Three-stage integrated energy system demand response strategy optimization method and system

Abstract

The invention discloses a three-stage integrated energy system demand response strategy optimization method and system, relates to the field of integrated energy optimization scheduling, and remarkably enhances the flexibility and response capability of a user side. The method comprises the steps of performing price demand response on a typical energy consumption main body based on a price elasticity matrix and load data of the typical energy consumption main body to obtain a load curve, and determining an initial output plan according to the load curve; determining a plurality of item data corresponding to the integrated energy system, and comparing each item data with a corresponding preset range; and if any one of the plurality of comparison results indicates that the project data is not in the preset range, determining the project data corresponding to the comparison result, and optimizing the initial output plan according to an optimization strategy associated with the project data until the plurality of latest project data are all in the corresponding preset range, thereby obtaining a target output plan.

Description

Technical Field

[0001] The present application relates to the field of integrated energy optimization and scheduling, and in particular to a method and system for optimizing a demand response strategy for a three-stage integrated energy system. Background Art

[0002] Under the severe challenges of global energy transformation and climate change, the optimized operation of integrated energy systems has become a key link in achieving efficient energy utilization and promoting low-carbon development. With the rapid development of demand-side management technology, achieving a dynamic balance between energy supply and demand through intelligent control has become an important way to improve the flexibility of energy systems and reduce carbon emissions. Among them, demand-side response, as a proactive means of supply and demand regulation, can guide users to change their electricity consumption behavior, enhance user participation and satisfaction, and is an important means to achieve "peak shaving and valley filling" and promote electricity market reform.

[0003] The current demand response strategy is to divide a day into peak, valley, and flat periods, and then formulate time-of-use electricity prices to guide users to use electricity and achieve "peak shaving and valley filling". However, the existing demand response strategy is relatively rigid and does not take into account the impact of carbon emissions, user comfort, and grid load rate on prices. Summary of the invention

[0004] In view of this, the present application provides a three-stage integrated energy system demand response strategy optimization method and system, the main purpose of which is to solve the problem that the current demand response strategy is relatively rigid and does not take into account the impact of carbon emissions, user comfort and grid load rate on prices.

[0005] According to the first aspect of the present application, a three-stage integrated energy system demand response strategy optimization method is provided, the method comprising:

[0006] Determine the load data of typical energy users in the integrated energy system, and set the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users, and industrial park energy users;

[0007] Based on the pre-constructed price elasticity matrix and the load data, a price demand response is performed on the typical energy-consuming entity to obtain a load curve corresponding to the typical energy-consuming entity, and an initial output plan is determined according to the load curve, wherein the initial output plan is a rural community output plan, an agricultural park output plan, and an industrial park output plan optimized by an alternating multiplier method;

[0008] According to the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate, and the user comfort is a single comfort index converted from electricity comfort and heat comfort;

[0009] If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, determining the project data corresponding to the comparison result, and optimizing the initial output plan according to the optimization strategy associated with the project data;

[0010] According to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, each project data is compared with the corresponding preset range, and when any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, thereby obtaining a target output plan.

[0011] Optionally, performing price demand response for the typical energy-consuming subject based on the pre-constructed price elasticity matrix and the load data, obtaining a load curve corresponding to the typical energy-consuming subject, and determining an initial output plan according to the load curve include:

[0012] The coefficient of variation is constructed based on the following formula,

[0013]

[0014] in, represents the initial amount of electric load in period t; P all Indicates the initial total amount of electrical load; T is 24h; represents the initial average value of the electric load; η is the real-time price fluctuation factor; represents the initial electricity price in period t; C(t) represents the real-time electricity price in period t, and θ (θ = θ1 + θ2 + θ3) represents the price fluctuation coefficient;

[0015] The autoelastic coefficient is constructed based on the following formula,

[0016]

[0017] Among them, ΔQ i and Q i is the change in electricity demand in time period i and the initial electricity demand; ΔP i and P i is the change in electricity price in time period i and the initial electricity price;

[0018] The cross elasticity matrix is ​​constructed based on the following formula:

[0019]

[0020] Among them, ΔP j and P j is the change in electricity price in time period j and the initial electricity price;

[0021] The price elasticity matrix is ​​constructed based on the self-variation coefficient, the self-elasticity coefficient and the cross-elasticity coefficient.

[0022]

[0023] Based on the following formula, the load value after price demand response is calculated using the self-variation coefficient and the price elasticity matrix:

[0024]

[0025] Among them, P DE (t) is the load value after participating in demand response in period t;

[0026] generating the load curve according to the load value, and determining an output plan matching the load curve based on the load curve;

[0027] The output plan is optimized by using an alternating multiplier method to obtain the initial output plan.

[0028] Optionally, the adopting the alternating multiplier method to optimize the output plan to obtain the initial output plan includes:

[0029] defining a Lagrange multiplier vector and a regularization parameter, and initializing optimization variables according to the output plan;

[0030] For each iteration, a Lagrangian function is constructed to optimize the optimization variables, and the optimized optimization variables are used to update the Lagrangian multiplier vector until the change in the optimization variables meets a preset convergence condition, thereby obtaining the initial output plan.

