Agricultural park energy storage capacity configuration optimization method based on load division

Abstract

The invention provides an agricultural park energy storage capacity configuration optimization method based on load division, and the method comprises the following steps: S1, carrying out the division processing according to the demand characteristic difference between different loads, and obtaining the typical daily power data of an agricultural park; s2, planning photovoltaic power priority supply and demand response conventional load configuration, and taking a power fluctuation ratio as a quantitative evaluation reliability index; s3, introducing a time-of-use electricity price strategy for supply and demand matching of agricultural load power, and realizing power consumption compensation and energy storage capacity configuration under the condition of optimal cash-out income of a peak-valley electricity price difference value; and S4, constructing a two-stage energy storage capacity configuration optimization model by taking the operation reliability and economy of the micro-grid system as objective functions. The invention aims to solve the problems of poor energy management planning effect, low energy comprehensive utilization rate and the like of a park micro-grid system due to large distributed photovoltaic energy fluctuation interval, large load characteristic difference and imbalance of energy storage construction cost and economic benefit in the existing micro-grid configuration technology.

Description

Technical Field

[0001] The present invention relates to the application field of renewable energy storage configuration, and in particular to an agricultural park energy storage capacity configuration optimization method based on load division. Background Art

[0002] Agricultural parks are endowed with abundant renewable resources, among which photovoltaic energy has great potential for utilization. However, with the transformation and development of new power industry models in agricultural parks, there are many types of loads in agricultural production, and there are large differences in power characteristics and electricity consumption ranges, which greatly affect the comprehensive utilization rate of clean energy and the economy and safety of the construction and operation of microgrid systems in agricultural parks. Therefore, to configure appropriate energy storage capacity, it is necessary not only to consider the economic imbalance caused by the energy storage investment and construction costs and the agricultural industry profit model, but also to consider the intermittent fluctuation characteristics of the interaction between renewable energy output and agricultural load demand, resulting in power uncertainty and poor operation reliability. Summary of the invention

[0003] The present invention proposes an optimization method for energy storage capacity configuration in an agricultural park based on load division, aiming to solve the problems in the existing microgrid configuration technology, such as large fluctuations and intermittents of distributed photovoltaic energy, high complexity of load demand differences, and imbalance between energy storage construction costs and economic benefits, which lead to poor energy management planning effects and low comprehensive energy utilization rate of the park microgrid system.

[0004] In one aspect of the present invention, the present invention proposes an agricultural park energy storage capacity configuration optimization method based on load division. According to an embodiment of the present invention, the following steps are included:

[0005] S1. Divide and process the loads according to their demand characteristics to obtain typical daily power data of the agricultural park;

[0006] S2. Plan the photovoltaic power supply and demand priority to respond to the conventional load configuration, set the root mean square envelope window to adjust and smooth the net load power fluctuation, and use the power fluctuation rate as a quantitative evaluation reliability indicator;

[0007] S3. Based on the net load power leveling results, a time-of-use electricity price strategy is introduced to match the agricultural load power supply and demand, and a particle swarm optimization algorithm is used to achieve energy storage capacity configuration with optimal power consumption compensation and peak-valley electricity price difference cashing benefits;

[0008] S4. Taking the reliability and economy of microgrid system operation as the objective function, a two-stage energy storage capacity configuration optimization model is constructed;

[0009] In addition, the method for optimizing the configuration of energy storage capacity in an agricultural park based on load division according to the above embodiment of the present invention may also have the following additional technical features:

[0010] In some embodiments of the present invention, in step S1, the park power load is divided into two categories: conventional load power that meets daily office needs and agricultural load power that meets agricultural production needs based on differences in basic characteristics of the load.

[0011] In some embodiments of the present invention, step S1 specifically includes the following steps:

[0012] S11. Obtaining a typical daily conventional load demand power curve through historical power data;

[0013] S12. Establish a mathematical model based on the agricultural load production type to obtain a typical daily load power curve.

