Wind-light-irrigation ditch multi-element integrated on-site power supply energy storage capacity configuration and energy scheduling method

Abstract

The invention relates to the technical field of new energy power generation and network construction, in particular to a wind-light-irrigation ditch multi-element integrated on-site power supply energy storage capacity configuration and energy scheduling method, which realizes efficient integration of energy and water resources by constructing a system comprising five modules, namely a wind power generation module, a photovoltaic power generation module, an energy storage system, a user load side module and an irrigation ditch system. The genetic dynamic smoothing system (GDS) introduced in the invention utilizes a genetic algorithm to dynamically adjust a filtering window, and the energy storage utilization efficiency is optimized. Besides, multi-objective optimization is carried out on the system through a non-dominated sorting genetic algorithm (NSGA-II), so that the net load and the energy loss are minimized, and the economical efficiency and the reliability of the system are improved. According to the integration scheme, the energy self-sufficiency capacity of rural areas is improved, the sustainability of agricultural production is promoted, and support is provided for diversified development of rural economy.

Description

Technical Field

[0001] The present invention relates to the technical field of renewable energy power generation network construction, and in particular to a wind-solar-irrigation canal multi-integrated on-site power supply energy storage capacity configuration and energy dispatching method. Background Art

[0002] The effective integration and utilization of clean energy has become an urgent need. Especially in remote areas or areas with large geographical restrictions, isolated microgrid systems are particularly important because they can operate independently without a central grid connection. These systems rely on renewable energy such as wind and solar power and are equipped with energy storage facilities to ensure the stability and reliability of power supply. In this context, some innovative solutions have begun to gain attention in rural areas, especially those strategies that use existing agricultural infrastructure to optimize energy and water management. This approach not only optimizes water resource management by integrating existing agricultural canal systems and renewable energy technologies, but also enhances the peak-shaving capacity of the power system and improves the overall energy efficiency and reliability of the system.

[0003] This type of system is specially designed to meet the needs of power supply and water resource management at the same time, ensuring the stability of power and water supply by precisely scheduling renewable energy and energy storage output. The design of this system fully considers the periodicity and seasonality of agricultural activities, achieving dual optimization of energy and water resources. In this way, not only the use of resources is optimized, but also the energy self-sufficiency of rural areas is improved, and the sustainability of agricultural production is enhanced. Compared with traditional large-scale pumped storage power stations, these small and flexible energy storage solutions have the advantages of low construction cost, short construction period and flexible geographical location requirements. In an islanded system, battery energy storage not only has to cope with daily load fluctuations, but also has to provide sufficient backup power when renewable energy output is insufficient. Therefore, its capacity configuration and energy scheduling strategy need to be carefully designed to meet all operational and safety requirements. Therefore, research on energy storage battery capacity configuration and energy scheduling in islanded microgrids is particularly important. These innovative strategies can not only improve the operating efficiency of the system by integrating agriculture and energy production, but also significantly reduce operation and maintenance costs, enhance the system's adaptability to environmental changes, promote the widespread use of clean energy, and achieve sustainable transformation of the energy structure. This provides new feasibility for solving energy supply problems in remote areas, while also promoting the diversified development of the rural economy. Summary of the invention

[0004] The purpose of this invention is to solve the shortcomings of the prior art and propose a wind-solar-irrigation canal multi-integrated local power supply energy storage capacity configuration and energy scheduling method, aiming to optimize the utilization of energy and water resources, improve the stability and economic benefits of the system, and ensure the continuity of energy supply and sustainable development of the environment in remote areas.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for configuring energy storage capacity and dispatching energy for wind-solar-irrigation canal multi-integrated local power supply, the specific steps of which are as follows:

[0007] Step 1: Construct a wind-solar storage irrigation system, which consists of five main modules: wind power generation module, photovoltaic power generation module, energy storage system module, user load side module and irrigation canal system module; the irrigation canal system module includes upper and lower reservoirs, irrigation canal power generation facilities and pumping equipment. The upper and lower reservoirs are connected to water sources. Both the upper and lower reservoirs have irrigation tasks. The irrigation canal power generation facilities generate electricity when releasing water, while the pumping equipment stores energy when needed. This capacity configuration and energy scheduling method collects historical data of wind and photovoltaic power generation, load demand data and irrigation water data, and obtains various operation scenarios of the microgrid system within the entire life cycle, which are classified into different typical time periods according to different climate characteristics;

