Configuration method and system for additionally distributing photovoltaic system in existing wind power plant

Abstract

The invention provides a configuration method and system for an added photovoltaic system of an existing wind power plant, and the method comprises the steps: obtaining the wind-solar power generation resource data of a built wind power plant, and calculating the wind-solar combined power generation output power of the wind power plant according to the wind-solar power generation resource data; setting an operation mode of the wind-solar combined power station according to the wind-solar combined power generation output power and the rated power of the main transformer, and calculating photovoltaic limited power utilization time; calculating the yield rate of photovoltaic power generation based on the photovoltaic limited power utilization time, taking the yield rate and the installation capacity of the photovoltaic system as different optimization targets, and optimizing the configuration scheme of additionally distributing the photovoltaic system in the wind power plant through a multi-target optimization algorithm with constraint conditions; and adding a photovoltaic system with a corresponding capacity in the wind power plant according to the obtained optimization result. According to the method, the capacity of the added photovoltaic system can be reasonably set in the wind power plant, the synergistic effect of the wind power plant and the photovoltaic system is maximized, and the overall utilization rate of renewable energy sources is improved.

Description

Technical Field

[0001] This application relates to the field of wind-solar co-generation design technology, and in particular to a configuration method and system for adding photovoltaic systems to existing wind farms. Background Technology

[0002] With the transformation of the global energy structure and the increasing severity of climate change, the development and utilization of renewable energy are receiving more and more attention. Wind power and solar power, as two major renewable energy technologies, have become important driving forces for energy transformation due to their clean and renewable characteristics. However, the intermittency and instability of wind and solar energy limit the continuity and reliability of their energy supply. To improve the utilization efficiency of renewable energy and the stability of the power grid, more and more research and practice are focusing on the synergistic cooperation between wind farms and photovoltaic power generation systems.

[0003] The combination of wind farms and photovoltaic (PV) power generation systems, known as wind-solar hybrid systems, can compensate for each other's power generation characteristics. Generally, wind power has higher efficiency at night or in winter, while PV power performs better during the day and in summer. By rationally configuring the ratio of wind to PV power, more stable and efficient energy output can be achieved, increasing the overall power generation of the system and reducing the pressure on the power grid.

[0004] However, despite the significant advantages of wind-solar hybrid systems, their design and implementation face numerous challenges. Related technologies cannot reasonably add photovoltaic systems to wind farms, resulting in several issues: First, the inability to rationally configure the installed capacity of wind power equipment and photovoltaic modules leads to data waste and grid instability problems; second, the collaborative control strategy for wind farms and photovoltaic systems needs optimization; and third, the factors considered during photovoltaic addition are relatively singular, resulting in poor operational efficiency. Summary of the Invention

[0005] This application aims to at least partially address one of the technical problems in the related art.

[0006] Therefore, the first objective of this application is to propose a configuration method for adding photovoltaic systems to existing wind farms. This method is based on a constrained multi-objective optimization method, which can reasonably set the capacity of the added photovoltaic system in the wind farm, maximize the synergistic effect between the wind farm and the photovoltaic system, and improve the overall utilization rate of renewable energy.

[0007] The second objective of this application is to propose a configuration system for adding photovoltaic systems to existing wind farms.

[0008] The third objective of this application is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, the first aspect of this application proposes a method for configuring an existing wind farm to add a photovoltaic system, comprising the following steps: Obtain wind and solar power generation resource data of the constructed wind farm, and calculate the combined wind and solar power generation output power of the wind farm based on the wind and solar power generation resource data; Determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power of the wind-solar combined power generation and the rated power, and calculate the photovoltaic curtailment utilization time based on the operation mode; The rate of return of photovoltaic power generation is calculated based on the photovoltaic curtailment time. The rate of return and the installed capacity of the photovoltaic system are used as different optimization objectives. The configuration scheme of adding photovoltaic system in the wind farm is optimized by a multi-objective optimization algorithm with constraints. The constraints are the minimum completion indicators of each preset optimization objective. Based on the optimization results obtained, photovoltaic systems of corresponding capacity are added to the wind farm, and the control strategy of the wind-solar combined power station is determined based on the operating mode.

[0010] Optionally, in one embodiment of this application, the operating mode is to maintain the normal operation of the wind farm and impose power curtailment on the photovoltaic system when the combined wind and solar power output exceeds the rated power. The calculation of the photovoltaic power curtailment utilization time based on the operating mode includes: determining the wind and solar power curtailment output based on the relationship between the sum of the total output of the wind farm and the uncurtailed output of the photovoltaic system and the rated power of the main transformer; calculating the photovoltaic system curtailment output based on the wind and solar power curtailment output and the total output of the wind farm; and calculating the photovoltaic power curtailment utilization time based on the photovoltaic system curtailment output and the capacity of the photovoltaic modules.

[0011] Optionally, in one embodiment of this application, the step of calculating the rate of return of photovoltaic power generation based on the photovoltaic curtailment time includes: determining the construction cost and operation and maintenance cost of the photovoltaic system; and using the photovoltaic curtailment time and the construction cost and operation and maintenance cost of the photovoltaic system as independent variables, calculating the rate of return of photovoltaic power generation through a relevant rate of return function.

