A solar pumping system photovoltaic array capacity optimization method and device
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- MECHANICS RES & DESIGN ACAD SICHUAN PROV
- Filing Date
- 2026-01-29
- Publication Date
- 2026-05-29
Smart Images
Figure CN121615877B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of new energy technology, and in particular to a method and apparatus for optimizing the capacity of a photovoltaic array in a solar-powered irrigation system. Background Technology
[0002] Pumping stations effectively solve problems related to agricultural irrigation and domestic water use, improving production efficiency and quality of life. Most pumping stations rely on the power grid for their pumps, but this limitation restricts their deployment in remote mountainous areas with insufficient grid coverage. Solar-powered pumping systems effectively address this deficiency. These systems generate electricity through photovoltaic arrays, supplying power to the pumps locally and significantly reducing reliance on the power grid.
[0003] Currently, the photovoltaic array capacity design of known solar-powered irrigation systems mainly adopts the following methods: (1) Empirical estimation method: a rough estimate is made based on the power of the water pump and the local sunshine conditions. This method lacks precise parameter correction, which often leads to unreasonable system configuration, either insufficient power generation or wasted investment. (2) Static calculation formula method: calculation is performed using fixed empirical coefficients. This method does not consider the influence of multiple factors such as the characteristics of solar energy resources in different regions and the operating characteristics of water pumps. (3) Software simulation method: simulation is performed using professional software such as PVsyst. This method requires detailed meteorological data, is complex to operate, and does not adequately consider the specific operating conditions of the irrigation station.
[0004] The known methods have the following main problems: (1) Ignoring resource differences: the total solar radiation, effective sunshine hours, seasonal and intraday variations in the region are significant, and a unified standard is likely to lead to overestimation / underestimation; (2) Ignoring pump / transmission differences: the efficiency of different pump types (such as centrifugal / volumetric pumps) and different power transmission forms (such as DC / AC / inverter transmission methods) is significantly different; (3) Insufficient water supply in low light scenarios: it is difficult to operate stably on cloudy days and during low light periods at dawn and dusk, and is often crudely solved by "stacking capacity or stacked energy storage", which causes investment and land pressure, that is, the initial investment and land pressure brought about by over-configuration of photovoltaic arrays and energy storage capacity.
[0005] Therefore, a photovoltaic array capacity optimization method that integrates solar energy resource zoning, pump / drive type, and low-light robustness is needed to minimize the photovoltaic array capacity and reduce energy storage requirements while meeting water supply constraints. Summary of the Invention
[0006] The purpose of this invention is to provide a method and apparatus for optimizing the capacity of a photovoltaic array in a solar irrigation system, addressing all or part of the problems mentioned above, so as to minimize the installed capacity design of the photovoltaic array and reduce the energy storage requirements while ensuring the robustness of the solar irrigation system.
[0007] The technical solution adopted in this invention is as follows:
[0008] A method for optimizing the capacity of a photovoltaic array in a solar-powered irrigation system, the method comprising:
[0009] Obtain the basic parameters of the solar-powered irrigation system, including design flow rate, total system head, solar resource level, pump type, electric drive type, system performance ratio, and solar array type;
[0010] Based on the peak water power of the system corrected by the aforementioned basic parameters, the basic photovoltaic array installed capacity is obtained.
[0011] Under the constraint of daily water supply, with the goal of minimizing the installed capacity of the photovoltaic array and the energy storage capacity, the minimum feasible installed capacity of the photovoltaic array is calculated based on the basic installed capacity of the photovoltaic array.
[0012] Furthermore, the daily water supply constraint includes a first constraint and a second constraint.
[0013] The first constraint is that, in a given set of random scenarios, the probability of achieving the daily water supply target reaches a predetermined probability.
[0014] The second constraint is that, within a given set of representative low-light scenarios, the daily water supply target must reach a predetermined number.
[0015] Furthermore, the energy storage capacity includes an upper limit for energy storage discharge power and a daily energy budget for energy storage; the upper limit for energy storage discharge power and the daily energy budget for energy storage are matched.
[0016] Furthermore, the peak water power of the system is corrected based on the aforementioned fundamental parameters, including:
[0017] The first peak water power is calculated using the design flow rate and the total system head.
[0018] Obtain the traffic correction factor Determine the pump correction factor based on the pump type. Determine the current transmission correction coefficient based on the described electric transmission type. Determine the solar energy correction factor based on the aforementioned solar energy resource level. Determine the array correction coefficient based on the aforementioned solar array configuration. ;
[0019] Through the flow correction coefficient Water pump correction factor and current transmission correction factor The first peak water power is corrected for equipment influencing factors to obtain the second peak water power;
[0020] Through the system performance ratio and the solar energy correction coefficient and array correction coefficient The second peak water power is corrected to obtain the third peak water power.
[0021] Furthermore, when calculating the minimum feasible photovoltaic array installed capacity, the effective power of the system exceeding the stable water pump power threshold at any time step in any scenario is constrained to be between the surplus power of the electrical power delivered to the water pump at that time step that exceeds the stable water pump power threshold and the surplus power of the second peak water power exceeding the stable water pump power threshold.
