Comprehensive energy planning method and related equipment

Abstract

The embodiment of the invention provides a comprehensive energy planning method and related equipment, and belongs to the technical field of energy planning. The method comprises the following steps: establishing a wind power generation model, a photovoltaic power generation model, a gas turbine power generation model and a cogeneration unit power generation model; establishing a target planning function according to the minimum cost constraint through the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model and the cogeneration unit power generation model; the target planning function comprises carbon emission limiting conditions for gas turbine power generation and cogeneration unit power generation; and splitting the target planning function into an inner circulation problem and an outer circulation problem through a planning optimization algorithm, solving the inner circulation problem, calculating to obtain initial scene data, and calculating to obtain a target planning scheme according to the initial scene data through the outer circulation problem. According to the embodiment of the invention, the feasibility and calculation efficiency of model solving can be effectively improved, and the adaptability of a planning scheme to multiple operation scenes is enhanced.

Description

Technical Field

[0001] This application relates to the field of energy planning technology, and in particular to a comprehensive energy planning method and related equipment. Background Technology

[0002] Against the backdrop of the accelerated global energy structure transition towards a clean and low-carbon future, integrated energy systems, as key infrastructure for achieving multi-energy synergy and improved energy efficiency, are increasingly becoming a cutting-edge research direction in the energy field. This system breaks down barriers between traditional energy systems by constructing a coupled network of multiple energy forms, including electricity, heat, cooling, gas, and hydrogen, enabling the cascade utilization and complementary coordination of energy. Specifically, combined heat and power (CHP) units can effectively achieve the joint production of electricity and heat, reducing energy conversion losses; electricity-to-gas technology can convert surplus electricity into gas for storage, enhancing system operational flexibility; and diversified energy storage facilities, such as thermal storage, electricity storage, and hydrogen storage, help smooth out fluctuations in renewable energy output and improve the overall absorption capacity of clean energy, thereby synergistically promoting the low-carbon and high-efficiency development of the energy system. Summary of the Invention

[0003] The main objective of this application is to propose a comprehensive energy planning method and related equipment, which can effectively improve the feasibility and computational efficiency of model solving and enhance the adaptability of the planning scheme to multiple operating scenarios.

[0004] To achieve the above objectives, one aspect of this application proposes a comprehensive energy planning method, the method comprising: Establish models for wind power generation, photovoltaic power generation, gas turbine power generation, and combined heat and power (CHP) unit power generation; Objective programming functions are established based on minimum cost constraints using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power (CHP) unit power generation model. The objective programming function includes constraints on the carbon emissions of the gas turbine power generation and the CHP unit power generation. The objective planning function is decomposed into an inner loop problem and an outer loop problem using a planning optimization algorithm. The inner loop problem is solved to obtain the initial scenario data. Based on the initial scenario data, the objective planning scheme is calculated using the outer loop problem.

[0005] In some embodiments, the target programming function further includes limitations on equipment capacity investment cost, operation and maintenance cost, energy purchase cost, capture cost, storage cost, and carbon trading cost; wherein the equipment capacity investment cost is used to describe the cost of deploying energy equipment.

[0006] In some embodiments, establishing an objective programming function based on a minimum cost constraint using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power (CHP) unit power generation model includes: The objective programming function is constructed using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power unit power generation model:

[0007] in, These are the decision variables for the first stage, representing the investment capacity of each piece of equipment; It is a set of source-load uncertainties, including the fluctuation values ​​of wind and solar power output and electricity-heat-cooling load; For the feasible region of uncertain variables; For the second-stage decision variables, it represents the situation under a given uncertainty scenario. The system operation decision. For equipment investment cost function, This is the system operating cost function.

[0008] In some embodiments, the step of decomposing the objective programming function into an inner loop problem and an outer loop problem using a planning optimization algorithm includes: The formula for decomposing the objective programming function into an outer loop problem using a planning optimization algorithm is shown below:

[0009] in, Minimize the introduced auxiliary variables It minimizes the objective function under the enumerated relaxation set S scenario; Represents the current set The number of scenarios in the problem is the number of times the original problem has been iterated. The formula for decomposing the objective programming function into an inner loop problem using a planning optimization algorithm is shown below:

[0010] The inner loop problem contains continuous variables. and discrete variables .

[0011] In some embodiments, solving the inner loop problem to calculate the initial scene data includes: The inner loop problem is decomposed into the minimum subproblem and the maximum subproblem. The minimum subproblem is then decomposed into the minimum inner management problem and the minimum inner support problem using an optimization planning algorithm. Solving the minimum inner support problem yields an optimized discrete set; the optimized discrete set contains data from multiple worst-case scenarios. The minimum subproblem is solved by enumerating and calculating the optimized discrete set through the minimum inner management problem.

[0012] In some embodiments, after decomposing the inner loop problem to obtain the minimum and maximum subproblems, the method further includes: The maximum subproblem is transformed into a minimum dual problem using Lagrange duality theory, and the minimum dual problem is solved directly to solve the inner loop problem and calculate the initial scene data.

[0013] In some embodiments, before calculating the target planning scheme based on the initial scenario data using the outer loop problem, the method further includes: If it is determined that the initial planning scheme does not meet the operational requirements in the target planning function, then the upper and lower bounds of the planning optimization algorithm are updated. If the upper bound and the lower bound do not satisfy the formula Then, a new initial scene data is obtained by resolving the problem; wherein, the... As the upper bound, the stated As the lower bound, the stated To limit parameters; If the upper bound and the lower bound satisfy the formula Then, based on the initial scenario data, the target planning scheme is calculated through the outer loop problem.

[0014] To achieve the above objectives, another aspect of this application proposes a comprehensive energy planning device, the device comprising: The model building module is used to build models for wind power generation, photovoltaic power generation, gas turbine power generation, and combined heat and power (CHP) unit power generation. The function establishment module is used to establish a target programming function based on the minimum cost constraint using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power unit power generation model; the target programming function includes the constraint conditions on the carbon emissions of the gas turbine power generation and the combined heat and power unit power generation. The problem-solving module is used to decompose the target planning function into an inner loop problem and an outer loop problem through a planning optimization algorithm, solve the inner loop problem to calculate the initial scene data, and calculate the target planning scheme based on the initial scene data through the outer loop problem.

[0015] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0016] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described above.