[0012] Optionally, in one embodiment of this application, the optimization objective of the multi-objective optimization algorithm further includes: the cost per unit of photovoltaic module and the fluctuation of the wind-solar combined power generation system. Before optimizing the configuration scheme, it further includes: determining the cost per unit of photovoltaic module based on the construction cost and operation and maintenance cost of the photovoltaic system; setting thresholds in the minimum completion indicators corresponding to the installed capacity of the photovoltaic system, the rate of return, the cost per unit of photovoltaic module, and the fluctuation of the wind-solar combined power generation system.

[0013] Optionally, in one embodiment of this application, the optimization of the configuration scheme for adding photovoltaic systems to the wind farm using a constrained multi-objective optimization algorithm includes: generating an initial population, wherein each individual in the population represents a configuration scheme for adding photovoltaic systems to the wind farm; calculating the function value of each individual for each optimization objective, and verifying whether each optimization objective meets the corresponding minimum completion index; performing non-dominated sorting and crowding calculation on the individuals based on the verification results, and performing genetic operations on the individuals based on the sorting results and crowding calculation results to generate the next generation population; updating the initial population to the next generation population, and iteratively performing evaluation, verification, sorting, and genetic operations until the iteration stopping condition is met; selecting an objective solution from the Pareto front obtained from the iteration, wherein the objective solution is a configuration scheme that meets the requirements of multiple optimization objectives.

[0014] Optionally, in one embodiment of this application, before optimizing the configuration scheme of adding photovoltaic systems in the wind farm using a multi-objective optimization algorithm with constraints, the method further includes: setting a weight coefficient corresponding to each optimization objective; the step of selecting the objective solution from the Pareto front obtained by iteration includes: selecting the objective solution from the Pareto front according to the weight coefficient of each optimization objective.

[0015] Optionally, in one embodiment of this application, the wind and solar power generation resource data includes the wind speed and total horizontal irradiance of the wind farm at each hour throughout the year. The step of calculating the combined wind and solar power generation output power of the wind farm based on the wind and solar power generation resource data includes: calculating the output power of a single wind turbine in the wind farm at each hour throughout the year based on the wind speed at each hour throughout the year; calculating the output power of a unit photovoltaic module at each hour throughout the year based on the total horizontal irradiance of the wind farm at each hour throughout the year; and determining the combined wind and solar power generation output power of the wind farm based on the output power of the single wind turbine and the output power of the unit photovoltaic module at each hour throughout the year.

[0016] To achieve the above objectives, a second aspect of this application proposes a configuration system for adding photovoltaic systems to existing wind farms, comprising the following modules: The first calculation module is used to acquire wind and solar power generation resource data of the constructed wind farm, and calculate the combined wind and solar power generation output power of the wind farm based on the wind and solar power generation resource data. The second calculation module is used to determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power of the wind-solar combined power generation and the rated power, and calculate the photovoltaic curtailment utilization time based on the operation mode. The optimization module is used to calculate the rate of return of photovoltaic power generation based on the photovoltaic curtailment time, and to use the rate of return and the installed capacity of the photovoltaic system as different optimization objectives. The module optimizes the configuration scheme of adding photovoltaic systems in the wind farm through a multi-objective optimization algorithm with constraints, wherein the constraints are the minimum completion indicators of each preset optimization objective. The configuration module is used to add photovoltaic systems of corresponding capacity to the wind farm based on the obtained optimization results, and to determine the control strategy of the wind-solar combined power station based on the operating mode.

[0017] To implement the above embodiments, a third aspect of this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the configuration method for adding photovoltaic systems to existing wind farms as described in the first aspect.

[0018] The technical solution provided by the embodiments of this application brings at least the following beneficial effects: Based on a determined operating mode of a wind-solar co-location site, this application calculates the curtailed output of photovoltaic power and the number of curtailed utilization hours. Then, based on the determined number of curtailed utilization hours, it determines the rate of return function of the photovoltaic power station. The rate of return of the photovoltaic power station is then used as an objective, with the minimum completion indicator of each objective as a constraint, for multi-objective optimization. By using a constrained multi-objective optimization algorithm, it ensures that the obtained integrated configuration solution of wind farm and photovoltaic power can meet the specific performance requirements of all key performance indicators, achieving the best trade-off among multiple objectives. Therefore, based on a constrained multi-objective optimization method, this application can reasonably set the capacity of the added photovoltaic system in the wind farm, maximizing the synergistic effect of the wind farm and photovoltaic system, improving the overall utilization rate of renewable energy, and is applicable to power generation scenarios under various seasons and weather conditions. It improves the rationality and reliability of adding photovoltaic systems to existing wind farms, which is conducive to the stable and efficient operation of wind and solar power stations.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein Figure 1 A flowchart illustrating a method for configuring an existing wind farm to add a photovoltaic system, as proposed in an embodiment of this application; Figure 2 This is a flowchart illustrating a multi-objective optimization process proposed in an embodiment of this application; Figure 3This is a schematic diagram of the configuration system for adding a photovoltaic system to an existing wind farm, as proposed in an embodiment of this application. Detailed Implementation

[0021] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0022] It should be noted that the wind-solar hybrid systems in the relevant embodiments face numerous challenges in design and implementation. First, a key issue is how to rationally configure the installed capacity of wind power and photovoltaics based on geographical location, meteorological conditions, and grid demand. Second, the physical and electrical integration of wind farms and photovoltaic systems requires overcoming technical obstacles, such as inverter configuration, electrical connections, and optimization of system control strategies. Furthermore, evaluating the operational efficiency of wind-solar combined power plants is an indispensable part of wind-solar hybrid system design, requiring comprehensive consideration of factors such as investment costs, operation and maintenance expenses, and electricity market prices. The lack of consideration for operational efficiency in the relevant embodiments results in low operational returns for the wind-solar hybrid systems. Therefore, this application proposes a scientific and reasonable capacity configuration method for adding photovoltaic systems to existing wind farms.