[0031] Optionally, the determining, based on the initial output plan, a plurality of project data corresponding to the integrated energy system, and comparing each project data with a corresponding preset range to obtain a plurality of comparison results, including:

[0032] According to the initial output plan, the carbon emissions are calculated based on the following formula:

[0033]

[0034] in, The actual carbon emissions of typical energy users; They represent the actual carbon emissions from electricity purchases from the power grid, gas boilers, and combined cooling, heating, and power; Indicates the CO2 consumed by power-to-gas conversion; T is 24h; α n , β n , γ n (n=1, 2, 3) represent the carbon emission calculation parameters of online electricity purchase, gas boiler and combined heating and cooling power generation respectively; P buy (t) represents the power purchased by the power grid during period t; P CCHP H is the equivalent power of each device in the combined cooling, heating and power generation; GB (t) represents the thermal power of the gas boiler during period t; P CCHP,e (t), P CCHP,h (t), Q CCHP,c (t) respectively represent the electric, thermal and cooling output power of the combined cooling, heating and power generation in the time period t; δ represents the carbon capture coefficient; the carbon emission is compared with the preset carbon emission range to obtain a first comparison result;

[0035] According to the initial output plan, the electricity comfort and heat comfort are calculated respectively, and the electricity comfort is calculated using the following formula:

[0036]

[0037] Among them, P cut (t) represents the load that can be reduced in period t; P shift (t) represents the load that can be translated in time period t; P load (t) represents the total electrical load in time period t, and the indoor illumination is calculated using the following formula:

[0038]

[0039] Where ρ represents the lighting power density; S represents the total lighting area; E i (t) represents the sunlight intensity at the i-th test point at time t; P l (t) represents the lighting load when natural lighting cannot meet the indoor illumination requirements at time t; n represents the number of lighting points;

[0040] The thermal comfort is calculated according to the following formula:

[0041] c2=α4T+β4E l (t)-γ4E l (t) 2 +D,

[0042] Among them, α4, β4, γ4, and D are correlation coefficients obtained by multivariate nonlinear regression analysis of actual data; T represents the correlated color temperature;

[0043] Based on the following formula, the electricity comfort and the heat comfort are converted into a single comfort index:

[0044]

[0045] Where M represents the theoretical minimum value of a single index; c i A dimensionless function representing a single indicator; a weight ratio of electricity comfort to heat comfort is set to [α1, α2] (α1+α2=1), and the single comfort indicator is compared with a preset comfort range to obtain a second comparison result;

[0046] According to the initial output plan, the grid load rate of the typical energy-consuming entity is calculated, and the grid load rate is compared with a preset grid load rate range to obtain a third comparison result.

[0047] Optionally, if any comparison result among the multiple comparison results indicates that the project data is not within a preset range, determining the project data corresponding to the comparison result, and optimizing the initial output plan according to an optimization strategy associated with the project data, includes:

[0048] If the first comparison result indicates that the carbon emissions are not within the preset carbon emissions range, the carbon emission index and carbon emissions of the typical energy user are determined according to the initial output plan, and the carbon emission index is the sum of the carbon emission index of the gas boiler, the carbon emission index of the combined cooling, heating and power generation, and the carbon emission index of the power grid purchased in the typical energy user;

[0049] According to the carbon emission index and the carbon emission amount, a carbon price model of the typical energy-consuming entity is constructed.

[0050]

[0051] Among them, F c represents the reward / penalty carbon price of the integrated energy system; p represents the basic carbon price; ε, κ represent the reward / penalty coefficients; l represents the interval length of carbon emissions, and the carbon price model is processed by interval dimensionless processing to obtain the price fluctuation coefficient of the typical energy-consuming entity

[0052]

[0053] Among them, e1 represents the converted price fluctuation coefficient, and the self-variation coefficient of the typical energy consumer is updated according to the price fluctuation coefficient, and the load curve of the typical energy consumer is optimized according to the updated self-variation coefficient, and the initial output plan is updated using the optimized load curve.

[0054] Optionally, if any comparison result among the multiple comparison results indicates that the project data is not within a preset range, determining the project data corresponding to the comparison result, optimizing the initial output plan according to an optimization strategy associated with the project data, and obtaining a target output plan, includes:

[0055] If the second comparison result indicates that the single comfort index is not within the preset comfort range, the single comfort index is normalized and calculated to obtain the price fluctuation coefficient of the typical energy user.

[0056]

[0057] Among them, c min With c max are the limit values ​​of comfort rewards and penalties respectively, e2 is the converted price impact coefficient, the self-variation coefficient of the typical energy user is updated according to the price fluctuation coefficient, and the load curve of the typical energy user is optimized according to the updated self-variation coefficient, and the initial output plan is updated using the optimized load curve.

[0058] Optionally, if any comparison result among the multiple comparison results indicates that the project data is not within a preset range, determining the project data corresponding to the comparison result, optimizing the initial output plan according to an optimization strategy associated with the project data, and obtaining a target output plan, includes:

[0059] If the third comparison result indicates that the grid load rate is not within the preset grid load rate range, the difference between the actual power purchase amount of the typical energy consumer and the range specified by the grid is calculated, and the difference is normalized and dimensionless to obtain the price fluctuation coefficient of the typical energy consumer.

[0060]

[0061] Among them, P max With P min are the upper and lower limits of the grid load respectively, P represents the amount of electricity purchased by the comprehensive energy system from the grid, e3 is the converted price impact coefficient, the self-variation coefficient of the typical energy consumer is updated according to the price fluctuation coefficient, and the load curve of the typical energy consumer is optimized according to the updated self-variation coefficient, and the initial output plan is updated using the optimized load curve.