[0017] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0018] The embodiments of this application include at least the following beneficial effects: This application provides a comprehensive energy planning method, device, electronic device, storage medium, and program product. This solution establishes wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power (CHP) unit power generation models; establishes a target planning function based on minimum cost constraints using the wind power generation models, photovoltaic power generation models, gas turbine power generation models, and CHP unit power generation models; the target planning function includes constraints on carbon emissions from gas turbine power generation and CHP unit power generation; the target planning function is decomposed into an inner loop problem and an outer loop problem using a planning optimization algorithm, and the inner loop problem is solved to calculate initial scenario data; the target planning scheme is calculated based on the initial scenario data using the outer loop problem. By implementing the embodiments of this application, a target programming function is established based on the minimum cost constraint using the established wind power generation model, photovoltaic power generation model, gas turbine power generation model, and combined heat and power unit power generation model. This allows for constrained planning of energy models included in the integrated energy system. The target programming function is then decomposed into an inner-loop problem and an outer-loop problem using a planning optimization algorithm. The inner-loop problem is solved to obtain initial scenario data. Based on the initial scenario data, the target planning scheme is calculated using the outer-loop problem. By simulating typical operating conditions, load characteristics, and renewable energy output characteristics, initial scenario data reflecting the basic operating state of the system is obtained. By decoupling operation optimization and planning design, the feasibility and computational efficiency of the model solution are effectively improved, and the adaptability of the planning scheme to multiple operating scenarios is enhanced. This provides more scientific and systematic decision support for the actual deployment of integrated energy systems. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an implementation environment provided in an embodiment of this application; Figure 2 This is a flowchart of the integrated energy planning method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the topology of an electro-carbon coupled integrated energy system in one embodiment; Figure 4 This is a schematic diagram of the integrated energy planning device provided in the embodiments of this application; Figure 5 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.

[0021] It is understood that the terms “first,” “second,” etc., used in this application may be used herein to describe various concepts, but unless otherwise stated, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of the embodiments of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words “if,” “when,” or “in response to a determination” as used herein may be interpreted as “when…” or “when…” or “in response to a determination.”

[0022] As used in this application, the terms "at least one", "multiple", "each", "any", etc., "at least one" includes one, two or more, "multiple" includes two or more, "each" refers to each of the corresponding multiples, and "any" refers to any one of the multiples.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0024] Before providing a detailed description of the embodiments of this application, some of the nouns and terms involved in the embodiments of this application will be explained first. The nouns and terms involved in the embodiments of this application are subject to the following interpretations.

[0025] 1) Power-to-Gas (P2G): A technology that converts electrical energy into gaseous energy such as natural gas through processes such as electrolysis for storage or utilization. This can improve system flexibility and enable the effective use of surplus electrical energy.

[0026] 2) Carbon Capture, Utilization and Storage (CCUS): A technology system based on separation, transport and storage technologies that captures carbon dioxide generated during energy consumption and utilizes it (e.g., reacting it with hydrogen to produce natural gas) or stores it to achieve carbon emission reduction.

[0027] 3) Tiered carbon trading mechanism: A carbon trading model that divides the difference between net carbon emissions and carbon quotas into different intervals, with each interval corresponding to a different carbon price coefficient (including reward and penalty modes). It guides the system to reduce carbon emissions through segmented pricing and requires linearization processing to embed into the planning model.

[0028] 4) Box-type uncertainty set: This set is used to characterize the fluctuation range of wind and solar power output on the source side and electric, heat and cooling loads on the load side. By determining the predicted value and maximum fluctuation range of the variables, it describes the possible value range of the uncertainty factors, but there is a potential problem that the planning results are too conservative.

[0029] In related technologies, the planning of integrated energy systems always faces the core challenge of synergistic optimization and mutual constraints between the dual objectives of economic efficiency and low carbon emissions. From an economic perspective, planning needs to take into account the overall life-cycle cost of equipment investment, including the purchase and installation of renewable energy units, diversified energy storage facilities, and carbon capture systems, as well as operation and maintenance expenses and external energy procurement costs, to ensure the system's acceptable economic feasibility throughout its entire life cycle. From a low-carbon perspective, it is necessary to use technologies such as carbon capture, utilization, and storage (CCUS) to reduce the system's carbon source intensity, and to effectively internalize carbon emission costs by optimizing the operation strategies of high-carbon units and introducing tiered carbon trading mechanisms, thereby achieving precise control over the system's total net carbon emissions.

[0030] The synergy between economic and low-carbon goals requires planning models that can not only accurately characterize multi-energy flow physical parameters such as energy conversion efficiency and equipment operating characteristics, but also integrate environmental and economic factors such as carbon emission reduction accounting and carbon trading costs. This necessitates seeking a Pareto optimal solution between the two while satisfying hard constraints such as multi-energy flow balance and equipment capacity. Furthermore, in actual operation, the system must address multiple uncertainties, including the randomness of wind and solar power output on the source side and the volatility of energy consumption on the load side. This further highlights the urgency and importance of constructing robust, detailed optimization models and efficient solution algorithms.

[0031] In view of this, this application provides a comprehensive energy planning method, which can effectively improve the feasibility and computational efficiency of model solving and enhance the adaptability of the planning scheme to multiple operating scenarios.

[0032] The integrated energy planning method provided in this application relates to the field of information technology. This integrated energy planning method can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the integrated energy planning method, but is not limited to the above forms.

[0033] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0034] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0035] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided in an embodiment of this application. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0036] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0037] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0038] Terminal 102 can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the application does not impose any limitations.

[0039] For example, based on Figure 1 The implementation environment shown in this application embodiment provides a comprehensive energy planning method. The following description uses the application of this comprehensive energy planning method in server 101 as an example. It can be understood that the comprehensive energy planning method can also be applied to terminal 102.

[0040] Figure 2This is an optional flowchart of the integrated energy planning method provided in the embodiments of this application. The subject executing the integrated energy planning method can be any of the aforementioned electronic devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S201 to S203.

[0041] Step S201: Establish wind power generation model, photovoltaic power generation model, gas turbine power generation model, and combined heat and power unit power generation model.

[0042] In the modeling of integrated energy systems, it is necessary to construct models of multiple types of power generation units, including wind power, photovoltaic power, gas turbine power, and combined heat and power (CHP) units. Specifically, the wind power model converts wind speed information from meteorological data into electrical power output using wind speed-power characteristic curves; the photovoltaic power generation model calculates its power generation capacity based on parameters such as irradiance and ambient temperature using the photoelectric conversion principle; the gas turbine model simulates its power generation process based on fuel input and thermodynamic cycle efficiency relationships; and the CHP unit model simultaneously describes the coupling characteristics of both electrical and thermal outputs, reflecting its waste heat recovery and utilization mechanism during power generation.

[0043] In some embodiments, wind power generation has the advantages of abundant primary resources, low cost, and high efficiency. However, the output of a wind turbine mainly depends on the wind speed, and its mathematical model is shown in formula (1): (1) In formula (1), , These represent the cut-in wind speed and the rated wind speed, respectively. Represents the cut-out wind speed; It is the real-time wind speed; This is the actual output power of the fan. This is the rated power of the fan.