[0023] The following description, with reference to the accompanying drawings, illustrates a method and system for configuring photovoltaic systems in existing wind farms, as proposed in an embodiment of the present invention.

[0024] Figure 1 A flowchart illustrating a method for configuring an existing wind farm to add a photovoltaic system, as proposed in this application, is shown below. Figure 1 The method includes the following steps: Step S101: Obtain wind and solar power generation resource data of the constructed wind farm, and calculate the combined wind and solar power output power of the wind farm based on the wind and solar power generation resource data.

[0025] Specifically, by surveying the wind power generation and photovoltaic power generation (hereinafter referred to as wind and solar power generation) resources of the existing wind power generation stations (hereinafter referred to as wind farms) and obtaining various types of data related to wind and solar power generation resources, including wind resource data, solar energy resource data and environmental data.

[0026] For example, wind resource data includes wind speed over 8760 hours per year for wind farms, and solar resource data includes total horizontal irradiance over 8760 hours per year for wind farms, where total irradiance includes both direct and diffuse irradiance.

[0027] Furthermore, based on the acquired wind and solar power resource data, the combined wind and solar power output of the wind farm is calculated, i.e., the total power transmitted by wind and solar power simultaneously. As one possible approach, the total power transmitted by wind and solar power simultaneously is determined from the perspective of the power generation of a single wind turbine and a single photovoltaic module.

[0028] In one embodiment of this application, calculating the combined wind and solar power output of a wind farm based on wind and solar power resource data includes: first, calculating the output power of a single wind turbine in the wind farm at each hour of the year based on the wind speed at each hour of the year; then, calculating the output power of a unit photovoltaic module at each hour of the year based on the total horizontal irradiance of the wind farm at each hour of the year; and finally, determining the combined wind and solar power output of the wind farm based on the output power of a single wind turbine at each hour of the year and the output power of a unit photovoltaic module at each hour of the year.

[0029] Specifically, in this embodiment, the power generation curve of a single wind turbine is first determined. Specifically, the hourly output power of a single wind turbine over a year of 8760 hours can be calculated using the following formula:

[0030] in, p w This represents the hourly output power of the wind turbine. p i This refers to the rated output power of the wind turbine. v c To activate the wind speed, v R Rated wind speed, v F This refers to the cutoff wind speed. It's understandable that when the wind speed is very low, below the starting wind speed, the unit is in a shutdown state. When the wind speed exceeds the starting wind speed, the generator set enters a variable power operation state, meaning that the power generation increases with the increase in wind speed. Once the rated power is reached, the unit will be limited to operating at the rated power. When the wind speed is too high, exceeding the unit's cutoff wind speed, for safety reasons, the unit will enter a shutdown protection state.

[0031] Therefore, the output power of the wind turbine in each hour can be calculated according to the above formula. By summing up the output power of each hour, the overall output power of 8760 hours throughout the year can be obtained. It is also possible to fit and generate the power generation curve of a single wind turbine.

[0032] Furthermore, the power generation curve per unit photovoltaic (e.g., 1kW photovoltaic module) is calculated based on the collected solar energy and temperature data from the wind farm.

[0033] Specifically, the output power of a single photovoltaic module can be calculated using the following formula: Unit photovoltaic (kW) output power = total irradiance on module surface × temperature correction factor C T The temperature correction factor is the annual average temperature reduction factor for solar cells, expressed as a percentage. The temperature correction factor can be determined using the critical temperature of the solar cells, as well as data such as the number of daytime hours at the wind farm throughout the year and the relationship between the daytime temperature of the solar cells and the critical temperature.

[0034] The total irradiance of the component surface can be determined by the following formula: Total irradiance of the module surface = f x (Solar resource data, azimuth angle, module tilt angle) The solar energy resource data includes multiple parameters such as the total hourly irradiance of the inclined plane, the hourly direct irradiance of the horizontal plane, the hourly diffuse irradiance of the horizontal plane, the total hourly irradiance of the horizontal plane, the hourly direct radiation ratio of the horizontal plane and the inclined plane, and the average ground reflectivity.

[0035] Therefore, based on the solar energy resource data of the wind farm for 8760 hours throughout the year, the output power of a unit photovoltaic module in each hour can be calculated.

[0036] Furthermore, multiplying the output power of a single photovoltaic (PV) module by the PV capacity yields the PV power output. Multiplying the power output of a single wind turbine by the number of turbines in the wind farm yields the total wind farm power output. Adding the PV power output to the total wind farm power output determines the combined wind and solar power output. Specifically, based on the hourly output power calculated above, the hourly combined wind and solar power output can be calculated. This data indicates the combined power output under the condition of wind and solar power co-location, given known wind and solar resources and determined parameters of the wind turbines and PV modules.