[0008] Step 2: Use the historical data of wind power generation, photovoltaic power generation and load demand to compile a net load data set, and perform spectrum analysis by applying Fourier transform to the net load data within a specified time period to determine the upper and lower limits of the sliding average filter window;

[0009] Step 3: Based on the operation scenario data of wind power generation, photovoltaic power generation and load demand throughout the life cycle, the initial energy storage system throughput reference power curve is obtained through the genetic dynamic smoothing system;

[0010] Step 4: Determine the energy dispatch strategy of the wind-solar-irrigation canal multi-integrated local power supply system. The specific operation strategy is as follows:

[0011] When there is a surplus of wind power and photovoltaic power generation in the microgrid, the irrigation canal system module is prioritized for storage; when the irrigation canal system module cannot absorb all the energy, the energy is transferred to the energy storage system module; when the energy storage system module cannot absorb the remaining capacity, wind and photovoltaic power generation are abandoned;

[0012] When the wind power generation and photovoltaic power generation in the microgrid are insufficient, the irrigation canal system module will be used to make up for the missing energy first; when the irrigation canal system module cannot make up all the energy, the energy storage system module will compensate for the remaining missing energy;

[0013] Step 5: Conduct a detailed analysis of the historical water level data of the irrigation canal system module, determine the concentrated allocation of water for irrigation tasks on extreme dates to adapt to the peak water demand during the critical growth stage of crops, and thus optimize the energy scheduling strategy; generate a historical output curve of irrigation water volume by constructing the irrigation operation history scenario of the entire life cycle of the irrigation canal system.

[0014] Preferably, in step 3, the following steps are specifically included:

[0015] Step 3.1: Determine the upper and lower limits of the sliding average filter window in the typical period, and use the average value of the sum of the upper and lower limits of the filter window as the initial filter window for adaptive filtering; calculate the net load value of the N sampling points before the typical time filtering;

[0016] Step 3.2: Calculate the sum of the net load values ​​and the distortion values ​​of N sampling points in the typical time period of the initial filtering window, and initialize the weights in the genetic dynamic smoothing system;

[0017] E=∑|P w(i) +P pv(i) -P load(i) |·Δt

[0018] E is the sum of the net load values ​​of N sampling points in a typical time period, P w(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P pv(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P load(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, and Δt is the sampling resolution of historical data;

[0019]

[0020] R n is the distortion value with filter window n, is the filtered data of the i-th historical sampling point, x i is the data before filtering of the i-th historical sampling point;

[0021] Among them, the sum of distortion values ​​indicates whether the deviation between the filtered data and the data before filtering is large or small, and the sum of net load values ​​indicates the power demand of the power system after removing the output of renewable energy in a typical period, reflecting the size of the demand for energy storage capacity;

[0022] Step 3.3: By using the improved non-dominated genetic algorithm (NSGA-II) and adjusting the weights in the genetic dynamic smoothing system, a suitable filtering window is found to reduce the net load while keeping the filtered data with the original characteristics and not too far away from the historical data;

[0023] Firstly, the improved non-dominated genetic algorithm (NSGA-II) is used to obtain the Pareto solution set with the minimum net load and the minimum distortion value as the target, and the window with the shortest weighted Euclidean distance is selected as the initial optimal window;

[0024] Adjust the weights in the genetic dynamic smoothing system. When the filtered data is distorted, the weight of the minimum distortion value is amplified; when the net load reduction effect is not obvious, the weight of the minimum net load value is amplified;

[0025] When the genetic dynamic smoothing system obtains the distortion value weight when the filter window is 1, it is the upper limit of the minimum distortion value weight, and the net load value weight at this time is the lower limit of the minimum net load value weight;

[0026]

[0027]

