Demand response mechanism optimization method, system, electronic device and storage medium

Abstract

The embodiment of the present invention provides a method, system, electronic device and storage medium for optimizing a demand response mechanism, which belongs to the field of integrated energy systems. The method includes: modeling an energy hub for each integrated energy system user, and inputting the prices of each upper-level energy, optimizing the energy hub to obtain optimized energy flow results with the goal of minimizing the cost of purchased energy; pre-building a user-side demand response model according to the multi-dimensional elasticity coefficient matrix of integrated energy; inputting the carbon intensity change rate and the optimized energy flow results into the user-side demand response model, and adjusting the energy hub if the demand response amount is greater than the set value. By introducing a multi-dimensional elasticity coefficient matrix, the spatiotemporal characteristics of the multi-user loads of the integrated demand response and their contribution to the low-carbon operation of the system are fully characterized, and the energy consumption pattern on the user side is optimized to promote efficient use of energy and reduce carbon emissions. At the same time, the energy hub modeling method is adopted according to the characteristics of different users to achieve low-carbon optimization scheduling of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to a demand response mechanism optimization method, system, electronic equipment and storage medium. Background Art

[0002] The user-level integrated energy system is located at the end of the energy network. It can fully coordinate the dispatch of various types of distributed resources and differentiated energy loads, and is an important development direction for distributed IES in the future. Demand response is an important way for the demand side to participate in the flexible interaction of the upper energy network. Users actively adjust their energy consumption behavior according to signals such as energy price signals to respond to the needs of the supply side, which is conducive to improving the flexibility of the dispatch of the integrated energy system.

[0003] Most existing studies focus on demand response strategies for one or a few energy types, ignoring the close coupling and conversion potential between multiple energy forms such as electricity, heat, and cold energy in the integrated energy system, and failing to give full play to the optimization advantages of the demand side for system operation. Moreover, as an indispensable part of modern energy consumption, user cold load is often marginalized in the existing IDR model, and the IDR model has not kept pace with the user needs of the integrated energy system. Secondly, the user load in IES not only has significant time-varying characteristics, but also shows unique spatial distribution characteristics due to factors such as user type, geographical location, and living habits. However, when describing these load characteristics, existing studies often use overly simplified classification methods, such as dividing only by energy type or typical load curves, ignoring the differences between users and the same user in different time periods. This extensive classification method makes it difficult to construct a unified indicator system that can accurately reflect the spatiotemporal characteristics of multi-user loads. Thirdly, the existing IDR model ignores the consideration of the user side in the study of user low-carbon demand response, and only considers the carbon emission cost into the total dispatch cost, so users cannot clearly understand the low-carbon degree of their own response. Summary of the invention

[0004] The purpose of the embodiments of the present invention is to provide a demand response mechanism optimization method, system, electronic device and storage medium, which are used to fully or at least partially solve the technical problems existing in the above-mentioned prior art.

[0005] In order to achieve the above object, an embodiment of the present invention provides a method for optimizing a demand response mechanism, including:

[0006] Energy hub modeling is carried out for each integrated energy system user, and the prices of each upper-level energy are input to minimize the cost of purchased energy, optimize the energy hub, and obtain the optimized energy flow result; the energy hub contains a variety of energy coupling devices, whose input ports are electricity, gas and heat purchased from the upper-level energy network, and whose output ports are the user's electricity, gas, heat and cooling loads;

[0007] A user-side demand response model is pre-built based on the comprehensive energy multi-dimensional elasticity coefficient matrix, the demand for electricity, gas, heat, and cold energy, and the price change rate of electricity, gas, heat, and cold. The comprehensive energy multi-dimensional elasticity coefficient matrix includes the price elasticity coefficient, the energy interaction elasticity coefficient, the carbon intensity sensitivity coefficient, the low-carbon interaction elasticity coefficient, and the cooperation willingness index.

[0008] The carbon intensity change rate and the optimized energy flow results are input into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends.

[0009] Optionally, energy hub modeling is performed for each integrated energy system user, including:

[0010] Read the state parameters of the coupling devices in the energy hubs of different types of users, and perform the initialization process to generate the port-branch association matrix, conversion characteristic matrix, input association matrix, output association matrix and energy conversion matrix;

[0011] Assigning initial load values ​​before user demand response, defining constraints and objective functions, optimizing energy hubs with the goal of minimizing purchased energy costs, wherein the constraints include coupling device constraints, user load regulation constraints, purchased power limits, and energy flow equations.

