Comprehensive energy system planning optimization method and computing device

Abstract

The invention discloses an integrated energy system planning optimization method and computing equipment, and the method comprises the steps: obtaining various equipment parameters, load demand data and energy prices in an integrated energy system, taking the lowest annual total cost of the integrated energy system as a first target, and taking the highest integrated energy efficiency as a second target; the total annual cost comprises the annual investment cost, the energy purchase cost, the annual operation and maintenance cost, the stepped carbon transaction cost and the wind curtailment and light curtailment penalty cost, and the comprehensive energy efficiency is used for representing the ratio of the total energy supply amount to the total energy consumption amount of each type of equipment in the comprehensive energy system; and solving the planning optimization model by using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, and determining the optimal capacity configuration of each type of equipment in the integrated energy system based on the optimal solution. According to the method, the optimal capacity configuration of each type of equipment in the integrated energy system can be reasonably planned on the premise of meeting the load requirement of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy systems, and in particular to a comprehensive energy system planning optimization method and computing equipment. Background Art

[0002] Integrated energy systems are complex coupled systems involving the input, conversion, storage, and output of multiple energy sources. Based on the coupled and complementary relationships between these energy sources, coordinated optimization of energy production, conversion, and storage within the system is required to ensure a low-carbon, economical, and stable energy supply. Based on the scale of services and the geographic coverage of the system, integrated energy systems can be categorized as interregional, regional, and user-level. An interregional integrated energy system is an energy transmission system that connects multiple regional integrated energy systems with energy production sites. Using large-scale power and gas transmission systems as a backbone, it facilitates long-distance energy transmission and aims to achieve energy complementarity and optimized allocation across regions, improving energy efficiency and supply security. A regional integrated energy system is an energy supply system based on energy stations and electricity, gas, and heat networks, connecting the energy supply and load sides. A user-level integrated energy system is a micro-energy station and its supporting network, located on the load side and formed by combining various distributed energy devices. It emphasizes cascaded energy utilization.

[0003] Achieving holistic and coordinated planning for integrated energy systems requires achieving multiple optimization goals, such as meeting diverse user load demands and ensuring economic and environmental benefits. This requires achieving the coordinated complementarity of heterogeneous energy sources and the coordinated optimization of multiple systems to build a clean, safe, economical, and efficient energy system. Currently, the planning and configuration methods and technologies for most park-level integrated energy systems in my country are incomplete and often fail to incorporate the actual conditions of the planning area (such as natural resource endowments and load demand characteristics). This leads to frequent wind and solar power curtailment, and a lack of coordinated planning measures across different energy sources.

[0004] How to rationally plan the optimal capacity configuration of various equipment in the integrated energy system while meeting the system load demand, thereby reducing the consumption of fossil fuels such as coal and promoting the development of hydrogen energy, is an urgent problem to be solved.

[0005] Therefore, an integrated energy system planning optimization method is needed. Summary of the Invention

[0006] To this end, the present invention provides a comprehensive energy system planning and optimization method to solve or at least alleviate the above problems.

[0007] According to one aspect of the present invention, a method for optimizing the planning of an integrated energy system is provided, which is executed in a computing device and includes: obtaining parameters of various types of equipment, load demand data, and energy prices in the integrated energy system, wherein the parameters of various types of equipment include the investment and construction costs and equipment energy efficiency of various types of equipment, the load demand data include electric load demand data and thermal load demand data, and the energy prices include electricity prices and gas prices; based on the parameters of various types of equipment, load demand data, and energy prices, a planning optimization model for the integrated energy system is constructed with the lowest annual total cost of the integrated energy system as a first goal and the highest comprehensive energy efficiency of the integrated energy system as a second goal, wherein the annual total cost includes annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and wind and solar power curtailment penalty cost, and the comprehensive energy efficiency is used to represent the ratio of the total energy supply of various types of equipment in the integrated energy system to the total energy consumption of various types of equipment; solving the planning optimization model using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, and determining the optimal capacity configuration of various types of equipment in the integrated energy system based on the optimal solution.

[0008] Optionally, in the integrated energy system planning optimization method according to the present invention, the planning optimization model of the integrated energy system is constructed with the lowest annual total cost of the integrated energy system as the first goal and the highest comprehensive energy efficiency of the integrated energy system as the second goal, including: constructing the lowest annual total cost objective function corresponding to the first goal based on the annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and wind and solar power abandonment penalty cost of the integrated energy system; constructing the highest comprehensive energy efficiency objective function corresponding to the second goal based on the ratio of the total energy supply of various types of equipment in the integrated energy system to the total energy consumption of various types of equipment; constructing the planning optimization model of the integrated energy system based on the lowest annual total cost objective function and the highest comprehensive energy efficiency objective function.

[0009] Optionally, in the integrated energy system planning optimization method according to the present invention, a planning optimization model of the integrated energy system is constructed based on the annual total cost minimum objective function and the comprehensive energy efficiency maximum objective function, including: constructing a planning optimization model of the integrated energy system based on the annual total cost minimum objective function, the comprehensive energy efficiency maximum objective function and multiple constraints of the integrated energy system; the multiple constraints include power balance constraints, system energy supply network constraints, system floor space constraints, energy storage unit equipment operation constraints, and other equipment component operation constraints; wherein the power balance constraints include electric load Power balance constraints, thermal power balance constraints; the system energy supply network constraints are used to limit the purchased electricity and gas of the integrated energy system to the corresponding ranges respectively; the system floor space constraints are used to indicate that the total installation floor space of all equipment in the integrated energy system does not exceed the upper limit of the usable area of ​​the integrated energy system; the energy storage unit equipment operation constraints include electrolyzer capacity constraints, fuel cell capacity constraints, heat storage tank heat storage constraints, and hydrogen storage tank hydrogen storage constraints; the remaining equipment component operation constraints are used to display the power and ramp rate of the remaining equipment components, and the remaining equipment components include gas turbines and fans.

[0010] Optionally, in the integrated energy system planning optimization method according to the present invention, the planning optimization model is solved using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, including: initializing the number of particles in the particle swarm, the initial position and initial velocity of each particle, the initial value of the optimal position of the particle, the initial value of the global historical optimal position of the particle swarm, initializing the initial temperature and termination temperature of the simulated annealing process, and setting the maximum number of iterations, wherein the particles are used to represent potential solutions in space; adjusting the inertia weight of the particles based on time-varying weights, and using a chaotic sequence to perform chaotic perturbation on the random parameters of the particle velocity in the iterative process to obtain random parameters of the chaotically perturbed particle velocity; iteratively updating the particle velocity and position based on the time-varying weights and the random parameters of the chaotically perturbed particle velocity; when all particles in the particle swarm are in the global historical optimal position of the particle swarm, performing a simulated annealing process on each particle to update the position of each particle, including: perturbing the current position of the particle to generate a candidate solution, and Determine the optimal position of the particle after iterative update; compare the fitness of the candidate position corresponding to the candidate solution with the optimal position of the particle after iterative update through the fitness function, wherein, if the fitness of the candidate position is better than the fitness of the optimal position of the particle after iterative update, then use the candidate position as the new optimal position of the particle; select the new optimal position of the particle with the best fitness from the new optimal positions of each particle in the particle swarm through the fitness function as the global current optimal position of the particle swarm, and compare the fitness of the global current optimal position of the particle swarm with the global historical optimal position of the particle swarm, if the fitness of the global current optimal position of the particle swarm is better, then use the global current optimal position of the particle swarm as the new global historical optimal position of the particle swarm; update the current temperature of the simulated annealing process, if the current temperature drops below the termination temperature, the current number of iterations reaches the maximum number of iterations, and the new global historical optimal position of the particle swarm meets the preset limit, then output the Pareto solution set; use the Topsis method to determine the optimal solution based on the Pareto solution set.

[0011] Optionally, the integrated energy system planning optimization method according to the present invention also includes: establishing a stepped carbon trading model that takes into account the carbon excess ratio, so as to determine the stepped carbon trading cost through the stepped carbon trading model, and the carbon excess ratio is used to represent the ratio of actual carbon emissions to carbon emission quotas.

[0012] Optionally, in the integrated energy system planning optimization method according to the present invention, the carbon excess ratio is calculated by the following formula:

[0013]

[0014] When the carbon excess ratio gradient is 0.1, the stepped carbon trading model is as follows:

[0015]

[0016] Among them, C JY represents the tiered carbon transaction cost; JY represents the unit price of carbon trading; α represents the price increase; θ represents the reward coefficient; E IES represents the carbon emission quota of the integrated energy system; Q IES Represents the actual carbon emissions of the integrated energy system.

[0017] Optionally, in the integrated energy system planning and optimization method according to the present invention, the calculation formulas for annual investment cost, energy purchase cost, annual operation and maintenance cost, and wind and solar curtailment penalty cost are as follows:

[0018]

[0019]

[0020] Among them, C ycost 、C buy 、C main 、C pt Respectively represent annual investment cost, energy purchase cost, annual operation and maintenance cost, and penalty cost for curtailing wind and solar power; represents the unit capacity investment cost of the kth type of equipment; D k represents the configuration capacity of the kth type of equipment; r and m represent the interest rate and service life in the annualized cost formula respectively; Respectively represent the electricity price and gas price in period t; Δt represents the unit time; O k represents the annual maintenance cost per unit capacity of equipment k; P k,D (t) represents the output of the kth device; β pt P represents the penalty coefficient for curtailing wind and solar power; pvwt (t) represents the wind and solar power curtailment at time t.

[0021] Optionally, in the integrated energy system planning and optimization method according to the present invention, the carbon emission sources in the integrated energy system include CHP units, gas boilers and purchased electricity.