[0022] Furthermore, the flow correction coefficient Based on system efficiency calibration, the mapping relationship between flow rate and system efficiency is indicated; the pump correction coefficient is... Based on pump type calibration, it indicates the power conversion efficiency for different pump types; the current transmission correction coefficient is... Based on the calibration of the current transmission form, it indicates the power conversion rate of different current transmission forms.
[0023] Furthermore, a solar energy correction coefficient is determined based on the aforementioned solar energy resource level. ,include:
[0024] The average daily total irradiance of the target area is obtained based on solar energy resource zoning;
[0025] The equivalent sunshine duration is calculated based on the average daily total irradiance.
[0026] The proportionality coefficient between the equivalent sunshine duration and the reference sunshine duration is used as the solar energy correction coefficient. .
[0027] Furthermore, based on the aforementioned basic photovoltaic array installed capacity, the minimum feasible photovoltaic array installed capacity is calculated, including:
[0028] The upper and lower boundaries of the photovoltaic array's installed capacity are initialized based on the aforementioned basic photovoltaic array installed capacity;
[0029] Iterate through the following steps until the upper and lower boundaries of the photovoltaic array's installed capacity converge:
[0030] Initialize the photovoltaic array capacity based on the upper and lower boundaries of the current photovoltaic array installed capacity;
[0031] Multiple candidate values for the upper limit of energy storage discharge power are set, and for each candidate value, the daily energy budget of energy storage and its upper and lower boundaries are initialized;
[0032] For each time step of each scenario, the available power of the photovoltaic array at the current time step is calculated step by step, and the effective power and the daily energy budget margin of the energy storage are updated based on the available power of the photovoltaic array.
[0033] The daily water supply is calculated based on the effective power at each time step, and a first constraint index corresponding to the first constraint condition and a second constraint index corresponding to the second constraint condition are calculated based on the daily water supply.
[0034] Determine whether both the first and second constraint indicators meet the standards. If yes, update the upper boundary of the daily energy budget based on the remaining daily energy budget and the current upper boundary of the daily energy budget. If no, update the lower boundary of the daily energy budget based on the remaining daily energy budget and the lower boundary of the daily energy budget.
[0035] Determine whether there exists at least one set of energy storage discharge power upper limit and corresponding energy storage daily energy budget among the multiple candidate values such that both the first constraint index and the second constraint index meet the standard. If yes, update the upper boundary of the photovoltaic array installed capacity to the current photovoltaic array installed capacity. If no, update the lower boundary of the photovoltaic array installed capacity to the current photovoltaic array installed capacity.
[0036] The final photovoltaic array installed capacity is calculated based on the upper and lower boundaries of the photovoltaic array installed capacity at the time of convergence. The energy storage daily energy budget with the minimum value at the time of convergence is taken as the final energy storage daily energy budget, and the candidate value that minimizes the energy storage daily energy budget at the time of convergence is taken as the upper limit of the final energy storage discharge power.
[0037] Furthermore, the conditions for the convergence of the upper and lower boundaries of the photovoltaic array's installed capacity are as follows:
[0038] The gap between the upper and lower boundaries of the current photovoltaic array installed capacity is within a predetermined proportion of the upper boundary of the current photovoltaic array installed capacity.
[0039] The present invention also provides a photovoltaic array capacity optimization device for a solar irrigation system. The device includes a processor and a storage medium, wherein the storage medium stores a computer program, and the processor runs the computer program to execute a method for optimizing the photovoltaic array capacity of a solar irrigation system.
[0040] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:
[0041] This application addresses the specific scenario of solar-powered irrigation systems, fully considering the equipment constraints and geographical characteristics of the irrigation station. It optimizes the photovoltaic array capacity based on practical considerations, ensuring that daily water supply is met while minimizing the deployment of the photovoltaic array capacity and reducing the energy storage capacity. This ensures a strong coupling between the capacity optimization results and the resource and target environments of the actual scenario, enhancing the practical value of the solution. This application fully considers the dual constraints of low-light and opportunistic scenarios, balancing the needs of most conventional environmental conditions and a few extreme environmental conditions, ensuring the robustness of the system. This application achieves rigorous capacity optimization through simple optimization modeling and iterative solutions; the method is simple to implement and facilitates coded deployment. Furthermore, this application's solution can be horizontally transferred to similar photovoltaic-driven scenarios (such as salt extraction, pastoral water supply, and remote pressurization) by simply replacing relevant parameters, demonstrating strong scalability and significant technological implications. Attached Figure Description
[0042] The present invention will be described by way of example and with reference to the accompanying drawings, wherein:
[0043] Figure 1 This is a flowchart of one implementation of a method for optimizing the capacity of a photovoltaic array in a solar-powered irrigation system.
[0044] Figure 2 This is a flowchart for calculating the minimum installed capacity of a photovoltaic array.
[0045] Figure 3 This refers to the irradiation scene distribution in one embodiment scenario.
[0046] Figure 4 This is a schematic diagram of the iterative process for solving the capacity of a photovoltaic array.