[0012] Optionally, the objective function is characterized as:

[0013] ;

[0014] In the formula, , , They are Comprehensive energy prices of electricity, natural gas and heat at any given moment; , , They are The amount of electricity, natural gas and heat purchased at any given moment.

[0015] Optionally, the constraint condition is characterized as:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022] In the formula, For the Coupling device in Input power at the moment; For the Coupling device in The input power upper limit at the moment; For the Coupling device in The lower limit of input power at the moment; To purchase energy, Energy flow matrix, is the load after user-side demand response, is the amount of energy purchased from the upper level, A is the generation port-branch association matrix, H is the conversion characteristic matrix of the coupling device, X is the input association matrix, Y is the output association matrix, and the energy conversion matrix Z is obtained.

[0023] Optionally, the user-side demand response model is characterized as:

[0024] ;

[0025] Where L represents the energy demand for electricity, gas, heat and cooling, S represents the user cooperation willingness index, M represents the comprehensive energy multidimensional elasticity coefficient matrix, ROC represents the price change rate of electricity, gas, heat and cooling and the carbon intensity change rate, and diag(L) represents the diagonal matrix form of L.

[0026] Optionally, the user cooperation willingness index is represented by:

[0027] ;

[0028] In the formula, is the frequency of use, is the consistency score, Score the peak time.

[0029] Optionally, the price change rate of electricity, gas, heat, and cooling and the carbon intensity change rate are represented as:

[0030]

[0031] In the formula, They are respectively expressed as the electricity price change rate, gas price change rate, heat price change rate, cooling price change rate, electricity carbon emission intensity change rate, gas carbon emission intensity change rate, heat carbon emission intensity change rate, cooling carbon emission intensity change rate, where They respectively represent the changes in electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, cooling carbon emission intensity before and after the user-side demand response. They respectively represent the electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, and cooling carbon emission intensity before user-side demand response.

[0032] On the other hand, the present invention also provides a demand response mechanism optimization system, comprising:

[0033] The energy hub modeling module is used to model the energy hub for each integrated energy system user, and input the prices of each superior energy source, with the goal of minimizing the cost of purchased energy, optimizing the energy hub, and obtaining the optimized energy flow result; among them, the EH contains a variety of energy coupling devices, whose input ports are electricity, gas and heat purchased from the superior energy network, and whose output ports are the user's electricity, gas, heat and cooling loads;

[0034] A user-side demand response model construction module is used to pre-construct a user-side demand response model according to a comprehensive energy multidimensional elasticity coefficient matrix, wherein the comprehensive energy multidimensional elasticity coefficient matrix includes a price elasticity coefficient, an energy interaction elasticity coefficient, a carbon intensity sensitivity coefficient, a low-carbon interaction elasticity coefficient, and a cooperation willingness index;

[0035] The optimization module is used to input the carbon intensity change rate and the optimized energy flow results into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends.

[0036] On the other hand, the present invention also provides an electronic device, comprising 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 above-mentioned demand response mechanism optimization method when executing the program.

[0037] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned demand response mechanism optimization method are implemented.

[0038] Through the above technical solutions, the low-carbon demand response mechanism in the integrated energy system is deeply studied. By introducing a multi-dimensional elasticity coefficient matrix, it not only covers the traditional indicator of price elasticity coefficient, but also adds dimensions such as energy interaction elasticity coefficient, carbon intensity sensitivity coefficient, low-carbon interaction elasticity coefficient and cooperation willingness index, so as to comprehensively characterize the spatiotemporal characteristics of multi-user loads in the integrated demand response and their contribution to the low-carbon operation of the system, accurately characterize and optimize the energy consumption pattern on the user side, so as to promote the efficient use of energy and significantly reduce carbon emissions. At the same time, according to the characteristics of different users of the integrated energy system, the energy hub modeling method is adopted to achieve efficient and low-carbon optimization scheduling of the system.

[0039] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings:

[0041] Figure 1 It is a flow chart of an implementation method of a demand response mechanism optimization method provided by an embodiment of the present invention;

[0042] Figure 2 This is a structural diagram of an energy hub provided by an embodiment of the present invention;

[0043] Figure 3 is a detailed implementation flow chart of a demand response mechanism optimization method provided by an embodiment of the present invention;

[0044] Figure 4 This is a comparison diagram of electric load changes in a common residential area provided by an embodiment of the present invention;

[0045] Figure 5 This is a comparison diagram of gas load changes in a common residential area provided by an embodiment of the present invention;

[0046] Figure 6 This is a comparison diagram of heat load changes in an ordinary residential area provided by an embodiment of the present invention;