[0062] According to the second aspect of the present application, a stage integrated energy system demand response strategy optimization system is provided, the system comprising:

[0063] The first determination module is used to determine the load data of typical energy users in the integrated energy system and set the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users and industrial park energy users;

[0064] A second determination module is used to respond to the price demand of the typical energy-consuming subject based on the pre-constructed price elasticity matrix and the load data, obtain the load curve corresponding to the typical energy-consuming subject, and determine the initial output plan according to the load curve, wherein the initial output plan is the rural community output plan, the agricultural park output plan and the industrial park output plan optimized by the alternating multiplier method;

[0065] A comparison module is used to determine multiple project data corresponding to the integrated energy system according to the initial output plan, and compare each project data with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate, and the user comfort is a single comfort index converted from electricity comfort and heat comfort;

[0066] An optimization module, configured to determine the project data corresponding to the comparison result if any comparison result among the plurality of comparison results indicates that the project data is not within a preset range, and optimize the initial output plan according to an optimization strategy associated with the project data;

[0067] The optimization module is used to re-determine multiple project data corresponding to the integrated energy system according to the optimized initial output plan, compare each project data with the corresponding preset range, and optimize the optimized initial output plan when any comparison result indicates that the project data is not within the preset range, until multiple comparison results all indicate that the project data is within the preset range, thereby obtaining a target output plan.

[0068] By means of the above technical scheme, the present application provides a three-stage integrated energy system demand response strategy optimization method and system. The present application first determines the load data of typical energy users in the integrated energy system, and sets the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users, and industrial park energy users. Next, based on the pre-constructed price elasticity matrix and load data, the price demand response is performed on the typical energy users, and the load curve corresponding to the typical energy users is obtained, and the initial output plan is determined according to the load curve. Further, according to the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with the corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort, and grid load rate. If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, the project data corresponding to the comparison result is determined, and the initial output plan is optimized according to the optimization strategy associated with the project data. Finally, according to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, and each project data is compared with the corresponding preset range. When any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, and the target output plan is obtained. This application not only optimizes the time-of-use electricity price strategy, but also enhances the economic driving force of small-scale users, allowing small-scale users to participate more actively in demand response. In addition, this method comprehensively considers multiple factors such as carbon emissions, user comfort, and grid load rate, and performs demand response in stages to flexibly respond to the fluctuations and uncertainties of new energy output, thereby significantly improving the overall flexibility and stability of the power system. This method helps to improve the overall efficiency of the energy system, reduce carbon emissions, and provide an innovative solution for promoting low-carbon development and responding to global energy challenges.

[0069] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0071] Figure 1A schematic diagram of a three-stage integrated energy system demand response strategy optimization method provided in an embodiment of the present application is shown;

[0072] Figure 2 A schematic diagram of a three-stage integrated energy system demand response strategy optimization method provided in an embodiment of the present application is shown;

[0073] Figure 3 A schematic diagram of a three-stage integrated energy system demand response strategy optimization method provided in an embodiment of the present application is shown;

[0074] Figure 4 A structural schematic diagram of a three-stage integrated energy system demand response strategy optimization system provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0075] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0076] Under the severe challenges of global energy transformation and climate change, the optimized operation of integrated energy systems has become a key link in achieving efficient energy utilization and promoting low-carbon development. With the rapid development of demand-side management technology, achieving a dynamic balance of energy supply and demand through intelligent control has become an important way to improve the flexibility of energy systems and reduce carbon emissions. However, traditional energy systems often ignore the flexibility and responsiveness of the user side, resulting in low energy utilization efficiency and difficulty in effectively responding to sudden changes in energy supply and demand. Demand-side response, as an active means of supply and demand regulation, can guide users to change their electricity consumption behavior, enhance user participation and satisfaction, and is an important means to achieve "peak shaving and valley filling" and promote electricity market reform. In existing studies, researchers usually divide a day into multiple periods of peak, valley and flat, and formulate time-of-use electricity prices to guide users to use electricity, but the effects of price incentives vary significantly for different types of users. For small-scale users, due to low electricity consumption and high response costs, existing price incentives often fail to generate sufficient economic driving force, while existing demand responses are relatively rigid and do not take into account the impact of carbon emissions, user comfort and grid load rate on prices. Therefore, the embodiment of the present application provides a three-stage integrated energy system demand response strategy optimization method, such as Figure 1 As shown, the method includes:

[0077] 101. Determine the load data of typical energy users in the integrated energy system and set up a real-time electricity price strategy for the integrated energy system. Typical energy users include rural community energy users, agricultural park energy users and industrial park energy users.

[0078] In the embodiment of the present application, the method for obtaining the load data of typical energy users is to collect load data through smart meters, obtain meter data and forecast data from the power grid. Real-time electricity price is an electricity price system that is dynamically adjusted according to the short-term supply and demand balance of the electricity market and the operating cost of the power system. It aims to accurately reflect the value of electricity, guide users to optimize electricity consumption behavior, and promote the stability of the electricity market and the efficient use of energy.

[0079] 102. Based on the pre-constructed price elasticity matrix and load data, the price demand response is carried out for typical energy users, the load curve corresponding to the typical energy users is obtained, and the initial output plan is determined according to the load curve. The initial output plan is the rural community output plan, agricultural park output plan and industrial park output plan optimized by the alternating multiplier method.

[0080] In the embodiment of the present application, the system introduces the self-variation coefficient, the self-elasticity coefficient and the cross-elasticity coefficient on the basis of introducing the trial electricity price for the typical energy-consuming subject in the integrated energy system, constructs the price elasticity matrix, and then responds to the price demand of the typical energy-consuming subject according to the price elasticity matrix and the load data, obtains the load curve corresponding to the typical energy-consuming subject, and determines the initial output plan according to the load curve. It should be noted that the distributed optimization of the three types of typical energy-consuming subjects can be achieved by optimizing the power generation side output plan of the energy supply system of rural communities, agricultural parks and industrial parks by using the alternating direction multiplier method (ADMM). Distributed optimization is an advanced computing strategy that uses parallel collaboration and information sharing between multiple computing nodes or intelligent agents to jointly solve a global optimization problem. This method decomposes large-scale complex problems into multiple sub-problems, assigns them to different computing nodes for processing, and then uses efficient communication protocols and coordination mechanisms to integrate the local solutions of each node into a global optimal solution. Distributed optimization not only improves computing efficiency, but also better adapts to complex and changeable network environments and dynamically changing optimization goals, and enhances the robustness and scalability of the system. In the fields of big data processing, cloud computing, smart grid, etc., distributed optimization has become one of the key technologies for solving large-scale optimization problems. In the embodiment of the present application, the output plan is formulated by the admm optimization algorithm according to the load curve of the power consumption side, and the specific process of determining the initial output plan is as follows:

[0081] 1. Based on the real-time electricity price, the self-variation coefficient, self-elasticity coefficient and cross-elasticity coefficient are introduced, and the price elasticity matrix is ​​constructed based on this.

