Low-carbon integrated energy system modeling method based on hybrid energy storage system

Abstract

The invention provides a low-carbon integrated energy system modeling method based on a hybrid energy storage system. And constructing a multi-dimensional constraint and cost optimization objective function, and establishing a global optimization framework of the integrated energy system. Wherein the balance constraint and the assembly constraint ensure dynamic matching of electric / hot / cold / gas multi-energy flow and cooperative operation of equipment, and the problem of unbalanced supply and demand of a traditional system is solved. The carbon emission constraint is divided through a dynamic carbon valence interval, the carbon emission and the carbon cost are in nonlinear correlation, a low-carbon technology path is guided, the green certificate constraint converts excess renewable energy power generation into economic benefits, and dual excitation of environmental protection and economy is formed. And the target function comprehensively optimizes equipment investment, maintenance cost and market income, and drives multi-energy complementation and full life cycle cost optimization. The system is adaptive to renewable energy fluctuation, load change and energy price fluctuation through optimal configuration of system output and equipment capacity, and economical and efficient operation under the low-carbon target is achieved.

Description

Technical Field

[0001] The present application relates to the fields of energy conversion and energy storage, and in particular to a low-carbon integrated energy system modeling method based on a hybrid energy storage system. Background Art

[0002] An integrated energy system can convert multiple forms of energy, using different types of energy carriers to optimize resource allocation. Specifically, it can systematically coordinate the consumption, production, and conversion of various energy forms. Using energy conversion equipment, it can achieve multi-energy conversion and flexible control of energy scheduling.

[0003] However, integrated energy systems suffer from inflexible scheduling and a struggle to balance economic and environmental performance. Therefore, a low-carbon integrated energy system modeling method based on hybrid energy storage systems is needed to enhance the complementary capabilities of various energy conversion devices within the system, thereby improving the system's energy conversion performance and energy scheduling flexibility while ensuring the system's economic performance and environmental performance. Summary of the Invention

[0004] The purpose of this application is to solve at least one of the above-mentioned technical defects, especially the technical defects in the existing technology that the integrated energy system scheduling is not flexible enough and the economy and environmental performance cannot be taken into account at the same time.

[0005] In a first aspect, the present application provides a low-carbon integrated energy system modeling method based on a hybrid energy storage system, the method comprising:

[0006] Determine constraints and objective functions based on the component distribution of the integrated energy system;

[0007] The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system;

[0008] According to the value of the objective function, the operating parameters of the integrated energy system are adjusted.

[0009] As an optional embodiment, the integrated energy system includes an electric, thermal, and cooling gas coupling network, wherein the coupling network includes an electric power system, a thermal system, a cooling system, and a gas reaction system;

[0010] Wherein, the energy storage unit includes a short-term energy storage unit and a long-term energy storage unit;

[0011] The power system includes a power supply unit and an energy storage unit. The power supply unit includes a renewable energy unit, an external power grid, a combined cooling, heating and power (CCHP) device, and a reversible solid oxide fuel cell (RSOC). The short-term energy storage unit is used to balance intraday power supply and demand fluctuations.

[0012] The thermal system is collaboratively heated by the CCHP, the RSOC power generation mode, and the gas boiler, and the heat load fluctuation is adjusted by the short-term energy storage unit;

[0013] The refrigeration system is provided by the CCHP in cooling mode in combination with an electric chiller, and the cooling load demand is matched by the short-term energy storage unit;

[0014] The gas reaction system synthesizes methane through the hydrogen generated by the RSOC through the external gas source input, and stores it in the long-term energy storage unit.

[0015] As an optional embodiment, the objective function includes a system cost function, which is used to indicate the difference between the total system cost and the total system benefit. The total system cost includes one or more of equipment investment cost, equipment maintenance cost, renewable energy penalty cost, carbon trading cost and energy purchase cost. The total system benefit includes energy sales benefit and / or green certificate benefit.

[0016] As an optional embodiment, the balance constraints include one or more of power balance, thermal balance, cooling capacity balance, hydrogen balance and methane balance, and the component constraints include one or more of renewable energy unit model, CCHP model, RSOC model, one-way energy transfer equipment model and energy storage system model.

[0017] As an optional implementation, the carbon emission constraint conditions include:

[0018] Dynamically divide carbon price ranges based on the difference between system carbon emissions and carbon emission quotas, and set incremental carbon trading costs based on the difference between the carbon price ranges;

[0019] Furthermore, the green certificate constraints include:

[0020] The portion of renewable energy power generation that exceeds the preset power generation reference value is converted into green certificate income.

[0021] As an optional implementation, the capacity configuration strategy of the short-term energy storage unit includes:

[0022] Determining the optimal economic capacity configuration of each energy storage unit based on the optimization results corresponding to the operating parameters of the integrated energy system;

[0023] Based on the renewable energy output forecast curve, optimize the energy storage charging and discharging time window to maximize the absorption of renewable energy;

[0024] Set energy storage charging and discharging efficiency constraints to limit energy conversion losses;

[0025] And, the scheduling strategy of the long-term energy storage unit includes:

[0026] During peak periods of renewable energy output, excess electricity is used to electrolyze water through RSOC to produce hydrogen and synthesize methane for storage;

[0027] During periods of renewable energy shortage, the stored hydrogen and methane can be released for use in CCHP and gas boilers;

[0028] Reduce external energy supply requirements based on inter-seasonal energy transfer conditions.

[0029] As an optional implementation manner, the switching condition of the RSOC working mode includes:

[0030] When the power supply-demand ratio is greater than the preset upper limit, it switches to electrolysis mode to produce hydrogen;

[0031] When the power supply-demand ratio is less than the preset lower limit, it switches to power generation mode;

[0032] The preset upper limit and the preset lower limit are adjusted in combination with the real-time carbon trading price signal dynamics.

