A method and system for cogeneration of thermal and electrical energy for inland antarctic environments

Abstract

The application provides a thermoelectricity collaborative energy supply method and system for inland Antarctic environment, and relates to the technical field of energy supply systems, and specifically comprises the following steps: collecting annual environmental data of a preset scientific research area, and extracting at least one typical week working condition by using a clustering algorithm; constructing a multi-objective optimization model containing decision variables, an optimization objective function and constraint conditions; iteratively solving the multi-objective optimization model by using a multi-objective optimization algorithm, wherein, in each iteration, a candidate system configuration scheme is simulated by using a preset dynamic thermal physical model, a battery dynamic state updating model and a multi-source energy supply control strategy under the typical week working condition to evaluate the corresponding objective function value; and obtaining the optimal configuration scheme of the energy system according to the result of the iterative solution.

Description

Technical Field

[0001] This invention relates to the field of energy supply system technology, and more specifically, to a heat-electricity synergistic clean energy supply method and system suitable for extreme environments such as polar inland areas. Background Technology

[0002] Due to their unique geographical location and climate, polar regions exhibit extreme natural conditions, including consistently low temperatures, intense snowstorms, and severe imbalances in day and night cycles. Under these conditions, the distribution of renewable energy sources such as wind and solar power exhibits significant seasonality and dramatic fluctuations in both time and space. These characteristics bring considerable uncertainty and challenges to the energy supply of scientific research platforms deployed in these areas, especially in scenarios requiring unattended, long-term operation. Traditional energy supply models are insufficient to meet their stringent requirements for stability and reliability. To enhance energy self-sufficiency, some polar research stations have begun deploying integrated systems combining wind, solar, and energy storage.

[0003] However, existing integrated wind, solar, and energy storage systems still have many shortcomings in their design and operation strategies. Firstly, these systems generally prioritize power dispatch, severely lacking a coordinated optimization mechanism with the thermal management system. In the extreme low-temperature environments of polar regions, ensuring stable temperature inside the cabin is not only a basic requirement but also a prerequisite for the normal operation of critical equipment such as communication modules and energy storage batteries. Existing technical solutions typically treat heat load as a static or isolated parameter, failing to fully consider the dynamic thermal characteristics of the cabin structure itself, such as its heat capacity, heat loss, and the differences in heat conduction characteristics caused by snow layers at different burial depths. This lack of thermal inertia modeling leads to insufficient coupling between the thermal management model and the energy dispatch strategy, preventing truly effective coordinated control between the thermal and electrical systems, thus affecting equipment operational safety and energy utilization efficiency.

[0004] Secondly, current technologies have not proposed a unified thermal-electricity synergistic optimization strategy that can take into account typical meteorological cycles and regional environmental differences. Current research mostly focuses on single dimensions such as capacity configuration of wind, solar, and energy storage systems, battery life degradation modeling, or simple economic assessments, lacking a framework that can integrate variable meteorological conditions for unified scheduling decisions. Wind speed, solar radiation intensity, and snow temperature variations differ significantly among different Antarctic sites; using modeling and design methods based on single operating conditions makes it difficult to ensure the energy system has broad adaptability and robustness. Furthermore, when energy storage batteries experience performance degradation and rapid capacity decay at low temperatures, the system often needs to frequently start diesel generators as a supplement, which not only significantly increases fuel consumption but also reduces the overall operating efficiency of the energy system.

[0005] Finally, at the optimization algorithm level, existing configuration methods also lack efficient solution mechanisms for such complex multi-objective problems. Optimizing energy systems requires balancing multiple conflicting objectives, such as ensuring thermal environment safety and improving fuel economy. Most existing configuration methods rely on traditional genetic algorithms or static multi-objective models. In the uncertain polar environment, these algorithms are prone to getting trapped in local optima and have low convergence efficiency, making them unsuitable for real-time decision-making. Therefore, there is an urgent need to develop an intelligent energy management method that can deeply couple with dynamic thermal environment models and efficiently solve multi-objective optimization problems to improve the overall robustness and intelligence of energy systems in unmanned polar platforms. Summary of the Invention

[0006] This invention provides a method for thermoelectric co-generation energy supply in the Antarctic inland environment, the method specifically including the following steps:

[0007] Collect year-round environmental data of the pre-designated scientific research area, and based on the year-round environmental data, use a clustering algorithm to extract at least one typical weekly working condition;

[0008] The annual environmental data includes average ground temperature, average wind speed, and average solar radiation intensity.

[0009] Construct a multi-objective optimization model that includes decision variables, an optimization objective function, and constraints; the decision variables include the number of various types of equipment in the energy system, and the optimization objective function includes maximizing thermal environment safety and minimizing total life cycle fuel consumption.

[0010] The multi-objective optimization model is iteratively solved using a multi-objective optimization algorithm. In each iteration, the candidate system configuration scheme is simulated under the typical weekly operating conditions using a preset dynamic thermophysical model, a battery dynamic state update model, and a multi-source power supply control strategy to evaluate its corresponding objective function value.

[0011] Based on the results of the iterative solution, the optimal configuration scheme of the energy system is obtained.

[0012] The step of extracting at least one typical weekly operating condition using a clustering algorithm includes: constructing a multidimensional weekly feature vector based on the average ground temperature, average wind speed, and average solar radiation intensity in the annual environmental data; clustering the multidimensional weekly feature vector to obtain at least one category; and selecting, from the at least one category, the real weekly data sequence with the smallest distance to the cluster center of that category as the typical weekly operating condition.

[0013] The dynamic thermophysical model is as follows:

[0014] in, This is the internal temperature of the chamber at the next moment. This represents the current internal temperature of the enclosure. For time step, The total effective heat capacity of the system, The average heating power within the current time step. The overall heat transfer coefficient of the system is . The current ambient temperature.

[0015] The battery dynamic state update model is used to update the battery's state of charge:

[0016] in, The state of charge at the next moment. The current state of charge. Total energy for charging The total energy of the discharge. The rated capacity of the battery pack. For charging efficiency, This refers to the discharge efficiency.

[0017] The multi-source energy supply control strategy follows the principle of energy utilization hierarchy, including: prioritizing renewable energy as the first priority to meet heating power demand and using redundant renewable energy power to charge batteries; when renewable energy is insufficient, activating energy storage batteries as the second priority to supplement supply, provided that the battery state of charge is higher than a preset minimum threshold; and activating diesel generators as a final guarantee when there is a heating power gap and the battery state of charge is lower than the minimum threshold.