[0082] Specifically, the coefficient of variation is first constructed based on the following formula 1:

[0083] Formula 1:

[0084] in, represents the initial amount of electric load in period t; P all Indicates the initial total amount of electrical load; T is 24h; represents the initial average value of the electric load; η is the real-time price fluctuation factor; represents the initial electricity price in period t; C(t) represents the real-time electricity price in period t, θ(θ=θ1+θ2+θ3) represents the price fluctuation coefficient; θ1, θ2, θ3 are the price fluctuation coefficient determined according to carbon emissions, the price fluctuation coefficient determined according to a single comfort index, and the price fluctuation coefficient determined according to the grid load rate, respectively.

[0085] Then, the autoelastic coefficient is constructed based on the following formula 2: ii Refers to the impact of electricity price changes in time period i on electricity demand in time period i

[0086] Formula 2:

[0087] Among them, ΔQ i and Q i is the change in electricity demand in time period i and the initial electricity demand; ΔP i and P i is the change in electricity price within time period i and the initial electricity price.

[0088] Next, the cross elasticity matrix is ​​constructed based on the following formula 3. The cross elasticity matrix ε ij represents the response of the change in electricity consumption in time period i to the change in electricity price in time period j,

[0089] Formula 3:

[0090] Among them, ΔP j and P j is the change in electricity price in time period j and the initial electricity price;

[0091] Finally, the price elasticity matrix is ​​constructed based on the self-variation coefficient, self-elasticity coefficient and cross-elasticity coefficient. A day is divided into n time periods, and the electricity price elasticity matrix E is expressed as:

[0092] Formula 4:

[0093] 2. Based on the pre-built price elasticity matrix and load data, the price demand response is carried out for typical energy users to obtain the load curve corresponding to the typical energy users.

[0094] Specifically, based on the following formula 5, the self-variation coefficient and the price elasticity matrix are used to calculate the load value after price demand response.

[0095] Formula 5:

[0096] Among them, P DE (t) is the load value after participating in demand response in period t.

[0097] 3. Generate a load curve based on the load value and determine the initial output plan based on the load curve.

[0098] In the embodiment of the present application, the system generates a load curve according to the load value, and determines the output plan matching the load curve based on the load curve. The output plan is optimized by the alternating multiplier method to obtain the initial output plan. Specifically, the Lagrange multiplier vector y is defined as (0) and regularization parameter ρ>0, and initialize the optimization variable x according to the output plan (0) and z (0) . Construct a Lagrangian function to optimize the optimization variable, and use the optimized optimization variable x (k+1) and z (k+1) Update the Lagrange multiplier vector y (k+1) , where α is the learning rate, for k = 0, 1, 2, ..., repeat the following steps:

[0099] (1)Update x (k+1) :x (k+1) =argmin x L(x,z (k) ,y (k) )

[0100] (2)Update z (k+1) :z (k+1) =argmin z L(x (k+1) ,z,y (k) )

[0101] (3)Update y (k+1) :y (k+1) =y (k) +ρ(Ax (k+1) +Bz (k+1) -c)

[0102] Until the change of the optimization variable meets the preset convergence condition, the initial output plan x is obtained. * =x(k+1) The convergence condition can be that the change in the optimization objective or the change in the optimization variable is less than a given threshold.

[0103] 103. According to the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with the corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate.

[0104] In the embodiment of the present application, the carbon emissions of the integrated energy system are first calculated according to the initial output plan. Since the park contains power-to-gas (P2G) equipment, which can convert part of CO2 into raw materials, the actual carbon emissions calculation can refer to the following formula 6:

[0105] Formula 6:

[0106] in, The actual carbon emissions of typical energy users; They represent the actual carbon emissions from electricity purchases from the power grid, gas boilers, and combined cooling, heating, and power; Indicates the CO2 consumed by power-to-gas conversion; T is 24h; α n , β n , γ n (n=1, 2, 3) represent the carbon emission calculation parameters of online electricity purchase, gas boiler and combined heating and cooling power generation respectively; P buy (t) represents the power purchased by the power grid during period t; P CCHP H is the equivalent power of each device in the combined cooling, heating and power generation; GB (t) represents the thermal power of the gas boiler during period t; P CCHP,e (t), P CCHP,h (t), Q CCHP,c (t) respectively represent the electric, thermal and cooling output power of the combined heat and power generation in time period t; δ represents the carbon capture coefficient; the carbon emissions are compared with the preset carbon emission range to obtain a first comparison result.