[0014] In some embodiments of the present invention, step S2 specifically includes the following steps:

[0015] S21, based on the load divided in step S1, plan the photovoltaic power priority supply and demand response to the conventional load configuration, set the RMS envelope window to smooth the net load power fluctuation, and establish the specific objective function f1 as follows:

[0016] f1=min(F RMS )

[0017]

[0018] P net (t) = P pv (t)-P load (t)

[0019] In the above formula, F RMS is the root mean square of the net load power, and its physical meaning is to measure the discrete fluctuation degree of the net load power; T represents the length of the sampling period; P net (t) is the net load power at time t, which is equal to the unbalanced power between the photovoltaic output and the conventional load demand; Ppv(t) is the photovoltaic output power at time t; P load (t) is the power demand of conventional load at time t;

[0020] Establish energy storage battery SOC constraints:

[0021] SOC min ≤SOC(t)≤SOC max

[0022] In the formula, SOC(t) represents the charge state of the energy storage system at time t; SOC max and SOC min are the minimum and maximum states of charge, respectively;

[0023] Establish energy storage battery charging and discharging power constraints:

[0024] -P charge,max ≤P ess (t)≤P charge,max

[0025] Where, the energy storage battery charging and discharging power constraint, P ess (t) represents the charging and discharging power of the energy storage system at time t; P charge,max and -P charge,max Respectively represent the maximum charging power and maximum discharging power of the energy storage system;

[0026] S22. Determine the quantitative evaluation reliability index. The power fluctuation rate λ is calculated as follows:

[0027]

[0028] Where maxP(t) and minP(t) represent the maximum and minimum photovoltaic power generation per unit time, respectively; P zjrl represents the total installed capacity of photovoltaic power generation; λ1 represents the power fluctuation rate threshold standard for grid-connected constraints.

[0029] In some embodiments of the present invention, the power fluctuation stabilization effect is reflected by comparing the change in the percentage of exceeding the threshold standard before and after stabilization:

[0030]

[0031] R below,ratio ≤5%

[0032] In the formula, R above,ratio and R below,ratio They represent the percentage of volatility exceeding the threshold and the percentage of volatility not exceeding the threshold within a specific period of time; r above,time 、r below,time 、r total,time They respectively represent the time period when the fluctuation rate exceeds the threshold, the time period when the fluctuation rate does not exceed the threshold, and the total selected specific time period; it is defined that the percentage of power fluctuation rate exceeding the threshold in a typical daily cycle is within a 5% error tolerance.

[0033] In some embodiments of the present invention, step S3 is specifically as follows:

[0034] Taking the net load power smoothing result obtained in step S2 as the input parameter, combined with its own objective function, constraints and related economic parameters, the PSO particle swarm optimization algorithm is used to continuously iterate and adjust to configure the energy storage capacity configuration scale with the optimal NPV net present value within the energy storage operation cycle. The specific objective function f2 is as follows:

[0035] f2=Max(F economy )

[0036]

[0037] In the above formula, F economy It is the formula for calculating the economic NPV. Its overall meaning refers to the objective function. The NPV net present value is an economic indicator for comprehensively evaluating the long-term benefits of the energy storage system. It calculates the present value of the benefits of the energy storage system during the entire operation cycle minus the costs. The larger the NPV, the better the return on investment and construction of the energy storage system. C0 represents the initial construction investment cost of the energy storage system; r is the discount rate; N is the normal operating life of the energy storage system, and B n is the total income in the nth year, C n is the energy storage operation and maintenance cost in the nth year;

[0038] B n =B n,sw +B n,tg +B n,bt

[0039] In the above formula, B n is the total revenue in the nth year, including the sales revenue of surplus electricity on the grid in the nth year B n,sw , Peak shaving and valley filling benefits B n,tg 、National subsidy income B n,bt ;

