Low-carbon operation method and system for integrated electric-thermal energy system under demand response

Abstract

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

Description

A low-carbon operation method and system for an electric-thermal integrated energy system under demand response Technical Field

[0001] The present invention relates to the technical field of optimized operation of an electric-thermal integrated energy system, and in particular to a low-carbon operation method and system for an electric-thermal integrated energy system under demand response. Background Art

[0002] IES is a key physical carrier of the Energy Internet. By building an IES that complements electricity and thermal energy, it will facilitate the large-scale development of renewable energy and distributed energy supply systems, thereby increasing the utilization rate of traditional primary energy and achieving sustainable energy development. Furthermore, the development of IES will help improve the security and self-healing capabilities of social systems and enhance human society's ability to withstand natural disasters, which is of great significance to national security.

[0003] According to the national 13th Five-Year Plan, the coordinated utilization of multiple energies, including large-scale renewable energy grid integration and interactive supply and demand among diverse users, is a key task in my country. The development, utilization, and coordinated management of demand-side resources within IES are key technologies and methods for energy deployment within the 13th Five-Year Plan. Low-carbon demand response, as an extension and expansion of traditional demand response, is an important strategy for promoting flexible interaction between demand-side resources and IES.

[0004] As energy systems continue to evolve and load types continue to increase, user-specific demands complicate accurate system scheduling and further complicate the coupling between the power grid and heat network. The coupling between different energy sources enables users to adjust their energy usage, leading to a shift in traditional electricity demand response towards low-carbon demand. Responding to low-carbon demand while reducing carbon emissions is a pressing issue.

[0005] Summary of the Invention

[0006] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid obscuring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0007] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a low-carbon operation method of an electric-thermal integrated energy system under demand response, which is used to solve the problems in the background technology.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In a first aspect, the present invention provides a method for low-carbon operation of an electric-thermal integrated energy system under demand response, comprising:

[0010] By collecting data from the cogeneration unit, building a cogeneration unit model and electric boiler unit constraints, we obtain the initial carbon quota allocation, and considering the carbon trading cost model, we build an electric and thermal integrated energy system model;

[0011] Based on the electric-thermal integrated energy system model, an optimal scheduling model is established to meet the operation constraints and maximize the benefits on the energy supply side;

[0012] Establish a carbon emission flow model to calculate the carbon emission flow, map the carbon emissions on the energy supply side to the load side, and establish a low-carbon demand response model on the demand side with the goal of maximizing consumer surplus;

[0013] A master-slave game model is used to describe the game process between the supply side and the demand side models, and a genetic algorithm is used to solve the master-slave game model to achieve optimal electric and thermal energy scheduling.

[0014] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response described in the present invention, wherein: initial carbon quota allocation is obtained, and a carbon trading cost model is considered to construct an electric-thermal integrated energy system model, including:

[0015] The initial carbon quota is allocated to CHP and coal-fired units using a free allocation method;

[0016] The carbon transaction cost model is expressed as follows: F CET =λ CET (E r -E IES )

[0017] Among them, F CET is the carbon trading cost, λ CET represents the carbon trading price, E r is the actual carbon emissions, E IES is the initial carbon emission quota of IES.

[0018] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response described in the present invention, an optimization scheduling model is established to meet the operation constraints and maximize the benefits on the energy supply side, including:

[0019] Establish power balance constraints, heat network power constraints, wind turbine constraints, and photovoltaic generator constraints under the conditions that the power grid and heat network operations meet;

[0020] The optimal scheduling model of the energy supply side is expressed as follows: max F es =F sale -FCET -F C -F idr

[0021] Among them, F IES is the total revenue on the energy supply side; F sale F is the income from energy sales; C is the power generation cost of coal-fired units; F idr It is the carbon reduction incentive available to users.

[0022] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response of the present invention, wherein: establishing a carbon emission flow model to calculate the carbon emission flow includes:

[0023] The carbon emission flow model of the power network considers the carbon potential of each node in the power network, the carbon emission flow model of the heat network considers the energy flow distribution in the water supply network and return water network, and the equipment carbon emission flow model of the cogeneration unit and electric boiler unit.