[0037] Step S102: Determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power and rated power of the wind-solar combined power generation, and calculate the photovoltaic curtailment utilization time based on the operation mode.

[0038] Specifically, by obtaining parameters such as the capacity and rated power of the main transformer of the existing wind farm, the limiting conditions for wind and solar power co-operation are determined so that the total power output of wind and solar power does not exceed the rated power of the main transformer. Based on these limiting conditions, the operating mode of wind and solar power co-operation can be determined.

[0039] In one embodiment of this application, the operating mode is to maintain the normal operation of the wind farm and impose power curtailment on the photovoltaic system when the combined wind and solar power output exceeds the rated power of the main transformer. That is, the wind and solar co-generation control strategy includes ensuring that the wind power generation is not restricted, and when the combined wind and solar power output exceeds the rated power of the main transformer, power curtailment is imposed on the photovoltaic system, thereby ensuring that the total combined wind and solar power output does not exceed the rated power of the main transformer.

[0040] Furthermore, based on the above operating mode, the power curtailment utilization time of the photovoltaic system can be calculated (in this application, the time during which the photovoltaic system can generate electricity using solar energy under power curtailment conditions is referred to as power curtailment utilization time).

[0041] In one embodiment of this application, calculating the solar power curtailment utilization time based on the operating mode includes: determining the wind power and solar power curtailment output based on the relationship between the sum of the total output of the wind farm and the uncurtailed output of the solar system and the rated power of the main transformer; calculating the solar system curtailment output based on the wind power and solar power curtailment output and the total output of the wind farm; and calculating the solar power curtailment utilization time based on the solar system curtailment output and the capacity of the solar modules.

[0042] Specifically, the normal output of wind and solar power under uncurtailed conditions (referred to as wind and solar uncurtailed output) equals the solar output calculated in step S101 plus the wind output. However, when the solar system is subject to curtailment, the curtailed output of wind and solar power can be calculated using the following formula: Wind and solar power curtailment output = np.where (Wind and solar power output without curtailment > Main transformer power, Main transformer power, Wind and solar power output without curtailment) This formula states that if the uncurtailed power output of wind and solar power is greater than the main transformer power, then the curtailed power output of wind and solar power equals the main transformer power, for example, 120. If the uncurtailed power output of wind and solar power is not greater than the main transformer power, then the curtailed power output of wind and solar power equals the uncurtailed power output of wind and solar power. Specifically, uncurtailed power output of wind and solar power = total wind farm output + uncurtailed power output of solar system. When determining the curtailed power output of wind and solar power, the hourly data calculated above can be compared to determine the corresponding curtailed power output for each hour.

[0043] Then, the power curtailment output of the photovoltaic system is calculated using the following formula: Photovoltaic power curtailment output = Wind and solar power curtailment output - Wind power output. That is, the power curtailment output of the photovoltaic system is obtained by subtracting the total output of the wind farm from the calculated wind and solar power curtailment output. Furthermore, when determining the photovoltaic power curtailment output, the hourly data calculated for each hour throughout the year can be compared to determine the corresponding photovoltaic power curtailment output for each hour.

[0044] Finally, the power curtailment output of the photovoltaic system is divided by the capacity of the photovoltaic modules to calculate the photovoltaic curtailment utilization time. Since the above embodiment is calculated hourly based on hourly data, in this step, to calculate the photovoltaic curtailment utilization hours, the power curtailment output of the photovoltaic system is divided by the capacity of the photovoltaic modules and then summed. The total number of photovoltaic curtailment utilization hours for the whole year is then calculated using the SUM function.

[0045] It should be noted that, in the case where the photovoltaic installation capacity has not yet been determined in this application, the unknown photovoltaic capacity can be eliminated in some cases by dividing by the capacity of the photovoltaic modules. In other cases, the photovoltaic capacity can be used as an input quantity into the subsequent optimization algorithm, and the installation capacity can be determined through optimization.

[0046] Step S103: Calculate the rate of return of photovoltaic power generation based on the photovoltaic curtailment time, and use the rate of return and the installed capacity of the photovoltaic system as different optimization objectives. Optimize the configuration scheme of adding photovoltaic systems to the wind farm through a multi-objective optimization algorithm with constraints, where the constraints are the minimum completion indicators of each preset optimization objective.

[0047] Specifically, in order to accurately assess the economic benefits of the wind-solar hybrid system, this application comprehensively considers multiple factors, uses the calculated photovoltaic curtailment time as the independent variable, and calculates the rate of return of photovoltaic power generation.

[0048] In one embodiment of this application, the calculation of the rate of return of photovoltaic power generation based on the photovoltaic curtailment time includes: determining the construction cost and operation and maintenance cost of the photovoltaic system; using the photovoltaic curtailment time and the construction cost and operation and maintenance cost of the photovoltaic system as independent variables, and calculating the rate of return of photovoltaic power generation through a relevant rate of return function.

[0049] Specifically, in this embodiment, the construction cost of the photovoltaic system can be determined using the following function: Photovoltaic system construction cost = f (Construction cost of photovoltaic power plant area + cost of photovoltaic transmission line + cost of upgrading substation) Then, the operation and maintenance cost of the photovoltaic system is determined by the following function: The operation and maintenance cost of a photovoltaic system = f (Land costs, personnel costs, material costs, other costs, maintenance costs...) The relevant functions for calculating construction costs and operation and maintenance costs can be determined according to the relevant specifications for constructing photovoltaic power plants. When calculating operation and maintenance costs, various factors can be considered based on the actual operation of the photovoltaic power plant. This application does not impose any restrictions on this.