[0040] C n =C n,yw Q n,ess

[0041] In the above formula, C n is the energy storage operation and maintenance cost in the nth year, C n,yw is the energy storage operation and maintenance cost per unit power generation, Q n,ess is the annual power generation of energy storage in the nth year;

[0042] P NET +P DG =P LOAD +P ESS

[0043] The above formula is the power balance constraint condition, where P NET Indicates the net load power after the first stage of stabilization; P DG Represents the power of the interconnection line between the microgrid and the grid; P LOAD Represents the power demand of agricultural load; P ESS Indicates the optimization of the energy storage system power. When it is a positive value, it means that the energy storage is in a charging state, and when it is a negative value, it means that the energy storage is in a discharging state;

[0044]

[0045] In the above formula, P ESSIndicates the configuration power of the energy storage system; Indicates the upper and lower limits of the energy storage system's configuration power; PV,zjrl Represents the total installed capacity of distributed photovoltaics.

[0046] In some embodiments of the present invention, step S4 is specifically as follows:

[0047] S41. Construct a two-stage energy storage capacity configuration optimization model;

[0048] S42. Introduce valid parameter data to complete model method verification analysis.

[0049] In some embodiments of the present invention, step S41 is specifically as follows: in the first stage, the instantaneous unbalanced power fluctuation after the conventional load power and photovoltaic power supply and demand response in step S2 is smoothed by utilizing the energy storage charging and discharging characteristics and the RMS envelope technology to ensure the reliable operation of the agricultural park microgrid system; in the second stage, the power demand response time-of-use electricity price strategy is used to guide the peak shaving and valley filling and power consumption compensation of the agricultural load system in step S3, and the NPV economic optimal energy storage capacity of the agricultural load under the supply and demand power balance optimization scheduling is configured.

[0050] In some embodiments of the present invention, step S42 is specifically as follows: according to the load division planned in step S1, the typical daily power data of the agricultural park is obtained, and the two-stage energy storage capacity configuration optimization model is established. The PSO algorithm is used in Matlab software to define relevant parameters, and the optimal result of the energy storage configuration is output through multiple optimization runs, and the energy storage configuration optimization model is verified and analyzed by example.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] Aiming at the new power industry model of agricultural parks, the inherent uncertainty of renewable energy power generation and the system safety and economic problems caused by the diversity of load demand, the present invention proposes an optimization method for the configuration of energy storage capacity in agricultural parks based on load division, taking the operation reliability and economy of the park microgrid system as the objective function. First, the photovoltaic power supply and demand response to conventional loads is planned, the instantaneous unbalanced power fluctuations are analyzed, and the root mean square envelope technology is used for smoothing to improve the operation reliability and safety of the agricultural park microgrid system; secondly, the smoothing results are used as the input parameters for economic optimization, and the time-of-use electricity price strategy is introduced to guide the energy storage to realize the peak shaving and valley filling of the load system, and the consumption compensation to reduce the impact of power fluctuations; finally, the particle swarm optimization algorithm is used to give the optimal solution, realize economic optimization, and improve the total benefits of the park configuration energy storage project. The simulation of the example shows that the method of the present invention can effectively solve the problems of poor energy management planning effect and low comprehensive energy utilization rate of the microgrid system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of energy storage capacity configuration optimization based on load division in an embodiment of the present invention;

[0054] Figure 2 is a typical daily photovoltaic output power curve of a park in an embodiment of the present invention;

[0055] Figure 3 is a typical daily conventional load demand power curve of a park in an embodiment of the present invention;

[0056] Figure 4 is a typical daily agricultural load demand power curve of the park in the embodiment of the present invention;

[0057] Figure 5 is a net load power smoothing fluctuation diagram in an embodiment of the present invention;

[0058] Figure 6 is a net load power fluctuation rate variation diagram in an embodiment of the present invention;

[0059] Figure 7 This is a diagram showing the results of a typical day configuration energy storage simulation operation in an embodiment of the present invention;