[0033] In a second aspect, the present application provides a low-carbon integrated energy system modeling device based on a hybrid energy storage system, the device comprising:

[0034] a determination module for determining constraints and objective functions based on the component distribution of the integrated energy system;

[0035] The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system;

[0036] The processing module is used to adjust the operating parameters of the integrated energy system according to the value of the objective function.

[0037] In a third aspect, the present application provides a computer device comprising one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method described in the first aspect are performed.

[0038] In a fourth aspect, the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the method described in the first aspect.

[0039] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages:

[0040] Based on any of the above-mentioned embodiments, the low-carbon integrated energy system modeling method proposed in this application constructs a market-driven energy optimization system through the deep integration of a multi-energy complementary architecture and hierarchical energy storage technology. At the system structure level, the coupled design of the electric, heat, and cooling multi-energy flow network breaks the isolated operation mode of traditional energy systems. The coordinated control of CCHP and RSOC enables on-demand conversion of multiple energy forms. Short-term energy storage units effectively smooth intraday load fluctuations, and the long-term energy storage system solves the spatiotemporal mismatch of energy supply and demand through cross-seasonal storage of hydrogen and methane. At the operational optimization level, the combined effect of a dynamic carbon price interval mechanism and a green certificate revenue model transforms environmental constraints into quantifiable economic parameters, driving the system to proactively reduce carbon emissions under the most cost-effective path. The coordinated modeling of energy conversion equipment and long- and short-term hybrid energy storage ensures optimal resource allocation within the system's safety boundaries. The real-time adjustment function of equipment operating parameters enhances adaptability to renewable energy volatility and market changes. In terms of technological innovation, the RSOC dual-mode switching strategy and the energy storage capacity hierarchical optimization algorithm significantly improve the comprehensive utilization efficiency of key equipment. The energy scheduling mechanism based on supply-demand ratio thresholds achieves precise matching of source, grid, load, and storage. Driven by both market mechanisms and technological innovation, this solution significantly improves the renewable energy absorption rate, reduces carbon emission intensity, and increases the utilization rate of energy storage systems while ensuring the system's economic efficiency. It provides a technically feasible and commercially sustainable solution for building a new power system, and effectively solves the problem of difficulty in coordinating the optimization of economic and environmental performance during the low-carbon transformation of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0042] Figure 1 A flowchart of a low-carbon integrated energy system modeling method based on a hybrid energy storage system provided in one embodiment of the present application;

[0043] Figure 2 A seasonal load and renewable energy parameter data graph provided for one embodiment of the present application;

[0044] Figure 3 An electricity-heating-cooling power balance scheduling diagram provided for one embodiment of the present application;

[0045] Figure 4 An operating curve of a hybrid energy storage system provided in one embodiment of the present application;

[0046] Figure 5 The system cost structure under different solutions provided for one embodiment of the present application;

[0047] Figure 6 A sensitivity analysis diagram of carbon quota trading and green certificate trading provided for one embodiment of this application;

[0048] Figure 7 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0050] The dramatic increase in load demand and the rapid decline in fossil energy resources have made the use of renewable energy a top priority. In the face of a mismatch between electricity load and renewable energy, integrated energy systems have been promoted and widely applied to improve the utilization of different energy types and reduce CO2 emissions. Integrated energy systems (IES) can convert multiple forms of energy and use different types of energy carriers to optimize resource allocation. Energy storage systems can store energy to address the temporal and spatial mismatch between load and renewable energy peaks. To achieve the "dual carbon goals," integrated energy systems that include hybrid energy storage systems have become a research hotspot for improving renewable energy absorption capacity and creating higher economic benefits.

[0051] IES can systematically coordinate the consumption, production, and conversion of various forms of energy. In recent years, natural gas streams have become an important energy carrier, in addition to electric heating and cooling loads. Hydrogen and natural gas are clean, sustainable energy carriers that can replace fossil fuels and reduce CO2 emissions. Utilizing energy conversion devices such as reversible solid oxide cells (RSOCs) and combined cooling, heating, and power (CCHPs), IES can achieve multi-energy conversion and flexible energy dispatch. However, little literature has addressed the complementary nature of RSOCs and CCHPs when utilizing different forms of electricity. Most current research focuses solely on system economics, ignoring the current need to reduce emissions and addressing green and low-carbon development requirements.

[0052] Currently, both the electricity and natural gas systems experience significant seasonal fluctuations in power generation and load. Wind power output is concentrated in winter and early spring, with lower output in summer. This creates a growing conflict between the seasonal peaks and valleys in natural gas production during winter and summer, necessitating the exploration of seasonal energy storage measures for both systems. Short-term energy storage is used for intraday load reduction and can significantly increase renewable energy consumption. Long-term energy storage focuses more on seasonal energy transfers, but suffers from inflexible scheduling and the ability to maintain a single state for extended periods. However, it can stabilize seasonal imbalances in renewable energy and load, achieving the effect of seasonal energy storage. Energy hubs enable the conversion of multiple energy sources, improving energy efficiency. CCHP systems and RSOCs are two common energy conversion devices. Combining energy conversion devices with short- and long-term energy storage can significantly improve the utilization of all energy sources in the system, playing a supporting role in supporting a high proportion of renewable energy.

[0053] Driven by the dual carbon goals, countries around the world have begun leveraging external markets to regulate their energy systems and reduce carbon emissions. Currently, there are two main types of carbon markets: Carbon Emission Trading (CET) and Green Certificate Trading (GCT). CET uses a "penalty" approach, reducing emissions by increasing the cost of carbon emissions. Green Certificate Trading (GCT) uses a "positive" approach, encouraging renewable energy production through incentives. By incorporating these carbon trading methods into the system's objective function, they influence system costs to control the system's total carbon emissions. Once a carbon trading market is established, the integrated energy system will reduce carbon emissions based on carbon market prices and actual conditions.