[0044] Photovoltaic power generation has significant characteristics such as environmental protection and energy saving. As a major distributed renewable energy source, its output is greatly affected by weather, light intensity and ambient temperature, and has strong randomness. Its photovoltaic power generation model can be shown in formula (2): (2) In formula (2): This indicates the maximum output power of the photovoltaic unit. For reference light intensity; The reference temperature is used. The photovoltaic output power and the arrangement of solar panels are closely related to the installation method of the photovoltaic power generation device. It is the power temperature coefficient of a photovoltaic cell; For working hours, the intensity of sunlight; The ambient temperature is when the photovoltaic panel is working. As can be seen from formula (2), the maximum power generation is positively correlated with the light intensity, but the light intensity has a very high degree of uncertainty. Therefore, there is a certain degree of deviation between the actual power generation of the photovoltaic unit and the predicted photovoltaic power.

[0045] In terms of energy efficiency, the combined heat and power (CHP) unit power generation model reduces energy losses during energy conversion and significantly improves the overall energy utilization rate compared to the traditional separate power generation and heating mode. From an economic perspective, it can reduce energy supply costs and reduce the expenditure of enterprises and users on electricity and heat. In terms of environmental protection, due to the improved energy utilization efficiency and reduced fuel consumption, the emissions of pollutants such as carbon dioxide and sulfur dioxide are reduced, which helps to alleviate environmental pollution problems. In addition, CHP can also enhance the stability and reliability of energy supply, especially when dealing with peak energy demand such as winter heating, it can ensure a stable supply of electricity and heat. The mathematical model established based on the working principle of CHP is shown in formula (3): (3) In formula (3), For cogeneration units in Total power output during the time period; , They are respectively in Electrical and thermal power of the time-limited cogeneration unit power generation model; , For cogeneration units in Natural gas consumption during a given period; , These are the electrical efficiency and thermal efficiency of a combined heat and power (CHP) unit, respectively. This refers to the calorific value of natural gas.

[0046] Micro-Gas Turbines (MTs) operate on the Brayton cycle. First, air enters the compressor through the inlet, where it is compressed, increasing its pressure. The compressed high-pressure air then enters the combustion chamber, where it mixes with injected fuel (usually natural gas or other combustible gases) and burns. The resulting high-temperature, high-pressure gas expands rapidly. This expanded gas drives the turbine to rotate. Part of the turbine's energy is used to power the compressor, while the remaining energy is output through a drive shaft to drive a generator or directly power other equipment. Throughout this process, the energy of the gas is gradually converted into mechanical energy, ultimately producing electricity or other forms of power. Its compact structure makes it suitable for various applications, including distributed energy systems.

[0047] The mathematical model relating the power of a micro gas turbine to its natural gas consumption is shown in equation (4): (4) In formula (4), For micro gas turbines in Heat loss during certain periods of the day; The electrical power of the micro gas turbine; , They are respectively Thermal power and cooling power of the time-limited bromine cooler; The conversion rate of the micro gas turbine; , , These are the heat loss coefficient, the heating coefficient of the bromine cooler, and the cooling coefficient, respectively. The flue gas recovery efficiency of the bromine cooler.

[0048] Battery energy storage systems (BESS) can primarily rely on electrochemical reactions to store and release electrical energy through two processes: charging and discharging. The ideal mathematical model for charging and discharging is shown in formula (5): (5) express The amount of electricity stored in the energy storage device at all times; express The power of the energy storage device at all times This represents charging efficiency, which is a constant. This represents the discharge efficiency, which is a constant. express The electrical power input to the energy storage device at any given time is the decision variable. express The output power of the energy storage device at any given time is the decision variable. for Time and The time difference between moments.

[0049] Heat storage tank (HST): Primarily based on a thermal energy storage medium, it achieves the storage and release of thermal energy through two processes: heat storage and heat release. The ideal mathematical model for heat storage and release is shown in formula (6): (6) This represents the heat generated by the energy storage device at time t; express The heat of the energy storage device at all times; This represents the charging efficiency, which is a constant. This represents the discharge efficiency, which is a constant. express The thermal power input to the energy storage device at any given time is the decision variable. express The output thermal power of the energy storage device at all times.

[0050] Hydrogen-Storage Tanks (H2STs) are primarily based on the gas / liquid storage principle, achieving hydrogen storage and release through two processes: filling and releasing hydrogen. The ideal mathematical model for filling and releasing hydrogen is shown below: (7) express The amount of electricity stored in the energy storage device at all times; express The amount of electricity stored in the energy storage device at all times; This represents the charging efficiency, which is a constant. This represents the discharge efficiency, which is a constant. express The electrical power input to the energy storage device at any given time is the decision variable; express The output power of the energy storage device at any given time is the decision variable.

[0051] Hydrogen fuel cells (HFCs) are primarily based on electrochemical redox reactions. They achieve energy conversion by reacting hydrogen and oxygen to generate electricity and produce water. The ideal mathematical model for their power generation is shown below: (8) in, , for Electrical and thermal power of hydrogen fuel cells during different time periods; , The output efficiency of hydrogen fuel cells in terms of electrical power and thermal power; for The hydrogen power input into the hydrogen fuel cell during the electrolyzer period; , These represent the upper and lower limits of the thermoelectric ratio of hydrogen fuel cells.

[0052] Carbon capture, utilization and storage (CCUS) is mainly based on separation, transport and storage technologies. It achieves carbon emission reduction by capturing, utilizing or storing carbon dioxide. The ideal mathematical model for carbon treatment is shown in formula (9): (9) for Power generation model of time-limited cogeneration unit, carbon capture, utilization and storage, and power consumption in the power-to-gas technology process; This represents the correlation between carbon capture and electricity consumption. This represents the amount of carbon dioxide captured.

[0053] Carbon capture, utilization, and storage (CCUS) is a complete industrial technology chain integrating capture, transportation, utilization, and storage, relying on the collaborative work of a series of physical equipment and infrastructure. Its core begins at emission sources such as coal-fired power plants and chemical plants, where carbon dioxide is captured and purified from flue gas using chemical absorption towers or advanced membrane separation equipment. This liquid or supercritical carbon dioxide is then transported to designated locations via high-pressure pipeline networks or dedicated transport vessels. Ultimately, this carbon dioxide is either injected into deep underground saline aquifers or depleted oil and gas fields for permanent geological storage, or used as a resource for oil recovery, chemical production, and synthetic fuels, forming a closed-loop engineering system from "source interception" to "end-of-pipe treatment." It serves as a key technological bridge connecting the low-carbon utilization of fossil fuels with the achievement of deep emission reduction.

[0054] Methane reactors (MR) are mainly based on the principles of catalytic or biotransformation reactions. They achieve material conversion and energy utilization through the decomposition, synthesis, or reforming of methane. The ideal mathematical model of the reaction is shown in formula (10): (10) In formula (10), for Gas power conversion of methane reactor during the time period; The conversion rate of the methane reactor; for The hydrogen power input into the methane reactor from the electrolyzer during this period; This represents the amount of carbon dioxide required for the methanation process in a methane reactor. for density; This represents the volume of natural gas produced by the methane reactor.