[0050] Furthermore, the rate of return of a photovoltaic power plant is calculated using the following function: The rate of return of a photovoltaic (PV) power plant = f(PV capacity, PV curtailment hours, electricity price, PV construction cost, PV operation and maintenance cost) In this application, the photovoltaic curtailment utilization hours calculated based on wind and solar power generation resource data and curtailment operation mode are used as input parameters to calculate the rate of return of the photovoltaic power station in actual operation, thereby improving the accuracy of the calculated rate of return.

[0051] Furthermore, the calculated rate of return and the installation capacity of the photovoltaic system to be determined are used as two optimization objectives. Combined with other key features that need to be considered, the configuration scheme for adding photovoltaic systems to wind farms is optimized through a multi-objective optimization algorithm with constraints.

[0052] In the multi-objective optimization problem of this application, in addition to finding a trade-off solution between the various optimization objectives, the wind-solar hybrid solution can also be ensured to meet specific performance requirements by setting a minimum performance index for each objective. As a possible implementation, a multi-objective optimization algorithm with constraints can be used, such as the constrained NSGA-II (Constrained Non-dominated Sorting Genetic Algorithm II) algorithm.

[0053] The constrained NSGA-II algorithm adds constraint handling capabilities to the NSGA-II algorithm. It introduces a penalty function or other mechanism to handle constraint violations, building upon non-dominated sorting and crowding sorting. This allows the algorithm to find the optimal Pareto solution set while satisfying all constraints.

[0054] In one embodiment of this application, the multi-objective optimization algorithm targets various optimization objectives, including, in addition to the aforementioned rate of return and the installed capacity of the photovoltaic system, the cost per unit of photovoltaic module and the volatility of the wind-solar combined power generation system. Before optimizing the configuration scheme, it further includes: determining the cost per unit of photovoltaic module based on the construction cost and operation and maintenance cost of the photovoltaic system; and setting thresholds in the minimum completion indicators corresponding to the installed capacity of the photovoltaic system, the rate of return, the cost per unit of photovoltaic module, and the volatility of the wind-solar combined power generation system.

[0055] Specifically, this embodiment first sets various optimization objectives and corresponding minimum completion targets for each objective. For example, for the installed capacity of a photovoltaic system, the objective is to maximize the installed capacity of the photovoltaic system, and the minimum completion target is that the installed capacity of the photovoltaic system must reach at least a specific value, which is a preset minimum installed capacity threshold.

[0056] Regarding the rate of return, the goal is to maximize the project's rate of return. The minimum target is that the rate of return of the photovoltaic system must reach a certain value, which is a preset minimum return threshold.

[0057] The goal for the cost of a unit photovoltaic module is to minimize the total investment cost. The minimum target is that the investment cost does not exceed a certain amount, which is a preset maximum allowable threshold.

[0058] To minimize system volatility, the goal is to reduce the fluctuation of power output of the combined wind and solar power system. The minimum target is that the fluctuation of the combined system output must not exceed a specific threshold, which is the maximum allowable deviation between the real-time output of the system and the normal average output. For example, the fluctuation of the output power of the combined wind and solar power system should not exceed 5% of the standard deviation.

[0059] Furthermore, in this embodiment, before optimizing the configuration scheme of adding photovoltaic systems in the wind farm using a multi-objective optimization algorithm with constraints, the method further includes setting a weight coefficient corresponding to each optimization objective.

[0060] Specifically, the weight coefficients corresponding to each optimization objective are set according to the importance of each optimization objective. They can be determined based on the actual needs and expected operational goals of configuring photovoltaic systems in wind farms. For example, when the goal is to maximize the installed capacity of photovoltaic systems to enhance wind-solar complementarity and grid stability, the weights of the installed capacity of photovoltaic systems can be set to 40%, the rate of return to 20%, the cost per photovoltaic module to 20%, and the volatility of wind-solar combined power generation systems to 20%.

[0061] Furthermore, based on the aforementioned multiple optimization objectives and the minimum completion indicators and weight coefficients for each objective, the constrained NSGA-II algorithm is used for optimization to determine the configuration scheme for adding photovoltaic systems to the wind farm. To more clearly illustrate the detailed process of finding the optimal solution using a multi-objective optimization algorithm in this application, an optimization method proposed in one embodiment of this application is described below as an example.

[0062] Figure 2 This is a flowchart of a multi-objective optimization process proposed in an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps: Step S201: Generate an initial population, where each individual in the population represents a configuration scheme for adding a photovoltaic system to a wind farm.

[0063] Specifically, this step initializes the population and generates an initial population. Each individual represents a possible wind and solar power configuration scheme. The population can be initialized randomly or in combination with the operating mode of the wind and solar combined power station determined in the above embodiments.

[0064] Step S202: Calculate the function value of each individual for each optimization objective, and check whether each optimization objective meets the corresponding minimum completion index.

[0065] Specifically, this step involves individual evaluation and constraint verification. That is, the function value of each optimization objective for each individual in the population is calculated, and the calculated objective function value is compared with the corresponding minimum performance indicator to verify whether each objective meets its respective minimum performance indicator.

