System energy efficiency optimization method and apparatus based on comprehensive energy demand, electronic device, and storage medium

Abstract

The present application provides a system energy efficiency optimization method and apparatus based on a comprehensive energy demand, an electronic device, and a storage medium. The method comprises: in response to a received target comprehensive energy demand, on the basis of comprehensive energy demand parameters, operation parameters of a target system, and environmental loss parameters of the target system, constructing an energy efficiency optimization function of the target system (S210); on the basis of a balance relationship between the energy supply and the energy consumption and a device operation demand of transmitting energy, constructing constraint conditions for optimizing the energy efficiency of the target system (S220); and on the basis of the energy efficiency optimization function and the constraint conditions, using a target algorithm to process operation parameters of a plurality of devices by taking the operation parameters of the plurality of devices in the target system as population individuals to obtain device parameters of the target system meeting the target comprehensive energy demand (S230).

Description

System energy efficiency optimization method, device, electronic device and storage medium based on comprehensive energy demand

[0001] This application claims priority to the Chinese patent application filed with the China Patent Office on November 24, 2023, with application number 202311575306.5, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of energy utilization technology, for example, to a system energy efficiency optimization method, device, electronic device and storage medium based on comprehensive energy demand. Background Art

[0003] With the development of energy technologies, the limitations of traditional electricity demand response technologies are gradually becoming apparent. On the one hand, load time shifting not only reduces user comfort to a certain extent, but also affects user output value and user satisfaction, and is significantly influenced by user preferences. On the other hand, with the accelerated advancement of integrated energy technologies and engineering construction, the trend towards energy diversification is becoming more prominent. The "multi-energy complementarity" characteristics of integrated energy systems can achieve conversion between electricity and other energy sources, making the overall external adjustability of the system more obvious.

[0004] Integrated energy demand response (IEDR) has emerged as an extension of traditional electricity demand response. It not only shares the characteristics of electricity demand response but also significantly expands the range of adjustable load resources when system reliability is threatened or when users change their energy supply methods during peak electricity prices. This is a beneficial supplement to traditional electricity demand-side management and demand response technologies. Therefore, research on IEDR terminals and the optimization of multi-energy application system responses has important theoretical and practical value.

[0005] Summary of the Invention

[0006] This application provides a system energy efficiency optimization method, device, electronic device and storage medium based on comprehensive energy demand.

[0007] According to the first aspect of the present application, a system energy efficiency optimization method based on comprehensive energy demand is provided, including: in response to the received target comprehensive energy demand, constructing an energy efficiency optimization function of the target system according to the comprehensive energy demand parameters, the operating parameters of the target system and the environmental loss parameters of the target system; based on the balance relationship between energy supply and energy consumption and the operating requirements of the equipment used to transmit energy, constructing constraints for optimizing the energy efficiency of the target system; based on the energy efficiency optimization function and the constraints, using the target algorithm, with the operating parameters of multiple devices in the target system as population individuals, processing the operating parameters of the multiple devices to obtain the device parameters of the target system that meet the target comprehensive energy demand.

[0008] According to an embodiment of the present application, the comprehensive energy demand parameters include actual terminal load parameters, terminal load adjustment parameters, and terminal estimated load parameters. Based on the comprehensive energy demand parameters, the operating parameters of the target system, and the environmental loss parameters of the target system, an energy efficiency optimization function for the target system is constructed, including: constructing a first function for characterizing energy usage based on the terminal estimated load parameters and the actual terminal load parameters; and constructing a second function for characterizing energy conversion based on the terminal load adjustment parameters, the operating parameters of the target system, and the environmental loss parameters of the target system.

[0009] According to an embodiment of the present application, the operating parameters of the target system include an operating time parameter, an operating energy consumption coefficient, a device quantity parameter, a device operating efficiency parameter, a device operating status parameter, a device operating energy consumption parameter, and a device operating power parameter. According to the terminal load adjustment parameter, the target system operating parameter, and the target system environmental loss parameter, a second function for characterizing the energy conversion status is constructed, including: constructing a first sub-function for characterizing energy loss according to the operating time parameter, the operating energy consumption coefficient, the device quantity parameter, the device operating efficiency parameter, and the device operating energy consumption parameter; constructing a second sub-function for characterizing device operating loss according to the device operating efficiency parameter; constructing a third sub-function for characterizing demand response subsidies according to the terminal load adjustment parameter; constructing a fourth sub-function for characterizing device depreciation loss according to the device quantity parameter, the device operating status parameter, and the device operating power parameter; and constructing a fifth sub-function for characterizing environmental resource loss during the pollutant emission treatment process of the target system according to the target system environmental loss parameter.

[0010] According to an embodiment of the present application, the terminal load adjustment parameters include a transferable load parameter, an adjustable load parameter, and a curtailable load parameter. Constructing a third sub-function for characterizing the demand response subsidy based on the terminal load adjustment parameters includes: constructing the third sub-function based on the transferable load parameter, the adjustable load parameter, and the curtailable load parameter.

[0011] According to an embodiment of the present application, the environmental loss parameters include a pollutant type parameter, a pollutant emission coefficient, and a treatment resource loss parameter corresponding to the pollutant type parameter. Constructing a fifth sub-function for characterizing environmental resource loss during the pollutant emission treatment process of the target system based on the environmental loss parameters of the target system includes: constructing the fifth sub-function based on the pollutant type parameter, the pollutant emission coefficient, and the treatment resource loss parameter corresponding to the pollutant type parameter.

[0012] According to an embodiment of the present application, based on the balance relationship between energy supply and energy consumption and the equipment operation requirements for transmitting energy, constraint conditions for optimizing the energy efficiency of the target system are constructed, including: based on the balance relationship between energy supply and energy consumption, a first constraint condition is constructed according to the terminal estimated load parameters, the terminal actual load parameters and the equipment operation energy consumption parameters of the target system; based on the equipment operation requirements for transmitting energy, a second constraint condition is constructed according to the equipment operation status parameters and the equipment operation power parameters.

[0013] According to an embodiment of the present application, based on an energy efficiency optimization function and constraints, a target algorithm is used, and the operating parameters of multiple devices in the target system are used as population individuals. The operating parameters of the multiple devices are processed to obtain device parameters of the target system that meet the target comprehensive energy demand, including: based on the energy efficiency optimization function and constraints, an target algorithm is used, and the operating parameters of multiple devices in the target system are used as population individuals to construct an initial population; according to a predetermined fitness function, the population individuals in the initial population are processed to obtain multiple fitnesses corresponding to the multiple population individuals; based on the multiple fitnesses, a target population individual is determined from the initial population; based on a predetermined genetic probability, a genetic operation is performed on the target population individual to generate an offspring population; when it is determined that the fitness of the offspring population and the fitness of the initial population meet predetermined conditions, an annealing operation is performed on the offspring population to obtain the device parameters of the target system.

[0014] According to an embodiment of the present application, based on a predetermined genetic probability, a genetic operation is performed on individuals of a target population to generate an offspring population, including: based on a predetermined crossover probability, performing a crossover operation on individuals of the target population to obtain a crossover offspring population; based on a predetermined mutation probability, performing a mutation operation on the crossover offspring population to obtain an offspring population.

[0015] According to an embodiment of the present application, the neighborhood corresponding to the offspring population includes I, where I is an integer greater than 1; an annealing operation is performed on the offspring population to obtain device parameters of the target system, including: using a neighborhood operator to process the population individuals in the i-th neighborhood corresponding to the offspring population to obtain a first function value corresponding to the population individuals in the i-th neighborhood for characterizing the energy usage status and a second function value for characterizing the energy conversion status; when it is determined that the current iteration round does not meet the iteration termination condition, returning to execute the processing operation for the i-th neighborhood and incrementing i; when it is determined that the current iteration round meets the iteration termination condition, determining the attribute parameters in the population individuals in the neighborhood corresponding to the largest first function value and the smallest second function value as the device parameters of the target system.

