Method and device for determining regulation capability of regional power grid

Abstract

The embodiment of the invention provides a method and device for determining the regulation capability of a regional power grid, relates to the technical field of power, and can improve the accuracy of determining the regulation capability of the power grid. The regional power grid regulation capability determination method comprises the steps of obtaining operation information of resources and new energy prediction information, wherein the operation information comprises a response curve, a load curve and an excitation price of the resources; based on the response curve, the load curve, the excitation price and a pre-constructed time-phased adjustment capability evaluation model of the regional power grid, determining the time-phased adjustment capability of the regional power grid; based on the new energy prediction information, determining a time-phased adjustment demand of the regional power grid; and determining the residual adjustment capability of the regional power grid based on the time-phased adjustment capability and the time-phased adjustment demand.

Description

Technical Field

[0001] The present application relates to the field of electric power technology, and in particular to a method and device for determining the regulation capability of a regional power grid. Background Art

[0002] The regulation capacity of a regional power grid of renewable energy refers to the ability of the regional power grid to respond to load fluctuations and fluctuations in renewable energy generation in different time periods. The significance of determining this regulation capacity is that renewable energy is random and intermittent, and fluctuations in its power generation may cause changes in parameters such as frequency and voltage in the power grid. Therefore, determining the regulation capacity of the regional power grid is crucial to ensure that the power grid can effectively absorb or output renewable energy in different time periods and maintain the stable operation of the system.

[0003] In addition, by determining the regulation capacity of the regional power grid, the grid operator can more reasonably dispatch traditional energy supply, energy storage facilities and load regulation resources, so as to make effective dispatch decisions when the load is peak or the new energy generation fluctuates greatly. This can not only reduce the peak load pressure and backup resource demand during peak hours, but also avoid other unnecessary backup costs caused by insufficient regulation capacity. In addition, the increase in the proportion of new energy generation requires the grid to have stronger regulation capacity. By determining the regulation capacity of the grid, it is possible to optimize the energy storage configuration and improve the grid's new energy absorption capacity.

[0004] However, most traditional methods for determining the regulation capacity of regional power grids are based on static analysis, relying on fixed static load and power data, and failing to reflect the characteristics of renewable energy fluctuations in real time. At the same time, these methods often ignore the role of external factors such as weather in the determination process, and fail to fully consider the impact of meteorological fluctuations on the regulation capacity of the power grid. In addition, the accuracy of traditional methods is limited, usually relying on linear regression or basic time series models, and can only provide an estimate of the daily average regulation capacity, and cannot determine a more accurate regulation capacity. Summary of the invention

[0005] In order to solve the above problems, the present application provides a method and device for determining the regulation capability of a regional power grid.

[0006] In a first aspect, a method for determining a regional power grid regulation capability is provided, comprising:

[0007] Obtain resource operation information and new energy forecast information. Operation information includes resource response curve, load curve and incentive price;

[0008] Determine the time-division regulation capability of the regional power grid based on the response curve, load curve, incentive price and a pre-built time-division regulation capability evaluation model of the regional power grid;

[0009] Determine the time-sharing regulation needs of the regional power grid based on new energy forecast information;

[0010] The remaining regulation capacity of the regional power grid is determined based on the time-based regulation capacity and time-based regulation demand.

[0011] Furthermore, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, including:

[0012] Inputting the new energy forecast information into a pre-built power forecast model to determine the new energy power in different time periods;

[0013] The time-division regulation demand of the regional power grid is determined based on the new energy power in different time periods and the pre-acquired target demand power.

[0014] Furthermore, the steps of constructing the power prediction model include:

[0015] A power prediction model is generated based on the acquired new energy historical prediction information and the denoising diffusion model acquired through new energy historical power training.

[0016] Further, the resources include: electric water heater load, calculated load and electrolytic copper load; the response curve of the resources includes: the response curve of the electric water heater load, the response curve of the calculated load and the response curve of the electrolytic copper load; the load curve of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; the incentive price of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load;

[0017] The time-division regulation capability of the regional power grid is determined based on the response curve, load curve, incentive price and the pre-built time-division regulation capability evaluation model of the regional power grid, including:

[0018] Inputting the response curve of the electric water heater load, the load curve of the electric water heater load and the incentive price into a pre-built regulation capacity model of the electric water heater load to determine the regulation capacity of the electric water heater load in different time periods;

[0019] Inputting the response curve of the calculated load, the load curve of the calculated load and the incentive price into a pre-built adjustment capacity model of the calculated load to determine the time-division adjustment capacity of the calculated load;

[0020] Inputting the response curve of the electrolytic copper load, the load curve of the electrolytic copper load and the incentive price into a pre-built electrolytic copper load regulation capacity model to determine the time-division regulation capacity of the electrolytic copper load;

[0021] The time-division regulation capacity of the electric water heater load, the time-division regulation capacity of the calculation load and the time-division regulation capacity of the electrolytic copper load are input into the time-division regulation capacity evaluation model of the regional power grid to determine the time-division regulation capacity of the regional power grid.

