A comprehensive energy management system and method for new energy power plants

Abstract

This invention relates to the field of new energy power station technology and discloses a comprehensive energy management system and method for new energy power stations. The energy management module, automatic voltage control module, automatic power control module, and wind power prediction module of this invention are all integrated into one piece of equipment, reducing the number of equipment in the control center of the new energy power station, thus reducing cost and floor space. It achieves multi-functional integration of control and dispatch operation of the new energy power station, reduces communication latency, and improves control efficiency. Furthermore, the wind power prediction module predicts the active power prediction curve of the new energy substations. The energy management module performs comprehensive calculations based on the acquired grid dispatch instructions, the operating data of each unit, and the active power prediction curve, allocating the active and reactive power of each new energy unit and controlling the centralized energy storage device to perform charging and discharging operations, thereby realizing comprehensive management of the new energy power station.

Description

Technical Field

[0001] This invention relates to the field of new energy power station technology, specifically to a comprehensive energy management system and method for new energy power stations. Background Technology

[0002] With the proposal of dual-carbon goals and "building a new power system with new energy as the mainstay", the power system with a high proportion of new energy has rapidly developed from local areas to the whole country. The weaknesses of new energy power generation in terms of voltage, frequency and damping support for the power grid have also been rapidly exposed, and the safe and stable operation of the large power grid is facing huge challenges. Therefore, higher requirements are put forward for the energy management system of new energy power plants.

[0003] However, the energy management systems for new energy power plants currently being developed have the following problems: 1. Low control efficiency: The control architecture of existing wind and solar power plants is complex, data transmission efficiency is low, the ability to control multiple types of objects is weak, and the active support function for the power grid is lacking, which can no longer meet the current and future development needs of new energy power plants; 2. Limited functionality: The energy management systems for existing new energy power plants only have a monitoring function, which is limited and cannot meet more complex control and operation requirements; 3. Inability to meet the needs of the electricity market: Currently, new energy power plants generally do not have the ability to predict the power output of new energy power plants, which cannot meet the growing needs of the new energy electricity market. Summary of the Invention

[0004] In view of this, the present invention provides a comprehensive energy management system and method for new energy power stations to solve the problem that traditional new energy power station management systems have limited functions and cannot meet the development needs of energy power stations.

[0005] In a first aspect, the present invention provides a comprehensive energy management system for new energy power plants. The system includes an energy management module, a wind power prediction module, an automatic power control module, and an automatic voltage control module. The wind power prediction module is used to predict a first active power prediction curve corresponding to the new energy substation and a second active power prediction curve corresponding to each generating unit. The new energy substation includes all generating units within the new energy power plant. The energy management module acquires grid dispatch instructions and, based on the active power demand for a target time period in the grid dispatch instructions, the actual active power of the current new energy power plant, and the predicted active power corresponding to the target time period in the first active power prediction curve, determines the active power adjustment target amounts for the new energy substation and / or the energy storage substation, controls the energy storage substation to adjust its active power output based on the corresponding active power adjustment target amounts, and sends the active power adjustment target amounts of the new energy substation to the automatic power control module. This refers to an energy storage power station in a new energy power station. The energy management module is used to determine the reactive power regulation amount based on the target line voltage demand in the power grid dispatch command and the obtained actual voltage value, and to determine the reactive power regulation target amount for the new energy substation based on the reactive power regulation amount, or to determine the reactive power regulation target amounts for the new energy substation and the energy storage substation respectively, control the energy storage substation to perform reactive power regulation based on the corresponding reactive power regulation target amount, and send the reactive power regulation target amount corresponding to the new energy substation to the automatic voltage control module; the automatic power control module is used to determine the active power allocation amount for each unit based on the second active power prediction curve corresponding to each unit and the active power regulation target amount of the new energy substation, and control each unit to adjust according to its corresponding active power allocation amount; the automatic voltage control module is used to determine the reactive power regulation allocation amount for each unit based on the reactive power regulation amount corresponding to the new energy substation, and control each unit to adjust according to its corresponding reactive power regulation allocation amount.

[0006] The integrated energy management system for new energy power stations provided by this invention integrates an energy management module, an automatic voltage control module, an automatic power control module, and a wind power prediction module into a single device. This reduces the number of devices required for the control center of new energy power stations, thereby reducing costs and floor space. It achieves multi-functional integration of control and dispatch operation for new energy power stations, reduces communication latency, and improves control efficiency. Furthermore, the wind power prediction module predicts the active power prediction curve of the new energy substations, and the energy management module performs comprehensive calculations based on the acquired grid dispatch instructions, the operating data of each unit, and the active power prediction curve to allocate the active and reactive power of each new energy unit and control the centralized energy storage device to perform charging and discharging operations, thus realizing the comprehensive management of new energy power stations.

[0007] In one optional embodiment, the system further includes a primary frequency regulation module, wherein the primary frequency regulation module is used to obtain the frequency deviation of the new energy substation, and when the frequency deviation is greater than a preset frequency regulation threshold, calculate the required frequency regulation capacity of the new energy substation, and control the discharge output power of the energy storage substation to perform frequency regulation operation based on the required frequency regulation capacity.

[0008] In one optional implementation, the automatic voltage control module includes a reactive power setting unit and a reactive power distribution unit. The reactive power setting unit is used to determine the reactive power adjustment distribution amount for each generator unit based on the reactive power adjustment amount corresponding to the new energy substation and the adjustable amount of each generator unit, and sends it to the reactive power distribution unit. The reactive power distribution unit distributes the reactive power adjustment distribution amount of each generator unit to each generator unit according to the distribution of the reactive power adjustment distribution amount of each generator unit, and performs reactive power adjustment operation.