[0018] In the objective function of minimizing total life cycle fuel consumption, total life cycle fuel consumption includes: the cumulative operating fuel consumption of the diesel generator under the typical weekly operating conditions; the transportation fuel consumption required to transport various types of equipment in the energy system to the preset scientific research area; and the maintenance fuel consumption generated by inspecting and maintaining the various types of equipment within an operating year.

[0019] The multi-objective optimization algorithm is a multi-objective particle swarm optimization algorithm; and the iterative solution steps include: during the iteration process, using an external archive to store and maintain non-dominated solutions; after the iteration terminates, the non-dominated solutions in the external archive constitute a Pareto optimal front, and the optimal configuration scheme is determined from the Pareto optimal front.

[0020] This specification also proposes a thermoelectric co-generation energy supply system for the Antarctic inland environment, the system comprising:

[0021] Data Acquisition Module: Collects annual environmental data of the preset scientific research area, and extracts at least one typical weekly working condition based on the annual environmental data using a clustering algorithm;

[0022] The annual environmental data includes average ground temperature, average wind speed, and average solar radiation intensity.

[0023] Model building module: Constructs a multi-objective optimization model that includes decision variables, optimization objective functions, and constraints; the decision variables include the number of various types of equipment in the energy system, and the optimization objective functions include maximizing thermal environment safety and minimizing total life cycle fuel consumption;

[0024] Optimization module: The multi-objective optimization model is solved iteratively using a multi-objective optimization algorithm. In each iteration, the candidate system configuration scheme is simulated under the typical weekly operating conditions using a preset dynamic thermophysical model, a battery dynamic state update model, and a multi-source power supply control strategy to evaluate its corresponding objective function value.

[0025] Scheme configuration module: Based on the results of iterative solution, the optimal configuration scheme of the energy system is obtained.

[0026] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned thermoelectric co-generation energy supply method for the Antarctic inland environment.

[0027] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described thermoelectric co-generation energy supply method for the Antarctic inland environment.

[0028] Compared with the prior art, the thermoelectric synergistic energy supply method and system for the Antarctic inland environment provided in this application embodiment has the following significant advantages:

[0029] This invention addresses the shortcomings of traditional polar energy systems that focus on power dispatch and neglect dynamic thermal management by constructing a deeply coupled system model and collaborative control strategy. The dynamic thermophysical model established in this invention closely links the thermal inertia parameters of the shell-type enclosure in polar snow environments, such as the total effective heat capacity and total heat transfer coefficient, with the energy dispatch of the energy system. This enables the energy control strategy to perform refined heating management based on accurate predictions of future internal temperatures, rather than simply responding to temperature exceedance alarms. This thermo-electric collaborative control mechanism ensures that unnecessary energy consumption is minimized while meeting the operating temperature requirements of critical internal equipment. It significantly reduces the reliance on high-frequency, low-efficiency starting of diesel generators in low-temperature environments, thereby greatly improving energy utilization efficiency while ensuring system reliability.

[0030] This invention proposes a complete system optimization design process based on real-world environmental data, significantly improving the scientific rigor and environmental adaptability of energy system configuration schemes. By employing clustering algorithms to perform pattern recognition on long-term, high-resolution measured environmental data throughout the year, typical and statistically representative weekly operating conditions are extracted. This invention overcomes the biases and limitations of traditional design methods that rely on annual averages or single extreme operating conditions. System optimization based on these typical weekly operating conditions ensures that the final determined equipment configuration (number of wind turbines, photovoltaic units, and batteries) exhibits good performance and robustness under various seasonal and extreme climatic conditions throughout the year. This avoids investment waste due to over-design or the risk of system collapse under extreme conditions due to under-design, enhancing the universality of the design scheme in different regions and variable climatic conditions in Antarctica.

[0031] This invention constructs a rigorous multi-objective optimization model for the complex system configuration problem and solves it using advanced intelligent optimization algorithms, significantly improving the intelligence level and decision-making efficiency of system design. This model not only takes ensuring thermal environment safety as the core performance objective but also considers the fuel consumption throughout the entire lifecycle as an economic objective, comprehensively taking into account fuel costs for equipment transportation, subsequent maintenance, and generator operation. This makes the optimization results more closely aligned with engineering realities and the overall considerations of logistical support. Employing an improved multi-objective particle swarm optimization algorithm, it can efficiently search for a set of Pareto optimal solutions that achieve the best trade-off between performance and cost within a vast configuration combination space. This provides engineering decision-makers with a series of quantitative alternatives, replacing the traditional model that relies on experience-based estimations. It can quickly obtain the optimal solution for wind-solar-storage co-configuration, significantly improving the operational intelligence and decision-making efficiency of energy systems in unattended polar scenarios. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the thermoelectric synergistic energy supply process for the Antarctic inland environment. Detailed Implementation

[0034] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0035] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. 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.

[0036] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number and aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0037] Additionally, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that practice can be carried out without these specific details.

[0038] This specification presents an embodiment of a thermoelectric co-generation energy supply method for the Antarctic inland environment, which specifically includes the following steps:

[0039] Collect year-round environmental data of the pre-designated scientific research area, and based on the year-round environmental data, use a clustering algorithm to extract at least one typical weekly working condition;

[0040] The annual environmental data includes average ground temperature, average wind speed, and average solar radiation intensity.

[0041] Construct a multi-objective optimization model that includes decision variables, an optimization objective function, and constraints; the decision variables include the number of various types of equipment in the energy system, and the optimization objective function includes maximizing thermal environment safety and minimizing total life cycle fuel consumption.

[0042] The multi-objective optimization model is iteratively solved using a multi-objective optimization algorithm. In each iteration, the candidate system configuration scheme is simulated under the typical weekly operating conditions using a preset dynamic thermophysical model, a battery dynamic state update model, and a multi-source power supply control strategy to evaluate its corresponding objective function value.

[0043] Based on the results of the iterative solution, the optimal configuration scheme of the energy system is obtained.

[0044] This embodiment details the data acquisition and typical scenario generation process for a specific application scenario in the method of the present invention. The ultimate goal of this stage is to provide a set of input data models, namely typical weekly operating conditions, that can accurately reflect the real environmental changes throughout the year in the Antarctic interior and inland areas and are computationally efficient and feasible for subsequent energy system configuration optimization and control strategy simulation.