[0024] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response of the present invention, a low-carbon demand response model on the demand side is established with the goal of maximizing consumer surplus, including:

[0025] The goal is to maximize consumer surplus, that is, the difference between the user's utility function and energy consumption function, which can be expressed as:

[0026] Among them, F user is the user side cost; F u,t is the utility function of the user at time t; F be The cost of purchasing electricity from the user side; F bh The cost of purchasing heat from the side supply of energy to the user;

[0027] Consider the carbon emission reduction incentives and low-carbon demand response constraints obtained on the user side.

[0028] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response described in the present invention, a master-slave game model is used to describe the game process between the energy supply side and the demand side model, including:

[0029] The energy supply side formulates carbon emission reduction incentive prices for each time period of the day, and the user side adjusts its energy demand based on the incentive price and carbon signal. At the same time, the energy supply side re-formulates its own incentive price pricing strategy based on the adjustment of energy demand, and establishes a master-slave game model with the energy supply side as the leader and the user side as the follower.

[0030] As a preferred solution of the low-carbon operation method of the electric-thermal integrated energy system under demand response of the present invention, wherein: using a genetic algorithm to solve the master-slave game model includes:

[0031] Set the initial population size, maximum number of iterations, population mutation rate, crossover probability, and convergence error;

[0032] Initially randomly generate the incentive price of the energy supply side of group u, transmit the parameters to the user side, and set the number of iterations k, k = k + 1;

[0033] The user side receives the incentive price and unit output of the m groups of energy supply side, calculates and retains the current income And return the energy demand to the energy supply side;

[0034] The energy supply side solves the unit output based on the energy demand returned by the demand side within a day and retains the current profit and the current optimal incentive price;

[0035] Select and mutate to generate new incentive prices and iterate repeatedly to obtain the benefits on the energy supply side and user-side consumer surplus

[0036] like but otherwise

[0037] like and The game is determined to have reached equilibrium and the iteration ends, otherwise it returns to the set number of iterations k;

[0038] Where ε is the precision.

[0039] In a second aspect, the present invention provides a low-carbon operation system for an electric-thermal integrated energy system under demand response, comprising:

[0040] The module for establishing a model of an integrated electric and thermal energy system collects data from the cogeneration unit, constructs a model of the cogeneration unit and constraints on the electric boiler unit, obtains the initial carbon quota allocation, and considers the carbon trading cost model to build an integrated electric and thermal energy system model.

[0041] An optimization scheduling model establishment module, which establishes an optimization scheduling model that satisfies operation constraints and maximizes energy supply side benefits based on the electric and thermal integrated energy system model;

[0042] A low-carbon demand response model establishment module establishes a carbon emission flow model to calculate the carbon emission flow, maps the carbon emissions on the energy supply side to the load side, and establishes a low-carbon demand response model on the demand side with the goal of maximizing consumer surplus;

[0043] The game model solving module adopts a master-slave game model to describe the game process between the supply side and the demand side models, and uses a genetic algorithm to solve the master-slave game model to achieve optimal electric and thermal energy scheduling.

[0044] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: the processor implements any step of the above method when executing the computer program.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above method is implemented.

[0046] Compared with the prior art, the beneficial effects of the invention are as follows: the present invention establishes a demand-side low-carbon demand response model and constructs an electric-thermal integrated energy system model by collecting data from the cogeneration units; based on the electric-thermal integrated energy system model, an optimization scheduling model that meets the operating constraints and maximizes the benefits on the energy supply side is established; at the same time, a carbon emission flow model is established to calculate the carbon emission flow, and the carbon emissions on the energy supply side are mapped to the load side. At the same time, with the goal of maximizing consumer surplus, a low-carbon demand response model on the demand side is established, and a genetic algorithm is used to solve the optimization scheduling model that maximizes the benefits on the energy supply side and the low-carbon demand response model on the demand side, to obtain a low-carbon optimization scheduling plan for the electric-thermal integrated energy system, and implement the scheduling of the electric-thermal integrated energy system; the present invention not only reduces the carbon emissions of the electric-thermal integrated energy system, but also reduces the cost of energy conservation and emission reduction, taking into account both low carbon and economic efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0048] FIG1 is an overall flow chart of a low-carbon operation method of an electric and thermal integrated energy system under demand response according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0052] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0053] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0054] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0055] Example 1

