Antarctic research station operation method based on virtual energy storage and waste heat recovery cooperation

Abstract

This application discloses a method for operating an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery, including: source-side modeling; load-side modeling; storage-side modeling; power balance constraints; obtaining a deterministic optimization model; and establishing a two-stage sub-Bruker optimization scheduling model based on the deterministic optimization model and the box-type uncertainty set to which the output of the photovoltaic array and wind turbine generator belongs. By introducing inaccurate tolerance during the iteration process, the solution accuracy and convergence threshold of the main problem are adaptively adjusted to solve the two-stage sub-Bruker optimization scheduling model, resulting in a scheduling scheme. A wind-solar-hydrogen-storage multi-energy complementary model based on real local Antarctic meteorological datasets is constructed, effectively overcoming the shortcomings of traditional models in predicting inaccuracies in extreme polar environments, accurately characterizing the output limitation characteristics caused by ice and snow cover, and ensuring uninterrupted and reliable power supply for the research station under extreme weather conditions.

Description

Technical Field

[0001] This application relates to the field of energy optimization, specifically to an operational method for Antarctic research stations based on the synergy of virtual energy storage and waste heat recovery. Background Technology

[0002] Antarctica holds irreplaceable strategic value as a crucial region for monitoring climate change and exploring the frontiers of Earth science. However, as a cutting-edge base for polar scientific research, Antarctic research stations have long relied on diesel generators for energy supply. Although this model boasts high power reliability thanks to mature fuel storage and transportation technologies, it may lead to environmental pollution, high carbon emissions, and significant logistics costs.

[0003] To elaborate, the traditional polar integrated energy model does not construct a dynamic output-limited model that considers the evolution of extreme Antarctic weather (such as wind turbine icing and photovoltaic snow cover), making it difficult to accurately capture the nonlinear dynamic spatiotemporal dependence between the extreme polar environment and the multi-energy complementary system, resulting in inaccurate assessment of the system's energy supply reliability during sudden weather changes.

[0004] Meanwhile, the traditional research station operation strategy does not consider the deep coupling of low-grade waste heat from the operation of hydrogen energy equipment with flexible heat load, and lacks the exploration of the time-domain flexible heat storage characteristics of building envelope and water heater tank, resulting in waste of waste heat resources and high consumption of fossil energy.

[0005] In addition, given the high uncertainty of power output from wind and solar power in Antarctica, there is a lack of a dynamic scheduling model that does not require pre-setting a prior probability distribution and can balance computational efficiency and robustness. This makes scheduling schemes prone to failure due to the scarcity of historical big data in the polar regions and large prediction biases, resulting in a significant decrease in the economic efficiency and risk resistance of the system.

[0006] Therefore, it is urgent to reduce carbon emissions from research stations in order to achieve a sustainable development plan for Antarctic scientific research. Summary of the Invention

[0007] To address the aforementioned issues, this application proposes a method for operating Antarctic research stations based on the synergy of virtual energy storage and waste heat recovery, including: Source-side modeling is performed; wherein, the source-side modeling includes: modeling a polar wind power generation model under icing conditions and a polar photovoltaic power generation model under snow conditions using meteorological parameters; and modeling a polar diesel generator output model based on temperature difference and a hydrogen energy equipment model describing the hydrogen production of a PEM electrolyzer using the first operating parameters corresponding to the energy production equipment. And load-side modeling is performed; wherein, the load-side modeling includes: modeling the electric heating model under thermal balance constraints and the conventional energy consumption model described by the power equation using the first working parameters corresponding to the energy production equipment and the building parameters corresponding to the scientific research station building; and modeling the waste heat recovery water heater model described by the thermal dynamic change equation of water temperature using the second working parameters corresponding to the energy consumption equipment. And perform storage-side modeling; wherein, the storage-side modeling includes: modeling a battery energy storage model under the constraints of multiple operating boundaries in the spatiotemporal dimension through the third operating parameters corresponding to the energy storage device, and a hydrogen storage tank model described by the balance equation of dynamic changes in hydrogen storage capacity; Power balance constraints are performed based on the source-side modeling, the load-side modeling, and the storage-side modeling. Based on the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function, a deterministic optimization model is obtained. Based on the deterministic optimization model and the box-type uncertainty set to which the output of the photovoltaic array and wind turbine generator belongs, a two-stage sub-Blu-ray bar optimization scheduling model is established. By introducing an imprecise tolerance during the iteration process, the accuracy of solving the main problem and the convergence threshold are adaptively adjusted to solve the two-stage sub-Bruker optimization scheduling model and obtain a scheduling scheme; the scheduling scheme is used to schedule at least one of the energy production equipment, the energy consumption equipment, and the energy storage equipment.

[0008] In one example, meteorological parameters are used to model polar wind power generation under icing conditions and polar photovoltaic power generation under snow-covered conditions. Specifically, this includes: Based on the actual wind speed at the hub height of the wind turbine in the meteorological parameters, a polar wind power generation model is obtained by establishing a cubic function fitting relationship between the wind turbine icing power loss coefficient and the wind turbine icing mass and introducing an icing growth model. Based on the actual light intensity and photovoltaic panel temperature in the meteorological parameters, a polar photovoltaic power generation model was obtained by establishing a cubic function fitting relationship between the photovoltaic snow-covered power loss coefficient and the photovoltaic snow-covered mass and introducing a snow-covered growth model.

[0009] In one example, the output model of a polar diesel generator based on temperature difference and the hydrogen energy equipment model describing the hydrogen production of a PEM electrolyzer are modeled using the first operating parameters corresponding to the energy production equipment. Specifically, this includes: Based on the output power of the diesel generator in the first operating parameters, a polar diesel generator output model is obtained by introducing a diesel consumption correction coefficient based on temperature difference and considering the upper and lower limits of output power. Using the operating power of the PEM electrolyzer and the rate at which the hydrogen fuel cell consumes hydrogen as inputs, a model of the hydrogen energy equipment is obtained by establishing a linear conversion relationship between the PEM electrolyzer's hydrogen production process and a mathematical model of the hydrogen fuel cell.

[0010] In one example, the electric heating model under thermal balance constraints and the conventional energy consumption model described by the power equation are modeled using the first operating parameters corresponding to the energy production equipment and the building parameters corresponding to the research station building; and the waste heat recovery water heater model described by the thermal dynamic change equation of water temperature is modeled using the second operating parameters corresponding to the energy consumption equipment. Specifically, this includes: Based on the electrical energy consumed by the electric heating equipment in the first working parameters and the thermal resistance and heat capacity parameters in the building parameters, the electric heating model is obtained by constructing the thermal balance constraints of the scientific research station's envelope and the indoor thermal balance constraints using a thermal resistance-heat capacity network based on the heating capacity of the electric heating equipment. Based on the heat generation power of the electrolyzer and hydrogen fuel cell and the heat recovery efficiency of the heat exchanger in the second working parameters, a waste heat recovery water heater model is obtained by establishing the thermal dynamic change equation of the water temperature in the water heater and setting the upper and lower limits of the user's comfortable water temperature. Based on the operating parameters of the conventional electrical load equipment in the first working parameters, a conventional energy consumption model is modeled by establishing the power equations corresponding to each conventional electrical load equipment and aggregating them.