[0054] In summary, some feasible implementations have the following limitations:

[0055] Insufficient complementarity in energy conversion devices: While existing technologies enable the conversion of multiple energy sources through devices such as reversible oxide solid state cells (RSOCs) and combined cooling, heating, and power systems (CCHPs), little literature addresses the complementarity of these devices when utilizing different forms of electricity. This results in a lack of flexibility in energy conversion and scheduling, preventing the system from fully leveraging the advantages of different energy sources.

[0056] Ignoring green and low-carbon demands: Most current research focuses on the economics of the system, ignoring the need to reduce carbon emissions. With the introduction of the "dual carbon goals," existing technologies fail to effectively integrate green and low-carbon demands, resulting in limited effectiveness in reducing carbon emissions.

[0057] Inflexible seasonal energy storage scheduling: While existing long-term energy storage systems can stabilize seasonal imbalances in renewable energy and load, their scheduling is inflexible and they can only maintain a single state for a long time. Short-term energy storage, used for intraday load reduction, can significantly increase renewable energy consumption, but in the long term, capacity reduction is more significant and costs are higher. This limits the system's flexibility and efficiency in responding to seasonal fluctuations.

[0058] Inadequate system integration of carbon trading mechanisms: While carbon trading mechanisms (such as carbon trading markets and greenhouse gas emissions control) have been introduced to reduce carbon emissions, they are insufficiently integrated into the overall energy system. Existing technologies fail to fully leverage price signals from the carbon trading market to optimize the system's carbon reduction strategies, resulting in insignificant carbon reduction results.

[0059] This application can improve the complementarity of energy conversion equipment, and by optimizing the coordinated work of energy conversion equipment such as RSOC and CCHP, improve the complementarity between different forms of electric energy, thereby enhancing the energy conversion and scheduling flexibility of the system; in the process of incorporating green and low-carbon demands into the design and optimization of the integrated energy system, by introducing low-carbon technologies and strategies, significantly reduce the carbon emissions of the system, and help achieve the "dual carbon goals"; improve the scheduling strategy of the long-term energy storage system so that it can respond more flexibly to seasonal fluctuations, and improve the efficiency of the system in seasonal energy storage and load balancing; deeply integrate carbon trading mechanisms (such as CET and GCT) with the integrated energy system, use the price signals of the carbon trading market, optimize the system's carbon emission reduction strategy, and thus significantly improve the system's carbon emission reduction effect.

[0060] Through the above improvements, this application improves the energy utilization efficiency, flexibility and carbon emission reduction capabilities of the integrated energy system, thereby better responding to the sharp increase in load demand and the rapid reduction of fossil energy, and promoting the widespread application of renewable energy.

[0061] In summary, the technical concept of this application is that the low-carbon integrated energy system modeling method proposed in this application constructs a market-driven energy optimization system through the deep integration of multi-energy complementary architecture and hierarchical energy storage technology. At the system structure level, the coupled design of the electric, heat and cooling multi-energy flow network breaks the isolated operation mode of the traditional energy system. The coordinated control of CCHP and RSOC realizes the on-demand conversion of multiple energy forms. The short-term energy storage unit effectively smooths the intraday load fluctuations. The long-term energy storage system solves the spatiotemporal mismatch problem of energy supply and demand through hydrogen-methane cross-seasonal storage. At the operational optimization level, the combined effect of the dynamic carbon price interval mechanism and the green certificate income model transforms environmental constraints into quantifiable economic parameters, driving the system to actively reduce carbon emissions under the cost-optimal path. The coordinated modeling of energy conversion equipment and long-term and short-term hybrid energy storage ensures that the system achieves the optimal resource configuration within the safety boundary. The real-time adjustment function of the equipment operating parameters enhances the adaptability to the volatility of renewable energy and changes in the market environment. In terms of technological innovation, the RSOC dual-mode switching strategy and energy storage capacity tiered optimization algorithm significantly improve the comprehensive utilization efficiency of key equipment. An energy dispatch mechanism based on supply-demand ratio thresholds achieves precise matching of sources, grids, loads, and storage. Driven by both market mechanisms and technological innovation, this solution significantly improves the renewable energy absorption rate, reduces carbon emissions, and increases energy storage system utilization while ensuring system economics. This provides a technically feasible and commercially sustainable solution for building a new power system, effectively addressing the difficulty in coordinating economic and environmental optimization during the low-carbon transition of integrated energy systems.

[0062] The method provided in this application is described in detail below based on corresponding implementation methods in some actual application scenarios.

[0063] See also Figure 1 , Figure 1 A flow chart of a low-carbon integrated energy system modeling method based on a hybrid energy storage system provided in one embodiment of the present application is shown in FIG. Figure 1 As shown, the method includes:

[0064] S101. Determine constraints and objective functions based on component distribution of the integrated energy system;

[0065] The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system;

[0066] S102: Adjust the operating parameters of the integrated energy system according to the value of the objective function.

[0067] This application constructs a multi-dimensional constraint and cost optimization objective function to establish a global optimization framework for an integrated energy system. Among them, balance constraints and component constraints ensure the dynamic matching of electricity / heat / cold / gas multi-energy flows and the coordinated operation of equipment, solving the problem of imbalance between supply and demand in traditional systems. Carbon emission constraints divide carbon prices into dynamic intervals, nonlinearly linking carbon emissions with carbon costs, and guide low-carbon technology paths. Green certificate constraints convert excess renewable energy power generation into economic benefits, forming a dual incentive for environmental protection and economy. The objective function comprehensively optimizes equipment investment, maintenance costs and market benefits, driving multi-energy complementarity and optimal life cycle costs. The system adapts to renewable energy fluctuations, load changes and energy price fluctuations through dynamic parameter adjustment to achieve economical and efficient operation under low-carbon goals.