[0016] The second aspect of the present application provides a system energy efficiency optimization device based on comprehensive energy demand, including: a first building module, a second building module and a processing module. The first building module is configured to respond to the received target comprehensive energy demand and construct an energy efficiency optimization function of the target system according to the comprehensive energy demand parameters, the operating parameters of the target system and the environmental loss parameters of the target system. The second building module is configured to construct constraints for optimizing the energy efficiency of the target system based on the balance relationship between energy supply and energy consumption and the operating requirements of the equipment used to transmit energy. The processing module is configured to process the operating parameters of multiple devices in the target system based on the energy efficiency optimization function and the constraints, using a target algorithm, with the operating parameters of multiple devices in the target system as population individuals, to obtain the device parameters of the target system that meet the target comprehensive energy demand.

[0017] The third aspect of the present application provides an electronic device, comprising: one or more processors; a memory configured to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the above-mentioned system energy efficiency optimization method based on comprehensive energy demand.

[0018] The fourth aspect of the present application also provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, causes the processor to execute the above-mentioned system energy efficiency optimization method based on comprehensive energy demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG1 shows an application scenario diagram of a method and apparatus for optimizing system energy efficiency based on comprehensive energy demand according to an embodiment of the present application;

[0020] FIG2 shows a flow chart of a method for optimizing system energy efficiency based on comprehensive energy demand according to an embodiment of the present application;

[0021] FIG3 shows a flowchart of processing the operating parameters of multiple devices in a target system using a target algorithm according to an embodiment of the present application, with the operating parameters of multiple devices in the target system as population individuals;

[0022] FIG4A is a schematic diagram showing the result of processing the operating parameters of multiple devices in a target system using a genetic algorithm according to an embodiment of the present application, with the operating parameters of the multiple devices in the target system as population individuals;

[0023] FIG4B is a schematic diagram showing the result of processing the operating parameters of multiple devices in the target system using the simulated annealing algorithm according to an embodiment of the present application, with the operating parameters of multiple devices in the target system as population individuals;

[0024] FIG4C is a schematic diagram showing the result of processing the operating parameters of multiple devices in the target system using the genetic simulated annealing algorithm according to an embodiment of the present application, with the operating parameters of multiple devices in the target system as population individuals;

[0025] FIG5 shows a structural block diagram of a system energy efficiency optimization device based on comprehensive energy demand according to an embodiment of the present application; and

[0026] FIG6 shows a block diagram of an electronic device suitable for implementing a system energy efficiency optimization method based on comprehensive energy demand according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] Hereinafter, embodiments of the present application will be described with reference to the accompanying drawings. These descriptions are merely exemplary and are not intended to limit the scope of the present application. In the following description, for ease of explanation, many details are set forth to provide an understanding of the embodiments of the present application. However, it is apparent that one or more embodiments may also be implemented without these details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present application.

[0028] The terms used herein are merely for describing the embodiments and are not intended to limit the present application. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0029] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. 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.

[0030] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with 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 systems having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0031] Integrated energy systems have been a research hotspot in recent years, with fruitful results in research on the optimized operation of multiple energy resources, including electricity, cooling, and heating. With the accelerated construction of new power systems, the urgency of research on energy supply security and demand response technologies has become increasingly prominent, but systematic research on integrated energy demand response is rare.

[0032] With the development of smart cities, improving grid power efficiency through information communication and Internet of Things technologies has become an important support for comprehensive energy utilization management. The research progress of technologies related to integrated energy demand response is as follows:

[0033] 1. By establishing a control center, developing equipment models and load forecasting models for a multi-energy integrated utilization system, and conducting simulation studies on the dispatchability, reliability, and, in particular, system integrity of the multi-energy integrated utilization system, the impact of distributed energy systems and energy storage devices on the power grid will be studied from the perspectives of voltage stability, load flow, power quality, frequency variation, fault current variation, system safety, and stability. 2. A modular multi-energy integrated residential hot water system features quick-install modules that can be flexibly adapted to different needs, equipped with standard interfaces for easy connection to air-source heat pumps and solar collectors. 3. Combining renewable energy sources, such as solar energy, with other energy sources to achieve integrated energy utilization. For example, a split air-source heat pump not only has the functions of a conventional air-source heat pump but also features physical and logical interfaces for interoperability with other heat sources, such as solar collectors and gas boilers. 4. Using small gas turbines to compensate for output fluctuations from renewable energy generation reduces expensive energy storage investments, achieving the goal of integrated multi-energy utilization.

[0034] In-depth research is underway in key areas such as distributed combined cooling, heating, and power (CCHP) system integration and design, gas turbine and waste heat utilization device development, and system operation and control. For example, a demonstration "energy island" is being constructed, using a micro-gas turbine as the core, combined with a direct-fired lithium bromide absorption chiller and waste heat boilers, for distributed energy system research. A distributed energy system, also using a micro-gas turbine as the core, is being developed for hotel applications.

[0035] In order to solve the problem of multi-energy optimization configuration, with the minimum system investment (equipment cost, operation and maintenance cost, equipment replacement cost) as the goal, the number of photovoltaic cells, the number of installed wind turbines, the height of wind turbine towers, etc. are used as optimization variables, and the power supply reliability, the upper and lower limits of wind turbine tower height, and the energy surplus ratio are used as constraints. The power supply configuration of the wind-solar-storage independent power supply system is optimized.

[0036] In this study of integrated energy management systems, based on short-term and ultra-short-term power forecasting, an energy optimization model suitable for independent power grids was established, taking into account the output characteristics of at least one micro-source and the load operating characteristics. The model, with minimizing operating costs as its objective function, investigates energy optimization and coordinated control strategies for the composite system on both day-ahead and intraday timescales.

[0037] For an independent microgrid system including wind power generation, diesel power generation, energy storage and seawater desalination, a study on energy management methods based on ultra-short-term power forecasting was carried out, and an energy management strategy based on ultra-short-term wind speed forecasting was proposed.

[0038] However, the accuracy of information data exchange between end users and application systems is low, and communication reliability is low. Existing multi-energy integrated utilization technologies have relatively limited functionality. Further research is needed to achieve regional multi-energy information integration and management, make decisions on integrated energy utilization based on different energy regulation needs, and improve energy efficiency. The development of key equipment for achieving integrated and optimized energy utilization is not yet mature.

[0039] In view of this, the present invention provides a method for optimizing system energy efficiency based on comprehensive energy demand. This method establishes a demand-side comprehensive energy system operation optimization model, takes comprehensive energy efficiency and resource loss as optimization objectives, and analyzes the model structure to clarify the characteristics and solution features of the optimization model. The optimization objectives can be selected from the perspectives of system resource friendliness, system environmental friendliness, and overall system energy efficiency, in order to maximize the energy efficiency of the comprehensive energy system and minimize resource loss.

[0040] In the technical solution of the present application, the user information involved (including user personal information, user image information, user device information, such as location information, etc.) and data (including data used for analysis, stored data, displayed data, etc.) are all information and data authorized by the user or fully authorized by multiple parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant countries and regions, 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.

[0041] FIG1 shows an application scenario diagram of a system energy efficiency optimization method and device based on comprehensive energy demand according to an embodiment of the present application.

[0042] As shown in FIG1 , the application scenario 100 according to this embodiment may include a grid-side load management system master station 101 , an integrated energy demand response terminal device 102 , and an integrated energy system 103 .

[0043] The system energy efficiency optimization method based on comprehensive energy demand provided in the present application can be executed by the comprehensive energy demand response terminal device 102. The comprehensive energy demand response terminal device 102 obtains the target comprehensive energy demand from the grid-side load management system master station 101, and based on the target comprehensive energy demand, executes the system energy efficiency optimization method based on comprehensive energy demand provided in the embodiment of the present application, obtains the equipment parameters of the comprehensive energy system that meets the target comprehensive energy demand, and generates a response instruction with the equipment parameters of the comprehensive energy system, and sends the response instruction to the comprehensive energy system 103, so that the equipment control module 1031 in the comprehensive energy system 103 controls the corresponding equipment to adjust the equipment parameters to the equipment parameters in the response instruction. At the same time, the comprehensive energy system 103 can also send the system operation status after adjusting the equipment parameters to the comprehensive energy demand response terminal device 102 in the form of feedback information, so as to realize the interaction between the comprehensive energy system and the comprehensive energy demand on the grid side.

[0044] According to an embodiment of the present application, the integrated energy demand response terminal device 102 may include a storage module 1021 , a processing module 1022 and a response module 1023 .