[0011] In one example, the battery energy storage model constrained by multiple operational boundaries in the spatiotemporal dimensions and the hydrogen storage tank model described by the balance equation of dynamic changes in hydrogen storage capacity are modeled using the third operating parameters corresponding to the energy storage device. Specifically, this includes: Based on the battery's operating parameters in the third operating parameters, a battery energy storage model is obtained by establishing multiple operating boundaries, including charging and discharging power constraints, state of charge constraints, charging and discharging state mutual exclusion constraints, and power balance constraints at the beginning and end of the scheduling cycle. Based on the hydrogen mass flow rate of the electrolyzer hydrogen production and fuel cell hydrogen consumption processes in the third working parameters, as well as the charging and discharging status flag of the hydrogen storage tank, a model of the hydrogen storage tank is obtained by establishing a balance equation for the dynamic change of hydrogen storage capacity in adjacent scheduling periods and setting minimum and maximum energy storage capacity constraints for the hydrogen storage tank.

[0012] In one example, a deterministic optimization model is obtained by integrating the source-side modeling, the load-side modeling, the storage-side modeling, the power balance constraints, and the energy optimization objective function, specifically including: With the goal of minimizing carbon emissions, an energy optimization objective function is constructed; the equipment corresponding to carbon emissions includes diesel generators, electric heating equipment, and water heaters. A deterministic optimization model is obtained by integrating the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function. The deterministic optimization model includes the energy optimization objective function and the corresponding optimization constraints. These constraints include inequality constraints and equality constraints within the model set, constraint relationships between optimization variables, and the predicted values ​​of uncertain variables for each time period. The model set includes the source-side modeling, load-side modeling, storage-side modeling, and power balance constraints.

[0013] In one example, based on the deterministic optimization model and the constructed box-type uncertainty set to which the photovoltaic array and wind turbine output belong, a two-stage sub-Bruker optimization scheduling model is established, specifically including: Construct a box-shaped uncertainty set to which the output of the photovoltaic array and the wind turbine generator belong; the uncertain variables in the box-shaped uncertainty set are the uncertain variables of the output of the photovoltaic array and the wind turbine generator introduced after considering uncertainties; Based on the deterministic optimization model, expressed in a compact typical matrix form, a two-stage sub-Bruker optimal scheduling model is established. The optimization variables in the first stage correspond to the charging and discharging states of the battery and the hydrogen storage system. The optimization variables in the second stage correspond to the wind power generation under icing conditions, the photovoltaic power generation under snow conditions, the output power of the diesel generator in time period t, the operating power of the electrolyzer in time period t, the output power of the hydrogen fuel cell in time period t, the electrical energy consumed by the electric heating equipment, the indoor temperature in time period t, the wall temperature of each numbered wall, the power of the water heater in time period t, the water temperature in the water heater tank in time period t, the heat power recovered by the electrolyzer and hydrogen fuel cell through the heat exchanger in time period t, the discharge power of the battery in time period t, the charging power of the battery in time period t, the power required by other equipment at the Antarctic research station, and the aforementioned uncertain variables.

[0014] In one example, by introducing an imprecise tolerance during the iteration process, the accuracy of solving the main problem and the convergence threshold are adaptively adjusted to solve the two-stage bibliometric optimization scheduling model, resulting in a scheduling scheme, specifically including: Based on the power output of wind and light under the worst-case scenario, the lower bound of the main problem is obtained by solving the main problem and updating the lower bound of the main problem, and by solving the main problem and recording the optimal decision value of the main problem; the main problem corresponds to the optimization variable in the first stage. Based on the optimal decision value of the primary problem, the value of the wind and solar power output under the worst-case scenario is obtained by solving the sub-problems; the sub-problems correspond to the optimization variables in the second stage. According to duality theory, the min problem in the max-min bi-level optimization corresponding to the subproblem is transformed into a max problem, and the max problems in the max-min bi-level optimization of the max problem domain are merged to obtain the dual problem; Based on the bilinear term in the dual problem, the box-shaped uncertainty set is reformulated as follows: whether to take the lower limit of the fluctuation range is represented by a binary variable, and an uncertainty adjustment parameter is introduced. For the bilinear term, it is linearized using the Big M method, and a continuous auxiliary variable is introduced to iteratively solve the subproblem. Based on the lower bound of the main problem and the upper bound of the subproblems, the system determines whether to stop iteration and return to the optimal solution, or to continue iteration, by judging whether the actual relative gap is close enough to the preset convergence gap.

[0015] In one example, the method further includes: If the actual relative gap does not meet the preset convergence gap, then the inaccurate tolerance is determined by the preset convergence gap, and the inaccurate tolerance is compared with the upper bound of the subproblem. If the actual relative gap after iteration satisfies the non-precise tolerance, then update the lower bound of the main problem, reduce the relative optimal gap, and return to the main problem for the next iteration; If the actual relative gap after iteration does not meet the non-precise tolerance, the subproblem is solved again, a new worst-case scenario is obtained, and the main problem is returned for iteration.

[0016] The operational method for Antarctic research stations based on the synergy of virtual energy storage and waste heat recovery proposed in this application can bring the following beneficial effects: 1. A wind-solar-hydrogen-storage multi-energy complementary model based on real meteorological datasets in Antarctica was constructed. This model effectively overcomes the shortcomings of traditional models in predicting inaccurate results in extreme polar environments, accurately depicts the power output limitation caused by ice and snow cover, and ensures uninterrupted and reliable power supply for the research station under extreme weather conditions.

[0017] 2. A low-carbon operation strategy that considers the collaboration between virtual energy storage system and waste heat recovery water heater is proposed, which effectively solves the problems of waste heat resources and heat supply and demand imbalance. Under the premise of fully guaranteeing the polar thermal environment and hot water demand, energy cascade utilization is realized, and carbon emissions are significantly reduced.

[0018] 3. A two-stage partial Bruker optimization strategy was proposed and solved using the DTA-C&CG algorithm. This effectively mitigated the impact of strong wind and solar fluctuations and the lack of precise probability distribution on the scheduling scheme. While ensuring that the calculation speed meets the real-time convergence requirements, it significantly improved the risk resistance and robustness of the low-carbon operation of the research station. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating the operation method of an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery in the embodiments of this application; Figure 2 This is a diagram illustrating the operational architecture of an Antarctic research station under one scenario in this application embodiment. Figure 3 This is a schematic diagram of a multi-energy complementary model for an Antarctic research station under one scenario in the embodiments of this application; Figure 4 This is a schematic diagram of the thermal dynamics model of the building envelope under one scenario in the embodiments of this application; Figure 5 This is a schematic diagram of an equivalent thermal resistance-thermal capacity network in one embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0022] To reduce carbon emissions during the operation of Antarctic Research Stations (ARS), this application proposes an operation method for Antarctic Research Stations based on the synergy of virtual energy storage and waste heat recovery (which involves a low-carbon operation strategy for Antarctic Research Stations that considers the synergy between a virtual energy storage system and a waste heat recovery water heater).