[0068] As an optional embodiment, the integrated energy system includes an electric, thermal, and cooling gas coupling network, wherein the coupling network includes an electric power system, a thermal system, a cooling system, and a gas reaction system;

[0069] Wherein, the energy storage unit includes a short-term energy storage unit and a long-term energy storage unit;

[0070] The power system includes a power supply unit and an energy storage unit. The power supply unit includes a renewable energy unit, an external power grid, a combined cooling, heating and power (CCHP) device, and a reversible solid oxide fuel cell (RSOC). The short-term energy storage unit is used to balance the fluctuations in power supply and demand during the day.

[0071] The thermal system is collaboratively heated by the CCHP, the RSOC power generation mode, and the gas boiler, and the heat load fluctuation is adjusted by the short-term energy storage unit;

[0072] The refrigeration system is provided by the CCHP in cooling mode in combination with an electric chiller, and the cooling load demand is matched by the short-term energy storage unit;

[0073] The gas reaction system synthesizes methane through the hydrogen generated by the RSOC through the external gas source input, and stores it in the long-term energy storage unit.

[0074] The multi-energy coupled network for electric heating and cooling designed in this application achieves deep coordination of energy forms and cross-timescale regulation. The coordinated power supply of renewable energy units in the power system and the external grid, combined with the bidirectional energy conversion capabilities of CCHP and RSOC, forms a multi-source complementary power supply structure, enhancing renewable energy absorption capacity. Short-term energy storage units rapidly respond to intraday load fluctuations, effectively mitigating the impact of intermittent wind and solar power output on system stability. The thermal system combines CCHP and RSOC power generation modes for heat supply, combined with the backup regulation capabilities of gas-fired boilers, to ensure heat supply reliability while reducing fossil energy consumption. The cooling system utilizes CCHP cooling mode and coordinated control of electric chillers to optimize cooling efficiency and enable peak-valley load shifting. The gas reaction system synthesizes methane from hydrogen produced by RSOC electrolysis and stores it in long-term energy storage units, overcoming seasonal storage capacity limitations and addressing inter-cycle energy imbalances. The closed-loop design of the multi-energy flow network enables energy complementarity among the subsystems, improving overall energy efficiency.

[0075] As an optional embodiment, the objective function includes a system cost function, which is used to indicate the difference between the total system cost and the total system benefit. The total system cost includes one or more of equipment investment cost, equipment maintenance cost, renewable energy penalty cost, carbon trading cost and energy purchase cost. The total system benefit includes energy sales benefit and / or green certificate benefit.

[0076] In this application, an IES system is established to implement the corresponding method, which considers a hybrid energy storage and conversion system as shown in the figure. The goal of IES is to achieve the lowest total system cost, which is defined as

[0077] C IES = C in + C e + C y + C pum -C gre -C sell (1)

[0078] Where C IES is the cost function of IES, C in Energy purchase cost, C e The one-time investment cost of different equipment, C y is the operating cost of the equipment, C pum is the penalty cost for curtailing renewable energy and demand response. C gre Indicates the green certificate income, C sell Represents the revenue from electricity sales.

[0079] The system's electricity, heat, cold, hydrogen, and methane balances are as follows:

[0080] (2)

[0081] (3)

[0082] (4)

[0083] (5)

[0084] (6)

[0085] The subscript t indicates the current cycle t or the previous cycle t-1. P, H, U, V, and G represent electricity, heat, cooling, hydrogen, and methane, respectively. Superscripts indicate their corresponding sources. For example, Formula 2 shows the power balance between the sum of the current PV generation, CCHP generation, RSOC generation, purchased power grid, and energy storage discharge power from the previous cycle and the current sum of RSOC electrolysis, energy storage charging, cooling power, and the electrical load.

[0086] The system cost function proposed in this application achieves total factor economic optimization through the dynamic balance of multi-dimensional costs and benefits. The normalized modeling of equipment investment costs simplifies the complexity of traditional periodic accounting and ensures the accuracy of economic forecasts in the planning stage. The introduction of renewable energy penalty costs drives the model to prioritize the consumption of clean energy and reduce the phenomenon of wind and solar power abandonment. The nonlinear calculation model of carbon trading costs accurately reflects the price transmission effect of the carbon emission rights market, prompting the system to actively choose low-carbon technology paths at the operational level. The dynamic hedging mechanism of energy purchase costs and sales revenue enhances the flexibility of the system in participating in energy market transactions. The quantitative modeling of green certificate revenue converts environmental protection indicators into economic parameters, forming a dual incentive mechanism of environmental and economic benefits. Through the comprehensive optimization of multiple cost items, the system automatically selects the resource allocation plan with the best life cycle cost under the premise of meeting low-carbon constraints.

[0087] As an optional embodiment, the balance constraints include one or more of power balance, thermal balance, cooling capacity balance, hydrogen balance and methane balance, and the component constraints include one or more of renewable energy unit model, CCHP model, RSOC model, one-way energy transfer equipment model and energy storage system model.

[0088] In some specific implementations, the renewable energy unit model uses a photovoltaic power station as a reference. Photovoltaic power generation consists of grid connection and photovoltaic power curtailment, as shown below:

[0089] (7)

[0090] (8)

[0091] Where, is the predicted photovoltaic power generation power, is the actual grid-connected photovoltaic power, It is the photovoltaic power reduction. is the solar power generation coefficient. is the capacity of the photovoltaic generator.

[0092] RSOC can operate in solid oxide fuel cell (SOFC) or solid oxide electrolysis cell (SOEC) mode.

[0093] (9)

[0094] (10)

[0095] In the formula is the calorific value of hydrogen, which is 33 kWh / kg. , It is the efficiency of converting hydrogen into electricity and heat.

[0096] For the SOEC mode of RSOC, the reaction consumes electrical energy to generate chemical energy in hydrogen:

[0097] (11)

[0098] In the formula is the conversion efficiency of electricity to hydrogen.

[0099] Since RSOC can only work in one mode at a time, the 0-1 variable is used in the formula to represent the working state of RSOC.

[0100] (12)

[0101] (13)

[0102] In the formula , Represents the power generation and electrolysis status of RSOC at time t. Indicates the capacity of RSOC.