[0056] 1 , which is a first embodiment of the present invention, provides a low-carbon operation method for an electric-thermal integrated energy system under demand response, including:

[0057] S1. By collecting data from the cogeneration unit, constructing the cogeneration unit model and the electric boiler unit constraints, obtaining the initial carbon quota allocation, and considering the carbon trading cost model, constructing the electric and thermal integrated energy system model;

[0058] Furthermore, the initial carbon quota is allocated to CHP and coal-fired units using a free allocation method;

[0059] Furthermore, the CHP (combined heat and power) unit model is constructed as follows:

[0060] Where a, b, and c are the burnup coefficients of GT (gas turbine) respectively; are the natural gas consumption and electric power output of GT respectively; GT is the output electrical and thermal power ratio of GT; are the heating efficiency and rated power generation efficiency of GT respectively; WHB is the output heat power; η WHB is the heating efficiency of WHB; It is the start-stop flag of GT; are the minimum and maximum values ​​of GT output power respectively; They are the minimum and maximum climbing rates of GT respectively;

[0061] Furthermore, the electric boiler (EB) satisfies the following constraints:

[0062] in, is the rated capacity of EB; and are the power consumption and heat output of EB respectively; η EB is the efficiency coefficient;

[0063] Specifically, the gratuitous distribution method is expressed as follows:

[0064] Among them, E IES is the initial carbon emission quota of IES; δ g E is the carbon emission quota per unit of natural gas consumption of the gas-fired unit; CHP 、E TP are the carbon quotas allocated to CHP and coal-fired units respectively; β is the conversion coefficient of GT power generation into heat supply; N TP is the amount of coal burned in the system; For the i p The output power of the coal-fired unit at time t;

[0065] It should be noted that the use of a free allocation method to deal with carbon quotas can help companies smoothly pass the carbon quota adaptation period, thereby enabling them to maintain their competitiveness and reduce carbon leakage, but it will reduce incentives for renewable energy;

[0066] Preferably, T is the scheduling period, and T=24;

[0067] Furthermore, the carbon transaction cost model is expressed as follows: F CET =λ CET (E r -E IES )

[0068] Among them, F CET is the carbon trading cost, λ CET represents the carbon trading price, E r is the actual carbon emissions, E IES The initial carbon emission quota of IES;

[0069] S2. Based on the electric and thermal integrated energy system model, establish an optimal scheduling model that meets the operating constraints and maximizes the benefits on the energy supply side;

[0070] Furthermore, the electric power balance constraints, heat network power constraints, wind turbine constraints and photovoltaic generator constraints are established under the conditions that the power grid and heat network operations meet;

[0071] Specifically, the electric power balance constraint is expressed as follows:

[0072] Among them, P L,t is the total electrical load; P WT,iw,t is the output power of the i-th wind turbine at time t; P PV,iv,t is the output power of the i-th photovoltaic generator set at time t; P CHP,iw,t is the output power of the i-th CHP unit at time t; N WT 、N PV 、N CHP They are the number of wind turbines, photovoltaic generators, and CHP units respectively;

[0073] Specifically, the heat network power constraint is expressed as follows:

[0074] Among them, H EB,ib,t is the thermal power of the i-th electric boiler at time t; H CHP,ic,t is the thermal power of the i-th CHP at time t; N EB is the number of electric boilers;

[0075] Specifically, the wind turbine constraints are expressed as follows:

[0076] Among them, P W,t is the actual output of the wind turbine at time t; is the predicted output value of the wind turbine at time t;

[0077] Specifically, the constraints of the photovoltaic generator set are expressed as follows:

[0078] Among them, P PV,t is the actual output of the photovoltaic generator set at time t; is the predicted output value of the photovoltaic generator set at time t;

[0079] It should be noted that considering the above constraints can improve the performance and operational efficiency of the entire integrated energy system;

[0080] Furthermore, the optimal scheduling model of the energy supply side is expressed as follows: max F es =F sale -F CET -F C -F idr