[0028] w2=1-w1

[0029] 0<w i <1

[0030] w i is the i-th target weight, w1 is the minimum net load value weight, w2 is the minimum distortion value weight, y i is the i-th normalized target value (minimum net load value and minimum distortion value), y i,n is the i-th target value when the filter window is n, y i,min is the minimum value of the i-th target value in the Pareto set when the filter window is n, y i,max is the maximum value of the i-th target value in the Pareto set when the filter window is n, and D(y) is the weighted Euclidean distance;

[0031] Step 3.4: Based on the scenario data within the entire life cycle, the energy storage system exchange power curve obtained by the genetic dynamic smoothing system under different minimum net load weights and minimum distortion value weights is obtained.

[0032] Preferably, in step 5, the initial water storage capacity is adjusted using the designed capacity of the irrigation canal to more effectively match the actual irrigation demand and optimize water resource management;

[0033] P p,i =a p (q p,i ) 2 +b p q p,i +c p

[0034] P ir,i =a ir (q ir,i ) 2 +b ir q ir,i +c ir

[0035] ΔP(i) =P load(i) -(P w(i) +P pv(i) )

[0036]

[0037] Among them, a p 、b p 、c p is the power factor, P p,i is the power generated at the i-th sampling point, q p,i is the water discharge flow rate of power generation at the i-th sampling point, a ir 、b ir 、c ir is the power factor of the pumped storage system, P ir,i is the pumped storage power at the i-th sampling point, q ir,i is the pumped storage water flow rate at the i-th sampling point, ΔP (i) is the deviation power at the i-th sampling moment, P e(i) ' is the energy storage throughput power after the combined dispatch of irrigation canal and energy storage;

[0038] E'=ρ·g·h·min{v up -v up ',v down -v down '}

[0039] Where E' is the energy that needs to be compensated by the auxiliary battery energy storage of the irrigation canal system in the primary energy throughput, ρ is the density of water (1000kg / m 3 ), g is the acceleration due to gravity (9.81m / s 2 ), h is the vertical height difference of water falling from high to low (m), v up is the surplus volume of water in the upper reservoir, v up ' is the irrigation water volume of the upper reservoir, v down is the residual volume of water in the lower reservoir, v down ' is the irrigation water volume of the lower reservoir;

[0040] Taking the economic efficiency, reliability and utilization rate of new energy of microgrid as multi-objectives, considering the irrigation canal water level constraint and energy storage charge state constraint, a multi-objective optimization model of local power supply and energy storage capacity configuration of wind-solar-irrigation canal multi-integration is constructed, and the improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the model.

[0041]

[0042] C total is the total investment cost of the system, C m is the total system investment cost in the mth year, r is the discount rate, C ini,nThe initial investment cost of the installed capacity of the nth distributed unit, C ope,n The initial investment cost of the nth distributed unit installation capacity, C ad Penalty cost for wind and solar curtailment, C loss Penalty for load loss;

[0043]

[0044] rcu is the utilization rate of new energy, LPSP is the load power shortage rate, P pv,m (i) is the photovoltaic output power in the i-th period of the m-th year, P w,m (i) is the wind power output in the i-th period of the m-th year, P L,m (i) is the load demand in the i-th period of the m-th year, P h,m (i) is the combined output power of the energy storage and irrigation canal system in the i-th period of the m-th year;

[0045] Constraints:

[0046]

[0047] P e-max is the upper limit of the throughput power per unit time of the energy storage system, v up-max is the upper limit of the water capacity of the upper reservoir, v down-max is the upper limit of the water capacity of the lower reservoir, q p,max is the upper limit of irrigation canal power generation, q ir,max For irrigation canal pumping storage absorption power limit, SOC sc,m (i) is the charge state of the energy storage system when the irrigation canal and energy storage system are coordinated at the i-th moment, SOC sc,max is the maximum state of charge of the energy storage system, SOC sc,min It is the minimum state of charge of the energy storage system.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. In the face of interannual changes in historical data resolution, the present invention supports independent analysis of each year's data to construct a corresponding energy storage throughput curve, and then generates an aggregated overall energy storage throughput curve by integrating the annual curves. This strategy shows a high degree of adaptability, allowing the system to effectively adapt to the diversity of data resolution, thereby optimizing the implementation of energy scheduling and management strategies, and ensuring the continuity and efficiency of energy supply.