[0023] In summary, firstly, considering the power output reduction caused by wind turbine icing and solar photovoltaic system snow accumulation in polar environments, an ARS (Augmented Regeneration System) wind-solar-hydrogen-storage multi-energy complementary model was constructed based on real Antarctic meteorological datasets. Secondly, based on the thermal storage characteristics of the ARS building envelope and water heater tank, a virtual energy storage system for ARS and a waste heat recovery water heater were implemented for coordinated operation. Finally, considering the uncertainty of wind and solar power output in Antarctica, a two-stage sub-Bruker optimization strategy for ARS was proposed. The optimization conservatism level was flexibly adjusted by setting uncertainty adjustment parameters, and the optimal solution to the original problem was obtained iteratively using the Dynamic Tolerance Adjusted Column and Constraint Generation (DTA-C&CG) algorithm. The results show that the proposed strategy can fully exploit the synergistic potential of the thermal storage characteristics of the ARS building envelope and water heater tank, significantly reducing carbon emissions from ARS operation while ensuring indoor thermal comfort and domestic hot water demand.

[0024] like Figure 1 As shown, this application provides an operator for an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery, including: S101: Perform source-side modeling; wherein, the source-side modeling includes: modeling the polar wind power generation model under the icing state of the wind turbine and the polar photovoltaic power generation model under the snow-covered state of the photovoltaic system using meteorological parameters; and modeling the polar diesel generator output model based on temperature difference and the hydrogen energy equipment model describing the hydrogen production of the PEM electrolyzer using the first operating parameters corresponding to the energy production equipment.

[0025] First, it should be noted that steps S101, S102, and S103 of this application correspond to source-side modeling, load-side modeling, and storage-side modeling, respectively. They are used for: source-side modeling, mainly for mathematically describing the output characteristics and operating boundaries of energy production equipment (wind power generation, photovoltaic power generation, diesel generators, PEM electrolyzers, and hydrogen fuel cells) within the research station; load-side modeling, mainly for mathematically describing the energy consumption characteristics and thermal dynamics of all energy-consuming equipment (electric heating and building envelope thermal dynamics, waste heat recovery water heaters, and conventional electrical equipment) within the research station; and storage-side modeling, mainly for mathematically describing the capacity dynamics and charging / discharging operating boundaries of energy storage equipment (batteries and hydrogen storage tanks) within the research station.

[0026] In this embodiment of the application, source-side modeling, load-side modeling, and storage-side modeling are described by steps S101, S102, and S103 respectively, only for the purpose of describing the three separately. In actual work, there is no strict order in which source-side modeling, load-side modeling, and storage-side modeling are executed. The three can be arranged in the corresponding order according to actual needs, or they can be executed in parallel.

[0027] like Figure 2 As shown, the operational framework of the Antarctic research station constructed in this application mainly consists of three parts: the source side, the storage side, and the load side. The source side includes wind turbines, photovoltaic arrays, diesel generators, electrolyzers, and fuel cells. The wind turbines, photovoltaic arrays, and diesel generators together form the basic power supply system, with multiple energy sources working together to ensure the operational needs of the research station. The electrolyzer consumes electricity to produce hydrogen, while the fuel cell uses hydrogen to generate electricity when needed. The storage side includes batteries and hydrogen storage tanks. Batteries, as a flexible resource, can be charged and discharged bidirectionally according to the real-time supply and demand status of the system to achieve dynamic energy balance. The hydrogen storage tanks receive and store hydrogen produced from the source-side electrolyzer and deliver hydrogen to the source-side fuel cells when needed. The load side includes all energy-consuming equipment within the research station, including electric heating equipment, gas analyzers, snow melting equipment, monitoring systems, and biological detection equipment, as well as communication and lighting equipment required for autonomous operation in the Antarctic environment. In addition, since the electrolyzer and hydrogen fuel cell generate a lot of waste heat during operation, a heat exchanger is used to recover the waste heat and use it as an auxiliary heat source for the water heater, thereby meeting the living needs of the scientific research personnel at the station.

[0028] Specifically, for source-side modeling, various models are explained and illustrated through multiple implementation examples.

[0029] Example 1, for a polar wind power generation model: Based on the actual wind speed at the turbine hub height in the meteorological parameters, a polar wind power generation model is obtained by establishing a cubic function fitting relationship between the wind turbine icing power loss coefficient and the wind turbine icing mass and introducing an icing growth model.

[0030] The output power of a wind turbine depends on the actual wind speed at the height of the turbine hub, as shown in Formula 1: Formula 1; in, This refers to the actual output power of the fan. This refers to the rated power of the fan; This refers to the actual wind speed of the fan; The cut-in wind speed of the fan; This refers to the rated wind speed of the fan; This refers to the cut-out velocity of the fan.

[0031] Wind turbine blade icing requires meeting critical conditions of ambient temperature ≤0℃, surface temperature ≤-5℃, and humidity >85%. The icing process is mainly affected by factors such as temperature, humidity, wind speed, liquid water content (LWC), and median volume diameter (MVD).

[0032] The power output of wind power generation under icing conditions can be expressed as shown in Formula 2: Formula 2; Where t is the time period, This refers to the power output of wind turbines under icing conditions. This represents the power output of wind power under normal conditions. The icing power loss coefficient of the wind turbine is related to the icing quality of the wind turbine, and its relationship can be fitted using a function, as shown in Formula 3: Formula 3; in, , , , It is a constant; For the quality of icing on the wind turbine.

[0033] The wind turbine icing growth model is shown in Formula 4: Formula 4; in, The collision coefficient; The adhesion coefficient; It is the accretion coefficient; This refers to the particle mass concentration. Effective particle velocity; This represents the effective cross-sectional area of ​​the object upon which the water droplet collides.

[0034] Example 2, for a polar photovoltaic power generation model: Based on the actual light intensity and photovoltaic panel temperature in the meteorological parameters, a polar photovoltaic power generation model is obtained by establishing a cubic function fitting relationship between the photovoltaic snow-covered power loss coefficient and the photovoltaic snow-covered mass, and introducing a snow-covered growth model.

[0035] The output power of a photovoltaic array is directly proportional to the light intensity and the illuminated area, as shown in Formula 5: Formula 5; in, This represents the actual output power of the photovoltaic array during time period t. This refers to the rated power of the photovoltaic array under standard parameters. This is the power degradation factor; This represents the actual light intensity. The light intensity of the photovoltaic array under standard conditions; This refers to the power temperature regulation coefficient. and The values ​​represent the photovoltaic panel temperature and the standard ambient temperature during time period t.

[0036] Photovoltaic panel temperature As shown in Formula Six: Formula Six; in, Temperature of the photovoltaic panel; The outdoor ambient temperature during time period t; is the radiation temperature coefficient.

[0037] Photovoltaic snow cover requires meeting critical conditions such as ambient temperature ≤0℃, snowfall >2.5mm, and sufficient water vapor. The snow cover process is mainly affected by factors such as snowfall, the dryness and wetness of the snow, and the tilt angle of the photovoltaic panels.

[0038] The output power of the photovoltaic array under snow cover conditions can be expressed as shown in Formula 7: Formula 7; in, This represents the photovoltaic power generation capacity under snow-covered conditions. This represents the photovoltaic power generation capacity under normal conditions. The photovoltaic snow-covered power loss coefficient is related to the quality of photovoltaic snow cover, and their relationship can be fitted using a function, as shown in Formula 8: Formula 8; in, , , , It is a constant; For the quality of snow cover on photovoltaic panels.

