A new energy station operation control optimization method, system and device

Abstract

The application relates to the technical field of station operation control, and discloses a new energy station operation control optimization method, system and equipment, which comprises the following steps: performing stability analysis on the output power of each new energy station in a target area to obtain the characteristic types of the new energy stations; controlling each new energy station to execute the time-sharing output plan output by an output planning model; performing real-time analysis on the load demand curve of the target area when the current output plan in the time-sharing output plan is executed, and calculating the power supply capacity margin of the target area; and analyzing the power supply capacity margin, and if the analysis result meets the scheduling condition, correcting the next time period output plan according to the resource scheduling quantity. Through the output planning of regional centralized control and the scheduling strategy of various resource sharing containing energy storage resources, the application improves the stability of new energy grid connection, the reliability of resource scheduling, ensures the balance between regional power supply and demand, and improves the safety and stability of power grid operation.

Description

Technical Field

[0001] This invention relates to the field of power station operation control technology, and in particular to an operation control optimization method, system and equipment for new energy power stations. Background Technology

[0002] New energy power plants play a crucial role in energy transition and low-carbon development. As an important carrier of clean energy supply, they are directly related to the stability and sustainability of the power system. With the development of regional economies and the increasing complexity of power system structures, multiple power plants exist within a regional power grid to ensure the region's power supply. These power plants include both non-new energy power plants and new energy power plants.

[0003] Currently, the power output planning of most new energy power plants is based on the planning of a single power plant, without taking into account the impact of other power plants in the region on regional power supply. This one-sidedness makes it difficult for new energy power plants in the region to form an overall optimal system, which is not conducive to the long-term stable operation of new energy power plants. Furthermore, the lack of overall consideration of various power resources during resource scheduling affects the stability of power operation in the region. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method, system, and equipment for optimizing the operation control of new energy power plants. By implementing a regional centralized output control and resource sharing scheduling strategy for new energy power plants, the invention aims to reduce output fluctuations at new energy power plants, improve the stability of new energy grid connection, and ensure a balance between regional power supply and demand.

[0005] In a first aspect, the present invention provides an operation control optimization method for renewable energy power stations, applicable to a target area with both renewable energy power stations and non-renewable energy power stations, the method comprising:

[0006] Stability analysis was performed on the output power of each new energy power station within the target area to obtain the characteristic type of each new energy power station;

[0007] Each of the aforementioned renewable energy power plants is controlled to execute a time-of-use power output plan output by a power output planning model, wherein the objective function of the power output planning model is used to indicate minimizing power output fluctuations, maximizing power output efficiency, and minimizing wind and solar curtailment rates, and the constraints of the power output planning model are obtained based on all the aforementioned characteristic types; and, when executing the current power output plan in the time-of-use power output plan, the load demand curve of the target area is analyzed in real time to calculate the power supply capacity margin of the target area; and, the power supply capacity margin is analyzed, and if the analysis result meets the scheduling conditions, the power output plan for the next time period in the time-of-use power output plan is modified according to the determined resource scheduling amount.

[0008] Furthermore, the step of performing stability analysis on the output power of each new energy power station within the target area to obtain the characteristic type of each new energy power station includes:

[0009] Calculate the power fluctuation rate based on the output power of each new energy power station in the target area;

[0010] Based on the comparison relationship between the power volatility and the volatility threshold, the characteristic type of the new energy power station is determined, and the characteristic type includes stable type and volatile type.

[0011] Furthermore, the steps for constructing the constraints include:

[0012] Calculate the output weighting coefficient of each new energy power station based on the characteristic type and the power fluctuation rate;

[0013] Based on the output weighting coefficient, the maximum output value of the station is corrected, and based on the minimum output value of the station and the corrected maximum output value of the station, the constraint conditions based on the output value of the station are obtained.

[0014] Furthermore, the step of performing real-time analysis on the obtained load demand curve of the target area and calculating the power supply capacity margin of the target area includes:

[0015] Based on the obtained load demand curve of the target area, the load demand of the target area in the next time period is obtained;

[0016] Based on the obtained power output plans for the next time period of the non-new energy power stations and the power output plans for the next time period of the new energy power stations, the power supply capacity of the target area for the next time period is obtained.

[0017] The difference between the power supply capacity for the next time period and the load demand for the next time period is used as the power supply capacity margin of the target area.

[0018] Furthermore, the step of revising the output plan for the next time period in the time-sharing output plan based on the determined resource scheduling amount includes:

[0019] The resource scheduling amount is determined based on the preset resource sharing pool and the power supply capacity margin;

[0020] Based on the scheduling strategy and the resource scheduling amount, the output plan for the next time period in the time-sharing output plan is modified.

[0021] Furthermore, the construction steps of the preset resource sharing pool include:

[0022] Based on the current adjustable capacity of various power resources of each regional power station in the target area, an initial resource sharing pool is constructed. The regional power stations include new energy power stations and non-new energy power stations, and the power resources include new energy resources, non-new energy resources and energy storage resources.

[0023] Based on the historical operating status of the power plants in the region, calculate the reliability coefficients of various power resources of the power plants in the region;

[0024] Calculate the resource mobilization index based on the current adjustable capacity, the reliability coefficient, and the preset response time coefficient;

[0025] Based on the comparison relationship between the resource call index and the index threshold, the initial resource sharing pool is updated to obtain the resource sharing pool.

[0026] Furthermore, after the step of obtaining the resource sharing pool, the method further includes:

[0027] The resource sharing pool is updated according to preset resource exit conditions, which include output fluctuation conditions of new energy resources, reliability coefficient conditions of non-new energy resources, and state of charge conditions of energy storage resources.