[0050] 2. The present invention not only improves energy self-sufficiency by integrating wind power generation, photovoltaic power generation and irrigation systems, but also realizes efficient integration of energy and water resources. This technical solution particularly emphasizes the advantages of double-layer linkage, with the upper layer mainly responsible for energy generation and preliminary scheduling, and the lower layer finely managing the distribution and use of energy and water resources.

[0051] 3. The genetic dynamic smoothing system (GDS) introduced in this invention makes energy scheduling more accurate, and significantly improves energy efficiency and system stability by dynamically adjusting the filter window of the energy storage system. This double-layer linkage design greatly enhances the flexibility and adaptability of the system in response to environmental changes and changes in load demand, optimizes resource utilization, and ensures the reliability of power supply and irrigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is the overall flow chart of the present invention;

[0053] Figure 2 This is a flow chart of the genetic dynamic smoothing system in the present invention;

[0054] Figure 3 A multi-objective optimization flow chart for energy storage capacity configuration in the present invention;

[0055] Figure 4 A comparison diagram of load deviation under different minimum distortion value weights in the present invention;

[0056] Figure 5 A bar chart for optimizing the irrigation strategy in the present invention;

[0057] Figure 6 This is a comparison chart of the energy storage efficiency optimization of the irrigation system in the present invention. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the advantages and features of the present invention, thereby making a clearer definition of the protection scope of the present invention. The embodiments described in the present invention are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0059] Reference Figure 1-6 , a wind-solar-irrigation canal multi-integrated local power supply energy storage capacity configuration and energy dispatching method, comprising the following steps:

[0060] like Figure 1 The flow chart shows:

[0061] Step 1: Construct a wind-solar-energy storage-irrigation system, which consists of five main modules: wind power generation module, photovoltaic power generation module, energy storage system module, user load side module and irrigation canal system module. Among them, the irrigation canal system includes upper and lower reservoirs, irrigation canal power generation facilities and pumping equipment. The lower reservoir is connected to the water source. Both the upper and lower reservoirs have irrigation tasks. The irrigation canal power generation facilities generate electricity when releasing water, while the pumps store energy when needed. Collect historical data on wind and photovoltaic power generation, load demand data and irrigation water data, and obtain various operating scenarios of the microgrid system throughout its life cycle, and classify them into different typical time periods according to different climate characteristics;

[0062] like Figure 2 The process is as follows:

[0063] Step 2: Use historical data of wind power generation, photovoltaic power generation and load demand to compile a net load data set. Perform spectrum analysis by applying Fourier transform to the net load data within a specified time period to determine the upper and lower limits of the sliding average filter window;

[0064] Step 3: Based on the operation scenario data of wind power generation, photovoltaic power generation and load demand throughout the life cycle, the initial energy storage system throughput reference power curve is obtained through the genetic dynamic smoothing system;

[0065] Step 3.1: Determine the upper and lower limits of the sliding average filter window in the typical period, and use the average value of the sum of the upper and lower limits of the filter window as the initial filter window for adaptive filtering; calculate the net load value of the N sampling points before the typical time filtering;

[0066] Step 3.2: Calculate the sum of the net load values ​​and the distortion values ​​of N sampling points in the typical time period of the initial filtering window, and initialize the weights in the genetic dynamic smoothing system;

[0067] E=∑|P w(i) +P pv(i) -P load(i) |·Δt

[0068] E is the sum of the net load values ​​of N sampling points in a typical time period, P w(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P pv(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P load(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, and Δt is the sampling resolution of historical data;

[0069]

[0070] R n is the distortion value with a filter window of n. is the filtered data of the i-th historical sampling point, xi is the data before filtering of the i-th historical sampling point;

[0071] The total distortion value indicates whether the data after filtering is larger or smaller than the data before filtering. The total net load value indicates the power demand of the power system after removing the output of renewable energy during a typical period, reflecting the demand for energy storage capacity.