[0047] Figure 7 This is a comparison diagram of cooling load changes in an ordinary residential area provided by an embodiment of the present invention;

[0048] Figure 8 This is a comparison diagram of the changes in electrical loads in an industrial area provided by an embodiment of the present invention;

[0049] Fig. 9 This is a comparison diagram of gas load changes in an industrial area provided by an embodiment of the present invention;

[0050] Fig.10 This is a comparison diagram of heat load changes in an industrial area provided by an embodiment of the present invention;

[0051] Fig.11 This is a comparison diagram of cooling load changes in an industrial area provided by an embodiment of the present invention;

[0052] Fig.12 It is a structural diagram of a demand response mechanism optimization system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0053] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0054] See also Figure 1 As shown, a flowchart of an implementation method of a demand response mechanism optimization method provided by an embodiment of the present invention includes the following execution steps:

[0055] Step 100: Model the energy hub for each integrated energy system user, input the prices of each upper-level energy, optimize the energy hub with the goal of minimizing the cost of purchased energy, and obtain the optimized energy flow result.

[0056] Among them, the energy hub contains a variety of energy coupling equipment, whose input port is electricity, gas and heat purchased from the superior energy network, and the output port is the user's electricity, gas, heat and cooling load.

[0057] It should be understood that the optimized energy flow result can be considered as the energy demand for electricity, gas, heating and cooling.

[0058] Specifically, the energy hub modeling for each integrated energy system user in step 100 may be performed by specifically performing the following steps:

[0059] S1000: Read the status parameters of the coupling devices in the energy hubs of different types of users, and perform the initialization process to generate the port-branch association matrix, conversion characteristic matrix, input association matrix, output association matrix and energy conversion matrix.

[0060] In some embodiments, the port-branch association vector defines the connection between the node ports and the branches. nodes (energy converters) and EH of branches (energy flows). Node Port The port-branch association vector is a dimension of A vector of , which defines the connections between this port and all branches. The elements of are:

[0061]

[0062] Note that each port can be connected to multiple branches. have ports, then the node The port-branch association matrix The size is , defined as follows:

[0063]

[0064] Therefore, the port-branch association matrix of a node contains all the information about the connections between this node and all branches.

[0065] In some embodiments, a node The conversion characteristic matrix Defines the energy conversion characteristics of the node. It has the same number of rows as the number of energy conversion processes and the same number of columns as the number of input and output ports. The columns are sorted by port number.

[0066] 1) Type 1: Single input port and single output port

[0067]

[0068] Since there is only one energy conversion process, the energy output is proportional to the input. Since there are two ports, the matrix The dimension is . Representation Converter The efficiency of is related to the input port, while "1" is related to the output port.

[0069] 2) Type 2: Single input port and multiple output ports

[0070] If the energy output of each output port is proportional to the energy input, then the number of rows is equal to the number of output ports. The corresponding line The elements are:

[0071]

[0072] in, From the input port to the output port The efficiency of the energy conversion process.

[0073] In some embodiments, the energy conversion matrix: Given the port branch association matrix and the converter characteristic matrix, the node of the node can be calculated Energy conversion matrix: .

[0074] System energy conversion matrix The node energy conversion matrix that combines all nodes in EH:

[0075] .

[0076] In some embodiments, an input correlation matrix is: is a matrix, which relates energy input to branch energy flows, for The elements in the equation can be defined as:

[0077]

[0078] In some embodiments, the output correlation matrix is: Similarly, the output correlation matrix is is a matrix, which relates the energy output to the branch energy flows, for The elements in the equation can be defined as:

[0079]

[0080] S1001: Assigning initial load values ​​before user demand response, defining constraints and objective functions, optimizing energy hubs with the goal of minimizing purchased energy costs, wherein the constraints include coupling device constraints, user load regulation constraints, purchased power limits, and energy flow equations.

[0081] Specifically, the objective function aims to minimize the cost of purchasing energy, that is, the total cost of purchasing electricity, natural gas, and heat. The objective function is represented as:

[0082] ;

[0083] In the formula, , , They are Comprehensive energy prices of electricity, natural gas and heat at any given moment; , , They are The amount of electricity, natural gas and heat purchased at any given moment.

[0084] Specifically, the constraint condition is characterized as follows:

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] ;

[0091] In the formula, is the input power of the nth coupling device at time t; is the upper limit of the input power of the nth coupling device at time t; is the lower limit of the input power of the nth coupling device at time t; To purchase the upper limit of energy, V energy flow matrix, is the load after user-side demand response, is the amount of energy purchased from the upper level, A is the generation port-branch association matrix, H is the conversion characteristic matrix of the coupling device, X is the input association matrix, Y is the output association matrix, and the energy conversion matrix Z is obtained.