[0039] The photovoltaic snow cover growth model is shown in Formula 9: Formula Nine; in, This refers to the influence coefficient of wind speed and direction. The coefficient representing the influence of snow moisture level; The coefficient representing the influence of the photovoltaic panel tilt angle; This refers to the amount of snowfall.

[0040] Example 3, for the output model of a polar diesel generator: Based on the output power of the diesel generator in the first operating parameters, a polar diesel generator output model is obtained by introducing a diesel consumption correction coefficient based on temperature difference and considering the upper and lower limits of output power.

[0041] In the extreme environment of Antarctica, low temperatures significantly affect the starting performance and operating efficiency of diesel generators. To accurately characterize the impact of ambient temperature on diesel consumption, this embodiment introduces a diesel consumption correction coefficient based on temperature difference, as shown in Formula 10: Formula 10; in, The diesel generator consumption during time period t; This is a correction factor based on temperature difference; For reference ambient temperature; It is a constant; This represents the power coefficient of the diesel generator. Let t be the output power of the diesel generator during the time period.

[0042] Since the power response speed of diesel generators is relatively fast compared to hourly dispatching, their gradeability constraint is not considered; only the output power constraint is considered, as shown in Formula 11: Formula 11; in, and These represent the minimum and maximum output power of the diesel generator, respectively.

[0043] Example 4, for a hydrogen energy equipment model: Using the operating power of the PEM electrolyzer and the rate at which the hydrogen fuel cell consumes hydrogen as inputs, a model of the hydrogen energy equipment is obtained by establishing a linear conversion relationship between the PEM electrolyzer's hydrogen production process and a mathematical model of the hydrogen fuel cell.

[0044] For PEM electrolyzers, due to the highly intermittent and fluctuating power output from Antarctic wind and solar power, frequent adjustments to the electrolyzer's operating status are necessary. Therefore, proton exchange membrane (PEM) electrolyzers are used as the hydrogen production equipment. The electrolytic hydrogen production process of the PEM electrolyzer is modeled, and the hydrogen production capacity of the PEM electrolysis hydrogen production equipment during time period t is calculated. As shown in Formula Twelve: Formula 12; in, The mass of hydrogen produced by electrolysis during time period t; The operating power of the electrolytic cell during time period t; To improve the operating efficiency of the electrolytic cell; It has the lowest calorific value of hydrogen.

[0045] For hydrogen fuel cells, the mathematical model is shown in Formula Thirteen: Formula Thirteen; in, The output power of the hydrogen fuel cell during time period t; For the power generation efficiency of fuel cells; Let t be the rate at which the fuel cell consumes hydrogen during time period t; It has the lowest calorific value of hydrogen.

[0046] S102: And perform load-side modeling; wherein, the load-side modeling includes: modeling the electric heating model under thermal balance constraints and the conventional energy consumption model described by the power equation using the first working parameters corresponding to the energy production equipment and the building parameters corresponding to the scientific research station building; and modeling the waste heat recovery water heater model described by the thermal dynamic change equation of water temperature using the second working parameters corresponding to the energy consumption equipment.

[0047] Specifically, for load-side modeling, the various models will still be explained and illustrated through multiple embodiments.

[0048] Example 5, for the electric heating model: Based on the electrical energy consumed by the electric heating equipment in the first working parameters and the thermal resistance and heat capacity parameters in the building parameters, the electric heating model is obtained by constructing the thermal balance constraints of the scientific research station's envelope and the indoor thermal balance constraints using a thermal resistance-heat capacity network based on the heating capacity of the electric heating equipment.

[0049] To ensure the thermal comfort of personnel within the station, electric heating equipment is used for heating. The electrothermal conversion relationship and power constraints are shown in Formulas 14 to 15: Formula Fourteen; Formula 15; in, The heat energy converted by the electric heating equipment during time period t; The electrothermal conversion efficiency of electric heating equipment; The electrical energy consumed by electric heating equipment; This is the upper limit of the operating power of electric heating equipment.

[0050] Based on the heating capacity of the electric heating equipment, a mathematical model of the Antarctic research station is further constructed using a thermal resistance-heat capacity network. The thermal resistance and heat capacity parameters of the research station are calculated as shown in Formulas 16 and 17. Formula Sixteen; Formula 17; in, It is the thickness of the i-th layer of material; It is the thermal conductivity of the i-th layer material; It is the total area through which heat flows; Density; For volume; R is the specific heat capacity of the i-th layer material, R is the thermal resistance, and C is the heat capacity.

[0051] The thermal balance constraints of the enclosure structure of Antarctic research stations can be described as shown in Equations 18 to 21: Formula 18; Formula 19; Formula 20; Formula 21; Where ij is the corresponding wall number in the enclosure structure, ij being 12 / 13 corresponds to the side without windows, and ij being 14 / 15 corresponds to the side with windows. The wall's heat capacity is categorized into the side with windows and the side without windows. For wall temperature; The indoor temperature during time period t; The temperature of adjacent nodes during time period t; The wall thermal resistance is divided into the side with windows and the side without windows; This is a 0-1 variable representing whether the wall receives external solar radiation. If the wall receives external solar radiation, then... Select 1 if the value is 1, otherwise select 0. The wall's heat absorption rate; This refers to the area of ​​the wall. This represents the light intensity in the corresponding direction of the wall.

[0052] The thermal balance constraints within the research station are shown in Formula 22: Formula 22; in, For the internal heat capacity of the research station; Outdoor temperature; Refers to adjacent nodes in a region; For window thermal resistance; The refractive index of the window; This represents the light intensity in the corresponding direction of the window; It serves as an internal heat source.

[0053] like Figures 3-5 The diagram illustrates the derivation logic of the virtual energy storage model for an Antarctic research station. Firstly, Figure 3The overall architecture of the multi-energy complementary system for the research station is presented. Based on this, and considering the complex heat transfer process of the building and the heat storage characteristics of the building envelope such as walls and windows, a system is constructed as follows: Figure 4 The thermal dynamic model of the building envelope shown is used to characterize its heat transfer and interaction mechanisms. To further achieve a quantitative description and solution of the building's thermal processes, a mathematical abstraction is performed, and a model is established as follows: Figure 5 The equivalent thermal resistance-thermal capacity network model is shown.

[0054] During normal operation of the research station, the indoor temperature It should be maintained within a suitable range, as shown in Formula 23: Formula 23; in, , These are the lower and upper limits of the indoor temperature at the research station, respectively.

[0055] Example 6, for a waste heat recovery water heater model: Based on the heat generation power of the electrolyzer and hydrogen fuel cell and the heat recovery efficiency of the heat exchanger in the second working parameters, a waste heat recovery water heater model is obtained by establishing a thermal dynamic change equation of water temperature in the water heater and setting upper and lower limits of user comfort water temperature.

[0056] Since the electrolyzer and hydrogen fuel cell generate a large amount of waste heat during operation, recovering this energy can significantly improve the energy utilization efficiency of the research station. This process is shown in Equations 24 to 26: Formula 24; Formula 25; Formula 26; in, The heat output of the electrolyzer and hydrogen fuel cell during time period t; The heat power recovered by the heat exchanger from the electrolyzer and hydrogen fuel cell during time period t; The heat recovery efficiency of the heat exchanger. The output power of the hydrogen fuel cell during time period t. This refers to the power generation efficiency of fuel cells.