[0009] Secondly, the present invention provides a comprehensive energy management method for new energy power plants, applied to the comprehensive energy management system for new energy power plants in the first aspect or any corresponding embodiment thereof. The method includes: predicting a first active power prediction curve corresponding to a new energy substation and a second active power prediction curve corresponding to each generating unit, wherein the new energy substation includes all generating units in the new energy power plant; obtaining a power grid dispatch instruction; and, based on the active power demand for a target time period in the power grid dispatch instruction, the actual active power of the current new energy power plant, and the predicted active power corresponding to the target time period in the first active power prediction curve, determining the active power adjustment target amounts for the new energy substation and / or the energy storage substation respectively, and controlling the energy storage substation to adjust its active power output based on the corresponding active power adjustment target amounts. The energy storage substation refers to the energy storage power station in the new energy power station. Based on the target line voltage demand in the power grid dispatch instruction and the obtained actual voltage value, the reactive power regulation amount is determined, and the reactive power regulation target amount of the new energy substation is determined based on the reactive power regulation amount, or the reactive power regulation target amounts of the new energy substation and the energy storage substation are determined respectively, and the energy storage substation is controlled to perform reactive power regulation based on the corresponding reactive power regulation target amount; based on the second active power prediction curve corresponding to each unit and the active power regulation target amount of the new energy substation, the active power allocation amount of each unit is determined, and each unit is controlled to adjust according to its corresponding active power allocation amount; based on the reactive power regulation amount corresponding to the new energy substation, the reactive power regulation allocation amount of each unit is determined, and each unit is controlled to adjust according to its corresponding reactive power regulation allocation amount.

[0010] In one optional implementation, when the active power demand is determined to be an active power load increase scenario, the active power demand includes at least an active power increment. The step of determining the active power adjustment target amounts for the new energy substation and / or energy storage substation based on the active power demand for the target time period in the grid dispatch instruction, the actual active power of the current new energy power station, and the predicted active power corresponding to the target time period in the first active power prediction curve, includes: calculating the adjustment margin value between the predicted active power corresponding to the target time period in the first active power prediction curve and the actual active power, and determining whether the adjustment margin value is less than the active power increment; if the adjustment margin value is not less than the active power increment, determining the active power adjustment target amount for the new energy substation as the active power increment; if the adjustment margin value is less than the active power increment, calculating the active power difference between the active power increment and the adjustment margin value, determining the active power adjustment target amount for the new energy substation as the adjustment margin value, and determining the active power adjustment target amount for the energy storage substation as the active power difference.

[0011] In one optional implementation, when the active power demand is determined to be an active power load reduction scenario, the active power demand includes at least an active power reduction amount. The step of determining the active power adjustment target amounts for the new energy substation and / or energy storage substation based on the active power demand for the target time period in the grid dispatch instruction, the actual active power of the current new energy power station, and the predicted active power corresponding to the target time period in the first active power prediction curve includes: subtracting the active power reduction amount from the current energy storage of the energy storage substation to obtain the adjusted energy storage, and determining whether the adjusted energy storage is... The adjustment of the energy storage substation reaches the lower limit of its active power regulation. If the adjusted energy storage does not reach the lower limit of the energy storage substation's active power regulation, the active power regulation target amount of the energy storage substation is determined as the active power reduction amount. If the adjusted energy storage reaches the lower limit of the energy storage substation's active power regulation, the active power regulation target amount of the energy storage substation is determined as the first regulation target amount. The active power regulation target amount of the new energy substation is determined as the difference between the active power reduction amount and the first regulation target amount, where the first regulation target amount is the difference between the current energy storage of the energy storage substation and the lower limit of the energy storage substation's active power regulation.

[0012] In one optional implementation, the reactive power regulation amount is a voltage regulation amount. The step of determining the reactive power regulation target amount for the new energy substation based on the reactive power regulation amount, or determining the reactive power regulation target amounts for the new energy substation and the energy storage substation respectively, includes: calculating the sum of the current voltage and voltage regulation amount of the new energy substation to obtain an adjusted voltage amount, and determining whether the adjusted voltage amount is within the normal voltage range of the new energy substation; if the adjusted voltage amount is within the normal voltage range of the new energy substation, determining the reactive power regulation target amount of the new energy substation as the voltage regulation amount; if the adjusted voltage amount is not within the normal voltage range of the new energy substation, determining the reactive power regulation target amount of the new energy substation as a first voltage regulation amount, and determining the reactive power regulation target amount of the energy storage substation as the difference between the voltage regulation amount and the first voltage regulation amount, where the first voltage regulation amount is the difference between the normal voltage threshold of the new energy substation and the adjusted voltage amount.

[0013] In one optional implementation, the first active power prediction curve corresponding to the new energy substation and the second active power prediction curve corresponding to each unit are predicted through the following steps: obtaining the meteorological parameters of the current environment of the new energy substation, inputting the meteorological parameters of the current environment into the power prediction model corresponding to each unit after training, to obtain the predicted first active power corresponding to each unit in the new energy substation, wherein different types of units correspond to different power prediction models; determining the first active power corresponding to the new energy substation based on the predicted first active power corresponding to each unit; wherein the power prediction model corresponding to the current unit is trained through the following steps: using training data of different environmental meteorological parameters and corresponding active power, training a preset neural network model corresponding to the current unit, and using a particle swarm optimization algorithm to optimize the preset neural network model to obtain the power prediction model corresponding to the current unit.

[0014] In one optional implementation, the preset neural network model is a BP neural network model. The optimization of the preset neural network model using the particle swarm optimization algorithm includes: iteratively executing a particle optimal solution determination operation until the iteration termination condition is met; using the updated position corresponding to the optimal particle in all particle sets after iteration termination as the initial weight of the BP neural network; the particle optimal solution determination operation includes: encoding the weights of each layer of the BP neural network as particles and initializing the initial positions of the particles; training the BP neural network model using current environmental meteorological parameters to obtain predicted active power, calculating the deviation between the predicted active power and the active power corresponding to the current meteorological parameters, and calculating the fitness value of each particle based on the deviation; comparing the fitness value of each particle with the fitness value of the optimal particle in all particle sets, updating the particle velocity and position, and updating the optimal particle in all particle sets.