[0055] Electrolyzers (ELs) are mainly based on the principle of electrolysis. They use electrical energy to drive the decomposition of electrolytes to produce gases (such as hydrogen and oxygen). The ideal mathematical model of electrolysis is shown in formula (11): (11) for Hydrogen power output from the electrolyzer during the time period; The conversion efficiency of hydrogen by electrolysis in an electrolyzer; for The electrical energy consumed by the electrolyzer during a given time period.

[0056] Electric Refrigerator (ER) is mainly based on electric energy to drive the refrigeration cycle. It achieves the transfer of heat from the low temperature end to the high temperature end through the phase change of the refrigerant. Its ideal mathematical model for refrigeration is shown in formula (12): (12) for Power consumption of the time-limited electric chiller; for The cooling capacity of the time-limited electric chiller; This refers to the electro-cooling conversion efficiency of the electric chiller.

[0057] Electric boilers (EB) are mainly based on the principle of converting electrical energy into heat energy. They achieve heat output by heating the working fluid through resistance heating. The ideal mathematical model for heating is shown in formula (13): (13) , for Electricity consumption and heat output of electric boilers during specific time periods; This refers to the heat production efficiency of the electric boiler.

[0058] By establishing the above-mentioned refined model, the output characteristics of different power sources under different operating conditions can be accurately characterized, providing key basic data support for the coordinated scheduling of multiple energy flows, capacity configuration optimization and low-carbon economic analysis of the system, thereby improving the accuracy and practicality of the planning and operation simulation of the entire integrated energy system.

[0059] Step S202: Establish a target programming function based on the minimum cost constraint using the wind power generation model, photovoltaic power generation model, gas turbine power generation model, and combined heat and power unit power generation model; the target programming function includes the constraint conditions on the carbon emissions of gas turbine power generation and combined heat and power unit power generation.

[0060] In some embodiments, the operational characteristics of various power generation units, such as wind power, photovoltaic power, gas turbine power, and combined heat and power (CHP) units, need to be integrated to construct an objective programming function with the minimum total cost as the core constraint. This objective programming function not only considers the investment, operation, and maintenance costs of each power generation method but also emphasizes the introduction of carbon emission constraints for fossil fuel units such as gas turbines and CHP units. Specifically, by setting carbon emission caps or unit output carbon emission intensity constraints, the external environmental costs are internalized into the optimization objective. In modeling, the objective function is typically embedded in the form of inequalities or penalty terms, thereby guiding the system's control strategy for high-carbon units while optimizing costs, promoting the synergistic achievement of clean energy consumption and carbon emission reduction goals.

[0061] The target programming function also includes certified emission reductions (CERs), also known as carbon emission allowances, which are allocated by the primary and secondary markets, covering equipment capacity investment costs, operation and maintenance costs, energy purchase costs, capture costs, storage costs, and carbon trading costs. Initial allowances are allocated by regulatory agencies in the primary market; this portion is the carbon emission allowance allocated by the government to enterprises. Within integrated energy systems, carbon emissions primarily originate from combined heat and power (CHP) units and micro gas turbines, followed by photovoltaic (PV) units and wind turbines.

[0062] Certified Emission Reductions (CERs), also known as carbon emission allowances, are allocated through primary and secondary markets. Initial allowances are allocated by regulatory agencies in the primary market; this portion consists of carbon emission allowances allocated by the government to businesses. Within integrated energy systems, carbon emissions primarily originate from combined heat and power (CHP) units and micro gas turbines, followed by photovoltaic (PV) units and wind turbines.

[0063] Consider allocating carbon allowances to combined heat and power (CHP) units, gas turbines, photovoltaic (PV) units, and wind turbines. After the introduction of the carbon trading market, the decision on whether to sell or purchase carbon allowances will be made based on the net carbon emissions within the system and the total carbon allowances allocated. The introduction of the carbon trading market means that the cost of carbon emissions has shifted from an external environment to an internal one, thus making carbon emission costs a crucial economic indicator within the system and having a significant impact on the capacity configuration and scheduling of the integrated energy system. Free carbon allowances are determined by the baseline method and are divided into two parts: power supply and heating. The carbon allowances obtained by the units according to the baseline method are shown in formula (14): (14) in, Carbon credits for free electricity supply Carbon quotas for free heating Free carbon credits.

[0064] Alternatively, the carbon quota for electricity supply can be calculated as shown in formula (15): (15) in, For electricity supply carbon quotas, The reference value for power supply to the unit. This is the unit cooling correction factor. Correction factor for power supply to the generating unit. This is the unit output correction factor. In order to be in The electrical power of the time-limited cogeneration unit power generation model. The electrical power of the micro gas turbine, This is a photovoltaic power generation model. This is a model for wind power generation.

[0065] Alternatively, the heating carbon quota can be calculated as shown in formula (16): (16) in, This is the baseline value for the unit's heating supply. In order to be in Thermal power of the time-limited cogeneration unit power generation model In order to be in Thermal power of the bromine cooler during the period.

[0066] The calculation model for net carbon emissions is shown in formula (17): (17) This represents the actual total carbon emissions of the entire IES; Calculate carbon emissions for purchasing electricity from the grid; Carbon emission coefficient per unit of electricity purchased; , These are the unit carbon emission coefficients for power supply and heat supply in the power generation model of a combined heat and power unit; , These are the carbon emission coefficients per unit of electricity and heat supplied by the micro gas turbine, respectively. For carbon emissions from gas turbines, Electricity purchased from the external power grid.

[0067] With the significant increase in the proportion of clean energy power generation such as wind and solar, the resulting increase in carbon quotas, and the reduction in actual carbon emissions due to the application of P2G technology, the integrated energy system has achieved zero or even negative carbon emissions under the artificially controlled carbon trading market indicators. Therefore, the expansion from tiered positive carbon trading to tiered positive and negative carbon trading is a historical trend. Compared with the traditional carbon trading method, the tiered carbon trading market can use parameters such as base price, price growth rate, and interval length, while tiered positive and negative carbon trading further limits the carbon emissions of the system through a reward and punishment model. The mathematical model of tiered carbon trading under the reward and punishment model can be shown in formula (18): (18) In the formula: for The cost of carbon trading at any given time The base price for carbon trading per unit in the trading market. This is the carbon emission equivalent range. For price growth rate, Let this be the compensation coefficient. By identity transformation, we get: (19) The tiered carbon trading mechanism, due to its non-linear nature involving segmented pricing, needs to be linearized into a linear constraint that can be incorporated into a planning model. Its core is to divide the difference between net carbon emissions and certified carbon reductions into several intervals, each corresponding to a different carbon price coefficient, by introducing auxiliary variables. , , , , and and continuous variables , , , , , and Achieve linearization. The value range is divided into 7 key intervals, including 3 negative tiers, a zero point, and 3 positive tiers, each interval corresponding to a different price growth rate. Linearizing the tiered carbon trading mechanism yields: (20) The seven-point function is represented as shown in formula (21): (twenty one) (twenty two) in, , , , , , as well as These are the weighting coefficients.