[0047] Figure 5 This is a map showing the daily water supply distribution of the Monte Carlo sample.
[0048] Figure 6 This is a map showing the daily water supply distribution under low light conditions. Detailed Implementation
[0049] All features disclosed in this specification, or all steps in all disclosed methods or processes, may be combined in any way, except for mutually exclusive features and / or steps.
[0050] Any feature disclosed in this specification (including any appended claims and abstract) may be replaced by other equivalent or similar features, unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is merely one example of a series of equivalent or similar features.
[0051] To address the problems of large deviations from actual needs, weak system operational robustness, and high investment and land occupation pressure in known photovoltaic array capacity design methods, this application proposes a photovoltaic array capacity optimization method and device for solar irrigation systems, aiming to minimize the photovoltaic array capacity and reduce energy storage requirements while meeting water supply constraints.
[0052] The photovoltaic array capacity optimization method and apparatus for solar-powered irrigation systems proposed in this application, under the constraints of a predetermined head and flow rate (or daily water supply), explicitly introduces multiple correction coefficients: a flow rate correction coefficient. Water pump correction factor Correction factor for electric transmission Solar energy resource correction coefficient Array correction coefficient We incorporate low-light robustness (such as low-light threshold and daily water supply achievement rate) into the optimization constraints; quickly obtain the minimum feasible photovoltaic array capacity through one-dimensional search / convex approximation, and provide a "necessary lower limit" suggestion for energy storage; thereby achieving the engineering goals of improving energy utilization efficiency, reducing initial investment and land use, and reducing reliance on energy storage.
[0053] like Figure 1 As shown in the embodiments of this application, the method for optimizing the photovoltaic array capacity of a solar-powered irrigation system includes the following steps:
[0054] S1. Obtain the basic parameters of the solar-powered irrigation system.
[0055] The solar-powered irrigation system comprises two main parts: a photovoltaic array (for generating electricity) and an irrigation pumping station (consuming electricity to supply water). The amount of electricity supplied depends on the required water volume and the amount of work done. The water volume is reflected by the flow rate, while the amount of work done is reflected by the pump head. Furthermore, in this embodiment, various factors affecting the power conversion rate are considered, such as equipment-related factors and meteorological factors (resource and environmental factors). Therefore, in an optional implementation, the basic parameters of the solar-powered irrigation system include:
[0056] (1) Design flow rate.
[0057] The design flow rate reflects the daily water supply target. It can be calculated directly from the design flow rate, or by converting the design daily water supply into the design flow rate. Indicates design flow rate, in units of .
[0058] (2) Total head of the system.
[0059] The total head of the system represents the pump's delivery height. It indicates that the unit is mThis reflects the amount of work done by the water pump on a unit volume of water.
[0060] (3) Solar energy resource level.
[0061] Because solar resource conditions vary significantly across different regions, solar resource levels are used to reflect the solar resource conditions of different solar resource zones. Each solar resource zone has a corresponding solar resource level. A solar correction factor is determined based on the solar resource level. .
[0062] As an optional implementation, the solar resource level is determined by the equivalent sunshine duration. Using a calibrated reference sunshine duration (e.g., 5 hours), the ratio of the equivalent sunshine duration of different solar resource levels / zones to this reference sunshine duration is used as the solar correction factor for the corresponding solar resource level / zone. .
[0063] Specifically, the solar energy correction factor is determined by the solar energy resource level. Includes:
[0064] Based on the solar energy resource zoning, the average daily total irradiance of the target area (i.e., the area where the solar irrigation system is deployed) is obtained, and then... This information can be obtained through authoritative sources.
[0065] Based on average daily total irradiance Calculate the equivalent sunshine duration. This is the conversion process, using... To represent the equivalent duration of sunshine, then .
[0066] The solar energy correction factor is the ratio of the equivalent sunshine duration to the reference sunshine duration. Reference sunshine duration is expressed as follows: ,but .
[0067] (4) Pump type.
[0068] Water pumps are classified into centrifugal pumps, positive displacement pumps, etc. Different types of water pumps may have different efficiencies in doing work on water flow under the same power output, which ultimately reflects different conversion rates of photovoltaic array power capacity.
[0069] Determine the pump correction factor based on the pump type. Water pump correction factor The pump correction factor is obtained based on the pump type calibration. Indicates the power conversion efficiency for different pump types. For example, calibrated for centrifugal pumps, The value is 0.9, while for positive displacement pumps, The value is only 0.78. Therefore, for a positive displacement pump, more electrical energy is needed to supply the same water flow rate as a centrifugal pump. In other words, under the same conditions, a larger photovoltaic array capacity needs to be deployed for a positive displacement pump.
[0070] (5) Electric drive form.
[0071] Electric drive systems are classified into DC drive and AC drive, etc. Different electric drive systems have different conversion rates in water pumps, which ultimately result in different conversion rates in the power capacity of photovoltaic arrays.