[0045] This embodiment is applied to an unmanned shell-type scientific research module deployed in the Panda 1100 region of the Antarctic inland ice sheet. This module needs to operate continuously year-round using its own energy system, without human intervention, providing a stable and reliable power supply and temperature environment for its internal scientific equipment (such as weather sensors, data acquisition and communication modules). The energy system design must be able to withstand the harsh challenges posed by the lack of sunlight, unstable wind resources, and extreme low temperatures during the polar night. Therefore, accurate modeling of the environmental characteristics of this region is the cornerstone of the successful design of the entire system.

[0046] Step 1: Environmental Data Collection and Characterization

[0047] To ensure the energy system design is well-suited to the environmental characteristics of the target area, step 1 involves acquiring a high-resolution, long-term field observation dataset. For example, annual measured data from the Panda1100 automated weather station from January 1st to December 31st, 2020, are selected. To comprehensively characterize the key external factors affecting the heat-electricity synergistic energy system, the following three core environmental variables are identified for collection: Wind speed: Wind speed data is collected at a height of 10 meters above the snow surface. Wind speed directly determines the instantaneous power generation of the wind turbine generators and is one of the main energy input sources in the system. Solar radiation intensity: Total solar radiation intensity data is collected at the horizontal plane. Solar radiation intensity determines the power generation capacity of the photovoltaic array, and its significant differences during polar days and nights are a core element that must be considered in the system design. Snow layer temperature: Ground temperature data is collected at a depth of 1 meter below the snow surface. Since the experimental cabin is partially buried in snow, the ground temperature represents the external ambient temperature that the cabin shell is in direct contact with. It is a key boundary condition for calculating the cabin's heat loss and assessing heating demand. Compared to the drastically changing air temperature, the ground temperature at this depth better reflects the stable thermal environment in which the cabin is located.

[0048] The above three sets of data were all recorded with a time resolution of 4 hours, forming a total of 2,190 discrete time points throughout the year, which constitute a basic database that can accurately describe the dynamic changes of wind, solar and thermal resources in the region.

[0049] Due to wind speed Solar radiation intensity The physical units and numerical ranges of the three variables—solar temperature (°C), geothermal temperature, and ground temperature—differ significantly. If directly used in subsequent cluster analysis, variables with larger numerical ranges (such as solar radiation intensity) will dominate distance calculations, masking the influence of other variables and distorting the clustering results. To address this issue, this step employs a min-max normalization method to preprocess the three sets of time-series data. The aim is to eliminate the influence of units of measurement, ensuring that each variable has equal weight in subsequent pattern recognition.

[0050]

[0051] in, These are the original collected values. and These are the minimum and maximum values ​​of the variable in the entire year's dataset, respectively. The result is a dimensionless value within the interval [0, 1] after processing. Through normalization, all external environmental inputs are placed on a uniform evaluation scale.

[0052] Step 2: Extraction of typical weekly working conditions based on K-means clustering

[0053] The sheer volume of data from 2190 time points throughout the year is prohibitively large for end-to-end energy system optimization simulations, resulting in extremely high computational costs. Therefore, a limited number (e.g., four) of highly representative seven-day (one-week) operating scenarios are scientifically extracted from the annual data using pattern recognition algorithms. These typical weeks cover the main seasonal climate characteristics and extreme weather combinations of the year, serving as standardized and efficient input for subsequent system optimization design and performance evaluation.

[0054] To identify weekly climate patterns, a sliding window method was used to process the normalized data for the entire year. A series of weekly samples were formed by capturing data in 7-day windows (42 four-hour time points) starting from the beginning of the year. To characterize the overall climate characteristics of each weekly sample, the average values ​​of three variables within each window were calculated, forming a three-dimensional weekly feature vector. ,in , , The first Normalized values ​​of weekly average ground temperature, average wind speed, and average solar radiation intensity.

[0055] The K-means algorithm was used to perform unsupervised clustering on the above weekly feature vector set, automatically dividing all weekly samples throughout the year into groups. The clusters are the most similar in terms of intrinsic characteristics. In this embodiment, the number of clusters is set. This is to generate typical climate models corresponding to the polar day period, polar night period, and two transitional periods, respectively. The algorithm flow is as follows:

[0056] Four cluster centers are randomly selected from all the weekly feature vectors. .

[0057] Repeat the assignment and update steps until convergence:

[0058] Allocation steps: For each week feature vector Calculate its relationship with the four cluster centers. Euclidean distance It is then assigned to the category represented by the nearest cluster center.

[0059] Update steps: After all the weekly vectors have been assigned, recalculate the geometric center of each category (i.e., the mean of all vectors within the category) and use it as the new cluster center.

[0060] Convergence: When the cluster centers no longer change, or the amount of change is less than the preset threshold, the algorithm converges, and all weeks of the year are successfully divided into 4 categories.

[0061] Determining the typical weekly representative:

[0062] The cluster centers obtained after the clustering algorithm converges are a mathematical average and may not correspond to any real-world week. Directly using such virtual week data for system simulation ignores the complex coupling relationships between variables in the real world (e.g., the misalignment between windy and sunny periods), leading to distorted simulation results. To ensure high realism and physical feasibility of the simulation input, this step uses the following method to determine the final typical week:

[0063] In each final category, the Euclidean distance between all the true weekly feature vectors in that category and the final cluster center of that category is calculated again, and the true weekly with the smallest distance is selected as the typical weekly representative of that category.

[0064] For example, one cluster in the clustering results clearly characterizes the extreme and severe conditions of "low temperature, no wind, and no light" during the polar night. In this embodiment, the week of August 3 to August 9, 2020 (W31) was determined to be the best representative of this category using the method described above. Therefore, the complete, unaveraged raw data sequence of wind speed, light intensity, and ground temperature for this week, containing 42 consecutive time points, was formally identified as the first typical weekly operating condition, used for subsequent evaluation and optimization of the energy system's performance under the most severe survival conditions. Similarly, three other typical weeks representing other operating conditions can be obtained.

[0065] Next, this embodiment details the process of constructing a dynamic thermophysical model for an unattended shell-type enclosure in Antarctica according to the present invention.