[0022] Furthermore, the steps of constructing a time-division regulation capability evaluation model for a regional power grid include:

[0023] Construct an initial capability assessment model based on the time fusion algorithm;

[0024] Based on the historical regulation capabilities of the acquired resources and the historical regulation capabilities of the regional power grid, an initial capacity assessment model is trained to generate a time-division regulation capacity assessment model for the regional power grid.

[0025] Further, the remaining regulation capacity of the regional power grid is determined based on the time-division regulation capacity and the time-division regulation demand, including:

[0026] The time-division regulation capacity and time-division regulation demand are input into a pre-built surplus regulation capacity determination model to determine the surplus regulation capacity of the regional power grid.

[0027] In a second aspect, the present application provides a device for determining a regional power grid regulation capability, comprising:

[0028] The acquisition module is used to obtain resource operation information and new energy forecast information. The operation information includes resource response curve, load curve and incentive price;

[0029] A first determination module is used to determine the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price and a pre-constructed time-division regulation capability evaluation model of the regional power grid;

[0030] The second determination module is used to determine the time-division regulation demand of the regional power grid based on the new energy prediction information;

[0031] The third determination module is used to determine the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand.

[0032] Furthermore, the second determining module includes:

[0033] A power determination unit, used to input the new energy prediction information into a pre-built power prediction model to determine the new energy power in different time periods;

[0034] The demand determination unit determines the time-division regulation demand of the regional power grid based on the new energy power in the time periods and the pre-acquired target demand power.

[0035] Furthermore, the steps of constructing the power prediction model include:

[0036] A power prediction model is generated based on the acquired new energy historical prediction information and the denoising diffusion model acquired through new energy historical power training.

[0037] Further, the resources include: electric water heater load, calculated load and electrolytic copper load; the response curve of the resources includes: the response curve of the electric water heater load, the response curve of the calculated load and the response curve of the electrolytic copper load; the load curve of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; the incentive price of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load;

[0038] The first determination module includes:

[0039] A first determination unit is used to input the response curve of the electric water heater load, the load curve of the electric water heater load and the incentive price into a pre-built regulation capacity model of the electric water heater load to determine the regulation capacity of the electric water heater load in different time periods;

[0040] A second determination unit is used to input the response curve of the calculated load, the load curve of the calculated load and the incentive price into a pre-built adjustment capacity model of the calculated load to determine the time-division adjustment capacity of the calculated load;

[0041] A third determination unit is used to input the response curve of the electrolytic copper load, the load curve of the electrolytic copper load and the incentive price into a pre-built electrolytic copper load regulation capacity model to determine the time-division regulation capacity of the electrolytic copper load;

[0042] The fourth determination unit inputs the time-division regulation capacity of the electric water heater load, the time-division regulation capacity of the calculation load and the time-division regulation capacity of the electrolytic copper load into the time-division regulation capacity evaluation model of the regional power grid to determine the time-division regulation capacity of the regional power grid.

[0043] Furthermore, the steps of constructing a time-division regulation capability evaluation model for a regional power grid include:

[0044] Construct an initial capability assessment model based on the time fusion algorithm;

[0045] Based on the historical regulation capabilities of the acquired resources and the historical regulation capabilities of the regional power grid, an initial capacity assessment model is trained to generate a time-division regulation capacity assessment model for the regional power grid.

[0046] Furthermore, the third determination module is specifically used for:

[0047] The time-division regulation capacity and time-division regulation demand are input into a pre-built surplus regulation capacity determination model to determine the surplus regulation capacity of the regional power grid.

[0048] In a third aspect, the present application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for determining the regulation capability of a regional power grid when executing the program.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for determining the regulation capability of a regional power grid.

[0050] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the method for determining the regulation capability of a regional power grid.