[0045] According to an embodiment of the present application, the storage module 1021 is configured to store the application required to execute the system energy efficiency optimization method based on comprehensive energy demand provided in the present application, ensuring that the program is stably stored in the comprehensive energy demand response terminal device 102 so that the relevant application can be called in time according to the comprehensive energy demand.

[0046] According to an embodiment of the present application, the processing module 1022 is configured to execute the application program in the storage module 1021 to execute the system energy efficiency optimization method based on comprehensive energy demand provided in the present application.

[0047] According to an embodiment of the present application, the response module 1023 may include a response control submodule 1023_1 and a feedback module 1023_2. The response control submodule 1023_1 is configured to send the device parameters of the integrated energy system that meet the target integrated energy demand, obtained by the processing module 1022, to the integrated energy system 103 in the form of a response instruction. The feedback module 1023_2 is configured to receive system operating status information of the integrated energy system 103 after adjusting the device parameters.

[0048] The number of grid-side load management system master stations 101, integrated energy demand response terminal devices 102, and integrated energy systems 103 in FIG1 is merely illustrative. Any number of grid-side load management system master stations, integrated energy demand response terminal devices, and integrated energy systems may be provided as required.

[0049] The following will describe the system energy efficiency optimization method based on comprehensive energy demand according to an embodiment of the present application based on the scenario described in FIG1 through FIG2 and FIG3 .

[0050] FIG2 shows a flow chart of a method for optimizing system energy efficiency based on comprehensive energy demand according to an embodiment of the present application.

[0051] As shown in FIG2 , the system energy efficiency optimization method based on comprehensive energy demand of this embodiment 200 includes operations S210 to S230 .

[0052] In operation S210 , in response to the received target comprehensive energy demand, an energy efficiency optimization function of the target system is constructed according to comprehensive energy demand parameters, operating parameters of the target system, and environmental loss parameters of the target system.

[0053] In operation S220 , based on the balance between energy supply and energy consumption and the operation requirements of equipment for transmitting energy, constraint conditions for optimizing energy efficiency of the target system are constructed.

[0054] In operation S230, based on the energy efficiency optimization function and the constraints, a target algorithm is used to process the operating parameters of multiple devices in the target system as population individuals to obtain device parameters of the target system that meet the target comprehensive energy demand.

[0055] According to an embodiment of the present application, the target system may be an integrated energy system. The target integrated energy demand may include the total amount of energy purchased by the user and the total load obtained from the external energy network. The total energy may include the total amount of electricity and natural gas. The total load may include the electrical load and the thermal load.

[0056] According to an embodiment of the present application, the comprehensive energy demand parameter may include a terminal actual load parameter, a terminal load adjustment parameter, and a terminal estimated load parameter.

[0057] According to an embodiment of the present application, the operating parameters of the target system may include: operating time parameters, operating energy consumption coefficient, equipment quantity parameters, equipment operating efficiency parameters, equipment operating status parameters, equipment operating energy consumption parameters and equipment operating power parameters.

[0058] According to an embodiment of the present application, the environmental loss parameters of the target system may include pollutant type parameters, pollutant emission coefficients, and governance resource loss parameters corresponding to the pollutant type parameters.

[0059] According to an embodiment of the present application, the energy efficiency optimization function of the target system may include a target system energy efficiency maximization function and a target system resource loss minimization function. The energy efficiency of the target system may represent the energy conversion efficiency of the target system, which may be determined based on the user's energy load, the total amount of purchased energy, and the energy quality coefficient of the relevant energy.

[0060] According to an embodiment of the present application, resource loss may include input energy loss, equipment operation loss, demand response subsidy loss, equipment depreciation loss and environmental pollution loss.

[0061] According to an embodiment of the present application, the balance relationship between energy supply and energy consumption may include the balance relationship between the supply and consumption of electrical energy and the balance relationship between the supply and consumption of thermal energy of the integrated energy system.

[0062] According to an embodiment of the present application, the equipment operation requirements for transmitting energy may represent a limited range of equipment operation parameters that all equipment in the integrated energy system must meet for operational stability during energy transmission.

[0063] According to the embodiments of the present application, a multi-objective optimization function, including maximizing comprehensive energy efficiency and minimizing resource loss, can be derived based on the energy efficiency optimization function and constraints. This multi-objective optimization function and constraints can be used as the optimization problem to meet the target comprehensive energy demand. Using a target algorithm, the operating parameters of multiple devices in the target system are processed as individuals in the population to obtain the device parameters of the target system that meet the target comprehensive energy demand.

[0064] According to an embodiment of the present application, the target algorithm may include: a genetic algorithm, a simulated annealing algorithm, or a genetic simulated annealing algorithm.

[0065] According to the embodiments of the present application, since the energy efficiency optimization function of the target system is constructed based on the comprehensive energy demand parameters, the operating parameters of the target system and the environmental loss parameters of the target system, the energy efficiency optimization of the comprehensive energy system based on the actual comprehensive energy demand on the demand side is achieved, and the technical effect of improving system energy efficiency and reducing system resource loss is achieved while meeting the target comprehensive energy demand.

[0066] According to an embodiment of the present application, an energy efficiency optimization function of the target system is constructed based on the comprehensive energy demand parameters, the operating parameters of the target system and the environmental loss parameters of the target system, which may include the following operations: constructing a first function for characterizing the energy usage status based on the terminal estimated load parameters and the terminal actual load parameters; constructing a second function for characterizing the energy conversion status based on the adjustment parameters of the terminal load, the operating parameters of the target system and the environmental loss parameters of the target system.

[0067] According to an embodiment of the present application, the terminal estimated load parameter can represent the total amount of energy that has been pre-purchased by the user side, for example, it can include the total amount of electric energy W e and the total amount of natural gas W g .

[0068] According to the embodiment of the present application, the actual load parameter of the terminal can represent the total energy load of the user side, for example, it can include the total electric load L e and the total heat load L h .

[0069] According to an embodiment of the present application, the first function may be expressed as formula (1):

[0070] Among them, W e Indicates the total amount of electricity that has been purchased in advance by the user; W g Indicates the total amount of natural gas that has been purchased in advance by the user; L e Indicates the total electric load on the user side; L h Indicates the total heat load on the user side; λ h Indicates the energy quality coefficient of heat load and natural gas; λ g The energy quality coefficient that represents the electrical load and electrical energy.

[0071] According to an embodiment of the present application, the energy quality coefficient of the electric load and the electric energy can be determined based on the combustion temperature of the natural gas when it is completely burned during operation of the integrated energy system and the ambient temperature during operation of the system. For example, the energy quality coefficient of the electric load and the electric energy can be calculated according to formula (2):

[0072] Wherein, T0 represents the ambient temperature when the system is running; T represents the combustion temperature when the natural gas is completely burned when the integrated energy system is running;

[0073] According to an embodiment of the present application, the energy quality coefficient of the heat load and natural gas can be determined based on the ambient temperature during system operation and the heating temperature on the user side. For example, the energy quality coefficient of the heat load and natural gas can be calculated according to formula (3):

[0074] Among them, T0 represents the ambient temperature when the system is running; T h Indicates the heating temperature on the user side.

[0075] According to an embodiment of the present application, the second function can represent the resource loss of the integrated energy system. Since resource loss can include input energy loss, equipment operation loss, demand response subsidy loss, equipment depreciation loss, and environmental pollution loss, the demand response subsidy loss function can be constructed using the adjustment parameters of the terminal load; the input energy loss function, equipment operation loss function, and equipment depreciation loss function can be constructed using the operating parameters of the target system; and the environmental pollution loss function can be constructed using the environmental loss parameters of the target system.

[0076] According to an embodiment of the present application, the second function may be expressed as formula (4): min C = C1 + C2 + C3 + C4 + C5 (4)

[0077] Among them, C represents the second function; C1 represents the input energy loss function; C2 represents the equipment operation loss function; C3 represents the demand response subsidy function; C4 represents the equipment depreciation loss function; C5 represents the environmental pollution loss function.

