Comprehensive energy demand response method, device and equipment based on simulation platform

Abstract

The present invention provides a comprehensive energy demand response method, device and equipment based on a simulation platform, which can be applied to the field of data processing and energy utilization technology. The method includes: in response to receiving a comprehensive energy demand for a target system from a human-computer interaction interface, obtaining device operation parameters and energy demand response transfer parameters of each device in the target system; based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between each device in the target system, using the simulation platform to process the device operation parameters, energy demand response transfer parameters and comprehensive energy demand to obtain an initial device operation strategy; and controlling the operation status of each device in the target system according to the initial device operation strategy, so that the target system can provide energy that meets the comprehensive energy demand to the user side with the minimum comprehensive energy consumption, taking into account multi-dimensional factors such as device power consumption, user trust, carbon emissions, and environmental pollution.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and energy utilization, and in particular to a comprehensive energy demand response method, device and equipment based on a simulation platform. Background Art

[0002] With the development of energy technology, energy demand and environmental pollution have become the focus of attention. Vigorously developing renewable energy and improving energy efficiency have become the choice for sustainable energy development. In the integrated energy system, the mutual conversion between various energy sources such as cold, heat, electricity, and gas has greatly increased the diversity of energy supply. At the same time, the energy demand of end users has gradually increased, and the demand for peak shaving and valley filling of various energy sources has continued to increase.

[0003] However, traditional power demand response is difficult to meet the above needs. As an extension of traditional power demand response, integrated energy demand response not only inherits the core advantages of traditional power demand response in the power market by adjusting user demand to balance supply and demand, relieve grid pressure, and improve power system flexibility, but also realizes the comprehensive integration and optimization of energy types and utilization methods on this basis. Therefore, it is of great theoretical and practical value to study the response optimization between integrated energy demand response terminals and multi-energy application systems. Summary of the invention

[0004] In view of the above problems, the present invention provides a comprehensive energy demand response method, device and equipment based on a simulation platform.

[0005] According to the first aspect of the present invention, a comprehensive energy demand response method based on a simulation platform is provided, comprising: in response to receiving a comprehensive energy demand for a target system from a human-computer interaction interface, obtaining device operating parameters and energy demand response transfer parameters of each device in the above-mentioned target system; wherein the above-mentioned energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; the above-mentioned user trust represents the proportion of the user group that has participated in the comprehensive energy response to the total user group; based on the balance relationship between energy supply and energy consumption and the operating constraint relationship between the various devices of the above-mentioned target system, using the simulation platform to process the above-mentioned device operating parameters, energy demand response transfer parameters and the above-mentioned comprehensive energy demand to obtain an initial device operating strategy; and controlling the operating status of each device of the above-mentioned target system according to the above-mentioned initial device operating strategy, so that the above-mentioned target system provides energy that meets the above-mentioned comprehensive energy demand to the above-mentioned user side with minimal energy consumption.

[0006] According to an embodiment of the present invention, based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the various devices of the target system, the simulation platform is used to process the device operation parameters, energy demand response transfer parameters and the comprehensive energy demand to obtain an initial device operation strategy, including: constructing an energy consumption function of the target system according to the device operation parameters, energy demand response transfer parameters and the comprehensive energy demand; constructing a first constraint condition according to the device operation parameters and the carbon trading reward parameters based on the balance relationship between energy supply and energy consumption; constructing a second constraint condition according to the device operation parameters based on the operation constraint relationship between the various devices; and calculating the initial device operation strategy based on the energy consumption function, the first constraint condition and the second constraint condition using a particle swarm algorithm.

[0007] According to an embodiment of the present invention, the above-mentioned equipment operation parameters include equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters, equipment operation power consumption parameters, equipment operation maintenance parameters and equipment pollution discharge parameters; the above-mentioned energy consumption function of the target system is constructed according to the above-mentioned equipment operation parameters, energy demand response transfer parameters and the above-mentioned comprehensive energy demand, including: constructing a first sub-function according to the above-mentioned equipment operation time parameters, the above-mentioned equipment quantity parameters, the above-mentioned equipment operation efficiency parameters, and the above-mentioned equipment operation power consumption parameters; constructing a second sub-function according to the above-mentioned equipment operation maintenance parameters, the above-mentioned equipment operation time parameters, the above-mentioned equipment quantity parameters and the above-mentioned equipment operation power consumption parameters; constructing a third sub-function according to the above-mentioned user trust, load reward parameters and the above-mentioned comprehensive energy demand; constructing a fourth sub-function according to the above-mentioned carbon trading reward parameters and the above-mentioned comprehensive energy demand; constructing a fifth sub-function according to the above-mentioned equipment pollution discharge parameters, the above-mentioned equipment operation time parameters, the above-mentioned equipment quantity parameters and the above-mentioned equipment operation power consumption parameters; and constructing the above-mentioned energy consumption function according to the above-mentioned first sub-function, the above-mentioned second sub-function, the above-mentioned third sub-function, the above-mentioned fourth sub-function and the above-mentioned fifth sub-function.

[0008] According to an embodiment of the present invention, the above-mentioned particle swarm algorithm is used to calculate the above-mentioned initial device operation strategy based on the above-mentioned energy consumption function, the above-mentioned first constraint condition and the above-mentioned second constraint condition, including: taking device operation parameters as particles, constructing an initial particle population based on the above-mentioned energy consumption function, the above-mentioned first constraint condition and the above-mentioned second constraint condition; calculating the fitness of each particle in the above-mentioned initial particle population based on user trust and the current iteration round; and iteratively optimizing the position of each particle in the above-mentioned initial particle population based on the above-mentioned fitness, so as to obtain the above-mentioned initial device operation strategy.

[0009] According to an embodiment of the present invention, the above-mentioned initial device operation strategy is obtained by iteratively optimizing the position of each particle in the above-mentioned initial particle population based on the above-mentioned fitness, including: determining an intermediate particle population from the above-mentioned initial particle population according to the initial fitness of each particle; and obtaining the intermediate position of each particle in the above-mentioned intermediate particle population; wherein the above-mentioned intermediate position represents the function value of the above-mentioned energy consumption function; calculating the energy consumption difference value based on the optimal position of each particle in the above-mentioned intermediate particle population and the optimal position of the population; when it is determined that the above-mentioned energy consumption difference value is greater than or equal to a predetermined threshold value and the above-mentioned current iteration round is less than the predetermined iteration round, updating the inertia weight, particle speed and particle position of each particle respectively according to user trust, predetermined inertia weight and predetermined learning parameters; and when it is determined that the above-mentioned energy consumption difference value is less than the predetermined threshold value or the above-mentioned current iteration round is equal to the above-mentioned predetermined iteration round, generating the above-mentioned initial device operation strategy according to the device operation parameters corresponding to each particle in the current iteration number.

[0010] According to an embodiment of the present invention, after controlling the operating status of each device of the target system according to the initial device operating strategy, the comprehensive energy demand response method based on the simulation platform further includes: obtaining actual operating status information of each device within a predetermined period;

[0011] The actual energy consumption information of the target system in the predetermined time period is calculated based on the actual operation status information; and the initial equipment operation strategy of the target system is adjusted by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period to obtain the target equipment operation strategy.

[0012] According to an embodiment of the present invention, the above-mentioned initial equipment operation strategy of the target system is adjusted by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period to obtain the target equipment operation strategy, including: in response to the difference value between the above-mentioned actual energy consumption information and the above-mentioned simulated energy consumption information being greater than or equal to the difference threshold, and the change trend of the above-mentioned actual energy consumption information is the same as the change trend of the above-mentioned simulated energy consumption information, adjusting the operation parameters of each device in the above-mentioned initial equipment operation strategy according to the above-mentioned difference value to generate the above-mentioned target equipment operation strategy; and in response to the difference value between the above-mentioned actual energy consumption information and the above-mentioned simulated energy consumption information being greater than or equal to the difference threshold, and the change trend of the above-mentioned actual energy consumption information is different from the change trend of the above-mentioned simulated energy consumption information, displaying energy consumption warning information through a visual interface, so as to obtain manual adjustment information for the above-mentioned initial equipment operation strategy, adjusting the initial equipment operation strategy of the above-mentioned target system, and obtaining the target equipment operation strategy.

[0013] A second aspect of the present invention provides a comprehensive energy demand response device based on a simulation platform, comprising: a first acquisition module, a simulation module and a control operation module.

[0014] The first acquisition module is used to obtain the equipment operation parameters and energy demand response transfer parameters of each equipment in the target system in response to receiving the comprehensive energy demand for the target system from the human-computer interaction interface; wherein the energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; the user trust represents the proportion of the user group that has participated in the comprehensive energy response to the total user group.

[0015] The simulation module is used to process the operating parameters of the above-mentioned equipment, the energy demand response transfer parameters and the above-mentioned comprehensive energy demand based on the balance relationship between energy supply and energy consumption and the operating constraint relationship between the various devices of the above-mentioned target system using the simulation platform to obtain the initial equipment operation strategy.