[0022] Optionally, in the integrated energy system planning and optimization method according to the present invention, the various types of equipment include production equipment, energy conversion equipment and energy storage equipment; the integrated energy system includes a renewable energy power generation unit, a hydrogen energy storage unit, a CHP unit and coupling auxiliary equipment; wherein, the renewable energy power generation unit includes distributed photovoltaic equipment and wind turbine equipment, the hydrogen energy storage unit includes a fuel cell, a hydrogen storage tank and an electrolyzer, the CHP unit includes a gas turbine and a waste heat boiler, and the coupling auxiliary equipment includes a gas boiler and a heat storage tank.

[0023] According to one aspect of the present invention, a computing device is provided, comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be suitable for execution by the at least one processor, and the program instructions include instructions for executing the integrated energy system planning optimization method as described above.

[0024] According to one aspect of the present invention, a computer program product is provided, comprising a computer program / instruction, wherein the computer program / instruction implements the method described above when executed by a processor.

[0025] According to one aspect of the present invention, a readable storage medium storing program instructions is provided. When the program instructions are read and executed by a computing device, the computing device executes the integrated energy system planning optimization method as described above.

[0026] According to the technical solution of the present invention, a comprehensive energy system planning optimization method is provided. The method obtains the parameters, load data, and energy prices of various equipment in the comprehensive energy system. Then, a planning optimization model of the comprehensive energy system is constructed with the goal of minimizing the annual total cost of the comprehensive energy system (including the tiered carbon trading cost) and maximizing the comprehensive energy efficiency. The planning optimization model is solved using a simulated annealing chaos particle swarm complementary algorithm to obtain the optimal solution. Based on the optimal solution, the optimal capacity configuration of various equipment in the comprehensive energy system is determined. Based on this, it is possible to rationally plan the optimal capacity configuration of various equipment in the comprehensive energy system while meeting the system load requirements.

[0027] Furthermore, by constructing the highest objective function for comprehensive energy efficiency, it is possible to achieve organic coordination among the subsystems of the integrated energy system, improve the efficiency and stability of the entire system, and take into account the performance of the integrated energy system, thereby reducing the overall energy purchase demand of the system.

[0028] In addition, the stepped carbon trading model established according to the present invention can accurately describe the extent to which the actual carbon emissions of the integrated energy system exceed the carbon emission quota. Moreover, when the actual carbon emissions are less than the carbon emission quota, different gradients of rewards can be given based on the carbon excess ratio gradient, thereby further incentivizing system operators to reduce carbon emissions.

[0029] In addition, the improved simulated annealing chaos particle swarm complementary algorithm according to the present invention takes into account the impact of inertia weight on the algorithm's solution speed, adopts time-varying weights during the particle evolution process to balance the global optimization process; uses a chaotic sequence to perform chaotic perturbations on each particle during the iterative process, thereby avoiding being trapped in local extreme values; and introduces a simulated annealing mechanism to ensure that particles can escape the local optimal solution when trapped in the local optimum, thus achieving global search. In the process of solving the planning optimization model using the simulated annealing chaos particle swarm complementary algorithm, the random perturbations of the simulated annealing process can help escape the local optimal solution during the search process, increasing the chance of finding the global optimal solution. At the same time, the simulated annealing chaos particle swarm complementary algorithm's updates to particle velocities and positions help quickly search for potential high-quality solution areas in the solution space, improving the algorithm's optimization performance and robustness.

[0030] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To achieve the above and related purposes, the present invention describes certain illustrative aspects in conjunction with the following description and accompanying drawings, which indicate various ways in which the principles disclosed in the present invention can be practiced, and all aspects and their equivalents are intended to fall within the scope of the claimed subject matter. The above and other purposes, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout the present disclosure, the same reference numerals generally refer to the same parts or elements.

[0032] Figure 1 The basic structure and energy flow diagram of the integrated energy system 100 provided in an embodiment of the present invention are shown;

[0033] Figure 2 A schematic diagram of a computing device 200 provided according to an embodiment of the present invention is shown;

[0034] Figure 3 A schematic flow chart of a comprehensive energy system planning and optimization method 300 according to an embodiment of the present invention is shown;

[0035] Figure 4 A schematic diagram of the overall operation strategy of the integrated energy system in a surplus power scenario according to an embodiment of the present invention is shown;

[0036] Figure 5 A schematic diagram of the overall operation strategy of the integrated energy system in a no-surplus-power scenario according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0037] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0038] For an integrated energy system, in order to rationally plan the optimal capacity configuration of various types of equipment in the integrated energy system while meeting the system load demand, an embodiment of the present invention proposes an integrated energy system planning optimization method.

[0039] The following first introduces an integrated energy system 100 provided by an embodiment of the present invention.

[0040] Figure 1 The basic structure and energy flow diagram of the integrated energy system 100 provided according to an embodiment of the present invention are shown.

[0041] like Figure 1 As shown, integrated energy system 100 includes various devices and end-user load requirements. Specifically, these devices may include production equipment, energy conversion equipment, and energy storage equipment. The subsystems within integrated energy system 100 can interact and couple with each other to meet the electrical and thermal load requirements of end users.

[0042] In some embodiments, the integrated energy system 100 may specifically be an integrated energy system that takes into account wind, solar, hydrogen and storage.

[0043] In some embodiments, the integrated energy system 100 may be a park integrated energy system that couples hydrogen energy storage and renewable energy power generation. The integrated energy system 100 includes a renewable energy power generation unit, a hydrogen energy storage unit, a combined heat and power (CHP) unit, and coupling auxiliary equipment. Among them, the renewable energy power generation unit includes distributed photovoltaic equipment and wind turbine equipment. The hydrogen energy storage unit includes a fuel cell, a hydrogen storage tank, and an electrolyzer. One of the typical features of the integrated energy system 100 is multi-energy coupling and complementarity. The combined heat and power unit (CHP unit) is a typical device among them, which can improve the multi-energy complementary characteristics of the heating system. The combined heat and power unit (CHP unit) includes a gas turbine and a waste heat boiler. The gas turbine and the waste heat boiler are often closely connected in the energy system and are an important component of the combined heat and power unit (CHP unit). Coupling auxiliary equipment includes a gas boiler, a heat storage tank, an inverter, etc.

[0044] The park (integrated energy system 100) includes an internal power grid and natural gas pipeline network, and is connected to the external power grid and gas grid. The system's electricity and heat load requirements are met through electricity and gas purchases. Renewable energy is converted to AC power by photovoltaic and wind turbines, then converted to DC power by inverters and supplied to the electrolyzers. The electrolyzers produce hydrogen and store it in hydrogen storage tanks, consuming electricity and converting it into hydrogen and heat. The hydrogen storage tanks supply hydrogen to fuel cells, which in turn consume hydrogen to generate electricity and heat. This hydrogen storage unit as a whole enables a specialized form of "combined heat and power." Furthermore, some hydrogen from the hydrogen storage tanks can be mixed into the natural gas pipeline network and supplied to electricity and heat loads via CHP units and gas-fired boilers, somewhat reducing gas purchase requirements. Natural gas enters the CHP unit and is converted into electricity and heat by a gas turbine. The combination of waste heat boilers and gas turbines improves energy efficiency and reduces pollution. Furthermore, to meet higher winter heat load demands, gas-fired boilers and thermal storage tanks are incorporated. Heat from the heating network can be supplied directly to heat users or stored in thermal storage tanks, improving overall energy efficiency.

[0045] In the above-mentioned energy flow process, renewable energy power generation equipment and energy storage technology work together to achieve temporal and spatial output complementarity. Compared with the configuration of energy storage equipment in conventional energy systems, since hydrogen energy needs to undergo multiple energy form conversions during production, storage, and use, the energy utilization efficiency is low, and the investment economics of the water electrolysis hydrogen storage system is poor, which greatly limits the application and development of hydrogen energy. However, in a multi-energy complementary integrated energy system, as an auxiliary means of absorbing renewable energy power generation, it can have considerable added value and environmental benefits. In this system, the hydrogen energy storage unit demonstrates a high degree of operational flexibility and the ability of multi-energy storage and supply, which can effectively absorb clean energy, reduce the system's dependence on external energy, and thus improve the overall economic efficiency, environmental benefits, and energy efficiency.

[0046] In an embodiment of the present invention, a computing device may be configured to execute integrated energy system planning and optimization method 300 to rationally plan the optimal capacity configuration of various devices in the integrated energy system while meeting system load requirements. Integrated energy system planning and optimization method 300 of the present invention is described below.

[0047] The following introduces a computing device 200 provided by an embodiment of the present invention.

[0048] Figure 2 FIG. 2 shows a schematic diagram of a computing device 200 provided according to an embodiment of the present invention. Figure 2As shown, in a basic configuration, computing device 200 includes at least one processing unit 202 and system memory 204. According to one aspect, depending on the configuration and type of computing device, processing unit 202 can be implemented as a processor. System memory 204 includes, but is not limited to, volatile storage (e.g., random access memory), non-volatile storage (e.g., read-only memory), flash memory, or any combination of such memories. According to one aspect, system memory 204 includes an operating system 205.

[0049] According to one aspect, operating system 205 is suitable for controlling the operation of computing device 200, for example. Furthermore, examples may be practiced in conjunction with graphics libraries, other operating systems, or any other application programs, and are not limited to any particular application or system. Figure 2 The basic configuration is shown in FIG. 2 by those components within the dashed lines. According to one aspect, the computing device 200 has additional features or functionality. For example, according to one aspect, the computing device 200 includes additional data storage devices (removable and / or non-removable), such as magnetic disks, optical disks, or tapes. Such additional storage Figure 2 In the figure, removable storage device 209 and non-removable storage device 210 are shown.