[0103] The one-way energy transfer equipment model takes the methanation reactor / gas boiler / electric compressor model as an example. These three devices are all one-way energy conversion devices. Taking the methanation equipment as an example, during the methanation process, the methane production can be calculated by considering the energy loss of hydrogen, as shown in the following formula.

[0104] (14)

[0105] In the formula is the efficiency of the methanation reaction. κ is the conversion coefficient between electrical energy and thermal energy, and its value is MJ / kWh. The corresponding calorific value of methane is 35.6 MJ / m 3 .

[0106] Energy storage system model, in order to reduce the impact of renewable energy uncertainty on the system, this application has established a variety of energy storage methods based on the principles of hydrogen energy, thermal energy, etc. The working principles of energy storage systems are similar, and now we take hydrogen energy storage as an example to explain as follows:

[0107] (15)

[0108] (16)

[0109] (17)

[0110] (18)

[0111] (19)

[0112] In the formula is the planned volume of the hydrogen storage tank. It is the maximum ramp power input / output of the hydrogen storage tank. is the input / output ratio of hydrogen rate. is a binary variable that defines the state of the tank.

[0113] In the CCHP model, the combined heat and power unit consists of a gas turbine and a lithium bromide heat pump. The lithium bromide heat pump can operate in both heating and cooling modes, thus achieving combined heat, power and cooling.

[0114] (20)

[0115] (twenty one)

[0116] (twenty two)

[0117] (twenty three)

[0118] In the formula Equivalent to the electrical power (kW) of the combined heating, cooling and power plant. Indicates the gas flow rate input to the CCHP device. , , Represent the conversion efficiency of electrical energy, thermal energy and cold energy respectively. , is a binary variable representing the operating mode of the CHP unit.

[0119] The multi-level constraint system constructed in this application provides three-dimensional guarantees for the safe and economical operation of the system. The power balance constraint eliminates the risk of frequency instability caused by supply and demand imbalance in traditional systems by matching the output of the power supply unit with the load demand in real time. The thermal balance constraint combines the thermal inertia characteristics of the short-term heat storage device to optimize the timing of thermal energy storage and release to reduce the peak pressure of the heating system. The cooling capacity balance constraint improves the energy efficiency of the refrigeration system through the coordinated control of the electric refrigerator and CCHP. The hydrogen balance constraint ensures the safe switching between the RSOC electrolysis mode and the power generation mode to avoid excessive pressure in the gas system. The output characteristic constraint of the renewable energy unit model accurately reflects the spatiotemporal distribution of wind and solar resources, improving the modeling reliability of the prediction data. The multivariate energy conversion efficiency parameter setting of the CCHP model truly restores the operating characteristics of the trigeneration equipment. The charge and discharge efficiency constraint of the energy storage system model effectively quantifies the impact of energy conversion losses on the system economy, forming a comprehensive guarantee mechanism covering system safety, efficiency and environmental protection.

[0120] As an optional implementation, the carbon emission constraint conditions include:

[0121] Dynamically divide carbon price ranges based on the difference between system carbon emissions and carbon emission quotas, and set incremental carbon trading costs based on the difference between the carbon price ranges;

[0122] Furthermore, the green certificate constraints include:

[0123] The portion of renewable energy power generation that exceeds the preset power generation reference value is converted into green certificate income.

[0124] Specifically, with regard to carbon emissions and green certificates, the specific implementation methods are as follows: the carbon emission process can be divided into the material acquisition, processing and equipment manufacturing stage, the material and equipment transportation stage, the construction and installation stage, the operation and maintenance stage, and the recycling and disposal stage, as shown below.

[0125] (twenty four)

[0126] In the formula Refers to the carbon emissions of photovoltaic power stations. Refers to the carbon emission intensity of photovoltaic power stations at different life cycle stages, which are 21.22, 1.07, 1.25, 2.48, and 0.52 g CO2e / (kW) respectively.

[0127] Carbon emissions during system operation mainly include two aspects. The carbon emissions of equipment in IES are shown in the following formula.

[0128] (25)

[0129] In the formula is the carbon emissions generated by the operation of the system. Burning 1m 2 The amount of carbon dioxide produced by natural gas is 2.165 kg / m 2 . is the carbon emission coefficient for electricity purchased from the grid, which is 0.997 kg / kWh.

[0130] The baseline method encourages businesses to proactively reduce emissions. Electricity buyers receive free carbon emission allowances as shown in the following formula.

[0131] (26)

[0132] (27)

[0133] (28)

[0134] (29)

[0135] (30)

[0136] In the formula Represents the initial free carbon allowance allocation for each device. , They represent the free carbon quota allocation coefficients of coal-fired equipment and electrical equipment, with values of 0.798kg / kWh and 0.385kg / kWh respectively.

[0137] The tiered carbon emission trading between the system and the carbon market, compared with the traditional carbon trading pricing mechanism, the tiered CET price is divided into multiple purchase ranges, as shown in the following formula.

[0138] (31)

[0139] Where C is the benchmark price of the carbon market. is the value of the price growth rate. is the length of the carbon emission interval.

[0140] Green Certificate Trading Model: A green certificate is an electronic certificate with a unique identification code issued by the government for each megawatt-hour of renewable energy generated by a power station. Excess renewable energy generation can be used to generate additional revenue through the sale of green certificates. The GCT model is as follows:

[0141] (32)

[0142] In the formula is the price of green certificates and is set at 12€ / (1 MWh). is the reference value of renewable energy power generation, which is 5×10 3 kWh.

