CN114386683A - Energy system operation optimization method and system based on carbon trading and demand response - Google Patents

Energy system operation optimization method and system based on carbon trading and demand response Download PDF

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CN114386683A
CN114386683A CN202111627914.7A CN202111627914A CN114386683A CN 114386683 A CN114386683 A CN 114386683A CN 202111627914 A CN202111627914 A CN 202111627914A CN 114386683 A CN114386683 A CN 114386683A
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张荣强
袁海山
叶昀
陈有强
孙靓
张超
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Abstract

The utility model provides an energy system operation optimization method and system based on carbon trading and demand response, comprising: respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user; constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the consumer, and simultaneously considers the using energy satisfaction degree of the user; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target; and optimizing and solving the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.

Description

Energy system operation optimization method and system based on carbon trading and demand response
Technical Field
The disclosure belongs to the technical field of operation optimization of comprehensive energy systems, and particularly relates to an energy system operation optimization method and system based on carbon trading and demand response.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
With the problem of resource shortage of environmental pollution becoming more and more serious and the aim of realizing double carbon targets of carbon peak reaching and carbon neutralization as soon as possible, a comprehensive energy system capable of simultaneously generating cold, heat, electricity and gas energy increasingly becomes the main direction of energy development and is also an important way for realizing energy reform.
The reasonable optimization of the comprehensive energy system is an important premise for realizing high-efficiency, economic and stable operation, so that at present, numerous scholars do numerous researches on the operation optimization of the comprehensive energy system, and currently, common optimization methods comprise linear programming, random optimization, robust optimization and the like.
Disclosure of Invention
In order to solve the above problems, the present disclosure provides a method and a system for optimizing operation of an energy system based on carbon trading and demand response, which can effectively improve the economy of an energy supply side and reduce the consumption cost of an energy consumption side, thereby improving the economy and environmental protection of the whole system and reducing the usage amount of energy and the emission amount of carbon dioxide.
According to a first aspect of the embodiments of the present disclosure, there is provided a method for optimizing operation of an energy system based on carbon trading and demand response, including:
respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the consumer, and simultaneously considers the using energy satisfaction degree of the user; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and optimizing and solving the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
Further, the outer layer optimization model aims at the maximum total system yield, and is specifically expressed as:
Figure BDA0003439256860000021
wherein the content of the first and second substances,
Figure BDA0003439256860000022
the price of the electricity sold by the energy supplier,
Figure BDA0003439256860000023
the price of the thermal energy sold by the energy supplier,
Figure BDA0003439256860000024
the price of cold energy sold by the energy supplier,
Figure BDA0003439256860000025
the cold energy power required by the user is the cold energy power,
Figure BDA0003439256860000026
for the demand of the thermal load after the demand response,
Figure BDA0003439256860000027
is the demand of the electric load after the demand response;
Figure BDA0003439256860000028
the cost of consuming natural gas for energy suppliers,
Figure BDA0003439256860000029
the cost of purchasing electrical energy from the grid for an energy provider,
Figure BDA00034392568600000210
and the operation and maintenance cost of each device of the energy supplier.
Further, the inner optimization model is a model that maximizes a difference between a utility function of a user and an energy cost, and is specifically expressed as:
Figure BDA00034392568600000211
wherein the content of the first and second substances,
Figure BDA00034392568600000212
is the utility function of the user.
Further, the method for nesting linear programming based on genetic algorithm specifically combines a particle swarm algorithm and linear programming solution, wherein the particle swarm algorithm adopts a genetic particle swarm algorithm, and the linear programming solution adopts a CPLEX solver.
According to a second aspect of the embodiments of the present disclosure, there is provided an energy system operation optimization system based on carbon trading and demand response, including:
the mathematical model construction unit is used for respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