[0060] Figure 8 This is a peak-shaving and valley-filling effect diagram of a typical daily energy storage configuration in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, the method for optimizing the configuration of energy storage capacity in an agricultural park based on load division includes the following steps:

[0063] Step S1: With the development and transformation of new power systems in the agricultural field, the load types are numerous and complex, and the characteristic requirements vary greatly. Therefore, based on the basic characteristic differences of the loads, the power loads in the park are divided into two categories: conventional load power that meets daily office needs and agricultural load power that meets agricultural production needs. The specific process is as follows:

[0064] Step S11, obtaining typical daily power data of the agricultural park. Figure 2As shown in the figure, the typical daily photovoltaic output power data collected from the historical data of a certain park has a sampling period of 1440 minutes and a sampling interval of 1 minute. Distributed photovoltaic energy has the significant disadvantage that "the duration and intensity of daily radiation vary in different seasons and are easily affected by the external environment, resulting in high uncertainty in output power fluctuations". Therefore, the typical daily data is mainly collected from the historical photovoltaic output data of the park in the past, with the day with the largest output power in a certain month as the typical daily data.

[0065] Step S12: perform division processing based on the differences in demand characteristics between different loads. Figure 3 As shown in the figure, conventional loads have a relatively stable power consumption pattern and low volatility, and the power demand is concentrated in the daytime in terms of interval distribution. The power curve is mostly normally distributed. Therefore, the typical daily conventional load curve can be obtained through historical power data, with a sampling period of 1440 minutes and a sampling interval of 1 minute. Figure 4 As shown in the figure, agricultural load has large volatility, seasonality, timeliness and time shiftability, and the interval distribution of electricity demand is closely related to production activities, with great elasticity and uncertainty. Therefore, it is necessary to establish a mathematical model according to the agricultural load production type to obtain a typical daily load power curve.

[0066] In the present invention, the agricultural load mainly considers two types: irrigation and drainage agricultural load and production agricultural load. The mathematical model of irrigation and drainage agricultural load is:

[0067]

[0068] In the above formula, Q water (t) represents the amount of water used for irrigation and drainage; A, K c are the planting area and crop coefficient respectively; P eff It represents effective precipitation (mm / d); ET0 represents reference evapotranspiration; n represents the total irrigation time of the park drainage and irrigation system; t represents the calculation starting from the first hour, and mainly expresses the summation process, starting from t being the first hour and accumulating until t=n.

[0069] P pg (t) = Q water (t)η

[0070] In the above formula, P pg (t) represents the electric load power of the electric irrigation and drainage system; η is the efficiency coefficient of the electric irrigation and drainage system.

[0071] The production agricultural load is mainly based on the charging of electric tractors after work. Due to the differences in production operations, the charging of motors is very random. Therefore, the Monte Carlo simulation method is used to establish a mathematical model:

[0072]

[0073] In the above formula, the charging load power P of the nth agricultural machine in time period t is calculated according to the charging time. t che,n The total load power P of all agricultural machinery in the time period t is obtained by adding up the charging loads of all agricultural machinery in the time period t. t che,N , and then the typical daily agricultural machinery charging load power curve is obtained, where N represents the total number of agricultural machinery in the park.

[0074] Step S2: Plan the photovoltaic power supply and demand priority to respond to the conventional load configuration, set the root mean square envelope window to adjust and smooth the net load power fluctuation, and use the power fluctuation rate as a quantitative evaluation reliability indicator. The specific process is as follows:

[0075] Step S21: Based on the load divided in step S1, plan the photovoltaic power supply and demand priority response to the conventional load configuration, set the RMS envelope window to smooth the net load power fluctuation, ensure the reliable operation of the agricultural park microgrid system, and establish the specific objective function as follows:

[0076] f1=min(F RMS )

[0077]

[0078] P net (t) = P pv (t)-P load (t)