[0143] The dynamic carbon trading mechanism and green certificate incentive mechanism designed in this application construct a market-driven low-carbon optimization framework. The dynamic carbon price interval division method simulates the price elasticity characteristics of the real carbon market through the carbon emission threshold classification, so that the system automatically triggers the deep emission reduction strategy when it approaches the quota threshold. The incremental carbon trading cost calculation model reflects the law of rising marginal emission reduction costs and guides the system to give priority to low-cost emission reduction technologies. The green certificate income conversion mechanism directly links excess power generation from renewable energy with environmental benefits, forming a positive economic incentive cycle. The dynamic adjustment function of the preset power generation reference value adapts to the changing needs of renewable energy quota policies in different regions. By deeply embedding the market trading mechanism into the system optimization model, the organic unity of policy guidance and market laws is achieved, and the potential profit space of carbon asset management is explored while meeting the mandatory emission reduction requirements.

[0144] As an optional implementation, the capacity configuration strategy of the short-term energy storage unit includes:

[0145] Determining the optimal economic capacity configuration of each energy storage unit based on the optimization results corresponding to the operating parameters of the integrated energy system;

[0146] Based on the renewable energy output forecast curve, optimize the energy storage charging and discharging time window to maximize the absorption of renewable energy;

[0147] Set energy storage charging and discharging efficiency constraints to limit energy conversion losses;

[0148] And, the scheduling strategy of the long-term energy storage unit includes:

[0149] During peak periods of renewable energy output, excess electricity is used to electrolyze water through RSOC to produce hydrogen and synthesize methane for storage;

[0150] During periods of renewable energy shortage, the stored hydrogen and methane can be released for use in CCHP and gas boilers;

[0151] Reduce external energy supply requirements based on inter-seasonal energy transfer conditions;

[0152] The energy storage capacity optimization strategy of this application realizes the refined management of the spatiotemporal distribution of energy. The minimum capacity threshold of the short-term energy storage unit is based on the statistical analysis of historical load fluctuation data to ensure the reliability of the basic regulation capability. The charging and discharging time window optimization algorithm is combined with the renewable energy output forecast curve to maximize the energy storage system's ability to absorb fluctuating power sources. The dynamic adjustment of the charging and discharging efficiency constraints balances the game relationship between energy conversion losses and scheduling benefits. The cross-seasonal scheduling strategy of the long-term energy storage unit converts short-term surplus electricity into high-energy-density gas fuel through hydrogen-methane dual-carrier storage technology. The seasonal energy transfer mechanism breaks through the timeliness limitations of traditional energy storage and alleviates the structural contradictions of energy supply and demand in winter and summer. The coordinated optimization of the hierarchical energy storage system enables the system to have the dual regulation capabilities of coping with intraday fluctuations and seasonal imbalances.

[0153] As an optional implementation manner, the switching condition of the RSOC working mode includes:

[0154] When the power supply-demand ratio is greater than the preset upper limit, it switches to electrolysis mode to produce hydrogen;

[0155] When the power supply-demand ratio is less than the preset lower limit, it switches to power generation mode;

[0156] The preset upper limit and the preset lower limit are adjusted in combination with the real-time carbon trading price signal dynamics.

[0157] The RSOC dynamic switching strategy designed in this application significantly improves the comprehensive utilization efficiency of energy conversion equipment. The mode switching threshold setting based on the power supply and demand ratio ensures that the working state of the equipment is accurately matched with the real-time needs of the system. The intelligent switching mechanism between the electrolysis mode and the power generation mode enables RSOC to have the dual functions of energy conversion and energy storage regulation. The dynamic adjustment function of the preset threshold is combined with the real-time carbon price signal to achieve the coordinated optimization of the equipment operation mode and the market environment. During the peak output period of renewable energy, the electrolysis hydrogen production mode is prioritized to increase the local consumption rate of clean energy. When the system has a power supply gap, it quickly switches to the power generation mode to reduce dependence on the external power grid. The linkage mechanism of the equipment control strategy and the carbon trading market forms a new regulation paradigm for the coordinated optimization of technical parameters and economic indicators, which greatly improves the operating efficiency of key equipment.

[0158] Table 1

[0159]

[0160] In a practical application scenario, when various implementation methods are combined, a low-carbon IES based on a hybrid energy storage system is used as an example to examine the effectiveness of the model. The device parameters in the IES are shown in Table 1.

[0161] In order to analyze the impact of multi-time-scale energy storage and energy conversion devices in IES, this application sets up the following five scenarios for comparative analysis.

[0162] 1) Scenario 1: IES is equipped with hybrid energy storage / conversion equipment such as CCHP and RSOC.

[0163] 2) Scenario 2: IES is equipped with a short-term energy storage system similar to HS, ES and CS, and equipped with CCHP and RSOC.

[0164] 3) Scenario 3: The IES is equipped with a long-term energy storage system similar to H2S and ES, and CCHP and RSOC are considered in the system.

[0165] 4) Scenario 5: IES is equipped with a hybrid energy storage system, but only CCHP is considered.

[0166] 5) Scenario 5: IES is equipped with a hybrid energy storage system, but only RSOC is considered.

[0167] Figure 2 A seasonal load and renewable energy parameter data diagram is provided for one embodiment of the present application. All of these seasonal load data and typical renewable energy generation coefficients are shown in FIG2 .

[0168] To verify the feasibility of the low-carbon integrated energy system model, an optimization evaluation was conducted on the proposed integrated energy system in Scheme 5, which includes RSOC, combined cooling, heating and power, and a hybrid energy storage system. Optimal scheduling was performed under the regulation of the electricity and carbon trading markets.

[0169] Figure 3 The power-heating-cooling power balance scheduling diagram provided for one embodiment of this application illustrates the power-heating-cooling power balance scheduling. Electricity is the energy conversion carrier for various devices. In this case, the energy balance of the power supply is far greater than the power load in all seasons. Because heating is relatively uniform and the national standard requires high-carbon emission equipment, the combined heating, cooling, and power system operates in heating mode most of the time. In summer, during the peak cooling load period (40-48 hours), the combined heating, cooling, and power system switches to cooling mode. In winter, when the heat load is high, both CCHP and SOEC modes of the RSOC operate at high power levels to meet the heat demand after methane storage during the two-season period. During the peak winter heat load period (73-85 hours), the GB also begins operating to meet heat demand and help consume methane from the gas storage tank. Long-term and short-term energy storage can address intraday and seasonal peak demand. Long-term energy storage (H2S and GS) can only operate in one mode per day. Given that electricity cannot be stored in large quantities for long periods of time, short-term energy storage must consume the stored energy within a day.