the optimization model construction unit is used for constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the user, and meanwhile, the using energy satisfaction degree of the user is considered; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and the optimization solving unit is used for carrying out optimization solving on the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
According to a third aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a program stored thereon, the program, when executed by a processor, implementing a method for optimizing the operation of an energy system based on carbon trading and demand response as described above.
According to a fourth aspect of the embodiments of the present disclosure, there is provided an electronic device, including a memory, a processor, and a program stored on the memory and executable on the processor, where the processor executes the program to implement the method for optimizing the operation of an energy system based on carbon trading and demand response.
Compared with the prior art, the beneficial effect of this disclosure is:
(1) the utility model provides an energy system operation optimization method and system based on carbon trading and demand response, which comprises the following steps that firstly, the operation optimization of a comprehensive energy system is divided into an inner layer and an outer layer, the outer layer is an energy supplier, and the inner layer is a flexible user; then, the outer-layer energy supplier establishes an optimization model based on the economy and the stepped carbon trading mechanism to optimize the energy selling price, the output condition of each energy device and the carbon trading price; the flexible users establish an optimization model based on economy and demand response to optimize energy demands of the flexible users; finally, using a genetic algorithm nested linear programming algorithm to solve the operation strategies of the energy suppliers and the flexible users; by introducing a carbon trading mechanism in the operation optimization of the energy system, the system can be promoted to reduce the emission of carbon dioxide, and the economical efficiency and the environmental protection performance of the energy system are improved.
(2) The scheme can effectively improve the economical efficiency of the energy supply side and reduce the consumption cost of the energy utilization side, thereby improving the economical efficiency and the environmental protection property of the whole system and reducing the use amount of energy and the emission amount of carbon dioxide.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart illustrating a method for optimizing operation of an energy system based on carbon trading and demand response according to a first embodiment of the present disclosure;
fig. 2 is a schematic diagram of a ladder carbon transaction mechanism according to a first embodiment of the disclosure;
fig. 3 is a detailed flowchart of a particle swarm algorithm in the first embodiment of the disclosure;
fig. 4 is a schematic diagram of the upper and lower layers of the energy system operation optimization system based on carbon trading and demand response according to the first embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims to provide an energy system operation optimization method based on carbon trading and demand response.
As shown in fig. 1, a method for optimizing operation of an energy system based on carbon trading and demand response includes:
respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the consumer, and simultaneously considers the using energy satisfaction degree of the user; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and optimizing and solving the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
Further, the outer layer optimization model aims at the maximum total system yield, and is specifically expressed as:
Figure BDA0003439256860000051
wherein the content of the first and second substances,
Figure BDA0003439256860000052
the price of the electricity sold by the energy supplier,
Figure BDA0003439256860000053
the price of the thermal energy sold by the energy supplier,
Figure BDA0003439256860000054
the price of cold energy sold by the energy supplier,
Figure BDA0003439256860000055
the cold energy power required by the user is the cold energy power,
Figure BDA0003439256860000056
for the demand of the thermal load after the demand response,
Figure BDA0003439256860000057
is the demand of the electric load after the demand response;
Figure BDA0003439256860000058
the cost of consuming natural gas for energy suppliers,
Figure BDA0003439256860000059
the cost of purchasing electrical energy from the grid for an energy provider,
Figure BDA00034392568600000510
and the operation and maintenance cost of each device of the energy supplier.
Further, the inner optimization model is a model that maximizes a difference between a utility function of a user and an energy cost, and is specifically expressed as:
Figure BDA00034392568600000511
wherein the content of the first and second substances,
Figure BDA00034392568600000512
is the utility function of the user.