[0028] Furthermore, the step of revising the output plan for the next time period in the time-sharing output plan according to the scheduling strategy and the resource scheduling amount includes:

[0029] Based on the preset scheduling priorities and calling conditions, scheduling resources are selected from the resource sharing pool to obtain the scheduling resource sequence corresponding to each scheduling priority;

[0030] Based on the preset priority scheduling weight and the resource scheduling amount, the actual scheduling amount of each scheduling resource in each scheduling resource sequence is calculated sequentially.

[0031] Based on the actual scheduling volume, the output plan for the next time period in the time-sharing output plan is revised.

[0032] Secondly, the present invention provides an operation control optimization system for new energy power stations, applied in a target area with both new energy power stations and non-new energy power stations, the system comprising:

[0033] The stability analysis module is used to perform stability analysis on the output power of each new energy power station in the target area and obtain the characteristic type of each new energy power station.

[0034] The power output plan execution module is used to control each of the new energy power plants to execute the time-of-use power output plan output by the power output planning model. The objective function of the power output planning model is used to indicate the minimization of power output fluctuation, the maximization of power output efficiency, and the minimization of wind and solar curtailment rate. The constraints of the power output planning model are obtained based on all the characteristic types. When executing the current power output plan in the time-of-use power output plan, the module performs real-time analysis on the load demand curve of the target area to calculate the power supply capacity margin of the target area. The module also analyzes the power supply capacity margin. If the analysis result meets the scheduling conditions, the module corrects the power output plan for the next time period in the time-of-use power output plan according to the determined resource scheduling amount.

[0035] Thirdly, embodiments of the present invention also provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0036] This invention provides a method, system, and equipment for optimizing the operation and control of renewable energy power plants. Based on the stability of renewable energy power plants, this invention performs regional centralized control output planning, which effectively reduces the probability of fluctuations in renewable energy output, thereby improving the stability of renewable energy grid connection. Through a resource scheduling strategy based on a resource sharing pool, it comprehensively schedules multiple resources, improving the timeliness and reliability of resource scheduling, thus ensuring the balance of regional power supply and demand, and further enhancing the safety and stability of power grid operation. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the operation control optimization method for new energy power stations in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the operation control optimization system for new energy power stations in an embodiment of the present invention;

[0039] Figure 3 This is an internal structural diagram of the computer device in an embodiment of the present invention.

[0040] Figure label:

[0041] 10. Stability analysis module; 20. Output plan execution module. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 The first embodiment of the present invention proposes an operation control optimization method for new energy power stations, which is applied to a target area with both new energy power stations and non-new energy power stations, and includes steps S10 to S20:

[0044] Step S10: Perform stability analysis on the output power of each new energy power station in the target area to obtain the characteristic type of each new energy power station;

[0045] Step S20: Control each of the new energy power plants to execute the time-of-use power output plan output by the power output planning model, wherein the objective function of the power output planning model is used to indicate minimizing power output fluctuations, maximizing power output efficiency, and minimizing wind and solar curtailment rates, and the constraints of the power output planning model are obtained based on all the characteristic types; and, when executing the current power output plan in the time-of-use power output plan, perform real-time analysis on the load demand curve of the target area to calculate the power supply capacity margin of the target area; and, analyze the power supply capacity margin, and if the analysis result meets the scheduling conditions, then revise the power output plan for the next time period in the time-of-use power output plan according to the determined resource scheduling amount.

[0046] In this invention, a new energy power station refers to the collection of all equipment below the grid connection point of a wind farm or solar power station centrally connected to the power system, including transformers, busbars, lines, converters, energy storage, wind turbines, photovoltaic power generation equipment, reactive power regulation equipment, and auxiliary equipment. New energy deployments are characterized by numerous locations, wide distribution, and dispersed capacity, posing certain scheduling challenges for dynamic coordination of power generation, grid, load, and storage. Therefore, centralized management and remote scheduling of new energy power station clusters within a certain area can improve the accuracy and timeliness of remote control command execution, as well as the security and compliance of control strategies. Based on the above reasons, this invention provides a method for regional centralized control of new energy power stations to optimize their operational output.

[0047] In this invention, the power output of new energy power plants is first planned through regional centralized control. Furthermore, the stability of each new energy power plant is incorporated into the planning model during the power output planning process. The stability of each new energy power plant is determined based on its output power. Specific steps include:

[0048] Calculate the power fluctuation rate based on the output power of each new energy power station in the target area;

[0049] Based on the comparison relationship between the power volatility and the volatility threshold, the characteristic type of the new energy power station is determined, and the characteristic type includes stable type and volatile type.

[0050] This embodiment analyzes the stability of all new energy power stations within the target area. These power stations include wind farms and solar power plants, among others. The output of these power stations is directly affected by weather conditions; changes in weather conditions, such as wind and solar power, cause fluctuations in their output. The severity of weather changes varies across regions, leading to different levels of output fluctuations in power stations within those regions. Therefore, this embodiment analyzes the stability of each power station separately. For consistency, wind farms and solar power plants are collectively referred to as "new energy power stations," meaning each power station is either a wind farm or a solar power plant.

[0051] Specifically, the output power of a new energy power plant is obtained over multiple preset time periods. The power fluctuation rate for each time period is calculated based on the output power. The power fluctuation rate is the quotient of the difference between the maximum and minimum power values ​​during that time period and the average power value. Then, the power fluctuation rate for that time period is compared with a preset fluctuation threshold. If it is lower than the fluctuation threshold, the power plant's output is considered to meet the stability requirements; otherwise, the power plant's output is considered to have large fluctuations. Based on the comparison results, the characteristic type of the power plant can be determined. The characteristic types include stable and fluctuating. Since different types of power stations have different requirements for power fluctuation rate, this implementation sets corresponding fluctuation thresholds according to the power station type to ensure the accuracy of the stability analysis results for each type of power station. It should be noted that due to the diversity of weather changes, the stability analysis of the power stations is continuous, that is, the characteristic type of the power station changes according to the current power output. In order to ensure the accuracy of the analysis results, in this embodiment, a power station whose power fluctuation rate exceeds the fluctuation threshold for two consecutive time periods can be regarded as a fluctuating power station, or a power station whose power fluctuation rate exceeds the fluctuation threshold for more than a preset percentage of time periods, such as more than 60% of the time periods, can be regarded as a fluctuating power station. The specific settings can be set according to the actual situation. This is only a preferred method and not a specific limitation.