[0066] In unmanned polar applications, one of the core tasks of energy management is maintaining the internal temperature of the cabin within a safe and tolerable range. Therefore, constructing a mathematical model that can accurately predict changes in cabin temperature due to external environmental factors and internal heating strategies is a prerequisite for achieving intelligent heat-electricity coordinated scheduling. This model forms the basis for subsequent energy scheduling strategy formulation and system configuration optimization, and its accuracy directly affects the system's energy efficiency and operational reliability. Inaccurate model predictions may lead to the system failing to provide timely heating when temperatures drop, causing equipment freezing damage; or excessive heating when temperatures are safe, resulting in the waste of valuable fuel and electricity.

[0067] Considering that the shell-type enclosure is a closed structure with good thermal insulation, and the internal air is disturbed by natural convection or weak equipment cooling fans, the temperature distribution can be assumed to be relatively uniform. In this case, using the lumped parameter method for modeling is a choice that balances model accuracy and computational efficiency. This method treats the entire enclosure (including its structure, internal air, and equipment) as a single heat capacity node with a uniform temperature, thereby simplifying the complex partial differential heat transfer problem into ordinary differential equations, greatly reducing the difficulty of solving the problem and making it possible to embed it into optimization algorithms that require rapid iterative calculations.

[0068] Construction of a continuous-time thermodynamic model based on the law of conservation of energy:

[0069] According to the first law of thermodynamics, also known as the law of conservation of energy, the rate of change of internal energy of a system is equal to the power of work done on the system by the surroundings plus the rate of heat transfer from the surroundings to the system. For the shell-type enclosure in this embodiment, the rate of change of its internal thermal energy (manifested as a change in temperature) is equal to the input power of the internal heat source minus the heat power lost through the outer shell. This can be expressed as the following differential equation of thermal balance:

[0070]

[0071] in, This represents the rate of change of the total heat energy inside the enclosure. It is the total effective heat capacity of the system, in units of It is a lumped parameter that comprehensively reflects the heat storage capacity of the enclosure structure, internal air, and all onboard equipment, embodying the system's thermal inertia. The larger the heat capacity, the smoother the temperature change within the enclosure. In this embodiment, based on the materials and structure of a typical polar unmanned cabin, the following parameters are set... . yes The average temperature inside the chamber at any given time, in units of . It is the derivative of internal temperature with respect to time, i.e., the rate of temperature change. represent The total heating power supplied to the housing by all internal heat sources at any given time, in units of This power is the direct output of the energy dispatch system, and its energy can come from electric heaters driven by photovoltaic / wind power generation, electric heaters driven by the discharge of energy storage batteries, or waste heat generated by diesel generators, etc. represent The total heat loss power that the enclosure dissipates to the external environment through its outer shell (walls, top, bottom) at any given time, expressed in units of... Heat loss mainly occurs through conduction and convection. This power is approximately a linear function of the internal and external temperature difference.

[0072]

[0073] in, It is the overall heat transfer coefficient of the system, with units of 1000 ppm. It is also a lumped parameter, characterizing the overall thermal insulation performance of the enclosure. The smaller the value, the better the thermal insulation performance. In this embodiment, it is set as follows: . yes The ambient temperature of the enclosure at any given time, in units of This temperature data is derived from a generated typical weekly operating condition time series.

[0074] Discrete model transformation for control system applications:

[0075] The continuous-time model described above accurately describes the physical processes of the thermal system. However, energy management systems are microprocessor-based digital control systems, and their decision-making and control are performed in discrete time steps. Therefore, the continuous differential equations must be converted into discrete difference equations so that they can be executed by a computer program. This embodiment uses the forward Euler method for discretization, with a time step of... Keeping consistent with the data collection interval, i.e. Hour.

[0076] Differential terms Approximate in difference form Substituting this into the continuous model equations, we get:

[0077]

[0078] After simplification, the discrete state update equation for predicting the internal temperature at the next moment can be obtained:

[0079]

[0080] in: This represents the sequence number of the current time step. It is the next time step predicted by the model. The internal temperature of the chamber at any given time. It is the known internal temperature at the current time step. At the current time step The average heating capacity provided by the energy system. It is the known external ambient temperature at the current time step.

[0081] Model application and boundary condition setting:

[0082] The resulting discretized thermodynamic model forms the core of the energy management system's prediction capabilities. At each decision-making moment... The controller can adjust the current measured internal temperature. External ambient temperature obtained from typical weekly data This model is used to simulate and evaluate different heating capacities. Future temperature The impact.

[0083] To ensure the safety of the scientific research equipment inside the enclosure, this embodiment sets clear operating boundary conditions for the thermodynamic model, namely, temperature safety constraints:

[0084]

[0085] in: This is the lowest safe temperature that the device can tolerate; in this embodiment, it is set to... . This is an upper temperature limit set to prevent overheating and excessive energy consumption; in this embodiment, it is set to [temperature value missing]. .

[0086] In summary, this embodiment constructs a parameterized and discretized dynamic thermophysical model.

[0087] Next, this embodiment details the construction of a dynamic state update model for the core energy storage unit of the system—the battery pack.

[0088] In unmanned energy systems in Antarctica, energy storage battery packs play a crucial central role. They bridge the gap between the intermittent and fluctuating supply of renewable energy sources (wind and solar) and the continuous and stable demands of scientific research. Specifically, the battery packs absorb and store excess energy when energy is abundant (e.g., during strong winds or polar days), and release energy when energy is scarce (e.g., during calm weather or polar nights) to ensure heating for the cabin and power for equipment. Therefore, establishing a dynamic model that can accurately track the real-time state of charge of the batteries is essential for the safe, efficient, and economical operation of the entire energy system. This model is the sole basis for upper-level energy dispatch strategies to make decisions (when to charge, when to discharge, and when to activate backup power).

[0089] Key Model Parameter Definitions: To construct a battery model that reflects real physical characteristics, its core parameters must first be defined. These parameters form the foundation of the model and remain consistent throughout subsequent simulations and optimizations.

[0090] Battery pack rated capacity ( This refers to the total energy that a battery pack can theoretically store under ideal conditions, measured in units of... It is determined by both the capacity of a single cell and the number of cells contained in the battery pack.

[0091]

[0092] in, This is the number of individual battery modules connected in parallel in the battery pack, and this value is one of the decision variables for subsequent system optimization configuration. This refers to the rated capacity of a single battery module. In this embodiment, a lithium iron phosphate battery is selected, and its single-module rated capacity is... .