[0016] The control operation module is used to control the operating status of each device of the above-mentioned target system according to the above-mentioned initial device operation strategy, so that the above-mentioned target system can provide energy that meets the above-mentioned comprehensive energy demand to the above-mentioned user side with minimum energy consumption.

[0017] A third aspect of the present invention provides an electronic device, comprising: one or more processors; a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0018] The fourth aspect of the present invention further provides a computer-readable storage medium on which a computer program or instruction is stored, and the steps of the above method are implemented when the above computer program or instruction is executed by a processor.

[0019] The fifth aspect of the present invention also provides a computer program product, including a computer program or instructions, which implement the steps of the above method when executed by a processor.

[0020] According to the comprehensive energy demand response method, device and equipment based on the simulation platform provided by the present invention, the comprehensive energy demand is set through a human-computer interaction interface, and the device operating parameters, energy demand response transfer parameters and comprehensive energy demand are processed by using the simulation platform while satisfying the balance relationship between energy supply and energy consumption and the operating constraint relationship between each device of the target system to obtain an initial device operation strategy, so that the target system can minimize energy consumption, and then provide energy that meets the comprehensive energy demand to the user side, and the energy demand response transfer parameters comprehensively consider user trust, load reward parameters and carbon trading reward parameters, so as to improve the energy efficiency of the target system and reduce the resource loss of the target system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above contents and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0022] Figure 1 An application scenario diagram of a comprehensive energy demand response method based on a simulation platform according to an embodiment of the present invention is shown;

[0023] Figure 2 A flowchart of a comprehensive energy demand response method based on a simulation platform according to an embodiment of the present invention is shown;

[0024] Figure 3 A schematic diagram showing the result of processing the operating parameters of multiple devices using a particle swarm algorithm according to an embodiment of the present invention;

[0025] Figure 4 A schematic diagram showing the power of the electric load on the user side after the operating state of each device of the target system is controlled according to the initial device operating strategy in four different scenarios according to an embodiment of the present invention;

[0026] Figure 5 A schematic diagram of real-time changes in user-side thermal load power is shown after the operating states of various devices of the target system are controlled according to the initial device operating strategy in four different scenarios according to an embodiment of the present invention;

[0027] Figure 6 A schematic diagram of real-time changes in cooling load power at the user side is shown in four different scenarios according to an embodiment of the present invention after the operating states of various devices of the target system are controlled according to the initial device operating strategy;

[0028] Figure 7 A flowchart of calculating an initial equipment operation strategy based on an energy consumption function, a first constraint condition, and a second constraint condition using a particle swarm algorithm according to an embodiment of the present invention is shown;

[0029] Figure 8 A workflow diagram of a simulation platform according to an initial device operation strategy according to an embodiment of the present invention is shown;

[0030] Fig. 9 A logical architecture diagram of a simulation platform according to an initial device operation strategy according to an embodiment of the present invention is shown;

[0031] Fig.10 A structural block diagram of a comprehensive energy demand response device based on a simulation platform according to an embodiment of the present invention is shown;

[0032] Fig.11 A block diagram of an electronic device suitable for implementing a comprehensive energy demand response method based on a simulation platform according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0033] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

[0034] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0035] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0036] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0037] In the technical solution of the present invention, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0038] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present invention provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0039] With the continuous growth of global energy demand and the diversified development of energy structure, Integrated Energy Demand Response (IEDR) as an innovative energy management strategy has emerged and has received increasing attention. The introduction of the concept of integrated energy demand response marks the extension and expansion of the traditional electricity demand response (DR) concept to a wider and deeper integrated energy system field. IEDR not only inherits the core advantages of DR in the power market by adjusting user demand to balance supply and demand, relieve grid pressure, and improve the flexibility of the power system, but also realizes the comprehensive integration and optimization of energy types and utilization methods on this basis.

[0040] Through comprehensive energy demand response, the scope of the adjustable load resource library has been unprecedentedly expanded, no longer limited to traditional power load management, but covers a variety of energy utilization forms including energy storage systems, distributed energy (such as solar energy, wind energy), cogeneration systems, etc. This diversified and intelligent resource scheduling method not only provides more adjustment means for the power system, but also brings users a more economical and environmentally friendly energy experience.

[0041] In the process of realizing the present invention, it is found that in the related technology, a series of modularly designed power generation and energy storage technologies are proposed to quantitatively analyze the decision-making of multiple influencing factors of complex systems. In terms of integrated energy systems, the impact of demand response of electricity, thermal energy and renewable integrated energy on decision-making is comprehensively considered. However, there is still room for improvement in multi-energy priority auxiliary decision-making, information data interaction accuracy and communication reliability.

[0042] In view of this, an embodiment of the present invention provides a comprehensive energy demand response method based on a simulation platform, including: in response to receiving a comprehensive energy demand for a target system from a human-computer interaction interface, obtaining device operating parameters and energy demand response transfer parameters of each device in the target system; wherein the energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; user trust represents the proportion of user groups that have participated in the comprehensive energy response to the total user group; based on the balance relationship between energy supply and energy consumption and the operating constraint relationship between each device of the target system, using the simulation platform to process the device operating parameters, energy demand response transfer parameters and comprehensive energy demand to obtain an initial device operating strategy; and controlling the operating status of each device of the target system according to the initial device operating strategy, so that the target system provides energy that meets the comprehensive energy demand to the user side with minimal energy consumption.

[0043] Figure 1 An application scenario diagram of comprehensive energy demand response decision-making based on a simulation platform according to an embodiment of the present invention is shown.

[0044] like Figure 1 As shown, the application scenario 100 of the comprehensive energy demand response decision-making method based on the simulation platform may include a grid-side load management system master station 101, a comprehensive energy demand response terminal device 102, a target system 103 and a user side 104.

[0045] According to an embodiment of the present invention, the comprehensive energy demand response method based on the simulation platform can be executed by the comprehensive energy demand response terminal device 102. The grid-side load management system master station 101 adjusts the comprehensive energy demand in real time based on the energy consumption habits and actual comprehensive energy demand of the user side 104. The comprehensive energy demand response terminal device 102 then obtains the comprehensive energy demand from the grid-side load management system master station 101, and based on the comprehensive energy demand, executes the comprehensive energy demand response method based on the simulation platform provided by the embodiment of the present invention, obtains the device parameters of the comprehensive energy demand response terminal device 102 that meet the comprehensive energy demand, and generates a response instruction with the device parameters, and sends it to the target system 103, so that the comprehensive target system 103 controls the corresponding device to adjust the device parameters to the device parameters in the response instruction. At the same time, the target system 103 can also send the system operation status after adjusting the device parameters to the comprehensive energy demand response terminal device 102 in the form of feedback information, so as to realize the interaction between the target system 103 and the grid-side energy demand, and let the target system 103 directly provide comprehensive energy to the user side 104 to meet the comprehensive energy use demand of the user side 104.

[0046] It should be understood that Figure 1The number of the power grid side load management system master station, integrated energy demand response terminal equipment, target system and users is only illustrative. According to actual needs, there can be any number of power grid side load management system master stations, integrated energy demand response terminal equipment, target system and users.

[0047] The following will be based on Figure 1 The scene described by Figure 2~Figure 7 The comprehensive energy demand response method based on the simulation platform according to an embodiment of the present invention is described in detail.

[0048] Figure 2 A flow chart of a comprehensive energy demand response method based on a simulation platform according to an embodiment of the present invention is shown.

[0049] like Figure 2 As shown, the comprehensive energy demand response method 200 based on the simulation platform of this embodiment includes operations S210 to S230.

[0050] In operation S210 , in response to receiving a comprehensive energy demand for a target system from a human-computer interaction interface, device operation parameters and energy demand response transfer parameters of each device in the target system are acquired.

[0051] In operation S220, based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the devices of the target system, the device operation parameters, energy demand response transfer parameters and comprehensive energy demand are processed using a simulation platform to obtain an initial device operation strategy.

[0052] In operation S230, the operation state of each device of the target system is controlled according to the initial device operation strategy, so that the target system can provide energy that meets the comprehensive energy demand to the user side with minimum energy consumption.

[0053] According to an embodiment of the present invention, a human-computer interaction interface may represent an interface for communication and interaction between a user and a computer. The human-computer interaction interface may include a user side and a supply side.

[0054] According to an embodiment of the present invention, the target system may represent an integrated energy system based on a simulation platform. The target system may include multiple devices. For example, the target system may include 20 devices. The device operation parameters of the device may represent the parameters of the device during operation. The device operation parameters of multiple devices may be different.

[0055] According to an embodiment of the present invention, the energy demand response transfer parameters may include user trust, load reward parameters and carbon trading reward parameters. User trust may characterize the proportion of user groups that have participated in the comprehensive energy response to the total user group. The corresponding comprehensive energy data may be set through the human-computer interaction interface. For example, the user side may set equipment operation parameters, etc. The user side may set user trust, etc.