[0068] The constraints are equivalently transformed into: (twenty three) Through the above linearization process, the original nonlinear carbon trading cost function is transformed into linear equality and inequality constraints with auxiliary variables, which can be directly embedded into the constraint system of the multi-stage robust capacity planning model to achieve coupled optimization constraints of carbon emission costs and equipment planning decisions; among them, equipment capacity investment cost is used to describe the cost of deploying energy equipment.

[0069] Specifically, integrated energy systems inevitably experience a certain degree of source load fluctuation during actual operation, therefore, the impact of source load uncertainties needs to be considered during the planning stage. The fluctuations in photovoltaic and wind power output, as well as cooling, heating, and electrical loads, are defined as a box-type uncertainty set U, as shown in formula (24): (twenty four) in, To represent an uncertain variable, an uncertain set can be taken. Any value in; , , , and The output of photovoltaic power, wind power, electrical load power, thermal load power, and cooling load power are represented in that order after considering uncertainties. , , , and These represent the predicted values ​​of photovoltaic power output, wind power output, electrical load power, thermal load power, and cooling load power, respectively, after considering uncertainties. , , , and These represent the maximum allowable fluctuations in photovoltaic power output, wind power output, electrical load power, thermal load power, and cooling load power, respectively.

[0070] However, directly applying the uncertainty set shown in formula (24) can lead to overly conservative planning, meaning that the source-load optimization results will all fall on the boundaries of the uncertainty set simultaneously, a situation that is extremely rare in reality. In the multi-stage robust planning problem of the integrated energy system of this invention, the photovoltaic output and wind power output values ​​are taken from the uncertainty set. The lower limit, while the electrical, heating and cooling loads take values ​​into the uncertain set. When the upper limit is reached, it is considered the most unfavorable situation for the planning problem. In addition, for the most unfavorable scenario of source-load time-series fluctuations, since it is often difficult to obtain a large number of relevant source-load time-series data samples during the planning stage before the park is built, the most unfavorable scenario of net power fluctuations between time series is not considered in the planning problem.

[0071] (25) in, , , , , , All are 0-1 variables; a value of 0 represents... The actual photovoltaic output, wind power output, electrical load, heat load, and cooling load at any given time are predicted values, with a value of 1 representing the actual output at any given time. The actual photovoltaic output, wind power output, electrical load, heat load, and cooling load at any given moment are on the boundary of the uncertainty set; This represents the uncertainty adjustment parameter for each uncertainty on a typical day in each year of the planning period; It can take the value 24.

[0072] In some embodiments, the objective programming function further includes limitations on equipment capacity investment cost, operation and maintenance cost, energy purchase cost, capture cost, storage cost, and carbon trading cost; wherein the equipment capacity investment cost is used to describe the cost of deploying energy equipment.

[0073] Specifically, the core objective is to minimize the total cost of the electricity-carbon coupled integrated energy system throughout its entire life cycle. The objective function covers multiple cost items, including equipment investment, operation and maintenance, energy procurement, carbon capture and storage, and carbon trading. The specific expression is as follows: Equipment capacity investment cost It can be shown in formula (26): (26) in, Equipment The unit capacity investment cost; Equipment The installed capacity; , These refer to the discount rate and the entire life cycle, respectively.

[0074] Operation and maintenance costs It can be shown in formula (27): (27) in, Equipment The unit operating and maintenance cost; Equipment of Output power during a given time period.

[0075] (28) The energy purchase cost can be represented by formula (29): (29) The capture cost can be expressed as shown in formula (30): (30) The cost of sealing can be expressed as shown in formula (31): (31) The carbon trading cost can be represented by formula (32): (32) In addition to formula (23), the capacity constraints of each device are expressed as shown in formula (33): (33) The power generation capacity of the combined heat and power (CHP) unit power generation model. This represents the minimum rated power output of the combined heat and power (CHP) unit power generation model. This represents the maximum rated power output of the combined heat and power (CHP) unit power generation model. The heat output of the combined heat and power (CHP) unit power generation model. The minimum rated heating power of the combined heat and power (CHP) unit power generation model. This represents the maximum rated heating power of the combined heat and power (CHP) unit power generation model. This refers to the operating power of the micro gas turbine. This is the minimum rated power of a micro gas turbine. This is the maximum rated power of the micro gas turbine; The operating power of the photovoltaic panel. This is the minimum rated power of the photovoltaic panel. This refers to the maximum rated power of the photovoltaic panel. This refers to the operating power of wind power generation. This is the minimum rated power for wind power generation. This is the maximum rated power of wind power generation; This refers to the operating power of the electrolytic cell. This is the minimum rated power of the electrolytic cell. This refers to the maximum rated power of the electrolytic cell; This refers to the operating power of the electric chiller. This is the minimum rated power of the electric chiller. This is the maximum rated power of the electric chiller; The operating power of the electric boiler. This is the minimum rated power of the electric boiler. This refers to the maximum rated power of the electric boiler. The operating power of the methane reactor, This is the minimum rated power for a methane reactor. This is the maximum rated power of the methane reactor; For the operating power of hydrogen fuel cells, This represents the minimum rated power of a hydrogen fuel cell. This represents the maximum rated power of the hydrogen fuel cell. The operating power of carbon capture, utilization and storage technology for power generation. This is the minimum rated power for power generation using carbon capture, utilization, and storage (CVC) technology. The maximum rated power for power generation using carbon capture, utilization and storage technology; This is the maximum rated power of the energy storage system. This is the minimum rated power of the energy storage system; The operating power of the thermal storage tank, This is the minimum rated power of the thermal storage tank. This is the maximum rated power of the thermal storage tank; The operating power of the hydrogen storage tank, This is the minimum rated power of the hydrogen storage tank. This represents the maximum rated power of the hydrogen storage tank. Due to limitations in equipment model and parameters, the output of each device in the integrated energy system must be less than its rated power. The integrated energy system needs to meet the power balance constraints of electricity, heat, cooling, and gas, as shown in the following formula: The power balance constraint is shown in equation (35): (35) The thermal power balance constraint is shown in equation (36): (36) The cold power balance constraint is shown in equation (37): (37) The hydrogen power balance constraint is shown in equation (38): (38) As an optional implementation, an objective programming function can be established based on a minimum cost constraint using wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power (CHP) unit power generation models, including: Using wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power (CHP) unit power generation models, the objective programming function is constructed as follows:

[0076] in, These are the decision variables for the first stage, representing the investment capacity of each piece of equipment; It is a set of source-load uncertainties, including the fluctuation values ​​of wind and solar power output and electricity-heat-cooling load; For the feasible region of uncertain variables; For the second-stage decision variables, it represents the situation under a given uncertainty scenario. The system operation decision. For equipment investment cost function, This is the system operating cost function. , , , , , , , , , , , Both are coefficient matrices formed by combining the previous constraints into a large matrix.