[0081] Furthermore, F sale =F se +F sh

[0082] Among them, F IES is the total revenue on the energy supply side; F sale F is the income from energy sales; C is the power generation cost of coal-fired units; F idr F is the carbon reduction incentive available to the user side; se 、F sh are the revenue from electricity and heat sales respectively; e,t ,λ h,t are the unit electricity price and heat price at time t respectively; P L,t 、H L,t are the electrical and thermal load powers at time t respectively;

[0083] S3. Establish a carbon emission flow model to calculate the carbon emission flow, map the carbon emissions on the energy supply side to the load side, and establish a low-carbon demand response model on the demand side with the goal of maximizing consumer surplus;

[0084] Furthermore, the established carbon emission flow models include the power network carbon emission flow model, the heat network carbon emission flow model, the equipment carbon emission flow model and the electric boiler carbon emission flow model;

[0085] Specifically, the power network carbon emission flow model uses the carbon potential of each node in the power network to calculate, which is expressed as follows:

[0086] in, is the carbon potential of node n in the power network at time t; L is the set of branches connected to node n into which the flow flows; P l,n,t is the power flowing from the lth branch into node n at time t; is the carbon flow density of branch l connected to node n at time t; M is the set of units connected to node n; P m,n,t is the output power of unit m connected to node n at time t; is the carbon emission intensity of unit m connected to node n, which is determined by the unit characteristics;

[0087] Specifically, the heat network carbon emission flow model considers the energy flow distribution in the water supply network and the return water network, which is expressed as follows:

[0088] in, are the carbon potential of node n in the heat supply network and return network at time t; are the sets of pipes flowing into and out of node n respectively; are the carbon flow densities of pipe l flowing into and out of node n at time t; are the water flow rates of pipe l in the water supply network and return network at time t respectively; are the outlet temperatures of pipe l flowing into and out of node n at time t, respectively;

[0089] Specifically, the equipment carbon emission flow model considers the combined heat and power unit and is expressed as follows:

[0090] in, is the carbon potential at the input of the CHP unit; Input airflow to the cogeneration unit; are the carbon potentials at the heat and electricity output ends of the cogeneration unit, respectively; are the thermal and electrical output powers of the cogeneration unit respectively;

[0091] Specifically, the carbon emission flow model of electric boilers is expressed as follows:

[0092] in, are the carbon potentials at the input and output ends of the electric boiler respectively;

[0093] Furthermore, the goal is to maximize consumer surplus, that is, the difference between the user's utility function and energy consumption function, which can be expressed as:

[0094] Among them, F user is the user side cost; F u,t is the utility function of the user at time t; F be The cost of purchasing electricity from the user side; F bh The cost of purchasing heat from the side supply of energy to the user;

[0095] Preferably, a quadratic form is used to quantify the total utility of the difference between the user's utility function and energy usage function, as follows:

[0096] Among them, e , α e 、υ h , α h are the user's preference coefficients for electricity and heat consumption, respectively, which can reflect the user's energy demand preference and affect the size of the demand;

[0097] Furthermore, the user's electricity and heat purchase costs are expressed as follows:

[0098] Furthermore, the carbon emission reduction incentives and low-carbon demand response constraints obtained by the user side are considered;

[0099] Specifically, the carbon emission reduction incentives obtained by the user side are as follows:

[0100] in, is the carbon dioxide emission reduction of the integrated energy system; Y is the number of load types. For electric and thermal loads, Y is 2; N is the number of electric and thermal load nodes in the system; are the decrease and increase of the electricity and heat load demand response load of node n at time t respectively; is the carbon potential corresponding to the electrical and thermal load node n at time t;

[0101] Specifically, low-carbon demand response constraints: λ idr,min ≤λ idr ≤λ idr,max

[0102] in, are 0-1 state variables indicating that the user is in load increase or load reduction; are the maximum limits for increase and decrease of electrical and thermal loads respectively; is the maximum change value of the total electricity and heat load throughout the day; idr,min ,λ idr,max are the upper and lower limits of the unit incentive price respectively;

[0103] It should be noted that the above constraints are established to address the reduction of renewable energy caused by the free allocation method, and the establishment of constraints can reduce user carbon emissions and respond to carbon demand;