[0066] Step S203: Based on the test results, perform non-dominated sorting and crowding calculation on individuals, and perform genetic operations on individuals based on the sorting results and crowding calculation results to generate the next generation population.

[0067] Specifically, this step first performs non-dominated ranking and crowding calculation for each individual. Individuals that meet the constraints are ranked non-dominated, while those that do not meet the constraints are penalized according to the degree of their constraint violation. Through non-dominated ranking, individuals in the population are assigned to a non-dominated layer, thus ensuring that the selected individuals belong to a relatively superior, non-inferior rank. Crowding calculation is achieved by comparing the crowding distance between individuals; specifically, the crowding degree of each individual can be calculated based on the function values ​​of its surrounding individuals.

[0068] Furthermore, individual selection, crossover, and mutation are performed. Based on the obtained sorting and crowding calculation results, individuals that meet the requirements are selected for genetic operations, including crossover and mutation operations, to generate a new generation of population.

[0069] Step S204: Update the initial population to the next generation population, and repeat the evaluation, verification, sorting and genetic operations until the iteration stopping condition is met.

[0070] Specifically, this step involves iterative calculations. For the newly generated next generation population, the operations of steps S202 and S203 above are repeated. That is, the population is continuously updated by iteratively evaluating, sorting, selecting, and genetically processing the newly generated population in each round. The next round of evaluation, sorting, selection, and genetic processing is then performed on the newly generated population until the iteration stopping condition is met, such as when the number of iterations reaches the set maximum number of iterations.

[0071] Step S205: Select the objective solution from the Pareto front obtained by iteration, wherein the objective solution is a configuration scheme that satisfies the requirements of multiple optimization objectives.

[0072] Specifically, this step involves result analysis and selection of the optimal solution. The Pareto optimal solution set obtained through iteration is the non-dominated solution set, which is the set of all non-dominated solutions in the multi-objective optimization problem. The Pareto optimal solution in this set achieves better results on all objective functions simultaneously. Since this embodiment sets multiple optimization objectives, the Pareto optimal solution set forms a surface in space, namely the Pareto front.

[0073] This step makes decisions based on the actual needs and strategy preferences of the wind farm, selecting the optimal solution suitable for the current needs from the final Pareto front. In this embodiment, selecting the target solution from the iteratively obtained Pareto front includes: selecting the target solution from the Pareto front based on the weight coefficients of each optimization objective. That is, the selection strategy for making the decision can be determined based on the weight coefficients of the optimization objectives set in the above embodiment.

[0074] Therefore, the constrained multi-objective optimization method described in the above embodiments ensures that the resulting solution not only achieves the best trade-off among multiple optimization objectives but also meets the minimum requirements of all key performance indicators. This provides an effective solution strategy for the complex system configuration problem of integrating wind farms and photovoltaic systems.

[0075] Step S104: Based on the obtained optimization results, add photovoltaic systems of corresponding capacity to the wind farm, and determine the control strategy of the wind-solar combined power station based on the operation mode.

[0076] Specifically, based on the installed capacity of the photovoltaic system included in the optimal solution obtained in the previous step, photovoltaic systems of corresponding capacity are configured in the existing wind farms. Furthermore, the control strategy for the wind-solar combined system is determined. For example, different time periods are set, such as when the combined wind and solar power output is about to reach the rated power of the main transformer and when it exceeds the rated power. To implement power curtailment for the photovoltaic system, control strategies are required, including the number of photovoltaic modules that need to be shut down and the start-up and shutdown methods of the photovoltaic system.

[0077] Therefore, this application can not only maximize the synergistic effect of wind farms and photovoltaic systems and improve the overall utilization rate of renewable energy, but also provide strong technical support for energy transition and climate change response.

[0078] In summary, the configuration method for adding photovoltaic (PV) systems to existing wind farms according to the embodiments of this application calculates the curtailment output and curtailment utilization hours of PV based on the determined operating mode of the wind-solar co-location site. Then, based on the determined curtailment utilization hours, it determines the rate of return function of the PV power plant. The rate of return of the PV power plant is then used as an objective, with the minimum completion indicator of each objective as a constraint, for multi-objective optimization. By using a constrained multi-objective optimization algorithm, it ensures that the obtained integrated configuration solution of wind farm and PV can meet the specific performance requirements of all key performance indicators, achieving an optimal trade-off among multiple objectives. Therefore, this method, based on a constrained multi-objective optimization approach, can reasonably set the capacity of the added PV system in the wind farm, maximize the synergistic effect of the wind farm and PV system, improve the overall utilization rate of renewable energy, and is applicable to power generation scenarios under various seasons and weather conditions. It improves the rationality and reliability of adding PV systems to existing wind farms, and is conducive to the stable and efficient operation of wind and solar power plants.

[0079] To achieve the above embodiments, this application also proposes a configuration system for adding photovoltaic systems to existing wind farms. Figure 3 This is a schematic diagram of the configuration system for adding a photovoltaic system to an existing wind farm, as proposed in an embodiment of this application.

[0080] like Figure 3 As shown, the system includes a first computing module 100, a second computing module 200, an optimization module 300, and a configuration module 400.

[0081] The first calculation module 100 is used to acquire wind and solar power generation resource data of the constructed wind farm and calculate the combined wind and solar power output power of the wind farm based on the wind and solar power generation resource data.