[0093] Charge / discharge efficiency ( Batteries inevitably experience energy losses during energy conversion, primarily dissipating as heat. Charging efficiency... This indicates the percentage of electrical energy input to the battery that is effectively converted into chemical energy and stored. This embodiment sets... This means that 10% of the energy is lost during the charging process. Discharge efficiency This indicates the percentage of chemical energy extracted from the battery that is effectively converted into electrical energy to supply the load. This embodiment sets... That is, 10% of the energy is lost during the discharge process.

[0094] Discrete-time State-Owned Capacity (SOC) update model based on energy conservation:

[0095] This model uses discrete time steps. (In this invention) The system performs iterative calculations (every hour) to update the battery's state of charge by tracking the energy inflow and outflow within each time step.

[0096] at any time step At the beginning, the energy stored in the battery was In one During the time period, the energy flowing into and out of the battery is the charging energy and discharging energy, respectively. According to the principle of energy conservation, in... At any given moment, the energy stored in the battery for:

[0097]

[0098] in, Is The energy effectively stored in the battery during a given period. It is equal to the total energy provided by an external power source (such as a wind or solar power system or a diesel engine) for charging. Multiply by the charging efficiency.

[0099]

[0100] Is The energy consumed from the battery during a given period to meet load demands. If the load needs to draw energy from the battery... The energy consumed inside the battery is actually more due to discharge losses.

[0101] The state update equation, expressed in terms of energy (in kWh), is obtained as follows:

[0102]

[0103] For ease of control and observation, the dimensionless state of charge (SOC) is typically used to characterize the battery state, which is defined as the ratio of the current stored energy to the rated capacity. .

[0104] Divide both sides of the state update equation by This yields the final SOC discrete-time update model:

[0105]

[0106] This equation describes the time step at any time step. Given the current state Charging energy and discharge energy This allows for accurate prediction of the state at the next moment. .

[0107] Boundary conditions and safety constraints for model operation:

[0108] To ensure the long-term, safe, and reliable operation of the battery pack, strict operating boundary conditions must be set in the model to prevent irreversible damage.

[0109] The SOC operating range constraint requires that the battery's state of charge must always be maintained between the preset upper and lower limits.

[0110]

[0111] Minimum state of charge To prevent deep discharge of the battery, a minimum permissible state of charge is set. Deep discharge severely damages the battery's chemical structure and shortens its cycle life. This embodiment sets... When the State of Charge (SOC) approaches this value, the control system must take intervention measures (such as starting the diesel engine). Maximum State of Charge To prevent overcharging, a maximum state of charge is set, typically 100%. Initial state of charge Set the initial state for simulation or system startup. In this embodiment, it is assumed that the battery is in a relatively healthy charging state when deployment is complete. Charging safety threshold This is not the physical boundary of the battery, but rather the target value set for the upper-level control strategy (especially the diesel engine start-stop strategy). When the diesel engine is triggered to charge the battery, its charging target is to reach... Instead of filling it completely, this is done to save fuel and allow for the inclusion of renewable energy sources. This embodiment sets... .

[0112] Next, this embodiment details the construction of an accurate fuel consumption model for the diesel generator, the final support unit of the system in the method of the present invention.

[0113] In the energy systems of unmanned research stations in Antarctica, diesel generators are crucial for ensuring the system's survival under extreme conditions (e.g., weeks of windless and dark weather leading to the depletion of both renewable energy generation and battery storage). However, the use of diesel generators is directly linked to high operating costs and immense logistical pressure, as fuel needs to be transported from the base thousands of kilometers away. Therefore, creating a mathematical model that can accurately predict fuel consumption is essential for the feasibility assessment of the entire system. This model serves as the direct basis for subsequent multi-objective optimization algorithms to weigh the trade-offs between operational reliability and fuel economy—the two core objectives. A coarse model can lead to optimization results that deviate from reality, potentially resulting in a system that is overly reliant on fuel or suffers from insufficient fuel reserves at critical moments.

[0114] The fuel efficiency of a diesel generator is not a constant value; it is closely related to its current output power load rate. Operation at low load rates is typically extremely inefficient, while efficiency is highest near the rated load. Simply using a fixed average generator efficiency to calculate fuel consumption introduces significant errors. To more accurately reflect this physical characteristic, this embodiment employs a linear fuel consumption rate model. This model expresses the generator's fuel consumption rate as a linear function of its output power, accurately describing the fuel consumption characteristics across the entire operating range from no-load to full-load.

[0115] Determining the generator output power: The diesel generator is configured in the control strategy to start only when necessary. Once started, it operates within a certain time step. The electrical energy that needs to be generated internally The demand consists of two parts: making up for the heating power gap, when neither renewable energy nor battery discharge can meet the current heating power required as calculated by the thermodynamic model. At that time, the resulting power gap To charge the battery, according to the battery model, when the battery's SOC falls below a minimum threshold... At this time, it needs to be charged to a safe threshold. This part of the demand is equivalent to charging power. .

[0116] Therefore, the generator is Total output power required at all times for:

[0117]

[0118] Throughout During the time period (4 hours in this invention), the generator needs to stably output this power, and the total electrical energy generated is:

[0119]

[0120] Establishment of a linear fuel consumption rate model:

[0121] The linear fuel consumption rate model established in this embodiment correlates the instantaneous fuel consumption rate of the generator (unit: liters / hour) with its output electrical power (unit: kW):

[0122]

[0123] in: Is the generator in Fuel consumption rate at any given time, in units of . Is the generator in The actual output power at any given time, in units of . This is the rated output power of the diesel generator, a fixed parameter that characterizes the generator's maximum continuous output capability. In this embodiment, a small diesel generator is selected, with a rated power... . It is the marginal fuel consumption coefficient, in units of The physical meaning of this coefficient is the extra liters of fuel required for the generator to produce an additional kilowatt-hour (1 kWh) of electricity. It mainly reflects the effective conversion efficiency of the engine. This is the no-load fuel consumption coefficient, a dimensionless parameter. The product of this coefficient and the rated power... This represents the generator in no-load condition (i.e.) Under these conditions, the fuel consumption rate is only required to maintain its own operation (overcoming internal friction, driving cooling and lubrication systems, etc.).

[0124] Based on the performance curves of typical small diesel generators, this embodiment selects empirical values ​​for the above coefficients that are consistent with reality: , .

[0125] Calculation of total fuel consumption for a single trip:

[0126] Based on the above fuel consumption rate model, the fuel consumption rate of a diesel generator during a single start-up and continuous operation over a time step can be calculated. The total volume of fuel consumed during the process. This volume represents the system's fuel consumption per run. :

[0127]

[0128] The result of this formula is in liters (L). ).