[0052] After obtaining the characteristic types of each power station, a regional centralized power output plan is performed on the new energy power stations in the target area according to the power output planning model. Specifically, in this embodiment, the power output planning model is a time-segmented power output planning model, including an objective function and constraints. The objective function is a multi-objective optimization function, with multiple optimization objectives being the minimization of power output fluctuation, the maximization of power output efficiency, and the minimization of wind and solar curtailment rates. Power output fluctuation is represented by the power output difference between adjacent time periods, and power output efficiency is represented by the ratio between the power output value of that time period and the rated power output value of that time period. The curtailment rate refers to the ratio of the amount of electricity (curtailed wind or solar power) that cannot be utilized and is discarded during wind or solar power generation due to insufficient grid absorption or problems with the power station itself, to the theoretical power generation. The discarded electricity is equal to the difference between the theoretical power generation and the actual power generation. The curtailment rate reflects the stability and reliability of power generation at new energy power plants. The lower the curtailment rate, the higher the efficiency of the power plant. The optimization targets of power output efficiency and curtailment rate actually implicitly include the limitations of weather changes and grid absorption changes on new energy power generation at different times.

[0053] The constraints of the power output planning model include power output constraints, node voltage amplitude constraints, line transmission power constraints, and energy storage scale constraints. Among them, the power output constraint means that the power output of each power station is constrained by the minimum and maximum power output values. The node voltage amplitude constraint means that the voltage amplitude of any node in the power station is constrained by the minimum and maximum voltage amplitude values. The line transmission power constraint means that the power of the transmission line cannot exceed the maximum line power value. The energy storage scale constraint means whether the power station has energy storage devices and the available storage capacity of the energy storage devices.

[0054] Based on the aforementioned objective function and constraints, solving them yields the time-of-use power output plan for the power plants. In reality, fluctuations in the power output of renewable energy power plants can impact the grid-connected power. Therefore, this embodiment, when planning power output, limits the power plant output based on its current stability level, thereby further ensuring the stability of the grid-connected power. Specifically, limiting the power plant output based on its stability level is mainly reflected in the output constraints, which include:

[0055] Calculate the output weighting coefficient of each new energy power station based on the characteristic type and the power fluctuation rate;

[0056] Based on the output weighting coefficient, the maximum output value of the station is corrected, and based on the minimum output value of the station and the corrected maximum output value of the station, the constraint conditions based on the output value of the station are obtained.

[0057] In this embodiment, the output weighting coefficient of each renewable energy power station is first determined according to its characteristic type. Assuming the initial value of the output weighting coefficient is 1, for stable power stations, whose output stability is high, the initial value can be used. For fluctuating power stations, whose output fluctuates significantly, the output weighting coefficient can be corrected based on the previously calculated power fluctuation rate. Specifically, the difference between the power fluctuation rate and the fluctuation threshold is used as the correction amount. The initial value is subtracted from the correction amount to obtain the corrected output weighting coefficient. The output weighting coefficient of each power station is used to adjust the maximum output value of the power station. Assuming that a power station's calculated power fluctuation rate is 20% during the current stability analysis, exceeding the fluctuation threshold by 10%, and the correction amount is 0.2 - 0.1 = 0.1, then the corrected output weighting coefficient is 1 - 0.1 = 0.9. Using the rated capacity of the power station as the initial maximum output value, the corrected maximum output value is 0.9 * rated capacity. The output weighting coefficient can directly limit the upper limit of power output of fluctuating power stations, thereby reducing the impact of single-station fluctuations and ensuring grid connection stability.

[0058] In a preferred embodiment, to further ensure the stability of power grid operation, the present invention sets a buffer ratio for the power station based on the power fluctuation rate, and further adjusts the maximum output value of the power station according to the buffer ratio. Specifically, the product of the power fluctuation rate and the preset ratio is used as the buffer ratio. For example, if the preset ratio is 50%, and the power fluctuation rate is 20%, then the buffer ratio is 20% * 50% = 10%. Then, the product of the current maximum output value and the buffer ratio is used as the safety redundancy, and the current maximum output value is subtracted from the safety redundancy to obtain the corrected maximum output value. In this embodiment, the maximum output correction based on the safety redundancy can be a second correction based on the maximum output value corrected by the output weight coefficient, or the safety redundancy can be used alone to correct the initial maximum output value. Taking the second correction as an example, with the rated capacity of the power station as the initial maximum output value, the maximum output value after correction by the output weight coefficient and the safety redundancy can be expressed as output weight coefficient * rated capacity * (1 - buffer ratio). In this embodiment, by forcibly reserving emergency space for power stations with high fluctuation levels through safety redundancy, it is possible to effectively avoid power output exceeding limits for fluctuating power stations.