[0104] S4. Use the master-slave game model to describe the game process between the supply side and the demand side models, and use the genetic algorithm to solve the master-slave game model to achieve optimal electric and thermal energy scheduling;

[0105] Furthermore, the energy supply side formulates carbon emission reduction incentive prices for each time period within a day, and the user side adjusts energy demand based on the incentive price and carbon signal. At the same time, the energy supply side re-formulates its own incentive price pricing strategy based on the adjustment of energy demand, with the energy supply side as the leader and the user side as the follower, establishing a master-slave game model;

[0106] Specifically, the established master-slave game model is expressed as follows: G={N,ρ es ,δ user ,f es ,f user}

[0107] Furthermore, the master-slave game model contains three elements: participants, strategies, and payoffs;

[0108] It should be noted that participants: the energy supply side and the user side are the two participants in the game model, and the participant set is represented by N = {es, user}; strategy: the leader's energy supply side strategy is the incentive price and unit output formulated for 24 hours, which can be represented as a set as ρ es ={λ idr ,P TP,i,t ,Q GS,i,t The strategy of the follower user side is the load at each moment, which can be expressed as a set as δ user ={P L,t ,Q L,t ,H L,t}; Benefit: supply side objective function; demand side objective function;

[0109] Furthermore, the nonlinear problem of the energy supply side strategy included in the model is solved by genetic algorithm;

[0110] Furthermore, the genetic algorithm solution process is as follows:

[0111] S401, setting the initial population size, maximum number of iterations, population mutation rate, crossover probability, and convergence error;

[0112] S402: Initially randomly generate incentive prices for the energy supply side of group u, transmit the parameters to the user side, and set the number of iterations k, k = k + 1;

[0113] S403: The user side receives the incentive price and unit output of the m groups of energy supply side, calculates and retains the current income And return the energy demand to the energy supply side;

[0114] It should be noted that the user side receives the incentive price and unit output of the m group energy supply side. It is necessary to use the YAMIP platform to use the CPLEX solver to solve the energy demand after the demand response in order to calculate and retain the current income.

[0115] S404. The energy supply side solves the unit output based on the energy demand returned by the demand side within a day and retains the current profit. and the current optimal incentive price;

[0116] S405, select and mutate to generate a new incentive price and repeat S402 to S404 to obtain the energy supply side benefits and user-side consumer surplus

[0117] S405, if but otherwise

[0118] S406, if and If the game reaches equilibrium, the iteration ends; otherwise, the process returns to S402;

[0119] Among them, ε is the accuracy;

[0120] It should be noted that by establishing a game model between the demand side and the supply side, the carbon emissions of the integrated energy system can be effectively reduced, thereby reducing the cost of energy conservation and emission reduction.

[0121] Furthermore, this embodiment also provides a low-carbon operation system for an electric and thermal integrated energy system under demand response, including:

[0122] The module for establishing a model of an integrated electric and thermal energy system collects data from the cogeneration unit, constructs a model of the cogeneration unit and constraints on the electric boiler unit, obtains the initial carbon quota allocation, and considers the carbon trading cost model to build an integrated electric and thermal energy system model.

[0123] An optimization scheduling model establishment module, which establishes an optimization scheduling model that satisfies operation constraints and maximizes energy supply side benefits based on the electric and thermal integrated energy system model;

[0124] A low-carbon demand response model establishment module establishes a carbon emission flow model to calculate the carbon emission flow, maps the carbon emissions on the energy supply side to the load side, and establishes a low-carbon demand response model on the demand side with the goal of maximizing consumer surplus;

[0125] The game model solving module adopts a master-slave game model to describe the game process between the supply side and the demand side models, and uses a genetic algorithm to solve the master-slave game model to achieve optimal electric and thermal energy scheduling.

[0126] This embodiment further provides a computer device applicable to a low-carbon operation method of an electric-thermal integrated energy system under demand response, including:

[0127] Memory and processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the low-carbon operation method of the electric and thermal integrated energy system under demand response as proposed in the above embodiment.

[0128] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0129] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the low-carbon operation method of the electric and thermal integrated energy system under demand response as proposed in the above embodiment.