[0015] In one optional implementation, the second active power prediction curve is updated based on meteorological parameters of the environment. The determination of the active power allocation for each unit based on the second active power prediction curve corresponding to each unit and the active power adjustment target of the new energy substation includes: determining the adjustable power of each unit based on the predicted active power for the target time period in the second active power prediction curve corresponding to each unit and the current actual active power of each unit; selecting a set of units whose adjustable power is greater than the active power adjustment target of the new energy substation; sorting the units in the set according to their adjustable power from largest to smallest and their evaluation indicators; selecting the unit at the top of the sorted list as the target unit; determining the active power allocation of the target unit as the active power adjustment target; and setting the active power allocation of any other unit besides the target unit to zero. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the integrated energy management system for new energy power stations according to an embodiment of the present invention;

[0018] Figure 2 This is a structural example diagram of a new energy power station according to an embodiment of the present invention;

[0019] Figure 3 This is a flowchart illustrating the integrated energy management method for new energy power stations according to an embodiment of the present invention;

[0020] Figure 4 This is a structural example diagram of a BP neural network according to an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] my country's power system reform has achieved remarkable results. With the implementation of green power trading, more and more new energy power generation companies are participating in power market transactions. In particular, in the process of new energy projects participating in spot trading, short-term and ultra-short-term power forecasts are important bases and foundations for spot trading. However, the mainstream new energy power forecasting systems in the market are configured based on the grid's technical requirements for the grid connection of new energy power plants and photovoltaic power plants. Different manufacturers' power forecasting systems are configured at the level of each new energy power plant, and their accuracy varies. The management is relatively decentralized. At the same time, the grid has extremely strict assessment of the power forecasting data uploaded by new energy power plants and photovoltaic power plants. There are clear requirements and penalties for the accuracy and stability of short-term and ultra-short-term reports. The main problems of the wind and solar power forecasting systems on the market are: (1) the accuracy rate is not up to standard; (2) the information obtained from power trading is scattered, and manual adjustment is entirely based on experience; (3) the coupling between power forecasting and power trading is insufficient, and it cannot provide strong support.

[0024] Currently, the main application of renewable energy power forecasting is in power planning and dispatch. With the advancement of power sector reform and the gradual establishment of the power trading market, power forecasting technology will also play a crucial role in power trading. For renewable energy power plants, the current "reporting quantity without price guarantee" method, which primarily participates in spot trading, will gradually transition to a "reporting quantity and pricing" method. For "reporting quantity without pricing," renewable energy plants only need to declare their power generation, not their electricity price. Therefore, the key factor is the accuracy of power generation forecasting. If the accuracy is not up to standard, they will face market deviation assessments. Regional power forecasting is a significant factor affecting electricity trading prices; therefore, power trading requires crucial technical support from power forecasting systems.

[0025] To ensure the safe and stable operation of the power grid while maximizing the integration of wind power, it is necessary to incorporate a renewable energy power forecasting system suitable for the spot market model into renewable energy power plants. This system should accurately predict the changing trends of renewable energy power plants. Simultaneously, primary frequency regulation, automatic generation control (AGC), and automatic voltage control (AVC) should be added to renewable energy power plants to achieve multi-functional integrated control of renewable energy power plants, from power forecasting and electricity market trading to active and reactive power regulation.

[0026] However, in traditional new energy power plants, devices such as AGC, AVC, Wind Power Prediction (WPP) and Energy Management System (EMS) are all configured independently. Too many intermediate devices and communication layers lead to higher equipment costs, lower control efficiency and worse reliability for the entire power plant.

[0027] This embodiment provides a comprehensive energy management system for new energy power stations, such as... Figure 1 As shown, the system includes an energy management module, a wind power prediction module, an automatic power control module, and an automatic voltage control module. The wind power prediction module predicts the first active power prediction curve corresponding to the renewable energy substation and the second active power prediction curves corresponding to each generating unit. The renewable energy substation includes all generating units within the renewable energy power plant. The energy management module obtains grid dispatch instructions and, based on the active power demand for the target time period in the grid dispatch instructions, the actual active power of the current renewable energy power plant, and the predicted active power corresponding to the target time period in the first active power prediction curve, determines the active power adjustment target for the renewable energy substation and / or the energy storage substation, respectively, and sends the active power adjustment target of the renewable energy substation to the automatic power control module. The energy storage substation refers to the energy storage power station within the renewable energy power plant. The first block is used to determine the reactive power regulation amount based on the target line voltage demand in the power grid dispatch command and the obtained actual voltage value, and to determine the reactive power regulation target amount of the energy storage substation based on the reactive power regulation amount, or to determine the reactive power regulation target amounts of the energy storage substation and the new energy substation respectively, and to send the reactive power regulation target amount corresponding to the new energy substation to the automatic voltage control module; the second block is used to determine the active power allocation amount of each unit based on the second active power prediction curve corresponding to each unit and the active power regulation target amount of the new energy substation, and to control each unit to adjust according to its corresponding active power allocation amount; the third block is used to determine the reactive power regulation allocation amount of the reactive power generator in each unit based on the reactive power regulation amount corresponding to the new energy substation, and to control each reactive power generator to adjust according to its corresponding reactive power regulation allocation amount.

[0028] like Figure 2As shown in the figure, this embodiment of the invention designs a multifunctional integrated energy management system for new energy power plants. This system integrates functional modules that were originally deployed independently in each new energy power plant, including an energy management module, an automatic power control module, an automatic voltage control module, and a wind power prediction module. The integrated energy management system can monitor and collect the operating data of all units and lines in the new energy substation through a Supervisory Control and Data Acquisition (SCADA) system, as well as obtain the operating data of the energy storage substation. The operating data of the energy storage substation may include, but is not limited to, the output active power and voltage value. For example only, the new energy substation includes all units in the new energy power plant, not limited to wind turbines and photovoltaic units, and the energy storage substation refers to a centralized energy storage device.