[0107] The user comfort is determined according to the initial output plan. The user comfort is a single comfort index converted from the electricity comfort and the heat comfort. Specifically, the electricity comfort and the heat comfort are calculated separately, and based on the following formula, the electricity comfort and the heat comfort are converted into a single comfort index. Specifically, the user's electricity comfort can be expressed as the ratio of the interruptible power load and the transferable power load participating in the dispatch in each period to the total power load. The dimensionless function of the power comfort is calculated using the following formula 7:

[0108] Formula 7:

[0109] Among them, P cut(t) represents the load that can be reduced in period t; P shift (t) represents the load that can be translated in time period t; P load (t) represents the total electrical load in time period t. When the indoor natural illumination is not enough to meet the comfort requirements, artificial lighting needs to be introduced through scientific calculations to supplement it to ensure that the indoor illumination meets the established standards. The illumination is calculated using the following formula 8:

[0110] Formula 8:

[0111] Where ρ represents the lighting power density; S represents the total lighting area; E i (t) represents the sunlight intensity at the i-th test point at time t, and the formula is shown in the following formula 9; P l (t) represents the lighting load when natural lighting cannot meet the indoor illumination requirements at time t; n represents the number of lighting points.

[0112] Formula 9: E i (t) = E zd (t)ω w (A+E sc (t)(ω D +ω S +ω R ))

[0113] Among them, E zd (t) represents the illuminance of direct sunlight on an unobstructed outdoor horizontal surface at time t; ω w Indicates the light transmittance of the window; A indicates the correction factor; E sc (t) represents the illuminance of sky scattered light on an outdoor unobstructed horizontal surface at time t; ω D Represents the direct light coefficient; ω S represents the sky light diffusion coefficient; ω R Represents the reflected light coefficient. Further, the dimensionless function of thermal comfort is calculated using the following formula 10:

[0114] Formula 10: c2=α4T+β4E l (t)-γ4E l (t) 2 +D

[0115] Among them, α4, β4, γ4, and D are correlation coefficients obtained by multivariate nonlinear regression analysis of actual data; T represents the correlated color temperature. The substitution synthesis method, i.e., the following formula 11, is used to synthesize the electricity comfort and the heat comfort into a single comfort. To avoid excessive fluctuations in comfort, the dimensionless interval processing is used to limit the minimum comfort index to the range of [M, 0]. The converted single comfort index is shown in the following formula 12:

[0116] Formula 11:

[0117] Formula 12:

[0118] Where M represents the theoretical minimum value of a single index; c i Represents a dimensionless function of a single indicator; ω i The weight of the function is represented, the weight ratio of thermal comfort to light comfort is set to [α1, α2] (α1+α2=1), and the single comfort index is compared with the preset comfort range to obtain the second comparison result.

[0119] According to the initial output plan, the grid load rate of the typical energy-consuming entity is calculated, and the grid load rate is compared with the preset grid load rate range to obtain the third comparison result. It should be noted that the grid load rate refers to the physical quantity of the ratio of the average load (electricity) to the maximum load, that is, "grid average load / grid designed maximum load".

[0120] 104. If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, the project data corresponding to the comparison result is determined, and the initial output plan is optimized according to the optimization strategy associated with the project data.

[0121] 1. If the first comparison result indicates that the carbon emissions are not within the preset carbon emissions range, the carbon emission index and carbon emissions of the typical energy-consuming entity are determined according to the initial output plan. The carbon emission index is the sum of the carbon emission index of the gas boiler, the carbon emission index of the combined heating and cooling power generation, and the carbon emission index of the power grid purchased electricity in the typical energy-consuming entity;

[0122] Based on carbon emission indicators and carbon emissions, a carbon price model for typical energy users is constructed, as shown in the following formula 13:

[0123] Formula 13:

[0124] Among them, F c represents the reward / penalty carbon price of the integrated energy system; p represents the basic carbon price; ε, κ represent the reward / penalty coefficients; l represents the interval length of carbon emissions, and the carbon price model is processed by interval dimensionless processing to obtain the price fluctuation coefficient of the typical energy-consuming entity. In order to avoid excessive fluctuations in carbon prices, the price fluctuation coefficient is obtained after interval dimensionless processing, as shown in the following formula 14:

[0125] Formula 14:

[0126] Wherein, e1 represents the price fluctuation coefficient after conversion, and the dimensionless formula of intervalization is shown in the following formula 15:

[0127] Formula 15:

[0128] And the self-variation coefficient of the typical energy-consuming entity is updated according to the price fluctuation coefficient. At this time, the trend range of the real-time electricity price in the self-variation coefficient can refer to the following formula 16:

[0129] Formula 16: C min (1+θ1)≤C(t)≤C max (1+θ1)

[0130] And optimize the load curve of the typical energy-consuming entity according to the updated self-variation coefficient, and use the optimized load curve to update the initial output plan.

[0131] 2. If the second comparison result indicates that the single comfort index is not within the preset comfort range, the single comfort index is normalized and calculated to obtain the price floating coefficient of the typical energy user, as shown in the following formula 17:

[0132] Formula 17:

[0133] Among them, c min With c max are the limit values ​​of comfort rewards and penalties respectively, e2 is the converted price impact coefficient, and the self-variation coefficient of the typical energy-consuming entity is updated according to the price fluctuation coefficient. At this time, the trend range of the real-time electricity price in the self-variation coefficient can refer to the following formula 18:

[0134] Formula 18: C min (1+θ1+θ2)≤C(t)≤C max (1+θ1+θ2)

[0135] And optimize the load curve of the typical energy-consuming entity according to the updated self-variation coefficient, and use the optimized load curve to update the initial output plan.

[0136] 3. If the third comparison result indicates that the grid load rate is not within the preset grid load rate range, the difference between the actual power purchase amount of the typical energy consumer and the range specified by the grid is calculated, and the difference is normalized and dimensionless to obtain the price floating coefficient of the typical energy consumer, as shown in the following formula 19:

[0137] Formula 19:

[0138] Among them, P max With P min are the upper and lower limits of the grid load, P represents the amount of electricity purchased by the integrated energy system from the grid, e3 is the converted price impact coefficient, and the self-variation coefficient of the typical energy-consuming entity is updated according to the price fluctuation coefficient. At this time, the trend range of the real-time electricity price in the self-variation coefficient can refer to the following formula 20:

[0139] Formula 20: C min (1+θ1+θ2+θ3)≤C(t)≤C max (1+θ1+θ2+θ3)

[0140] And optimize the load curve of the typical energy-consuming entity according to the updated self-variation coefficient, and use the optimized load curve to update the initial output plan.