[0051] The technical solution provided in the embodiment of the present application determines the time-division regulation capacity of the regional power grid by inputting multiple variables in the operation information of the resource into the time-division regulation capacity evaluation model of the regional power grid, thereby improving the accuracy of the calculation. Compared with a simple linear regression or time series model, it can better reflect the influence of multiple parameters in the power grid, making the determination of the time-division regulation capacity more accurate. Furthermore, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, taking into account the characteristics of new energy fluctuations, making the determination process more accurate and comprehensive. Ultimately, the remaining regulation capacity of the regional power grid is determined by the time-division regulation capacity and time-division regulation demand of the regional power grid, which can help power grid operators identify supply and demand gaps, optimize the power grid dispatch plan, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate some embodiments of this specification or technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 A flowchart of a method for determining the regulation capability of a regional power grid provided in an embodiment of the present application;

[0054] Figure 2 A flowchart of another method for determining the regulation capability of a regional power grid provided in an embodiment of the present application;

[0055] Figure 3 A flowchart of another method for determining the regulation capability of a regional power grid provided in an embodiment of the present application;

[0056] Figure 4A flowchart of another method for determining the regulation capability of a regional power grid provided in an embodiment of the present application;

[0057] Figure 5 A schematic diagram of the structure of a device for determining the regulation capability of a regional power grid provided in an embodiment of the present application;

[0058] Figure 6 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in some embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on some embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0060] It should be noted that the terms "first", "second", etc. in the specification and claims of this document and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of this document described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment. It should be noted that the acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of relevant laws and regulations.

[0061] As described in the background technology, the traditional method for determining the regulation capability has several limitations, which are mainly reflected in the following aspects:

[0062] 1. Traditional determination methods mainly rely on static load and power data, which cannot dynamically capture the fluctuation characteristics of renewable energy generation. Since renewable energy such as wind and solar energy have significant randomness and intermittency, dynamic regulation capacity assessment needs to be performed on a finer time scale, and static analysis methods are difficult to achieve real-time response.

[0063] 2. Renewable energy generation is heavily dependent on meteorological conditions, such as wind speed, light intensity, and temperature. However, existing methods often ignore these key factors or only use simple statistical models for estimation, failing to fully capture the profound impact of meteorological fluctuations on the grid's regulation capabilities.

[0064] 3. Traditional methods often rely on linear regression or basic time series models when predicting renewable energy power, which limits their ability to predict renewable energy output in future time periods. Therefore, when formulating regulation plans, it is easy to over-regulate or under-regulate.

[0065] 4. Traditional methods cannot provide detailed analysis of grid regulation capabilities at the hourly level, and can only provide daily average or grid-wide regulation capability assessments. This rough determination method is difficult to effectively cope with the rapid fluctuations in renewable energy generation, bringing challenges to grid dispatching.

[0066] Based on this, the present application provides a method for determining the regulation capability of a regional power grid, such as Figure 1 As shown, it is a flow chart of a method for determining the regulation capability of a regional power grid provided in an embodiment of the present application, comprising the following steps:

[0067] S101. Obtain resource operation information and new energy forecast information.

[0068] The operation information includes the resource response curve, load curve and incentive price.

[0069] S102: Determine the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price, and a pre-constructed time-division regulation capability evaluation model of the regional power grid.

[0070] S103. Determine the time-based regulation demand of the regional power grid based on the new energy forecast information.

[0071] S104: Determine the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand.

[0072] The technical solution provided in the embodiment of the present application determines the time-division regulation capacity of the regional power grid by inputting multiple variables in the operation information of the resource into the time-division regulation capacity evaluation model of the regional power grid, thereby improving the accuracy of the calculation. Compared with a simple linear regression or time series model, it can better reflect the influence of multiple parameters in the power grid, making the determination of the time-division regulation capacity more accurate. Furthermore, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, taking into account the characteristics of new energy fluctuations, making the determination process more accurate and comprehensive. Ultimately, the remaining regulation capacity of the regional power grid is determined by the time-division regulation capacity and time-division regulation demand of the regional power grid, which can help power grid operators identify supply and demand gaps, optimize the power grid dispatch plan, and reduce operating costs.

[0073] The following is an explanation of each of the above steps:

[0074] S101. Obtain resource operation information and new energy forecast information.

[0075] Among them, the operating information includes the response curve, load curve and incentive price of the resources. The resources include: electric water heater load, calculated load and electrolytic copper load. The new energy forecast information includes: new energy load data, wind speed information, wind direction information, light intensity information, temperature information, air humidity information and air pressure information.