[0057] The thermal dynamics of water temperature in a water heater can be represented as shown in Formula 27: Formula 27; in, The water temperature in the water heater tank during time period t; The ambient temperature during time period t; The power of the water heater during time period t; and These are the thermal resistance and heat capacity of the water heater, respectively; W is the water capacity of the water heater tank. This represents the water consumption of users during time period t.

[0058] To ensure user comfort, the water temperature inside the water heater must be maintained within a suitable range, as expressed by formula twenty-eight: Formula 28; in, , These are the lower and upper limits of the user's comfortable water temperature, respectively.

[0059] Example 7, for a conventional energy consumption model: Based on the operating parameters of the conventional electrical load equipment in the first working parameters, a conventional energy consumption model is modeled by establishing the power equations corresponding to each conventional electrical load equipment and aggregating them.

[0060] The conventional electrical load within the research station mainly consists of gas analyzers, snow melting systems, monitoring equipment, biological detection equipment, and basic lighting equipment. The overall model is shown in Formula 29: Formula 29; in, Power required for other equipment at the Antarctic research station; This refers to the output power of the gas analyzer. Output power for the snow melting system; To monitor the output power of the equipment; The total power of the biological detection equipment during time period t; The output power of the lighting equipment.

[0061] The output power of the snow melting system is shown in formulas 30 to 31: Formula 30; Formula 31; in, The average operating power of the snow melting equipment during time period t; The total domestic water demand within the research station during time period t; Wastewater recycling rate; The unit heat consumption required to convert 1 kg of outdoor snow into room temperature liquid water during time period t; The electrothermal conversion efficiency of snow melting equipment; The latent heat of fusion of ice; , These are the specific heat capacities of water and snow, respectively. The outlet water temperature of the snow melting system; The outdoor ambient temperature is t during the time period.

[0062] The output power of the monitoring system is shown in Formula 32: Formula 32; in: The number of sensors; This refers to the rated power of a single node of the sensor. The average load factor of the sensor; To calculate the energy consumption coefficient; For data processing speed; Storage energy consumption coefficient; The data write rate that the monitoring station needs to store during time period t; This refers to the communication energy consumption coefficient. This is the transmission rate percentage coefficient; This represents the maximum data transmission rate.

[0063] Biological detection equipment mainly includes autoclaves, constant temperature incubators, centrifuges, and vacuum drying ovens. Its total power during time period t can be expressed as shown in Formula 33: Formula 33; in, The heat requirement of the autoclave during time period t; The electrothermal conversion efficiency of the autoclave; Set the temperature for the constant temperature incubator; The indoor temperature during time period t; Equivalent thermal resistance of the constant temperature incubator; For the heating efficiency of the constant temperature incubator; Auxiliary power for the constant temperature incubator; Let be the load factor of the centrifuge during time period t; This refers to the rated power of the centrifuge. The heating heat requirement of the vacuum drying oven during time period t; For the heating efficiency of the vacuum drying oven; The power of the vacuum pump in the vacuum drying oven.

[0064] S103: And perform storage-side modeling; wherein, the storage-side modeling includes: modeling a battery energy storage model under the constraints of multiple operating boundaries in the spatiotemporal dimension and a hydrogen storage tank model described by the balance equation of dynamic changes in hydrogen storage capacity through the third operating parameters corresponding to the energy storage device.

[0065] Example 8, for a battery energy storage model: Based on the battery's operating parameters in the third operating parameter, a battery energy storage model is obtained by establishing multiple operating boundaries, including charging and discharging power constraints, state of charge constraints, mutual exclusion constraints of charging and discharging states, and power balance constraints at the beginning and end of the scheduling cycle.

[0066] The constraints that a battery must meet during operation include charge / discharge power constraints, state of charge constraints, mutual exclusion constraints between charge / discharge states, and power balance constraints at the beginning and end of the scheduling cycle, as shown in Equations 34 to 37 respectively: Formula 34; Formula thirty-five; Formula Thirty-Six; Formula thirty-seven; in, Let t be the discharge power of the battery during time period t; The charging power of the battery during time period t; The charging and discharging efficiency of the battery; This is a battery charge / discharge status indicator; 1 indicates charging, and 0 indicates discharging. This refers to the maximum allowable charge and discharge power of the battery. The scheduling period is 24 hours. This refers to the battery capacity during the initial dispatch period. , These are the minimum and maximum energy storage capacities allowed for the battery during the scheduling process, respectively.

[0067] Example 9, for a hydrogen storage tank model: Based on the hydrogen mass flow rate of the electrolyzer hydrogen production and fuel cell hydrogen consumption processes in the third working parameters, as well as the charging and discharging status flag of the hydrogen storage tank, a model of the hydrogen storage tank is obtained by establishing a balance equation for the dynamic change of hydrogen storage capacity in adjacent scheduling periods and setting minimum and maximum energy storage capacity constraints for the hydrogen storage tank.

[0068] Considering the hydrogen production process in the electrolyzer and the hydrogen consumption process in the fuel cell, the amount of hydrogen stored in the hydrogen storage tank during adjacent scheduling periods can be expressed as shown in Formula 38: Formula 38; The operational constraints of a hydrogen storage system can be expressed as shown in Equations 39 to 42: Formula 39; Formula 40; Formula 41; Formula 42; in, , They are respectively The amount of hydrogen stored in the hydrogen storage tank during time period t; The operating power of the fuel cell during time period t; For fuel cell operating efficiency; The operating power of the electrolytic cell during time period t; To improve the operating efficiency of the electrolytic cell; This is a flag indicating the charge / discharge status of the hydrogen storage system; 1 indicates that the electrolyzer is in operation, and 0 indicates that the fuel cell is in operation. , These are the maximum permissible operating power for the electrolyzer and the fuel cell, respectively. This refers to the capacity of the hydrogen storage tank during the initial scheduling period. , These represent the minimum and maximum energy storage capacities allowed for the hydrogen storage tanks during the scheduling process, respectively.

[0069] S104: Perform power balance constraints based on the source-side modeling, the load-side modeling, and the storage-side modeling.

[0070] Specifically, the overall balance constraints are shown in Equation 43: Formula 43; The meanings of each parameter have been explained above and will not be repeated here.

[0071] S105: Based on the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function, a deterministic optimization model is obtained. Based on the deterministic optimization model and the box-type uncertain set to which the output of the photovoltaic array and wind turbine generator belongs, a two-stage sub-Blu-ray bar optimization scheduling model is established.

[0072] With the goal of minimizing carbon emissions, an energy optimization objective function is constructed. The equipment corresponding to carbon emissions includes diesel generators, electric heating equipment, and water heaters. The operational objective of the Antarctic research station optimization model is to minimize carbon emissions. This paper considers the carbon emissions from diesel generators, electric heating equipment, and water heaters, as shown in Formula 44. Formula 44; in, This represents the total carbon emissions of the Antarctic research station. Carbon emissions from diesel generators at Antarctic research stations; Carbon emissions from electric heating equipment inside the research station; Carbon emissions from water heaters inside the research station; The total time period is 24 hours.