[0029] The wind power prediction module (WPP) of this invention can predict the first active power prediction curve of the new energy substation and the second active power prediction curve corresponding to each unit within a specific future time period based on meteorological information sent by the new energy substation, including but not limited to parameters such as wind speed, wind direction, temperature, and air pressure. The method of predicting active power is not limited; for example, a mathematical module can be used to establish a prediction correspondence between meteorological parameters and active power, which is only an example. Figure 2 As shown, the energy management module can receive grid dispatch instructions via 104 communication. These instructions may include, but are not limited to, active power demand for a target time period. This can be an active power adjustment or an active power demand, such as requiring an increase of 10 watts in active power output, or a target active power demand of 100 watts. This is just one example. The energy management module can then obtain the predicted active power for the target time period based on the first active power prediction curve. Furthermore, it can compare this with the actual active power and active power demand of the current renewable energy power station to determine the respective active power adjustment target for the renewable energy substation and / or energy storage substation. The method of active power adjustment is not limited. Active power adjustment can be prioritized for new energy substations. If it is detected that the adjustable active power of the new energy substation can cover the active power demand, then only the active power adjustment target amount of the new energy substation is allocated as the active power demand, and the energy storage substation does not need to be allocated. If it is detected that the adjustable active power of the new energy substation cannot cover the active power demand, then the remaining unadjusted active power can be calculated and determined as the active power adjustment target amount of the energy storage substation. The active power adjustment target amount of the new energy substation is its adjustable active power. Active power adjustment can also be prioritized for the energy storage substation. This is just an example.

[0030] In this embodiment of the invention, after determining the active power adjustment target amounts for the new energy substation and the energy storage substation respectively, the energy management module can control the energy storage substation to adjust its active power output based on the corresponding active power adjustment target amounts. It can also send the active power adjustment target amounts of the new energy substation to the automatic power control module (AGC). The AGC can determine the predicted active power of each unit during the target time period based on the second active power prediction curve corresponding to each unit. The AGC can queue the units according to the order of predicted active power from largest to smallest, and then allocate the active power adjustment target amounts of the new energy substations to the corresponding units in that order. Finally, it sends unit control commands to the units for execution. This is merely an example.

[0031] The power grid dispatching command in this embodiment of the invention also includes the target line voltage demand. The energy management module can receive the actual line voltage value sent by the electrical measurement device, then calculate the deviation between the target line voltage demand and the actual line voltage value, and multiply it by a preset voltage droop coefficient to determine the reactive power regulation amount. Then, it can determine the reactive power regulation target amounts for the energy storage substation and the new energy substation respectively. Reactive power regulation can be preferentially undertaken by the new energy substation. It is determined whether the reactive power regulation margin of the new energy substation meets the reactive power regulation amount. If it does not meet the reactive power regulation amount, the remaining reactive power regulation amount can be calculated, and the energy storage substation can be controlled to use a reactive power generator or... The power compensator performs power compensation; the reactive power regulation target of the new energy substation is determined as its reactive power regulation margin, and the reactive power regulation target of the new energy substation is sent to the automatic voltage control module. The automatic voltage control module can determine the reactive power regulation allocation in each unit based on the reactive power regulation of the new energy substation, and control each unit to adjust according to its corresponding reactive power regulation allocation. Here, the unit is generally a wind turbine for reactive power regulation. That is, after the energy management module completes the power allocation between substations, the automatic power control module performs the active power allocation between units, and the automatic voltage control module performs the reactive power allocation between units. This is just an example.

[0032] The integrated energy management system for new energy power stations provided by this invention integrates an energy management module, an automatic voltage control module, an automatic power control module, and a wind power prediction module into a single device. This reduces the number of devices required for the control center of new energy power stations, thereby reducing costs and floor space. It achieves multi-functional integration of control and dispatch operation for new energy power stations, reduces communication latency, and improves control efficiency. Furthermore, the wind power prediction module predicts the active power prediction curve of the new energy substations, and the energy management module performs comprehensive calculations based on the acquired grid dispatch instructions, the operating data of each unit, and the active power prediction curve to allocate the active and reactive power of each new energy unit and control the centralized energy storage device to perform charging and discharging operations, thus realizing the comprehensive management of new energy power stations.

[0033] In one optional implementation, the system further includes a primary frequency regulation module, wherein the primary frequency regulation module is used to obtain the frequency deviation of the new energy substation, and when the frequency deviation is greater than a preset frequency regulation threshold, calculate the required frequency regulation capacity of the new energy substation, and control the discharge output power of the energy storage substation to perform frequency regulation operation based on the required frequency regulation capacity.

[0034] like Figure 2 As shown, the integrated energy management system for new energy power plants also integrates a primary frequency regulation module, which controls the energy storage substation to fully undertake the frequency regulation task of the new energy substation. The primary frequency regulation module can obtain the frequency deviation of the new energy substation and compare the frequency deviation with the preset frequency regulation threshold. When the frequency deviation is greater than the preset frequency regulation threshold, it can calculate the required frequency regulation capacity of the new energy substation and control the discharge output power of the energy storage substation to complete the frequency regulation task based on the required frequency regulation capacity of the new energy substation, thereby improving the AGC control effect of the power plant.

[0035] Specifically, the automatic voltage control module includes a reactive power setting unit and a reactive power distribution unit. The reactive power setting unit is used to determine the reactive power adjustment allocation for each generator unit based on the reactive power adjustment amount corresponding to the new energy substation and the adjustable amount of each generator unit, and sends it to the reactive power distribution unit. The reactive power distribution unit distributes the reactive power adjustment allocation to each generator unit according to the allocation of the reactive power adjustment allocation for each generator unit and performs reactive power adjustment operations.