[0078] According to the embodiments of the present application, an energy efficiency optimization function is constructed from the perspective of maximizing the energy efficiency and minimizing resource consumption of the integrated energy system, and equipment parameters can be adjusted in a timely manner according to the target integrated energy demand, so as to achieve the goal of improving the energy efficiency of the integrated energy system and reducing the resource loss of the integrated energy system while meeting the integrated energy demand on the user side.

[0079] The operating parameters of the target system include operating time parameters, operating energy consumption coefficient, equipment quantity parameters, equipment operating efficiency parameters, equipment operating status parameters, equipment operating energy consumption parameters and equipment operating power parameters.

[0080] According to an embodiment of the present application, a first sub-function for characterizing energy loss is constructed based on an operation duration parameter, an operation energy consumption coefficient, a device quantity parameter, a device operation efficiency parameter, and a device operation energy consumption parameter. The first sub-function can characterize an input energy loss function.

[0081] For example, the first sub-function can be constructed according to formula (4-1):

[0082] Among them, c g (t) represents the energy consumption value of electric energy in the period t, c e (t) represents the energy consumption value of natural gas in time period t; T represents the total operation time of the integrated energy system, N represents the total number of devices in the integrated energy system, η i represents the operating efficiency of the i-th device, g n W represents the natural gas consumption of the i-th device in the operation phase per unit time. e (t) represents the amount of electricity used during the period t.

[0083] According to an embodiment of the present application, a second sub-function for characterizing equipment operation loss is constructed based on the equipment operation efficiency parameter. The second sub-function can characterize the equipment operation loss function.

[0084] For example, the second sub-function can be constructed according to formula (4-2):

[0085] Among them, c r，i is the unit operating loss of the i-th device, P i (t) is the output power of the i-th device in time period t.

[0086] According to an embodiment of the present application, a third sub-function for representing the demand response subsidy is constructed based on the adjustment parameter of the terminal load. The third sub-function can represent the demand response subsidy function.

[0087] According to an embodiment of the present application, the terminal load adjustment parameters include a transferable load parameter, an adjustable load parameter, and a curtailable load parameter. The third sub-function is constructed based on the transferable load parameter, the adjustable load parameter, and the curtailable load parameter.

[0088] For example, the third sub-function can be constructed according to formula (4-3):

[0089] Among them, c TL represents the unit compensation parameter of the transferable load; c RL Indicates the number of compensation units that can reduce load; c AL Indicates the unit compensation parameter of the adjustable heat load; E TL (t) represents the actual total amount of transferable load during the period t, E RL (t) represents the actual total amount of load reduction that can be achieved during the period t, H AL (t) represents the actual total change of the adjustable heat load during the period t.

[0090] According to an embodiment of the present application, a fourth sub-function for characterizing equipment depreciation loss is constructed based on the equipment quantity parameter, the equipment operating state parameter, and the equipment operating power parameter. The fourth sub-function can characterize the equipment depreciation loss function.

[0091] For example: the fourth sub-function can be constructed according to formula (4-4).

[0092] Among them, c inv,i represents the full usage loss of the i-th device, λ r Indicates the equipment recovery factor, P m,i represents the rated power of the i-th device, t m,irepresents the maximum usable time of the i-th device, P i (t) represents the output power of the i-th device in time period t.

[0093] According to an embodiment of the present application, a fifth sub-function is constructed based on the environmental loss parameters of the target system to characterize the environmental resource loss during the pollutant emission treatment process of the target system. The fifth sub-function can characterize the environmental pollution loss function.

[0094] According to an embodiment of the present application, the environmental loss parameters include pollutant type parameters, pollutant emission coefficients and governance resource loss parameters corresponding to the pollutant type parameters; based on the environmental loss parameters of the target system, a fifth sub-function is constructed to characterize the environmental resource loss in the pollutant emission treatment process of the target system, including: constructing the fifth sub-function based on the pollutant type parameters, the pollutant emission coefficients and the governance resource loss parameters corresponding to the pollutant type parameters.

[0095] For example: the fifth sub-function can be constructed according to formula (4-5).

[0096] Among them, c p represents the treatment loss of the pth pollutant, P represents the total number of pollutant types, η r Indicates the emission coefficient of pollutants, P i (t) represents the output power of the i-th device in time period t.

[0097] According to the embodiments of the present application, the demand response subsidy loss and environmental pollution loss are added to the resource loss function of the integrated energy system, which can adapt to the response scenario of the energy gradient demand on the current user side, so as to respond to the changing factors such as the comprehensive energy demand change and the comprehensive energy demand subsidy change in time during the peak energy usage period, adjust the equipment parameters of the integrated energy system, and meet the comprehensive energy demand on the user side.

[0098] According to the embodiments of the present application, during the energy efficiency optimization process of an integrated energy system, the balance between energy supply and consumption, as well as the stability of equipment during energy transmission, are key to ensuring the normal operation of the integrated energy system. Therefore, constraints can be constructed based on the operational requirements of the balance between energy supply and consumption, as well as the stability of equipment during energy transmission.

[0099] For example, based on the balance between energy supply and energy consumption, the first constraint condition is constructed according to the terminal estimated load parameters, the terminal actual load parameters, and the equipment operation energy consumption parameters of the target system. The first constraint condition can be shown as formula (5):

[0100] Among them, W el(t) represents the electric load demand on the user side during the period t, W ed (t) represents the power consumption of the electric drive equipment in the integrated energy system during the period t, W hl (t) represents the heat load demand on the user side during period t, W hd (t) represents the heat consumption of the heat-driven equipment in the integrated energy system during the period t, W g (t) represents the natural gas usage in period t.

[0101] According to an embodiment of the present application, based on the equipment operation requirements of the transmission energy, the second constraint condition is constructed according to the equipment operation state parameters and the equipment operation power parameters. The second constraint condition can be shown as formula (6):

[0102] Among them, P min Indicates the lower limit of the output power of the integrated energy system equipment; P max Indicates the upper limit of the output power of the integrated energy system equipment, △P min Indicates the lower limit of the ramp rate of the integrated energy system equipment; △P max Indicates the upper limit of the ramp rate of the integrated energy system equipment. θ(t) is the start or stop state of the equipment. θ(t) = 1 indicates that the equipment is on, and θ(t) = 0 indicates that the equipment is stopped. △P i (t) represents the ramp rate of the i-th device in the integrated energy system.

[0103] FIG3 shows a flowchart of processing the operating parameters of multiple devices in a target system using a target algorithm according to an embodiment of the present application, with the operating parameters of the multiple devices in the target system as population individuals.

[0104] As shown in FIG. 3 , the process 300 of processing operating parameters of multiple devices may include operations S3301 to S3313 .

[0105] In operation S3301, the population is initialized.

[0106] According to the embodiments of the present application, an initial population can be constructed based on an energy efficiency optimization function and constraints, using a target algorithm and taking the operating parameters of multiple devices in a target system as population individuals.

[0107] In operation S3302, a fitness value is calculated.

[0108] According to an embodiment of the present application, population individuals in the initial population may be processed according to a predetermined fitness function to obtain multiple fitnesses corresponding to the multiple population individuals.

[0109] According to an embodiment of the present application, target population individuals are determined from the initial population based on multiple fitnesses. For example, population individuals with fitness greater than a fitness threshold are selected as target population individuals.

[0110] According to an embodiment of the present application, the fitness value can be adaptively adjusted using formula (7):

[0111] Among them, T' is the inverse of the current evolution generation, and gen is the set total number of iterations.

[0112] In operation S3303, selection, crossover, and mutation are performed.

[0113] According to an embodiment of the present application, based on a predetermined genetic probability, a genetic operation is performed on the target population individuals to generate an offspring population. The predetermined genetic probability may include a predetermined crossover probability P cr and the predetermined mutation probability P mu Based on a predetermined crossover probability, a crossover operation is performed on the target population individuals to obtain a crossover offspring population; and based on a predetermined mutation probability, a mutation operation is performed on the crossover offspring population to obtain the offspring population.