[0072] The correction factor for current transmission is determined by the type of electric power transmission. Water pump correction factor This is based on pump type calibration, indicating the power conversion efficiency for different pump types. For example, calibrated to drive a pump via a DC link, The value is 0.85, and the water pump is driven by an AC (including inverter) link transmission. The value is 0.75. Therefore, the installed capacity of photovoltaic arrays with different power transmission methods also varies.
[0073] (6) System performance ratio.
[0074] System performance ratio This is expressed as the energy conversion efficiency of the photovoltaic array, reflecting the ratio of actual output power to installed capacity. For the same power demand, different system performance levels require different photovoltaic array capacities.
[0075] (7) Solar array form.
[0076] The type of solar array refers to the tracking method of the photovoltaic array, such as single-axis tracking or dual-axis tracking. Different types of solar arrays have different effects on the overall output power of the photovoltaic array. Therefore, for the same power demand, the required photovoltaic array capacity will also be different for different types of solar arrays.
[0077] Solar array configuration determines array correction factor It can be obtained through the annual average irradiance gain ratio or the simulated output ratio. The solar energy correction factor is determined by the solar energy resource level. Similarly, using a calibrated reference array configuration (usually a photovoltaic array with a fixed tilt angle as a reference), the ratio of the annual equivalent power generation (equivalent to the annual equivalent irradiance) of different solar array configurations relative to that reference array configuration is used as the array correction factor for the corresponding solar array configuration. This is represented as:
[0078] ;
[0079] In the formula, The annual average equivalent irradiance under the photovoltaic array tracking method used (such as single-axis or dual-axis);
[0080] The annual average equivalent irradiance using a fixed tilt array tracking method.
[0081] Based on practical calculations, The value range is usually [1, 1.35].
[0082] The above basic parameters, from the perspectives of equipment or resource conditions, affect to varying degrees the effective conversion efficiency of photovoltaic array capacity into final water supply power.
[0083] In addition, factors affecting the optimization of photovoltaic array capacity also include system efficiency, namely the overall water supply efficiency of the solar-powered irrigation system, which is calibrated by the ratio of the actual water supply flow to the design flow. This application embodiment introduces a flow correction coefficient. To couple the impact of system efficiency on photovoltaic array capacity, this flow correction coefficient The mapping relationship between flow rate and system efficiency is indicated by calibrating the data using recorded water supply data. Through experimental statistics, its value typically ranges from [value range missing]. .
[0084] S2. Based on the peak water power of the system with basic parameters, the installed capacity of the basic photovoltaic array is obtained.
[0085] As an optional implementation, the peak water power of the system based on fundamental parameter correction includes:
[0086] S21. Calculate the first peak water power by using the design flow rate and the total head of the system.
[0087] The first peak water power represents the unit energy required to complete the water supply. Let represent the first peak water power, then:
[0088] ;
[0089] In the formula, These represent the density of water and the acceleration due to gravity, respectively. , .
[0090] S22. Obtain the flow correction coefficient Determine the pump correction factor based on the pump type. Determine the correction factor for current transmission based on the type of electric transmission. Determine the solar energy correction factor based on the solar energy resource level. Determine the array correction coefficient based on the solar array configuration. .
[0091] The above correction factors ( The methods for obtaining ) have been explained in the previous text and will not be repeated here.
[0092] S23, using flow correction coefficient Water pump correction factor and current transmission correction factor The second peak water power is obtained by correcting for equipment influencing factors on the first peak water power.
[0093] The second peak water power is the electrical power required to account for the influencing factors of the incoming equipment. Let the second peak water power be represented, then:
[0094] .
[0095] S24, through system performance ratio and solar energy correction coefficient and array correction coefficient The second peak water power is corrected to obtain the third peak water power.
[0096] The third peak hydropower represents the electrical power required to meet the environmental resource impact factors, i.e., the minimum electrical power needed to satisfy the design flow rate, serving as the basic photovoltaic array installed capacity. Let's represent this. Then we have:
[0097] .
[0098] S3. Under the condition of meeting the daily water supply constraint, with the goal of minimizing the photovoltaic array installed capacity and energy storage capacity, calculate the minimum feasible photovoltaic array installed capacity based on the basic photovoltaic array installed capacity.
[0099] Different solar resource zones have different sunshine conditions, and even within the same solar resource zone, the sunshine intensity varies daily, exhibiting a certain degree of fluctuation. To improve the robustness of the photovoltaic array, this application considers providing minimum necessary energy storage compensation for the photovoltaic array during periods of weak sunshine. That is, in scenarios where the photovoltaic array is insufficient to support the water supply of the pumping station due to insufficient sunshine intensity, energy storage components / equipment supplement the energy supply. This avoids the rigid dependence on large-capacity energy storage components (i.e., all the electricity generated by the photovoltaic array is stored in the energy storage components, and then the energy is supplied to the pumping station entirely by the energy storage components).
[0100] The primary objective of photovoltaic array capacity optimization is to minimize the installed capacity of the photovoltaic array while meeting the daily water supply constraint. .
[0101] Furthermore, in a preferred embodiment of this application, the robustness requirement of the photovoltaic array is also considered, namely, that the daily water supply constraint condition is still met under low light conditions (i.e., low light scenario). Based on this objective, the daily water supply constraint condition in this embodiment includes a first constraint condition and a second constraint condition.