[0036] The Automatic Voltage Control (AVC) module in this embodiment of the invention divides the reactive power control strategy into two layers: a reactive power setting unit and a reactive power distribution unit. The reactive power setting unit executes a reactive power comprehensive optimization algorithm and issues the corresponding reactive power setpoint to the reactive power distribution layer for each unit. The reactive power comprehensive optimization algorithm is not limited and can be a capacity-proportional method, an average distribution method, or a method proportional to the actual active power generated by the unit, etc. Using the capacity-proportional method, the reactive power adjustment component of each unit can be determined based on the reactive power adjustment amount corresponding to the new energy substation and the adjustable amount of each unit. As an example, the reactive power distribution unit distributes the reactive power reference setpoint of the wind farm, which has been constrained by reactive power limits, to each wind turbine and wind farm reactive power compensation device such as SVC. SVC is a typical grid-connected compensation device in a flexible AC transmission system. Its basic function is to absorb or transmit continuously adjustable reactive power from the grid to the grid to maintain a constant voltage at the installation point.

[0037] According to an embodiment of the present invention, a method for integrated energy management of new energy power stations is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0038] This embodiment provides a comprehensive energy management method for new energy power stations, which is applied to the aforementioned comprehensive energy management system for new energy power stations. Figure 3 This is a flowchart of a comprehensive energy management method for new energy power stations according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0039] Step 301: Predict the first active power prediction curve corresponding to the new energy substation and the second active power prediction curve corresponding to each unit. For details, please refer to the above embodiment, which will not be repeated here.

[0040] Among them, the new energy substation includes all the generating units in the new energy power station.

[0041] Specifically, the prediction curves for the first active power of the new energy substation and the second active power of each generating unit are predicted through the following steps: Meteorological parameters of the current environment of the new energy substation are obtained, and these parameters are input into the trained power prediction models corresponding to each generating unit to obtain the predicted first active power of each generating unit in the new energy substation. Different types of generating units correspond to different power prediction models. Based on the predicted first active power of each generating unit, the first active power of the new energy substation is determined. The power prediction model corresponding to the current generating unit is trained through the following steps: Using training data of different environmental meteorological parameters and corresponding active power, a preset neural network model corresponding to the current generating unit is trained, and the preset neural network model is optimized using a particle swarm optimization algorithm to obtain the power prediction model corresponding to the current generating unit.

[0042] This invention takes into account the uncertainty of wind speed and the strong randomness of power generation in new energy power plants, making it difficult to find an accurate mathematical model to describe them. However, artificial neural networks have powerful self-learning capabilities and can effectively identify inherent patterns by learning from historical data. Based on meteorological parameters such as wind speed, wind direction, temperature, and air pressure, as well as power curves, different power prediction models are used for different types of new energy units. For example, wind turbines are divided into direct-drive and doubly-fed types, and their corresponding power prediction models will certainly be different. Units of the same model can generally use one model and one set of model parameters. The power prediction model can be put into operation synchronously with the new energy units. In practical applications, the meteorological parameters of the current environment of the new energy substation are obtained and input into the trained power prediction models corresponding to each unit. This yields the second active power prediction curve for each unit in the new energy substation. Then, the second active power prediction curves corresponding to all units can be integrated to obtain the first active power curve for the new energy substation.

[0043] The power prediction model corresponding to the current unit in this embodiment of the invention can be trained using training data of different environmental meteorological parameters and corresponding active power to train a preset neural network model, or the particle swarm optimization algorithm can be used to optimize the weights and other parameters of the neural network model to obtain the optimal trained power prediction model.

[0044] Furthermore, the preset neural network model is a BP neural network model. The particle swarm optimization algorithm is used to optimize the preset neural network model, including: iteratively executing the particle optimal solution determination operation until the iteration termination condition is met; using the updated position of the optimal particle in all particle sets after iteration termination as the initial weight of the BP neural network; the particle optimal solution determination operation includes: encoding the weights of each layer of the BP neural network as particles and initializing the initial positions of the particles; training the BP neural network model using current environmental meteorological parameters to obtain the predicted active power, calculating the deviation between the predicted active power and the active power corresponding to the current meteorological parameters, and calculating the fitness value of each particle based on the deviation; comparing the fitness value of each particle with the fitness value of the optimal particle in all particle sets, updating the particle velocity and position, and updating the optimal particle in all particle sets.

[0045] The preset neural network model in this embodiment of the invention is a BP neural network model (a multi-layer feedforward network trained by the backpropagation algorithm), such as... Figure 4 As shown, taking four meteorological parameters—wind speed, wind direction, temperature, and air pressure—as the input to a BP neural network, and active power as the output, with a hidden layer of five neurons, the output of each hidden layer node is as follows:

[0046]

[0047] The output of the output layer node is:

[0048]

[0049] Its standard algorithm uses the mean squared error function as the error function expression, where Let p be the expected output of output node j, and p be the number of input samples.

[0050]

[0051] Define the output layer error variable and the hidden layer error variable as follows:

[0052]

[0053] The adjustment amounts for the weights of the output layer and hidden layer, determined by the error variable, are as follows:

[0054]

[0055] The threshold correction amounts for the output layer and hidden layer, determined by the error variable, are as follows:

[0056]

[0057] Therefore, by modifying the adjustment amounts of the weights and thresholds for each layer determined by the above formula, we obtain the adjustment formula for the BP network model:

[0058]

[0059] This invention employs an improved example algorithm to perform predictions after optimizing a BP neural network. Firstly, the traditional Particle Swarm Optimization (PSO) algorithm is an evolutionary computational technique inspired by the regularity of flocking bird behavior. It utilizes swarm intelligence to establish a simplified model, the mathematical expression of which is:

[0060]

[0061] In the formula and ω represents the update speed of particle at time k+1 and k, respectively; ω is the particle inertia weight; c1 and c2 are the individual particle and global learning factors, respectively; r1 and r2 are both random numbers in the range [0, 1]. It is a locally optimal particle; The globally optimal particle; These represent the positions of the particle at times k+1 and k, respectively.