[0129] This model allows the energy management system to determine the power output of generators based on demand. It can predict the amount of fuel that will be consumed during this startup in real time and accurately.

[0130] Next, this embodiment details how the present invention formulates a multi-source energy supply control strategy with clear priorities and clear execution logic for the entire heat-electric synergistic energy system.

[0131] This control strategy serves as the intelligent hub connecting system status perception (temperature, power level, renewable energy availability) with action execution (energy dispatch). Its core design objective is to maximize the use of non-fuel-consuming renewable energy sources while ensuring the absolute safety of the thermal environment inside the research cabin, protecting the health of energy storage batteries, and minimizing the frequency and duration of startup of the high-cost, logistically burdensome backup power source, the diesel generator. To achieve this objective, this strategy adheres to the following two core principles:

[0132] Thermal management is the absolute priority: at all times, ensure that the cabin temperature does not fall below the minimum safe threshold. This is the highest priority task in the system, and all energy dispatch decisions must first satisfy this constraint.

[0133] Energy utilization hierarchy principle: When meeting heating and power supply needs, energy use strictly follows the hierarchy order of renewable energy → energy storage batteries → diesel generators. That is, if clean energy can be used, energy storage is not needed, and if energy storage can be used, fuel oil is not needed.

[0134] System state awareness and demand calculation: in each control cycle (time step) At the start of the hour, the control system first collects and calculates the current time. The system status and requirements.

[0135] State variable inputs include: internal temperature This refers to the current value measured by the cabin temperature sensor. External ambient temperature. This refers to the current value read from a defined series of typical weekly operating condition data. Battery state of charge. This refers to the current value provided by the battery management system. Available power from renewable energy sources. That is, the total electrical power that can be generated at present, predicted by power models of wind turbines and photovoltaics based on wind speed and solar intensity data.

[0136] Heating power demand calculation ( The control system calculates the future temperature based on the current internal temperature. Within a given time period, to maintain the temperature above the safe lower limit The required average heating power. This is obtained through the inverse solution of a discretized thermodynamic model:

[0137]

[0138] This formula ensures that when Already higher than When the system provides sufficient power to offset heat loss, it only needs to provide enough power to compensate for the heat loss; while when Below At that time, the system will provide additional power to restore it to its normal operating level. .

[0139] Priority-based energy dispatch execution logic: After determining the heating power demand... Then, the control system performs energy dispatch according to the following strict priority order:

[0140] Priority 1: Direct supply of renewable energy; the system first checks the available power of renewable energy sources. Dispatch rules: Prioritize the use of all available renewable energy sources to meet heating demand. Power allocation: Renewable energy power used for heating. After meeting heating needs, the remaining redundant renewable energy power... This portion of the power will be used to charge the battery.

[0141] Priority 2: Supplemental supply via energy storage batteries. If the heating demand is still not fully met after Priority 1, i.e., a power gap exists. If the battery's state of charge is higher than its minimum safe threshold, then the energy storage battery will be activated to replenish it. Scheduling rule: When the battery's state of charge is higher than its minimum safe threshold... Under the premise that the remaining heating gap is met by batteries.

[0142] Decision-making conditions and execution: If Then the battery discharge power is set to The corresponding discharge energy is Otherwise (i.e.) The battery is not discharging. Heating gap It will be passed on to the next priority processing.

[0143] Priority 3: Diesel generator as the final backup. The diesel generator is the system's last resort and is only activated when neither renewable energy nor battery storage can meet the minimum heat demand. Dispatch rule: The diesel generator is activated only when there is a heating power shortfall and the battery state of charge has reached or fallen below the minimum threshold.

[0144] Decision-making conditions and execution: If ( And ( Then, the diesel generator will be started. Power setting: The generator needs to simultaneously perform two tasks: make up for the current heating shortfall and charge the battery to a safe threshold. First, the calculation will move the battery from its current position. Charge to Energy required: Considering charging efficiency, the average charging power required to achieve this charging is: Therefore, the total output power of the generator is set as follows: Then, the fuel consumption for this run was calculated based on the fuel consumption model.

[0145] Otherwise, the diesel generator remains off.

[0146] System status update:

[0147] After the decision-making and execution of a control cycle are completed, the system will call the dynamic thermophysical model and the dynamic state update model according to the actual energy flow to update the internal temperature and battery SOC state, in preparation for the decision-making of the next time step.

[0148] Next, this embodiment details the configuration design of the entire thermoelectric co-energy system in this invention, constructing a complete multi-objective optimization model.

[0149] Designing an energy system for an unmanned Antarctic research station essentially involves finding the optimal balance between conflicting performance metrics and cost constraints. For example, increasing the number of photovoltaic panels and batteries can improve the system's survivability during the polar night (performance metrics), but it also drastically increases fuel consumption for equipment transportation and maintenance (cost constraints). Relying on experience or single-objective optimization for design often leads to overlooking some aspects and fails to find the globally optimal configuration.

[0150] The purpose of this embodiment is to abstract and paradigmatize this complex engineering trade-off problem into a precise multi-objective optimization model.

[0151] The decision variables are the objects of this optimization problem; they directly define the physical configuration of an energy system. In this embodiment, the decision variable is a three-dimensional integer vector. :

[0152]

[0153] in: Number of wind turbine generator sets installed (unit: units). : Number of photovoltaic modules installed (unit: modules). : Number of energy storage battery modules installed (unit: modules).

[0154] Based on polar transportation capabilities and engineering experience, a reasonable optimization search space was set for these decision variables:

[0155]

[0156]

[0157]

[0158] And all decision variables must be positive integers, i.e. .

[0159] Construction of the objective function: The objective function is used to evaluate any given configuration. Criteria for determining the degree of superiority or inferiority. This model contains two conflicting objective functions.

[0160] Objective function 1: Maximize thermal environment safety ( This objective function enhances the system's ability to cope with extreme weather conditions. Under the coldest typical weekly operating conditions, the peak cabin temperature achievable by a configuration directly reflects its thermal assurance capability and robustness. A higher peak temperature means a greater safety margin. The objective function is expressed mathematically as follows:

[0161]

[0162] in, It represents the set of all time steps within a complete typical weekly simulation cycle. In a given configuration Under the following conditions, the control strategy is implemented and the results are obtained through simulation using a thermodynamic model. The cabin temperature at any given time.