[0056] According to an embodiment of the present invention, energy supply can represent energy input to a target system. Energy consumption can represent energy consumption by a target system. Operation constraint relationships between devices can represent constraints between multiple devices of a target system. A simulation platform can represent a platform for processing device operation parameters, energy demand response transfer parameters, and comprehensive energy demand.

[0057] According to an embodiment of the present invention, the initial device operation strategy can characterize the initial device operation mode of multiple devices in the target system. Based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the devices in the target system, the device operation parameters, energy demand response transfer parameters and comprehensive energy demand can be processed using a simulation platform to obtain the initial device operation strategy.

[0058] According to an embodiment of the present invention, different device operation strategies may correspond to different operation states of each device of the target system. The operation state of each device of the target system may be controlled according to the initial device operation strategy, so that the target system provides energy that meets the comprehensive energy demand to the user side with the minimum energy consumption.

[0059] According to an embodiment of the present invention, the comprehensive energy demand is set through a human-computer interaction interface. Under the condition of satisfying the balance relationship between energy supply and energy consumption and the operating constraints between the devices of the target system, the device operating parameters, energy demand response transfer parameters and comprehensive energy demand are processed by using a simulation platform to obtain an initial device operating strategy, so that the target system can minimize energy consumption, and then provide energy that meets the comprehensive energy demand to the user side. The energy demand response transfer parameters comprehensively consider multi-dimensional factors such as device power consumption, user trust, carbon emissions, and environmental pollution, so as to improve the energy efficiency of the target system and reduce the resource loss of the target system.

[0060] According to an embodiment of the present invention, based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between each device of the target system, a simulation platform is used to process the device operation parameters, energy demand response transfer parameters and comprehensive energy demand to obtain an initial device operation strategy, including: constructing an energy consumption function of the target system according to the device operation parameters, energy demand response transfer parameters and comprehensive energy demand; constructing a first constraint condition according to the device operation parameters and carbon trading reward parameters based on the balance relationship between energy supply and energy consumption; constructing a second constraint condition according to the device operation parameters based on the operation constraint relationship between each device; and calculating the initial device operation strategy based on the energy consumption function, the first constraint condition and the second constraint condition using a particle swarm algorithm.

[0061] According to an embodiment of the present invention, the energy consumption function of the target system can characterize the energy consumption level of the target system. The energy consumption of the target system can include external energy purchase energy consumption, maintenance energy consumption of equipment operation loss, comprehensive energy demand response subsidy, carbon trading subsidy and environmental pollution control energy consumption. The energy consumption function of the target system can be constructed based on equipment operation parameters, energy demand response transfer parameters and comprehensive energy demand, as shown in the following formula (1):

[0062] (1)

[0063] in, represents the energy consumption function of the target system, represents the external energy purchase energy consumption function, The maintenance energy consumption function representing the equipment operation loss, represents the comprehensive energy demand response subsidy function, represents the carbon trading reward subsidy function, Represents the energy consumption function of environmental pollution control.

[0064] According to an embodiment of the present invention, in the target system power consumption decision process, the balance between energy supply and energy consumption and the stability of the equipment during energy transmission are the key to ensuring the normal operation of the target system. Therefore, constraints can be constructed based on the balance between energy supply and energy consumption and the stability requirements of the equipment during energy transmission.

[0065] According to an embodiment of the present invention, based on the balance relationship between energy supply and energy consumption, a first constraint condition can be constructed according to equipment operation parameters and carbon trading reward parameters, as shown in the following formula (2):

[0066] (2)

[0067] Among them, among them, express The total amount of electricity load on the user side during the period, express Energy loss of the target system's electrical energy-driven equipment during the period, express The total heat load on the user side during the period, express The total cooling load on the user side during the period, express The energy consumption of the driving equipment in the target system during the period, Indicates electric energy driven equipment exist The output power during the period, Indicates internal energy driven device exist The output power during the period, Indicates internal energy driven device operating efficiency.

[0068] According to an embodiment of the present invention, the second constraint condition may be constructed based on the operation constraint relationship between the devices and the device operation parameters, as shown in the following formula (3):

[0069] (3)

[0070] in, Indicates the upper limit of the output power of the target system's electric energy drive equipment. Indicates the lower limit of the output power of the target system's electric energy drive equipment. Indicates the upper limit of the output power of the device that can be driven in the target system. Indicates the lower limit of the output power of the driving device in the target system. Indicates the upper limit of the ramp rate of the target system's electric energy drive equipment. Indicates the lower limit of the ramp rate of the target system's electric energy drive equipment. Indicates the upper limit of the ramp rate that can be driven in the target system. Indicates the lower limit of the ramp rate that can be driven by the target system. Indicates the start or stop status of the device. Indicates that the device is started. Indicates that the device is stopped.

[0071] According to an embodiment of the present invention, a particle swarm algorithm is a random search algorithm based on group collaboration, which can simulate the social behavior in a flock of birds or a school of fish. In the particle swarm algorithm, each potential solution is represented as a particle in the search space, and these particles search for the optimal solution through collaboration and information sharing between individuals. In an embodiment of the present invention, the operating parameters of the devices in the target system can be regarded as particles.

[0072] Table 1 Particle swarm algorithm related parameter settings

[0073]

[0074] Table 2 Target system related parameter settings

[0075]

[0076] According to an embodiment of the present invention, in order to stimulate and encourage users to shave peak loads and fill valley loads and decide on the energy consumption mode of users, the electricity price mechanism on the user side is improved, and a time-of-use electricity price strategy is adopted as shown in Table 3 below:

[0077] Table 3 User-side time-of-use electricity price table

[0078]

[0079] According to an embodiment of the present invention, the parameters of the particle swarm algorithm are shown in Table 1, the parameters of the target system are shown in Table 2, and the load reward parameters are shown in Table 3. The particle swarm algorithm can be used to calculate the initial equipment operation strategy based on the energy consumption function of the above formula (1), the first constraint condition of the above formula (2) and the second constraint condition of the above formula (3).

[0080] According to the embodiment of the present invention, based on the energy consumption function of the target system and when the constraint conditions are met, the initial equipment operation strategy can be calculated, thereby improving the accuracy of the initial equipment operation strategy.

[0081] According to an embodiment of the present invention, an energy consumption function of a target system is constructed according to equipment operation parameters, energy demand response transfer parameters and comprehensive energy demand, including: constructing a first sub-function according to equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters and equipment operation power consumption parameters; constructing a second sub-function according to equipment operation maintenance parameters, equipment operation time parameters, equipment quantity parameters and equipment operation power consumption parameters; constructing a third sub-function according to user trust, load reward parameters and comprehensive energy demand; constructing a fourth sub-function according to carbon trading reward parameters and comprehensive energy demand; constructing a fifth sub-function according to equipment pollution discharge parameters, equipment operation time parameters, equipment quantity parameters and equipment operation power consumption parameters; and constructing an energy consumption function according to the first sub-function, the second sub-function, the third sub-function, the fourth sub-function and the fifth sub-function.

[0082] According to an embodiment of the present invention, the equipment operation parameters may include equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters, equipment operation power consumption parameters, equipment operation maintenance parameters and equipment sewage discharge parameters.

[0083] According to an embodiment of the present invention, a first sub-function may be constructed according to a device operation time parameter, a device quantity parameter, a device operation efficiency parameter, and a device operation power consumption parameter. The first sub-function may also be called an external energy purchase energy consumption function, as shown in the following formula (4):

[0084] (4)

[0085] in, express The target system power purchase price during the period, express The target system gas purchase price during the time period.

[0086] According to an embodiment of the present invention, a second sub-function may be constructed based on the equipment operation maintenance parameter, the equipment operation time parameter, the equipment quantity parameter, and the equipment operation power consumption parameter. The second sub-function may also be called a maintenance energy consumption function of equipment operation loss, as shown in the following formula (5):

[0087] (5)

[0088] in, Indicates electric energy driven equipment The operation and maintenance cost coefficient, Indicates internal energy driven device Operation and maintenance cost coefficient.

[0089] According to an embodiment of the present invention, a third sub-function may be constructed based on user trust, load reward parameters and comprehensive energy demand. The third sub-function may also be called a comprehensive energy demand response subsidy function considering user trust, as shown in the following formula (6):

[0090] (6)

[0091] in, Indicates user trust. Indicates the unit subsidy power consumption of the electric load, represents the unit subsidy power consumption of the heat load, represents the unit subsidy power consumption of cooling load, express The electric load shifted in response to comprehensive energy demand during the period, express The heat load transferred in response to the comprehensive energy demand during the period, express Cooling load shifted in response to comprehensive energy demand during the time period.