[0072] Step 3.3: By using the improved non-dominated genetic algorithm (NSGA-II) and adjusting the weights in the genetic dynamic smoothing system, a suitable filtering window is found to reduce the net load while keeping the filtered data with the original characteristics and not too far away from the historical data;

[0073] Firstly, the improved non-dominated genetic algorithm (NSGA-II) is used to obtain the Pareto solution set with the minimum net load and the minimum distortion value as the target, and the window with the shortest weighted Euclidean distance is selected as the initial optimal window;

[0074] Adjust the weights in the genetic dynamic smoothing system. When the filtered data is distorted, the weight of the minimum distortion value is amplified. When the net load reduction effect is unclear, the weight of the minimum net load value is amplified.

[0075] When the genetic dynamic smoothing system obtains the distortion value weight when the filter window is 1, it is the upper limit of the minimum distortion value weight, and the net load value weight at this time is the lower limit of the minimum net load value weight;

[0076]

[0077] w2=1-w1

[0078] 0<w i <1

[0079] w i is the i-th target weight, w1 is the minimum net load value weight, w2 is the minimum distortion value weight, y i is the i-th normalized target value (minimum net load value and minimum distortion value), y i,n is the i-th target value when the filter window is n, y i,min is the minimum value y of the i-th target value in the Pareto set when the filter window is n i,max is the maximum value of the i-th target value in the Pareto set when the filter window is n, and D(y) is the weighted Euclidean distance;

[0080] Step 3.4: Based on the scenario data within the entire life cycle, the energy storage system exchange power curve obtained by the genetic dynamic smoothing system under different minimum net load weights and minimum distortion value weights is obtained.

[0081] like Figure 3 The flow chart shows:

[0082] Step 4: Determine the energy dispatch strategy of the wind-solar-irrigation canal multi-integrated local power supply system. The specific operation strategy is as follows:

[0083] When there is a surplus of wind power and photovoltaic power generation in the microgrid, the irrigation canal system is prioritized for storage. When the irrigation canal system cannot absorb all of the energy, the energy is transferred to the energy storage system. When the energy storage system cannot absorb the remaining capacity, wind and photovoltaic power are abandoned.

[0084] When the wind power generation and photovoltaic power generation in the microgrid are insufficient, the irrigation canal system will be used to make up for the missing energy first. When the irrigation canal system cannot make up all the energy, the energy storage system will make up for the remaining missing energy.

[0085] like Figure 4 As shown in the figure, taking the one-day dispatching process of village-level load (electricity load of 20 rural users) in Qinghai Province as an example, the weight selection has a significant impact on the filtering effect and the performance of the energy storage system. If the minimum distortion value weight is too large, the net load reduction effect will be poor, and if the minimum distortion value weight is too small, the filtered data will be distorted;

[0086] Step 5: Detailed analysis of the historical water level data of the irrigation canal system was conducted to determine the centralized allocation of irrigation water on the critical date to meet the peak water demand of the key growth stage of the crop, thereby optimizing the energy scheduling strategy. By constructing the historical irrigation operation scenario of the irrigation canal system throughout its life cycle, a historical output curve of irrigation water volume was generated. Using the design capacity of the irrigation canal, the initial water storage capacity was adjusted and reduced to more effectively match the actual irrigation demand and optimize water resource management;

[0087] P p,i =a p (q p,i ) 2 +b p q p,i +c p

[0088] P ir,i =a ir (q ir,i ) 2 +b ir q ir,i +c ir

[0089] ΔP (i) =P load(i) -(P w(i) +P pv(i) )

[0090]

[0091] Among them, a p , b p 、c p is the power factor, P p,i is the power generated at the i-th sampling point, q p,i is the water discharge flow rate of power generation at the i-th sampling point, a ir , b ir 、c ir is the pumped storage power factor, P ir,i is the pumped storage power at the i-th sampling point, q ir,i is the pumped storage water flow rate at the i-th sampling point, ΔP (i) is the deviation power at the i-th sampling moment, P e(i) ' is the energy storage throughput power after the combined dispatch of irrigation canal and energy storage;