[0082] The second calculation module 200 is used to determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power and rated power of the wind-solar combined power generation, and calculate the photovoltaic curtailment utilization time based on the operation mode.

[0083] The optimization module 300 is used to calculate the rate of return of photovoltaic power generation based on the photovoltaic curtailment time. It takes the rate of return and the installed capacity of the photovoltaic system as different optimization objectives, and optimizes the configuration scheme of adding photovoltaic systems in wind farms through a multi-objective optimization algorithm with constraints. The constraints are the minimum completion indicators of each preset optimization objective.

[0084] The configuration module 400 is used to add photovoltaic systems of corresponding capacity to the wind farm based on the obtained optimization results, and to determine the control strategy of the wind-solar combined power station based on the operation mode.

[0085] Optionally, in one embodiment of this application, the second calculation module 200 is specifically used to: determine the wind power and photovoltaic curtailment output based on the relationship between the sum of the total output of the wind farm and the uncurtailed output of the photovoltaic system and the rated power of the main transformer; calculate the curtailment output of the photovoltaic system based on the wind power and photovoltaic curtailment output and the total output of the wind farm; and calculate the photovoltaic curtailment utilization time based on the curtailment output of the photovoltaic system and the capacity of the photovoltaic modules.

[0086] Optionally, in one embodiment of this application, the optimization module 300 is specifically used to: determine the construction cost and operation and maintenance cost of the photovoltaic system; and use the photovoltaic curtailment time and the construction cost and operation and maintenance cost of the photovoltaic system as independent variables to calculate the rate of return of photovoltaic power generation through a relevant rate of return function.

[0087] Optionally, in one embodiment of this application, the optimization module 300 is specifically used to: determine the cost of a unit photovoltaic module based on the construction cost and operation and maintenance cost of the photovoltaic system; and set thresholds among the minimum completion indicators corresponding to the installation capacity, rate of return, cost of a unit photovoltaic module, and fluctuation of the wind-solar combined power generation system of the photovoltaic system.

[0088] Optionally, in one embodiment of this application, the optimization module 300 is specifically used for: generating an initial population, wherein each individual in the population represents a configuration scheme for adding photovoltaic systems to a wind farm; calculating the function value of each individual for each optimization objective, and verifying whether each optimization objective meets the corresponding minimum completion index; performing non-dominated sorting and crowding calculation on the individuals based on the test results, and performing genetic operations on the individuals based on the sorting results and crowding calculation results to generate the next generation population; updating the initial population to the next generation population, and cyclically performing evaluation, verification, sorting, and genetic operations until the iteration stopping condition is met; selecting the target solution from the Pareto front obtained from the iteration, wherein the target solution is a configuration scheme that meets the requirements of multiple optimization objectives.

[0089] Optionally, in one embodiment of this application, the optimization module 300 is further configured to: set the weight coefficients corresponding to each optimization objective; and select the target solution from the Pareto front surface according to the weight coefficients of each optimization objective.

[0090] Optionally, in one embodiment of this application, the first calculation module 100 is specifically used to: calculate the output power of a single wind turbine in the wind farm at each hour of the year based on the wind speed of the wind farm at each hour of the year; calculate the output power of a unit photovoltaic module at each hour of the year based on the total horizontal irradiance of the wind farm at each hour of the year; and determine the combined wind and solar power output power of the wind farm based on the output power of a single wind turbine at each hour of the year and the output power of a unit photovoltaic module at each hour of the year.

[0091] It should be noted that the description of the configuration method for adding photovoltaic systems to existing wind farms described above also applies to the system in this embodiment, and the implementation principle is the same, so it will not be repeated here.

[0092] In summary, the configuration system for adding photovoltaic systems to existing wind farms according to the embodiments of this application, based on a constrained multi-objective optimization method, can reasonably set the capacity of the added photovoltaic system in the wind farm, maximize the synergistic effect between the wind farm and the photovoltaic system, improve the overall utilization rate of renewable energy, and is applicable to power generation scenarios under various seasons and weather conditions. It improves the rationality and reliability of adding photovoltaic systems to existing wind farms and is conducive to the stable and efficient operation of wind and solar power stations.

[0093] To implement the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the configuration method for adding photovoltaic systems to existing wind farms as described in the first aspect of the present application.

[0094] In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the use of illustrative expressions for the above terms in multiple embodiments or examples does not imply that these embodiments or examples are identical. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0095] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0096] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0097] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0098] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0099] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0100] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0101] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for configuring an existing wind farm to add a photovoltaic system, characterized in that, Includes the following steps: Obtain wind and solar power generation resource data of the constructed wind farm, and calculate the combined wind and solar power generation output power of the wind farm based on the wind and solar power generation resource data; Determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power of the wind-solar combined power generation and the rated power, and calculate the photovoltaic curtailment utilization time based on the operation mode; The rate of return of photovoltaic power generation is calculated based on the photovoltaic curtailment time. The rate of return and the installed capacity of the photovoltaic system are used as different optimization objectives. The configuration scheme of adding photovoltaic system in the wind farm is optimized by a multi-objective optimization algorithm with constraints. The constraints are the minimum completion indicators of each preset optimization objective. Based on the optimization results obtained, photovoltaic systems of corresponding capacity are added to the wind farm, and the control strategy of the wind-solar combined power station is determined based on the operating mode.