Further, the method for nesting the linear programming based on the genetic algorithm adopts a GA-PSO algorithm, and a CPLEX solver is adopted for solving the linear programming.
Further, the outer layer mathematical model comprises a plurality of equipment operation models, and the equipment operation models comprise but are not limited to a gas turbine, a waste heat recovery device, a gas boiler, an absorption refrigerator and an electric refrigerator.
Further, the inner-layer mathematical model is a demand response model of the user, and comprises that the user performs corresponding electric load transfer and heat load reduction according to the electricity price and the heat price.
Specifically, for ease of understanding, the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings:
the disclosure provides an energy system operation optimization method based on carbon trading and demand response, which comprises the following steps:
step 1: optimizing and dividing the comprehensive energy system into an inner layer and an outer layer, and establishing a mathematical model of each layer;
step 2: establishing a double-layer optimization model based on a carbon transaction mechanism and demand response;
and 3, solving decision variables of the energy suppliers and the flexible users by adopting a genetic algorithm nested linear programming algorithm.
The optimization of the comprehensive energy system in the step 1 is divided into an inner layer and an outer layer, wherein the inner layer and the outer layer are respectively an outer layer energy supplier, namely a combined cooling, heating and power system, cold, heat and electric energy are supplied to users at the same time, and an optimization model is established based on economy and a stepped carbon transaction mechanism to optimize the energy sale price, the output condition of each energy device and the carbon transaction price; the inner layer is a flexible user that optimizes the energy demand of different energy sources based on energy economy and comfort according to the energy price established by the outer energy supplier.
Further, the inner layer and the outer layer in the step 1 comprise operation models of all devices in an outer layer energy supplier and a demand response model of an inner layer flexible user.
(1) Mathematical model construction of outer energy suppliers
The main energy supply equipment in the outer energy supplier is a gas turbine, a waste heat recovery device, a gas boiler, an absorption refrigerator and an electric refrigerator.
The gas turbine model can be expressed by the relationship between the generating efficiency and the output electric power as follows:
Figure BDA0003439256860000061
wherein the content of the first and second substances,
Figure BDA0003439256860000062
in order to achieve the power generation efficiency of the gas turbine,
Figure BDA0003439256860000063
and PnElectric power output and rated power output for the gas turbine are provided.
Part of waste heat can be recovered in the power generation process of the gas turbine, and the mathematical model of the recovered waste heat is as follows:
Figure BDA0003439256860000064
wherein eta is1Is the gas turbine heat dissipation loss coefficient.
The absorption chiller can convert the waste heat recovered from the gas turbine into cold energy to supply cold load, and the output cold power model is as follows:
Figure BDA0003439256860000071
wherein eta iscThe refrigerating efficiency of the absorption type refrigerator is improved.
The waste heat recovery device can further convert the waste heat recovered by the gas turbine to supply heat load, and the output heat power is as follows:
Figure BDA0003439256860000072
wherein eta ishIs the thermal efficiency of the waste heat recovery equipment.
The electric refrigerator supplies cold load by consuming electric energy to generate cold energy, and the mathematical model is as follows:
Figure BDA0003439256860000073
wherein the content of the first and second substances,
Figure BDA0003439256860000074
the refrigerating capacity of the electric refrigerating machine at the moment t,
Figure BDA0003439256860000075
consuming electrical energy, COP, for electric refrigerationecIs the refrigeration coefficient of the electric refrigerator.
(2) User demand response mathematical model construction
The user demand response model is that the user makes corresponding electric load transfer and heat load reduction according to the electricity price and the heat price, and the user transfer electric load is defined as
Figure BDA0003439256860000076
Reduce the thermal load of
Figure BDA0003439256860000077
The new electrical load and thermal load requirements obtained after the demand response are respectively as follows:
Figure BDA0003439256860000078
Figure BDA0003439256860000079
wherein the content of the first and second substances,
Figure BDA00034392568600000710
and
Figure BDA00034392568600000711
respectively representing the original electric load demand and heat load demand of a user at the moment t.
Further, the carbon transaction mechanism used in step 2 is a stepped carbon transaction mechanism, and the model thereof is as follows:
wherein the carbon transaction cost of the generator set i is specifically represented as:
Figure BDA0003439256860000081
Figure BDA0003439256860000082
wherein the content of the first and second substances,