[0079] In the above formula, F RMS is the root mean square of the net load power, and its physical meaning is to measure the discrete fluctuation degree of the net load power; T represents the length of the sampling period; P net (t) is the net load power at the tth moment, which is equal to the unbalanced power between the photovoltaic output and the conventional load demand, also known as the shortage rate; P pv (t) is the photovoltaic power output at time t; P load (t) is the power demand of conventional load at time t;

[0080] SOC min ≤SOC(t)≤SOC max

[0081] In order to ensure the safe and economical operation of the energy storage system, it is necessary to consider the constraints on its state of charge and charging and discharging power. The above formula is the SOC constraint of the energy storage battery. SOC(t) represents the charge state of the energy storage system at time t, which is defined as the ratio of the energy storage capacity to the actual rated capacity of the energy storage; SOC max and SOC minThey are the minimum and maximum charge states respectively. In order to prevent the energy storage from being overcharged or over-discharged and affecting the operating life, the general value range is defined between (0, 1).

[0082] -P charge,max ≤P ess (t)≤P charge,max

[0083] The above formula is the energy storage battery charging and discharging power constraint, P ess (t) represents the charging and discharging power of the energy storage system at time t; P charge,max and -P charge,max They represent the maximum charging power and maximum discharging power of the energy storage system respectively.

[0084] Step S22: Determine the quantitative index of operation reliability. The power fluctuation rate threshold standard is based on the national standard GB / T 19964-2012 "Technology for Photovoltaic Power Station Access to Power System" and the State Grid Enterprise Standard Q / GDW1617-2015 "Technical Regulations for Photovoltaic Power Station Access to Power Grid". The active power change rate of the photovoltaic power station should meet the requirements for safe and stable operation of the power system. The power fluctuation rate λ is calculated as follows:

[0085]

[0086] Where maxP(t) and minP(t) represent the maximum and minimum photovoltaic power generation per unit time, respectively; P zjrl represents the total installed capacity of photovoltaic power generation; λ1 represents the power fluctuation rate threshold standard for grid-connected constraints.

[0087] In order to more clearly show the change of power fluctuation rate before and after smoothing, the concept of threshold ratio is introduced. Its physical meaning is mainly to reflect the power fluctuation stabilization effect by comparing the change of the ratio of exceeding the threshold standard before and after smoothing.

[0088]

[0089] R below,ratio ≤5%

[0090] In the formula, R above,ratio and R below,ratio They represent the percentage of volatility exceeding the threshold and the percentage of volatility not exceeding the threshold within a specific period of time; r above,time 、r below,time 、r total,time They respectively represent the time period when the fluctuation rate exceeds the threshold, the time period when the fluctuation rate does not exceed the threshold, and the total selected specific time period; it is defined that the percentage of power fluctuation rate exceeding the threshold in a typical daily cycle is within a 5% error tolerance.

[0091] Step S3: Based on the net load power leveling result, the time-of-use electricity price strategy is introduced to match the agricultural load power supply and demand, and the particle swarm optimization algorithm is used to achieve the energy storage capacity configuration with the optimal power consumption compensation and peak-valley electricity price difference cashing benefits. The specific process is as follows;

[0092] Taking the net load power leveling result obtained in step S2 as the input parameter, combined with its own objective function, constraints and related economic parameters, the PSO particle swarm optimization algorithm is used to continuously iterate and adjust, and the energy storage capacity configuration scale with the optimal NPV net present value within the energy storage operation cycle is configured to improve the economic benefits of the agricultural park in the photovoltaic energy storage project and determine the optimal scheduling configuration plan. The specific objective function is as follows:

[0093] f2=Max(F economy )

[0094]

[0095] In the above formula, NPV is an economic indicator that comprehensively evaluates the long-term benefits of energy storage systems. It calculates the present value of the benefits of energy storage systems during the entire operation cycle minus the costs. The larger the NPV, the better the return on investment and construction of the energy storage system. C0 represents the initial construction investment cost of energy storage; r is the discount rate; N is the normal operating life of the energy storage system, and B n is the total income in the nth year, C n is the energy storage operation and maintenance cost in the nth year.