[0050] As stated above, according to one aspect, a program module 203 is stored in the system memory 204. According to one aspect, the program module 203 may include one or more application programs, and the present invention is not limited to the type of application program. For example, the application program may include an email and contact application program, a word processing application program, a spreadsheet application program, a database application program, a slide show application program, a drawing or computer-aided application program, a web browser application program, etc.

[0051] In an embodiment according to the present invention, the program module 203 includes a plurality of program instructions for executing the integrated energy system planning and optimization method 300 of the present invention.

[0052] According to one aspect, examples may be practiced on a circuit comprising discrete electronic components, a packaged or integrated electronic chip containing logic gates, a circuit utilizing a microprocessor, or a single chip containing electronic components or a microprocessor. Figure 2Each or many components shown in can be integrated into a system on a chip (SOC) on a single integrated circuit to practice examples. According to one aspect, such an SOC device may include one or more processing units, a graphics unit, a communication unit, a system virtualization unit, and various application functions, all of which are integrated (or "burned") onto a chip substrate as a single integrated circuit. When operated via SOC, the functions described in the present invention can be operated via dedicated logic integrated with other components of the computing device 200 on a single integrated circuit (chip). Other technologies capable of performing logical operations (such as AND, OR, and NOT) can also be used to practice embodiments of the present invention, and the other technologies include but are not limited to mechanical, optical, fluid, and quantum technologies. In addition, embodiments of the present invention can be practiced in a general-purpose computer or in any other circuit or system.

[0053] According to one aspect, the computing device 200 may also have one or more input devices 212, such as a keyboard, a mouse, a pen, a voice input device, a touch input device, etc. Output devices 214 may also be included, such as a display, speakers, a printer, etc. The aforementioned devices are examples, and other devices may also be used. The computing device 200 may include one or more communication connections 216 that allow communication with other computing devices 218. Examples of suitable communication connections 216 include, but are not limited to: RF transmitter, receiver, and / or transceiver circuitry; Universal Serial Bus (USB), parallel, and / or serial ports.

[0054] As used in the present invention, the term computer-readable medium includes computer storage media. Computer storage media can include volatile and non-volatile, removable and non-removable media implemented with any method or technology for storing information (e.g., computer-readable instructions, data structures, or program modules). System memory 204, removable storage device 209, and non-removable storage device 210 are all examples of computer storage media (i.e., memory storage). Computer storage media can include random access memory (RAM), read-only memory (ROM), electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical storage, cassette tape, magnetic tape, magnetic disk storage or other magnetic storage device, or any other product that can be used to store information and can be accessed by computing device 200. According to one aspect, any such computer storage medium can be a part of computing device 200. Computer storage media does not include carrier waves or other propagated data signals.

[0055] According to one aspect, communication media is implemented by computer-readable instructions, data structures, program modules, or other data in a modulated data signal (e.g., a carrier wave or other transport mechanism), and includes any information delivery media. According to one aspect, the term "modulated data signal" describes a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media.

[0056] In an embodiment according to the present invention, the computing device 200 is configured to execute the integrated energy system planning and optimization method 300. The computing device 200 includes one or more processors and one or more readable storage media storing program instructions. When the program instructions are configured to be executed by the one or more processors, the computing device executes the integrated energy system planning and optimization method 300 according to the embodiment of the present invention.

[0057] In some embodiments, the computing device 200 executing the integrated energy system planning and optimization method 300 in the embodiment of the present invention may be a terminal or a server.

[0058] The integrated energy system planning optimization method 300 in the embodiment of the present invention is described in detail below.

[0059] Figure 3 FIG. 3 is a flow chart showing a method 300 for optimizing integrated energy system planning according to an embodiment of the present invention. Figure 3 As shown, the integrated energy system planning optimization method 300 includes the following steps 310 to 330.

[0060] Step 310: The computing device 200 may obtain various equipment parameters, load demand data, and energy prices in the integrated energy system.

[0061] In an embodiment of the present invention, load demand data includes electrical load demand data and thermal load demand data. Various equipment parameters include relevant parameters such as the investment and construction costs of various types of equipment and equipment energy efficiency. Specifically, various equipment parameters may include energy conversion equipment parameters and energy storage equipment parameters. Energy conversion equipment parameters include the equipment type, investment cost, operation and maintenance cost, equipment energy efficiency, and service life of the energy conversion equipment; energy storage equipment parameters include the equipment type, investment cost, operation and maintenance cost, self-damage rate, operating efficiency, and service life of the energy storage equipment.

[0062] Energy prices include electricity prices (electricity prices) and natural gas prices (natural gas prices). This primarily takes into account the need to purchase electricity from the external power grid and natural gas from external sources when the integrated energy system's internal energy supply is insufficient. In some embodiments, the electricity price may be determined based on the time-of-use electricity price published by the power management department, while the natural gas price may be the local average price, for example, 2.7 yuan / m³.

[0063] Step 320, the computing device 200 can construct a planning optimization model for the integrated energy system based on various equipment parameters, load demand data, and energy prices, with the lowest annual total cost of the integrated energy system as the first goal and the highest comprehensive energy efficiency of the integrated energy system as the second goal. The annual total cost includes the annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and wind and solar power abandonment penalty cost. Comprehensive energy efficiency is used to represent the ratio of the total energy supply of various types of equipment in the integrated energy system to the total energy consumption of various types of equipment (that is, the ratio of the total energy output of various types of equipment to the total energy input of various types of equipment).

[0064] It's important to note that comprehensive energy efficiency represents the efficiency with which a multi-energy system, with its complementary advantages, utilizes multiple energy sources. Comprehensive energy efficiency is the ratio of the total energy supply to the total energy consumption of each device in the system, or the ratio of the total energy output to the total energy input. This total energy does not include renewable energy generation; purchased electricity and gas are considered as input energy.

[0065] In step 330, the computing device 200 can use a simulated annealing chaos particle swarm optimization algorithm to solve the planning optimization model to obtain an optimal solution. Furthermore, the optimal capacity configuration of various devices in the integrated energy system can be determined based on the optimal solution.

[0066] In this embodiment of the present invention, the optimal solution corresponds to an optimal planning result, which includes the optimal capacity configuration of various types of equipment in the integrated energy system. Based on this, in step 330, it can be determined based on the optimal solution that the optimal planning result includes the optimal capacity configuration of various types of equipment in the integrated energy system (e.g., including CHP units, wind turbine equipment, photovoltaic equipment, etc.).

[0067] It should be noted that the simulated annealing chaos particle swarm complementary algorithm is obtained by improving the particle swarm algorithm in the present invention, and the specific algorithm process will be described in detail in the following embodiments.

[0068] In some embodiments, mathematical modeling can be performed in advance for the renewable energy power generation unit, hydrogen energy storage unit, combined heat and power unit (CHP unit), and coupling auxiliary equipment in the integrated energy system 100, as follows.

[0069] 1) Mathematical modeling of renewable energy power generation units

[0070] Renewable energy generation units include photovoltaic equipment and wind turbines. Photovoltaic power generation utilizes the photovoltaic effect to convert solar energy into electrical energy through semiconductor cells. In practical applications, the output power of photovoltaic power generation is affected by various factors, including light intensity and the area illuminated.

[0071] When modeling photovoltaic equipment, the component surface temperature, radiation intensity, and external ambient temperature can be used to characterize the output power of photovoltaic power generation, as follows:

[0072]

[0073] Where, P PV (t) is the power generation of the photovoltaic power generation system at time t; f PV is the performance attenuation parameter of the photovoltaic module, which is taken as 0.8; is the rated power of photovoltaic modules under standard conditions; G v (t) is the actual light intensity during the t period; G S The light intensity received by the component under standard environment is 1kW / m 2 ;k PV is the adjustment coefficient between power and temperature; T c 、T r are the actual temperature and standard temperature of the photovoltaic cell respectively. The standard temperature is 25℃.

[0074] Wind power generation is the process of converting wind energy into electrical energy. The core component is the wind turbine. When the actual wind speed in the environment reaches or exceeds the preset cut-in wind speed threshold of the wind turbine, the device starts normal operation mode. In this process, wind energy can be converted into mechanical energy, driving the wind turbine blades to rotate, and then driving the generator rotor to convert energy. According to the relationship between wind speed and wind power output power, considering that the wind power generation system can be connected to the grid, the cut-in wind speed v is set. i and cut-out wind speed v o When the wind turbine is generating power, the relationship between wind speed and output power can be established as follows:

[0075]

[0076] Where, P WT (t) is the power generation of the wind turbine power generation system at time t; v i is the cut-in wind speed; v o is the cut-out wind speed; v(t) is the actual wind speed in the area where the wind turbine is located at time t; is the rated power of the wind turbine; v r is the rated wind speed of the wind turbine.

[0077] 2) Mathematical modeling of CHP unit

[0078] Cogeneration technology aims to improve energy efficiency by effectively recovering waste heat generated during the power generation process. Under traditional power generation systems, a large amount of waste heat is directly discharged into the atmosphere in the form of flue gas, resulting in low energy efficiency. Cogeneration systems utilize waste heat recovery devices to recover and utilize waste heat to meet the system's thermal load requirements. During operation, the generator sets are powered by natural gas, which is converted into mechanical energy through a combustion process. The recovered waste heat can be used for a variety of purposes, such as heating domestic water, providing building heating and cooling, and producing industrial steam. By comprehensively utilizing waste heat, cogeneration systems significantly improve the overall energy efficiency of the entire energy system. Compared to traditional independent power and heat supply methods, cogeneration technology has achieved a qualitative leap in energy efficiency, making a significant contribution to energy conservation, emission reduction, and sustainable development.