[0092] According to an embodiment of the present invention, a fourth sub-function may be constructed according to the carbon trading reward parameter and the comprehensive energy demand. The fourth sub-function may also be called a carbon trading reward subsidy function. , then the fourth sub-function is as follows formula (7):

[0093] (7)

[0094] in, represents the unit transaction price in the carbon trading market, Indicates the system's carbon trading reward coefficient, Indicates the total amount of carbon trading in the system.

[0095] According to an embodiment of the present invention, the equipment pollution discharge parameters may include pollutant type parameters, pollutant emission coefficients, and treatment power consumption parameters corresponding to the pollutant type parameters. A fifth sub-function may be constructed based on the equipment pollution discharge parameters, equipment operation time parameters, equipment quantity parameters, and equipment operation power consumption parameters. The fifth sub-function may also be called an environmental pollution control energy consumption function, as shown in the following formula (8):

[0096] (8)

[0097] in, Indicates The power consumption of the treatment of pollutants Indicates electric energy driven equipment The pollutant emission coefficient, Indicates internal energy driven device The pollutant emission coefficient.

[0098] According to an embodiment of the present invention, an energy consumption function of a target system may be constructed according to the first sub-function, the second sub-function, the third sub-function, the fourth sub-function and the fifth sub-function.

[0099] According to an embodiment of the present invention, the energy consumption function of the target system comprehensively considers multi-dimensional factors such as load reward parameters, carbon trading reward parameters and equipment emission parameters, so that it can adapt to the response scenario of the energy gradient demand on the current user side, so as to respond promptly to changing factors such as energy demand changes and energy demand response subsidies during the peak energy usage period, adjust the initial equipment parameters of the target system, and meet the comprehensive energy needs of the user side.

[0100] According to an embodiment of the present invention, a particle swarm algorithm is used to calculate an initial device operation strategy based on an energy consumption function, a first constraint and a second constraint, including: taking device operation parameters as particles, constructing an initial particle population based on the energy consumption function, the first constraint and the second constraint; calculating the fitness of each particle in the initial particle population based on user trust and the current iteration round; and iteratively optimizing the position of each particle in the initial particle population based on the fitness to obtain the initial device operation strategy.

[0101] According to an embodiment of the present invention, when the device operating parameters are particles, the particle swarm algorithm can be used to construct an initial particle population based on the energy consumption function, the first constraint condition and the second constraint condition, thereby completing the initialization of the particle population.

[0102] According to an embodiment of the present invention, the user trust can be preset. According to the preset user trust and the current iteration round, the fitness of each particle in the initial particle population can be calculated as follows:

[0103] (9)

[0104] in, It represents the inverse of the current iteration number, and j represents the number of the particle.

[0105] According to an embodiment of the present invention, each particle may correspond to an initial position, and the position of each particle in the initial particle population may be iteratively optimized based on fitness to obtain an initial device operation strategy.

[0106] According to an embodiment of the present invention, taking into account the user's trust, a particle swarm algorithm can be used to determine the trust of each particle, and then the position of the particles is iteratively optimized to obtain an initial equipment operation strategy, which is more conducive to the user side's participation in the regulation process of comprehensive energy demand response.

[0107] According to an embodiment of the present invention, in order to verify the effectiveness of the target system operation decision considering the initial device operation strategy, the following four scenarios are set for simulation. Scenario S1 represents the operation of the target system without considering the initial device operation strategy. Scenario S2 represents the operation of the target system of the initial device operation strategy, and the user trust is 100%. Scenario S3 represents the operation of the target system considering the initial device operation strategy, and the user trust is 80%. Scenario S4 represents the operation of the target system considering the initial device operation strategy, and the user trust is 50%.

[0108] Figure 3 A schematic diagram showing the result of processing the operating parameters of multiple devices using a particle swarm algorithm according to an embodiment of the present invention is shown.

[0109] like Figure 3 As shown, in the process of particle swarm algorithm processing, the operating energy consumption of the four scenarios finally obtained are respectively Scenario S1: 5094.06 kJ, Scenario S2: 3898.07 kJ, Scenario S3: 4265.87 kJ and Scenario S4: 4676.55 kJ, and the number of iterations required to iterate to the optimal solution are respectively Scenario S1: 168 generations, Scenario S2: 154 generations, Scenario S3: 159 generations and Scenario S4: 161 generations. Therefore, the number of iterations required for the decision-making process of the target system under scenario S2 is the least, and it has the fastest iteration speed. The operating energy consumption of the target system obtained by the particle swarm algorithm under scenario S2 is lower than that obtained by the particle swarm algorithm in the other three scenarios, indicating that the consideration of the comprehensive energy demand response scheme and the higher the user trust, the more conducive to the decision-making operation of the target system, and the lower the operating energy consumption. The iteration process of the particle swarm algorithm is also very smooth, without particularly obvious data fluctuations, and will eventually converge to a steady-state value, indicating that the energy consumption function constructed by the particle swarm algorithm is solved, and the equipment operation parameters obtained are effective.

[0110] Figure 4 A schematic diagram of the electric load power at the user side is shown in four different scenarios after the operating status of each device of the target system is controlled according to the initial device operating strategy according to an embodiment of the present invention.

[0111] like Figure 4 As shown, by comparing scenarios S2, S3 and S4 under the introduction of the initial equipment operation strategy with scenario S1 without the introduction of the initial equipment operation strategy, it can be found that the peak-to-valley difference of the electric load power in scenarios S2, S3 and S4 before and after the comprehensive energy demand response decreased by 12.29%, 9.45% and 6.83% respectively.

[0112] Figure 5 A schematic diagram of real-time changes in thermal load power at the user side is shown after the operating status of each device of the target system is controlled according to the initial device operating strategy in four different scenarios according to an embodiment of the present invention.

[0113] like Figure 5 As shown, by comparing scenario S2, scenario S3 and scenario S4 under the introduction of the initial equipment operation strategy with scenario S1 without the introduction of the initial equipment operation strategy, it can be found that the peak-to-valley difference of the heat load power of scenario S2, scenario S3 and scenario S4 before and after the comprehensive energy demand response decreased by 10.92%, 7.56% and 4.11% respectively.

[0114] Figure 6A schematic diagram of real-time changes in cooling load power at the user side is shown in four different scenarios after the operating status of each device of the target system is controlled according to the initial device operating strategy according to an embodiment of the present invention.

[0115] like Figure 6 As shown, by comparing the scenarios S2, S3 and S4 under the introduction of the initial equipment operation strategy with the scenario S1 without the introduction of the initial equipment operation strategy, it can be found that the peak-to-valley difference of the cooling load power of scenarios S2, S3 and S4 before and after the comprehensive energy demand response decreased by 9.85%, 6.42% and 2.87% respectively.

[0116] According to the embodiment of the present invention, the user's electric load, heat load and cooling load are all smoothed to a certain extent after the introduction of the initial equipment operation strategy. This is because after considering the initial equipment operation strategy, the user can reasonably adjust his energy consumption strategy within a certain degree of satisfaction change range according to the time-of-use electricity price information, and because the target system considers the comprehensive energy demand response subsidy mechanism, that is, the load change amount transferred or reduced by the user is converted into an incentive subsidy to the user, further stimulating the user to actively participate in the energy adjustment process, so that the user transfers the peak load to the valley load, and makes reasonable adjustments in the normal period, effectively smoothing the user's load fluctuation, that is, reducing the peak-valley difference of the load before and after the response.

[0117] According to an embodiment of the present invention, the position of each particle in the initial particle population is iteratively optimized based on fitness to obtain an initial device operation strategy, including: determining an intermediate particle population from the initial particle population according to the initial fitness of each particle; and obtaining the intermediate position of each particle in the intermediate particle population; calculating an energy consumption difference value based on the optimal position of each particle in the intermediate particle population and the optimal position of the population; when it is determined that the energy consumption difference value is greater than or equal to a predetermined threshold and the current iteration round is less than the predetermined iteration round, updating the inertia weight, particle speed and particle position of each particle respectively according to user trust, predetermined inertia weight and predetermined learning parameters; and when it is determined that the energy consumption difference value is less than the predetermined threshold or the current iteration round is equal to the predetermined iteration round, generating an initial device operation strategy according to the device operation parameters corresponding to each particle in the current iteration number.

[0118] According to an embodiment of the present invention, each particle may correspond to a fitness, and the fitness of each particle may be different. The intermediate particle population may be determined from the initial particle population according to the initial fitness of each particle. For example, by comparing the initial fitness of each particle with a fitness threshold, particles having a fitness greater than the fitness threshold may be determined as particles in the intermediate particle population. Screening the initial particle population to obtain the intermediate particle population may play a role in screening inferior solutions to a certain extent.

[0119] According to an embodiment of the present invention, the intermediate position may represent the function value of the energy consumption function. When the intermediate particle population is determined, the intermediate position of each particle in the intermediate particle population may also be obtained.