[0096] NPV is the difference between the discounted value of future cash flows generated by an investment and the project investment cost. It is an economic indicator that comprehensively evaluates the long-term benefits of an energy storage system.

[0097] B n =B n,sw +B n,tg +B n,bt

[0098] In the above formula, B n is the total revenue in the nth year, mainly including the sales revenue of surplus electricity on the grid in the nth year B n,sw , Peak shaving and valley filling benefits B n,tg 、National subsidy income B n,bt wait.

[0099] C n =C n,yw Q n,ess

[0100] In the above formula, C n is the energy storage operation and maintenance cost in the nth year, C n,yw is the energy storage operation and maintenance cost per unit power generation, Q n,essis the annual power generation of energy storage in the nth year.

[0101] P NET +P DG =P LOAD +P ESS

[0102] The above formula is the power balance constraint condition, where P NET Indicates the net load power after the first stage of stabilization; P DG Represents the power of the interconnection line between the microgrid and the grid; P LOAD Represents the power demand of agricultural load; P ESS Indicates the optimized energy storage system power. The value can be positive or negative. When it is a positive value, it means the energy storage is in a charging state. When it is a negative value, it means the energy storage is in a discharging state.

[0103]

[0104] The configuration scale of the energy storage system must first meet the mandatory storage ratio requirements of new energy projects, that is, in principle, no less than 10% of the installed capacity of new energy projects. In the above formula, P ESS Indicates the configuration power of the energy storage system; Represents the upper and lower limits of the energy storage system’s configuration power, P PV,zjrl Represents the total installed capacity of distributed photovoltaics.

[0105] Step S4: Taking the reliability and economy of the microgrid system as the objective function, a two-stage energy storage capacity configuration optimization model is constructed. The specific process is as follows:

[0106] Step S41, construct a two-stage energy storage capacity configuration optimization model. In the first stage, for the instantaneous unbalanced power fluctuation after the conventional load power and photovoltaic power supply and demand response in step S2, the energy storage charging and discharging characteristics are used to smooth the RMS envelope technology, and the initial window variable U is set. n , perform the first linear iteration, i.e. K n =1, and judge whether the current net load power RMS value meets the quantitative evaluation index of operation reliability. If it does not meet the optimal operation reliability, update the window variable U n , to update the number of iterations K n =K n ±1, when the optimal operation reliability is met, the optimal power required to smooth the fluctuation is output to ensure the reliable operation of the agricultural park microgrid system; in the second stage, for the optimal power required to smooth the fluctuation obtained in step S2, combined with the NPV economic objective function and constraints in step S3, the parameters are initialized by the PSO algorithm to perform the first optimization iteration, namely K m= 1, according to the DR time-of-use electricity price strategy, the peak shaving and valley filling and power consumption compensation of the agricultural load system are guided, and the particle position is continuously updated. If the economic optimum is not met, the optimization parameters are adjusted and the number of iterations K is updated. m =K m ±1, when the economic optimum is met, the energy storage optimization configuration results and simulation operation results are output.

[0107] Step S42, introduce effective parameter data to complete the model method verification analysis. According to the load division planned in step S1, the typical daily power data of the agricultural park is obtained. Combined with the two-stage energy storage capacity configuration optimization model established in step S41, the PSO algorithm is used in Matlab software to define the appropriate number of particles, number of iterations, inertia factor, learning factor and other parameters. The optimal result of the energy storage configuration is output through multiple runs of optimization, and the energy storage configuration optimization model is verified and analyzed by example. The specific results of the example verification are as follows:

[0108] like Figure 5 , Figure 6 As shown in the figure, the net load power curve of the park on a typical day tends to be flat. After the power fluctuation rate is smoothed, the overall proportion of exceeding the threshold is reduced from 46.4% to 5.0%, and the maximum value of the net load power fluctuation rate is reduced from 23% to 10%. The results show that the net load power smoothing effect on a typical day of the park meets the quantitative evaluation standards and improves the operating reliability of the microgrid system.