[0079] The CHP power generation unit mainly includes gas internal combustion engines, gas turbines, steam turbines and other equipment. Internal combustion engines have high energy utilization efficiency, but their application in large CHP systems is limited. Gas turbines have more advantages in power output and efficiency and are suitable for large CHP systems. Steam turbines are widely used in cogeneration, but they have slow startup speeds and long response times. Gas turbines have faster startup and response speeds, which can better meet the peak load requirements of the power grid. In addition, the thermal energy utilization efficiency of gas turbines is higher than that of steam turbines. Gas turbines can generate electricity and heat energy simultaneously during operation, realizing cascaded energy utilization. Its heat energy can be recycled and used in heating, hot water or industrial processes, thereby improving energy utilization efficiency. The present invention combines gas turbines with waste heat boilers to model and analyze the cogeneration unit.

[0080]

[0081] Where, F GT (t) is the gas consumption of the gas turbine at time t; P GT (t) is the power generated by the gas turbine at time t; Δt is the time step, which is 1h in the present invention; η GT is the power generation efficiency of the gas turbine; L gas is the calorific value of natural gas (kWh / m 3 );γ hl is the heat loss coefficient of the gas turbine; Q GT (t) is the heat generation power of the gas turbine at time t; μ rwhb is the waste heat steam recovery efficiency; η WHB is the heat generation efficiency of the waste heat boiler; P WHB (t) is the heat production power of the waste heat boiler at time t.

[0082] In the cogeneration CHP unit of the present invention, natural gas and part of hydrogen provide fuel for the power generation unit. Waste heat is recovered by a waste heat boiler and transferred to the system heat network cycle to meet the heat load demand. Hydrogen is a high calorific value fuel. By adding hydrogen to natural gas, the total energy of the fuel can be increased, thereby improving energy utilization efficiency. Since hydrogen produces almost no carbon emissions during the combustion process and the combustion products are pure, adding hydrogen to natural gas can reduce overall carbon dioxide emissions. Therefore, hydrogen-mixed gas units have received widespread attention and have been put into actual operation in many places. In the process of consuming hydrogen-mixed gas for thermal power production, in order to ensure the safe and stable operation of the CHP unit, the volume ratio of hydrogen mixed in its gas is generally controlled within the range of 10% to 20%. After considering the hydrogen mixed in the gas network, the mathematical model of the entire gas unit in the system is as follows:

[0083]

[0084] Where Q gas (t) is the input power of the gas generator set at time t; P gas (t) is the output power of the gas generator set at time t; η gas is the energy utilization efficiency of the gas generator set; V gas (t) is the natural gas consumption at time t; is the hydrogen mixing ratio at time t; V H2 (t) is the amount of hydrogen consumed by the gas generator set at time t; L H2 is the calorific value of hydrogen (kWh / m 3 ).

[0085] 3) Mathematical modeling of hydrogen energy storage units

[0086] The hydrogen energy storage unit is a key equipment combination for constructing a low-carbon park integrated energy system according to the present invention. It is specifically composed of an electrolyzer, a hydrogen storage tank and a fuel cell. The electrolyzer and the fuel cell can participate in the heat network circulation with hot water as the working fluid while realizing power to gas to power (P2G2P), meeting part of the heat load demand. The role of the hydrogen storage tank is to compress and store hydrogen. The hydrogen energy storage unit has the following three main operating modes: First, when there is surplus electricity in the system or the electricity price is at a low point, the electrolyzer starts to produce hydrogen and stores it in the gas storage tank, which is converted into hydrogen and heat energy by consuming electricity; second, when the power grid is insufficient or the electricity price is at a peak price, the amount of electricity purchased should be reduced, and the hydrogen storage tank provides hydrogen to the fuel cell, which consumes hydrogen to provide electricity and heat energy, realizing a special form of "combined heat and power" through a heat exchanger; third, considering that the hydrogen in the hydrogen storage tank can also be partially mixed into the gas network, the electric heat load is supplied through CHP and gas boilers, which reduces the system's gas purchase demand to a certain extent. The following is a detailed analysis of the mathematical model of the hydrogen energy storage unit and its multi-energy storage and supply capabilities.

[0087] Electrolyzer Modeling:

[0088] The primary site for hydrogen production by water electrolysis is the electrolyzer. Efficient hydrogen production using electrolyzers is key to renewable energy hydrogen production. Mainstream water electrolysis hydrogen production technologies include alkaline water electrolysis (ALK), anion exchange membrane electrolysis (AEM), proton exchange membrane electrolysis (PEM), and solid oxide electrolysis (SOEC). A comparison of their technical characteristics is shown in Table 1. Currently, alkaline electrolyzer technology has achieved commercialization and a relatively mature industry chain. However, it is not suitable for intermittent power sources such as wind and solar power, and its hydrogen purity is lower than that of PEM and SOEC electrolyzers. PEM water electrolysis hydrogen production technology uses a proton exchange membrane as the solid electrolyte. Compared to ALK electrolyzers, PEM electrolyzers offer advantages such as high current density, high hydrogen purity, and fast response time, making them more suitable for integration with wind, solar, and energy storage technologies. With the acceleration of domestic substitution, the cost of proton exchange membranes in my country is expected to further decline. The core of the AEM electrolyzer consists of cathode materials, anode materials, and anion exchange membrane. Compared to ALK and PEM technologies, AEM combines the advantages of both. However, anion exchange membranes currently struggle to balance efficiency and equipment lifespan, and AEM electrolyzers are still in the laboratory development stage. SOEC uses solid oxides as electrolytes to electrolyze gaseous water at high temperatures. While safe, pollution-free, and highly efficient, the high-temperature environment places high demands on materials, sealing, and assembly technology. The present invention can employ a proton exchange membrane electrolyzer as the water electrolysis device.

[0089] The amount of hydrogen output by the proton exchange membrane electrolyzer is mainly related to the power of the power supply and the conversion efficiency of the electrolyzer. The electrolyzer mainly generates hydrogen by electrolyzing water for storage. Its hydrogen production efficiency is:

[0090]

[0091] Where η el is the hydrogen production efficiency of the electrolyzer; P et (t) is the power consumption of the electrolytic cell at time t; H el (t) is the hydrogen production power of the electrolyzer at time t.

[0092] The present invention considers expressing the three energy forms (electrical and thermal hydrogen) in the hydrogen energy storage unit in a unified form of power / energy. The actual hydrogen volume can be obtained by converting the energy / power of hydrogen into its own calorific value. At the same time, considering the electro-thermal coupling characteristics, the electrothermal balance equation of the electrolyzer is as follows:

[0093]

[0094] Where, T el (t) is the electrolyte temperature at time t; C el is the heat capacity of the electrolytic cell; is the heat loss power of the electrolytic cell environment; It absorbs heat for the cooling circulating water of the electrolytic cell.

[0095] The cooling water of the electrolyzer transfers heat energy through a heat exchanger, contributing to the heat network of the park's integrated energy system. This means that the heat absorbed by the cooling water is equivalent to the external heating power supplied by the electrolyzer. In Equation 2-8, the electrolyzer's thermoelectric balance equation can be expressed as follows: the electrolyzer's power consumption is equivalent to the sum of the hydrogen production power, the ambient heat loss power, the external heating power, and the heat absorbed by the electrolyte chemical reaction. Similarly, this equation can be used to derive the thermoelectric balance equation for the fuel cell described below.

[0096] Fuel Cell Modeling:

[0097] The working mechanism of hydrogen fuel cells relies primarily on the chemical reaction between hydrogen and oxygen, converting chemical energy into electrical energy. In recent years, rapid advances in hydrogen fuel cell technology have accelerated the development of the hydrogen energy sector. These fuel cells offer numerous advantages, including compact size, environmental friendliness, low noise operation, and high energy conversion efficiency. They have also found widespread application in a wide range of fields, including aviation and automotive. The core components of a hydrogen fuel cell are the anode plate, cathode plate, and electrolyte membrane. During operation, hydrogen and air flow through specific intake lines to the anode and cathode, respectively, passing through the diffusion layers on the electrodes and ultimately reaching the electrolyte membrane. On the anode side, hydrogen is decomposed into hydrogen ions and electrons by a catalyst. A large number of electrons accumulate to form the negative electrode of the fuel cell. The hydrogen ions travel through the electrolyte membrane to the cathode, where they react with oxygen to produce water. During this process, the cathode serves as the positive electrode of the fuel cell.

[0098] When the system load is at its peak, the power grid is insufficient, or the electricity price is at peak times, the fuel cell will consume hydrogen to achieve combined heat and power. The fuel cell's hydrogen consumption and power generation efficiency is:

[0099]

[0100] Where η fc is the power generation efficiency of the fuel cell; P fc (t) is the power generated by the fuel cell at time t; H fc (t) is the hydrogen power consumed by the fuel cell at time t.

[0101] Similar to the electrolyzer, the thermoelectric balance equation for the fuel cell is as follows:

[0102]

[0103] Where, T fc (t) is the operating temperature of the fuel cell; C fc is the heat capacity of the fuel cell; is the heat loss power of the fuel cell environment; It is the heat absorption power of the electrolytic cell cooling circulating water.

[0104] Hydrogen storage tank modeling:

[0105] Part of the hydrogen produced by the electrolyzer is mixed into the natural gas pipeline network, and the other part enters the hydrogen storage tank. Hydrogen storage tanks are similar to heat storage tanks and energy storage batteries. They can store excess hydrogen and release it when needed to supply hydrogen fuel cells for power generation, thereby improving the system's regulation capabilities. This article uses low-pressure hydrogen storage, with a pressure of generally 2 to 5 MPa. It is generally used in scenarios where large-scale hydrogen storage or low gas storage density requirements are required. Such as industrial hydrogen storage, seasonal hydrogen storage, etc. Combined with the conversion relationship between hydrogen power and volume, the process of storing and releasing hydrogen in hydrogen storage tanks is described in terms of energy:

[0106]

[0107] Where H SOC (t) is the hydrogen storage ratio of the hydrogen storage tank at time t; H hs (t) is the hydrogen storage capacity of the hydrogen storage tank at time t; Q hs is the rated capacity of the hydrogen storage tank; is the rate of change of hydrogen storage ratio in the hydrogen storage tank at time t; For hydrogen storage efficiency; To release hydrogen efficiency; is the rate of change of hydrogen storage ratio in the hydrogen storage tank at time t; is the hydrogen doping power at time t.