[0062] To enhance global search capabilities, a dynamic learning factor is employed. As the algorithm iterates, c1 gradually decreases, reducing the learning ability of individual particles, while c2 gradually increases, strengthening the particles' global cognitive ability. The specific implementation is as follows:

[0063]

[0064] Compared to traditional BP neural networks, which suffer from slow convergence, susceptibility to local minima, and sensitivity to initial weights and thresholds, an improved particle swarm optimization (IPSO) algorithm is used to optimize the connection weights of the BP neural network, encoding the weights as particles. This method ensures the efficiency and accuracy of short-term renewable energy power prediction. The optimization process of the BP neural network using IPSO involves encoding the weights of each layer as particles, as follows:

[0065] 1. Determine the structure of the BP neural network, including the number of nodes in the input layer, hidden layer, and output layer, and calculate the total number of weights.

[0066] 2. Encode particles, and concatenate all values ​​into an array according to Equation 13, where W ih1 V represents the weights from the input layer to the hidden layer. ho1 This represents the weights from the hidden layer to the output layer.

[0067] p best =[W ih1 W ih2 ,…,W ih(n×m) V ho1 V ho2 ,…,V ho(m×p) (13)

[0068] 3. Initialize particles: During the initialization phase of the IPSO algorithm, the initial positions of the particles, i.e., the BP network weights, can be randomly generated.

[0069] 4. Calculate fitness: For each particle in the particle swarm, calculate its fitness value according to Equation 3 based on its weight and bias.

[0070] 5. Update velocity and position: Based on the particle's own optimal solution and the optimal solution in the swarm (i.e., the fitness of the best particle in the entire particle set), as well as some weighting coefficients, update the particle's velocity and position.

[0071] Specifically, the new velocity and position are calculated using Equations 14 and 15.

[0072]

[0073] 6. Update the global optimal solution (i.e., the best particle in the entire particle set) and the individual optimal solution. After each iteration, update the global optimal solution and the individual optimal solution based on the fitness value. If the fitness of a particle exceeds the fitness of the global optimal solution, then it is adopted as the new global optimal solution; if the fitness of a particle exceeds the fitness of its individual optimal solution, then it is adopted as the new individual optimal solution.

[0074] 7. Repeat steps 4 and 5 until the conditions for stopping iteration are met. The conditions for stopping iteration can be set according to the actual situation (such as reaching the maximum number of iterations, the fitness value reaching a preset threshold, or the fitness value stabilizing).

[0075] 8. Output the optimal result, use the optimal result as the initial weight value of the BP neural network model, and start training the BP neural network.

[0076] This invention optimizes a preset neural network using a particle swarm optimization algorithm to obtain the optimal power prediction model, ensuring the efficiency and accuracy of short-term renewable energy power prediction.

[0077] Step S302: Obtain the power grid dispatch instruction. Based on the active power demand for the target time period in the power grid dispatch instruction, the actual active power of the current renewable energy power station, and the predicted active power corresponding to the target time period in the first active power prediction curve, determine the active power adjustment target amounts for the renewable energy substation and / or energy storage substation respectively, and control the energy storage substation to adjust the active power output based on the corresponding active power adjustment target amounts. For details, please refer to the above embodiment, which will not be repeated here.

[0078] Among them, energy storage substations refer to energy storage power stations in new energy power plants.

[0079] Specifically, when the active power demand is determined to be an active power load increase scenario, the active power demand must include at least the active power increment. The adjustment margin values ​​for the predicted active power and actual active power corresponding to the target time period in the first active power prediction curve are calculated, and it is determined whether the adjustment margin value is less than the active power increment. If the adjustment margin value is not less than the active power increment, the active power adjustment target amount for the new energy substation is determined as the active power increment. If the adjustment margin value is less than the active power increment, the active power difference between the active power increment and the adjustment margin value is calculated, and the active power adjustment target amount for the new energy substation is determined as the adjustment margin value, and the active power adjustment target amount for the energy storage substation is determined as the active power difference.

[0080] In this embodiment of the invention, the energy management module receives a power grid dispatch command and determines that the active power demand in the power grid dispatch command includes at least an active power increment. If this indicates an active power load increase scenario, the module can prioritize increasing the total active power setpoint of the renewable energy substation. It then calculates the adjustment margin value between the predicted active power and the actual active power corresponding to the target time period in the first active power prediction curve, and determines whether the adjustment margin value can cover the active power increment. If the adjustment margin value can cover the active power increment (i.e., the adjustment margin value is not less than the active power increment), the module will proceed accordingly. When the active power adjustment target of the new energy substation is determined as the active power increment, it is only adjusted by the new energy substation. When the adjustment margin value cannot cover the active power increment (i.e., the adjustment margin value is less than the active power increment), the energy storage substation is controlled to output electrical energy to compensate for the active power. The active power difference between the active power increment and the adjustment margin value is calculated. Then, the active power adjustment target of the new energy substation can be determined as the adjustment margin value, and the active power adjustment target of the energy storage substation can be determined as the active power difference. This is just an example.

[0081] In scenarios of increased active power load, this invention prioritizes increasing the total active power of the new energy substations. When the active power increase margin of the new energy substations cannot cover the increase in active power, the invention controls the energy storage substations to output electrical energy to compensate for the active power, thereby ensuring accurate and effective scheduling of the new energy power plants.

[0082] In one optional implementation, when the active power demand is determined to be an active power load reduction scenario, the active power demand includes at least an active power reduction amount. The active power reduction amount is subtracted from the current energy storage of the energy storage substation to obtain the adjusted energy storage, and it is determined whether the adjusted energy storage reaches the lower limit of the active power regulation of the energy storage substation. If the adjusted energy storage does not reach the lower limit of the active power regulation of the energy storage substation, the active power regulation target amount of the energy storage substation is determined to be the active power reduction amount. If the adjusted energy storage reaches the lower limit of the active power regulation of the energy storage substation, the active power regulation target amount of the energy storage substation is determined to be the first regulation target amount. The active power regulation target amount of the new energy substation is determined to be the difference between the active power reduction amount and the first regulation target amount, where the first regulation target amount is the difference between the current energy storage of the energy storage substation and the lower limit of the active power regulation of the energy storage substation.