[0076] Exemplarily, the resource operation information and new energy forecast information can be expressed by the following formula:

[0077] X(t)=[X water (t),X comput (t),X copper (t),X load (t),X speed (t),X direct (t),

[0078] X solar (t),X temper (t),X humid (t),X press (t)]

[0079] H(t)=[H water (t),H comput (t),H copper (t)]

[0080] Among them, X(t) is the input information of the data, X water (t) is the response curve of the electric water heater load in time period t, X comput (t) is the response curve of the calculated load in time period t, X copper (t) is the response curve of electrolytic copper load in time period t, X load (t) is the new energy load data of period t, X speed (t) is the wind speed information of time period t, X direct (t) is the wind direction information in time period t, X solar (t) is the light intensity information of time period t, X temper (t) is the temperature information of time period t, X humid (t) is the air humidity information of time period t, X press (t) is the air pressure information of time period t, H(t) is the load curve information of the resource, and H water (t) is the load curve of the electric water heater in period t, H comput(t) is the load curve of the calculated load in time period t, H copper (t) Load curve of electrolytic copper load in time period t.

[0081] S102: Determine the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price, and a pre-constructed time-division regulation capability evaluation model of the regional power grid.

[0082] In some embodiments, based on the response curves, load curves and incentive prices of different types of resources, the time period regulation capabilities of resources corresponding to different types of resources are determined. Further, based on the time period regulation capabilities of different types of resources, the time period regulation capabilities of the regional power grid are determined.

[0083] S103. Determine the time-based regulation demand of the regional power grid based on the new energy forecast information.

[0084] In some embodiments, the new energy power in different time periods is determined based on the new energy prediction information, and is further input into a pre-built demand model based on the acquired target demand power to determine the time-division regulation demand of the regional power grid.

[0085] S104: Determine the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand.

[0086] In some embodiments, the remaining regulation capacity of the regional power grid is determined based on the time-divided regulation capacity and the time-divided regulation demand.

[0087] In some embodiments, the resources include: electric water heater load, calculated load and electrolytic copper load; the response curve of the resources includes: the response curve of the electric water heater load, the response curve of the calculated load and the response curve of the electrolytic copper load; the load curve of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; the incentive price of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; Figure 2 As shown, the time-division regulation capability of the regional power grid is determined based on the response curve, the load curve, the incentive price and a pre-built time-division regulation capability evaluation model of the regional power grid, which can be specifically implemented as the following steps:

[0088] S201. Inputting the response curve of the electric water heater load, the load curve of the electric water heater load and the incentive price into a pre-built regulation capability model of the electric water heater load to determine the regulation capability of the electric water heater load in different time periods.

[0089] The pre-built regulation capability model of the electric water heater load is as follows:

[0090]

[0091] In the regulation capacity model of electric water heater load, is the time-segment regulation capability of the electric water heater load (specifically, the regulation capability of the electric water heater load in time period t), TFT water () is the regulation capacity model of the electric water heater load, X water (t) is the response curve of the electric water heater load (specifically, the response curve of the electric water heater load in time period t), H water (t) is the load curve of the electric water heater load (specifically, the load curve of the electric water heater load in time period t), and P(t) is the incentive price (specifically, the incentive price in time period t).

[0092] S202: Input the response curve of the calculated load, the load curve of the calculated load, and the incentive price into a pre-built adjustment capacity model of the calculated load to determine the time-division adjustment capacity of the calculated load.

[0093] The pre-built computational load regulation capability model is as follows:

[0094]

[0095] In the load regulation capacity model, To calculate the load regulation capacity in different time periods (specifically, to calculate the load regulation capacity in time period t), TFT comput () is the load regulation capacity model, X comput (t) is the response curve of the calculated load (specifically, the response curve of the calculated load in time period t), H comput P(t) is the load curve of the calculated load (specifically, the load curve of the calculated load in time period t), and P(t) is the incentive price (specifically, the incentive price in time period t).

[0096] S203, inputting the response curve of the electrolytic copper load, the load curve of the electrolytic copper load and the incentive price into a pre-built electrolytic copper load regulation capacity model to determine the time-divided regulation capacity of the electrolytic copper load.

[0097] Among them, the pre-built regulation capacity model of electrolytic copper load is as follows:

[0098]

[0099] In the regulation capacity model of electrolytic copper load, is the time-division regulation capability of the electrolytic copper load (specifically, the regulation capability of the electrolytic copper load in time period t), TFT copper () is the regulation capacity model of electrolytic copper load, X copper(t) is the response curve of electrolytic copper load (specifically, the response curve of electrolytic copper load in time period t), H copper P(t) is the load curve of the electrolytic copper load (specifically, the load curve of the electrolytic copper load in time period t), and P(t) is the incentive price (specifically, the incentive price in time period t).