[0141] 105. According to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, and each project data is compared with the corresponding preset range. When any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, thereby obtaining the target output plan.

[0142] In the actual operation process, Figure 2 and Figure 3As shown, the system will construct an initial price elasticity matrix based on the initial self-variation coefficient, self-elasticity coefficient and cross-elasticity coefficient, and then respond to the price demand of the integrated energy system based on the initial price elasticity matrix to obtain a load curve and an initial output plan. In the embodiment of the present application, the initial output plan indicates the output plan of each park energy supply equipment unit in the typical energy-consuming subject. Further, according to the initial output plan, it is judged whether the carbon emissions of the park are within the preset range. If it is not within the preset range, the energy side issues a price guidance factor, introduces the price guidance factor into the step carbon price and performs dimensionless processing to obtain a price floating coefficient related to the emission. Next, the self-variation coefficient is updated using the price floating coefficient, and then the initial output plan is updated using the new self-variation coefficient. According to the optimized initial output plan, it is judged again whether the carbon emissions of the park are within the preset range. If not, the initial output plan is continuously optimized until the carbon emissions of the park are within the preset range, and the next step of comfort judgment is performed. According to the initial output plan, the electricity comfort and heat comfort are determined, and the electricity comfort and heat comfort are converted into a single comfort index to judge whether the single comfort index is within the preset range. If it is not within the preset range, the energy side issues a price guidance factor, introduces the price guidance factor into the electricity and / or heat comfort and performs dimensionless processing to obtain a price floating coefficient related to the user comfort. Next, the price floating coefficient is used to update the self-variation coefficient, and then the new self-variation coefficient is used to update the initial output plan. According to the optimized initial output plan, it is judged again whether the single comfort index is within the preset range. If not, the initial output plan is continuously optimized until the single comfort index is within the preset range, and the next step of grid load rate judgment is performed. According to the initial output plan, it is judged whether the grid load rate is within the preset range. If it is not within the preset range, the energy side issues a price guidance factor, introduces the price guidance factor into the source-load supply and demand balance and performs dimensionless processing to obtain a price floating coefficient related to the grid load rate. Next, the price floating coefficient is used to update the self-variation coefficient, and then the new self-variation coefficient is used to update the initial output plan. According to the optimized initial output plan, it is judged again whether the park grid load rate is within the preset range. If not, the initial output plan is continuously optimized until the park grid load rate is within the preset range, and the target output plan is obtained.

[0143] The embodiment of the present application formulates guiding factors based on carbon emissions, user comfort, and grid load rate as targets, and performs demand response in three stages, which better alleviates the problem of tight power supply and demand and further enhances user participation and satisfaction.

[0144] The method provided in the embodiment of the present application first determines the load data of typical energy users in the integrated energy system, and sets the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users, and industrial park energy users. Next, based on the pre-constructed price elasticity matrix and load data, a price demand response is performed on the typical energy users, and a load curve corresponding to the typical energy users is obtained, and an initial output plan is determined based on the load curve. Further, based on the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort, and grid load rate. If any of the multiple comparison results indicates that the project data is not within the preset range, the project data corresponding to the comparison result is determined, and the initial output plan is optimized according to the optimization strategy associated with the project data. Finally, according to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, and each project data is compared with the corresponding preset range. When any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, and the target output plan is obtained. This application not only optimizes the time-of-use electricity price strategy, but also enhances the economic driving force of small-scale users, allowing small-scale users to participate more actively in demand response. In addition, this method comprehensively considers multiple factors such as carbon emissions, user comfort, and grid load rate, and performs demand response in stages to flexibly respond to the fluctuations and uncertainties of new energy output, thereby significantly improving the overall flexibility and stability of the power system. This method helps to improve the overall efficiency of the energy system, reduce carbon emissions, and provide an innovative solution for promoting low-carbon development and responding to global energy challenges.

[0145] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a three-stage integrated energy system demand response strategy optimization system, such as Figure 4 As shown, the device includes: a first determination module 401, a second determination module 402, a comparison module 403, and an optimization module 404.

[0146] The first determination module 401 is used to determine the load data of typical energy users in the integrated energy system and set the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users and industrial park energy users;

[0147] The second determination module 402 is used to perform price demand response for the typical energy-consuming subject based on the pre-constructed price elasticity matrix and the load data, obtain the load curve corresponding to the typical energy-consuming subject, and determine the initial output plan according to the load curve, wherein the initial output plan is the rural community output plan, the agricultural park output plan and the industrial park output plan optimized by the alternating multiplier method;

[0148] The comparison module 403 is used to determine multiple project data corresponding to the integrated energy system according to the initial output plan, and compare each project data with the corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate, and the user comfort is a single comfort index converted from electricity comfort and heat comfort;

[0149] The optimization module 404 is used to determine the project data corresponding to the comparison result if any comparison result among the multiple comparison results indicates that the project data is not within the preset range, and optimize the initial output plan according to the optimization strategy associated with the project data;

[0150] The optimization module 404 is used to re-determine multiple project data corresponding to the integrated energy system based on the optimized initial output plan, compare each project data with the corresponding preset range, and optimize the optimized initial output plan when any comparison result indicates that the project data is not within the preset range, until multiple comparison results all indicate that the project data is within the preset range, thereby obtaining a target output plan.