[0059] It is important to note that power output efficiency is expressed as effective power generation. The main reason for wind and solar power curtailment is the misalignment between peak power output and peak load. When the grid's absorption capacity is insufficient (e.g., transmission channel congestion, limited load demand), simply pursuing maximum efficiency may lead to increased wind and solar power curtailment. To achieve synergistic optimization of efficiency and power curtailment, this embodiment can solve the power output planning model in two ways. One is to use multi-objective optimization algorithms, such as non-dominated sorting genetic algorithms, particle swarm optimization algorithms, and simulated annealing algorithms, to solve the power output planning model, thereby obtaining a set of Pareto optimal solutions. Then, according to different time periods, the corresponding solutions are selected from the Pareto optimal solution set to obtain the time-of-use power output plan for each renewable energy plant. For example, during peak load periods when grid absorption is ample, output efficiency is selected as the optimal parameter for solution set selection. During off-peak periods when grid absorption is strained, wind and solar curtailment rates are selected as the optimal parameter for solution set selection, thus achieving optimal output planning for different time periods. Another approach is to normalize each optimization objective, converting it into a dimensionless representation, and using negative numbers to convert maximization into minimization. Then, weights are set for each optimization objective, and a linear combination is performed to convert the multi-objective optimization function into a single-objective optimization function. The weights of each normalized parameter are then adjusted according to different time periods. For example, during periods of ample grid absorption, the weight of output efficiency is increased, while during periods of strained absorption, the weight of wind and solar curtailment rates is increased. By considering the specific grid conditions at different time periods, the optimal output plan for that time period is selected, thus obtaining the optimal output of new energy power plants at different time periods, i.e., the time-of-use output plan. The specific solution steps for the output planning model can refer to the solution steps of conventional single-objective or multi-objective optimization algorithms, which will not be elaborated here.

[0060] This embodiment improves the rationality of power output planning for new energy power plants by conducting regional centralized power output planning and analyzing the correlation between power plants within the region, thereby ensuring the safety and stability of power output from new energy power plants.

[0061] In a preferred embodiment, based on the multiple optimization objectives of the aforementioned power output planning model, this embodiment also considers the impact of the spot market on power plant output, taking maximizing power plant revenue as a new optimization objective. That is, the objective function of the power output planning model is constructed based on minimizing power output fluctuations, maximizing power output efficiency, minimizing wind and solar curtailment rates, and maximizing power plant revenue. Specifically, power plant revenue is represented by the difference between actual power output revenue and cost power output revenue. Actual power output revenue is the product of the output value and the actual electricity price, while cost power output revenue is the product of the output value and the cost electricity price. Specifically, the cost electricity price refers to the levelized cost per kilowatt-hour (LCOE) of the renewable energy power plant. The LCOE is the generation cost calculated by first leveling the costs and generation over the project's lifecycle, i.e., the present value of costs over the lifecycle / the present value of generation over the lifecycle. The actual electricity price refers to the day-ahead market bid price in the electricity spot market, for example, using the peak electricity price of the current month as the upper limit of the bid price, while the lower limit is implemented according to the parameters stipulated by the competent authority. In this embodiment, the price factor of the spot market is incorporated into the objective function to further optimize the power output plan of the power station, thereby improving the overall revenue of new energy power stations in the region while ensuring the stability of power supply.

[0062] By solving the above power output planning model, the time-of-use power output plan of each new energy power station in the target area can be obtained. The time-of-use power output plan is then converted into power station control commands and sent to each power station for execution. Preferably, during the execution process, the local optimal power output can be re-solved for that period based on the intraday spot price update data accessed every 15 minutes, and the power output allocation for that period can be adjusted, while other periods remain unchanged.

[0063] Due to changes in external loads, the power system needs to schedule power resources according to changes in power supply and demand. Therefore, the output plan of new energy power plants is not fixed, but is adjusted in a timely manner according to changes in load demand. When conducting output adjustment analysis, it is necessary to combine the output of all power plants in the target area for unified scheduling to ensure the balance of power grid supply and demand.

[0064] In this embodiment, when performing resource scheduling, it is necessary to analyze the power supply capacity of new energy power stations and non-new energy power stations within the target area in conjunction with the load demand of the target area. The power supply capacity margin of the target area is calculated by combining the power supply capacity and load demand. The specific steps include:

[0065] Based on the obtained load demand curve of the target area, the load demand of the target area in the next time period is obtained;

[0066] Based on the obtained power output plans for the next time period of the non-new energy power stations and the power output plans for the next time period of the new energy power stations, the power supply capacity of the target area for the next time period is obtained.

[0067] The difference between the power supply capacity for the next time period and the load demand for the next time period is used as the power supply capacity margin of the target area.

[0068] In this embodiment, the load demand for the next time period is first obtained based on the load demand curve of the target area. Preferably, the load demand curve of the target area is predicted by a load forecasting model, which can be constructed based on a neural network model, such as a long short-term memory neural network model. For the trained load forecasting model, real-time regional load parameters of the target area are obtained, including time parameters, meteorological parameters, and historical load data. Then, the regional load data is converted into load features, including time features (such as minutes, weekday / weekend, and holiday markings), meteorological features (such as temperature, humidity, and wind speed), and load features (such as the proportion of industrial electricity consumption and the growth rate of residential electricity consumption). The load features, after normalization and other preprocessing, are input into the load forecasting model to obtain the load demand curve. Since the shorter the time span predicted by the load forecasting model, the more accurate the prediction result, in this embodiment, the load demand curve for the next time period is selected according to the time period division of the time-sharing power output plan to calculate the load demand of the target area for the next time period.

[0069] Then, based on the next-period output plans of all renewable energy power plants and all non-renewable energy power plants within the target area, the next-period power supply capacity of all power plants within the target area is obtained. Non-renewable energy power plants include thermal power plants and pumped storage hydropower stations. In this embodiment, the output plans of non-renewable energy power plants are used as known data, and their output plans can be obtained from the regional power grid dispatch center. Finally, the difference between the next-period power supply capacity and the next-period load demand is used as the power supply capacity margin of the target area. To ensure the accuracy of the power supply capacity margin, the load demand curve used for each power supply capacity margin calculation needs to be re-predicted. It should be noted that the next-period output plan in this embodiment does not specifically refer to the next period adjacent to the current period in the time-sharing output plan. Depending on changes in external load and limitations in calculation efficiency, the period used for calculating the power supply capacity margin can be the next period adjacent to the current period or the next period several periods away from the current period. The specific period selection can be determined based on the actual situation.