Figure BDA0003439256860000083
as a baseline carbon emission price; wherein E isiFor actual carbon emission, Qc iIs carbon emission amount, NcNumber of power generating units consuming coal, NgThe number of generator sets consuming natural gas, N is the total number of generator sets, eta is a carbon quota increase coefficient, and delta is a carbon trading quota increase coefficient; fig. 2 is a schematic diagram of a ladder carbon transaction mechanism.
The actual carbon emission of the system mainly comes from a gas turbine, a gas boiler and a power grid in a combined cooling heating and power system, and can be expressed as follows:
Figure BDA0003439256860000084
wherein, γiIs the carbon emission coefficient of the ith carbon dioxide emission source,
Figure BDA0003439256860000085
the output power of the ith carbon dioxide emission source at the moment t.
The optimization models in the step 2 are the optimization models of the outer energy suppliers and the optimization models of the inner users, firstly, the optimization models of the energy suppliers have the maximum optimization target of the total system income, which can be expressed as:
Figure BDA0003439256860000086
Figure BDA0003439256860000087
wherein the content of the first and second substances,
Figure BDA0003439256860000091
the price of the electricity sold by the energy supplier,
Figure BDA0003439256860000092
the price of the thermal energy sold by the energy supplier,
Figure BDA0003439256860000093
the price of cold energy sold by the energy supplier,
Figure BDA0003439256860000094
the cold energy power required by the user is the cold energy power,
Figure BDA0003439256860000095
for the demand of the thermal load after the demand response,
Figure BDA0003439256860000096
is the demand of the electric load after the demand response; .
Figure BDA0003439256860000097
The cost of consuming natural gas for energy suppliers,
Figure BDA0003439256860000098
the cost of purchasing electrical energy from the grid for an energy provider,
Figure BDA0003439256860000099
and the operation and maintenance cost of each device of the energy supplier.
The cost of fuel consumed by the gas turbine and the gas boiler when operating can be expressed in the form of a quadratic function, that is:
Figure BDA00034392568600000910
wherein the content of the first and second substances,
Figure BDA00034392568600000911
and
Figure BDA00034392568600000912
the output electric power of the gas turbine and the output thermal power of the boiler at the moment t are respectively; a ise，be，ce(ah，bh，ch) Is a cost factor of the gas turbine (boiler).
The power grid electricity purchase cost can be expressed as:
Figure BDA00034392568600000913
the system operation and maintenance cost can be expressed as:
Figure BDA00034392568600000914
wherein, muiThe operating maintenance cost coefficient for the ith device of the energy supplier,
Figure BDA00034392568600000915
power is output for the energy source of the ith device.
The optimization objective of the flexible user is the maximum residual of the consumer, and the user's energy consumption satisfaction degree is considered, so the utility function of the user is introduced, that is, the optimization objective function of the user is the difference between the utility function of the user and the energy consumption cost, which is expressed as:
Figure BDA00034392568600000916
wherein the content of the first and second substances,
Figure BDA00034392568600000917
is a utility function for the user, which can be expressed as:
Figure BDA00034392568600000918
wherein v ise、αe、vh、αhThe preference coefficients of the user for the consumption of the electric energy and the heat energy are used for reflecting the preference of the user for the energy demand and influencing the energy demand, and because the cold energy demand of the user does not respond to the demand, the satisfaction degree of the user for the cold energy demand is not considered.
Further, the specific flow chart of the particle swarm algorithm in step 3 is shown in fig. 3, and the linear programming solution is implemented by a CPLEX solver, specifically, the particle swarm algorithm adopts a genetic particle swarm algorithm (GA-PSO), and the linear programming solution adopts the CPLEX solver.
Example two:
the embodiment aims to provide an energy system operation optimization system based on carbon trading and demand response.
As shown in fig. 4, an energy system operation optimization system based on carbon trading and demand response includes:
the mathematical model construction unit is used for respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
the optimization model construction unit is used for constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the user, and meanwhile, the using energy satisfaction degree of the user is considered; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and the optimization solving unit is used for carrying out optimization solving on the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment one. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The energy system operation optimization method and system based on carbon trading and demand response can be realized, and have wide application prospects.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.