[0092] In some embodiments, see Figure 2 As shown, it is a structural diagram of an energy hub provided by an embodiment of the present invention. The energy hub (EH) contains a variety of energy coupling devices, which can be regarded as an integrated energy conversion node, capable of realizing mutual conversion between multiple energy sources, and can flexibly process and distribute different types of energy according to load demand. The input port is electricity (green line), gas (orange line), and heat (red line) purchased from the upper energy network, and the output port is the user's electricity, gas, heat, and cold (blue line) load. EH includes coupling equipment cogeneration CHP, absorption chiller ARG, electric boiler EB, gas boiler GB, power-to-gas P2G, gas turbine GE, electric chiller ERG, and photovoltaic power generation group PV. Modeling EH requires modeling the energy conversion devices it contains and their connections. Based on the standardized matrix modeling of graph theory, the characteristics of the energy converter and its topology are represented in matrix form.

[0093] Step 101: Pre-build a user-side demand response model based on the comprehensive energy multi-dimensional elasticity coefficient matrix, the electricity, gas, heat, and cooling energy demand, and the electricity, gas, heat, and cooling price change rates.

[0094] Among them, the comprehensive energy multidimensional elasticity coefficient matrix includes price elasticity coefficient, energy interaction elasticity coefficient, carbon intensity sensitivity coefficient, low-carbon interaction elasticity coefficient and cooperation willingness index.

[0095] In some embodiments, the price elasticity coefficient is mainly divided into the self-elasticity coefficient and the cross elastic coefficient , respectively representing the ratio of the energy consumption change rate to the energy price change rate in the same time period and different time periods. The load demand and price changes in each period can be associated through the price elasticity matrix.

[0096]

[0097] Where: Indicates the energy type, which can be electricity / gas / heat / cold, represented by , , ,Q; for After implementing user-side demand response during the period Energy consumption change; for Before implementing user-side demand response Use energy; , for , After implementing user-side demand response during the period Changes in energy prices; , for , Before implementing user-side demand response Energy prices.

[0098] In some embodiments, a day is divided into The self-elasticity coefficient and cross-elasticity coefficient can be written as × Elasticity matrix, the expression is as follows:

[0099]

[0100] In the formula, is the X energy price elasticity coefficient matrix, is the self-elastic coefficient, is the cross elastic coefficient, where the subscript , .

[0101] In some embodiments, the energy interaction elasticity coefficient is mainly divided into the self-elasticity coefficient and the cross elastic coefficient , representing the same time period and different time periods respectively The energy change rate and The ratio of the rate of change of energy prices. This coefficient is used to measure Energy demand for The sensitivity of energy price changes and the degree of Energy demand conversion capabilities.

[0102]

[0103] In the formula, for After implementing user-side demand response during the period Energy consumption change; for Before implementing user-side demand response Use energy; , for , After implementing user-side demand response during the period Changes in energy prices; , for , Before implementing user-side demand response Energy prices.

[0104] In some embodiments, a day is divided into The self-elasticity coefficient and cross-elasticity coefficient can be written as × Elasticity matrix, the expression is as follows:

[0105]

[0106] In the formula, is the energy interaction transfer matrix from X energy to Y energy, is the self-elastic coefficient, is the cross elastic coefficient, where the subscript , .

[0107] In some embodiments, the carbon strength sensitivity coefficient is mainly divided into the self-elastic coefficient and the cross elastic coefficient , representing the same time period and different time periods respectively The ratio of energy change rate to carbon price change rate is used to quantify the sensitivity of energy demand to carbon price changes. Through carbon trajectory tracking, the carbon emission cost is transferred from the source side to the user side. By increasing the user's preference for low-carbon energy, it guides them to choose a more environmentally friendly energy consumption method, realizes low carbon demand response (LCDR), and achieves carbon reduction on the user side.

[0108]

[0109] Where: , for , Change in carbon emission intensity after implementing user-side demand response during the period; , for , Carbon emission intensity before implementing user-side demand response in the time period, for After implementing user-side demand response during the period Energy consumption change; for Before implementing user-side demand response Use energy.

[0110] In some embodiments, a day is divided into The self-elasticity coefficient and cross-elasticity coefficient can be written as × Elasticity matrix, the expression is as follows:

[0111]

[0112] In the formula, is the X energy carbon intensity sensitivity coefficient matrix, is the self-elastic coefficient, is the cross elastic coefficient, where the subscript , .