[0163] Objective function 2: Minimize total lifecycle fuel consumption ( The objective function aims to minimize the system's logistical dependencies and long-term operating costs. Fuel consumption includes not only generator operation but also the fuel required to transport equipment to the Antarctic interior and for subsequent periodic maintenance, which together constitute the system's total lifecycle fuel cost. The mathematical expression of objective function two is as follows:

[0164] Among them, operating fuel consumption This refers to the cumulative operating fuel consumption of the diesel generator within a typical weekly simulation cycle.

[0165] Transportation fuel consumption It is to configure All the necessary equipment and fuel were transported from the inland base to the target site in one go. ,in, , , These refer to the mass of a single wind turbine, a single photovoltaic unit, and a single battery, respectively. For transportation distance; This is the fuel consumption coefficient per unit mass distance. Maintenance fuel consumption. This refers to the fuel consumption for transportation caused by equipment inspection and maintenance within an operating year.

[0166]

[0167] in, These are the average number of maintenance visits per year for wind turbines, photovoltaic panels, and batteries, respectively. These represent the number of devices that can be covered during each maintenance check. One-way distance for vehicle inspection; This is a rounding function, representing the minimum number of trips required to complete all equipment maintenance.

[0168] Setting constraints: Constraints are any feasible configuration scheme All of these must meet certain physical boundaries and safety bottom lines.

[0169] Thermal safety constraints: Throughout the entire simulation cycle, the internal temperature of the cabin must always be maintained within the preset safe range.

[0170] in, , .

[0171] Battery health constraints: To prevent permanent damage caused by over-discharge of the battery, its state of charge must never fall below the minimum safe threshold.

[0172]

[0173] in, .

[0174] Feasible domain constraint for decision variables: Decision variables must take values ​​within their predefined integer range.

[0175] This embodiment constructs the complex energy system configuration problem as a complete search for an optimal solution vector. To simultaneously optimize the objective function vector A model that satisfies all constraints.

[0176] Next, this embodiment details how the improved multi-objective particle swarm optimization algorithm is used in the method of the present invention to solve the constructed multi-objective optimization model and finally determine the optimal system configuration scheme.

[0177] The optimization problem in a multi-objective optimization model has the following characteristics: 1) multi-objective nature, meaning a trade-off needs to be made between two conflicting objectives: thermal environmental safety and life-cycle fuel consumption; 2) decision variables are integers; 3) the evaluation process of the objective function is computationally expensive, because each candidate configuration requires running a complete system thermo-electric dynamic simulation based on a typical weekly time series. Traditional mathematical programming methods are difficult to apply to such complex engineering optimization problems. Therefore, this embodiment uses an improved multi-objective particle swarm optimization algorithm.

[0178] Before the algorithm starts executing, the particle swarm and related parameters need to be initialized.

[0179] Population generation: Randomly generate a population containing... The initial population of particles. Each particle This represents a candidate system configuration scheme, its location vector. Randomly generated within the search space of the decision variables: At the same time, a velocity vector is randomly initialized for each particle. .

[0180] Individual and Global Optimal Initialization: For each particle Initialize its individual historical best position The initial value is its initial position. .

[0181] External repository initialization: Create an empty external repository. It is used to store and maintain non-dominated solutions discovered throughout the iteration process.

[0182] Control parameter settings: Population size Maximum number of iterations Learning factors , The external archive has a maximum capacity of 100.

[0183] The algorithm passes The optimal solution is searched in the nth iteration. In each iteration, for each particle in the population Perform the following operations:

[0184] 1. Candidate solution evaluation: Evaluate the particle's current position vector. The established complete system model (including thermodynamic, battery, and fuel consumption models) and control strategy are invoked, and simulations are performed under typical weekly operating conditions. After the simulation, the two objective function values ​​corresponding to this configuration scheme are obtained. and And verify whether it satisfies all constraints.

[0185] 2. Individual optimal position update: Compare the current positions of particles. Its individual historical best position .if Dominate (Right now Non-inferior in all objectives And is strictly superior to at least one objective. Then update .

[0186] 3. External Repository Update: Update all non-dominated solutions in the current population to the external repository. The solutions in the database are merged. Then, the Pareto dominance relation is applied again to remove all dominated solutions in the new set, resulting in an updated archive. If the number of solutions in the updated archive exceeds the capacity limit, a crowding distance sorting mechanism is used for pruning: the density of solutions around each solution (i.e., crowding) is calculated, and solutions located in the most crowded regions are removed first to maintain the diversity of the solution set.

[0187] 4. Global optimal guidance position selection: for each particle From the current external archives Select a globally optimal boot position To avoid premature convergence caused by all particles flying to the same point, a tournament selection method based on crowding distance is adopted: two solutions are randomly selected from the database, and the solution with a larger crowding distance (i.e., the one located in a sparser region) wins and is selected as the guiding position for that particle. .

[0188] 5. Particle velocity and position update: The dynamic inertia weight calculation adopts a linear decreasing strategy to adjust the inertia weight. This encourages global exploration in the early stages of the algorithm and promotes fine-grained local search in the later stages.

[0189]

[0190] in, , .

[0191] Speed ​​update: Speed ​​is updated according to the standard particle swarm optimization formula.

[0192] ,in It is a random number between [0,1].

[0193] Position Update: Update the particle's position.

[0194] Integer mapping and boundary handling: Since the decision variables are integers, the updated position vector... Perform rounding operations (e.g., rounding to the nearest integer) and ensure that the value is within the preset search space.

[0195] Output of the optimal solution set and determination of the solution: When the algorithm reaches the maximum number of iterations Then, the iteration process terminates. At this point, the external archive... All the non-dominated solutions stored in the database together constitute the final solution set of the optimization problem—the Pareto optimal front.

[0196] This frontier provides decision-makers with a range of optimal system configuration options. For example, it might include: Option A (Economical): This option has the lowest total lifecycle fuel consumption, but its peak cabin temperature under extreme conditions may also be the lowest among all options. Option B (Balanced): This solution achieves a good balance between fuel consumption and thermal safety. Solution C (Robust): This solution offers the strongest thermal safety performance and provides the highest cabin temperature margin, but it also has the highest transportation and maintenance costs.

[0197] Ultimately, engineering decision-makers can select the configuration that best meets their needs from this Pareto optimal frontier, based on the project's actual budget, risk tolerance, and specific requirements for thermal comfort.