[0100] S204, input the time-division regulation capability of the electric water heater load, the time-division regulation capability of the calculation load, and the time-division regulation capability of the electrolytic copper load into the time-division regulation capability evaluation model of the regional power grid to determine the time-division regulation capability of the regional power grid.

[0101] Among them, the time-division regulation capability evaluation model of the regional power grid is as follows:

[0102]

[0103] in, is the time-division regulation capability of the regional power grid (specifically, the regulation capability of the regional power grid in time period t), TFT grid () is the time-division regulation capability assessment model of the regional power grid, is the time-segment regulation capability of the electric water heater load (specifically, the regulation capability of the electric water heater load in time period t), To calculate the load regulation capacity in different time periods (specifically, to calculate the load regulation capacity in time period t), It is the regulation capacity of the electrolytic copper load in different time periods (specifically, the regulation capacity of the electrolytic copper load in time period t).

[0104] It should be noted that, as shown in the above-mentioned time-phased adjustment capability evaluation model of each resource, the above-mentioned models are all constructed based on the temporal fusion transformer (TFT) algorithm. Specifically, the initial model is as follows:

[0105]

[0106] in, is the time-based regulation capability of resources (specifically, the regulation capability of resources in time period t), i is the type of resource (such as the electric water heater load, computing load and electrolytic copper load mentioned above), TFT() is the time-based regulation capability evaluation model of resources built based on the time fusion algorithm, P(t) is the incentive price (specifically, the incentive price in time period t), and the loss function of the above model is as follows (mainly used to evaluate the error between the model prediction value and the actual value):

[0107]

[0108] Among them, L is the model prediction value, T is the total time, and t is the time period. is the predicted resource adjustment capability in different time periods, Y i (t) is the actual resource adjustment capability in different time periods.

[0109] It is understandable that the incentive price in the above formula is generally used to represent an economic means for incentivizing or adjusting load-side behavior in the power market. Exemplarily, the incentive price can be a compensation or reward mechanism for the operating cost, efficiency or availability of certain types of power generation resources (such as renewable energy generation) or power equipment (such as demand response resources). The incentive price P(t) is independent of the type of resource, so the same incentive price is used in the above models.

[0110] In some embodiments, Figure 3 As shown in FIG. 1 , the steps of constructing a time-division regulation capability evaluation model for a regional power grid include:

[0111] S301. Construct an initial capability assessment model based on a time fusion algorithm.

[0112] For example, an initial capacity assessment model is constructed based on a time fusion algorithm, the regulation capacity of resources is selected as the input of the model, and the time-divided regulation capacity of the regional power grid is selected as the output of the model. The architecture of the initial capacity assessment model is designed, such as convolutional layers, recurrent layers, and fusion layers, to ensure that the model can effectively capture local and global features in the relevant data.

[0113] S302: Based on the acquired historical regulation capabilities of the resources and the historical regulation capabilities of the regional power grid, an initial capability assessment model is trained to generate a time-division regulation capability assessment model of the regional power grid.

[0114] Exemplarily, the historical regulation capacity of resources and the historical regulation capacity of regional power grids are divided into training sets and test sets, the training set is used for model training, and the test set is used for model verification and evaluation. The initial capacity assessment model is trained using the training set, and the model parameters are adjusted by an optimization algorithm (such as gradient descent) to minimize the difference between the predicted value and the actual value. Furthermore, the trained initial capacity assessment model is verified using the test set to evaluate the prediction accuracy and generalization ability of the model, and the model is adjusted and optimized according to the verification results, such as adjusting the model architecture, adding or reducing features, adjusting training parameters, etc., and finally generating a time-divided regulation capacity assessment model for the regional power grid.

[0115] In some embodiments, Figure 4 As shown, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, including:

[0116] S401: Input new energy prediction information into a pre-built power prediction model to determine the new energy power in different time periods.

[0117] The input of the power prediction model is as follows:

[0118] X D (t) = X load (t),X speed (t),X direct (t),X solar (t),X temper (t),X humid (t),X press (t)

[0119] In the above formula, X D (t) is the input data of the power prediction model, X load (t) is the new energy load data of period t, X speed (t) is the wind speed information of time period t, X direct (t) is the wind direction information in time period t, X solar (t) is the light intensity information of time period t, X temper (t) is the temperature information of time period t, X humid (t) is the air humidity information of time period t, X press (t) is the air pressure information of time period t.

[0120] S402: Determine the time-division regulation demand of the regional power grid based on the new energy power in each time period and the pre-acquired target demand power.