[0114] The neighborhood corresponding to the offspring population includes I, where I is an integer greater than 1; performing an annealing operation on the offspring population to obtain the device parameters of the target system includes: using a neighborhood operator to process the population individuals in the i-th neighborhood corresponding to the offspring population to obtain a first function value corresponding to the population individuals in the i-th neighborhood for characterizing energy usage status and a second function value corresponding to the energy conversion status; if it is determined that the current iteration round does not meet the iteration termination condition, returning to perform the processing operation on the i-th neighborhood and incrementing i; if it is determined that the current iteration round meets the iteration termination condition, determining the attribute parameters of the population individuals in the neighborhood corresponding to the largest first function value and the smallest second function value as the device parameters of the target system. The largest first function value refers to the largest first function value corresponding to the I neighborhoods, and the smallest second function value refers to the smallest second function value corresponding to the I neighborhoods.

[0115] According to an embodiment of the present application, the predetermined crossover probability P crTwo parent chromosomes are selected for a crossover operation to produce offspring chromosomes. A repair operator is used to repair missing points during the crossover process, addressing both missing and duplicated chromosomes. The repair operator essentially involves a comparison and selection process. The crossover operator is applied to paired individuals, exchanging chromosome genes with a certain probability to produce individuals with new characteristics. A higher crossover probability expands the search space, but also increases the probability of corruption. A lower crossover probability can sluggish the search. The typical range for the crossover probability is 0.25 to 1.00.

[0116] According to an embodiment of the present application, according to the predetermined mutation probability P mu The crossover-treated daughter chromosomes are selected and mutated to produce mutant daughter chromosomes. Using the mutation operator, a portion of the genes in the selected individuals are modified to alternate alleles with a certain probability. The mutation probability is an auxiliary search operation in the algorithm, introduced to maintain population diversity. While the mutation probability can effectively prevent the loss of important genes, excessively high mutation frequencies can disrupt the algorithm. The generally predetermined range for the mutation probability is 0.001 to 0.1.

[0117] In operation S3304, it is determined whether the fitness is in a continuously decreasing state. If so, operation S3305 is executed; if not, the process returns to operation S3303 to update the population.

[0118] According to the embodiment of the present application, it is determined whether the fitness value of the optimal solution of the population is in a continuous decline state. If the above state occurs, the current optimal solution S is output and operation S3305 is executed to perform an annealing operation. Otherwise, operation S3303 continues to perform crossover and mutation operations. Only by performing an annealing operation on the fitness that is already in a continuous decline state can it be of specific significance, indicating that the operation of the genetic algorithm in the first stage has played a role and found the direction in which the solution declines the fastest. Therefore, it is necessary to continue to perform an annealing operation along this direction to obtain the optimal solution. The introduction of the annealing idea can allow a small number of parents with high fitness to compete with the offspring, accelerate the evolution and solution speed, and also ensure the population evolution efficiency and the algorithm solution speed and accuracy.

[0119] In operation S3305, an annealing operation is performed.

[0120] When it is determined that the fitness of the offspring population and the fitness of the initial population meet a predetermined condition, an annealing operation is performed on the offspring population to obtain device parameters of the target system.

[0121] In operation S3306, a small disturbance is performed on the current solution to generate a solution to be operated.

[0122] According to the embodiment of the present application, the current temperature is set to Tp = T1, and the number of iterations at this temperature is L p Set to 0, and set the optimal solution S obtained in operation S3304 as the current solution S n Since global optimization problems require a search method that combines local and global search, the former ensures search accuracy, while the latter ensures avoidance of local optimality. A sufficiently large initial value T1 can enable the algorithm to converge and traverse all feasible solutions in the search space. If it is too large or too small, the global optimal solution cannot be achieved.

[0123] In operation S3307, it is determined whether the new solution is the current optimal solution. If so, operation S3308 is performed; if not, operation S3309 is performed.

[0124] In operation S3308, the new solution is accepted.

[0125] In operation S3309, the new solution is accepted according to the annealing rule.

[0126] According to the embodiment of the present application, if T p >T end Then continue the annealing operation; otherwise, end the algorithm and output the current solution as the optimal solution. Termination temperature T end It is usually 0, but it will consume a lot of simulation time. As the temperature approaches 0, the surrounding conditions are almost the same. So find a temperature that is low enough to be acceptable.

[0127] In operation S3310, it is determined whether the number of iterations has been reached. If so, operation S3311 is executed; if not, the process returns to operation S3306.

[0128] According to the embodiment of the present application, if L p >L, then L p =L p +1, and go to step S3305, otherwise let L p =0,L p =T p xR T , and go to operation S3311. That is, if the maximum number of iterations (step length) is reached, the algorithm ends and the optimal solution and final step length are output; otherwise, the annealing operation continues. The step length L controls the adequacy and accuracy of the algorithm optimization and should be adjusted according to the actual situation and specific problems; the cooling rate R T The value is generally 0.5 to 0.99. This parameter determines the speed of the cooling process. The larger the parameter, the slower the cooling process, which indirectly affects the increase in the number of iterations and can increase the probability of global optimization. The parameter relationship between two adjacent generations is T i+1 =R T T i , R T is the cooling rate; if Ti Thermal equilibrium has been reached, then T i+1 Only a small number of state changes are needed to reach a stable equilibrium state, reducing the inner loop.

[0129] In operation S3311, determine whether the output condition is met. If so, perform operation S3312; if not, perform operation S3313.

[0130] In operation S3312, the result is output.

[0131] In operation S3313, the temperature is slowly lowered and the number of iterations is reset, and the annealing operation is returned to be performed.

[0132] According to the embodiment of the present application, a neighborhood operator is used to generate a neighborhood solution, and it is determined whether the neighborhood solution is better than the current solution S n If the condition is satisfied, the current solution is used to replace the neighborhood solution. Otherwise, the better value is used to replace the suboptimal value, and the operation is repeated. It is necessary to define ΔE=E(x t+1 )-E(x t ), E() is the objective function value of the simulated annealing operation. According to the Metropolis criterion, if △E<0, where x t Represents the current operator state, then accept the new solution and take it as the current solution, otherwise follow the acceptance probability function Determine whether to accept the new solution, where T represents the current temperature.

[0133] According to the embodiments of the present application, the final output result can be determined by judging whether the current number of iterations or the optimal solution meets the output conditions. The output result obtained is the parameters of multiple devices that meet the target comprehensive energy demand and maximize the energy efficiency and minimize resource loss of the comprehensive energy system.

[0134] According to an embodiment of the present application, when executing the system energy efficiency optimization method provided by the present application, the formulas (1), (2), (3), (4), (4-1), (4-2), (4-3), (4-4) and (4-5) described above can be combined into a multi-objective optimization mathematical model, and based on the constraints of formulas (5) and (6), a genetic annealing algorithm is executed for calculation.

[0135] According to the embodiments of the present application, the hardware environment for executing the system energy efficiency optimization method provided in the present application is as follows: Windows 10 operating system, the central processing unit (CPU) is Intel Core i7-9750H (2.6GHz / L3 12M), the memory is 8GB, and programming is performed using the MATLAB 2021b environment.

[0136] According to the embodiment of the present application, when executing the method of the embodiment of the present application, the following parameters can be pre-set: the maximum number of iterations L is 500 times, the population size Np is 200, the generation gap GAPG is 0.7, the mutation probability P is 0. mu is 0.05, crossover probability P cr is 0.8, cooling rate R T is 0.98, the initial temperature T0 is 100, and the ending temperature T end is 0.

[0137] According to an embodiment of the present application, the model parameters of the multi-objective optimization mathematical model are as follows:

[0138] T0 is 25℃, T1 is 1500℃, T h is 30℃. h ,λ g They are 0.17 and 0.93 respectively, and the energy quality coefficient of electric load and electric energy are both 1. g (t), c e (t) are all random numbers, ranging from [2.5×10 4 J, 3.7×10 4 J] and [2.3×10 3 J, 6.8×10 3 J]; T is 20h, N is 10; η i is a random number with a value range of [0.7, 0.8]; g n It is a random number, and its value range is [1.2m 3 , 1.4m 3 ]; W e (t) is set to 120kWh (average amount). r,i is a random number with a value range of [5×10 3 , 7×10 3 ];P i (t) is a random number, ranging from [1kW, 3.5kW]. TL 、c RL 、c AL 3×10 2 , 2×10 2 With 4×10 2 , E TL (t), E RL (t), H AL (t) are 13kWh, 12kWh, and 14kWh (average). inv,i is a random number with a value range of [6×10 6 J, 8×10 6 J];λ r is 0.8, P m,iis a random number with a value range of [2kW, 5kW], t m,i 43800h (5 years). p The value is {5×10 3 , 4×10 3 , 8×10 3 , 6×10 3}, P is 4, η r is 0.7. min With P max 1kW and 3.5kW respectively, △P min and △P max 75W and 300W respectively.