[0073] The values ​​are shown in formulas 45 to 47: Formula 45; Formula 46; Formula 47; in, Carbon emission factors for diesel fuel; This refers to the power output of the diesel generator. For the operating efficiency of diesel generators; Carbon emission factors for electric heating equipment; This refers to the operating power of the electric heating equipment. Carbon emission factors of water heaters; This refers to the operating power of the water heater.

[0074] A deterministic optimization model is obtained by integrating source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function. The deterministic optimization model includes the energy optimization objective function and the optimization constraints corresponding to the energy optimization objective function. The optimization constraints include: inequality constraints in the model set, equality constraints in the model set, constraint relationships satisfied between optimization variables, and the values ​​of uncertain variables being the predicted values ​​for each time period. The model set includes source-side modeling, load-side modeling, storage-side modeling, and power balance constraints.

[0075] When the output fluctuations of wind turbines and photovoltaic arrays are not considered, the deterministic optimization model of the Antarctic research station can be obtained, which can be expressed in a compact form as shown in Equation 48: Formula 48; Where x and y are optimization variables, and their specific expressions are shown in Formula 49: Formula 49; in, For the battery's charge / discharge status indicator, The charging / discharging status indicator bit of the hydrogen storage system; Wind power generation capacity under icing conditions Photovoltaic power generation under snow cover conditions The output power of the diesel generator during time period t, The operating power of the electrolytic cell during time period t The output power of the hydrogen fuel cell during time period t The electrical energy consumed by electric heating equipment Indoor temperature during time period t The wall temperature of each numbered wall is respectively The power of the water heater during time period t The water temperature in the water heater tank during time period t The heat power recovered by the heat exchanger from the electrolyzer and hydrogen fuel cell during time period t, The discharge power of the battery during time period t The charging power of the battery during period t, This provides the power required for other equipment at the Antarctic research station.

[0076] c is the column vector of coefficients corresponding to the objective function; D, K, F, G and Here is the coefficient matrix of the variables under the corresponding constraints; d, k, and h are constant column vectors. The inequality constraints in the model can be represented as the first row of Formula 48 (with the row containing "subject to, st" as the first row), including Formulas 11, 15, 23, 26, 28, 36, and 41; the equality constraints can be represented as the second row, including Formulas 1-10, 12-14, 16-22, 24-25, 27, 29-33, 37-38, and 42-43; the third row represents the constraint relationship satisfied between x and y under the optimization variables, including Formulas 34-35 and 39-40; the fourth row represents the predicted values ​​of the uncertain variables in the deterministic optimization model for the Antarctic research station, as shown in Formula 50. Formula fifty; in, , These represent the predicted output values ​​of the photovoltaic array and wind turbine generator at different time periods.

[0077] The above model is a conventional deterministic optimization problem, and the resulting solutions are often too "risky" and difficult to meet the needs of Antarctic research stations. Therefore, it is necessary to take into account the impact of uncertainty in the model.

[0078] Construct a box-shaped uncertainty set for the output of the photovoltaic array and the wind turbine generator; the uncertain variables in the box-shaped uncertainty set are the uncertain variables of the output of the photovoltaic array and the wind turbine generator introduced after considering uncertainties, as shown in Formula 51: Formula 51; in, For box-shaped uncertain sets, and These are the uncertainties in the output of the photovoltaic array and the wind turbine generator introduced after considering uncertainties; and These represent the maximum permissible fluctuation deviations for uncertain variables.

[0079] Based on a deterministic optimization model, expressed in a compact typical matrix form, and a two-stage partial Bruker optimal scheduling model is established, the two-stage partial Bruker optimal scheduling model for Antarctic research stations considering the uncertainty of wind and solar power output constructed in this application embodiment can be expressed in the following compact typical matrix form, as shown in Formula 52: Formula 52; The first stage problem is the outermost min, with the optimization variable x, which corresponds to the charging and discharging state of the battery and the charging and discharging state of the hydrogen storage system. The second stage problem is the inner max-min, with optimization variables u and y. y corresponds to the wind power generation under icing conditions, the photovoltaic power generation under snow conditions, the output power of the diesel generator in time period t, the working power of the electrolyzer in time period t, the output power of the hydrogen fuel cell in time period t, the electrical energy consumed by the electric heating equipment, the indoor temperature in time period t, the wall temperature of each numbered wall, the power of the water heater in time period t, the water temperature in the water heater tank in time period t, the heat power recovered by the electrolyzer and hydrogen fuel cell through the heat exchanger in time period t, the discharge power of the battery in time period t, the charging power of the battery in time period t, and the conventional electrical load. u corresponds to the uncertain variable.

[0080] The min problem is equivalent to the objective function in Formula 44, which represents minimizing carbon emissions from Antarctic research stations. Indicates a given set When optimizing the feasible region of variable y, the specific expression is shown in Formula 53 below: Formula 53; in, , , , These are dual variables used to handle the minimization problem in the second stage.

[0081] S105: By introducing an imprecise tolerance during the iteration process, the accuracy of solving the main problem and the convergence threshold are adaptively adjusted to solve the two-stage sub-Bruker optimization scheduling model and obtain a scheduling scheme; the scheduling scheme is used to schedule at least one of the energy production equipment, the energy consumption equipment, and the energy storage equipment.

[0082] To address the low efficiency of traditional two-stage bilabial bar optimization methods, this application proposes a DTA-C&CG algorithm. This algorithm introduces an imprecise tolerance during the iteration process. It adaptively adjusts the accuracy of solving the main problem and the convergence threshold, effectively avoiding redundant iterations while ensuring the accuracy of the global optimal solution, and significantly improving the model's solution efficiency.

[0083] Specifically, based on the wind and solar power output under the worst-case scenario, the lower bound of the main problem is obtained by solving the main problem and updating the lower bound of the main problem, and by solving the main problem and recording the optimal decision value of the main problem; the main problem corresponds to the optimization variables in the first stage.

[0084] Let the relative optimal gap of the main problem be... This is used to indicate the precision of the calculation result for the main problem. At the same time, in order to achieve... The verification and adjustment require the introduction of a preset convergence gap. Non-precise tolerance The iteration number k and the valid iteration index identifier I.

[0085] Decomposing Formula 52, the main problem is shown in Formula 54 below: Formula 54; Where k is the current iteration number, with an initial value of 1; I is the effective optimal iteration index, with an initial value of 0; LB is the lower bound of the main problem, with an initial value of 0, used to dynamically improve the solution accuracy of the main problem; The solution to the subproblem after the l-th iteration; This represents the value of the wind and light output under the worst-case scenario after the l-th iteration.

[0086] When according to When solving the main problem, we obtain the upper bound of the main problem. Lower Boundary If the conditions are met If the result obtained in this iteration is better, then the effective iteration count I=k is updated, and the lower bound of the main problem is also adjusted. As the new lower bound (LB), And record the optimal decision value of the main problem as Then substitute it into the subproblem to solve it.

[0087] Based on the optimal decision value of the primary problem, the value of wind and solar power output under the worst-case scenario is obtained by solving the sub-problems; the sub-problems correspond to the optimization variables in the second stage.

[0088] The decomposed subproblems are in the form shown in Formula 55: Formula 55; According to duality theory, the min problem in the max-min bi-level optimization corresponding to the subproblem is transformed into the max problem, and the max problems in the max-min bi-level optimization of the max problem domain are merged to obtain the dual problem.