[0121] In some embodiments, the new energy power in different time periods and the pre-acquired target demand power are input into a pre-built demand model to determine the time-based regulation demand of the regional power grid.

[0122] Among them, the pre-built demand model is as follows:

[0123]

[0124] P demand (t+1)=max(0,ΔP(t+1))

[0125] In the above formula, is the predicted renewable energy power in different time periods, P plan (t+1) is the target power demand obtained in advance, ΔP(t+1) is the power value exceeding the target power demand, P demand (t+1) is the time-division regulation demand of the regional power grid. In the above formula, the power value exceeding the target power demand is determined by the difference between the new energy power in the time period and the pre-acquired target power demand. Further, the time-division regulation demand of the regional power grid is determined based on the power value exceeding the target power demand.

[0126] It can be understood that when ΔP(t+1) is greater than 0, the power value of renewable energy generation is greater than the pre-acquired target power demand, and it is necessary to call on the internal resources of the regional power grid for adjustment. When ΔP(t+1) is less than or equal to 0, the power value of renewable energy generation is less than or equal to the pre-acquired target power demand, and no adjustment is required, and the time-sharing adjustment demand of the regional power grid is 0.

[0127] In some embodiments, the step of constructing a power prediction model includes:

[0128] A power prediction model is generated based on the denoising diffusion probabilistic models (DDPM) obtained by training the historical prediction information of new energy and the historical power of new energy.

[0129] For example, new energy prediction information is selected as the input of the DDPM model, new energy power is selected as the output of the DDPM model, and the architecture of the DDPM model is designed, such as convolutional layer, recurrent layer and fusion layer. In addition, the noise of data collection needs to be considered to avoid affecting the prediction results of the model.

[0130] In addition, after designing the architecture of the DDPM model, it needs to be trained to eventually generate a power prediction model.

[0131] Exemplarily, the acquired new energy historical prediction information and new energy historical power are divided into a training set and a test set, the training set is used for model training, and the test set is used for model verification and evaluation. The initial capacity evaluation model is trained using the training set, and the model parameters are adjusted by an optimization algorithm (such as gradient descent) to minimize the difference between the predicted value and the actual value. Furthermore, the trained initial capacity evaluation model is verified using the test set to evaluate the prediction accuracy and generalization ability of the model, and the model is adjusted and optimized according to the verification results, such as adjusting the model architecture, adding or reducing features, adjusting training parameters, etc., to finally generate a power prediction model.

[0132] It should be noted that the training process of the power prediction model includes a forward diffusion process and a reverse diffusion process. The forward diffusion process is used to train the input data X D (t) Add noise ε to construct a noisy time series. The forward diffusion process is as follows:

[0133]

[0134] In the above formula, is the k-th step of the noisy time series, α (k)is the adjustment parameter for controlling the noise size in the kth step, ε is the added noise, and K is the total number of diffusion steps.

[0135] The reverse diffusion process is used to gradually remove noise from the power prediction model and predict the renewable energy power in different time periods. The reverse diffusion process is as follows:

[0136]

[0137] In the above formula, is the predicted renewable energy power in different time periods, DDPM() is the inverse diffusion process model of the power prediction model built based on the denoising diffusion model, is the k-th step noisy time series, and ε is the added noise.

[0138] In some embodiments, determining the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand may be specifically implemented as the following steps:

[0139] The time-division regulation capacity and time-division regulation demand are input into a pre-built surplus regulation capacity determination model to determine the surplus regulation capacity of the regional power grid.

[0140] The remaining regulation capacity determination model is as follows:

[0141]

[0142] In the above formula, TJ(t) is the remaining regulation capacity of the regional power grid (specifically, the remaining regulation capacity of the regional power grid in time period t), is the time-division regulation capability of the regional power grid (specifically, the time-division regulation capability of the regional power grid in time period t), P demand (t) is the time-divided regulation demand of the regional power grid (specifically, the time-divided regulation demand of the regional power grid in time period t), and the remaining regulation capacity of the regional power grid is used to characterize the adequacy of the regulation capacity of the regional power grid.

[0143] The technical solution provided in the embodiment of the present application determines the time-division regulation capacity of the regional power grid by inputting multiple variables in the operation information of the resource into the time-division regulation capacity evaluation model of the regional power grid, thereby improving the accuracy of the calculation. Compared with a simple linear regression or time series model, it can better reflect the influence of multiple parameters in the power grid, making the determination of the time-division regulation capacity more accurate. Furthermore, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, taking into account the characteristics of new energy fluctuations, making the determination process more accurate and comprehensive. Ultimately, the remaining regulation capacity of the regional power grid is determined by the time-division regulation capacity and time-division regulation demand of the regional power grid, which can help power grid operators identify supply and demand gaps, optimize the power grid dispatch plan, and reduce operating costs.