[0077] Specifically, (40) Among them, formula (39) is the abstract matrix representation of the above many formulas, and in formula (40) This is the set of decision variables for the first stage, representing the investment capacity of each piece of equipment; It is a set of source-load uncertainties, including the fluctuation values ​​of wind and solar power output and electricity-heat-cooling load; For the feasible region of uncertain variables; The set of decision variables for the second stage represents the variables under a given uncertainty scenario. The system operation decision. For equipment investment cost function, Z is the system operating cost function. Z is the set of 0-1 variables linearized from energy storage devices and the tiered carbon trading mechanism. By constructing such planning models that take into account both economic efficiency and low-carbon orientation, we can provide a more scientific basis for decision-making in the planning stage for system structure design, unit combination and operation strategy that is more in line with the "dual carbon" goal, thereby enhancing the comprehensive environmental and economic benefits of integrated energy systems throughout their entire life cycle.

[0078] Step S203: The objective planning function is split into an inner loop problem and an outer loop problem using a planning optimization algorithm. The inner loop problem is solved to obtain the initial scenario data. Based on the initial scenario data, the objective planning scheme is calculated using the outer loop problem.

[0079] In some embodiments, a hierarchical solution strategy is adopted, decomposing the overall objective programming function into two interrelated sub-problems: an inner loop and an outer loop. The inner loop problem mainly focuses on system operation simulation and typical scenario generation. By simulating multi-energy flow coupled operation, load characteristics, and renewable energy output, initial scenario data reflecting the basic operating state of the system is obtained. The outer loop problem, based on the scenario data output from the inner loop, comprehensively optimizes from the perspective of top-level planning, such as system structure design and equipment capacity configuration, and finally generates a planning scheme that meets economic and low-carbon goals.

[0080] Specifically, since the second-stage optimization problem of the obtained model contains 0-1 decision variables, the sub-problem stages of the commonly used C&CG algorithm for two-stage robust optimization cannot be directly solved using strong duality theory. Therefore, the NC&CG algorithm is used to solve this problem. The first stage determines the investment capacity of each device; the second stage optimizes the system's operational decisions under different scenarios and obtains the worst-case scenario. Two-stage robust optimization, through a max-min form objective function, can find a scenario with uncertain source loads. Under this scenario, the system's required second-stage operating cost is maximized, which is the worst-case scenario. If the first-stage planning scheme can meet the operational requirements under the worst-case scenario, it proves that the proposed planning scheme has good robustness.

[0081] In some embodiments, the outer loop problem that can be decomposed from the objective programming function using a planning optimization algorithm is shown in the following formula:

[0082] in, Minimize the introduced auxiliary variables It minimizes the objective function under the enumerated relaxation set S scenario; Represents the current set The number of scenarios in the problem is the number of times the original problem has been iterated. The formula for decomposing the objective programming function into an inner loop problem using a programming optimization algorithm is shown below:

[0083] The inner loop problem contains continuous variables. and discrete variables .

[0084] Furthermore, the NC&CG algorithm decomposes the problem into an outer column-and-constraints generation (C&CG) loop and an inner C&CG loop. The core idea is that the inner C&CG finds the most unfavorable scenario u and returns this unfavorable scenario to the outer C&CG. The outer C&CG enumerates the obtained scenarios and finds the first-stage decision scheme.

[0085] The outer loop is the master problem (MP). Each time the inner loop finds the most unfavorable scenario, it adds it to set S. Set S is a discrete subset of the uncertainty set U. The MP problem does not consider all scenarios in the uncertainty set U, so MP is a relaxation problem of the original problem, and set S is the relaxation set. MP enumerates all scenarios in the relaxation set S to find a first-stage decision variable that optimizes the objective function value under the current most unfavorable scenario. The MP model is shown in (41): (41) in, Minimize the introduced auxiliary variables It minimizes the objective function under the enumerated relaxation set S scenario; Represents the current set The number of scenarios in the problem is equal to the number of times the original problem has been iterated.

[0086] Solving for MP yields the continuous variables for the first-stage decision. and objective function Because MP does not consider uncertain sets. Therefore, it may not have considered the truly worst-case scenario, hence the corresponding objective function value. The value is not greater than the objective function value of the original problem. That is, the objective function obtained by MP solution corresponds to the lower bound of the original problem, and the lower bound of the original problem is updated. .

[0087] The inner loop is a subproblem (SP) of the original. The SP is the solution obtained by the MP. Given the input, determine the decision for this stage. The worst-case scenario The model for the subproblem SP is shown in (42): (42) This dual-loop structure effectively improves the feasibility and computational efficiency of model solving by decoupling operation optimization and top-level design, enhances the adaptability of planning results to multiple operating conditions, and provides important methodological support for the long-term low-carbon economic operation of the system.

[0088] The inner-level min problem of the subproblem also contains continuous variables. and discrete variables Therefore, this two-layer problem cannot be directly transformed into a single-layer problem using duality theory. It is necessary to use NC&CG to decompose the inner C&CG cycle into an inner MP and an inner SP.

[0089] Furthermore, the inner loop problem is broken down into the minimum subproblem and the maximum subproblem. The minimum subproblem is then further broken down using an optimization programming algorithm to obtain the minimum inner management problem and the minimum inner support problem. The minimum inner support problem is solved to obtain an optimized discrete set. The optimized discrete set contains multiple worst-case scenario data. The minimum inner management problem is used to enumerate and calculate the optimized discrete set to solve the minimum subproblem.

[0090] Specifically, it could be setting a lower bound for the inner loop problem. upper bound value Inner MP enumeration To find the most unfavorable The inner SP is based on the given... Find the best in this scenario And add it to the discrete set that MP needs to enumerate. , This represents all the inner MP that need to be enumerated. The set constitutes the problem. The specific solution steps are as follows: (43) Given the input u* to the inner MP, the inner SP problem is transformed into a single-layer problem that can be directly solved. However, the scenarios provided by the inner MP for the inner SP... It is not necessarily an indeterminate set In the most unfavorable scenario, the objective function of the inner SP solution corresponds to the lower bound of the inner C&CG loop. Solving for and updating the lower bound of the inner C&CG loop is then necessary. .

[0091] By breaking down the internal circulation problem in a hierarchical manner, we can systematically identify and optimize responses to operational risks, enhance the robustness of planning schemes against uncertainties, and provide key model support for the safe and stable operation of integrated energy systems in highly volatile environments.

[0092] Optionally, the maximum subproblem can be transformed into a minimum dual problem using Lagrange duality theory, and the minimum dual problem can be solved directly to solve the inner loop problem and calculate the initial scene data.