[0070] After calculating the power supply capacity margin, the power supply capacity margin is compared with the preset margin threshold to determine whether the power supply capacity meets the load demand. In order to ensure the stability of the power supply, a buffer space needs to be reserved. The margin threshold is set according to the percentage of the load demand, for example, the margin threshold is 5% of the load demand in the next period. Furthermore, the margin threshold can be flexibly adjusted for the load demand in different periods. For example, in extreme weather conditions, the margin threshold can be increased to cope with sudden load fluctuations.

[0071] When the predicted power supply capacity margin for the next time period is less than the margin threshold, in order to avoid power shortages or insufficient power supply and ensure stable power supply, resource scheduling is required. Based on the determined resource scheduling amount, the power output plan for the next time period in the time-sharing power output plan is revised. The specific steps include:

[0072] The resource scheduling amount is determined based on the preset resource sharing pool and the power supply capacity margin;

[0073] Based on the scheduling strategy and the resource scheduling amount, the output plan for the next time period in the time-sharing output plan is modified.

[0074] This embodiment determines the resource scheduling amount through a preset resource sharing pool and power supply capacity margin. First, the resource sharing pool is described, and the construction steps of the resource sharing pool include:

[0075] Based on the current adjustable capacity of various power resources of each regional power station in the target area, an initial resource sharing pool is constructed. The regional power stations include new energy power stations and non-new energy power stations, and the power resources include new energy resources, non-new energy resources and energy storage resources.

[0076] Based on the historical operating status of the power plants in the region, calculate the reliability coefficients of various power resources of the power plants in the region;

[0077] Calculate the resource mobilization index based on the current adjustable capacity, the reliability coefficient, and the preset response time coefficient;

[0078] Based on the comparison relationship between the resource call index and the index threshold, the initial resource sharing pool is updated to obtain the resource sharing pool.

[0079] In this embodiment, the resource sharing pool is constructed based on the capacities of all regional power stations within the target area. Here, the regional power stations include new energy power stations and non-new energy power stations. If there are independent energy storage power stations within the target area, the regional power stations can also include independent energy storage power stations. Since the power generation of power stations can be divided into real-time power generation and energy storage power generation, the power resources can be divided into new energy resources, non-new energy resources, and energy storage resources according to the attribute types of power resources. Among them, new energy resources refer to the power resources corresponding to the real-time power generation devices of new energy power stations, non-new energy resources refer to the power resources corresponding to the real-time power generation devices of non-new energy power stations, and energy storage resources refer to the power resources corresponding to the energy storage devices supporting each new energy power station and non-new energy power station. If there are independent energy storage power stations, the energy storage resources also include the power resources corresponding to the independent energy storage power stations.

[0080] According to the maximum adjustable capacity and real-time capacity of various types of power resources, determine the current adjustable capacity of various types of power resources of the regional power stations. For new energy resources and non-new energy resources, the maximum adjustable capacity is the maximum output when the power station is fully loaded, and the real-time capacity is the current actual output. The difference between the two is the current adjustable capacity. Of course, for new energy power stations, the maximum adjustable capacity also needs to consider weather factors and time period factors. For energy storage resources, the current adjustable capacity is determined according to its state of charge. Then, based on the current adjustable capacity of various types of power resources of the regional power stations, construct an initial resource sharing pool. It can be understood that the initial resource sharing pool includes regional power stations, various types of power resources, and the corresponding current adjustable capacity.

[0081] Then, according to the historical operation status of each regional power station, calculate the reliability coefficient of various types of power resources of each regional power station. The historical operation status is mainly used to judge whether various types of power resources have experienced failures. If there are no failures within a preset historical period, such as the last 30 days, the reliability coefficient is 1. If there have been failures, the coefficient is reduced each time a failure occurs. For example, it is reduced by 0.2 for each failure, so as to obtain the reliability coefficient of various types of power resources of each regional power station. At the same time, according to the different attribute types of power resources, response time coefficients for various types of power resources are preset. For example, energy storage resources can achieve rapid response to resource scheduling, so their response time coefficients are relatively high. Among non-new energy resources, the response times of different energy sources are also different. For example, the response times of water turbine units, gas turbine units, fuel turbine units, and coal-fired turbine units decrease in sequence. Although new energy resources do not have as short a response time as energy storage resources, their response speed is higher than that of non-new energy resources. Therefore, based on the response speeds of various types of power resources, corresponding response time coefficients are set. Preferably, the response time coefficient of the energy storage device is set to 1, new energy is set to 0.9, and hydropower, gas, fuel, and coal-fired turbine units are set to 0.8, 0.7, 0.6, and 0.4 respectively.

[0082] Then, the resource call index of each type of power resource is obtained by multiplying its current adjustable capacity, reliability coefficient, and response time coefficient in sequence. If the resource call index is less than the index threshold, the resource is regarded as an inefficient resource and removed from the initial resource sharing pool. It can be seen that since the resource call index is a product relationship, the main parameter is the current adjustable capacity, and the current adjustable capacity is corrected according to the reliability coefficient and response time coefficient to ensure that the resources in the resource sharing pool have high reliability, high response speed and sufficient adjustable capacity.

[0083] To further ensure the stability of power grid operation, preferably, the resources in the resource sharing pool can also be updated in real time. Specific steps include:

[0084] The resource sharing pool is updated according to preset resource exit conditions, which include output fluctuation conditions of new energy resources, reliability coefficient conditions of non-new energy resources, and state of charge conditions of energy storage resources.