[0089] Considering that the max-min bi-level optimization is an NP-hard problem, it is usually difficult to solve. However, when (x, u) is given, the inner minimization is a linear problem. The min can be transformed into the max form according to duality theory, and then merged with the outer max problem. The resulting dual problem is shown in Equation 56. Formula fifty-six; Based on the bilinear term in the dual problem, the box-shaped uncertainty set is reformulated as follows: whether to take the lower limit of the fluctuation range is represented by a binary variable, and the form of uncertainty adjustment parameter is introduced.

[0090] The formula contains bilinear terms. For the Antarctic research station corresponding to the embodiments of this application, when the wind and solar power output is taken as the left boundary of Formula 51, the carbon emissions generated by the Antarctic research station are higher. Therefore, the uncertainty set U to which the wind and solar power output belongs can be restated as shown in Formula 57: Formula 57; in, This is a binary variable; a value of 1 indicates that the wind and solar power output reaches the minimum of the uncertain set. and These are the uncertainty adjustment parameters corresponding to the output of the photovoltaic array and the wind turbine generator, respectively, with values ​​ranging from 0 to... The integer within represents the total number of time periods within the scheduling cycle where the wind and solar power output values ​​are at the lower limit of the fluctuation range described in Formula 57. The smaller the value, the more risky the model is, and therefore it can be used to adjust the conservatism of the scheme.

[0091] For the bilinear term, we linearize it using the Big M method and introduce a continuous auxiliary variable to iteratively solve the subproblem.

[0092] However, since substituting the expression for the uncertain variable in Formula 57 into Formula 56 results in a product of a continuous variable and a binary variable, the Big M method is used to linearize it, yielding Formulas 58 and 59: Formula fifty-eight; Formula 59; in, , For continuous auxiliary variables, The upper bound of the dual variable can be a sufficiently large positive real number.

[0093] Based on the lower bound of the main problem and the upper bound of the subproblems, the system determines whether to stop iteration and return to the optimal solution, or to continue iteration, by judging whether the actual relative gap is close enough to the preset convergence gap.

[0094] Record the worst-case scenario obtained from the subproblem and update the upper bound UB of the original problem. When the actual relative gaps are sufficiently close, as shown in Equation 60, stop the iteration and return the optimal solution. : Formula Sixty; If the actual relative gap does not meet the preset convergence gap, the inaccurate tolerance is determined by the preset convergence gap, and the inaccurate tolerance is compared with the upper bound of the subproblem.

[0095] If Formula 60 is not met, then the non-precise tolerance needs to be compared according to Formula 61: Formula 61; in, The actual target value of the main problem. To ensure system reliability, the following non-precise tolerances must be met. It should satisfy the conditions shown in Formula 62: Formula 62; If the actual relative gap after iteration satisfies the non-precise tolerance, then update the lower bound of the main problem, reduce the relative optimal gap, and return to the main problem for the next round of iteration; if the actual relative gap after iteration does not satisfy the non-precise tolerance, then resolve the subproblem, obtain the new worst-case scenario, and return to the main problem for iteration.

[0096] If Equation 61 holds, then let k = I and update the lower bound of the main problem. At the same time, reduce the relative optimal gap. Then return to the main problem for the next iteration; if formula sixty-one does not hold, the subproblems need to be solved again to obtain the new worst-case scenario, and then return to the main problem for iteration.

[0097] 1. A wind-solar-hydrogen-storage multi-energy complementary model based on real meteorological datasets in Antarctica was constructed. This model effectively overcomes the shortcomings of traditional models in predicting inaccurate results in extreme polar environments, accurately depicts the power output limitation caused by ice and snow cover, and ensures uninterrupted and reliable power supply for the research station under extreme weather conditions.

[0098] 2. A low-carbon operation strategy that considers the collaboration between virtual energy storage system and waste heat recovery water heater is proposed, which effectively solves the problems of waste heat resources and heat supply and demand imbalance. Under the premise of fully guaranteeing the polar thermal environment and hot water demand, energy cascade utilization is realized, and carbon emissions are significantly reduced.

[0099] 3. A two-stage partial Bruker optimization strategy was proposed and solved using the DTA-C&CG algorithm. This effectively mitigated the impact of strong wind and solar fluctuations and the lack of precise probability distribution on the scheduling scheme. While ensuring that the calculation speed meets the real-time convergence requirements, it significantly improved the risk resistance and robustness of the low-carbon operation of the research station.

[0100] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for operating an Antarctic scientific research station based on virtual energy storage and waste heat recovery cooperation, characterized in that, include: Source-side modeling is performed; wherein, the source-side modeling includes: modeling a polar wind power generation model under icing conditions and a polar photovoltaic power generation model under snow conditions using meteorological parameters; and modeling a polar diesel generator output model based on temperature difference and a hydrogen energy equipment model describing the hydrogen production of a PEM electrolyzer using the first operating parameters corresponding to the energy production equipment. And load-side modeling is performed; wherein, the load-side modeling includes: modeling the electric heating model under thermal balance constraints and the conventional energy consumption model described by the power equation using the first working parameters corresponding to the energy production equipment and the building parameters corresponding to the scientific research station building; and modeling the waste heat recovery water heater model described by the thermal dynamic change equation of water temperature using the second working parameters corresponding to the energy consumption equipment. And perform storage-side modeling; wherein, the storage-side modeling includes: modeling a battery energy storage model under the constraints of multiple operating boundaries in the spatiotemporal dimension through the third operating parameters corresponding to the energy storage device, and a hydrogen storage tank model described by the balance equation of dynamic changes in hydrogen storage capacity; Power balance constraints are performed based on the source-side modeling, the load-side modeling, and the storage-side modeling. Based on the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function, a deterministic optimization model is obtained. Based on the deterministic optimization model and the box-type uncertainty set to which the output of the photovoltaic array and wind turbine generator belongs, a two-stage sub-Blu-ray bar optimization scheduling model is established. By introducing an imprecise tolerance during the iteration process, the accuracy of solving the main problem and the convergence threshold are adaptively adjusted to solve the two-stage sub-Bruker optimization scheduling model and obtain a scheduling scheme; the scheduling scheme is used to schedule at least one of the energy production equipment, the energy consumption equipment, and the energy storage equipment.

2. The Antarctic research station operation method based on virtual energy storage and waste heat recovery collaboration according to claim 1, characterized in that, Using meteorological parameters, models were created for polar wind power generation under icing conditions and polar photovoltaic power generation under snow-covered conditions. Specifically, this included: Based on the actual wind speed at the hub height of the wind turbine in the meteorological parameters, a polar wind power generation model is obtained by establishing a cubic function fitting relationship between the wind turbine icing power loss coefficient and the wind turbine icing mass and introducing an icing growth model. Based on the actual light intensity and photovoltaic panel temperature in the meteorological parameters, a polar photovoltaic power generation model was obtained by establishing a cubic function fitting relationship between the photovoltaic snow-covered power loss coefficient and the photovoltaic snow-covered mass and introducing a snow-covered growth model.