[0108] 4) Mathematical modeling of coupling auxiliary equipment

[0109] Gas Boiler Modeling:

[0110] Gas-fired boilers are gas-heat coupled devices. Compared to electric and oil-fired boilers, they offer higher energy conversion efficiency and lower costs. In a park's integrated energy system, combining gas-fired boilers with CHP units can achieve cascaded energy utilization. Specifically, gas-fired boilers can serve as supplementary equipment to CHP units, providing additional heat when the heat generated by the CHP unit is insufficient to meet demand. The operating model is as follows:

[0111]

[0112] Where η GB is the energy conversion efficiency of the gas boiler; Q GB (t) is the heating power of the gas boiler at time t; The lower and upper limits of the heating power of the gas boiler; It is the lower and upper limits of the gas boiler's ramp output.

[0113] Energy storage battery modeling:

[0114] The incorporation of a large amount of renewable energy into the power grid has brought peak-shaving and frequency-regulating pressure to the power system, while reducing the overall regulation capability of the system, making it unstable in the face of uncertain risks, thus posing new challenges to the safe and stable operation of the power system. As an effective solution, energy storage batteries can store surplus electricity generated by new energy power generation equipment and charge it through the power grid when needed. This type of battery has a high degree of flexibility and strong support capabilities for the power grid. In order to ensure that it can continue to supply power during peak power load periods, it is necessary to ensure that the battery's storage capacity reaches or is close to the upper limit. During the operation of energy storage batteries, there are two processes: storing electrical energy and releasing electrical energy. Constructing an output model for energy storage batteries also requires separate research from the above two processes. The mathematical model for storing electrical energy is:

[0115]

[0116] The mathematical model of energy storage battery releasing electrical energy is:

[0117]

[0118] Where, δ e is the power consumption rate of the energy storage device itself; P i e 、 Energy storage and energy release power for energy storage batteries; E SOC(t+1) 、E SOC(t) The battery capacity during the period t+1 and t; The efficiency of storing and releasing electrical energy; is the rated capacity of the energy storage battery.

[0119] Thermal storage tank modeling:

[0120] Thermal storage tanks function similarly to energy storage batteries in integrated energy systems, storing and releasing thermal energy. They can balance and regulate the system's thermal load and provide strong thermal support. They typically consist of a sealed container and a storage medium. They utilize the density difference between cold and hot water to store both simultaneously within the tank, achieving both heat storage and release. The mathematical model for storing and releasing thermal energy is:

[0121]

[0122] Where Q HS (t) is the heat capacity of the heat storage tank at time t; τ loss is the heat loss rate of the heat storage tank; is the charging efficiency of the thermal storage tank; Q is the heat released by the heat storage tank between t0 and t; HS(t0) is the heat storage capacity of the heat storage tank at the initial time t0; is the heat storage capacity of the heat storage tank between t0 and t; is the heat release efficiency of the heat storage tank.

[0123] In some embodiments, the two scenarios of surplus power and no surplus power can be divided according to whether the integrated energy system can meet the user's electricity load demand (whether there is surplus power), to study planning strategies and formulate operation strategies suitable for different scenarios. The heat generation of the equipment is determined by analyzing the size of the power supply load within the integrated energy system. In this way, the system gives priority to meeting the demand of the electricity load, generates electricity through fuel cells, and generates heat energy at the same time. If the heat generated cannot meet the demand, it can be supplemented by other forms of heat sources, such as heat storage tanks, CHP units and gas boilers. The hydrogen energy storage unit can also be regarded as a cogeneration device. As a storage device different from conventional forms, it determines its own operation strategy according to the local peak and valley time-of-use electricity prices and the output of new energy power generation. The electrolyzer and fuel cell can provide heat to the system while working, reducing the system's energy purchase demand and further improving the reliability of energy supply.

[0124] Figure 4 A schematic diagram of the overall operation strategy of the integrated energy system in a surplus power scenario according to an embodiment of the present invention is shown; Figure 5 A schematic diagram of the overall operation strategy of the integrated energy system in a no-surplus-power scenario according to an embodiment of the present invention is shown.

[0125] like Figure 4 As shown in the figure, when the integrated energy system can meet the user's electricity load demand, that is, when there is surplus electricity within the integrated energy system, there is no need to consider peak and valley electricity prices. Only the heat load demand can be considered to formulate a reasonable operation strategy. The integrated energy system can prioritize water electrolysis to produce hydrogen. The cooling circulating water of the electrolyzer device begins to absorb heat and transfer heat to the heat network. If the heat load demand is met at this time, the thermal storage tank will store heat. When there is a gap in the heat load, the thermal storage tank will make up for the shortfall in the heat load. If the heat load demand is still not met, natural gas will need to be purchased. The hydrogen storage tank will release some hydrogen and mix it into the natural gas pipeline network. The CHP unit and gas boiler will provide heat energy to meet the heat load demand of the integrated energy system.

[0126] like Figure 5As shown in the figure, when there is a gap in the electricity load within the integrated energy system and it cannot meet the electricity load demand of users, that is, when there is no surplus electricity in the integrated energy system, it is necessary to adjust the operation strategy of the integrated energy system in combination with the peak and valley electricity prices and the seasonal output characteristics of renewable energy. First, considering the situation where the thermal load can be met, when it is in the peak electricity price period, the level of electricity purchase should be reduced, and fuel cell power generation should be given priority. If it still cannot meet the electricity load demand, the heat-to-electricity ratio can be adjusted through the CHP unit to increase the proportion of electricity output and make up for the electricity load gap; when it is in the valley electricity price period, the role of hydrogen energy storage should be brought into play, and a large amount of hydrogen should be produced and stored. At the same time, the heat energy generated during the hydrogen production process is stored in the heat storage tank.

[0127] Secondly, consider the situation where the heat load cannot be met. In this case, there is a gap in both the electrical load and the thermal load. During peak electricity prices, fuel cells should be used to supply electricity and heat to fill the load gap. If the load gap still exists, heat storage tanks will be needed for heat supply and external electricity purchases will be required. Among them, during off-peak electricity prices, a large amount of hydrogen should be produced and stored until it is full. After the heat load gap is met, the residual heat is collected by the heat storage tank. If the heat load gap is still not met, natural gas will need to be purchased and CHP units and gas boilers will be used for heating. During normal electricity prices, the actual operation of the system should be combined with more consideration of the seasonal output characteristics of renewable energy. Among them, during periods of abundant clean energy, hydrogen storage can be used to convert excess electricity into hydrogen energy for storage, and provide heat energy in the electricity-hydrogen-electricity conversion process to meet the heat load demand and fully absorb renewable energy. During periods of scarcity of clean energy, the use of hydrogen storage should be reduced, and more use should be made of fuel cells and CHP units to meet the electricity and heat load demand.

[0128] In some embodiments, before constructing the planning optimization model, a tiered carbon trading model that considers the carbon excess ratio can be pre-established. This allows the tiered carbon trading costs to be determined through the tiered carbon trading model and incorporated into the annual total cost. The carbon excess ratio represents the ratio of actual carbon emissions to carbon emission quotas. Thus, the tiered carbon trading costs within the annual total cost can be determined using the tiered carbon trading model.

[0129] It's important to note that Carbon Emissions Trading (CET) treats CO2 emission rights as a commodity, with buyers and sellers trading them through a carbon trading platform. This can encourage assessed entities to reduce their carbon emissions through cost incentives, ultimately achieving the goal of reducing total carbon emissions and controlling greenhouse gas emissions.

[0130] The tiered carbon trading mechanism consists of carbon emission quotas, actual carbon emissions, and tiered carbon trading costs that take into account excess emissions. In the power industry, my country's current carbon trading mechanism primarily uses a baseline method to allocate carbon emission quotas. This baseline method stipulates that units of the same model have the same emission standards, ensuring fairness in the initial allocation of carbon emission rights. If actual carbon emissions are less than the carbon emission quota, some carbon emission quotas can be sold to generate revenue; otherwise, some carbon emission rights must be purchased to meet assessment requirements.

[0131] In some embodiments of the present invention, the carbon emission sources in the integrated energy system 100 include CHP units, gas boilers, and purchased electricity. Among them, the grid carbon emission factor is the ratio of the total carbon emissions of all power generation companies in a certain area to the total power supply of the local power grid. The actual carbon emissions of gas-fired unit-related equipment are calculated based on the output power of the equipment, rather than the gas purchase volume and the CO2 generated by the combustion of natural gas per unit volume, because the electrolyzer and hydrogen storage tank will send part of the hydrogen into the system gas network, and the combustion products of hydrogen do not contain CO2. Based on this, a mathematical model can be established for the initial carbon emission quota and the actual carbon emissions.

[0132] The mathematical model of the initial carbon emission quota is as follows:

[0133] E IES =E buy +E CHP +E GB (14)

[0134]

[0135] Where, E IES 、E buy 、E CHP 、E GB represent the carbon emission quotas (t) of the integrated energy system, purchased electricity, CHP unit, and gas boiler, respectively; Respectively represent the carbon emission quota per unit of purchased electricity power and per unit of thermal power of energy supply equipment (t); Represents the electrothermal power conversion parameter; P i (t) represents the purchased electricity power at time t; T represents the planning period (h).