[0144] It should be noted that the information collected in this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0145] It should be noted that the technical solution provided in this application provides users with corresponding operation entrances for them to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered.

[0146] Figure 5 A device for determining the regulation capability of a regional power grid is provided in an embodiment of the present application. The device for determining the regulation capability of a regional power grid is used to execute the above-mentioned method for determining the regulation capability of a regional power grid, such as Figure 5 As shown, the device for determining the regional power grid regulation capability includes: an acquisition module, a first determination module, a second determination module and a third determination module.

[0147] The acquisition module is used to obtain resource operation information and new energy forecast information. The operation information includes resource response curve, load curve and incentive price;

[0148] A first determination module is used to determine the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price and a pre-constructed time-division regulation capability evaluation model of the regional power grid;

[0149] The second determination module is used to determine the time-division regulation demand of the regional power grid based on the new energy prediction information;

[0150] The third determination module is used to determine the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand.

[0151] Furthermore, the second determining module includes:

[0152] A power determination unit, used to input the new energy prediction information into a pre-built power prediction model to determine the new energy power in different time periods;

[0153] The demand determination unit determines the time-division regulation demand of the regional power grid based on the new energy power in the time periods and the pre-acquired target demand power.

[0154] Furthermore, the steps of constructing the power prediction model include:

[0155] A power prediction model is generated based on the acquired new energy historical prediction information and the denoising diffusion model acquired through new energy historical power training.

[0156] Further, the resources include: electric water heater load, calculated load and electrolytic copper load; the response curve of the resources includes: the response curve of the electric water heater load, the response curve of the calculated load and the response curve of the electrolytic copper load; the load curve of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; the incentive price of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load;

[0157] The first determination module includes:

[0158] A first determination unit is used to input the response curve of the electric water heater load, the load curve of the electric water heater load and the incentive price into a pre-built regulation capacity model of the electric water heater load to determine the regulation capacity of the electric water heater load in different time periods;

[0159] A second determination unit is used to input the response curve of the calculated load, the load curve of the calculated load and the incentive price into a pre-built adjustment capacity model of the calculated load to determine the time-division adjustment capacity of the calculated load;

[0160] A third determination unit is used to input the response curve of the electrolytic copper load, the load curve of the electrolytic copper load and the incentive price into a pre-built electrolytic copper load regulation capacity model to determine the time-division regulation capacity of the electrolytic copper load;

[0161] The fourth determination unit inputs the time-division regulation capacity of the electric water heater load, the time-division regulation capacity of the calculation load and the time-division regulation capacity of the electrolytic copper load into the time-division regulation capacity evaluation model of the regional power grid to determine the time-division regulation capacity of the regional power grid.

[0162] Furthermore, the steps of constructing a time-division regulation capability evaluation model for a regional power grid include:

[0163] Construct an initial capability assessment model based on the time fusion algorithm;

[0164] Based on the historical regulation capabilities of the acquired resources and the historical regulation capabilities of the regional power grid, an initial capacity assessment model is trained to generate a time-division regulation capacity assessment model for the regional power grid.

[0165] Furthermore, the third determination module is specifically used for:

[0166] The time-division regulation capacity and time-division regulation demand are input into a pre-built surplus regulation capacity determination model to determine the surplus regulation capacity of the regional power grid.

[0167] The technical solution provided in the embodiment of the present application determines the time-division regulation capacity of the regional power grid by inputting multiple variables in the operation information of the resource into the time-division regulation capacity evaluation model of the regional power grid, thereby improving the accuracy of the calculation. Compared with a simple linear regression or time series model, it can better reflect the influence of multiple parameters in the power grid, making the determination of the time-division regulation capacity more accurate. Furthermore, the time-division regulation demand of the regional power grid is determined based on the new energy forecast information, taking into account the characteristics of new energy fluctuations, making the determination process more accurate and comprehensive. Ultimately, the remaining regulation capacity of the regional power grid is determined by the time-division regulation capacity and time-division regulation demand of the regional power grid, which can help power grid operators identify supply and demand gaps, optimize the power grid dispatch plan, and reduce operating costs.