[0092] E'=ρ·g·h·min{v up -v up ',v down -v down '}

[0093] Among them, E' is the energy reduced by the irrigation canal system in one throughput, ρ is the density of water (1000kg / m 3 ), g is the acceleration due to gravity (9.81m / s 2 ), h is the vertical height difference of water falling from high to low (m), v up is the surplus volume of water in the upper reservoir, v up ' is the irrigation water volume of the upper reservoir, v down is the residual volume of water in the lower reservoir, v down ' is the irrigation water volume of the lower reservoir;

[0094] like Figure 5 As shown in the figure, taking potatoes, a common crop on the Qinghai Plateau, as an example, the FAOPenman-Monteith model is used based on weather data and soil data to adopt an extreme daily irrigation rate and a centralized irrigation strategy to reduce irrigation time and frequency, which can enhance the efficiency of the energy storage system and reduce the cost and pressure of energy supply.

[0095] Taking the economic performance, reliability and utilization rate of new energy of microgrid as the objectives, considering the irrigation canal water level constraint and energy storage charge state constraint, a multi-objective optimization model of local power supply and energy storage capacity configuration of wind-solar-irrigation canal multi-integration is constructed, and the improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the model.

[0096]

[0097] C total is the total investment cost of the system, Cm is the total system investment cost in the mth year, r is the discount rate, C ini,n The initial investment cost of the installed capacity of the nth distributed unit, C ope,n The initial investment cost of the nth distributed unit installation capacity, C ad Penalty cost for wind and solar curtailment, C loss Penalty for load loss;

[0098]

[0099] rcu is the utilization rate of new energy, LPSP is the load power shortage rate, P pv,m (i) is the photovoltaic output power in the i-th period of the m-th year, P w,m (i) is the wind power output in the i-th period of the m-th year, P L,m (i) is the load demand in the i-th period of the m-th year, P h,m (i) is the combined output power of the energy storage and irrigation canal system in the i-th period of the m-th year;

[0100] Constraints:

[0101]

[0102] P e-max is the upper limit of the throughput power per unit time of the energy storage system, v up-max is the upper limit of the water capacity of the upper reservoir, v down-max is the upper limit of the water capacity of the lower reservoir, q p,max is the upper limit of irrigation canal power generation, q ir,max For irrigation canal pumping storage absorption power limit, SOC sc,m (i) is the charge state of the energy storage system when the irrigation canal and energy storage system are coordinated at the i-th moment, SOC sc,max is the maximum state of charge of the energy storage system, SOC sc,min It is the minimum state of charge of the energy storage system.

[0103] like Figure 6 As shown, the energy storage throughput before and after the introduction of the irrigation canal system is compared, demonstrating that by integrating the irrigation system and energy storage management, the demand for energy storage can be effectively reduced, and better resource allocation and utilization can be achieved in rural areas or intensively irrigated areas.

[0104] In summary, the present invention realizes efficient integration of energy and water resources by constructing a system including five modules: wind power generation, photovoltaic power generation, energy storage system, user load side and irrigation canal system. The introduced genetic dynamic smoothing system (GDS) uses genetic algorithm to dynamically adjust the filter window and optimize the efficiency of energy storage utilization. In addition, through the non-dominated sorting genetic algorithm (NSGA-II), the system performs multi-objective optimization to minimize net load and energy loss, and improve the economy and reliability of the system. The present invention not only improves the energy self-sufficiency of rural areas, but also promotes the sustainability of agricultural production, providing support for the diversified development of rural economy.