[0136] The mathematical model of actual carbon emissions is as follows:

[0137]

[0138] Where Q IES , Q buy 、 represent the actual carbon emissions of the integrated energy system, purchased electricity, and natural gas, respectively; represents the carbon emission factor of the power grid (kg / kWh); G i (t) represents the gas purchase volume rate; Indicates the carbon emission coefficient of natural gas (kg / m 3 ).

[0139] Under the existing tiered carbon trading mechanism, the length of carbon emission intervals is determined by the amount of emissions exceeding the carbon emission quota. If integrated energy systems with widely varying scales use the same tiered carbon trading model, the length of the intervals becomes difficult to quantify (when the carbon trading interval is too long, the tiered carbon trading mechanism's constraint on the system's carbon emissions becomes weaker). Furthermore, a tiered carbon trading mechanism based on interval length cannot accurately describe the extent to which a system's actual carbon emissions exceed its carbon emission quota.

[0140] Therefore, in some embodiments of the present invention, before constructing the planning optimization model, a tiered carbon trading model can be pre-established that takes into account the carbon excess ratio (the ratio of actual carbon emissions to carbon emission quotas). By dividing the carbon excess ratio into gradients, the larger the carbon excess ratio, the higher the purchase price of carbon emission rights in the corresponding interval.

[0141] The carbon excess ratio can be calculated using the following formula:

[0142]

[0143] When the carbon excess ratio gradient is 0.1, the stepped carbon trading model is as follows:

[0144]

[0145] Where C JY represents the tiered carbon transaction cost; JY represents the unit price of carbon trading; α represents the price increase, which is 20%; θ represents the reward coefficient, which is 0.2. IES represents the carbon emission quota of the integrated energy system; Q IES Represents the actual carbon emissions of the integrated energy system.

[0146] As mentioned above, in some embodiments, the carbon emission sources in the integrated energy system include CHP units, gas boilers, and purchased electricity. The carbon emission quota E of the integrated energy system is IES It can be calculated according to the above formulas (14)-(17). IES It can be calculated according to the above formulas (18)-(20).

[0147] It can be understood that the stepped carbon trading model established according to the embodiment of the present invention can accurately describe the extent to which the actual carbon emissions of the integrated energy system exceed the carbon emission quota. Moreover, when the actual carbon emissions are less than the carbon emission quota, different gradients of rewards can be given based on the carbon excess ratio gradient, thereby further incentivizing system operators to reduce carbon emissions.

[0148] The tiered carbon trading model established according to the present invention can determine the tiered carbon trading costs, and further, when constructing a planning optimization model, the tiered carbon trading costs can be included in the annual total cost.

[0149] Specifically, in some embodiments, the specific method of constructing the planning optimization model of the integrated energy system with the lowest annual total cost of the integrated energy system as the first goal and the highest comprehensive energy efficiency of the integrated energy system as the second goal is as follows: first, based on the annual investment cost, energy purchase cost, annual operation and maintenance cost, ladder carbon trading cost (i.e., the ladder carbon trading cost determined by the above-mentioned ladder carbon trading model), and wind and solar power abandonment penalty cost of the integrated energy system, a target function of the lowest annual total cost corresponding to the first goal can be constructed; and, based on the ratio of the total energy supply of various types of equipment in the integrated energy system to the total energy consumption of various types of equipment, a target function of the highest comprehensive energy efficiency corresponding to the second goal can be constructed. Furthermore, based on the target function of the lowest annual total cost and the target function of the highest comprehensive energy efficiency, a planning optimization model of the integrated energy system can be constructed.

[0150] The annual total cost minimum objective function constructed based on the annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and wind and solar curtailment penalty cost of the integrated energy system is as follows:

[0151] minf1=C ycost +C buy +C main +C JY +C pt (twenty three)

[0152] Where C ycost 、C buy 、C main 、C JY 、C pt They respectively represent the annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and penalty cost for wind and solar power curtailment of the integrated energy system.

[0153] Among them, the tiered carbon trading cost C JY The calculation formula of is shown in the above formulas (21)-(22).

[0154] Annual investment cost C ycost , energy purchase cost C buy , Annual operation and maintenance cost Cmain , Penalty cost for curtailing wind and solar power C pt The specific calculation formula is as follows:

[0155]

[0156] Where, represents the unit capacity investment cost of the kth type of equipment; D k represents the configuration capacity of the kth type of equipment; r and m represent the interest rate and service life in the annualized cost formula respectively; They represent the electricity price and gas price in time period t respectively; Δt represents the unit time, which can be 1h; O k represents the annual maintenance cost per unit capacity of equipment k; P k,D (t) represents the output of the kth device; β pt P represents the penalty coefficient for curtailing wind and solar power, which is 0.2 yuan / kWh; pvwt (t) represents the wind and solar power curtailment at time t.

[0157] It should be noted that the maximum comprehensive energy efficiency objective function represents the efficiency level of a multi-energy complementary integrated energy system. This objective function enables the coordinated utilization of various subsystems within the integrated energy system, improving overall system efficiency and stability while also balancing system performance and reducing the system's overall energy procurement needs.

[0158] Based on the ratio of the total energy supply of various types of equipment in the integrated energy system to the total energy consumption of various types of equipment, the highest comprehensive energy efficiency objective function is constructed as follows:

[0159]

[0160] Where G k Indicates the energy supply of the kth type of equipment; W j Indicates the energy consumption of the kth type of equipment.

[0161] In addition, in some embodiments, when constructing a planning optimization model, the integrated energy system needs to meet multiple constraints. That is, the planning optimization model of the integrated energy system can be constructed based on the objective function of minimizing the total annual cost, the objective function of maximizing the comprehensive energy efficiency, and multiple constraints of the integrated energy system. The multiple constraints of the integrated energy system include power balance constraints, system energy supply network constraints, system floor space constraints, energy storage unit equipment operation constraints, and other equipment components (including gas turbines, fans, etc.) operation constraints.

[0162] Among them, the power balance constraints include electric load power balance constraints and thermal power balance constraints.

[0163] The power balance constraint of the electric load is shown in formula (29).

[0164] P i (t)+P PV (t)+P WT (t)+P GT (t)+P fc (t) = P load (t)+P et (t) (29)

[0165] Where, P load (t)——electricity load demand at time t.

[0166] The thermal power balance constraint is shown in formula (30).

[0167]

[0168] Where H load (t) represents the heat load demand at time t.

[0169] The system energy supply network constraints are used to limit the purchased energy (purchased electricity and purchased gas) of the integrated energy system to the corresponding range. The system energy supply network constraints are shown in formula (31).

[0170]

[0171] Where, Indicates the maximum volume limit rate of gas purchase; P i max Indicates the maximum limit on purchased electricity power.

[0172] When planning and constructing an integrated energy system, it is necessary to consider the number of devices selected for the integrated energy system and the space required for the devices (installation area). Specifically, the system area constraint is used to indicate that the total installation area of ​​all devices in the integrated energy system cannot exceed the upper limit of the usable area of ​​the integrated energy system, as shown in Equation (32).

[0173]

[0174] Where, represents the installation area of ​​the kth device; Indicates the upper limit of the usable area of ​​the integrated energy system.

[0175] The operating constraints of the energy storage unit equipment include the electrolyzer capacity constraint, the fuel cell capacity constraint, the heat storage capacity constraint of the heat storage tank, and the hydrogen storage capacity constraint of the hydrogen storage tank, as shown in formula (33).

[0176]

[0177] Where Y el Indicates the maximum capacity of the electrolytic cell; Y fc Indicates the maximum capacity of the fuel cell; Indicates the minimum and maximum heat storage capacity of the heat storage tank; Indicates the minimum and maximum hydrogen storage ratios of the hydrogen storage tank.

[0178] The operating constraints of the remaining equipment components are used to display the power and ramp rate of the remaining equipment components (including gas turbines, fans, etc.), as shown in formula (34).

[0179]

[0180] Where Q k (t), Q k (t-1) represents the output power of the kth type of equipment at time t and the output power at time t-1 respectively; Respectively represent the minimum power and maximum power of the kth category equipment; They represent the ramp rate of reducing output and the ramp rate of increasing output of the k-th type of equipment respectively.

[0181] In some embodiments, a particle swarm algorithm can be used to solve the planning optimization model. The particle swarm algorithm uses the foraging behavior of bird flocks as a prototype. Through mutual learning between particles, it continuously approaches the optimization target. It treats potential solutions as particles in space, evaluates the quality of each particle by constructing a fitness function, finds the direction of each particle, and continuously searches for the optimal solution. The basic elements of the particle swarm algorithm can be expressed as follows:

[0182] PSO=(N,V,X,F,K) (35)

[0183] Where N is the number of particles; V is the particle evolution speed; X is the position of the particle; F is the fitness function; K is the number of iterations.

[0184] The speed and position of particle evolution during the iteration process can be expressed as follows:

[0185]

[0186] That is to say, the velocity and position of the particle can be iteratively updated through the above equations (36) and (37).

[0187] Where, ω i is the inertia weight of particle i; c1 and c2 are the individual learning factor and the group learning factor respectively; r1 and r2 are random parameters in the interval (0,1) and the evolution constant coefficient; The position of the i-th particle after k iterations; is the particle velocity after k+1 iterations; is the optimal position of particle i after iteration k times; g k is the global historical optimal position of the particle swarm.

[0188] The iterative formula for the global historical optimal position of the particle swarm is:

[0189]

[0190] Where, represents the fitness value of the i-th particle after iterating k+1 times; f(g k ) represents the global optimal position fitness value.