[0170] Figure 4 The operating curves of the hybrid energy storage system provided for one embodiment of the present application illustrate the operation of five energy storage devices. In spring, despite low load, the system does not activate long-term energy storage tanks due to low renewable energy generation. Simultaneously, due to low methane production and relatively high thermal load, thermal energy storage reaches its peak. In summer, when renewable energy generation reaches its maximum, the system chooses to store more electricity in the form of long-term chemical energy storage of hydrogen and methane to meet long-term demand. The cooling load is significantly higher than the heating load, so the combined cooling, heat, and power (CCHP) system operates in cooling mode part of the time. Because most hydrogen and methane are stored in the energy storage system, thermal energy storage can be maximized to compensate for any shortfalls. In autumn, when all load types are low, hydrogen storage is released to produce methane to meet CCHP consumption and RSOC operation in SOFC mode. In winter, the methane storage tanks begin to degas, and CCHP fully supplies electricity and heating loads, allowing the cold storage to meet the shortfall in cooling demand.

[0171] Figure 5 This radar chart shows the system cost structure for different scenarios under one embodiment of this application. Scenario 1 is the most comprehensive, featuring both long-term and short-term energy storage, with RSOC and CCHP as energy hubs. Scenario 1 has the highest installed renewable energy capacity (2724.25 kW) and a lower energy curtailment rate. Compared to the other four scenarios, Ce and C_gre are the highest in S1, while C_in is lower, as the majority of energy comes from renewable sources. In contrast, Scenarios 5 lack CCHP to consume methane, and the RSOC can only operate in one mode (SOFC / SOEC) per hour, resulting in a lower capacity for hydrogen production and consumption. Consequently, renewable energy utilization efficiency is low, resulting in the highest C_pum. Due to low methane consumption, the system's total carbon emissions are minimized, resulting in the lowest C_c. In Scenarios 4, without RSOC to produce green hydrogen, all methane consumed in the trigeneration system comes from the natural gas grid, resulting in the highest carbon trading price, C_c. Options 2 and 3 compare long-term and short-term energy storage. Short-term storage has the advantage of significantly increasing the utilization of renewable energy. Long-term storage can also increase the installed capacity of the combined cooling, heating, and power (CCHP) system, thereby reducing system costs.

[0172] Table 2

[0173]

[0174] Table 2 shows the equipment dispatch capacity and additional carbon emissions under various circumstances. Additional carbon emissions under different circumstances. S1 maximizes energy storage capacity, minimizes system costs, and reflects the optimal resource allocation. From S2, it can be seen that the lack of long-term energy storage will reduce the utilization of short-term energy storage. In Scheme 3, due to RSOC capacity and natural gas pipelines, long-term energy storage remains unchanged. The input and output constraints of natural gas pipelines limit hydrogen production and storage tank capacity. Combined cooling, heating and power has the advantage of generating thermal energy, and the energy peak of dissolved energy is consistent with the load peak. Therefore, CCHP tends to utilize more photovoltaic energy. In contrast, RSOC focuses on the use of wind energy,

[0175] Figure 6 A sensitivity analysis chart for carbon quota trading and green certificate trading, provided for one embodiment of this application, shows the impact of growth rate, CET benchmark price, and interval length on carbon emissions, total costs, and installed equipment capacity. Increasing C and θ increases the system's carbon emission costs by increasing total costs and reducing carbon emissions. The θ and l curves show that the changing trends of the θ and l curves are consistent. Figure 5 The curve shows a step-like pattern, with total carbon emissions decreasing as total costs increase. As C increases, wind power generation increases, the planned capacity of thermal storage equipment increases, and the planned capacity of combined cooling, heating, and power (CHP), a high-carbon, methane-consuming system, decreases. The carbon trading mechanism increases the utilization rate of renewable energy and enables low-carbon operation of the integrated energy system.

[0176] The present application also provides a low-carbon integrated energy system modeling device based on a hybrid energy storage system, the device comprising:

[0177] a determination module for determining constraints and objective functions based on the component distribution of the integrated energy system;

[0178] The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system;

[0179] The processing module is used to adjust the operating parameters of the integrated energy system according to the value of the objective function.

[0180] The low-carbon integrated energy system modeling and device proposed in this application establishes a market-driven energy optimization system through the deep integration of a multi-energy complementary architecture and hierarchical energy storage technology. At the system structure level, the coupled design of the electric, heat, and cooling multi-energy flow network breaks the isolated operation mode of traditional energy systems. The coordinated control of CCHP and RSOC enables on-demand conversion of multiple energy forms. Short-term energy storage units effectively smooth intraday load fluctuations, and the long-term energy storage system addresses the spatiotemporal mismatch between energy supply and demand through cross-seasonal storage of hydrogen and methane. At the operational optimization level, the combined effect of a dynamic carbon price range mechanism and a green certificate revenue model transforms environmental constraints into quantifiable economic parameters, driving the system to proactively reduce carbon emissions under the most cost-effective path. The coordinated modeling of energy conversion equipment and long- and short-term hybrid energy storage ensures optimal resource allocation within the system's safety boundaries. The real-time adjustment of equipment operating parameters enhances adaptability to renewable energy volatility and market changes. In terms of technological innovation, the RSOC dual-mode switching strategy and the energy storage capacity hierarchical optimization algorithm significantly improve the comprehensive utilization efficiency of key equipment. The energy scheduling mechanism based on supply-demand ratio thresholds achieves precise matching of sources, grids, loads, and storage. Driven by both market mechanisms and technological innovation, this solution significantly improves the renewable energy absorption rate, reduces carbon emission intensity, and increases the utilization rate of energy storage systems while ensuring the system's economic efficiency. It provides a technically feasible and commercially sustainable solution for building a new power system, and effectively solves the problem of difficulty in coordinating the optimization of economic and environmental performance during the low-carbon transformation of the integrated energy system.