Claims (10)

1. An energy system operation optimization method based on carbon trading and demand response is characterized by comprising the following steps:
respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the consumer, and simultaneously considers the using energy satisfaction degree of the user; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and optimizing and solving the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
2. The method as claimed in claim 1, wherein the outer layer optimization model is based on the maximum total profit of the system, and is specifically expressed as:
Figure FDA0003439256850000011
wherein the content of the first and second substances,
Figure FDA0003439256850000012
the price of the electricity sold by the energy supplier,
Figure FDA0003439256850000013
the price of the thermal energy sold by the energy supplier,
Figure FDA0003439256850000014
the price of cold energy sold by the energy supplier,
Figure FDA0003439256850000015
the cold energy power required by the user is the cold energy power,
Figure FDA0003439256850000016
for the demand of the thermal load after the demand response,
Figure FDA0003439256850000017
is the demand of the electric load after the demand response;
Figure FDA0003439256850000018
the cost of consuming natural gas for energy suppliers,
Figure FDA0003439256850000019
the cost of purchasing electrical energy from the grid for an energy provider,
Figure FDA00034392568500000110
and the operation and maintenance cost of each device of the energy supplier.
3. The method as claimed in claim 1, wherein the inner optimization model is a model that maximizes the difference between the utility function and the energy cost of the user, and is specifically expressed as:
Figure FDA0003439256850000021
wherein the content of the first and second substances,
Figure FDA0003439256850000022
is the utility function of the user.
4. The method of claim 1, wherein the carbon trading mechanism model is expressed as follows:
Figure FDA0003439256850000023
Figure FDA0003439256850000024
wherein the content of the first and second substances,
Figure FDA0003439256850000025
for the base carbon emission price, C (E)i) Carbon transaction cost for genset i; wherein, among others,
Figure FDA0003439256850000026
as a baseline carbon emission price; wherein E isiFor actual carbon emission, Qc iIs carbon emission amount, NcNumber of power generating units consuming coal, NgFor the number of generator sets consuming natural gas, N is the total number of generator sets, eta is a carbon quota growth coefficient, and delta is a carbon trading quota growth coefficient.
5. The method for optimizing the operation of the energy system based on the carbon trading and the demand response as claimed in claim 1, wherein the method for nesting the linear programming based on the genetic algorithm specifically adopts a combination of a particle swarm algorithm and a linear programming solution, wherein the particle swarm algorithm adopts a GA-PSO algorithm, and the linear programming solution adopts a CPLEX solver.
6. The method of claim 1, wherein the outer mathematical model comprises a plurality of plant operational models including, but not limited to, gas turbines, waste heat recovery devices, gas boilers, absorption chillers, and electric chillers.
7. The method as claimed in claim 1, wherein the inner mathematical model is a demand response model of the user, and the inner mathematical model includes the user making corresponding electric load transfer and heat load reduction according to the electricity price and the heat price.
8. An energy system operation optimization system based on carbon trading and demand response, comprising:
the mathematical model construction unit is used for respectively constructing mathematical models of an inner layer and an outer layer of the comprehensive energy system, wherein the inner layer and the outer layer comprise an outer layer energy supplier and an inner layer flexible user;
the optimization model construction unit is used for constructing an inner-layer optimization model and an outer-layer optimization model respectively based on a carbon transaction mechanism and a demand response mechanism; the inner-layer optimization model aims at the maximum residual of the user, and meanwhile, the using energy satisfaction degree of the user is considered; introducing a carbon trading mechanism into the outer layer optimization model, and optimizing an energy sale price, an energy equipment output condition and a carbon trading price by taking the maximum system profit as a target;
and the optimization solving unit is used for carrying out optimization solving on the inner and outer layer optimization models based on a genetic algorithm nested linear programming method to obtain decision variables of energy suppliers and flexible users.
9. A computer-readable storage medium, on which a program is stored, the program, when executed by a processor, implementing a method for optimizing the operation of an energy system based on carbon trading and demand response according to any one of claims 1 to 7.
10. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor executes the program to implement a method for optimizing the operation of an energy system based on carbon trading and demand response according to any one of claims 1 to 7.
CN202111627914.7A 2021-12-28 2021-12-28 Energy system operation optimization method and system based on carbon trading and demand response Pending CN114386683A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757552A (en) * 2022-04-25 2022-07-15 广西大学 Method and system for constructing multi-main-body complementary low-carbon operation strategy of multi-energy system

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114757552A (en) * 2022-04-25 2022-07-15 广西大学 Method and system for constructing multi-main-body complementary low-carbon operation strategy of multi-energy system

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