[0191] To address the shortcomings of existing standard particle swarm algorithms, such as premature maturation and a tendency to fall into local optimality, the present invention proposes an improved particle swarm algorithm: a simulated annealing chaos particle swarm complementary algorithm. This improved simulated annealing chaos particle swarm complementary algorithm first considers the impact of inertia weight on the algorithm's solution speed and adopts time-varying weights during particle evolution to balance the global optimization process. Second, a chaotic sequence is used to perturb each particle during the iteration process, thereby preventing it from falling into local extremes. Furthermore, a simulated annealing mechanism is introduced to ensure that particles can escape from the local optimal solution when trapped, achieving global search.

[0192] Specifically, ω i The larger the value of (inertia weight of particle i), the faster the algorithm will solve the problem, but it is also easy to fall into the local optimal situation. Therefore, in some embodiments of the present invention, the inertia weight ω of the particle can be realized based on the time-varying weight. i Appropriate adjustments are made to enable particles to have different exploration capabilities during the evolution process to balance the global optimization process.

[0193] The time-varying weight can be expressed as follows:

[0194]

[0195] Where, f min (k) is the minimum fitness of the particle swarm; w min 、w max are the minimum and maximum inertia weights respectively; f a (k) is the average fitness of the particle swarm.

[0196] In other words, the particle's inertia weight ω can be adjusted and updated by equation (39): i .

[0197] In some embodiments of the present invention, a chaotic sequence can be used to perform chaotic perturbation on the random parameters r1 and r2 of the particle velocity in the iterative process to obtain the random parameters of the chaotically perturbed particle velocity. Specifically, as shown in (40) and (41). It should be noted that the chaotic sequence can be specifically a Logistic model chaotic sequence. The Logistic model chaotic sequence is, that is, under certain parameter conditions, the sequence generated by the Logistic mapping exhibits chaotic behavior. The chaotic sequence seems random, but it is actually generated by a certain mathematical rule. Each particle in the iterative process is subjected to chaotic perturbations to prevent the particle from falling into a local extreme value.

[0198] The random parameter of the chaotically disturbed particle velocity can be expressed as follows:

[0199]

[0200] Where, is the random parameter of the chaotically perturbed particle velocity.

[0201] From the above, we can see that when all particles are in the global historical optimal position g of the particle swarm k When , the particle swarm stops searching and falls into the local optimal solution. At this time, the simulated annealing mechanism can be introduced to perform the simulated annealing process on each particle to update the position of each particle. Specifically, by perturbing the current position of the particle to generate a new candidate solution, the poor particle has a probability (probability P) to be in the optimal position of the particle swarm. The particle continues to fly, which ensures that the particle swarm jumps out of the local optimal solution and conducts a global search. Among them, the probability P gradually decreases as the temperature T decreases, and the termination temperature T can be set. f , when the temperature is lower than the termination temperature T f When , the simulated annealing process is exited and the global optimum is reached.

[0202] The iterative formula for the updated optimal position of particle i is:

[0203]

[0204] Where c is a random value in the interval (0,1). i T is the probability of the particle at temperature T, which can be expressed as follows:

[0205]

[0206] The factors affecting the cooling rate and the temperature iteration method are analyzed. When the particle swarm is in a dispersed state, the more dispersed it is, the lower the maturity is. Further iterative optimization is required (at the current temperature), which means the cooling rate needs to be reduced. Conversely, the cooling rate needs to be increased. The dispersion degree of the particle swarm can be expressed as:

[0207]

[0208] The mathematical equation for the cooling strategy is as follows:

[0209] T(k+1)=T(k)λ(1-σ) (45)

[0210] Where T(k) is the temperature after k iterations; λ is the temperature drop coefficient, which is used to determine the temperature drop rate.

[0211] In some embodiments, the specific process of solving the planning optimization model using the simulated annealing chaos particle swarm complementary algorithm is as follows:

[0212] First, initialize the particle swarm. This includes initializing the number of particles in the swarm, the initial position and velocity of each particle, the initial value of the particle's optimal position, and the initial value of the swarm's global historical optimal position. Furthermore, initialize parameters such as the initial and termination temperatures of the simulated annealing process. Finally, set the maximum number of iterations for the simulated annealing chaos particle swarm complementary algorithm. It's important to note that particles represent potential solutions in space. A fitness function can be constructed to evaluate the quality of each particle and determine its direction of progress, continuously searching for the optimal solution.

[0213] The inertia weight of the particle is adjusted based on the time-varying weight; and the random parameters r1 and r2 of the particle velocity in the iteration process are chaotically disturbed by using a chaotic sequence to obtain the random parameters of the chaotically disturbed particle velocity. Specifically, the inertia weight of the particle can be adjusted by using the above formula (39). The random parameters r1 and r2 of the particle velocity in the iteration process can be chaotically disturbed by using the above formulas (40) and (41) to obtain the random parameters r1 and r2 of the particle velocity in the iteration process. n k .

[0214] Based on the above time-varying weights and the random parameters of the chaotically perturbed particle velocity, the particle velocity and position are iteratively updated, and the fitness function value of the particle after the iterative update can be calculated. If the fitness function value of the particle after this iterative update is less than the extreme value of the global historical fitness function, the extreme value of the global historical fitness function is updated; otherwise, the extreme value of the global historical fitness function is not updated. Here, the particle velocity and position can be iteratively updated using the above equations (36) and (37).

[0215] When all particles in the particle swarm are at the global historical optimal position of the particle swarm, a simulated annealing process is performed on each particle to update the position of each particle. Specifically, the process includes: perturbing the current position of the particle (random perturbations can be performed near the current position of the particle) to generate a candidate solution (candidate particle), and determining the optimal position of the particle after iterative update by using the above formula (42). Furthermore, the fitness of the candidate position corresponding to the candidate solution and the optimal position of the particle after iterative update can be compared by using the fitness function. If the fitness of the candidate position is better than the fitness of the optimal position of the particle after iterative update, the candidate position is used as the new optimal position of the particle; if the fitness of the candidate position is worse than the fitness of the optimal position of the particle after iterative update, the above formula (43) is used to determine whether the optimal position of the particle after iterative update is used as the new optimal position of the particle. Specifically, the fitness function value of the candidate position corresponding to the candidate solution and the fitness function value of the optimal position of the particle after iterative update can be calculated. By comparing the fitness function value of the candidate position and the fitness function value of the optimal position of the particle after iterative update, the position with better fitness between the candidate position and the optimal position of the particle after iterative update can be determined. If the fitness of the candidate position is better than the fitness of the optimal position of the particle after iterative update, the candidate position is used as the new optimal position of the particle; if the fitness of the candidate position is worse than the fitness of the optimal position of the particle after iterative update, whether the optimal position of the particle after iterative update is used as the new optimal position of the particle is determined according to the above formula (43).

[0216] The fitness function is used to select the best new optimal position of the particle with the best fitness from the new optimal positions of each particle in the particle swarm as the current global optimal position of the particle swarm. Furthermore, the fitness function can be used to compare the fitness of the current global optimal position of the particle swarm with the fitness of the global historical optimal position of the particle swarm. If the fitness of the current global optimal position of the particle swarm is better, then the current global optimal position of the particle swarm can be used as the new global historical optimal position of the particle swarm.

[0217] Then, the current temperature of the simulated annealing process can be updated, and the above steps can be iteratively executed until the current temperature drops below the termination temperature, the current number of iterations reaches the maximum number of iterations, and the new global historical optimal position of the particle swarm meets the preset limit. The simulated annealing chaotic particle swarm complementary algorithm can be terminated and the Pareto solution set (multiple feasible solution sets) can be output.

[0218] Specifically, after each update of the current temperature of the simulated annealing process, it is determined whether the current temperature has dropped below the termination temperature, whether the current number of iterations has reached the maximum number of iterations, and whether the new global historical optimal position of the particle swarm meets the preset limit. If the current temperature has dropped below the termination temperature, the current number of iterations has reached the maximum number of iterations, and the new global historical optimal position of the particle swarm meets the preset limit, the simulated annealing chaotic particle swarm complementary algorithm can be terminated and the Pareto solution set (multiple sets of feasible solutions) can be output. At the same time, information such as the number of algorithm iterations and the convergence curve can be output.

[0219] In some embodiments, after obtaining the Pareto solution set, the Topsis method can be used to determine the optimal solution (corresponding to the optimal planning result) based on the Pareto solution set. Furthermore, the optimal planning result can be determined based on the optimal solution (the optimal planning result includes the optimal capacity configuration of various types of equipment in the integrated energy system). In other embodiments, the Pareto solution set can also be analyzed using a comprehensive scoring method or a gray correlation analysis method to obtain the optimal solution.

[0220] It should be noted that when solving planning optimization models using the simulated annealing chaos particle swarm complementary algorithm of the present invention, the random perturbations of the simulated annealing process can help escape local optimal solutions during the search process, increasing the chance of finding a global optimal solution. Furthermore, the updates to particle velocities and positions in the simulated annealing chaos particle swarm complementary algorithm help quickly search for potential high-quality solutions in the solution space, improving the algorithm's optimization performance and robustness.

[0221] According to the integrated energy system planning optimization method 300 of the present invention, various equipment parameters, load data, and energy prices in the integrated energy system are obtained. Then, a planning optimization model for the integrated energy system is constructed with the goal of minimizing the annual total cost of the integrated energy system (including the cost of tiered carbon trading) and maximizing the overall energy efficiency. The planning optimization model is solved using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution. Based on the optimal solution, the optimal capacity configuration of various types of equipment in the integrated energy system is determined. Based on this, it is possible to rationally plan the optimal capacity configuration of various types of equipment in the integrated energy system while meeting the system load requirements.