[0113] In some embodiments, the low carbon interaction elasticity coefficient is expressed as:

[0114]

[0115] In the formula, , for , Change in carbon emission intensity after implementing user-side demand response during the period; , for , Carbon emission intensity before implementing user-side demand response in the time period, for After implementing user-side demand response during the period Energy consumption change; for Before implementing user-side demand response Use energy.

[0116] In some embodiments, a day is divided into The self-elasticity coefficient and cross-elasticity coefficient can be written as × Elasticity matrix, the expression is as follows:

[0117]

[0118] In the formula, is the low-carbon interaction elasticity coefficient matrix of X energy to Y energy, is the self-elastic coefficient, is the cross elastic coefficient, where the subscript , .

[0119] In some embodiments, the user cooperation willingness index is calculated by comprehensively considering the user's usage frequency, usage consistency, and usage during peak hours. , The higher the value, the greater the potential of the user to participate in demand response. To quantify this indicator, the three major influencing factors are explained as follows. For ease of explanation, each of the above factors is normalized within the range of [0:1].

[0120] ;

[0121] In the formula, is the frequency of use, is the consistency score, Score the peak time.

[0122] If any factor is low, the scores are multiplied. For simplicity, we use the minimum-maximum normalized value of all consumers, and get ∈[0:1] to display the results.

[0123] In some embodiments, IES multidimensional elasticity coefficient matrix: Based on the above indicators, a multidimensional elasticity matrix is ​​constructed as follows:

[0124]

[0125] In the formula, They are electricity price elasticity coefficient matrix, gas price elasticity coefficient matrix, heat price elasticity coefficient matrix, and cooling price elasticity coefficient matrix respectively; They are the interactive transfer matrices of power-to-gas, power-to-heat, and power-to-cold energy respectively; They are gas-to-electricity, gas-to-heat, and gas-to-cooling energy interactive transfer matrices respectively; They are heat-to-electricity, heat-to-gas, and heat-to-cold energy interactive transfer matrices; They are the energy interactive transfer matrices of cold-to-electricity, cold-to-gas, and cold-to-heat; They are electricity price elasticity coefficient matrix, gas price elasticity coefficient matrix, heat price elasticity coefficient matrix, and cooling price elasticity coefficient matrix respectively; They are the interactive transfer matrices of power-to-gas, power-to-heat, and power-to-cold energy respectively; They are gas-to-electricity, gas-to-heat, and gas-to-cooling energy interactive transfer matrices respectively; They are heat-to-electricity, heat-to-gas, and heat-to-cold energy interactive transfer matrices; They are the energy interactive transfer matrices of cold-to-electricity, cold-to-gas, and cold-to-heat; They are the electric carbon intensity sensitivity coefficient matrix, gas carbon intensity sensitivity coefficient matrix, thermal carbon intensity sensitivity coefficient matrix, and cold carbon intensity sensitivity coefficient matrix; They are the low-carbon interaction elasticity coefficient matrices for power-to-gas, power-to-heat, and power-to-cooling respectively; They are the low-carbon interaction elasticity coefficient matrices of gas-to-electricity, gas-to-heat, and gas-to-cooling respectively; They are the low-carbon interaction elasticity coefficient matrices of heat-to-electricity, heat-to-gas, and heat-to-cooling respectively; They are the low-carbon interaction elasticity coefficient matrices of cold-to-electricity, cold-to-gas, and cold-to-heat, respectively.

[0126] This matrix is ​​used to describe the influence and relationship of different factors, such as energy prices, carbon prices and user behavior, on the demand for electrical and thermal energy. By collecting and analyzing a large amount of historical data, based on a large amount of actual historical data, and using data-driven methods, a mathematical model between various factors and energy demand is established. Specifically, the elements in the multidimensional elasticity matrix represent the sensitivity (i.e. elasticity) of demand changes when a certain factor changes. Given the energy price and energy flow in each period, the prices of electricity, gas, cooling and heating for users can be tracked. At the same time, given the rate of change of carbon intensity, the rate of change can be obtained. :

[0127]

[0128] In the formula, They are respectively expressed as the electricity price change rate, gas price change rate, heat price change rate, cooling price change rate, electricity carbon emission intensity change rate, gas carbon emission intensity change rate, heat carbon emission intensity change rate, cooling carbon emission intensity change rate, where They respectively represent the changes in electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, cooling carbon emission intensity before and after the user-side demand response. They respectively represent the electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, and cooling carbon emission intensity before user-side demand response.