[0109] like Figure 7 , Figure 8 As shown in the figure, the agricultural park is equipped with energy storage to achieve low storage and high discharge, and the load system has obvious power peak shaving and valley filling effect before and after the energy storage is configured. Figure 8 The typical daily load curve before energy storage is configured is shown by the dotted line in the figure, with a sample variance and standard deviation of 6.02 and 2.45 respectively; the typical daily load curve after energy storage is configured is shown by the solid line, with a sample variance and standard deviation of 2.27 and 1.65 respectively. Compared with the power fluctuation before energy storage is configured, the overall power fluctuation is reduced by 32%, which effectively improves the system operation reliability while increasing the system revenue.

[0110] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, they shall all fall within the protection scope of the present invention.

Claims

1. The method for optimizing energy storage capacity configuration in agricultural parks based on load division is characterized in that: The following steps are involved: S1. Divide and process the loads according to their demand characteristics to obtain typical daily power data of the agricultural park; S2. Plan the photovoltaic power supply and demand priority to respond to the conventional load configuration, set the root mean square envelope window to adjust and smooth the net load power fluctuation, and use the power fluctuation rate as a quantitative evaluation reliability indicator; S3. Based on the net load power leveling results, a time-of-use electricity price strategy is introduced to match the agricultural load power supply and demand, and a particle swarm optimization algorithm is used to achieve energy storage capacity configuration with optimal power consumption compensation and peak-valley electricity price difference cashing benefits; S4. Taking the operation reliability and economy of the microgrid system as the objective function, a two-stage energy storage capacity configuration optimization model is constructed.

2. The method for optimizing the configuration of energy storage capacity in an agricultural park based on load division according to claim 1 is characterized in that: In step S1, based on the differences in basic characteristics of the loads, the power loads in the park are divided into two categories: conventional load power that meets daily office needs and agricultural load power that meets agricultural production needs.

3. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 2 is characterized in that: Step S1 specifically includes the following steps: S11. Obtaining a typical daily conventional load demand power curve through historical power data; S12. Establish a mathematical model based on the agricultural load production type to obtain a typical daily load power curve.

4. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 1 is characterized in that: Step S2 specifically includes the following steps: S21, based on the load divided in step S1, plan the photovoltaic power priority supply and demand response to the conventional load configuration, set the RMS envelope window to smooth the net load power fluctuation, and establish the specific objective function f1 as follows: f1=min(F RMS ) P net (t)=P pv (t)-P load (t) In the above formula, F RMS is the root mean square of the net load power; T represents the length of the sampling period; P net (t) is the net load power at the tth moment, which is equal to the unbalanced power between the photovoltaic output and the conventional load demand; P pv (t) is the photovoltaic power output at time t; P load (t) is the power demand of conventional load at time t; Establish energy storage battery SOC constraints: SOC min ≤SOC(t)≤SOC max In the formula, SOC(t) represents the charge state of the energy storage system at time t; SOC max and SOC min are the minimum and maximum states of charge, respectively; Establish energy storage battery charging and discharging power constraints: -P charge,max ≤P ess (t)≤P charge,max Where, the energy storage battery charging and discharging power constraint, P ess (t) represents the charging and discharging power of the energy storage system at time t; P charge,max and -P charge,max Respectively represent the maximum charging power and maximum discharging power of the energy storage system; S22. Determine the quantitative evaluation reliability index. The power fluctuation rate λ is calculated as follows: Where maxP(t) and minP(t) represent the maximum and minimum photovoltaic power generation per unit time, respectively; P zjrl represents the total installed capacity of photovoltaic power generation; λ1 represents the power fluctuation rate threshold standard for grid-connected constraints.

5. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 4 is characterized in that: The power fluctuation stabilization effect can be reflected by comparing the changes in the proportion of exceeding the threshold standard before and after the stabilization: R below,ratio ≤5％ In the formula, R above,ratio and R below,ratio They represent the percentage of volatility exceeding the threshold and the percentage of volatility not exceeding the threshold within a specific period of time; r above,time 、r below,time 、r total,time They respectively represent the time period when the fluctuation rate exceeds the threshold, the time period when the fluctuation rate does not exceed the threshold, and the total selected specific time period; it is defined that the percentage of power fluctuation rate exceeding the threshold in a typical daily cycle is within a 5% error tolerance.

6. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 1 is characterized in that: Step S3 is as follows: Taking the net load power smoothing result obtained in step S2 as the input parameter, combined with its own objective function, constraints and related economic parameters, the PSO particle swarm optimization algorithm is used to continuously iterate and adjust to configure the energy storage capacity configuration scale with the optimal NPV net present value within the energy storage operation cycle. The specific objective function f2 is as follows: f2=Max(F economy ) In the above formula, F economy is the formula for calculating the economic NPV. The NPV net present value is an economic indicator for comprehensively evaluating the long-term benefits of the energy storage system. It calculates the present value of the benefits of the energy storage system over the entire operating cycle minus the costs. C0 represents the initial construction investment cost of the energy storage system. r is the discount rate. N is the normal operating life of the energy storage system. B n is the total income in the nth year, C n is the energy storage operation and maintenance cost in the nth year; B n =B n,sw +B n,tg +B n,bt In the above formula, B n is the total revenue in the nth year, including the sales revenue of surplus electricity on the grid in the nth year B n,sw , Peak shaving and valley filling benefits B n,tg 、National subsidy income B n,bt ; C n =C n,yw Q n,ess In the above formula, C n is the energy storage operation and maintenance cost in the nth year, C n,yw is the energy storage operation and maintenance cost per unit power generation, Q n,ess is the annual power generation of energy storage in the nth year; P NET +P DG =P LOAD +P ESS The above formula is the power balance constraint condition, where P NET Indicates the net load power after the first stage of stabilization; P DG Represents the power of the interconnection line between the microgrid and the grid; P LOAD Represents the power demand of agricultural load; P ESS Indicates the optimization of the energy storage system power. When it is a positive value, it means that the energy storage is in a charging state, and when it is a negative value, it means that the energy storage is in a discharging state; In the above formula, P ESS Indicates the configuration power of the energy storage system; Indicates the upper and lower limits of the energy storage system’s configuration power; P PV,zjrl Represents the total installed capacity of distributed photovoltaics.

7. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 1 is characterized in that: Step S4 is specifically as follows: S41. Construct a two-stage energy storage capacity configuration optimization model; S42. Introduce valid parameter data to complete model method verification analysis.

8. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 7 is characterized in that: Step S41 is as follows: In the first stage, the instantaneous unbalanced power fluctuation after the conventional load power and photovoltaic power supply and demand response in step S2 is smoothed by using the energy storage charging and discharging characteristics and the RMS envelope technology to ensure the reliable operation of the agricultural park microgrid system; in the second stage, the power demand response time-of-use electricity price strategy is used to guide the peak shaving and valley filling of the agricultural load system and power consumption compensation in step S3, and the NPV economic optimal energy storage capacity of the agricultural load under the supply and demand power balance optimization scheduling is configured.

9. The method for optimizing energy storage capacity configuration in an agricultural park based on load division according to claim 7 is characterized in that: Step S42 is specifically as follows: According to the load division planned in step S1, the typical daily power data of the agricultural park is obtained, and combined with the established two-stage energy storage capacity configuration optimization model, the PSO algorithm is used in Matlab software to define relevant parameters, and the optimal result of the energy storage configuration is output through multiple optimization runs, and the energy storage configuration optimization model is verified and analyzed by example.

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