[0083] In this embodiment of the invention, the energy management module receives a grid dispatch command and determines that the active power demand in the grid dispatch command includes at least an active power reduction. If this is determined to be an active power load reduction scenario, the active power output of the energy storage substation can be reduced first. The active power reduction is first subtracted from the current energy storage of the energy storage substation to obtain the adjusted energy storage. It then determines whether the adjusted energy storage reaches the lower limit of the active power regulation of the energy storage substation (to ensure the normal operation of the energy storage substation). If the adjusted energy storage does not reach the lower limit of the active power regulation of the energy storage substation, it can be determined that only the energy storage substation will perform charging operations to complete the active power reduction task. If the adjusted energy storage reaches the lower limit of the active power regulation of the energy storage substation, the active power regulation target amount of the energy storage substation can be determined as the first regulation target amount. The difference between the active power reduction and the first regulation target amount is calculated as the active power regulation target amount of the new energy substation.

[0084] Step S303: Based on the target line voltage demand in the power grid dispatch command and the obtained actual voltage value, determine the reactive power regulation amount, and based on the reactive power regulation amount, determine the reactive power regulation target amount for the new energy substation, or determine the reactive power regulation target amounts for the new energy substation and the energy storage substation respectively, and control the energy storage substation to perform reactive power regulation based on the corresponding reactive power regulation target amounts. For details, please refer to the above embodiments, which will not be repeated here.

[0085] Step S304: Based on the second active power prediction curve corresponding to each unit and the active power adjustment target of the new energy substation, determine the active power allocation of each unit, and control each unit to adjust according to its corresponding active power allocation. For details, please refer to the above embodiment, which will not be repeated here.

[0086] Specifically, the second active power prediction curve is updated based on the meteorological parameters of the environment. Based on the predicted active power for the target time period in the second active power prediction curve corresponding to each unit and the current actual active power of each unit, the adjustable power of each unit is determined. A set of units with adjustable power greater than the active power adjustment target of the new energy substation is selected. The units in the set are sorted according to the order of adjustable power from largest to smallest and the evaluation indicators of each unit. The unit at the top of the sort is selected as the target unit, and the active power allocation of the target unit is determined as the active power adjustment target. The active power allocation of any other unit is zero.

[0087] The second active power prediction curve in this embodiment of the invention is affected by meteorological parameters such as wind speed, wind direction, temperature and air pressure. Therefore, it changes in real time according to the changes in meteorological parameters of the environment. The automatic power control module AGC can determine the adjustable power of each unit based on the predicted active power of the second active power prediction curve corresponding to the target time period and the current actual active power of each unit. It can pre-select the set of units whose adjustable power is greater than the active power adjustment target of the new energy substation. That is, when the adjustable power in the target time period is less than the active power adjustment target of the new energy substation, the unit does not participate in active power allocation. Only when the adjustable power is greater than the active power adjustment target will active power allocation be performed. Furthermore, based on the power generation prediction method provided by the wind power prediction module, all units in the unit set can be sorted according to the order of adjustable power from largest to smallest, as well as comprehensive indicators such as cumulative power curtailment, technical level, and assessment status. Then, the unit at the top of the sorting results is selected as the target unit, and the active power allocation of the target unit is determined as the active power adjustment target. The active power allocation of any other unit is zero. Since the predicted active power of the units is affected by various meteorological parameters, the predicted active power of the units may change in real time, and the sorting of the units also changes. Therefore, the units can be sorted in real time and power allocated in turn to ensure the accuracy and flexibility of active power adjustment.

[0088] This invention sorts the units according to factors such as adjustable power and comprehensive indicators, and then prioritizes the allocation of active power based on the sorting results, thereby enhancing system stability. Reasonable power allocation helps maintain the stability of system frequency and power, and thus enhances the stability of the entire new energy power station.

[0089] Step S305: Based on the reactive power regulation amount corresponding to the new energy substation, determine the reactive power regulation allocation amount for each generating unit, and control each generating unit to adjust according to its corresponding reactive power regulation allocation amount. For details, please refer to the above embodiment, which will not be repeated here.

[0090] The comprehensive energy management method for new energy power stations provided by this invention integrates an energy management module, an automatic voltage control module, an automatic power control module, and a wind power prediction module into a single device. This reduces the number of devices required for the control center of new energy power stations, resulting in reduced costs and floor space (30% reduction in floor space and 40% reduction in cost). It achieves multi-functional integration of control and dispatch operation for new energy power stations, reduces communication latency, improves control efficiency, and increases the annual power generation of new energy power stations by more than 2%, bringing significant economic benefits. Furthermore, the wind power prediction module predicts the active power prediction curve of the new energy substations, and the energy management module performs comprehensive calculations based on the acquired grid dispatch instructions, the operating data of each unit, and the active power prediction curve to allocate the active and reactive power of each new energy unit and control the centralized energy storage device for charging and discharging operations, thus realizing comprehensive management of the new energy power station.

[0091] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take a processor 10 as an example.

[0092] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0093] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0094] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0095] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0096] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 5 The bus connection is taken as an example.