[0093] When dealing with the internal circulation problem in integrated energy system planning, the largest subproblem obtained after decomposition is transformed into an equivalent minimal dual problem using Lagrange duality theory. This transformation process integrates the original constraints into the objective function by introducing Lagrange multipliers, thereby reconstructing the complex original optimization problem into a dual form with better mathematical properties. Based on this, the obtained minimal dual problem is solved directly, and gradient-based algorithms or dual decomposition methods can usually be used to effectively obtain global or near-optimal solutions.

[0094] The discrete set obtained by MP enumeration in the inner loop and SP optimization in the inner loop In To find the discrete set worst-case scenario .enumerate After that, the inner min problem only involves continuous variables. At this point, the Lagrange duality theory can be used to transform the inner min problem into a max problem, thereby transforming the max-min bilayer problem into a max single-layer problem that can be solved by the solver. The simplified model of the inner MP using duality theory is shown in equation (44): (44) in, , , , These are the introduced Lagrange dual variables, corresponding to the inequality constraints and equality constraints in the constraints, respectively. Represents the enumerated discrete set In The number of iterations is equal to the number of iterations the inner loop has already performed.

[0095] Because there are dual variables in formula (44) With uncertain variables The multiplied bilinear terms will make the equation an uncertain variable. Substitute into formula (44) and linearize using the Big M method.

[0096] (45) in, It is an introduced auxiliary variable; when , ;when , ; This represents the uncertain variables of the source load in the forecast. When the wind and solar power output on the source side reaches the lower limit of the uncertainty set, and the electric, heat, and cooling loads on the load side reach the upper limit of the uncertainty set, the objective function of the inner MP is maximized, which conforms to the most unfavorable scenario. Therefore, we can conclude that: .

[0097] The maximum subproblem is transformed into a minimum dual problem using Lagrange duality theory, and this minimum dual problem is directly solved to obtain the inner loop problem. Initial scenario data is then calculated, preserving the optimization characteristics of the original problem while significantly improving computational efficiency and numerical stability. By solving this minimum dual problem, the overall solution to the inner loop problem is achieved, and initial scenario data reflecting the typical operating characteristics of the system is obtained. This provides a crucial input foundation for the system-level planning optimization of the outer loop, thereby enhancing the overall planning model's solveability and engineering practicality under complex constraints.

[0098] In some embodiments, if it is determined that the initial planning scheme does not meet the operational requirements in the objective programming function, then the upper and lower bounds of the planning optimization algorithm are updated; if the upper and lower bounds do not satisfy the formula... Then, a new initial scene data is obtained by resolving the problem; where, For the upper realm, The lower realm The upper and lower bounds are constrained parameters; if the upper and lower bounds satisfy the formula... Then, the target planning scheme is calculated based on the initial scenario data through the outer loop problem.

[0099] The inner MP does not take into account all possible scenarios. discrete sets It does not necessarily include the formula (45) that truly minimizes it. Therefore, the objective function of the inner MP loop corresponds to the upper bound of the inner C&CG loop. Solve for and update the upper bound of the inner C&CG loop:

[0100] Furthermore, if the upper and lower bounds of the inner C&CG loop do not satisfy the following formula:

[0101] in, If the initial planning scheme is set to 0.001, and it is determined that the initial planning scheme does not meet the operational requirements of the objective programming function, then the lower and upper bounds of the inner loop problem are reset, and the iteration loop continues. If it is determined that the initial planning scheme meets the operational requirements of the objective programming function, then the inner loop ends, and the worst-case scenario is output. Solving equation (42) yields the objective function of the inner C&CG loop. Because MP is input to SP in the first stage of decision-making. It is not necessarily optimal for the original problem, so the objective function solved by SP corresponds to the upper bound of the original problem, and the upper bound of the original problem is updated. .

[0102] Determine if the iteration of the original problem has ended:

[0103] in, If the difference between the upper and lower bounds of the original problem does not satisfy the formula (47), then continue to solve the outer C&CG loop and the inner C&CG loop; if the formula (47) is satisfied, the iteration can be stopped, and the optimization decision and objective function of the original problem can be obtained.

[0104] This boundary control and convergence judgment mechanism effectively ensures the numerical stability and solution efficiency of the planning model during the iteration process. By dynamically adjusting the solution range, it ensures that the final solution not only meets the operational constraints but also has good engineering applicability.

[0105] Figure 3This is a schematic diagram of the topology of an electro-carbon coupled integrated energy system in one embodiment, such as... Figure 3 As shown, renewable energy power generation equipment such as photovoltaic and wind power directly supplies electricity through clean energy, reducing initial carbon emissions; simultaneously, it is connected to the grid as an external energy supplement, meeting the power balance requirements of the integrated energy system coupled with electricity and carbon. Combined heat and power (CHP) units and micro gas turbines, as core energy supply equipment, output electricity and heat power by burning fuels such as natural gas, accompanied by carbon dioxide emissions, which are the main source of system carbon emissions. Carbon dioxide generated by CHP units and micro gas turbines is captured through a carbon capture, utilization, and storage (CCUS) system, forming a linkage mechanism of "energy consumption-carbon emissions-carbon capture". Part of the captured carbon dioxide enters the methane reactor, reacting with hydrogen produced by the electrolyzer to generate natural gas, achieving carbon recycling; the other part can be directly stored, reducing net carbon emissions. Battery energy storage, thermal storage tanks, and hydrogen storage tanks are configured to achieve the spatiotemporal transfer of electricity, heat, and hydrogen respectively: battery energy storage smooths out fluctuations in wind and solar power output, thermal storage tanks match peak-valley differences in heat load, and hydrogen storage tanks store hydrogen produced by the electrolyzer, providing feedstock for the methane reactor or hydrogen fuel cell. Hydrogen fuel cells can convert hydrogen energy into electrical and thermal power, improving the system's energy supply flexibility. Through auxiliary equipment such as electric chillers and electric boilers, electrical energy is converted into cooling and heating power to meet the diverse load demands of users, forming a multi-energy complementary energy supply network of "electricity-heat-cooling-hydrogen". The system calculates net carbon emissions and connects with external tiered carbon trading markets, enabling the buying and selling of carbon allowances based on certified carbon reductions, thus internalizing carbon emission costs and ultimately constructing a closed-loop system of "energy production-consumption-carbon governance-market trading".