[0102] The first constraint is that, within a given set of random scenarios, the probability of achieving the daily water supply target reaches a predetermined probability. Since it pertains to random scenarios, this first constraint can also be called a chance constraint.
[0103] As mentioned earlier, even within the same solar resource zone, the daily solar radiation intensity cannot be exactly the same, and the intensity also varies at different times of day. Therefore, in one optional implementation, this application considers using the average daily water supply target achievement rate over multiple days to represent the probability that the daily water supply target will be achieved within a given set of random scenarios. At the computer logic level, this involves counting the number of days in a randomly given set of observation days that the daily water supply target was achieved, and then dividing that number by the total number of observation days. This achievement probability must reach a relatively large proportion (e.g., 0.9) to satisfy the first constraint condition.
[0104] The second constraint is that, within a given set of representative low-light scenarios, the daily water supply target reaches a predetermined number. This second constraint is specifically designed for low-light scenarios, emphasizing the robustness of the photovoltaic array to low-light conditions; therefore, it can also be called a scenario constraint.
[0105] The second constraint requires that, among the given N (N is a positive integer) representative low-light scenarios, at least M (M is a positive integer, not higher than N) low-light scenarios achieve the daily water supply target for that day.
[0106] By designing opportunity constraints and scenario constraints, the optimization results are made more robust, taking into account the vast majority of normal sunshine scenarios and a few extreme weather scenarios.
[0107] In addition, this application also designed a secondary objective for the daily water supply constraint, namely the reduction objective: to minimize the energy storage capacity while meeting the main objective, thereby reducing the construction and land use pressure to a greater extent while ensuring that the water supply demand is met.
[0108] As a supplementary energy supply unit, when optimizing the capacity of the photovoltaic array, the energy storage capacity design includes the upper limit of energy storage discharge power S and the daily energy budget B, so as to comprehensively constrain the energy storage capacity from both the discharge and storage dimensions. Among them, the upper limit of energy storage discharge power S and the daily energy budget B are matched, that is, the two correspond to each other. When one of them is determined (such as the daily energy budget B), the other can be determined.
[0109] Furthermore, when calculating the minimum feasible photovoltaic array capacity, the effective power exceeding the stable water pump power threshold of the system is constrained at any time step k in any scenario s. All of these are within the time step of the electrical power supplied to the water pump by the system. Exceeding the stable water supply power threshold of the water pump The surplus power, to the second peak water power Exceeding the stable water supply power threshold of the water pump Between the surplus power, that is: .
[0110] In summary, the objective function for minimizing the installed capacity of the photovoltaic array and the energy storage capacity can be expressed as:
[0111]
[0112] in The primary goal for photovoltaic array installation capacity is to minimize it; This is the upper limit of the energy storage discharge power; The daily energy budget for energy storage has been explained above; In the context The Each time step is determined by the power output of the energy storage components actually discharged to the bus. In the context The The electrical power delivered to the pump at each time step; In the context The The effective power exceeding the pump stability threshold at each time step is expressed as: ; and The secondary weight represents the weight of the impact of energy storage capacity on the primary objective, and can take values of [value missing]. ; This is a Monte Carlo sample set used for frequency approximation of chance constraints; for The number of samples in the sample; A representative set of low-light scenarios; The second constraint condition requires that, as the threshold of the scenario constraint, the second constraint condition is met. At least one of the scenarios has Achieve the goal Intraday discrete time index; This represents the total number of time steps within the day. For time step; For the context Next Relative irradiance at each moment The value ranges from 0 to 1. A value of 0 indicates no effective irradiation, while a value of 1 indicates that the current irradiation intensity has reached the daily peak level. The equivalent performance coefficient; As mentioned earlier, the minimum stable water inlet threshold power can be taken as... ,if If the value is 0, it means that water supply is ineffective; , is the proportionality coefficient of the equivalent flow rate; The target daily water supply; For the context The total water supply for the day; (or other similar values) represent the chance constraint threshold, indicating the lower limit of the probability of achieving water supply on that day; This is an indicator function; it takes the value 1 if the condition is true, and 0 otherwise.
[0113] Solving the above objective function yields the minimum feasible photovoltaic array installed capacity. Furthermore, based on the minimum photovoltaic array installed capacity, the corresponding storage capacity is obtained; that is, the minimum feasible capacity is the upper limit of the energy storage discharge power. and the corresponding daily energy budget for energy storage .
[0114] As an optional implementation method, such as Figure 2 As shown, the above is based on the installed capacity of the basic photovoltaic array. Calculate the minimum feasible photovoltaic array installed capacity. ,include:
[0115] S31, Based on the installed capacity of basic photovoltaic arrays Initialize the upper and lower boundaries of the photovoltaic array's installed capacity.
[0116] by Indicates the lower boundary of the installed capacity of photovoltaic arrays, with This represents the upper limit of the installed capacity of the photovoltaic array. For example, initialization... , .