[0129] It can be deduced that the change in energy demand after demand response is:

[0130]

[0131] In the formula, is the change in demand for electricity, gas, heat and cooling after demand response, is the electricity, gas, heating and cooling demand before demand response, diag The diagonal matrix form of , M is the multidimensional elastic coefficient matrix, ROC is the rate of change, and S is the user cooperation willingness index.

[0132]

[0133] In the formula, It is the electricity, gas, heating and cooling demand of users after demand response. is the change in demand for electricity, gas, heat and cooling after demand response, It is the electricity, gas, heating and cooling demand before demand response.

[0134] The user-side demand response model of the integrated energy system is as follows:

[0135]

[0136] In the formula It is the electricity, gas, heating and cooling demand of users after demand response. is the change in demand for electricity, gas, heat and cooling after demand response, The electricity, gas, heating and cooling demand before demand response. The diagonal matrix form of M is a multidimensional elasticity coefficient matrix, ROC is the rate of change, and S is the user cooperation willingness index. Specifically, price-based demand response (PBDR) actively participates in load regulation, regulates user energy consumption behavior through price signals, and combines user autonomy and market orientation. Electricity, gas, heat, and air conditioning in the integrated energy system have certain commodity attributes, so user energy consumption has certain price elasticity characteristics. In order to ensure the rationality of user response energy consumption and accurately evaluate the user's demand response potential and carbon emission sensitivity, this patent defines the price elasticity coefficient, energy substitution elasticity coefficient, carbon emission sensitivity coefficient, and low-carbon interaction elasticity coefficient to form a comprehensive energy multidimensional elasticity coefficient matrix, and quantify the user's potential willingness to cooperate, introduce it into the multidimensional elasticity coefficient matrix, and finally construct a comprehensive energy system PBDR model based on this. The user-side demand response model is characterized as:

[0137] ;

[0138] Where L represents the energy demand for electricity, gas, heat and cooling, S represents the user cooperation willingness index, M represents the comprehensive energy multidimensional elasticity coefficient matrix, ROC represents the price change rate of electricity, gas, heat and cooling and the carbon intensity change rate, and diag(L) represents the diagonal matrix form of L.

[0139] Step 102: Input the carbon intensity change rate and the optimized energy flow results into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends.

[0140] See also Figure 3 As shown, it is a detailed implementation flow chart of a demand response mechanism optimization method provided by an embodiment of the present invention. First, the coupling device status and parameters in the EH of different types of users are read to implement the EH initialization process, generate the port-branch association matrix, the conversion characteristic matrix, the input association matrix, the output association matrix and the energy conversion matrix, assign the initial load value (△L=0) before the user demand response, define the constraints including coupling device constraints, user load regulation constraints, purchased power restrictions, energy flow equations, etc., with the goal of minimizing the purchased energy cost, input the prices of each energy at the upper level, optimize the calculation to obtain the optimized energy flow results, track the user-side electrical heating and cooling prices, and then track the price change rate to achieve economic dispatch. Then, based on the optimized energy flow results and the carbon intensity change rate (△CEI / CEI), the user-side electrical heating and cooling prices are tracked to achieve intuitive response on the user side. Finally, if the demand response amount (△L) is greater than the set value (ε), indicating that there is still room for demand response, it is returned to the user load regulation constraint to continue iterating until the demand response amount is less than the set value.

[0141] In some embodiments, in order to verify the feasibility and IDR effectiveness of the comprehensive energy low-carbon demand response model considering the temporal and spatial characteristics of the load proposed in this application, the following example is set: The basis for the energy prices in this example is as follows: According to the announcement on the agency power purchase price in March 2024, the peak and valley period definition of the spring period in the announcement is adopted. 10:00-15:00 every day is the valley period, of which 11:00-14:00 is the deep valley, 17:00-22:00 is the peak period, of which 17:00-20:00 is the peak, and the rest of the time is the normal period; according to the reference data provided by the CEIC statistical database, the price of civil natural gas is 2.750 yuan / cubic meter, and the calorific value of natural gas is 9.59475kWh, and the calculated natural gas price is 0.2866 yuan / kWh; according to the publicly announced centralized heating charge price standard, the heating price is charged according to heat metering, and the heating price is 0.16 yuan / kWh (44.45 yuan / GJ). By analyzing ordinary residential areas, high-end residential areas, and industrial areas, we can get Figure 4-Figure 7The result diagram of the demand response of ordinary residential areas is shown. It can be seen from the figure that ordinary residential areas are more sensitive to the changes in energy prices after tracking. The overall response trend is to move more to low-price periods, which can achieve staggered energy use of gas and heat loads, reduce pressure during peak hours, and minimize energy costs for users. Taking the megawatt power scale of this load as an example, it can reduce about 770 yuan. However, due to the limitation of the small number of coupling devices in the EH of residential areas, the conversion capacity of different energy loads still needs to be improved. The energy consumption curve of high-end residential areas is basically the same as that of ordinary residential areas, but high-end residential areas are not sensitive to changes in energy prices and carbon emission intensity after tracking, and the user cooperation willingness index is low. From the perspective of load time characteristics, the overall response trend is to move less to low-price periods than ordinary residential areas, and the energy cost reduction is not obvious. Other means can be considered to promote the demand response method of such users.