[0085] In this embodiment, corresponding resource exit conditions are pre-set for different types of power resources. Based on these conditions, the exit criteria for various power resources in the resource sharing pool are determined. Specifically, for new energy resources, the exit condition is output fluctuation, represented by the output power fluctuation rate. If the power fluctuation rate exceeds a pre-set threshold (e.g., 15%) for several consecutive periods, the resource is removed from the resource sharing pool. For non-new energy resources, the exit condition is the reliability coefficient. If the reliability coefficient is less than a threshold (e.g., less than 0.5), the resource is removed from the resource sharing pool. For energy storage resources, the exit condition is the state of charge (SOC) of the energy storage device. For example, energy storage devices with an SOC of less than 30% are removed from the resource sharing pool. Additionally, charging and discharging efficiency can also be used as an exit condition; for example, energy storage devices with a charging and discharging efficiency of less than 80% will be removed from the resource sharing pool. In this embodiment, the resource status in the resource sharing pool is updated in real-time based on a preset time step, such as refreshing the resource status every 5 minutes, to ensure the timeliness and effectiveness of various power resources in the resource sharing pool. It should be noted that the resource exit conditions in this embodiment are only preferred conditions. Other exit conditions can be set according to the actual situation of different regions, and no specific limitation is made here.

[0086] For the aforementioned resource sharing pool, this embodiment determines the required resource scheduling amount based on the comparison relationship between the power supply capacity margin and the margin threshold. The required resource scheduling amount is the margin threshold minus the power supply capacity margin. The resource scheduling amount extracted from the resource sharing pool is the minimum value between the required resource scheduling amount and the total adjustable capacity in the pool. If the required resource scheduling amount exceeds the total adjustable capacity in the pool, the excess part needs to be scheduled across regions. This embodiment only describes the part scheduled within the region.

[0087] After obtaining the resource allocation amount, resources can be allocated from the resource sharing pool according to the preset allocation strategy. If the resource allocation involves renewable energy power plants, the next time period output plan in the time-of-use output plan of the renewable energy power plants is revised according to the allocation results. In this embodiment, the allocation strategy is based on allocation priority settings. First, different priorities are set for different types of power resources. Specifically, energy storage resources have the characteristics of fast response and zero carbon emissions, so they have the first priority. Non-renewable energy resources have the second priority, and renewable energy resources have the third priority due to their intermittent and highly volatile characteristics. When retrieving resources from the resource sharing pool, energy storage resources can be selected first for scheduling according to scheduling priority. During scheduling, energy storage resources are selected sequentially according to their resource call index. If energy storage resources cannot meet the resource scheduling needs, non-new energy resources are selected sequentially from non-new energy resources according to their resource call index to continue allocating the remaining resource scheduling amount. If non-new energy resources still cannot meet the resource scheduling needs and there is still a remaining scheduling amount, new energy resources are selected sequentially from new energy resources according to their resource call index to continue allocating the resource scheduling amount. If a new energy resource is scheduled, the next time period output plan of the corresponding new energy power station is adjusted based on the allocated resource scheduling amount.

[0088] In a preferred embodiment, considering the peak-shaving situation of renewable energy output and electricity load, such as when photovoltaic output is at its peak and electricity load is at its trough in the afternoon, this embodiment sets the priority of various power resources according to the load status. During the trough period, renewable energy resources are set as the first priority, energy storage resources as the second priority, and non-renewable energy resources as the third priority. In other periods, energy storage resources are set as the first priority, non-renewable energy resources as the second priority, and renewable energy resources as the third priority. Then, resources are scheduled according to the order of scheduling priority and the order of resource call index under the same priority. Based on the scheduling results, it is determined whether the output plan of the renewable energy power station for the next period needs to be revised.

[0089] In another preferred embodiment, to avoid over-reliance on a single resource and disperse the probability of failure, this embodiment also provides another resource scheduling method, the specific steps of which include:

[0090] Based on the preset scheduling priorities and calling conditions, scheduling resources are selected from the resource sharing pool to obtain the scheduling resource sequence corresponding to each scheduling priority;

[0091] Based on the preset priority scheduling weight and the resource scheduling amount, the actual scheduling amount of each scheduling resource in each scheduling resource sequence is calculated sequentially.

[0092] Based on the actual scheduling volume, the output plan for the next time period in the time-sharing output plan is revised.

[0093] In this embodiment, scheduling priorities are first set, with energy storage resources as the first priority, non-new energy resources as the second priority, and new energy resources as the third priority. Different calling conditions are set for power resources with different scheduling priorities. According to the calling conditions, the scheduling resource sequence corresponding to each scheduling priority is selected. Preferably, for energy storage resources, the calling conditions are the state of charge and charge / discharge efficiency, such as a state of charge greater than 70% and a charge / discharge efficiency greater than 85%; for non-new energy resources, the calling conditions are the unit ramp rate and current output, such as a unit ramp rate greater than or equal to 20% / min and the ratio of current output to maximum output less than 60%; for new energy resources, the calling condition is output fluctuation, such as a power fluctuation rate less than or equal to 5% within two consecutive cycles. Based on the calling conditions, scheduling resources under each scheduling priority are selected from the resource sharing pool, and combined with the resource calling index, the scheduling resource sequence corresponding to each scheduling priority is obtained.

[0094] To avoid over-reliance on a single resource, this embodiment pre-sets a corresponding priority scheduling weight for each scheduling priority, with a, b, and c representing the priority scheduling weights of the first priority, second priority, and third priority, respectively, and a+b+c=1. Preferably, a is 0.5, b is 0.4, and c is 0.1. Priority dispatch weights are used to limit the upper limit of power resource dispatch under different dispatch priorities. The value obtained by multiplying the priority dispatch weight by the resource dispatch amount is the upper limit of power resource dispatch under that priority. Furthermore, the upper limit of power resource dispatch under that priority is also limited by the total adjustable capacity of the dispatch resource sequence. Therefore, for the dispatch resource sequence corresponding to the first priority, its dispatch upper limit is the minimum value between the total adjustable capacity of the energy storage resource sequence and a*resource dispatch amount; for the second priority, it is the minimum value between the total adjustable capacity of the non-new energy resource sequence and b*resource dispatch amount; and for the third priority, it is the minimum value between the total adjustable capacity of the new energy resource sequence and c*resource dispatch amount. Based on the dispatch upper limit of each dispatch resource sequence, dispatch amounts are allocated sequentially to each power resource according to the current adjustable capacity of each power resource in each sequence, resulting in the actual dispatch amount of each dispatch resource in the sequence. For the dispatch resource sequence of new energy resources, the output plan for the next time period in the time-sharing output plan of the corresponding new energy power station is revised according to the actual dispatch amount of each new energy resource in the sequence.

