation and energy load of the next day.

[0085] In this embodiment, a long short-term memory network (LSTM) is used to realize the day-ahead forecast of photovoltaic power and load in the park, and the forecast data is sent to the park data center to calculate the photovoltaic load ratio on the forecast day.

[0086] Step S.5: Construct a park optimization scheduling model that takes into account the reasonable photovoltaic consumption range, and use the genetic-differential algorithm (GA-DE) to solve the scheduling model.

[0087] In this embodiment, the objective is to minimize the comprehensive operation cost of the park, and the objective function is constructed as follows:

[0088] minF1=C1+C2+C3-C4

[0089] In the formula, F1 is the total cost of park operation; C1 is the electricity purchase cost; C2 is the photovoltaic absorption cost; C3 is the equipment operation and maintenance cost; C4 is the electricity sales income.

[0090] The electricity purchase cost is:

[0091] In the formula, C e (t) is the unit electricity purchase cost at time t; P e(t) is the power purchased by the park from the power grid at time t; T is the dispatching period, which is 24 hours.

[0092] Furthermore, the photovoltaic consumption cost is:

[0093]

[0094] Where k1 and k2 are the unit power penalty cost coefficients for photovoltaic consumption outside the reasonable range; P pv (t) is the photovoltaic output of the park at time t.

[0095] In this embodiment, the equipment operation and maintenance cost mainly includes the operation loss of photovoltaic and energy storage equipment, and the formula is:

[0096] C3=C pv +C ess

[0097] Among them, the operating cost of the photovoltaic power generation device in the park is C pv The calculation method is as follows:

[0098]

[0099] Where: c pv The electricity cost of photovoltaic power generation equipment taking into account operation, maintenance and depreciation factors.

[0100] Furthermore, the energy storage system charging and discharging loss cost C ess The calculation method is as follows:

[0101]

[0102] Where: c ess is the cost coefficient of charging and discharging loss of the energy storage system; P cha (t) is the charging power of the energy storage system at time t; P dis (t) is the discharge power of the energy storage system at time t;

[0103] In this embodiment, the optimal dispatching model of the park considering the reasonable consumption range of photovoltaic power generation is adopted, and its constraints include:

[0104] Electric power balance constraints:

[0105] P e (t)+P pv (t)+P dis (t) = P sell (t)+P load (t)+P cha (t)

[0106] Where P load (t) is the power load of the park at time t.

[0107] The photovoltaic output power constraint is:

[0108]

[0109] In the formula, is the maximum output power of the distributed photovoltaic power station during period t.

[0110] The most ideal state for the absorption of new energy is full absorption, but this will aggravate the fluctuation of the power system and affect the safe and stable operation of the power system. Therefore, the photovoltaic absorption rate constraint is:

[0111]

[0112] In the formula, It is the minimum photovoltaic power consumption rate allowed by the park power grid.

[0113] Energy storage equipment constraints:

[0114]

[0115] In the formula, are the charging and discharging power of energy storage device m at time t respectively; are the maximum charging and discharging rates of energy storage device m; C m is the maximum capacity of the energy storage device; E m (t) is the storage energy of energy storage device m at time t; are the maximum and minimum storage capacities of the energy storage device respectively.

[0116] Step S.6: Control the park's photovoltaic, energy storage devices and user energy-using equipment based on the solution of the park's optimal scheduling model.

[0117] In this embodiment, the collaborative energy intelligent control terminal can realize flexible access to metering and sensing devices on the user side and the distribution side, and has functions such as data collection, precise metering, energy management, and fault reporting. In addition, the collaborative energy intelligent control terminal includes an optical fiber communication interface, a remote communication module, an analog quantity acquisition module, etc., which can quickly transmit and process park data. In order to facilitate staff to monitor the park, it is also equipped with an LCD display screen, which can automatically display information such as communication mode, signal strength, abnormal alarm, and operating status, which can well meet the application scenarios of the park.

[0118] The following is combined with Figure 2 The solution of the park scheduling model involved in S5 and S.6 of the present invention is further described in detail. The scheduling model solution in this embodiment is solved by using a genetic-differential algorithm (GA-DE), and the specific steps are as follows:

[0119] First, initialize the population, and each individual in the population represents the photovoltaic, energy storage, and charging pile scheduling strategies of the park; then, after determining the lowest operating cost of the park as the objective function, calculate the fitness of each individual, and individuals with higher fitness represent scheduling strategies with lower operating costs of the park; further, randomly select three different individual mutations from the population to generate new candidate individuals, and cross the generated candidate individuals with the original individuals to obtain new individuals; by comparing the fitness of the new individuals after differential crossover with the original individuals, select individuals with higher fitness to enter the next generation. Finally, when the scheduling strategy that minimizes the operating cost of the park is found or the number of iterations reaches the upper limit, the algorithm outputs the optimal operation strategy of the park as the final result. If the termination condition is not met, continue the iterative optimization process of the algorithm.

[0120] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A park scheduling method considering the reasonable consumption range of photovoltaic power, characterized by: Step S.1, obtaining the historical operation data of the park and preprocessing it; the operation data includes: park load, photovoltaic absorption rate and corresponding unit operation cost of the park; Step S.2, construct a nonlinear regression model fitting the unit operation cost of the park with the photovoltaic load ratio and the photovoltaic absorption rate; Step S.3, after obtaining the equation of the fitted nonlinear regression model, solve the photovoltaic absorption rate when the unit operation cost of the park is the lowest under different photovoltaic load ratios, that is, the optimal photovoltaic absorption rate, and then take a margin of 3% above and below as the reasonable photovoltaic absorption range; Step S.4: Based on the photovoltaic power generation and energy load data of the park in the past year and combined with different weather conditions, a day-ahead forecast is made for the photovoltaic power generation and energy load of the next day; Step S.5, constructing a park optimization scheduling model that takes into account the reasonable consumption range of photovoltaic power, and using the genetic-difference algorithm (GA-DE) to solve the scheduling model; Step S.6: Control the park's photovoltaic, energy storage devices and user energy-using equipment based on the solution of the park's optimal scheduling model.

2. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 1, characterized in that: In step S.2, the specific steps of constructing a nonlinear regression model for fitting the unit operating cost of the park with the photovoltaic load ratio and the photovoltaic consumption rate are as follows: Y=β0+β1X1+β2X1 2 +β3X2+β4X2 2 +β5X1X2+ε Among them, Y is the unit operating cost of the park, X1 and X2 are the photovoltaic load ratio and photovoltaic absorption rate respectively, β0 is the intercept, β1, β2, β3, β4, β5 are regression coefficients, and ε is the error term; The least square method is used to estimate the regression coefficients β0, β1, β2, β3, β4, and β5, and the mean square error is selected as the error term ε. After the goodness of fit test, the nonlinear model is obtained; The coefficient of determination for the goodness-of-fit test is as follows: Among them, R 2 is the coefficient of determination, SSR is the regression sum of squares, SSE is the residual sum of squares, and SST is the total deviation sum of squares; R 2 The closer it is to 1, the better the model fits the data.

3. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 1, characterized in that: In step S.3, the specific steps for the reasonable photovoltaic consumption area are as follows: In the formula, λ pv (t) is the photovoltaic absorption rate of the park power grid system at time t; are the minimum and maximum values ​​of the reasonable photovoltaic consumption range, The maximum value can be 100%.

4. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 3, characterized in that: In step S.3.1, the objective function is constructed with the lowest comprehensive operation cost of the park as the goal: minF1=C1+C2+C3-C4 In the formula, F1 is the total cost of park operation; C1 is the cost of electricity purchase; C2 is the cost of photovoltaic consumption; C3 is the equipment operation and maintenance cost; C4 is the income from electricity sales; The electricity purchase cost is: In the formula, C e (t) is the unit electricity purchase cost at time t; P e (t) is the power purchased by the park from the power grid at time t; T is the dispatching period, which is 24 hours.

5. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 4, characterized in that: In step S.3.1.1, the PV consumption cost is: Where k1 and k2 are the unit power penalty cost coefficients for photovoltaic consumption outside the reasonable range; P pv (t) is the photovoltaic output of the park at time t.

6. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 4, characterized in that: In step S.3.1.2, the equipment operation and maintenance costs mainly include the operating losses of photovoltaic and energy storage equipment, and the formula is: C3=C pv +C ess Among them, the operating cost of the photovoltaic power generation device in the park is C pv The calculation method is as follows: Where: c pv The electricity cost of photovoltaic power generation equipment taking into account operation, maintenance and depreciation factors.

7. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 6, characterized in that: In step S.3.1.2.1, the energy storage system charging and discharging loss cost C ess The calculation method is as follows: Where: c ess is the cost coefficient of charging and discharging loss of the energy storage system; P cha (t) is the charging power of the energy storage system at time t; P dis (t) is the discharge power of the energy storage system at time t.

8. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 1, characterized in that: In step S.5.1, the park optimization scheduling model with reasonable photovoltaic consumption range is considered, and its constraints include: electric power balance constraint, photovoltaic output power constraint, photovoltaic consumption rate constraint and energy storage equipment constraint.

9. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 8, characterized in that: The electric power balance constraint is: P e (t)+P pv (t)+P dis (t)=P sell (t)+P load (t)+P cha (t) Where P load (t) is the power load of the park at time t.

10. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 1, characterized in that: In step S.5.2, the genetic-differential algorithm (GA-DE) is used to solve the scheduling model. The specific steps are as follows: Initializing the population means randomly generating a set of strategies as the initial exploration space of the algorithm. After determining the lowest park operation cost as the objective function, the initial parameters of the population size, crossover rate, and mutation rate are determined. The individuals in the population are represented by different scheduling schemes, such as park energy storage configuration and charging strategies for charging piles. The fitness function is the core of the genetic algorithm. The higher the fitness, the lower the cost of the solution. When calculating the fitness of individuals in the population, the algorithm evaluates the fitness of each solution to determine which solutions have a greater chance of survival and reproduction in the next generation. Each iteration generates a new population P k In each iteration, the algorithm evaluates the individuals of the current population according to the fitness function and generates the next generation population P through operations such as selection, crossover, and mutation. k+1 ; When the configuration scheme with the lowest cost is found or the number of iterations reaches the upper limit, the algorithm outputs the scheme with the lowest operation cost of the park as the final result. If the termination condition is not met, the algorithm continues to iterate and optimize.

11. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 8, characterized in that: The photovoltaic output power constraint is: In the formula, is the maximum output power of the distributed photovoltaic power station during period t.

12. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 8, characterized in that: The most ideal state for the absorption of new energy is full absorption, but this will aggravate the fluctuation of the power system and affect the safe and stable operation of the power system; therefore, the photovoltaic absorption rate is constrained as follows: In the formula, It is the minimum photovoltaic power consumption rate allowed by the park power grid.

13. A park scheduling method considering the reasonable photovoltaic consumption range according to claim 8, characterized in that: Energy storage equipment constraints: In the formula, are the charging and discharging power of energy storage device m at time t respectively; are the maximum charging and discharging rates of energy storage device m; C m is the maximum capacity of the energy storage device; E m (t) is the storage energy of energy storage device m at time t; are the maximum and minimum storage capacities of the energy storage device respectively.

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