[0142] In some embodiments, the industrial zone demand response result map is shown in Figure 8-Figure 11 As shown in the figure, the industrial zone is more sensitive to the tracked changes in energy prices and carbon intensity. The overall response trend is to move more to low-price periods, and is more inclined to use low-carbon intensity equipment, reducing pressure and carbon emissions during peak hours, and reducing energy costs. The comprehensive cost savings before and after IDR are shown in Table 1. Taking the load power scale of 100 megawatts as an example, it can save about 16,000 yuan. In addition, the industrial zone has a rich variety of coupled equipment in the EH, and the conversion capacity of different energy loads is high.

[0143] Table 1: Comprehensive cost savings before and after IDR

[0144]

[0145] From the above simulation results, it can be seen that the comprehensive energy low-carbon demand response model proposed in this application that takes into account the temporal and spatial characteristics of loads can make reasonable and economical low-carbon demand response strategies for loads with different spatial characteristics, and guide multiple users with complex temporal and spatial characteristics to autonomously adjust their energy consumption behaviors, so as to promote the efficient use of energy and realize the low-carbon and flexible operation of the comprehensive energy system.

[0146] Compared with the existing IDR model, this application breaks the limitations of DR of a single or a few types of energy loads, and comprehensively considers the four major loads of electricity, gas, heat, and cold; considers the temporal and spatial characteristics of user loads, refines load classification, and builds a unified indicator system, which can continuously adjust demand response parameters according to actual operation data and user feedback, and refine and optimize IDR strategies. In addition, in the process of promoting low-carbon transformation, users, as the main body of energy consumption, their low-carbon awareness and participation are crucial. For this reason, this application starts from the user side and directly guides users to understand and respond to the impact of their energy use behavior on carbon emissions. Users can autonomously adjust their energy use behavior based on the tracked energy price signals and carbon intensity signals, showing a high degree of autonomy and economic rationality, and on the premise of meeting the user's actual demand response capabilities, to the greatest extent possible to achieve low-carbon economic demand response.

[0147] See also Fig.12 As shown, it is a structural diagram of a demand response mechanism optimization system provided by an embodiment of the present invention, including:

[0148] The energy hub modeling module 120 is used to model the energy hub for each integrated energy system user, and input the prices of each upper-level energy, with the goal of minimizing the purchased energy cost, optimize the energy hub, and obtain the optimized energy flow result; wherein, the EH contains a variety of energy coupling devices, whose input ports are electricity, gas and heat purchased from the upper-level energy network, and whose output ports are the user's electricity, gas, heat and cooling loads;

[0149] A user-side demand response model construction module 121 is used to pre-construct a user-side demand response model according to a comprehensive energy multi-dimensional elasticity coefficient matrix, wherein the comprehensive energy multi-dimensional elasticity coefficient matrix includes a price elasticity coefficient, an energy interaction elasticity coefficient, a carbon intensity sensitivity coefficient, a low-carbon interaction elasticity coefficient, and a cooperation willingness index;

[0150] The optimization module 122 is used to input the carbon intensity change rate and the optimized energy flow results into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends.

[0151] On the other hand, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the demand response mechanism optimization method described in any one of the above embodiments are implemented.

[0152] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the demand response mechanism optimization method described in any of the above embodiments are implemented.

[0153] 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 combining 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.) containing computer-usable program codes.

[0154] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the 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 generate 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.

[0155] 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 including 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.

[0156] 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. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0157] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0158] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0159] 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 magnetic 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.

[0160] 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.

[0161] The above are only embodiments of the present application and are 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 protection scope of the present application.