[0198] This specification also proposes a thermoelectric co-generation energy supply system for the Antarctic inland environment, the system comprising:

[0199] Data Acquisition Module: Collects annual environmental data of the preset scientific research area, and extracts at least one typical weekly working condition based on the annual environmental data using a clustering algorithm;

[0200] The annual environmental data includes average ground temperature, average wind speed, and average solar radiation intensity.

[0201] Model building module: Constructs a multi-objective optimization model that includes decision variables, optimization objective functions, and constraints; the decision variables include the number of various types of equipment in the energy system, and the optimization objective functions include maximizing thermal environment safety and minimizing total life cycle fuel consumption;

[0202] Optimization module: The multi-objective optimization model is solved iteratively using a multi-objective optimization algorithm. In each iteration, the candidate system configuration scheme is simulated under the typical weekly operating conditions using a preset dynamic thermophysical model, a battery dynamic state update model, and a multi-source power supply control strategy to evaluate its corresponding objective function value.

[0203] Scheme configuration module: Based on the results of iterative solution, the optimal configuration scheme of the energy system is obtained.

[0204] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the aforementioned thermoelectric co-generation energy supply method for the Antarctic inland environment.

[0205] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described thermoelectric co-generation energy supply method for the Antarctic inland environment.

[0206] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0207] In this specification, the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the descriptions of the embodiments described later are relatively simple, and relevant parts can be referred to the descriptions of the foregoing embodiments.

[0208] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A thermoelectric co-energy supply method for the Antarctic inland environment, characterized by, The method comprises: collecting annual environmental data of a preset scientific exploration area, and extracting at least one typical week working condition based on the annual environmental data by using a clustering algorithm; the annual environmental data comprises average ground temperature, average wind speed and average solar radiation intensity; a multi-objective optimization model comprising decision variables, an optimization objective function and constraint conditions is constructed; the decision variables comprise the number of various types of equipment in the energy system, and the optimization objective function comprises maximizing thermal environmental safety and minimizing life cycle fuel consumption; a multi-objective optimization algorithm is used to iteratively solve the multi-objective optimization model, wherein in each iteration, a candidate system configuration scheme is simulated by using a preset dynamic thermal physical model, a battery dynamic state updating model and a multi-source energy supply control strategy under the typical week working condition to evaluate the corresponding objective function value; an optimal configuration scheme of the energy system is obtained according to the result of the iterative solution; the step of extracting at least one typical week working condition by using the clustering algorithm comprises: constructing a multi-dimensional week feature vector according to the average ground temperature, the average wind speed and the average solar radiation intensity in the annual environmental data; clustering the multi-dimensional week feature vector to obtain at least one category; and selecting a real week data sequence with the minimum distance to the category cluster center in the at least one category as the typical week working condition; the dynamic thermal physical model is: wherein, Tbox(t+1) is the box interior temperature at the next time instant, Tbox(t) is the box interior temperature at the current time instant, dt is the time step, Ctot is the total effective heat capacity of the system, Pavg(t) is the average heating power in the current time step, Ktot is the total heat transfer coefficient of the system, Tenv(t) is the outside ambient temperature at the current time instant; the battery dynamic state updating model is used to update the state of charge of the battery: wherein, SoCnextis the state of charge at the next time instant, SoCcurrentis the state of charge at the current time instant, Qtotalchargeis the total charge energy, Qtotaldischargeis the total discharge energy, Crateis the battery pack rated capacity, ηchargeis the charge efficiency, ηdischargeis the discharge efficiency.

2. A method for the co-production of energy from heat in the interior of the Antarctic environment according to claim 1, characterized by the fact that: the multi-source energy supply control strategy follows the principle of energy utilization hierarchy, comprising: taking renewable energy as the first priority to meet the heating power demand, and using redundant renewable energy power to charge the battery; when the renewable energy is insufficient, starting the energy storage battery as the second priority to supplement the supply on the premise that the state of charge of the battery is higher than a preset minimum threshold; and when there is a heating power gap and the state of charge of the battery is lower than the minimum threshold, starting the diesel generator as the final guarantee.

3. A method of co-generated energy supply by heat and electricity for the inland environment of Antarctica according to claim 2, characterized in that: In the objective function of minimizing the life cycle fuel consumption, the life cycle fuel consumption comprises: cumulative running fuel consumption of the diesel generator under the typical week working condition; transportation fuel consumption required for transporting various types of equipment in the energy system to the preset scientific exploration area; and maintenance fuel consumption generated by inspection and maintenance of the various types of equipment within an operating year.

4. A method of co-generated energy supply by heat and electricity for the inland environment of Antarctica according to claim 3, characterized in that: The multi-objective optimization algorithm is a multi-objective particle swarm optimization algorithm; and the step of iterative solution comprises: in the iteration process, storing and maintaining non-dominated solutions in an external archive; after the iteration is terminated, the non-dominated solutions in the external archive constitute a Pareto optimal front, and the optimal configuration scheme is determined from the Pareto optimal front.

5. A thermoelectric coenergetic energy supply system for the Antarctic inland environment, the system being used to perform a method of thermoelectric coenergetic energy supply for the Antarctic inland environment according to any one of claims 1-4, characterized by, The system comprises: a collection module configured to collect annual environmental data of a preset scientific exploration area, and extract at least one typical week working condition based on the annual environmental data by using a clustering algorithm; the annual environmental data comprises average ground temperature, average wind speed and average solar radiation intensity; A model construction module: constructing a multi-objective optimization model including decision variables, an optimization objective function and constraint conditions; the decision variables include the number of various types of equipment in the energy system, and the optimization objective function includes maximizing the safety of the thermal environment and minimizing the life cycle fuel consumption; An optimization module: iteratively solving the multi-objective optimization model using a multi-objective optimization algorithm, wherein in each iteration, a candidate system configuration scheme is simulated using a preset dynamic thermal physical model, a battery dynamic state updating model and a multi-source energy supply control strategy under the typical weekly operating condition to evaluate the corresponding objective function value; A scheme configuration module: obtaining the optimal configuration scheme of the energy system according to the result of the iterative solution. 6.An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements a method for heat and power co-energy supply in the Antarctic inland environment according to any one of claims 1-4 when executing the computer program. 7.A computer readable storage medium storing a computer program, wherein the computer program is executed by a processor to implement a method for heat and power co-energy supply in the Antarctic inland environment according to any one of claims 1-4.

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