[0117] Iterate through the following steps (S32~S37) until the upper and lower boundaries of the photovoltaic array's installed capacity converge:
[0118] S32. Initialize the photovoltaic array installed capacity based on the upper and lower boundaries of the current photovoltaic array installed capacity.
[0119] Pick This refers to the midpoint between the upper and lower boundaries of the photovoltaic array's installed capacity. Since the initial installed capacity of the photovoltaic array is at this point... This is not the final determined photovoltaic array installed capacity. To avoid misunderstanding that it is the final determined photovoltaic array installed capacity, the photovoltaic array installed capacity before convergence will be expressed as...P Therefore, the initial photovoltaic array installed capacity is expressed as: .
[0120] S33. Set multiple candidate values for the upper limit of energy storage discharge power, and initialize the daily energy budget of energy storage and its upper and lower boundaries for each candidate value.
[0121] For example, a stable water supply power threshold based on arithmetic progression. Multiple candidate values for the upper limit of energy storage discharge power are set. For example, setting six candidate values would be represented as follows: ,in Let represent the i-th candidate value. For each candidate value, initialize the upper bound of the daily energy budget B for energy storage. lower boundary Based on the upper and lower boundaries of the daily energy budget B for energy storage, initialize... .
[0122] S34. For each time step k of each scenario s, calculate the available power of the photovoltaic array at the current time step step by step. Based on the available power of the photovoltaic array Update effective power And the daily energy budget B for energy storage. For example, if there is a power shortage in the output power of the photovoltaic array at a certain time step, the energy storage unit needs to provide power, and the daily energy budget B for energy storage at this time step will decrease.
[0123] In one alternative implementation, at each time step k of each scenario s, the power gap for the current time step is calculated: That is, the available power of the photovoltaic array at the current time step. Below the water pump's stable water supply power threshold The time difference is considered; if it exceeds this threshold, the gap is zero. Then, the discharge power of the operating energy storage unit is calculated: Based on this, the electrical power at the pump end is calculated: Last updated effective power: Update the daily energy budget B balance for energy storage. .
[0124] S35. Calculate the daily water supply based on the effective power at each time step, and calculate the first constraint index corresponding to the first constraint condition and the second constraint index corresponding to the second constraint condition based on the daily water supply.
[0125] Daily water supply for: .
[0126] The first constraint indicator of statistics for: .
[0127] The second constraint of statistics for: .
[0128] S36. Determine the first constraint index Second constraint index If all targets are met, the upper boundary of the daily energy budget is updated based on the remaining daily energy budget and the current upper boundary of the daily energy budget. If not, the lower boundary of the daily energy budget is updated based on the remaining daily energy budget and the lower boundary of the daily energy budget.
[0129] The first constraint requires that the target for achieving the first constraint indicator be such that, within a given set of random scenarios, the probability of achieving the daily water supply target reaches a predetermined probability. The second constraint requires that, within a given set of representative low-light scenarios, the daily water supply target must reach a predetermined quantity. Determine whether both constraints are satisfied simultaneously. If so, update the upper boundary of the daily energy budget based on the remaining daily energy budget and the current upper boundary of the daily energy budget. Otherwise, update the lower boundary of the daily energy budget based on the daily energy budget surplus and the lower boundary of the daily energy budget, i.e., take... .
[0130] S37. Determine multiple candidate values (i.e., the 6 in the example above). In the given conditions, does there exist at least one set of upper limits for energy storage discharge power S and corresponding daily energy budget B such that both the first and second constraint indicators are met? If so, then update the upper limit of the photovoltaic array installed capacity to the current photovoltaic array installed capacity, i.e., update... If not, then update the lower boundary of the photovoltaic array installed capacity to the current photovoltaic array installed capacity, i.e., update... .
[0131] By repeating the above iterative operation, the upper and lower boundaries of the photovoltaic array's installed capacity are continuously updated until the difference between the current upper and lower boundaries of the photovoltaic array's installed capacity is within a predetermined proportion of the current upper boundary of the photovoltaic array's installed capacity. For example, until... .
[0132] S38. Calculate the final photovoltaic array installed capacity based on the upper and lower boundaries of the photovoltaic array installed capacity at convergence. The energy storage daily energy budget with the minimum at convergence is taken as the final energy storage daily energy budget, and the candidate value that minimizes the energy storage daily energy budget at convergence is taken as the upper limit of the final energy storage discharge power.
[0133] Once the convergence condition is met, the final installed capacity of the photovoltaic array can be determined. In the same iteration when the convergence condition is met, among multiple candidate values, the calculated minimum daily energy budget B is taken as the final daily energy budget, and the corresponding upper limit of energy storage discharge power S is taken as the final upper limit of energy storage discharge power. In this way, the energy storage discharge power is increased. Optimization.
[0134] The photovoltaic array capacity optimization method for solar irrigation systems provided in the above embodiments has the following advantages:
[0135] 1. It is specifically designed for solar-powered irrigation systems in areas with low light resources.