[0130] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0131] Example 2

[0132] Referring to Tables 1 to 3, a second embodiment of the present invention provides a low-carbon operation method for an electric-thermal integrated energy system under demand response, including:

[0133] In order to verify the impact of carbon trading and low-carbon demand response implementation on system scheduling results and the rationality of the master-slave game, the following four scenarios are set for analysis and comparison:

[0134] 1. Carbon trading and low-carbon demand response are not considered;

[0135] 2. Considering carbon trading but not low-carbon demand response;

[0136] 3. Consider traditional carbon trading and low-carbon demand response with fixed price incentives;

[0137] 4. Consider traditional carbon trading and optimize the scheduling of both supply and demand under the master-slave game;

[0138] Table 1 Functional side scheduling results and costs in each scenario

[0139] Table 2 System carbon emissions under each scenario

[0140] Table 3 Comparison of demand-side results between scenario 3 and scenario 4

[0141] From the comparison of Scenario 1 and Scenario 2 in Table 1 and Table 2, it can be seen that due to the increase in carbon trading costs in Scenario 2, the overall benefits are reduced, but under the influence of carbon trading, the carbon emissions of Scenario 2 are reduced by 1217.1t; compared with Scenario 3 and Scenario 2, it can be seen that carbon trading only allocates and clears carbon quotas for each unit in the power network based on the current actual situation. Therefore, under Scenario 2, carbon trading mainly affects the output scheduling of each device in the power network, while under Scenario 3, low-carbon demand response is carried out for the electric and thermal integrated energy system, and the total benefits of the system increase. At the same time, carbon emissions are reduced by 1286.8t compared with Scenario 2; compared with Scenario 4 and Scenario 3, it can be seen that the fixed price incentive in Scenario 3 can have a certain effect, but fails to fully tap the carbon reduction potential of the system. In Scenario 4, the incentive price is obtained as a pending strategy after the master-slave game equilibrium;

[0142] As shown in Table 3, Scenario 4 increases revenue by RMB 194,000 and reduces carbon emissions by 2,313.1 tons compared to Scenario 3. At the same time, it increases the revenue on the energy supply side and the consumer surplus on the user side.

[0143] In summary, the method of the present invention can adjust prices to incentivize users while increasing the revenue on the energy supply side and the consumer surplus on the user side, thereby reducing carbon emissions.

[0144] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Furthermore, 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 code. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and interpreted scripting language JavaScript, etc.

[0145] The present application is described with reference to the flow chart and / or block diagram of the method, device (system), and computer program product according to the embodiment of the present application. It should be understood that each flow process and / or box in the flow chart and / or block diagram and the combination of the flow process and / or box in the flow chart and / or block diagram can be realized 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 processing machine or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for realizing the function specified in one flow chart flow or multiple flows and / or one box or multiple boxes of the block diagram.

[0146] These computer program instructions may also be stored in a computer-readable memory that can direct 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 a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0147] 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, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0148] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0149] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A low-carbon operation method for an integrated electric-thermal energy system under demand response, characterized in that, Including: By collecting data in the combined heat and power (CHP) unit, constructing the CHP unit model and the constraint conditions of the electric boiler unit, obtaining the initial carbon quota allocation, and considering the carbon trading cost model, an integrated electric-thermal energy system model is constructed; Based on the integrated electric-thermal energy system model, an optimal scheduling model that satisfies the operation constraint conditions and maximizes the profit of the energy supply side is established; A carbon emission flow model is established to calculate the carbon emission flow, mapping the carbon emissions of the energy supply side to the load side. Meanwhile, with the goal of maximizing the consumer surplus, a low-carbon demand response model for the demand side is established; The master-slave game model is used to describe the game process between the energy supply side and the demand side models, and the genetic algorithm is used to solve the master-slave game model to achieve the optimal electric-thermal energy scheduling.

2. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 1, characterized in that Obtaining the initial carbon quota allocation, and considering the carbon trading cost model, constructing the integrated electric-thermal energy system model, including: The initial carbon quota is allocated to CHP and coal-fired units by the free allocation method; The carbon trading cost model is expressed as follows: F CET = λ CET (E r - E IES ) Among them, F CET is the carbon trading cost, and λ CET represents the carbon trading price. E r is the actual carbon emission, and E IES is the initial carbon emission quota of the IES.

3. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 2, wherein, Establishing an optimal scheduling model that satisfies the operation constraint conditions and maximizes the profit of the energy supply side, including: Establishing the electric power balance constraint, the heat network power constraint, the wind turbine unit constraint, and the photovoltaic generator unit constraint under the condition that the power grid and the heat network operate satisfactorily; The optimal scheduling model of the energy supply side is expressed as follows: max F es = F sale -F CET -F C -F idr Among them, F IES is the total revenue of the energy supply side; F sale is the revenue from selling energy; F C is the power generation cost of coal-fired units; F idr is the carbon reduction incentive that can be obtained on the user side.

4. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 2 or 3, characterized in that, Establishing a carbon emission flow model to calculate the carbon emission flow, including: The power network carbon emission flow model of the carbon potential of each node in the power network, considering the energy flow distribution in the water supply network and the return water network The heat network carbon emission flow model of the energy flow distribution in the middle, and the equipment carbon emission flow models of the CHP unit and the electric boiler unit.

5. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 4, wherein, With the goal of maximizing the consumer surplus, establishing a low-carbon demand response model for the demand side, including: The maximization of consumer surplus, which is the difference between the user's utility function and energy consumption function, is expressed as: Among them, F user is the cost on the user side; F u,t is the utility function of the user at time t; F be is the electricity purchase cost from the energy supply side to the user side; F bh is the heat purchase cost from the energy supply side to the user side; Considering the carbon emission reduction incentives obtained on the user side and the low-carbon demand response constraints.

6. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 5, characterized in that Using the master-slave game model to describe the game process between the energy supply side and the demand side models, including: The energy supply side formulates the carbon emission reduction incentive price for each time period within a day. The user side adjusts the energy consumption demand according to the incentive price and the carbon signal. At the same time, the energy supply side re-formulates its own incentive price pricing strategy according to the adjustment of the energy consumption demand. Taking the energy supply side as the leader and the user side as the follower, a master-slave game model is established.

7. The low-carbon operation method of the electric-thermal integrated energy system under demand response according to claim 6, wherein Using the genetic algorithm to solve the master-slave game model, including: Setting the initial population size, the maximum number of iterations, the population mutation rate, the crossover probability, and the convergence error; Randomly generating u groups of incentive prices for the energy supply side initially, transmitting the parameters to the user side, setting the number of iterations k, k = k + 1; The user side receives the incentive prices and unit outputs of m energy supply sides, calculates and retains the current revenue And returning the energy consumption demand to the energy supply side; The energy supply side solves the unit output according to the energy consumption demand returned by the demand side within one day and retains the current revenue And the current optimal incentive price; Select, mutate to generate a new incentive price and repeat the iteration to obtain the energy supply side revenue With the consumer surplus on the user side If Then Otherwise If And Then it is determined that the game reaches equilibrium, and the iteration ends. Otherwise, return To the set number of iterations k; where ε is the precision.

8. A low-carbon operation system for an integrated electric-thermal energy system under demand response, based on the low-carbon operation method for an integrated electric-thermal energy system under demand response according to any one of claims 1 to 7, characterized in that, Including: The integrated electric-thermal energy system model establishment module, by collecting data in the CHP unit, constructs the CHP unit model and the constraint conditions of the electric boiler unit, obtains the initial carbon quota allocation, and considering the carbon trading cost model, constructs the integrated electric-thermal energy system model; The optimal scheduling model establishment module, based on the integrated electric-thermal energy system model, establishes an optimal scheduling model that satisfies the operation constraint conditions and maximizes the profit of the energy supply side; The low-carbon demand response model establishment module establishes a carbon emission flow model to calculate the carbon emission flow, maps the carbon emissions on the energy supply side to the load side, and establishes a low-carbon demand response model on the demand side with the goal of maximizing consumer surplus; The game model solving module adopts a master-slave game model to describe the game process between the supply side and the demand side models, and uses a genetic algorithm to solve the master-slave game model to achieve optimal electric and thermal energy scheduling.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: 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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