[0151] The system provided in the embodiment of the present application first determines the load data of typical energy users in the integrated energy system, and sets the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users, and industrial park energy users. Next, based on the pre-constructed price elasticity matrix and load data, a price demand response is performed on the typical energy users, and a load curve corresponding to the typical energy users is obtained, and an initial output plan is determined based on the load curve. Further, based on the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort, and grid load rate. If any of the multiple comparison results indicates that the project data is not within the preset range, the project data corresponding to the comparison result is determined, and the initial output plan is optimized according to the optimization strategy associated with the project data. Finally, according to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, and each project data is compared with the corresponding preset range. When any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, and the target output plan is obtained. This application not only optimizes the time-of-use electricity price strategy, but also enhances the economic driving force of small-scale users, allowing small-scale users to participate more actively in demand response. In addition, this method comprehensively considers multiple factors such as carbon emissions, user comfort, and grid load rate, and performs demand response in stages to flexibly respond to the fluctuations and uncertainties of new energy output, thereby significantly improving the overall flexibility and stability of the power system. This method helps to improve the overall efficiency of the energy system, reduce carbon emissions, and provide an innovative solution for promoting low-carbon development and responding to global energy challenges.

[0152] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by hardware, or by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0153] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present application.

[0154] Those skilled in the art will appreciate that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more devices different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple sub-modules.

[0155] The above application serial numbers are for description only and do not represent the advantages or disadvantages of the implementation scenarios.

[0156] The above disclosure only discloses several specific implementation scenarios of the present application. However, the present application is not limited thereto, and any changes that can be conceived by technicians in this field should fall within the scope of protection of the present application.

Claims

1. A three-stage integrated energy system demand response strategy optimization method, characterized in that: include: Determine the load data of typical energy users in the integrated energy system, and set the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users, and industrial park energy users; Based on the pre-constructed price elasticity matrix and the load data, a price demand response is performed on the typical energy-consuming entity to obtain a load curve corresponding to the typical energy-consuming entity, and an initial output plan is determined according to the load curve, wherein the initial output plan is a rural community output plan, an agricultural park output plan, and an industrial park output plan optimized by an alternating multiplier method; According to the initial output plan, multiple project data corresponding to the integrated energy system are determined, and each project data is compared with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate, and the user comfort is a single comfort index converted from electricity comfort and heat comfort; If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, determining the project data corresponding to the comparison result, and optimizing the initial output plan according to the optimization strategy associated with the project data; According to the optimized initial output plan, multiple project data corresponding to the integrated energy system are determined again, each project data is compared with the corresponding preset range, and when any comparison result indicates that the project data is not within the preset range, the optimized initial output plan is optimized until multiple comparison results indicate that the project data is within the preset range, thereby obtaining a target output plan.

2. The method according to claim 1, characterized in that The step of performing price demand response on the typical energy-consuming subject based on the pre-constructed price elasticity matrix and the load data, obtaining a load curve corresponding to the typical energy-consuming subject, and determining an initial output plan according to the load curve includes: The coefficient of variation is constructed based on the following formula, in, represents the initial amount of electric load in period t; P all Indicates the initial total amount of electrical load; T is 24h; represents the initial average value of the electric load; η is the real-time price fluctuation factor; represents the initial electricity price in period t; C(t) represents the real-time electricity price in period t, and θ (θ = θ1 + θ2 + θ3) represents the price fluctuation coefficient; The autoelastic coefficient is constructed based on the following formula, Among them, ΔQ i and Q i is the change in electricity demand in time period i and the initial electricity demand; ΔP i and P i is the change in electricity price in time period i and the initial electricity price; The cross elasticity matrix is ​​constructed based on the following formula: Among them, ΔP j and P j is the change in electricity price in time period j and the initial electricity price; The price elasticity matrix is ​​constructed based on the self-variation coefficient, the self-elasticity coefficient and the cross-elasticity coefficient. Based on the following formula, the load value after price demand response is calculated using the self-variation coefficient and the price elasticity matrix: Among them, P DE (t) is the load value after participating in demand response in period t; generating the load curve according to the load value, and determining an output plan matching the load curve based on the load curve; The output plan is optimized by using an alternating multiplier method to obtain the initial output plan.

3. The method according to claim 2, characterized in that The step of optimizing the output plan by using the alternating multiplier method to obtain the initial output plan includes: defining a Lagrange multiplier vector and a regularization parameter, and initializing optimization variables according to the output plan; For each iteration, a Lagrangian function is constructed to optimize the optimization variables, and the optimized optimization variables are used to update the Lagrangian multiplier vector until the change in the optimization variables meets a preset convergence condition, thereby obtaining the initial output plan.