[0222] Furthermore, by constructing the highest objective function for comprehensive energy efficiency, it is possible to achieve organic coordination among the subsystems of the integrated energy system, improve the efficiency and stability of the entire system, and take into account the performance of the integrated energy system, thereby reducing the overall energy purchase demand of the system.

[0223] In addition, the stepped carbon trading model established according to the present invention can accurately describe the extent to which the actual carbon emissions of the integrated energy system exceed the carbon emission quota. Moreover, when the actual carbon emissions are less than the carbon emission quota, different gradients of rewards can be given based on the carbon excess ratio gradient, thereby further incentivizing system operators to reduce carbon emissions.

[0224] In addition, the improved simulated annealing chaos particle swarm complementary algorithm according to the present invention takes into account the impact of inertia weight on the algorithm's solution speed, adopts time-varying weights during the particle evolution process to balance the global optimization process; uses a chaotic sequence to perform chaotic perturbations on each particle during the iterative process, thereby avoiding being trapped in local extreme values; and introduces a simulated annealing mechanism to ensure that particles can escape the local optimal solution when trapped in the local optimum, thus achieving global search. In the process of solving the planning optimization model using the simulated annealing chaos particle swarm complementary algorithm, the random perturbations of the simulated annealing process can help escape the local optimal solution during the search process, increasing the chance of finding the global optimal solution. At the same time, the simulated annealing chaos particle swarm complementary algorithm's updates to particle velocities and positions help quickly search for potential high-quality solution areas in the solution space, improving the algorithm's optimization performance and robustness.

[0225] By way of example and not limitation, readable media include readable storage media and communication media. Readable storage media store information such as computer-readable instructions, data structures, program modules, or other data. Communication media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and include any information delivery medium. Combinations of any of the above are also included within the scope of readable media.

[0226] In the description provided herein, the algorithms and displays are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems may also be used in conjunction with the examples of the present invention. Based on the above description, it is apparent that the structure required for constructing such systems is well understood. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​may be utilized to implement the present invention described herein, and the description of specific languages ​​above is provided for the purpose of disclosing the preferred embodiment of the present invention.

[0227] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0228] Similarly, it should be understood that in order to streamline the disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0229] Those skilled in the art will appreciate that the modules, units, or components of the devices in the examples disclosed herein may be arranged in the device described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the aforementioned examples may be combined into one module or further divided into multiple submodules.

[0230] Unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved and is not intended to imply that the objects so described must have a given order in time, space, ranking, or in any other manner.

Claims

1. A method for optimizing integrated energy system planning, executed in a computing device, comprising: Obtain various equipment parameters, load demand data, and energy prices in the comprehensive energy system, wherein the various equipment parameters include the investment and construction costs and equipment energy efficiency of various equipment, the load demand data includes electrical load demand data and thermal load demand data, and the energy prices include electricity prices and gas prices; Based on the parameters of the various equipment, load demand data, and energy prices, a planning optimization model for the integrated energy system is constructed with the lowest annual total cost of the integrated energy system as the first goal and the highest comprehensive energy efficiency of the integrated energy system as the second goal, wherein the annual total cost includes annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and penalty cost for abandoned wind and solar power, and the comprehensive energy efficiency is used to represent the ratio of the total energy supply of various equipment in the integrated energy system to the total energy consumption of various equipment; The planning optimization model is solved by using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, and the optimal capacity configuration of various types of equipment in the integrated energy system is determined based on the optimal solution.

2. The method of claim 1, wherein: The planning optimization model of the integrated energy system is constructed with the lowest annual total cost of the integrated energy system as the first goal and the highest comprehensive energy efficiency of the integrated energy system as the second goal, including: Based on the annual investment cost, energy purchase cost, annual operation and maintenance cost, tiered carbon trading cost, and penalty cost for wind and solar power abandonment of the comprehensive energy system, a minimum annual total cost objective function corresponding to the first goal is constructed; Based on the ratio of the total energy supply of various types of equipment in the comprehensive energy system to the total energy consumption of various types of equipment, constructing a maximum comprehensive energy efficiency objective function corresponding to the second goal; Based on the objective function of minimizing the total annual cost and the objective function of maximizing the comprehensive energy efficiency, a planning optimization model for the comprehensive energy system is constructed.

3. The method of claim 2, wherein: Based on the objective function of the lowest annual total cost and the objective function of the highest comprehensive energy efficiency, a planning optimization model of the comprehensive energy system is constructed, including: Based on the objective function of minimizing the total annual cost, the objective function of maximizing the comprehensive energy efficiency, and multiple constraints of the comprehensive energy system, a planning optimization model of the comprehensive energy system is constructed; The multiple constraints include power balance constraints, system energy supply network constraints, system floor space constraints, energy storage unit equipment operation constraints, and other equipment component operation constraints; wherein the power balance constraints include electric load power balance constraints and thermal power balance constraints; the system energy supply network constraints are used to limit the purchased electricity and purchased gas of the integrated energy system within corresponding ranges; the system floor space constraints are used to indicate that the total installed floor space of all equipment in the integrated energy system does not exceed the upper limit of the usable area of ​​the integrated energy system; the energy storage unit equipment operation constraints include electrolyzer capacity constraints, fuel cell capacity constraints, heat storage tank heat storage constraints, and hydrogen storage tank hydrogen storage constraints; the other equipment component operation constraints are used to display the power and ramp rate of the other equipment components, and the other equipment components include gas turbines and fans.

4. The method according to any one of claims 1 to 3, wherein: The planning optimization model is solved by using a simulated annealing chaos particle swarm complementary algorithm to obtain an optimal solution, including: Initializing and setting the number of particles in the particle swarm, the initial position and initial velocity of each particle, the initial value of the optimal position of the particle, the initial value of the global historical optimal position of the particle swarm, the initial temperature and the termination temperature of the simulated annealing process, and setting the maximum number of iterations, wherein the particles are used to represent potential solutions in space; The inertia weight of the particle is adjusted based on the time-varying weight, and the random parameters of the particle velocity in the iteration process are chaotically disturbed by using a chaotic sequence to obtain the random parameters of the chaotically disturbed particle velocity; Iteratively updating the speed and position of the particle based on the time-varying weight and the random parameter of the chaotically disturbed particle speed; When all particles in the particle swarm are at the global historical optimal position of the particle swarm, a simulated annealing process is performed on each particle to update the position of each particle, including: perturbing the current position of the particle to generate a candidate solution, and determining the optimal position of the particle after iterative update; comparing the fitness of the candidate position corresponding to the candidate solution with the optimal position of the particle after iterative update through a fitness function, wherein if the fitness of the candidate position is better than the fitness of the optimal position of the particle after iterative update, the candidate position is used as the new optimal position of the particle; Through the fitness function, the new optimal position of the particle with the best fitness is selected from the new optimal positions of each particle in the particle swarm as the global current optimal position of the particle swarm, and the fitness of the global current optimal position of the particle swarm is compared with the global historical optimal position of the particle swarm. If the fitness of the global current optimal position of the particle swarm is better, the global current optimal position of the particle swarm is used as the new global historical optimal position of the particle swarm; Update the current temperature of the simulated annealing process, and if the current temperature drops below the termination temperature, the current number of iterations reaches the maximum number of iterations, and the new global historical optimal position of the particle swarm meets the preset limit, then output the Pareto solution set; The Topsis method is used to determine the optimal solution based on the Pareto solution set.

5. The method according to any one of claims 1 to 4, wherein: Also includes: A stepped carbon trading model taking into account a carbon excess ratio is established so as to determine the stepped carbon trading cost through the stepped carbon trading model. The carbon excess ratio is used to represent the ratio of actual carbon emissions to carbon emission quotas.

6. The method of claim 5, wherein: The carbon excess ratio is calculated using the following formula: When the carbon excess ratio gradient is 0.1, the stepped carbon trading model is as follows: Among them, C JY represents the tiered carbon trading cost; JY represents the unit price of carbon trading; α represents the price increase; θ represents the reward coefficient; E IES represents the carbon emission quota of the comprehensive energy system; Q IES Represents the actual carbon emissions of the integrated energy system.

7. The method according to any one of claims 1 to 6, wherein: The calculation formulas for annual investment cost, energy purchase cost, annual operation and maintenance cost, and penalty cost for abandoned wind and solar power are as follows: Among them, C ycost , C buy , C main , C pt They represent annual investment cost, energy purchase cost, annual operation and maintenance cost, and penalty cost for wind and solar power abandonment respectively; represents the unit capacity investment cost of the kth type of equipment; D k represents the configuration capacity of the kth type of equipment; r and m represent the interest rate and service life in the annualized cost formula respectively; Respectively represent the electricity price and gas price in time period t; Δt represents the unit time; O k represents the annual maintenance cost per unit capacity of equipment k; P k,D (t) represents the output of the kth device; β pt P represents the penalty coefficient for wind and solar power abandonment; pvwt (t) represents the wind and solar power curtailment at time t.

8. The method of claim 6, wherein: The carbon emission sources in the integrated energy system include CHP units, gas boilers and purchased electricity.

9. The method according to any one of claims 1 to 8, wherein: The various types of equipment include production equipment, energy conversion equipment and energy storage equipment; The integrated energy system includes a renewable energy power generation unit, a hydrogen energy storage unit, a CHP unit and coupling auxiliary equipment; wherein the renewable energy power generation unit includes distributed photovoltaic equipment and wind turbine equipment, the hydrogen energy storage unit includes a fuel cell, a hydrogen storage tank and an electrolyzer, the CHP unit includes a gas turbine and a waste heat boiler, and the coupling auxiliary equipment includes a gas boiler and a heat storage tank.

10. A computing device comprising: at least one processor; and A memory storing program instructions, wherein the program instructions are configured to be processed by the at least one processor, and the program instructions include instructions for processing the method according to any one of claims 1 to 9.

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