[0095] Furthermore, to ensure that resource allocation is fully utilized, this embodiment employs an iterative approach for resource allocation based on the scheduled resource sequence. First, a first round of allocation is performed according to the steps described above. If all resource allocation cannot be completed in the first round—for example, if a scheduled resource sequence is empty or the total adjustable capacity of the scheduled resource sequence is less than the product of the priority scheduling weight and the resource allocation—then it is determined whether any of the three scheduled resource sequences has a total adjustable capacity that has not been fully allocated. If so, the remaining resource allocation is assigned to that sequence. If the allocated allocation for all three sequences is equal to the total adjustable capacity of the sequence, and the resource allocation is still... If there are remaining resources, a second round of screening is conducted. In this second round, the conditions for requesting resources are more lenient than in the first round. By adjusting the threshold, three scheduling resource sequences are selected for the second round. For these sequences, the initial priority scheduling weights are set according to the steps of the first round, and then the resource scheduling quantities are allocated. If the second round still cannot achieve complete allocation of resource scheduling quantities, in the third round, resource scheduling quantities are allocated sequentially to various types of power resources in the resource sharing pool according to scheduling priority and resource request index, until the resource scheduling quantities are allocated. If all resources in the resource sharing pool cannot meet the resource scheduling quantity, cross-regional scheduling is required. During the above scheduling process, if a new energy power station participates in resource scheduling, its output plan for the next time period is adjusted based on the actual allocated scheduling quantity. This resource scheduling method in this embodiment avoids over-reliance on a single resource, disperses fault risks, and thus improves the stability of power grid operation.

[0096] This embodiment provides an operation control optimization method for new energy power plants. Based on the stability of new energy power plants, the present invention performs regional centralized control output planning, which can effectively reduce the probability of new energy output fluctuations, thereby improving the stability of new energy grid connection. Through a resource scheduling strategy based on a resource sharing pool, multiple resources are comprehensively scheduled, which can improve the timeliness and reliability of resource scheduling, thereby ensuring the balance of regional power supply and demand, and further enhancing the safety and stability of power grid operation.

[0097] Please see Figure 2 Based on the same inventive concept, the second embodiment of this invention proposes an operation control optimization system for a new energy power station, comprising:

[0098] The stability analysis module 10 is used to perform stability analysis on the output power of each new energy power station in the target area to obtain the characteristic type of each new energy power station.

[0099] The power output plan execution module 20 is used to control each of the new energy power plants to execute the time-sharing power output plan output by the power output planning model. The objective function of the power output planning model is used to indicate the minimization of power output fluctuation, the maximization of power output efficiency, and the minimization of wind and solar curtailment rate. The constraints of the power output planning model are obtained based on all the characteristic types. When executing the current power output plan in the time-sharing power output plan, the module performs real-time analysis on the load demand curve of the target area to calculate the power supply capacity margin of the target area. The module analyzes the power supply capacity margin, and if the analysis result meets the scheduling conditions, the module corrects the power output plan for the next time period in the time-sharing power output plan according to the determined resource scheduling amount.

[0100] The technical features and effects of the operation control optimization system for new energy power plants proposed in this embodiment are the same as those of the method proposed in this embodiment, and will not be repeated here. Each module in the above-mentioned operation control optimization system for new energy power plants can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in a computer device in hardware form, or it can be stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0101] Furthermore, embodiments of the present invention also propose a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0102] Please see Figure 3 The diagram illustrates the internal structure of a computer device in one embodiment. This computer device can specifically be a terminal or a server. The computer device includes a processor, memory, network interface, display, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an operation control optimization method for new energy power plants. The display screen of the computer device can be a liquid crystal display (LCD) or an e-ink display. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0103] Those skilled in the art will understand that Figure 3The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computing devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.

[0104] In summary, the present invention proposes an operation control optimization method, system, and equipment for renewable energy power plants. The method involves performing stability analysis on the output power of each renewable energy power plant within a target area to obtain the characteristic type of each power plant; controlling each power plant to execute a time-of-use power output plan output by a power output planning model, wherein the objective function of the power output planning model indicates minimizing power output fluctuations, maximizing power output efficiency, and minimizing wind and solar curtailment rates, and the constraints of the power output planning model are obtained based on all the characteristic types; and, when executing the current power output plan in the time-of-use power output plan, performing real-time analysis on the obtained load demand curve of the target area to calculate the power supply capacity margin of the target area; and, analyzing the power supply capacity margin, if the analysis result meets the scheduling conditions, then correcting the power output plan for the next time period in the time-of-use power output plan based on the determined resource scheduling amount. This invention uses regional centralized control for power output planning based on the stability of new energy power plants, which effectively reduces the probability of fluctuations in new energy power output and improves the stability of new energy grid connection. Through a resource scheduling strategy based on a resource sharing pool, it comprehensively schedules multiple resources, improves the timeliness and reliability of resource scheduling, ensures the balance of regional power supply and demand, and further enhances the safety and stability of power grid operation.

[0105] The various embodiments in this specification are described in a progressive manner. For directly identical or similar parts of the embodiments, refer to each other. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0106] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.