[0105] The description and practice disclosed in the present invention are easy to think and understand for ordinary technicians in the technical field, and several improvements and modifications can be made without departing from the principles of the present invention. Therefore, modifications or improvements made without departing from the spirit of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A wind-solar-irrigation canal multi-integrated local power supply energy storage capacity configuration and energy dispatching method, characterized in that: The specific steps are as follows: Step 1: Construct a wind-solar-storage-irrigation system, which consists of five modules: wind power generation module, photovoltaic power generation module, energy storage system module, user load side module and irrigation canal system module; the irrigation canal system module includes upper and lower reservoirs, irrigation canal power generation facilities and pumping equipment. The upper and lower reservoirs are connected to water sources. Both the upper and lower reservoirs have irrigation tasks. The irrigation canal power generation facilities generate electricity when releasing water, while the pumping equipment stores energy when needed. The capacity configuration and energy scheduling method first collects historical data on wind and photovoltaic power generation, load demand data and irrigation water data, and obtains various operation scenarios of the microgrid system within the entire life cycle, and classifies them into different typical time periods according to different climate characteristics. Step 2: Use the historical data of wind power generation, photovoltaic power generation and load demand to compile a net load data set, and perform spectrum analysis by applying Fourier transform to the net load data within a specified time period to determine the upper and lower limits of the sliding average filter window; Step 3: Based on the operation scenario data of wind power generation, photovoltaic power generation and load demand throughout the life cycle, the initial energy storage system throughput reference power curve is obtained through the genetic dynamic smoothing system; Step 4: Determine the energy dispatch strategy of the wind-solar-irrigation canal multi-integrated local power supply system. The specific operation strategy is as follows: When there is a surplus of wind power and photovoltaic power generation in the microgrid, the irrigation canal system module is prioritized for storage; when the irrigation canal system module cannot absorb all the energy, the energy is transferred to the energy storage system module; when the energy storage system module cannot absorb the remaining capacity, wind and photovoltaic power generation are abandoned; When the wind power generation and photovoltaic power generation in the microgrid are insufficient, the irrigation canal system module will be used to make up for the missing energy first; when the irrigation canal system module cannot make up all the energy, the energy storage system module will compensate for the remaining missing energy; Step 5: Conduct a detailed analysis of the historical water level data of the irrigation canal system module, determine the concentrated allocation of water for irrigation tasks on extreme dates to adapt to the peak water demand during the critical growth stage of crops, and thus optimize the energy scheduling strategy; generate a historical output curve of irrigation water volume by constructing the irrigation operation history scenario of the entire life cycle of the irrigation canal system.

2. A method for configuring energy storage capacity and dispatching energy for wind-solar-irrigation canal multi-integrated local power supply according to claim 1, characterized in that: In step 3, the following steps are specifically included: Step 3.1: Determine the upper and lower limits of the sliding average filter window within a typical period, and use the average value of the sum of the upper and lower limits of the filter window as the initial filter window for adaptive filtering; Calculate the net load value of N sampling points before typical time filtering; Step 3.2: Calculate the sum of the net load values ​​and the distortion values ​​of N sampling points in the typical time period of the initial filtering window, and initialize the weights in the genetic dynamic smoothing system; E=Σ|P w(i) +P pv(i) -P load(i) |·Δt E is the sum of the net load values ​​of N sampling points in a typical time period, P w(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P pv(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, P load(i) is the wind power generation at the i-th sampling point in a certain characteristic time period, and Δt is the sampling resolution of historical data; R n is the distortion value with filter window n, is the filtered data of the i-th historical sampling point, x i is the data before filtering of the i-th historical sampling point; Among them, the sum of distortion values ​​indicates whether the deviation between the filtered data and the data before filtering is large or small, and the sum of net load values ​​indicates the power demand of the power system after removing the output of renewable energy in a typical period, reflecting the size of the demand for energy storage capacity; Step 3.3: Find a suitable filtering window by using the improved non-dominated genetic algorithm NSGA-II and adjusting the weights in the genetic dynamic smoothing system, so as to reduce the net load while keeping the filtered data with the original characteristics and not too far away from the historical data; Firstly, the improved non-dominated genetic algorithm NSGA-II is used to obtain the Pareto solution set with the minimum net load and the minimum distortion value as the target, and the window with the shortest weighted Euclidean distance is selected as the initial optimal window; Adjust the weights in the genetic dynamic smoothing system. When the filtered data is distorted, the weight of the minimum distortion value is amplified; when the net load reduction effect is not obvious, the weight of the minimum net load value is amplified; When the genetic dynamic smoothing system obtains the distortion value weight when the filter window is 1, it is the upper limit of the minimum distortion value weight, and the net load value weight at this time is the lower limit of the minimum net load value weight; w2=1-w1 0＜w i ＜1 w i is the i-th target weight, w1 is the minimum net load value weight, w2 is the minimum distortion value weight, y i is the normalized target value of the ith i,n is the i-th target value when the filter window is n, y i,min is the minimum value of the i-th target value in the Pareto set when the filter window is n, y i,max is the maximum value of the i-th target value in the Pareto set when the filter window is n, and D(y) is the weighted Euclidean distance; Step 3.4: Based on the scenario data within the entire life cycle, the energy storage system exchange power curve obtained by the genetic dynamic smoothing system under different minimum net load weights and minimum distortion value weights is obtained.