3. The Antarctic research station operation method based on virtual energy storage and waste heat recovery collaboration according to claim 1, characterized in that, Using the first operating parameters corresponding to the energy production equipment, modeling is performed on the output model of the polar diesel generator based on temperature difference and the hydrogen energy equipment model describing the hydrogen production of the PEM electrolyzer, specifically including: Based on the output power of the diesel generator in the first operating parameters, a polar diesel generator output model is obtained by introducing a diesel consumption correction coefficient based on temperature difference and considering the upper and lower limits of output power. Using the operating power of the PEM electrolyzer and the rate at which the hydrogen fuel cell consumes hydrogen as inputs, a model of the hydrogen energy equipment is obtained by establishing a linear conversion relationship between the PEM electrolyzer's hydrogen production process and a mathematical model of the hydrogen fuel cell.

4. The Antarctic research station operation method based on virtual energy storage and waste heat recovery collaboration according to claim 1, characterized in that, Modeling is performed on the electric heating model under thermal balance constraints and the conventional energy consumption model described by the power equation, using the first working parameters corresponding to the energy production equipment and the building parameters corresponding to the scientific research station building. Furthermore, by using the second operating parameters corresponding to the energy-consuming equipment, a waste heat recovery water heater model described by the thermal dynamic change equation of water temperature is modeled, specifically including: Based on the electrical energy consumed by the electric heating equipment in the first working parameters and the thermal resistance and heat capacity parameters in the building parameters, the electric heating model is obtained by constructing the thermal balance constraints of the scientific research station's envelope and the indoor thermal balance constraints using a thermal resistance-heat capacity network based on the heating capacity of the electric heating equipment. Based on the heat generation power of the electrolyzer and hydrogen fuel cell and the heat recovery efficiency of the heat exchanger in the second working parameters, a waste heat recovery water heater model is obtained by establishing the thermal dynamic change equation of the water temperature in the water heater and setting the upper and lower limits of the user's comfortable water temperature. Based on the operating parameters of the conventional electrical load equipment in the first working parameters, a conventional energy consumption model is modeled by establishing the power equations corresponding to each conventional electrical load equipment and aggregating them.

5. The Antarctic research station operation method based on virtual energy storage and waste heat recovery collaboration according to claim 1, characterized in that, By using the third operating parameters corresponding to the energy storage equipment, we model the battery energy storage model under the constraints of multiple operating boundaries in the spatiotemporal dimensions, and the hydrogen storage tank model described by the balance equation of dynamic changes in hydrogen storage capacity. Specifically, this includes: Based on the battery's operating parameters in the third operating parameters, a battery energy storage model is obtained by establishing multiple operating boundaries, including charging and discharging power constraints, state of charge constraints, charging and discharging state mutual exclusion constraints, and power balance constraints at the beginning and end of the scheduling cycle. Based on the hydrogen mass flow rate of the electrolyzer hydrogen production and fuel cell hydrogen consumption processes in the third working parameters, as well as the charging and discharging status flag of the hydrogen storage tank, a model of the hydrogen storage tank is obtained by establishing a balance equation for the dynamic change of hydrogen storage capacity in adjacent scheduling periods and setting minimum and maximum energy storage capacity constraints for the hydrogen storage tank.

6. The Antarctic research station operation method based on virtual energy storage and waste heat recovery collaboration according to claim 1, characterized in that, Based on the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function, a deterministic optimization model is obtained, specifically including: With the goal of minimizing carbon emissions, an energy optimization objective function is constructed; the equipment corresponding to carbon emissions includes diesel generators, electric heating equipment, and water heaters. A deterministic optimization model is obtained by integrating the source-side modeling, load-side modeling, storage-side modeling, power balance constraints, and energy optimization objective function. The deterministic optimization model includes the energy optimization objective function and the corresponding optimization constraints. These constraints include inequality constraints and equality constraints within the model set, constraint relationships between optimization variables, and the predicted values ​​of uncertain variables for each time period. The model set includes the source-side modeling, load-side modeling, storage-side modeling, and power balance constraints.

7. The method for operating an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery as described in claim 6, characterized in that, Based on the aforementioned deterministic optimization model, and the constructed box-type uncertainty set to which the output of the photovoltaic array and wind turbine generator belongs, a two-stage sub-Bruker optimization scheduling model is established, specifically including: Construct a box-shaped uncertainty set to which the output of the photovoltaic array and the wind turbine generator belong; the uncertain variables in the box-shaped uncertainty set are the uncertain variables of the output of the photovoltaic array and the wind turbine generator introduced after considering uncertainties; Based on the deterministic optimization model, expressed in a compact typical matrix form, a two-stage sub-Bruker optimal scheduling model is established. The optimization variables in the first stage correspond to the charging and discharging states of the battery and the hydrogen storage system. The optimization variables in the second stage correspond to the wind power generation under icing conditions, the photovoltaic power generation under snow conditions, the output power of the diesel generator in time period t, the operating power of the electrolyzer in time period t, the output power of the hydrogen fuel cell in time period t, the electrical energy consumed by the electric heating equipment, the indoor temperature in time period t, the wall temperature of each numbered wall, the power of the water heater in time period t, the water temperature in the water heater tank in time period t, the heat power recovered by the electrolyzer and hydrogen fuel cell through the heat exchanger in time period t, the discharge power of the battery in time period t, the charging power of the battery in time period t, the power required by other equipment at the Antarctic research station, and the aforementioned uncertain variables.

8. The method for operating an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery as described in claim 7, characterized in that, By introducing an imprecise tolerance during the iteration process, the accuracy of solving the main problem and the convergence threshold are adaptively adjusted to solve the two-stage sub-Bruker optimization scheduling model, resulting in a scheduling scheme, specifically including: Based on the power output of wind and light under the worst-case scenario, the lower bound of the main problem is obtained by solving the main problem and updating the lower bound of the main problem, and by solving the main problem and recording the optimal decision value of the main problem; the main problem corresponds to the optimization variable in the first stage. Based on the optimal decision value of the primary problem, the value of the wind and solar power output under the worst-case scenario is obtained by solving the sub-problems; the sub-problems correspond to the optimization variables in the second stage. According to duality theory, the min problem in the max-min bi-level optimization corresponding to the subproblem is transformed into a max problem, and the max problems in the max-min bi-level optimization of the max problem domain are merged to obtain the dual problem; Based on the bilinear term in the dual problem, the box-shaped uncertainty set is reformulated as follows: whether to take the lower limit of the fluctuation range is represented by a binary variable, and an uncertainty adjustment parameter is introduced. For the bilinear term, it is linearized using the Big M method, and a continuous auxiliary variable is introduced to iteratively solve the subproblem. Based on the lower bound of the main problem and the upper bound of the subproblems, the system determines whether to stop iteration and return to the optimal solution, or to continue iteration, by judging whether the actual relative gap is close enough to the preset convergence gap.

9. The method for operating an Antarctic research station based on the synergy of virtual energy storage and waste heat recovery as described in claim 8, characterized in that, The method further includes: If the actual relative gap does not meet the preset convergence gap, then the inaccurate tolerance is determined by the preset convergence gap, and the inaccurate tolerance is compared with the upper bound of the subproblem. If the actual relative gap after iteration satisfies the non-precise tolerance, then update the lower bound of the main problem, reduce the relative optimal gap, and return to the main problem for the next iteration; If the actual relative gap after iteration does not meet the non-precise tolerance, the subproblem is solved again, a new worst-case scenario is obtained, and the main problem is returned for iteration.

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