[0136] Most known methods are general photovoltaic capacity estimation methods or require fine-grained weather-load simulation software, and do not incorporate the stable hydraulic threshold and pump / transmission characteristics of the pumping station; the method provided in this application uses... , Indicators such as "whether water can be supplied" are directly incorporated into the optimization model, and the optimization capacity results are strongly coupled with the water supply target, making them more in line with the on-site needs.
[0137] 2. Resource partitions are transferable, and the solar energy correction coefficient is standardized, reducing reliance on early data.
[0138] Through parameters By standardizing regional resource differences (annual radiation / equivalent sunshine hours) to a unified caliber, regional differences can be reflected, while reducing reliance on long-term historical data and complex simulations. This approach is suitable for large-scale comparisons and rapid convergence of solutions.
[0139] 3. Robust provability: Opportunity constraints + scenario dual constraints, taking into account both most and extreme scenarios, the optimized photovoltaic array has strong robustness.
[0140] Known methods either only provide an annual average of available hours or conservatively scale up to a single worst-case day. The method proposed in this application uses... The chance constraint (example value, other values within the range of 0.1 to the left and right) ensures that the daily target is achieved in most weather conditions. Combined with the scenario constraint, it sets boundaries for several representative low-light sunshine scenarios, while controlling tail risk and over-conservatism.
[0141] 4. Easy to implement.
[0142] Employing a "bisection on the outer layer + grid / bisection on the inner layer" approach combined with a monotonicity-based decision logic of "contracting the upper boundary upon feasibility," the system performs a bisection search on the photovoltaic array capacity in the outer layer and a grid / bisection search in the inner layer. Using small grids, The minimum necessary daily energy is obtained by using one-dimensional binary search and the rule of feasibility shrinking the upper bound. It can achieve stable convergence without commercial solvers. The process is simple, the convergence is clear, the implementation cost is low, and it is easy to implement and reproduce in engineering. The statistical indicators and judgment criteria are clear, which is easy for third-party review and verification, and it is suitable for rapid iteration in engineering.
[0143] 5. Highly scalable.
[0144] The method proposed in this application relies on only four elements: "load threshold - equivalent output - opportunity / scenario dual constraints - two-layer solution". It can be transferred to scenarios such as photovoltaic direct-drive salt extraction, pastoral water supply, and remote pressurization. By replacing the threshold / output conversion coefficient and scenario set, the technical solution can be reused horizontally.
[0145] For example, setting the head Design / Peak Flow Physical parameters: ; ; ; ; ; ; ; The Monte Carlo sample size was 50, and the number of low-light representative scenes was 10. The daily running time was 8 hours, and the time step was 2 minutes.
[0146] The calculated peak water power is Corrected water pump power Basic photovoltaic array installed capacity .
[0147] The solution process iterated a total of 6 times, and the result of each iteration was:
[0148] Iteration 1: scope Determined as feasible;
[0149] Iteration 2: Range shrinks to Determined as infeasible;
[0150] Iteration 3: Determined as infeasible;
[0151] Iteration 4: Determined as infeasible;
[0152] Iteration 5: Determined as infeasible;
[0153] Iteration 6: It was determined to be feasible and met the convergence condition.
[0154] Based on the scenario settings, the irradiation scenario distribution results can be obtained as follows: Figure 3 As shown.
[0155] Based on the objective function, the convergence result of the solution is as follows: Figure 4 As shown.
[0156] The results of water supply distribution are as follows Figure 5 As shown.
[0157] Water supply performance in low-light conditions (taking 10 samples as an example) is as follows: Figure 6 As shown.
[0158] The final calculated installed capacity of the photovoltaic array is:
[0159] 27.265KW; in the optimal energy storage budget, In other words, in this embodiment, no energy storage is required to meet the constraints. The basic photovoltaic array installed capacity is... It is important to note the basic photovoltaic array installed capacity. The nominal photovoltaic array installed capacity lower limit (baseline value) quickly estimated according to "ideal / baseline operating conditions" is the instantaneous matching capacity required at the best illumination time, taking into account losses. It is the minimum photovoltaic array capacity required to meet the daily water supply constraint (and the opportunity constraint and scenario constraint), taking into account the low light conditions and system fluctuations.
[0160] Based on the concept of this application, embodiments of this application also provide a photovoltaic array capacity optimization device for a solar irrigation system. The device includes a processor and a storage medium, the storage medium storing a computer program, and the processor running the computer program to execute the aforementioned photovoltaic array capacity optimization method for a solar irrigation system.
[0161] This invention is not limited to the specific embodiments described above. The invention extends to any new feature or combination disclosed in this specification, as well as any new method or process step or combination disclosed herein.
Claims
1. A method for optimizing the capacity of a photovoltaic array in a solar-powered irrigation system, characterized in that, The methods include: Obtain the basic parameters of the solar-powered irrigation system, including design flow rate, total system head, solar resource level, pump type, electric drive type, system performance ratio, and solar array type; Based on the peak water power of the system corrected by the aforementioned basic parameters, the basic photovoltaic array installed capacity is obtained. Under the constraint of daily water supply, with the goal of minimizing the installed capacity of photovoltaic array and energy storage capacity, the minimum feasible installed capacity of photovoltaic array is calculated based on the basic installed capacity of photovoltaic array. The daily water supply constraint includes a first constraint and a second constraint. The first constraint is that, in a given set of random scenarios, the probability of achieving the daily water supply target reaches a predetermined probability. The second constraint is that, within a given set of representative low-light scenarios, the daily water supply target must reach a predetermined number.
2. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 1, characterized in that, The energy storage capacity includes an upper limit for energy storage discharge power and a daily energy budget for energy storage; the upper limit for energy storage discharge power and the daily energy budget for energy storage are matched.
3. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 2, characterized in that, The peak water power of the system is corrected based on the aforementioned basic parameters, including: The first peak water power is calculated using the design flow rate and the total system head. Obtain the traffic correction factor Determine the pump correction factor based on the pump type. Determine the current transmission correction coefficient based on the described electric transmission type. Determine the solar energy correction factor based on the aforementioned solar energy resource level. Determine the array correction coefficient based on the aforementioned solar array configuration. ; Through the flow correction coefficient Water pump correction factor and current transmission correction factor The first peak water power is corrected for equipment influencing factors to obtain the second peak water power; Through the system performance ratio and the solar energy correction coefficient and array correction coefficient The second peak water power is corrected to obtain the third peak water power.
4. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 3, characterized in that, When calculating the minimum feasible photovoltaic array capacity, the effective power of the system exceeding the stable water pump power threshold is constrained at any time step in any scenario. The effective power is between the surplus power of the electrical power delivered to the water pump at that time step that exceeds the stable water pump power threshold and the surplus power of the second peak water power that exceeds the stable water pump power threshold.
5. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 3, characterized in that, The flow correction coefficient Based on system efficiency calibration, the mapping relationship between flow rate and system efficiency is indicated; the pump correction coefficient is... Based on pump type calibration, it indicates the power conversion efficiency for different pump types; the current transmission correction coefficient is... Based on the calibration of the current transmission form, it indicates the power conversion rate of different current transmission forms.
6. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 3, characterized in that, The solar energy correction factor is determined based on the solar energy resource level. ,include: The average daily total irradiance of the target area is obtained based on solar energy resource zoning; The equivalent sunshine duration is calculated based on the average daily total irradiance. The proportionality coefficient between the equivalent sunshine duration and the reference sunshine duration is used as the solar energy correction coefficient. .
7. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 4, characterized in that, Based on the aforementioned basic photovoltaic array installed capacity, the minimum feasible photovoltaic array installed capacity is calculated, including: The upper and lower boundaries of the photovoltaic array's installed capacity are initialized based on the aforementioned basic photovoltaic array installed capacity; Iterate through the following steps until the upper and lower boundaries of the photovoltaic array's installed capacity converge: Initialize the photovoltaic array capacity based on the upper and lower boundaries of the current photovoltaic array installed capacity; Multiple candidate values for the upper limit of energy storage discharge power are set, and for each candidate value, the daily energy budget of energy storage and its upper and lower boundaries are initialized; For each time step of each scenario, the available power of the photovoltaic array at the current time step is calculated step by step, and the effective power and the daily energy budget margin of the energy storage are updated based on the available power of the photovoltaic array. The daily water supply is calculated based on the effective power at each time step, and a first constraint index corresponding to the first constraint condition and a second constraint index corresponding to the second constraint condition are calculated based on the daily water supply. Determine whether both the first and second constraint indicators meet the standards. If yes, update the upper boundary of the daily energy budget based on the remaining daily energy budget and the current upper boundary of the daily energy budget. If no, update the lower boundary of the daily energy budget based on the remaining daily energy budget and the lower boundary of the daily energy budget. Determine whether there exists at least one set of energy storage discharge power upper limit and corresponding energy storage daily energy budget among the multiple candidate values such that both the first constraint index and the second constraint index meet the standard. If yes, update the upper boundary of the photovoltaic array installed capacity to the current photovoltaic array installed capacity. If no, update the lower boundary of the photovoltaic array installed capacity to the current photovoltaic array installed capacity. The final photovoltaic array installed capacity is calculated based on the upper and lower boundaries of the photovoltaic array installed capacity at the time of convergence. The energy storage daily energy budget with the minimum value at the time of convergence is taken as the final energy storage daily energy budget, and the candidate value that minimizes the energy storage daily energy budget at the time of convergence is taken as the upper limit of the final energy storage discharge power.
8. The method for optimizing the photovoltaic array capacity of a solar-powered irrigation system as described in claim 7, characterized in that, The conditions for the convergence of the upper and lower boundaries of the photovoltaic array's installed capacity are as follows: The gap between the upper and lower boundaries of the current photovoltaic array installed capacity is within a predetermined proportion of the upper boundary of the current photovoltaic array installed capacity.
9. A photovoltaic array capacity optimization device for a solar-powered irrigation system, characterized in that, The device includes a processor and a storage medium storing a computer program, the processor running the computer program to perform the photovoltaic array capacity optimization method for a solar irrigation system as described in any one of claims 1-8.