3. The method for configuring the energy storage capacity and dispatching the wind-solar-irrigation canal multi-integrated local power supply according to claim 1 is characterized in that: In step 5, the initial water storage capacity is adjusted using the designed capacity of the irrigation canal to more effectively match the actual irrigation demand and optimize water resource management; P p,i =a p (q p,i ) 2 +b p q p,i +c p P ir,i =a ir (q ir,i ) 2 +b ir q ir,i +c ir ΔP (i) =P load(i) -(P w(i) +P pv(i) ) Among them, a p 、b p 、c p is the power factor, P p,i is the power generated at the i-th sampling point, q p,i is the water discharge flow rate of power generation at the i-th sampling point, a ir 、b ir 、c ir is the power factor of the pumped storage system, P ir,i is the pumped storage power at the i-th sampling point, q ir,i is the pumped storage water flow rate at the i-th sampling point, ΔP (i) is the deviation power at the i-th sampling moment, P e(i) ' is the energy storage throughput power after the combined dispatch of irrigation canal and energy storage; E'=ρ·g·h·min{v up -v up ',v down -v down '} Where E' is the energy that needs to be compensated by the auxiliary battery energy storage of the irrigation canal system in the primary energy throughput, ρ is the density of water 1000kg / m 3 , g is the acceleration due to gravity 9.81m / s 2 h is the vertical height difference of water falling from high to low, m, v up is the surplus volume of water in the upper reservoir, v up ' is the irrigation water volume of the upper reservoir, v down is the residual volume of water in the lower reservoir, v down ' is the irrigation water volume of the lower reservoir; Taking the economic efficiency, reliability and utilization rate of new energy of microgrid as multi-objectives, considering the irrigation canal water level constraint and energy storage charge state constraint, a multi-objective optimization model of local power supply and energy storage capacity configuration of wind-solar-irrigation canal multi-integration is constructed, and the improved non-dominated sorting genetic algorithm NSGA-II is used to solve the model. C total is the total investment cost of the system, C m is the total system investment cost in the mth year, r is the discount rate, C ini,n The initial investment cost of the installed capacity of the nth distributed unit, C ope,n The initial investment cost of the nth distributed unit installation capacity, C ad Penalty cost for wind and solar curtailment, C loss Penalty for load loss; rcu is the utilization rate of new energy, LPSP is the load power shortage rate, P pv,m (i) is the photovoltaic output power in the i-th period of the m-th year, P w,m (i) is the wind power output in the i-th period of the m-th year, P L,m (i) is the load demand in the i-th period of the m-th year, P h,m (i) is the combined output power of the energy storage and irrigation canal system in the i-th period of the m-th year; Constraints: P e-max is the upper limit of the throughput power per unit time of the energy storage system, v up-max is the upper water capacity limit of the upper reservoir, v down-max is the upper limit of the water capacity of the lower reservoir, q p,max is the upper limit of irrigation canal power generation, q ir,max For irrigation canal pumping storage absorption power limit, SOC sc,m (i) is the charge state of the energy storage system when the irrigation canal and energy storage system are coordinated at the i-th moment, SOC sc,max is the maximum state of charge of the energy storage system, SOC sc,min It is the minimum state of charge of the energy storage system.

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