2. The configuration method according to claim 1, characterized in that, The operating mode is to maintain the normal operation of the wind farm and impose power curtailment on the photovoltaic system when the combined wind and solar power output exceeds the rated power. The calculation of the photovoltaic curtailment utilization time based on the operating mode includes: The curtailed power output of wind power and photovoltaic system is determined based on the relationship between the sum of the total output of wind farm and the uncurtailed output of photovoltaic system and the rated power of the main transformer. Calculate the curtailed power output of the photovoltaic system based on the curtailed power output of the wind power and the total power output of the wind farm; The photovoltaic curtailment utilization time is calculated based on the power output of the photovoltaic system and the capacity of the photovoltaic modules.

3. The configuration method according to claim 1, characterized in that, The calculation of the rate of return on photovoltaic power generation based on the photovoltaic curtailment period includes: Determine the construction and operation and maintenance costs of the photovoltaic system; Using the photovoltaic curtailment time, the construction cost, and the operation and maintenance cost of the photovoltaic system as independent variables, the rate of return of the photovoltaic power generation is calculated through a relevant rate of return function.

4. The configuration method according to claim 3, characterized in that, The optimization objectives of the multi-objective optimization algorithm also include: the cost per unit of photovoltaic module and the fluctuation of the wind-solar combined power generation system. Before optimizing the configuration scheme, it also includes: The cost of a unit photovoltaic module is determined based on the construction cost and operation and maintenance cost of the photovoltaic system. The threshold values ​​in the minimum completion indicators corresponding to the installed capacity of the photovoltaic system, the rate of return, the cost per unit of photovoltaic module, and the fluctuation of the wind-solar combined power generation system are set.

5. The configuration method according to claim 4, characterized in that, The optimization of the configuration scheme for adding photovoltaic systems to the wind farm using a constrained multi-objective optimization algorithm includes: An initial population is generated, wherein each individual in the population represents a configuration scheme for adding a photovoltaic system to the wind farm; Calculate the function value of each individual for each of the optimization objectives, and verify whether each of the optimization objectives meets the corresponding minimum completion index; Based on the test results, individuals are non-dominated ordination and crowding degree is calculated. Genetic operations are then performed on individuals based on the ordination results and crowding degree calculation results to generate the next generation population. The initial population is updated to the next generation population, and the evaluation, verification, sorting and genetic operations are performed cyclically until the iteration stopping condition is met. Select an objective solution from the Pareto front obtained through iteration, wherein the objective solution is a configuration scheme that satisfies the requirements of multiple optimization objectives.

6. The configuration method according to claim 5, characterized in that, Before optimizing the configuration scheme of adding photovoltaic systems to the wind farm using a constrained multi-objective optimization algorithm, the method further includes: Set the weight coefficients corresponding to each of the optimization objectives; The selection of the target solution from the Pareto front obtained through iteration includes: The objective solution is selected from the Pareto front based on the weight coefficients of each of the optimization objectives.

7. The configuration method according to claim 1, characterized in that, The wind and solar power resource data includes the wind speed and total horizontal irradiance of the wind farm at each hour throughout the year. The calculation of the combined wind and solar power output of the wind farm based on the wind and solar power resource data includes: Based on the wind speed of the wind farm at each hour throughout the year, calculate the output power of a single wind turbine in the wind farm at each hour throughout the year; Calculate the output power of a unit photovoltaic module for each hour of the year based on the total horizontal irradiance of the wind farm at each hour throughout the year; The combined wind and solar power output of the wind farm is determined based on the output power of the single wind turbine at each hour throughout the year and the output power of the unit photovoltaic module at each hour throughout the year.

8. A configuration system for adding photovoltaic systems to existing wind farms, characterized in that, include: The first calculation module is used to acquire wind and solar power generation resource data of the constructed wind farm, and calculate the combined wind and solar power generation output power of the wind farm based on the wind and solar power generation resource data. The second calculation module is used to determine the rated power of the main transformer of the wind farm, set the operation mode of the wind-solar combined power station according to the output power of the wind-solar combined power generation and the rated power, and calculate the photovoltaic curtailment utilization time based on the operation mode. The optimization module is used to calculate the rate of return of photovoltaic power generation based on the photovoltaic curtailment time, and to use the rate of return and the installed capacity of the photovoltaic system as different optimization objectives. The module optimizes the configuration scheme of adding photovoltaic systems in the wind farm through a multi-objective optimization algorithm with constraints, wherein the constraints are the minimum completion indicators of each preset optimization objective. The configuration module is used to add photovoltaic systems of corresponding capacity to the wind farm based on the obtained optimization results, and to determine the control strategy of the wind-solar combined power station based on the operating mode.

9. The configuration system according to claim 8, characterized in that, The second calculation module is specifically used for: The curtailed power output of wind power and photovoltaic system is determined based on the relationship between the sum of the total output of wind farm and the uncurtailed output of photovoltaic system and the rated power of the main transformer. Calculate the curtailed power output of the photovoltaic system based on the curtailed power output of the wind power and the total power output of the wind farm; The photovoltaic curtailment utilization time is calculated based on the power output of the photovoltaic system and the capacity of the photovoltaic modules.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the configuration method for adding photovoltaic systems to existing wind farms as described in any one of claims 1-7.

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