[0097] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0098] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0099] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A comprehensive energy management method for new energy power stations, characterized in that, The method includes: The prediction curves for the first active power of the new energy substation and the second active power of each generator unit are predicted. The new energy substation includes all generator units in the new energy power station. Obtain power grid dispatch instructions, and based on the active power demand for the target time period in the power grid dispatch instructions, the actual active power of the current new energy power station, and the predicted active power corresponding to the target time period in the first active power prediction curve, determine the active power adjustment target amount for the new energy substation and the energy storage substation respectively, and control the energy storage substation to adjust the active power output based on the corresponding active power adjustment target amount, wherein the energy storage substation represents the energy storage power station in the new energy power station; Based on the target line voltage demand in the power grid dispatching instruction and the actual voltage value obtained, the reactive power regulation amount is determined, and the reactive power regulation target amount for the new energy substation and the energy storage substation is determined based on the reactive power regulation amount, and the energy storage substation is controlled to perform reactive power regulation based on the corresponding reactive power regulation target amount. Based on the second active power prediction curve corresponding to each unit and the active power adjustment target of the new energy substation, the active power allocation of each unit is determined, and each unit is controlled to adjust according to its corresponding active power allocation. Based on the reactive power regulation corresponding to the new energy substation, determine the reactive power regulation allocation for each unit, and control each unit to adjust according to its corresponding reactive power regulation allocation. The active power demand includes at least the active power increment. When the active power demand is determined to be an active power load increase scenario, the adjustment margin value of the predicted active power and the actual active power corresponding to the target time period in the first active power prediction curve is calculated, and it is determined whether the adjustment margin value is less than the active power increment. If the adjustment margin value is not less than the active power increment, the active power adjustment target of the new energy substation is determined to be the active power increment. If the adjustment margin value is less than the active power increment, calculate the active power difference between the active power increment and the adjustment margin value, determine the active power adjustment target of the new energy substation as the adjustment margin value, and determine the active power adjustment target of the energy storage substation as the active power difference value.

2. The method according to claim 1, characterized in that, When the active power demand is determined to be an active power load reduction scenario, the active power demand includes at least the active power reduction amount. The determination of the active power adjustment target amounts for the new energy substation and the energy storage substation, based on the active power demand for the target time period in the grid dispatch instruction, the actual active power of the current new energy power station, and the predicted active power corresponding to the target time period in the first active power prediction curve, includes: Subtract the active power reduction from the current energy storage of the energy storage substation to obtain the adjusted energy storage, and determine whether the adjusted energy storage reaches the lower limit of the active power adjustment of the energy storage substation. If the adjusted energy storage does not reach the lower limit of the active power regulation of the energy storage substation, then the active power regulation target of the energy storage substation is determined to be the active power reduction amount. If the adjusted energy storage reaches the lower limit of the active power adjustment of the energy storage substation, the active power adjustment target of the energy storage substation is determined as the first adjustment target, and the active power adjustment target of the new energy substation is determined as the difference between the active power reduction and the first adjustment target. The first adjustment target is the difference between the current energy storage of the energy storage substation and the lower limit of the active power adjustment of the energy storage substation.

3. The method according to claim 1, characterized in that, The reactive power regulation amount is a voltage regulation amount. Determining the reactive power regulation target amounts for the new energy substation and the energy storage substation based on the reactive power regulation amount includes: Calculate the sum of the current voltage and voltage regulation of the new energy substation to obtain the adjusted voltage amount, and determine whether the adjusted voltage amount is within the normal voltage range of the new energy substation; If the adjusted voltage is within the normal voltage range of the new energy substation, the reactive power regulation target of the new energy substation is determined as the voltage regulation amount. If the adjusted voltage is not within the normal voltage range of the new energy substation, the reactive power regulation target of the new energy substation is determined as the first voltage regulation, and the reactive power regulation target of the energy storage substation is determined as the difference between the voltage regulation and the first voltage regulation. The first voltage regulation is the difference between the normal voltage threshold of the new energy substation and the adjusted voltage.

4. The method according to claim 1, characterized in that, The following steps are used to predict the first active power prediction curve for the new energy substation and the second active power prediction curve for each unit: The meteorological parameters of the current environment of the new energy substation are obtained, and the meteorological parameters of the current environment are input into the power prediction model corresponding to each unit after training, so as to obtain the predicted first active power of each unit in the new energy substation. Different types of units correspond to different power prediction models. Based on the predicted first active power corresponding to each of the aforementioned generating units, the first active power corresponding to the new energy substation is determined. The power prediction model for the current generating unit is trained through the following steps: Using training data of different environmental meteorological parameters and corresponding active power, a preset neural network model corresponding to the current unit is trained, and the preset neural network model is optimized using the particle swarm optimization algorithm to obtain the power prediction model corresponding to the current unit.

5. The method according to claim 4, characterized in that, The preset neural network model is a BP neural network model, and the optimization process of the preset neural network model using the particle swarm optimization algorithm includes: The particle optimal solution determination operation is performed iteratively until the iteration termination condition is met. The updated position of the optimal particle in all particle sets after iteration termination is used as the initial weights of the BP neural network. The particle optimal solution determination operation includes: The weights of each layer of the BP neural network are encoded as particles, and the initial positions of the particles are initialized. The predicted active power is obtained by training the BP neural network model using the current environmental meteorological parameters, and the deviation between the predicted active power and the active power corresponding to the current meteorological parameters is calculated. The fitness value of each particle is then calculated based on the deviation. The fitness value of each particle is compared with the fitness value of the best particle in the entire particle set, and the particle velocity and position are updated, as well as the best particle in the entire particle set is updated.

6. The method according to claim 1, characterized in that, The second active power prediction curve is updated based on meteorological parameters of the surrounding environment. The determination of the active power allocation for each unit, based on the second active power prediction curve corresponding to each unit and the active power adjustment target of the new energy substation, includes: Based on the predicted active power corresponding to the target time period in the second active power prediction curve of each unit and the current actual active power of each unit, the adjustable power of each unit is determined. Select a set of generating units whose adjustable power output is greater than the active power adjustment target of the renewable energy substation. The units in the set are sorted according to the order of their adjustable power from largest to smallest and the evaluation indicators of each unit. Select the unit ranked first as the target unit, determine the active power allocation of the target unit as the active power regulation target, and set the active power allocation of any other unit to zero.

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