[0106] Steps S201 to S203 as illustrated in this embodiment involve establishing wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power (CHP) unit power generation models; establishing a target programming function based on minimum cost constraints using these models; the target programming function includes constraints on carbon emissions from gas turbine power generation and CHP unit power generation; decomposing the target programming function into an inner loop problem and an outer loop problem using a planning optimization algorithm; solving the inner loop problem to obtain initial scenario data; and calculating the target programming scheme based on the initial scenario data using the outer loop problem. The established wind power generation model, photovoltaic power generation model, gas turbine power generation model, and CHP unit power generation model are then used to achieve the desired outcome. The combined heat and power (CHP) unit generation model establishes a target programming function based on minimum cost constraints. This function can constrain the planning of energy models included in the integrated energy system. Through a planning optimization algorithm, the target programming function is decomposed into an inner-loop problem and an outer-loop problem. The inner-loop problem is solved to obtain initial scenario data. Based on the initial scenario data, the target planning scheme is calculated through the outer-loop problem. By simulating typical operating conditions, load characteristics, and renewable energy output characteristics, initial scenario data reflecting the basic operating state of the system is obtained. By decoupling operation optimization and planning design, the feasibility and computational efficiency of the model solution can be effectively improved, and the adaptability of the planning scheme to multiple operating scenarios can be enhanced. This provides more scientific and systematic decision support for the actual deployment of integrated energy systems.

[0107] Please see Figure 4 This application also provides a comprehensive energy planning device that can implement the above-described method. The device includes: Model building module 401 is used to build wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power unit power generation models; The function establishment module 402 is used to establish a target programming function based on the minimum cost constraint using wind power generation model, photovoltaic power generation model, gas turbine power generation model, and combined heat and power unit power generation model; the target programming function includes the carbon emission constraints for gas turbine power generation and combined heat and power unit power generation. The problem-solving module 403 is used to decompose the objective programming function into an inner loop problem and an outer loop problem through a planning optimization algorithm, solve the inner loop problem to obtain the initial scenario data, and calculate the objective programming scheme based on the initial scenario data through the outer loop problem.

[0108] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0109] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0110] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0111] Please see Figure 5 , Figure 5 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application. The memory 502 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called and executed by the processor 501 using the methods described in the embodiments of this application. The input / output interface 503 is used to implement information input and output; The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504); The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0112] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0113] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0114] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0115] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0116] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0117] The integrated energy planning method, apparatus, electronic equipment, storage medium, and program product provided in this application establish wind power generation models, photovoltaic power generation models, gas turbine power generation models, and combined heat and power (CHP) unit power generation models. Based on minimum cost constraints, a target planning function is established using these models. The target planning function includes constraints on carbon emissions from gas turbine power generation and CHP unit power generation. A planning optimization algorithm decomposes the target planning function into an inner-loop problem and an outer-loop problem. The inner-loop problem is solved to obtain initial scenario data. Based on the initial scenario data, the outer-loop problem is used to calculate the target planning scheme. This effectively improves the feasibility and computational efficiency of the model solution, enhances the adaptability of the planning scheme to multiple operating scenarios, and thus provides more scientific and systematic decision support for the actual deployment of integrated energy systems.

[0118] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0119] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0120] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0121] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0122] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0123] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0124] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0125] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0126] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0127] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0128] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A comprehensive energy planning method, characterized in that, The method includes the following steps: Establish models for wind power generation, photovoltaic power generation, gas turbine power generation, and combined heat and power (CHP) unit power generation; Objective programming functions are established based on minimum cost constraints using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power (CHP) unit power generation model. The objective programming function includes constraints on the carbon emissions of the gas turbine power generation and the CHP unit power generation. The objective planning function is decomposed into an inner loop problem and an outer loop problem using a planning optimization algorithm. The inner loop problem is solved to obtain the initial scenario data. Based on the initial scenario data, the objective planning scheme is calculated using the outer loop problem.

2. The method according to claim 1, characterized in that, The objective programming function also includes limitations on equipment capacity investment cost, operation and maintenance cost, energy purchase cost, capture cost, storage cost, and carbon trading cost; wherein, the equipment capacity investment cost is used to describe the cost of deploying energy equipment.

3. The method according to claim 2, characterized in that, The step of establishing an objective programming function based on the minimum cost constraint using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power unit power generation model includes: The objective programming function is constructed using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power unit power generation model: in, These are the decision variables for the first stage, representing the investment capacity of each piece of equipment; It is a set of source-load uncertainties, including the fluctuation values ​​of wind and solar power output and electricity-heat-cooling load; For the feasible region of uncertain variables; For the second-stage decision variables, it represents the situation under a given uncertainty scenario. The system operation decision. For equipment investment cost function, This is the system operating cost function.

4. The method according to claim 1, characterized in that, The step of decomposing the objective programming function into an inner loop problem and an outer loop problem using a planning optimization algorithm includes: The formula for decomposing the objective programming function into an outer loop problem using a planning optimization algorithm is shown below: in, Minimize the introduced auxiliary variables It minimizes the objective function under the enumerated relaxation set S scenario; Represents the current set The number of scenarios in the problem is the number of times the original problem has been iterated. The formula for decomposing the objective programming function into an inner loop problem using a planning optimization algorithm is shown below: The inner loop problem contains continuous variables. and discrete variables .

5. The method according to claim 4, characterized in that, Solving the inner loop problem to obtain initial scene data includes: The inner loop problem is decomposed into the minimum subproblem and the maximum subproblem. The minimum subproblem is then decomposed into the minimum inner management problem and the minimum inner support problem using an optimization planning algorithm. Solving the minimum inner support problem yields an optimized discrete set; the optimized discrete set contains data from multiple worst-case scenarios. The minimum subproblem is solved by enumerating and calculating the optimized discrete set through the minimum inner management problem.

6. The method according to claim 5, characterized in that, After decomposing the inner loop problem to obtain the minimum and maximum subproblems, the method further includes: The maximum subproblem is transformed into a minimum dual problem using Lagrange duality theory, and the minimum dual problem is solved directly to solve the inner loop problem and calculate the initial scene data.

7. The method according to any one of claims 1 to 5, characterized in that, Before calculating the target planning scheme based on the initial scenario data through the outer loop problem, the method further includes: If it is determined that the initial planning scheme does not meet the operational requirements in the target planning function, then the upper and lower bounds of the planning optimization algorithm are updated. If the upper bound and the lower bound do not satisfy the formula Then, a new initial scene data is obtained by resolving the problem; wherein, the... As the upper bound, the stated As the lower bound, the stated To limit parameters; If the upper bound and the lower bound satisfy the formula Then, based on the initial scenario data, the target planning scheme is calculated through the outer loop problem.

8. A comprehensive energy planning device, characterized in that, The device includes: The model building module is used to build models for wind power generation, photovoltaic power generation, gas turbine power generation, and combined heat and power (CHP) unit power generation. The function establishment module is used to establish a target programming function based on the minimum cost constraint using the wind power generation model, the photovoltaic power generation model, the gas turbine power generation model, and the combined heat and power unit power generation model; the target programming function includes the constraint conditions on the carbon emissions of the gas turbine power generation and the combined heat and power unit power generation. The problem-solving module is used to decompose the target planning function into an inner loop problem and an outer loop problem through a planning optimization algorithm, solve the inner loop problem to calculate the initial scene data, and calculate the target planning scheme based on the initial scene data through the outer loop problem.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

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