[0120] According to an embodiment of the present invention, the energy consumption difference value can be calculated based on the optimal position of each particle in the intermediate particle population and the optimal position of the population. For example, when the predetermined threshold is 0.001, the energy consumption difference value satisfies the following formula (10):

[0121] (10)

[0122] in, Indicates The optimal position of a particle, Represents the optimal position of the intermediate particle population.

[0123] According to an embodiment of the present invention, the predetermined number of iterations may be affected by a specific problem, depending on the difficulty or specific type of the problem. The larger the predetermined number of iterations, the more complex the problem model, the larger the search space range of the algorithm, and the easier it is to find the global optimal solution. However, the algorithm operation will also take longer, increasing the workload. Therefore, the maximum number of iterations should be balanced. For example, the predetermined number of iterations can be set to 300. The inertia weight can be characterized to control the development of the particle swarm algorithm and the global search capability, and can measure the weight of each particle on the current velocity integration. When the inertia weight is large, the global optimization capability is strong and the local search capability is weak. When the inertia weight is small, the global optimization capability is weak and the local search capability is strong.

[0124] According to an embodiment of the present invention, the inertia weight may include a fixed weight and a dynamic weight. The fixed weight is a constant and remains unchanged, so that each particle always has the same development and search capabilities. The dynamic weight is generally a set change interval, which gradually decreases according to a certain rule during the change process. The measurement indicators are the change range and the decreasing rate, and each particle can have different development and search capabilities at different stages. The usual practice is to take a larger value for the inertia weight in the early stage, which has a stronger global search capability, and take a smaller value in the later stage, which is convenient for precise search to jump out of the local optimum.

[0125] According to an embodiment of the present invention, when it is determined that the energy consumption difference value is greater than or equal to a predetermined threshold and the current iteration round is less than a predetermined iteration round, the inertia weight, particle speed and particle position of each particle can be updated respectively according to the user trust, the predetermined inertia weight and the predetermined learning parameter, wherein the inertia weight of each particle is updated. , as follows formula (11):

[0126] (11)

[0127] in, represents the maximum inertia weight, represents the minimum inertia weight, L represents the scheduled iteration round, and k represents the current iteration number.

[0128] According to an embodiment of the present invention, the learning factor and All particles in the intermediate particle population can be continuously adjusted to The optimal position of a particle and the optimal position of the intermediate particle population The function of the particle trajectories is to control the experience of each particle and the intermediate particle population during the search process, influence and change the particle motion trajectory, and help the information exchange between particles.

[0129] According to an embodiment of the present invention, if two learning factors and If both are 0, the particle will maintain the current speed Flying to the boundary of the search space, it is difficult to find the global optimal solution due to the limited space range. If the learning factor is 0, this model is called a “social model”. The particles lack the ability to self-recognize and fall into a local optimum. is 0, this model is called the “cognitive model”, there is no information exchange between particles, and the probability of finding the global optimal solution is very low. Therefore, we can assume , and dynamically updates the learning factor with the number of iterations, so that the experience between each particle and the intermediate particle population can be shared, and the information interaction between particles can be strengthened, making the process of finding the optimal solution more accurate. Learning factor and The value range can be set to , ensure that the learning factors are equal during dynamic update .

[0130] According to an embodiment of the present invention, the particle velocity It can characterize the distance and direction of the next iteration of the particle. It is necessary to ensure that the particle has a maximum speed limit in each dimension to clamp the speed of the particle in the process of flying to the search space. Within the range, the particle speed can be set by the user. A suitable value should be selected within the range. If it is too large, the particles will fly over the optimal search area, and if it is too small, it will not be able to search for the global optimal solution. Both extremes will cause them to fall into the local optimum. Therefore, a reasonable value should be selected to control the particle speed within a suitable range. The particle speed can be adjusted together with the inertia weight to achieve the purpose of initialization. In this embodiment, the particle speed can be adjusted. Take it as 10.

[0131] According to an embodiment of the present invention, the particle velocity of a particle may be updated according to the following formula (12) and the particle position may be updated according to the following formula (13).

[0132] (12)

[0133] (13)

[0134] in, represents the position of the jth particle, represents the particle velocity of the jth particle, represents the updated position of the jth particle, represents the updated particle velocity of the jth particle, and Respectively represent two value ranges Random numbers can increase the randomness of the search process.

[0135] According to an embodiment of the present invention, when it is determined that the energy consumption difference value is less than a predetermined threshold or the current iteration round is equal to a predetermined iteration round, an initial device operation strategy can be generated according to the device operation parameters corresponding to each particle in the current iteration number.

[0136] According to an embodiment of the present invention, the energy consumption difference value can be determined for the optimal position of the intermediate particle population and the optimal position of the population, and when the energy consumption difference value is greater than or equal to a predetermined threshold and the current iteration round is less than the predetermined iteration round, the inertia weight, particle speed and particle position of each particle are updated, until the energy consumption difference value is less than the predetermined threshold or the current iteration round is equal to the predetermined iteration round, an initial device operation strategy is generated, thereby improving the accuracy of the generated initial device operation strategy, thereby meeting the comprehensive energy needs of the user side.

[0137] Figure 7 A flow chart of calculating an initial equipment operation strategy based on an energy consumption function, a first constraint condition, and a second constraint condition by using a particle swarm algorithm according to an embodiment of the present invention is shown.

[0138] like Figure 7 As shown, the process 700 of obtaining the initial device operation strategy includes operations S710 to S790.

[0139] In operation S710 , a particle population is initialized.

[0140] In operation S720, the fitness of each particle in the initial particle population is calculated, and the position of each particle in the initial particle population is obtained.

[0141] In operation S730, an intermediate particle population is determined according to the initial fitness of each particle, and an intermediate position of each particle in the intermediate particle population is obtained.

[0142] In operation S740, an optimal position of each particle in the intermediate particle population and a population optimal position are determined.

[0143] In operation S750, it is determined whether the current solution satisfies the termination condition. If so, operation S760 is performed, and if not, operation S770 is performed.

[0144] In operation S760, an initial device operation policy is generated.

[0145] In operation S770, the inertia weight and the learning factor are updated.

[0146] In operation S780, the speed and position of each particle are updated, and the fitness of the particle is obtained.

[0147] In operation S790, the updated optimal position of each particle in the intermediate particle population and the optimal position of the population are determined, and then operation S740 is performed to determine whether a termination condition is met.

[0148] According to an embodiment of the present invention, after controlling the operating status of each device of the target system according to the initial device operating strategy, the comprehensive energy demand response method based on the simulation platform also includes: obtaining actual operating status information of each device within a predetermined time period; calculating the actual energy consumption information of the target system within the predetermined time period based on the actual operating status information; and adjusting the initial device operating strategy of the target system by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period to obtain the target device operating strategy.

[0149] According to an embodiment of the present invention, after the operation status of each device of the target system is controlled according to the initial device operation strategy, the actual operation status information of each device within a predetermined period can be obtained. For example, the predetermined period can be 24 hours.

[0150] According to an embodiment of the present invention, the actual energy consumption information of the target system within a predetermined time period can be calculated based on the actual operating status information, and the actual energy consumption information can be compared with the simulated energy consumption information of each predetermined time period. The initial device operation strategy of the target system can be adjusted to obtain the target device operation strategy.

[0151] According to an embodiment of the present invention, the initial equipment operation strategy of the target system can be dynamically adjusted according to the actual energy consumption information, thereby obtaining the target equipment operation strategy of the target system, improving the operation efficiency of the target system, and further meeting the comprehensive energy needs of the user side.

[0152] According to an embodiment of the present invention, by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period, the initial equipment operation strategy of the target system is adjusted to obtain the target equipment operation strategy, including: in response to the difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to the difference threshold, and the change trend of the actual energy consumption information is the same as the change trend of the simulated energy consumption information, adjusting the operation parameters of each device in the initial equipment operation strategy according to the difference value to generate the target equipment operation strategy; and in response to the difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to the difference threshold, and the change trend of the actual energy consumption information is different from the change trend of the simulated energy consumption information, displaying energy consumption warning information through a visual interface to obtain manual adjustment information for the initial equipment operation strategy, adjusting the initial equipment operation strategy of the target system, and obtaining the target equipment operation strategy.

[0153] According to an embodiment of the present invention, when the difference threshold can be set based on experience, and when the difference value between the actual energy consumption information and the simulated energy consumption information is greater than or equal to the difference threshold, and the change trend of the actual energy consumption information is the same as the change trend of the simulated energy consumption information, it can be characterized that the actual energy consumption is within a controllable range, and the operating parameters of each device in the initial device operation strategy can be adjusted according to the difference value to generate a target device operation strategy. For example, the device operating parameters can be reduced or increased in proportion to the difference value to adjust the operating parameters of each device in the initial device operation strategy, thereby generating the target device operating parameters. For another example, the device operating parameters can be reduced or increased by weighting according to the difference value to adjust the operating parameters of each device in the initial device operation strategy, thereby generating the target device operating parameters.