[0139] In order to demonstrate the algorithm execution effect of the embodiment of the present application, the following description is made with reference to FIG. 4A to FIG. 4C .

[0140] FIG4A is a schematic diagram showing the result of processing the operating parameters of multiple devices in a target system using a genetic algorithm according to an embodiment of the present application, with the operating parameters of the multiple devices in the target system as population individuals.

[0141] As shown in Figure 4A, during the genetic algorithm processing, the algorithm running time is 476.2s, and the final resource loss is 4256.83X10 4 J, the number of iterations required to iterate to the optimal solution is 259 generations.

[0142] FIG4B is a schematic diagram showing the result of processing the operating parameters of multiple devices in the target system using the simulated annealing algorithm according to an embodiment of the present application, with the operating parameters of the multiple devices in the target system as population individuals.

[0143] As shown in Figure 4B, based on the simulation algorithm processing, the algorithm running time is 460.8s, and the final resource loss is 4287.54X10 4 J, the number of iterations required to iterate to the optimal solution is 278 generations.

[0144] FIG4C is a schematic diagram showing the result of processing the operating parameters of multiple devices in the target system using the genetic simulated annealing algorithm according to an embodiment of the present application, with the operating parameters of the multiple devices in the target system as population individuals.

[0145] As shown in Figure 4C, during the process of genetic simulation algorithm processing, the algorithm running time is 425.6s, and the final resource loss is 4243.54X04 4 J, the number of iterations required to iterate to the optimal solution is 235 generations.

[0146] According to the embodiment of the present application, based on the same model parameters, the genetic algorithm, the simulated annealing algorithm and the genetic simulated annealing algorithm are executed multiple times, and the comparison results are shown in Table 1 and Table 2:

[0147] Table 1 Comparison of average resource loss of five executions of different optimization algorithms

[0148] According to the embodiments of the present application, it can be seen from Table 1 that the average resource loss obtained based on the genetic simulated annealing algorithm is lower than the average resource loss obtained by other algorithms, indicating that the energy efficiency optimization function constructed by the present application is solved using the genetic simulated annealing algorithm, and the obtained equipment operating parameters can effectively reduce the resource loss of the integrated energy system.

[0149] Table 2 Comparison of average energy efficiency of five executions of different optimization algorithms

[0150] According to the embodiments of the present application, it can be seen from Table 2 that the average resource loss obtained based on the genetic simulated annealing algorithm is higher than the average resource loss obtained by other algorithms, indicating that the energy efficiency optimization function constructed by the present application is solved using the genetic simulated annealing algorithm, and the obtained equipment operating parameters can effectively improve the energy efficiency of the integrated energy system.

[0151] Based on the above-mentioned system energy efficiency optimization method based on comprehensive energy, the present application also provides a system energy efficiency optimization device based on comprehensive energy demand. The device will be described below in conjunction with Figure 5.

[0152] FIG5 shows a structural block diagram of a system energy efficiency optimization device based on comprehensive energy demand according to an embodiment of the present application.

[0153] As shown in FIG. 5 , the system energy efficiency optimization device 500 based on comprehensive energy demand in this embodiment includes a first construction module 510 , a second construction module 520 and a processing module 530 .

[0154] The first construction module 510 is configured to construct an energy efficiency optimization function for the target system based on the received target comprehensive energy demand parameters, the target system operating parameters, and the target system environmental loss parameters in response to the received target comprehensive energy demand. In one embodiment, the first construction module 510 can be configured to perform operation S210 described above, which is not further described here.

[0155] The second building block 520 is configured to construct constraints for optimizing the energy efficiency of the target system based on the balance between energy supply and energy consumption and the operating requirements of the equipment used to transmit energy. In one embodiment, the second building block 520 can be configured to perform the operation S220 described above, which will not be repeated here.

[0156] Processing module 530 is configured to utilize a target algorithm based on the energy efficiency optimization function and constraints, using the operating parameters of multiple devices in the target system as population individuals, to process the operating parameters of the multiple devices to obtain device parameters of the target system that meet the target comprehensive energy demand. In one embodiment, processing module 530 can be configured to perform operation S230 described above, which will not be further described here.

[0157] According to an embodiment of the present application, the comprehensive energy demand parameter includes an actual terminal load parameter, an adjustment parameter for the terminal load, and an estimated terminal load parameter; and the first construction module 510 includes a first construction submodule and a second construction submodule. The first construction submodule is configured to construct a first function representing energy usage based on the estimated terminal load parameter and the actual terminal load parameter. The second construction submodule is configured to construct a second function representing energy conversion based on the adjustment parameter for the terminal load, the operating parameters of the target system, and the environmental loss parameters of the target system.

[0158] According to an embodiment of the present application, the operating parameters of the target system include an operating time parameter, an operating energy consumption coefficient, a device quantity parameter, a device operating efficiency parameter, a device operating status parameter, a device operating energy consumption parameter, and a device operating power parameter; the second construction submodule includes a first construction unit, a second construction unit, a third construction unit, a fourth construction unit, and a fifth construction unit. The first construction unit is configured to construct a first sub-function for characterizing energy loss based on the operating time parameter, the operating energy consumption coefficient, the device quantity parameter, the device operating efficiency parameter, and the device operating energy consumption parameter. The second construction unit is configured to construct a second sub-function for characterizing device operating loss based on the device operating efficiency parameter. The third construction unit is configured to construct a third sub-function for characterizing demand response subsidies based on the adjustment parameter of the terminal load. The fourth construction unit is configured to construct a fourth sub-function for characterizing equipment depreciation loss based on the device quantity parameter, the device operating status parameter, and the device operating power parameter. The fifth construction unit is configured to construct a fifth sub-function for characterizing environmental resource loss in the pollutant emission treatment process of the target system based on the environmental loss parameter of the target system.

[0159] According to an embodiment of the present application, the adjustment parameters of the terminal load include transferable load parameters, adjustable load parameters and reducible load parameters; the third construction unit includes a first construction sub-unit, which is configured to construct a third sub-function based on the transferable load parameters, adjustable load parameters and reducible load parameters.

[0160] According to an embodiment of the present application, the environmental loss parameters include pollutant type parameters, pollutant emission coefficients and governance resource loss parameters corresponding to the pollutant type parameters; the fifth construction unit includes a second construction sub-unit, which is configured to construct a fifth sub-function based on the pollutant type parameters, the pollutant emission coefficients and the governance resource loss parameters corresponding to the pollutant type parameters.

[0161] According to an embodiment of the present application, the second construction module 520 includes a third construction submodule and a fourth construction submodule. The third construction submodule is configured to construct a first constraint condition based on the balance between energy supply and energy consumption, according to the terminal estimated load parameter, the terminal actual load parameter, and the target system's equipment operating energy consumption parameter. The fourth construction submodule is configured to construct a second constraint condition based on the equipment operating requirements of the transmitted energy, according to the equipment operating state parameter and the equipment operating power parameter.

[0162] According to an embodiment of the present application, the processing module 530 includes a fifth construction submodule, a processing submodule, a determination submodule, a generation submodule, and an acquisition submodule. The fifth construction submodule is configured to construct an initial population based on an energy efficiency optimization function and constraints, using a target algorithm and the operating parameters of multiple devices in the target system as population individuals. The processing submodule is configured to process the population individuals in the initial population according to a predetermined fitness function to obtain multiple fitnesses corresponding to the multiple population individuals. The determination submodule is configured to determine the target population individuals from the initial population based on the multiple fitnesses. The generation submodule is configured to perform genetic operations on the target population individuals based on a predetermined genetic probability to generate an offspring population. The acquisition submodule is configured to perform an annealing operation on the offspring population when it is determined that the fitness of the offspring population meets the fitness of the initial population and satisfies a predetermined condition, thereby obtaining the device parameters of the target system.