[0181] For other device-side implementations, they are expanded corresponding to the method-side implementations. The specific implementations and technical effects can refer to the corresponding device-side descriptions.

[0182] It should be noted that it should be understood that the division of the various modules of the above device is merely a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. Moreover, these modules can all be implemented in the form of software called by a processing element; or they can all be implemented in the form of hardware; or some modules can be implemented in the form of software called by a processing element, and some modules can be implemented in the form of hardware. For example, the processing module can be a separately established processing element, or it can be integrated into a chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and called by a processing element of the above device to perform the functions of the above-mentioned module. The implementation of other modules is similar. In addition, these modules can all or partly be integrated together, or they can be implemented independently. The processing element here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or by instructions in the form of software.

[0183] Schematically, as Figure 7 As shown, Figure 7 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of the present application. The computer device 300 can be provided as a server. Figure 7 Computer device 300 includes a processing component 302, which further includes one or more processors, and a memory resource represented by memory 301 for storing instructions executable by processing component 302, such as an application. The application stored in memory 301 may include one or more modules, each corresponding to a set of instructions. In addition, processing component 302 is configured to execute the instructions to perform the method of any of the above embodiments.

[0184] The computer device 300 may further include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate based on an operating system stored in the memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or the like.

[0185] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0186] An embodiment of the present application provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute a method as provided in any embodiment.

[0187] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0188] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referenced to each other.

[0189] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Determine the constraints and objective function based on the component distribution of the integrated energy system; in, The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system; According to the value of the objective function, the operating parameters of the integrated energy system are adjusted.

2. The method according to claim 1, characterized in that The integrated energy system includes an electric, thermal, and cooling gas coupling network, wherein the coupling network includes an electric power system, a thermal system, a cooling system, and a gas reaction system; Wherein, the energy storage unit includes a short-term energy storage unit and a long-term energy storage unit; The power system includes a power supply unit and an energy storage unit. The power supply unit includes a renewable energy unit, an external power grid, a combined cooling, heating and power (CCHP) device, and a reversible solid oxide fuel cell (RSOC). The short-term energy storage unit is used to balance the fluctuations in power supply and demand during the day. The thermal system is collaboratively heated by the CCHP, the RSOC power generation mode, and the gas boiler, and the heat load fluctuation is adjusted by the short-term energy storage unit; The refrigeration system is provided by the CCHP in cooling mode in combination with an electric chiller, and the cooling load demand is matched by the short-term energy storage unit; The gas reaction system synthesizes methane through the hydrogen generated by the RSOC through the external gas source input, and stores it in the long-term energy storage unit.

3. The method according to claim 2, characterized in that The objective function includes a system cost function, which is used to indicate the difference between the total system cost and the total system benefit. The total system cost includes one or more of equipment investment cost, equipment maintenance cost, renewable energy penalty cost, carbon trading cost and energy purchase cost. The total system benefit includes energy sales benefit and / or green certificate benefit.

4. The method according to claim 3, characterized in that The balance constraints include one or more of power balance, thermodynamic balance, cooling capacity balance, hydrogen balance and methane balance, and the component constraints include one or more of renewable energy unit model, CCHP model, RSOC model, one-way energy transfer equipment model and energy storage system model.

5. The method according to claim 3, characterized in that The carbon emission constraints include: Dynamically divide carbon price ranges based on the difference between system carbon emissions and carbon emission quotas, and set incremental carbon trading costs based on the difference between the carbon price ranges; Furthermore, the green certificate constraints include: The portion of renewable energy power generation that exceeds the preset power generation reference value is converted into green certificate income.

6. The method according to any one of claims 2 to 5, characterized in that: The capacity configuration strategy of the short-term energy storage unit includes: Determining the optimal economic capacity configuration of each energy storage unit based on the optimization results corresponding to the operating parameters of the integrated energy system; Based on the renewable energy output forecast curve, optimize the energy storage charging and discharging time window to maximize the absorption of renewable energy; Set energy storage charging and discharging efficiency constraints to limit energy conversion losses; And, the scheduling strategy of the long-term energy storage unit includes: During peak periods of renewable energy output, excess electricity is used to electrolyze water through RSOC to produce hydrogen and synthesize methane for storage; During periods of renewable energy shortage, the stored hydrogen and methane can be released for use in CCHP and gas boilers; Reduce external energy supply requirements based on inter-seasonal energy transfer conditions.

7. The method according to any one of claims 2 to 5, characterized in that: The switching conditions of the RSOC working mode include: When the power supply-demand ratio is greater than the preset upper limit, it switches to electrolysis mode to produce hydrogen; When the power supply-demand ratio is less than the preset lower limit, it switches to power generation mode; The preset upper limit and the preset lower limit are adjusted in combination with the real-time carbon trading price signal dynamics.

8. A low-carbon integrated energy system modeling device based on a hybrid energy storage system, characterized in that: The device comprises: a determination module for determining constraints and objective functions based on the component distribution of the integrated energy system; The constraints include balance constraints, component constraints, carbon emission constraints, and green certificate constraints, and the objective function is used to indicate the total cost of the integrated energy system; The processing module is used to adjust the operating parameters of the integrated energy system according to the value of the objective function.

9. A computer device, characterized in that: The method comprises one or more processors and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the method according to any one of claims 1 to 7 are performed.

10. A storage medium, characterized in that: The storage medium stores computer-readable instructions, which, when executed by one or more processors, enable the one or more processors to perform the steps of the method according to any one of claims 1 to 7.

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