[0154] According to an embodiment of the present invention, when the difference value between the actual energy consumption information and the simulated energy consumption information is greater than or equal to the difference threshold, and the changing trend of the actual energy consumption information is different from the changing trend of the simulated energy consumption information, it can be characterized that the actual energy consumption is in an uncontrollable range, and the operating parameters of each device in the initial device operation strategy can be manually adjusted to generate the target device operating parameters.

[0155] According to an embodiment of the present invention, adjustment methods for operating parameters of different devices can be provided based on the difference value between actual energy consumption information and simulated energy consumption information and the change trend of actual energy consumption information and simulated energy consumption information, thereby improving the flexibility and accuracy of the generated target device operating parameters.

[0156] Figure 8 A workflow diagram of a simulation platform according to an initial device operation strategy according to an embodiment of the present invention is shown.

[0157] like Figure 8As shown, the workflow 800 of the simulation platform according to the initial device operation strategy includes operations S810 to S830.

[0158] In operation S810, the supply side and the user side set initial equipment operation parameters through the human-computer interaction interface layer and transmit them to the database information layer.

[0159] In operation S820, the demand response method layer reads the parameters of the database information layer, generates and executes a suitable comprehensive energy demand response plan, and monitors the execution of the plan in real time and performs statistical analysis on the user's historical energy consumption data.

[0160] In operation S830, the modified or newly added comprehensive energy demand response scheme, initial equipment operation parameters, and initial equipment operation strategy are stored in the database information layer.

[0161] Fig. 9 A logical architecture diagram of a simulation platform according to an initial device operation strategy according to an embodiment of the present invention is shown.

[0162] like Fig. 9 As shown, the simulation platform according to the initial equipment operation strategy includes a human-computer interaction interface layer 910 , a demand response method layer 920 and a database information layer 930 .

[0163] According to an embodiment of the present invention, the human-computer interaction interface layer 910 may include a supply side 911 and a user side 104. The human-computer interaction interface layer 910 is used to set corresponding comprehensive energy data and transmit the data to the database information layer 930. For example, the comprehensive energy data set by the supply side 911 may be system operation parameters, pollution parameters, subsidy parameters, etc. The comprehensive energy data set by the user side 104 may be user trust, user energy preference, etc.

[0164] According to an embodiment of the present invention, the demand response method layer 920 is used to make decisions and execute the comprehensive energy demand response plan based on the data read by the database information layer. The generation of the comprehensive energy demand response plan aims to minimize the energy consumption function while satisfying the constraints of the target system operation, and transmits the comprehensive energy demand response plan information to the database information layer 930. It is also possible to monitor the execution of the comprehensive energy demand response plan and statistically analyze the user's historical energy consumption data.

[0165] According to an embodiment of the present invention, the database information layer 930 is used to store various integrated energy demand response parameters, integrated energy demand response policy information and integrated energy demand response solutions.

[0166] According to an embodiment of the present invention, the demand response method layer 920 may include a reading module 921, a decision module 922, and an implementation management module 923. The reading module 921 is used to read the comprehensive energy demand response related data from the database information layer. The decision module 922 is used to generate a suitable comprehensive energy demand response plan, with the goal of minimizing the operating energy consumption function while satisfying the constraints of the target system operation. The implementation management module 923 is used to execute the generated comprehensive energy demand response plan, monitor the execution of the comprehensive energy demand response plan, and statistically analyze the user's historical energy consumption data.

[0167] According to an embodiment of the present invention, the implementation management module 923 may include a solution implementation unit 9231 and an execution monitoring and analysis unit 9232. The solution implementation unit 9231 is used to execute the generated comprehensive energy demand response solution. The execution monitoring and analysis unit 9232 is used to monitor the execution of the comprehensive energy demand response solution and statistically analyze the user's historical energy consumption data, which may be actual energy consumption information generated by the operation according to the initial equipment operation strategy, or may be simulated energy consumption information simulated by the simulation platform according to the initial equipment operation strategy.

[0168] According to an embodiment of the present invention, the database information layer 930 may include a response information library module 931, a response policy library module 932, and a response solution library module 933. The database information layer 930 is used to store various historical comprehensive energy demand response parameter information. The response policy library module 932 is used to store various comprehensive energy demand response policy information. The response solution library module 933 is used to store historical comprehensive energy demand response solutions.

[0169] Based on the above-mentioned comprehensive energy demand response method based on the simulation platform, the present invention also provides a comprehensive energy demand response device based on the simulation platform. Fig.10 The device is described in detail.

[0170] Fig.10 A structural block diagram of a comprehensive energy demand response device based on a simulation platform according to an embodiment of the present invention is shown.

[0171] like Fig.10 As shown, the comprehensive energy demand response device 1000 based on the simulation platform of this embodiment includes a first acquisition module 1010 , a simulation module 1020 and a control operation module 1030 .

[0172] The first acquisition module 1010 is used to obtain the equipment operation parameters and energy demand response transfer parameters of each device in the target system in response to receiving the comprehensive energy demand for the target system from the human-computer interaction interface; wherein the energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; user trust represents the proportion of the user group that has participated in the comprehensive energy response to the total user group. In one embodiment, the first acquisition module 1010 can be used to perform the operation S210 described above, which will not be repeated here.

[0173] The simulation module 1020 is used to process the equipment operation parameters, energy demand response transfer parameters and comprehensive energy demand using the simulation platform based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the various devices of the target system to obtain an initial equipment operation strategy. In one embodiment, the simulation module 1020 can be used to perform the operation S220 described above, which will not be repeated here.

[0174] The control operation module 1030 is used to control the operation state of each device of the target system according to the initial device operation strategy, so that the target system can provide energy that meets the comprehensive energy demand to the user side with minimum energy consumption. In one embodiment, the control operation module 1030 can be used to perform the operation S230 described above, which will not be repeated here.

[0175] According to an embodiment of the present invention, the simulation module 1020 includes:

[0176] The first construction submodule is used to construct an energy consumption function of the target system according to equipment operation parameters, energy demand response transfer parameters and comprehensive energy demand.

[0177] The second construction submodule is used to construct the first constraint condition based on the balance relationship between energy supply and energy consumption, according to equipment operation parameters and carbon trading reward parameters.

[0178] The third construction submodule is used to construct the second constraint condition based on the operation constraint relationship between the devices and the device operation parameters.

[0179] The calculation submodule is used to calculate the initial equipment operation strategy based on the energy consumption function, the first constraint condition and the second constraint condition by using the particle swarm algorithm.

[0180] According to an embodiment of the present invention, the equipment operation parameters include equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters, equipment operation power consumption parameters, equipment operation maintenance parameters and equipment sewage discharge parameters; the first construction submodule includes:

[0181] The first construction unit is used to construct a first sub-function according to a device operation time parameter, a device quantity parameter, a device operation efficiency parameter, and a device operation power consumption parameter.

[0182] The second construction unit is used to construct a second sub-function according to the equipment operation and maintenance parameters, the equipment operation time parameters, the equipment quantity parameters and the equipment operation power consumption parameters.

[0183] The third construction unit is used to construct a third sub-function according to user trust, load reward parameters and comprehensive energy demand.

[0184] The fourth construction unit is used to construct a fourth sub-function according to the carbon trading reward parameters and the comprehensive energy demand.

[0185] The fifth construction unit is used to construct a fifth sub-function according to the equipment pollution discharge parameter, the equipment operation time parameter, the equipment quantity parameter and the equipment operation power consumption parameter.

[0186] The sixth constructing unit is used to construct an energy consumption function according to the first sub-function, the second sub-function, the third sub-function, the fourth sub-function and the fifth sub-function.

[0187] According to an embodiment of the present invention, the computing submodule includes:

[0188] The seventh construction unit is used to construct an initial particle population based on the energy consumption function, the first constraint condition and the second constraint condition, taking the equipment operation parameters as particles.

[0189] The calculation unit is used to calculate the fitness of each particle in the initial particle population based on the user trust and the current iteration round.

[0190] The unit is obtained, which is used to optimize the position of each particle in the initial particle population through iteration based on fitness, so as to obtain the initial device operation strategy.

[0191] According to an embodiment of the present invention, obtaining a unit includes:

[0192] The determination subunit is used to determine the intermediate particle population from the initial particle population according to the initial fitness of each particle; and obtain the intermediate position of each particle in the intermediate particle population; wherein the intermediate position represents the function value of the energy consumption function.

[0193] The calculation subunit is used to calculate the energy consumption difference value based on the optimal position of each particle in the intermediate particle population and the optimal position of the population.

[0194] The updating subunit is used to update the inertia weight, particle speed and particle position of each particle according to the user trust, the predetermined inertia weight and the predetermined learning parameter when it is determined that the energy consumption difference value is greater than or equal to a predetermined threshold and the current iteration round is less than the predetermined iteration round.