Claims

1. A method for optimizing a demand response mechanism, characterized in that: include: Energy hub modeling is carried out for each integrated energy system user, and the prices of each upper-level energy are input to minimize the cost of purchased energy, optimize the energy hub, and obtain the optimized energy flow result; the energy hub contains a variety of energy coupling devices, whose input ports are electricity, gas and heat purchased from the upper-level energy network, and whose output ports are the user's electricity, gas, heat and cooling loads; A user-side demand response model is pre-built based on the comprehensive energy multi-dimensional elasticity coefficient matrix, the demand for electricity, gas, heat, and cold energy, and the price change rate of electricity, gas, heat, and cold. The comprehensive energy multi-dimensional elasticity coefficient matrix includes the price elasticity coefficient, the energy interaction elasticity coefficient, the carbon intensity sensitivity coefficient, the low-carbon interaction elasticity coefficient, and the cooperation willingness index. The carbon intensity change rate and the optimized energy flow result are input into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends. Among them, the user-side demand response model is characterized as follows: ； In the formula, L represents the energy demand for electricity, gas, heat and cold, S represents the user cooperation willingness index, M represents the comprehensive energy multidimensional elasticity coefficient matrix, ROC represents the price change rate of electricity, gas, heat and cold and the carbon intensity change rate, and diag(L) represents the diagonal matrix form of L; Among them, the user cooperation willingness index is represented as: ； In the formula, is the frequency of use, is the consistency score, Score the peak time; Among them, the price change rate of electricity, gas, heat, and cooling and the carbon intensity change rate are characterized as follows: In the formula, They are respectively expressed as the electricity price change rate, gas price change rate, heat price change rate, cooling price change rate, electricity carbon emission intensity change rate, gas carbon emission intensity change rate, heat carbon emission intensity change rate, cooling carbon emission intensity change rate, where They respectively represent the changes in electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, cooling carbon emission intensity before and after the user-side demand response. They respectively represent the electricity price, gas price, heat price, cooling price, electricity carbon emission intensity, gas carbon emission intensity, heat carbon emission intensity, and cooling carbon emission intensity before user-side demand response.

2. The demand response mechanism optimization method according to claim 1, characterized in that: Energy hub modeling is performed for each integrated energy system user, including: Read the state parameters of the coupling devices in the energy hubs of different types of users, and perform the initialization process to generate the port-branch association matrix, conversion characteristic matrix, input association matrix, output association matrix and energy conversion matrix; Assigning initial load values ​​before user demand response, defining constraints and objective functions, optimizing energy hubs with the goal of minimizing purchased energy costs, wherein the constraints include coupling device constraints, user load regulation constraints, purchased power limits, and energy flow equations.

3. The method for optimizing the demand response mechanism according to claim 2, characterized in that: The objective function is characterized as: ； In the formula, , , They are Comprehensive energy prices of electricity, natural gas and heat at any given moment; , , They are The amount of electricity, natural gas and heat purchased at any given moment.

4. The method for optimizing the demand response mechanism according to claim 2, characterized in that: The constraints are characterized as follows: ； In the formula, is the input power of the nth coupling device at time t; is the upper limit of the input power of the nth coupling device at time t; is the lower limit of the input power of the nth coupling device at time t; To purchase the upper limit of energy, V energy flow matrix, is the load after user-side demand response, is the amount of energy purchased from the upper level, A is the generation port-branch association matrix, H is the conversion characteristic matrix of the coupling device, X is the input association matrix, Y is the output association matrix, and the energy conversion matrix Z is obtained.

5. A demand response mechanism optimization system applied to the demand response mechanism optimization method according to any one of claims 1 to 4, characterized in that: include: The energy hub modeling module is used to model the energy hub for each integrated energy system user, and input the prices of each superior energy source, with the goal of minimizing the cost of purchased energy, optimizing the energy hub, and obtaining the optimized energy flow result; among them, the EH contains a variety of energy coupling devices, whose input ports are electricity, gas and heat purchased from the superior energy network, and whose output ports are the user's electricity, gas, heat and cooling loads; A user-side demand response model construction module is used to pre-construct a user-side demand response model according to a comprehensive energy multidimensional elasticity coefficient matrix, wherein the comprehensive energy multidimensional elasticity coefficient matrix includes a price elasticity coefficient, an energy interaction elasticity coefficient, a carbon intensity sensitivity coefficient, a low-carbon interaction elasticity coefficient, and a cooperation willingness index; The optimization module is used to input the carbon intensity change rate and the optimized energy flow results into the user-side demand response model. If the demand response amount is greater than the set value, the energy hub is adjusted. If the demand response amount is less than the set value, the optimization ends.

6. 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 demand response mechanism optimization method as described in any one of claims 1-4 are implemented.

7. A non-transitory 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 demand response mechanism optimization method as described in any one of claims 1 to 4 are implemented.

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