4. The method according to claim 1, characterized in that The method further comprises: determining a plurality of project data corresponding to the integrated energy system according to the initial output plan, comparing each project data with a corresponding preset range, and obtaining a plurality of comparison results, including: According to the initial output plan, the carbon emissions are calculated based on the following formula: in, The actual carbon emissions of typical energy users; They represent the actual carbon emissions from electricity purchases from the power grid, gas boilers, and combined cooling, heating, and power; Indicates the CO2 consumed by power-to-gas conversion; T is 24h; α n , β n , γ n (n=1, 2, 3) represent the carbon emission calculation parameters of online electricity purchase, gas boiler and combined heating and cooling power generation respectively; P buy (t) represents the power purchased by the power grid during period t; P CCHP H is the equivalent power of each device in the combined cooling, heating and power generation; GB (t) represents the thermal power of the gas boiler during period t; P CCHP,e (t), P CCHP,h (t), Q CCHP,c (t) respectively represent the electric, thermal and cooling output power of the combined cooling, heating and power generation in the time period t; δ represents the carbon capture coefficient; the carbon emission is compared with the preset carbon emission range to obtain a first comparison result; According to the initial output plan, the electricity comfort and heat comfort are calculated respectively, and the electricity comfort is calculated using the following formula: Among them, P cut (t) represents the load that can be reduced in period t; P shift (t) represents the load that can be translated in time period t; P load (t) represents the total electrical load in time period t, and the indoor illumination is calculated using the following formula: Where ρ represents the lighting power density; S represents the total lighting area; E i (t) represents the sunlight intensity at the i-th test point at time t; P l (t) represents the lighting load when natural lighting cannot meet the indoor illumination requirements at time t; n represents the number of lighting points; The thermal comfort is calculated according to the following formula: c2=α4T+β4E l (t)-γ4E l (t) 2 +D, Among them, α4, β4, γ4, and D are correlation coefficients obtained by multivariate nonlinear regression analysis of actual data; T represents the correlated color temperature; Based on the following formula, the electricity comfort and the heat comfort are converted into the single comfort index: Where M represents the theoretical minimum value of a single index; c i A dimensionless function representing a single indicator; a weight ratio of electricity comfort to heat comfort is set to [α1, α2] (α1+α2=1), and the single comfort indicator is compared with a preset comfort range to obtain a second comparison result; According to the initial output plan, the grid load rate of the typical energy-consuming entity is calculated, and the grid load rate is compared with a preset grid load rate range to obtain a third comparison result.

5. The method according to claim 4, characterized in that If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, determining the project data corresponding to the comparison result, and optimizing the initial output plan according to the optimization strategy associated with the project data, including: If the first comparison result indicates that the carbon emissions are not within the preset carbon emissions range, the carbon emission index and carbon emissions of the typical energy user are determined according to the initial output plan, and the carbon emission index is the sum of the carbon emission index of the gas boiler, the carbon emission index of the combined cooling, heating and power generation, and the carbon emission index of the power grid purchased in the typical energy user; According to the carbon emission index and the carbon emission amount, a carbon price model of the typical energy-consuming entity is constructed. Among them, F c represents the reward / penalty carbon price of the integrated energy system; p represents the basic carbon price; ε, κ represent the reward / penalty coefficients; l represents the interval length of carbon emissions, and the carbon price model is processed by interval dimensionless processing to obtain the price fluctuation coefficient of the typical energy-consuming entity Among them, e1 represents the converted price fluctuation coefficient, and the self-variation coefficient of the typical energy consumer is updated according to the price fluctuation coefficient, and the load curve of the typical energy consumer is optimized according to the updated self-variability coefficient, and the initial output plan is updated using the optimized load curve.

6. The method according to claim 4, characterized in that If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, determining the project data corresponding to the comparison result, optimizing the initial output plan according to the optimization strategy associated with the project data, and obtaining the target output plan, including: If the second comparison result indicates that the single comfort index is not within the preset comfort range, the single comfort index is normalized and calculated to obtain the price fluctuation coefficient of the typical energy user. Among them, c min With c max are the limit values ​​of comfort rewards and penalties respectively, e2 is the converted price impact coefficient, the self-variation coefficient of the typical energy user is updated according to the price fluctuation coefficient, and the load curve of the typical energy user is optimized according to the updated self-variation coefficient, and the initial output plan is updated using the optimized load curve.

7. The method according to claim 5, characterized in that If any comparison result among the multiple comparison results indicates that the project data is not within the preset range, determining the project data corresponding to the comparison result, optimizing the initial output plan according to the optimization strategy associated with the project data, and obtaining the target output plan, including: If the third comparison result indicates that the grid load rate is not within the preset grid load rate range, the difference between the actual power purchase amount of the typical energy consumer and the range specified by the grid is calculated, and the difference is normalized and dimensionless to obtain the price fluctuation coefficient of the typical energy consumer. Among them, P max With P min are the upper and lower limits of the grid load respectively, P represents the amount of electricity purchased by the comprehensive energy system from the grid, e3 is the converted price impact coefficient, the self-variation coefficient of the typical energy consumer is updated according to the price fluctuation coefficient, and the load curve of the typical energy consumer is optimized according to the updated self-variation coefficient, and the initial output plan is updated using the optimized load curve.

8. A three-stage integrated energy system demand response strategy optimization system, characterized in that: include: The first determination module is used to determine the load data of typical energy users in the integrated energy system and set the integrated energy system to implement a real-time electricity price strategy, wherein the typical energy users include rural community energy users, agricultural park energy users and industrial park energy users; A second determination module is used to respond to the price demand of the typical energy-consuming subject based on the pre-constructed price elasticity matrix and the load data, obtain the load curve corresponding to the typical energy-consuming subject, and determine the initial output plan according to the load curve, wherein the initial output plan is the rural community output plan, the agricultural park output plan and the industrial park output plan optimized by the alternating multiplier method; A comparison module is used to determine multiple project data corresponding to the integrated energy system according to the initial output plan, and compare each project data with a corresponding preset range to obtain multiple comparison results, wherein the project data includes carbon emissions, user comfort and grid load rate, and the user comfort is a single comfort index converted from electricity comfort and heat comfort; An optimization module, configured to determine the project data corresponding to the comparison result if any comparison result among the plurality of comparison results indicates that the project data is not within a preset range, and optimize the initial output plan according to an optimization strategy associated with the project data; The optimization module is used to re-determine multiple project data corresponding to the integrated energy system according to the optimized initial output plan, compare each project data with the corresponding preset range, and optimize the optimized initial output plan when any comparison result indicates that the project data is not within the preset range, until multiple comparison results all indicate that the project data is within the preset range, thereby obtaining a target output plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. 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 according to any one of claims 1 to 7 are implemented.

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