[0195] The generating subunit is used to generate an initial device operation strategy according to the device operation parameters corresponding to each particle in the current iteration number when it is determined that the energy consumption difference value is less than a predetermined threshold or the current iteration round is equal to the predetermined iteration round.

[0196] According to an embodiment of the present invention, after controlling the operating state of each device of the target system according to the initial device operation strategy, the comprehensive energy demand response method based on the simulation platform further includes:

[0197] The second acquisition module is used to acquire the actual operation status information of each device within a predetermined period of time.

[0198] The calculation module is used to calculate the actual energy consumption information of the target system within a predetermined time period according to the actual operation status information.

[0199] The adjustment module is used to adjust the initial equipment operation strategy of the target system by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period to obtain the target equipment operation strategy.

[0200] According to an embodiment of the present invention, the adjustment module includes:

[0201] The first adjustment unit is used to respond to the difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to a difference threshold, and the change trend of the actual energy consumption information is the same as the change trend of the simulated energy consumption information, adjust the operating parameters of each device in the initial device operation strategy according to the difference value, and generate a target device operation strategy.

[0202] The second adjustment unit is used to respond to the difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to the difference threshold, and the change trend of the actual energy consumption information is different from the change trend of the simulated energy consumption information, and display the energy consumption warning information through a visual interface to obtain manual adjustment information for the initial equipment operation strategy, adjust the initial equipment operation strategy of the target system, and obtain the target equipment operation strategy.

[0203] According to an embodiment of the present invention, any multiple modules among the first acquisition module 1010, the simulation module 1020 and the control operation module 1030 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module.

[0204] According to an embodiment of the present invention, at least one of the first acquisition module 1010, the simulation module 1020 and the control operation module 1030 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or may be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the first acquisition module 1010, the simulation module 1020 and the control operation module 1030 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be executed.

[0205] Fig.11 A block diagram of an electronic device suitable for implementing a comprehensive energy demand response method based on a simulation platform according to an embodiment of the present invention is shown.

[0206] like Fig.11 As shown, the electronic device 1100 according to an embodiment of the present invention includes a processor 1101, which can perform various appropriate actions and processes according to the program in ROM 1102 or the program loaded from the storage part 1108 to RAM 1103. The processor 1101 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include an onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0207] In RAM 1103, various programs and data required for the operation of electronic device 1100 are stored. Processor 1101, ROM 1102 and RAM 1103 are connected to each other via bus 1104. Processor 1101 performs various operations of the method flow according to the embodiment of the present invention by executing the program in ROM 1102 and / or RAM 1103. It should be noted that the program can also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 can also perform various operations of the method flow according to the embodiment of the present invention by executing the program stored in the one or more memories.

[0208] According to an embodiment of the present invention, the electronic device 1100 may further include an I / O interface 1105, which is also connected to the bus 1104. The electronic device 1100 may further include one or more of the following components connected to the I / O interface 1105: an input portion 1106 including a keyboard, a mouse, etc.; an output portion 1107 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1108 including a hard disk, etc.; and a communication portion 1109 including a network interface card such as a LAN card, a modem, etc. The communication portion 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the I / O interface 1105 as needed. A removable medium 1111, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1110 as needed, so that a computer program read therefrom is installed into the storage portion 1108 as needed.

[0209] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0210] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, it may include but is not limited to: a portable computer disk, a hard disk, a RAM (Random Access Memory), a ROM (Read Only Memory), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present invention, the computer-readable storage medium may include the ROM 1102 and / or RAM 1103 described above and / or one or more memories other than ROM 1102 and RAM 1103.

[0211] The embodiment of the present invention also includes a computer program product, which includes a computer program, and the computer program includes a program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the comprehensive energy demand response method based on the simulation platform provided by the embodiment of the present invention.

[0212] The computer program executes the above functions defined in the system / device of the embodiment of the present invention when it is executed by the processor 1101. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0213] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1109, and / or installed from a removable medium 1111. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0214] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1109, and / or installed from the removable medium 1111. When the computer program is executed by the processor 1101, the above functions defined in the system of the embodiment of the present invention are performed. According to the embodiment of the present invention, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0215] According to an embodiment of the present invention, the program code for executing the computer program provided by the embodiment of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0216] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0217] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present invention may be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention may be combined and / or combined in various ways. All of these combinations and / or combinations fall within the scope of the present invention.

[0218] The embodiments of the present invention are described above. However, these embodiments are only for the purpose of illustration, and are not intended to limit the scope of the present invention. Although each embodiment is described above, it does not mean that the measures in each embodiment cannot be used in combination advantageously. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A comprehensive energy demand response method based on a simulation platform, characterized in that: The method comprises: In response to receiving the comprehensive energy demand for the target system from the human-computer interaction interface, obtaining the equipment operation parameters and energy demand response transfer parameters of each equipment in the target system; wherein the energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; the user trust represents the proportion of the user group that has participated in the comprehensive energy response to the total user group; Based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the devices of the target system, the device operation parameters, energy demand response transfer parameters and the comprehensive energy demand are processed using a simulation platform to obtain an initial device operation strategy; and According to the initial device operation strategy, the operation status of each device of the target system is controlled so that the target system can provide energy that meets the comprehensive energy demand to the user side with minimum energy consumption; wherein, Based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the devices of the target system, the device operation parameters, energy demand response transfer parameters and the comprehensive energy demand are processed using a simulation platform to obtain an initial device operation strategy, including: constructing an energy consumption function of the target system according to the equipment operation parameters, the energy demand response transfer parameters and the comprehensive energy demand; Based on the balance relationship between energy supply and energy consumption, according to the equipment operation parameters and the carbon trading reward parameters, a first constraint condition is constructed as follows: Among them, among them, express The total amount of electricity load on the user side during the period, express Energy loss of the target system's electrical energy-driven equipment during the period, express The total heat load on the user side during the period, express The total cooling load on the user side during the period, express The energy consumption of the driving equipment in the target system during the period, Indicates electric energy driven equipment exist The output power during the period, Indicates internal energy driven device exist The output power during the period, Indicates internal energy driven device operational efficiency; Indicates the total amount of carbon trading in the system; Based on the operation constraint relationship between the devices and the device operation parameters, a second constraint condition is constructed as follows: in, Indicates the upper limit of the output power of the target system's electric energy drive equipment. Indicates the lower limit of the output power of the target system's electric energy drive equipment. Indicates the upper limit of the output power of the device that can be driven in the target system. Indicates the lower limit of the output power of the driving device in the target system. Indicates the upper limit of the ramp rate of the target system's electric energy drive equipment. Indicates the lower limit of the ramp rate of the target system's electric energy drive equipment. Indicates the upper limit of the ramp rate that can be driven in the target system. Indicates the lower limit of the ramp rate of the device that can be driven in the target system; Indicates the start or stop status of the device. Indicates that the device is started. Indicates that the device is stopped; and Using a particle swarm algorithm, based on the energy consumption function, the first constraint condition and the second constraint condition, the initial equipment operation strategy is calculated; The equipment operation parameters include equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters, equipment operation power consumption parameters, equipment operation maintenance parameters and equipment sewage discharge parameters; The step of constructing the energy consumption function of the target system according to the equipment operation parameters, the energy demand response transfer parameters and the comprehensive energy demand includes: According to the equipment operation time parameter, the equipment quantity parameter, the equipment operation efficiency parameter, and the equipment operation power consumption parameter, a first sub-function is constructed; the first sub-function is an external energy purchase energy consumption function, as shown below: in, express The target system power purchase price during the period, express The target system gas purchase price during the time period; It represents the output power of electric energy driven device i at time t; Indicates internal energy driven device exist Output power during the time period; Indicates internal energy driven device operational efficiency; A second sub-function is constructed according to the equipment operation maintenance parameter, the equipment operation time parameter, the equipment quantity parameter and the equipment operation power consumption parameter; the second sub-function is a maintenance energy consumption function of equipment operation loss, as shown below: in, Indicates electric energy driven equipment The operation and maintenance cost coefficient, Indicates internal energy driven device Operation and maintenance cost coefficient; According to the user trust, load reward parameters and the comprehensive energy demand, a third sub-function is constructed; the third sub-function is a comprehensive energy demand response subsidy function, as shown below: in, Indicates user trust. Indicates the unit subsidy power consumption of the electric load, represents the unit subsidy power consumption of the heat load, represents the unit subsidy power consumption of cooling load, express The electric load shifted in response to comprehensive energy demand during the period, express The heat load transferred in response to the comprehensive energy demand during the period, express Cooling load shifted in response to comprehensive energy demand during the time period; According to the carbon trading reward parameter and the comprehensive energy demand, a fourth sub-function is constructed, and the fourth sub-function is a carbon trading reward subsidy function, as shown below: in, represents the unit transaction price in the carbon trading market, Indicates the system's carbon trading reward coefficient, Indicates the total amount of carbon trading in the system; According to the equipment pollution discharge parameter, the equipment operation time parameter, the equipment quantity parameter and the equipment operation power consumption parameter, a fifth sub-function is constructed, and the fifth sub-function is an environmental pollution control energy consumption function, as shown below: in, Indicates The power consumption of the treatment of pollutants Indicates electric energy driven equipment The pollutant emission coefficient, Indicates internal energy driven device The pollutant emission coefficient; The energy consumption function is constructed according to the first sub-function, the second sub-function, the third sub-function, the fourth sub-function and the fifth sub-function, as shown below: in, represents the energy consumption function of the target system, represents the external energy purchase energy consumption function, The maintenance energy consumption function representing the equipment operation loss, represents the comprehensive energy demand response subsidy function, represents the carbon trading reward subsidy function, Represents the energy consumption function of environmental pollution control.