Claims

1. A method for optimizing the operation control of a new energy power station, characterized in that, The method, applied to target areas with both renewable energy power stations and non-renewable energy power stations, includes: Stability analysis was performed on the output power of each new energy power station within the target area to obtain the characteristic type of each new energy power station; Each of the aforementioned renewable energy power plants is controlled to execute a time-of-use power output plan output by a power output planning model, wherein the objective function of the power output planning model is used to indicate minimizing power output fluctuations, maximizing power output efficiency, and minimizing wind and solar curtailment rates, and the constraints of the power output planning model are obtained based on all the aforementioned characteristic types; and, when executing the current power output plan in the time-of-use power output plan, the load demand curve of the target area is analyzed in real time to calculate the power supply capacity margin of the target area; and, the power supply capacity margin is analyzed, and if the analysis result meets the scheduling conditions, the power output plan for the next time period in the time-of-use power output plan is revised according to the determined resource scheduling amount; The step of performing stability analysis on the output power of each new energy power station within the target area to obtain the characteristic type of each new energy power station includes: Calculate the power fluctuation rate based on the output power of each new energy power station in the target area; Based on the comparison relationship between the power fluctuation rate and the fluctuation threshold, the characteristic type of the new energy power station is determined, and the characteristic type includes stable type and fluctuating type; The steps for constructing the constraints include: Calculate the output weighting coefficient of each new energy power station based on the characteristic type and the power fluctuation rate; Based on the output weighting coefficient, the maximum output value of the station is corrected, and based on the minimum output value of the station and the corrected maximum output value of the station, the constraint conditions based on the output value of the station are obtained. The step of revising the output plan for the next time period in the time-sharing output plan based on the determined resource scheduling amount includes: The resource scheduling amount is determined based on the preset resource sharing pool and the power supply capacity margin; Based on the scheduling strategy and the resource scheduling amount, the output plan for the next time period in the time-sharing output plan is modified.

2. The operation control optimization method for new energy power stations according to claim 1, characterized in that, The step of performing real-time analysis on the load demand curve of the target area and calculating the power supply capacity margin of the target area includes: Based on the obtained load demand curve of the target area, the load demand of the target area in the next time period is obtained; Based on the obtained power output plans for the next time period of the non-new energy power stations and the power output plans for the next time period of the new energy power stations, the power supply capacity of the target area for the next time period is obtained. The difference between the power supply capacity for the next time period and the load demand for the next time period is used as the power supply capacity margin of the target area.

3. The operation control optimization method for new energy power stations according to claim 1, characterized in that, The steps for constructing the preset resource sharing pool include: Based on the current adjustable capacity of various power resources of each regional power station in the target area, an initial resource sharing pool is constructed. The regional power stations include new energy power stations and non-new energy power stations, and the power resources include new energy resources, non-new energy resources and energy storage resources. Based on the historical operating status of the power plants in the region, calculate the reliability coefficients of various power resources of the power plants in the region; Calculate the resource mobilization index based on the current adjustable capacity, the reliability coefficient, and the preset response time coefficient; Based on the comparison relationship between the resource call index and the index threshold, the initial resource sharing pool is updated to obtain the resource sharing pool.

4. The operation control optimization method for new energy power stations according to claim 3, characterized in that, Following the step of obtaining the resource sharing pool, the method further includes: The resource sharing pool is updated according to preset resource exit conditions, which include output fluctuation conditions of new energy resources, reliability coefficient conditions of non-new energy resources, and state of charge conditions of energy storage resources.

5. The operation control optimization method for new energy power stations according to claim 3, characterized in that, The step of revising the next time period output plan in the time-sharing output plan according to the scheduling strategy and the resource scheduling amount includes: Based on the preset scheduling priorities and calling conditions, scheduling resources are selected from the resource sharing pool to obtain the scheduling resource sequence corresponding to each scheduling priority; Based on the preset priority scheduling weight and the resource scheduling amount, the actual scheduling amount of each scheduling resource in each scheduling resource sequence is calculated sequentially. Based on the actual scheduling volume, the output plan for the next time period in the time-sharing output plan is revised.

6. A system for optimizing the operation control of a new energy power station, characterized in that, The system, applicable to target areas with both renewable energy power stations and non-renewable energy power stations, includes: The stability analysis module is used to perform stability analysis on the output power of each new energy power station within the target area, and to obtain the characteristic type of each new energy power station; including: Calculate the power fluctuation rate based on the output power of each new energy power station in the target area; Based on the comparison relationship between the power fluctuation rate and the fluctuation threshold, the characteristic type of the new energy power station is determined, and the characteristic type includes stable type and fluctuating type; The power output plan execution module is used to control each of the new energy power plants to execute the time-of-use power output plan output by the power output planning model. The objective function of the power output planning model is used to indicate minimizing power output fluctuations, maximizing power output efficiency, and minimizing wind and solar curtailment rates. The constraints of the power output planning model are obtained based on all the characteristic types. Furthermore, when executing the current power output plan in the time-of-use power output plan, the module performs real-time analysis on the obtained load demand curve of the target area to calculate the power supply capacity margin of the target area. The module also analyzes the power supply capacity margin; if the analysis result meets the scheduling conditions, the module corrects the power output plan for the next time period in the time-of-use power output plan based on the determined resource scheduling amount. The steps for constructing the constraints include: Calculate the output weighting coefficient of each new energy power station based on the characteristic type and the power fluctuation rate; Based on the output weighting coefficient, the maximum output value of the station is corrected, and based on the minimum output value of the station and the corrected maximum output value of the station, the constraint conditions based on the output value of the station are obtained. The step of revising the output plan for the next time period in the time-sharing output plan based on the determined resource scheduling amount includes: The resource scheduling amount is determined based on the preset resource sharing pool and the power supply capacity margin; Based on the scheduling strategy and the resource scheduling amount, the output plan for the next time period in the time-sharing output plan is modified.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

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