[0163] According to an embodiment of the present application, the generation submodule includes a crossover unit and a mutation unit. The crossover unit is configured to perform a crossover operation on individuals of the target population based on a predetermined crossover probability to obtain a crossover offspring population. The mutation unit is configured to perform a mutation operation on the crossover offspring population based on a predetermined mutation probability to obtain an offspring population.

[0164] According to an embodiment of the present application, the neighborhood corresponding to the offspring population includes I, where I is an integer greater than 1; the obtaining submodule includes: a first processing unit, a second processing unit, and a determining unit. The first processing unit is configured to use a neighborhood operator to process the population individuals in the i-th neighborhood corresponding to the offspring population, and obtain a first function value corresponding to the population individuals in the i-th neighborhood for characterizing the energy usage status and a second function value for characterizing the energy conversion status. The second processing unit is configured to, when it is determined that the current iteration round does not meet the iteration termination condition, return to execute the processing operation for the i-th neighborhood and increment i. The determining unit is configured to, when it is determined that the current iteration round meets the iteration termination condition, determine the attribute parameters in the population individuals in the neighborhood corresponding to the largest first function value and the smallest second function value as the device parameters of the target system.

[0165] According to an embodiment of the present application, any multiple modules in the first building block 510, the second building block 520 and the processing module 530 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. According to an embodiment of the present application, at least one of the first building block 510, the second building block 520 and the processing module 530 can 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 can 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 modes of software, hardware and firmware or in an appropriate combination of any of them. Alternatively, at least one of the first building module 510 , the second building module 520 , and the processing module 530 may be at least partially implemented as a computer program module, and when the computer program module is executed, the corresponding function may be performed.

[0166] FIG6 shows a block diagram of an electronic device suitable for implementing a system energy efficiency optimization method based on comprehensive energy demand according to an embodiment of the present application.

[0167] As shown in Figure 6, the electronic device 600 according to an embodiment of the present application includes a processor 601, which can perform a variety of appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an ASIC), etc. The processor 601 may also include an onboard memory configured for cache purposes. The processor 601 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 application.

[0168] The RAM 603 stores various programs and data required for the operation of the electronic device 600. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the programs in the ROM 602 and / or RAM 603 to perform various operations according to the method flow of the embodiment of the present application. The programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the programs stored in the one or more memories to perform various operations according to the method flow of the embodiment of the present application.

[0169] According to an embodiment of the present application, the electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to the bus 604. The electronic device 600 may further include one or more of the following components connected to the I / O interface 605: an input portion 606 including a keyboard, a mouse, etc.; an output portion 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage portion 608 including a hard disk; and a communication portion 609 including a network interface card such as a local area network (LAN) card or a modem. The communication portion 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed in the drive 610 as needed, so that computer programs read from the removable media can be installed into the storage portion 608 as needed.

[0170] This application also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of this application is implemented.

[0171] According to an embodiment of the present application, a computer-readable storage medium may be a non-volatile computer-readable storage medium, such as a portable computer disk, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, EPROM), a flash memory, a portable compact disk read-only memory (Compact Disc Read Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present application, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above and / or one or more memories other than ROM 602 and RAM 603. The storage medium may be a non-transitory storage medium.

[0172] The present application also includes a computer program product comprising a computer program containing program code for executing the method shown in the flowchart. When the computer program product is executed in a computer system, the program code is used to cause the computer system to implement the item recommendation method provided in the present application.

[0173] The computer program executes the above functions defined in the system / device of the embodiment of the present application when the computer program is executed by the processor 601. According to the embodiment of the present application, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0174] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including wireless, wired, or any suitable combination thereof.

[0175] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-mentioned functions defined in the system of the embodiment of the present application are performed. According to the embodiment of the present application, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0176] According to an embodiment of the application, the program code for executing the computer program provided by an embodiment of the application can be written in any combination of one or more programming languages, and these computing programs can be implemented using high-level processes and / or object-oriented programming languages ​​and / or assembly / machine languages. Programming languages ​​include such as Java, C++, python, "C" language or similar programming languages. The program code can be executed completely on the user computing device, partially on the user device, partially on the remote computing device, or completely 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 LAN or a wide area network (WAN), or can be connected to an external computing device (such as using an Internet service provider to connect through the Internet).

[0177] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the part of the above-mentioned module, program segment or code includes one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that 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. 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 by a dedicated hardware-based system that performs the prescribed function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0178] The features described in the various embodiments and / or claims of this application may be combined and / or coupled in various ways, even if such combinations and / or couplings are not explicitly described in this application. The features described in the various embodiments and / or claims of this application may be combined and / or coupled in various ways. All such combinations and / or couplings fall within the scope of this application.

Claims

1. A system energy efficiency optimization method based on comprehensive energy demand, comprising: In response to the received target comprehensive energy demand, constructing an energy efficiency optimization function of the target system according to the comprehensive energy demand parameter, the operating parameters of the target system and the environmental loss parameters of the target system; Based on the balance between energy supply and energy consumption and the operation requirements of equipment used to transmit energy, construct constraints for optimizing the energy efficiency of the target system; Based on the energy efficiency optimization function and the constraint conditions, using a target algorithm, taking the operating parameters of multiple devices in the target system as population individuals, processing the operating parameters of the multiple devices to obtain device parameters of the target system that meet the target comprehensive energy demand; The energy efficiency optimization function is as follows: Among them, W e Indicates the total amount of electric energy that has been purchased in advance by the user; W g Indicates the total amount of natural gas that has been purchased in advance by the user; L e Indicates the total amount of electric load on the user side; L h Indicates the total heat load on the user side; h Represents the energy quality coefficient of heat load and natural gas; λ g The energy quality coefficient that represents the electrical load and electrical energy; min C=C1+C2+C3+C4+C5 (2); in: Among them, c g (t) represents the energy consumption value of electric energy in the period t, c e (t) represents the energy consumption value of natural gas in time period t; T represents the total operation time of the integrated energy system, N represents the total number of equipment in the integrated energy system, η i represents the operating efficiency of the ith device, g n represents the natural gas usage of the ith equipment in the operation phase per unit time, W e (t) represents the amount of electricity used in time period t; Among them, c r，i is the unit operating loss of the ith device, P i (t) is the output power of the ith device in time period t; Among them, c TL represents the unit compensation parameter of the transferable load; c RL Indicates the unit compensation number of the load that can be reduced; c AL Indicates the unit compensation parameter of the adjustable heat load; E TL (t) represents the actual total amount of transferable load within the period t, E RL (t) represents the actual total amount of load reduction that can be reduced in period t, H AL (t) represents the actual total change of the adjustable heat load during the period t; Among them, c inv,i represents the full usage loss of the i-th device, λ r Indicates the equipment recovery factor, P m,i represents the rated power of the ith device, t m,i represents the maximum usable time of the i-th device, P i (t) represents the output power of the ith device in time period t; Among them, c p represents the treatment loss of the pth pollutant, P represents the total number of pollutant types, η r Indicates the emission coefficient of the pollutant; The constraints are as follows: Among them, W el (t) represents the electric load demand on the user side during the period t, W ed (t) represents the power consumption of the electric drive equipment in the comprehensive energy system during the period t, W hl (t) represents the heat load demand on the user side during period t, W hd (t) represents the heat consumption of the heat-driven equipment in the integrated energy system during the period t; Among them, P min Indicates the lower limit of the output power of the integrated energy system equipment; P max Indicates the upper limit of the output power of the integrated energy system equipment, △P min Indicates the lower limit of the ramp rate of the integrated energy system equipment; △P max Indicates the upper limit of the ramp rate of the integrated energy system equipment. θ(t) is the start or stop state of the equipment. θ(t) = 1 indicates that the equipment is on, and θ(t) = 0 indicates that the equipment is stopped. The target algorithm includes any one of the following: a genetic algorithm, a simulated annealing algorithm or a genetic simulated annealing algorithm.

2. The method according to claim 1, wherein: The comprehensive energy demand parameters include actual terminal load parameters, terminal load adjustment parameters and terminal estimated load parameters; the energy efficiency optimization function of the target system is constructed according to the comprehensive energy demand parameters, the operating parameters of the target system and the environmental loss parameters of the target system, including: Constructing a first function for characterizing energy usage status according to the terminal estimated load parameter and the terminal actual load parameter; A second function for characterizing the energy conversion status is constructed according to the adjustment parameters of the terminal load, the operating parameters of the target system and the environmental loss parameters of the target system.