2. The method according to claim 1, characterized in that The using of a particle swarm algorithm to calculate the initial equipment operation strategy based on the energy consumption function, the first constraint condition and the second constraint condition includes: Taking the equipment operation parameters as particles, constructing an initial particle population based on the energy consumption function, the first constraint condition and the second constraint condition; Based on the user trust and the current iteration round, calculate the fitness of each particle in the initial particle population; and The initial device operation strategy is obtained by iteratively optimizing the position of each particle in the initial particle population based on the fitness.

3. The method according to claim 2, characterized in that The step of iteratively optimizing the position of each particle in the initial particle population based on the fitness to obtain the initial device operation strategy includes: Determine an intermediate particle population from the initial particle population according to the initial fitness of each particle; and obtain an intermediate position of each particle in the intermediate particle population; wherein the intermediate position represents a function value of the energy consumption function; Calculating the energy consumption difference value based on the optimal position of each particle in the intermediate particle population and the optimal position of the population; When it is determined that the energy consumption difference value is greater than or equal to a predetermined threshold and the current iteration round is less than a predetermined iteration round, respectively updating the inertia weight, particle speed and particle position of each particle according to the user trust, the predetermined inertia weight and the predetermined learning parameter; and When it is determined that the energy consumption difference value is less than a predetermined threshold or the current iteration round is equal to the predetermined iteration round, the initial device operation strategy is generated according to the device operation parameters corresponding to each particle in the current iteration number.

4. The method according to any one of claims 1 to 3, characterized in that: After controlling the operating state of each device of the target system according to the initial device operating strategy, the method further includes: Obtaining actual operating status information of each device within a predetermined period of time; Calculating the actual energy consumption information of the target system within the predetermined period according to the actual operating status information; and By comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period, the initial equipment operation strategy of the target system is adjusted to obtain the target equipment operation strategy.

5. The method according to claim 4, characterized in that The step of adjusting the initial device operation strategy of the target system by comparing the actual energy consumption information of each predetermined time period with the simulated energy consumption information of each predetermined time period to obtain the target device operation strategy includes: In response to a difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to a difference threshold, and a change trend of the actual energy consumption information is the same as a change trend of the simulated energy consumption information, adjusting an operating parameter of each device in the initial device operating strategy according to the difference value to generate the target device operating strategy; and In response to the difference value between the actual energy consumption information and the simulated energy consumption information being greater than or equal to a difference threshold, and the changing trend of the actual energy consumption information is different from the changing trend of the simulated energy consumption information, energy consumption warning information is displayed through a visual interface to obtain manual adjustment information for the initial equipment operation strategy, adjust the initial equipment operation strategy of the target system, and obtain the target equipment operation strategy.

6. A comprehensive energy demand response device based on a simulation platform, characterized in that: The device comprises: A first acquisition module is used to obtain, in response to receiving the comprehensive energy demand for the target system from the human-computer interaction interface, the device operation parameters of each device in the target system and the energy demand response transfer parameters; wherein the energy demand response transfer parameters include user trust, load reward parameters and carbon trading reward parameters; the user trust represents the proportion of the user group that has participated in the comprehensive energy response to the total user group; A simulation module, for processing the equipment operation parameters, energy demand response transfer parameters and the comprehensive energy demand using a simulation platform based on the balance relationship between energy supply and energy consumption and the operation constraint relationship between the equipment of the target system to obtain an initial equipment operation strategy; and A control operation module, used to control the operation state of each device of the target system according to the initial device operation strategy, so that the target system can provide energy that meets the comprehensive energy demand to the user side with minimum energy consumption; Wherein, the simulation module is used for: constructing an energy consumption function of the target system according to the equipment operation parameters, the energy demand response transfer parameters and the comprehensive energy demand; Based on the balance relationship between energy supply and energy consumption, according to the equipment operation parameters and the carbon trading reward parameters, a first constraint condition is constructed as follows: Among them, among them, express The total amount of electricity load on the user side during the period, express Energy loss of the target system's electrical energy-driven equipment during the period, express The total heat load on the user side during the period, express The total cooling load on the user side during the period, express The energy consumption of the driving equipment in the target system during the period, Indicates electric energy driven equipment exist The output power during the period, Indicates internal energy driven device exist The output power during the period, Indicates internal energy driven device operational efficiency; Indicates the total amount of carbon trading in the system; Based on the operation constraint relationship between the devices and the device operation parameters, a second constraint condition is constructed as follows: in, Indicates the upper limit of the output power of the target system's electric energy drive equipment. Indicates the lower limit of the output power of the target system's electric energy drive equipment. Indicates the upper limit of the output power of the device that can be driven in the target system. Indicates the lower limit of the output power of the driving device in the target system. Indicates the upper limit of the ramp rate of the target system's electric energy drive equipment. Indicates the lower limit of the ramp rate of the target system's electric energy drive equipment. Indicates the upper limit of the ramp rate that can be driven in the target system. Indicates the lower limit of the ramp rate of the device that can be driven in the target system; Indicates the start or stop status of the device. Indicates that the device is started. Indicates that the device is stopped; Using a particle swarm algorithm, based on the energy consumption function, the first constraint condition and the second constraint condition, the initial equipment operation strategy is calculated; The equipment operation parameters include equipment operation time parameters, equipment quantity parameters, equipment operation efficiency parameters, equipment operation power consumption parameters, equipment operation maintenance parameters and equipment sewage discharge parameters; The step of constructing the energy consumption function of the target system according to the equipment operation parameters, the energy demand response transfer parameters and the comprehensive energy demand includes: According to the equipment operation time parameter, the equipment quantity parameter, the equipment operation efficiency parameter, and the equipment operation power consumption parameter, a first sub-function is constructed; the first sub-function is an external energy purchase energy consumption function, as shown below: in, express The target system power purchase price during the period, express The target system gas purchase price during the time period; It represents the output power of electric energy driven device i at time t; Indicates internal energy driven device exist Output power during the time period; Indicates internal energy driven device operational efficiency; A second sub-function is constructed according to the equipment operation maintenance parameter, the equipment operation time parameter, the equipment quantity parameter and the equipment operation power consumption parameter; the second sub-function is a maintenance energy consumption function of equipment operation loss, as shown below: in, Indicates electric energy driven equipment The operation and maintenance cost coefficient, Indicates internal energy driven device Operation and maintenance cost coefficient; According to the user trust, load reward parameters and the comprehensive energy demand, a third sub-function is constructed; the third sub-function is a comprehensive energy demand response subsidy function, as shown below: in, Indicates user trust. Indicates the unit subsidy power consumption of the electric load, represents the unit subsidy power consumption of the heat load, represents the unit subsidy power consumption of cooling load, express The electric load shifted in response to comprehensive energy demand during the period, express The heat load transferred in response to the comprehensive energy demand during the period, express Cooling load shifted in response to comprehensive energy demand during the time period; According to the carbon trading reward parameter and the comprehensive energy demand, a fourth sub-function is constructed, and the fourth sub-function is a carbon trading reward subsidy function, as shown below: in, represents the unit transaction price in the carbon trading market, Indicates the system's carbon trading reward coefficient, Indicates the total amount of carbon trading in the system; According to the equipment pollution discharge parameter, the equipment operation time parameter, the equipment quantity parameter and the equipment operation power consumption parameter, a fifth sub-function is constructed, and the fifth sub-function is an environmental pollution control energy consumption function, as shown below: in, Indicates The power consumption of the treatment of pollutants Indicates electric energy driven equipment The pollutant emission coefficient, Indicates internal energy driven device The pollutant emission coefficient; The energy consumption function is constructed according to the first sub-function, the second sub-function, the third sub-function, the fourth sub-function and the fifth sub-function, as shown below: in, represents the energy consumption function of the target system, represents the external energy purchase energy consumption function, The maintenance energy consumption function representing the equipment operation loss, represents the comprehensive energy demand response subsidy function, represents the carbon trading reward subsidy function, Represents the energy consumption function of environmental pollution control.

7. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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