[0168] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer device, and specifically, the computer device may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0169] An embodiment of the present invention provides a computer device, including a memory and a processor, the memory is used to store information including program instructions, the processor is used to control the execution of the program instructions, and when the program instructions are loaded and executed by the processor, the steps of the embodiment of the method for determining the regulation capability of the regional power grid are implemented. For a specific description, please refer to the embodiment of the method for determining the regulation capability of the regional power grid.

[0170] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer device 600 suitable for implementing an embodiment of the present application.

[0171] like Figure 6 As shown, the computer device 600 includes a central processing unit (CPU) 601, which can perform various appropriate operations and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the computer device 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0172] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal feedback device (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed as needed as the storage section 608.

[0173] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program tangibly contained on a machine-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 609, and / or installed from the removable medium 611.

[0174] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0175] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0176] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0177] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0178] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0179] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0180] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0181] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0182] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0183] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0184] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0185] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for determining the regulation capability of a regional power grid, characterized in that: include: Acquiring resource operation information and new energy forecast information, wherein the operation information includes a response curve, a load curve, and an incentive price of the resource; Determining the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price and a pre-constructed time-division regulation capability evaluation model of the regional power grid; Determining the time-division regulation demand of the regional power grid based on the new energy forecast information; The remaining regulation capacity of the regional power grid is determined based on the time-division regulation capacity and the time-division regulation demand.

2. The method according to claim 1, characterized in that The determining of the time-division regulation demand of the regional power grid based on the new energy prediction information includes: Inputting the new energy prediction information into a pre-built power prediction model to determine the new energy power in different time periods; The time-division regulation demand of the regional power grid is determined based on the new energy power in the time division and the pre-acquired target demand power.

3. The method according to claim 2, characterized in that The steps of constructing the power prediction model include: The power prediction model is generated by training a denoising diffusion model acquired based on the acquired new energy historical prediction information and the new energy historical power.

4. The method according to claim 1, characterized in that: The resources include: electric water heater load, calculated load and electrolytic copper load; the response curves of the resources include: the response curve of the electric water heater load, the response curve of the calculated load and the response curve of the electrolytic copper load; the load curves of the resources include: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; the incentive price of the resources includes: the load curve of the electric water heater load, the load curve of the calculated load and the load curve of the electrolytic copper load; The determining the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price and a pre-built time-division regulation capability evaluation model of the regional power grid includes: Inputting the response curve of the electric water heater load, the load curve of the electric water heater load and the incentive price into a pre-built regulation capacity model of the electric water heater load to determine the regulation capacity of the electric water heater load in different time periods; Inputting the response curve of the computing load, the load curve of the computing load and the incentive price into a pre-built computing load regulation capacity model to determine the time-division regulation capacity of the computing load; Inputting the response curve of the electrolytic copper load, the load curve of the electrolytic copper load and the incentive price into a pre-built electrolytic copper load regulation capacity model to determine the time-division regulation capacity of the electrolytic copper load; The time-division regulation capability of the electric water heater load, the time-division regulation capability of the calculation load and the time-division regulation capability of the electrolytic copper load are input into the time-division regulation capability evaluation model of the regional power grid to determine the time-division regulation capability of the regional power grid.

5. The method according to claim 4, characterized in that The steps of constructing the time-division regulation capability evaluation model of the regional power grid include: Construct an initial capability assessment model based on the time fusion algorithm; The initial capacity evaluation model is trained based on the acquired historical regulation capacity of the resource and the historical regulation capacity of the regional power grid to generate a time-division regulation capacity evaluation model of the regional power grid.

6. The method according to claim 1, characterized in that The determining the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand includes: The time-division regulation capacity and the time-division regulation demand are input into a pre-built surplus regulation capacity determination model to determine the surplus regulation capacity of the regional power grid.

7. A device for determining the regulation capability of a regional power grid, characterized in that: include: An acquisition module, used to acquire resource operation information and new energy forecast information, wherein the operation information includes a response curve, a load curve and an incentive price of the resource; A first determination module is used to determine the time-division regulation capability of the regional power grid based on the response curve, the load curve, the incentive price and a pre-constructed time-division regulation capability evaluation model of the regional power grid; A second determination module is used to determine the time-division regulation demand of the regional power grid based on the new energy prediction information; The third determination module is used to determine the remaining regulation capacity of the regional power grid based on the time-division regulation capacity and the time-division regulation demand.

8. An electronic 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 program, the steps of the method for determining the regional power grid regulation capability described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for determining the regional power grid regulation capability as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instruction is executed by a processor, the steps of the method for determining the regional power grid regulation capability as described in any one of claims 1 to 6 are implemented.

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