3. The method according to claim 2, wherein: The operating parameters of the target system include operating time parameters, operating energy consumption coefficient, equipment quantity parameters, equipment operating efficiency parameters, equipment operating status parameters, equipment operating energy consumption parameters and equipment operating power parameters; The constructing a second function for characterizing the energy conversion status according to the adjustment parameter of the terminal load, the operation parameter of the target system and the environmental loss parameter of the target system comprises: Constructing a first sub-function for characterizing energy loss according to the operation duration parameter, the operation energy consumption coefficient, the equipment quantity parameter, the equipment operation efficiency parameter and the equipment operation energy consumption parameter; According to the equipment operation efficiency parameter, construct a second sub-function for characterizing equipment operation loss; constructing a third sub-function for characterizing the demand response subsidy according to the adjustment parameter of the terminal load; Constructing a fourth sub-function for characterizing equipment depreciation loss according to the equipment quantity parameter, the equipment operation state parameter and the equipment operation power parameter; According to the environmental loss parameters of the target system, a fifth sub-function is constructed for characterizing the environmental resource loss during the pollutant emission treatment process of the target system.

4. The method according to claim 3, wherein: The adjustment parameters of the terminal load include transferable load parameters, adjustable load parameters and curtailable load parameters; The step of constructing a third sub-function for characterizing the demand response subsidy according to the adjustment parameter of the terminal load includes: The third sub-function is constructed according to the transferable load parameter, the adjustable load parameter and the curtailable load parameter.

5. The method according to claim 3, wherein: The environmental loss parameters include pollutant type parameters, pollutant emission coefficients and governance resource loss parameters corresponding to the pollutant type parameters; The fifth sub-function for characterizing the environmental resource loss during the pollutant emission treatment process of the target system is constructed according to the environmental loss parameter of the target system, comprising: The fifth sub-function is constructed according to the pollutant type parameter, the pollutant emission coefficient and the control resource loss parameter corresponding to the pollutant type parameter.

6. The method according to claim 1, wherein: The constraint conditions for optimizing the energy efficiency of the target system based on the balance relationship between energy supply and energy consumption and the operation requirements of the equipment used to transmit energy include: Based on the balance relationship between the energy supply and the energy consumption, a first constraint condition is constructed according to the terminal estimated load parameter, the terminal actual load parameter and the equipment operation energy consumption parameter of the target system; Based on the equipment operation requirements of the transmission energy, according to the equipment operation status parameters and equipment operation power parameters, a The second constraint.

7. The method according to claim 1, wherein: The method of processing the operating parameters of multiple devices in the target system as population individuals based on the energy efficiency optimization function and the constraint conditions, and obtaining the device parameters of the target system that meet the target comprehensive energy demand, includes: Based on the energy efficiency optimization function and the constraint conditions, using the target algorithm, and taking the operating parameters of multiple devices in the target system as population individuals, an initial population is constructed; According to a predetermined fitness function, the population individuals in the initial population are processed to obtain a plurality of fitnesses corresponding to the plurality of population individuals; Based on the multiple fitnesses, determining a target population individual from the initial population; Based on a predetermined genetic probability, performing genetic operations on the target population individuals to generate an offspring population; When it is determined that the fitness of the offspring population and the fitness of the initial population meet a predetermined condition, an annealing operation is performed on the offspring population to obtain device parameters of the target system.

8. The method according to claim 7, wherein: The step of performing genetic operations on the target population individuals based on a predetermined genetic probability to generate an offspring population includes: Based on a predetermined crossover probability, performing a crossover operation on the target population individuals to obtain a crossover offspring population; and Based on a predetermined mutation probability, a mutation operation is performed on the crossover offspring population to obtain the offspring population.

9. The method according to claim 7, wherein: The neighborhood corresponding to the offspring population includes I, where I is an integer greater than 1; performing an annealing operation on the offspring population to obtain the device parameters of the target system includes: Using a neighborhood operator to process population individuals in a j-th neighborhood corresponding to the offspring population, obtaining a first function value corresponding to the population individuals in the j-th neighborhood for characterizing energy usage conditions and a second function value corresponding to the energy conversion conditions; If it is determined that the current iteration round does not meet the iteration termination condition, return to execute the processing operation for the j-th neighborhood and increment j; When it is determined that the current iteration round satisfies the iteration termination condition, the attribute parameters of the population individuals in the neighborhood corresponding to the largest first function value and the smallest second function value are determined as the device parameters of the target system.

10. A system energy efficiency optimization device based on comprehensive energy demand, comprising: A first building module is configured to respond to the received target comprehensive energy demand and build an energy efficiency optimization function of the target system according to the comprehensive energy demand parameter, the operating parameters of the target system and the environmental loss parameters of the target system; A second building module is configured to build a constraint condition for optimizing the energy efficiency of the target system based on a balance relationship between energy supply and energy consumption and an operation requirement of equipment for transmitting energy; A processing module is configured to use a target algorithm based on the energy efficiency optimization function and the constraint condition to obtain the target The operating parameters of multiple devices in the system are population individuals, and the operating parameters of the multiple devices are processed to obtain the device parameters of the target system that meet the target comprehensive energy demand; The energy efficiency optimization function is as follows: Among them, W e Indicates the total amount of electric energy that has been purchased in advance by the user; W g Indicates the total amount of natural gas that has been purchased in advance by the user; L e Indicates the total amount of electric load on the user side; L h Indicates the total heat load on the user side; h Represents the energy quality coefficient of heat load and natural gas; λ g The energy quality coefficient that represents the electrical load and electrical energy; min C=C1+C2+C3+C4+C5 (2); in: Among them, c g (t) represents the energy consumption value of electric energy in the period t, c e (t) represents the energy consumption value of natural gas in time period t; T represents the total operation time of the integrated energy system, N represents the total number of equipment in the integrated energy system, η i represents the operating efficiency of the ith device, g n represents the natural gas usage of the ith equipment in the operation phase per unit time, W e (t) represents the amount of electricity used in time period t; Among them, c r，i is the unit operating loss of the ith device, P i (t) is the output power of the ith device in time period t; Among them, c TL represents the unit compensation parameter of the transferable load; c RL Indicates the unit compensation number of the load that can be reduced; c AL Indicates the unit compensation parameter of the adjustable heat load; E TL (t) represents the actual total amount of transferable load within the period t, E RL (t) represents the actual total amount of load reduction that can be reduced in period t, H AL (t) represents the actual total change of the adjustable heat load during the period t; Among them, c inv,i represents the full usage loss of the i-th device, λ r Indicates the equipment recovery factor, P m,i represents the rated power of the ith device, t m,i represents the maximum usable time of the i-th device, P i (t) represents the output power of the ith device in time period t; Among them, c p represents the treatment loss of the pth pollutant, P represents the total number of pollutant types, η r Indicates the emission coefficient of pollutants, P i (t) represents the output power of the ith device in time period t; The constraints are as follows: Among them, W el (t) represents the electric load demand on the user side during the period t, W ed (t) represents the power consumption of the electric drive equipment of the comprehensive energy system during the period t, W hl (t) represents the heat load demand on the user side during period t, W hd (t) represents the heat consumption of the thermal drive equipment of the integrated energy system during the period t; Among them, P min Indicates the lower limit of the output power of the integrated energy system equipment; P max Indicates the upper limit of the output power of the integrated energy system equipment, △P min Indicates the lower limit of the ramp rate of the integrated energy system equipment; △P max Indicates the upper limit of the ramp rate of the integrated energy system equipment. θ(t) is the start or stop state of the equipment. θ(t) = 1 indicates that the equipment is on, and θ(t) = 0 indicates that the equipment is stopped. The target algorithm includes any one of the following: a genetic algorithm, a simulated annealing algorithm or a genetic simulated annealing algorithm.

11. An electronic device, comprising: one or more processors; a storage device configured to store one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors are enabled to execute the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having executable instructions stored thereon, wherein: When the executable instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 9.

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