Industrial park heat supply model and scale configuration method based on new energy on-site heating and conveying

Abstract

The invention relates to the field of energy engineering, and provides an industrial park heat supply system scale configuration method based on new energy on-site heating and conveying. The problem that a heat supply scene cannot be deeply adapted in the prior art is solved by constructing and determining a first type of configuration parameter set, performing iterative optimization on each group to obtain an optimal second type of parameters, determining a target configuration scheme based on comprehensive evaluation, integrally and collaboratively optimizing the scale of the new energy and heat energy storage components and considering system economic indexes; the method has the advantages that the economical efficiency and reliability of the industrial park heat supply system are improved, and the energy waste and the heat supply gap are reduced through collaborative optimization of the scales of the new energy and heat energy storage assemblies.

Description

Technical Field

[0001] This application relates to the field of energy engineering, and more specifically, to a method for the large-scale configuration of an industrial park heating system based on local heat generation and transmission from new energy sources. Background Technology

[0002] In the field of energy supply for industrial parks, with the increasing demand for clean heating, systems based on localized heating and transmission of new energy sources are gradually gaining attention. However, existing general-purpose production simulation software is mainly designed for traditional power systems and lacks the ability to deeply adapt to the heating scenarios of industrial parks. These software programs cannot directly build integrated simulation models that include new energy power supply components, solar thermal collectors, electrothermal conversion components, and thermal energy storage components, and are particularly unable to handle the dynamic interaction mechanisms in the context of the electricity market. Specifically, existing technologies often ignore the impact of time-of-use pricing on system operation strategies during large-scale deployment. For example, they fail to simulate the logic of actively storing heat during off-peak hours to optimize peak-hour heating, leading to distorted economic assessments. At the same time, for periods when new energy absorption is difficult (such as peak wind or solar power output but low heat load), existing models lack quantitative analysis of the flexible adjustment capabilities of electrothermal conversion components and thermal energy storage components, failing to effectively avoid energy waste or heating gaps. Furthermore, scale-based configuration methods typically rely on static load curves and simplified environmental assumptions, failing to adequately consider the dynamic changes over the entire 8760-hour cycle and the real-time degradation effects of environmental factors such as air quality on solar thermal efficiency. This makes it difficult to balance clean heating goals with economic benefits in actual operation. Existing technologies also tend to optimize the scale of individual components in isolation rather than adopting an integrated and coordinated approach, resulting in insufficient matching between wind power, photovoltaic, electric heating, and thermal storage systems, and failing to fully realize the overall project's revenue potential. These issues highlight the significant limitations of existing scale-based configuration methods in supporting industrial parks to achieve the functional goal of "clean heating as the main source, with surplus renewable energy connected to the grid and transmitted."

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this application is to provide a method for configuring the scale of an industrial park heating system based on local heat generation and transmission of new energy sources. This method has the advantages of improving the economy and reliability of the industrial park heating system and reducing energy waste and heating gaps by synergistically optimizing the scale of new energy sources and thermal energy storage components.

[0005] This application provides a method for the large-scale configuration of an industrial park heating system based on local heat generation and transmission from new energy sources. The technical solution is as follows: This method is based on a heating system simulation module, which is used to build and run a digital model of the heating system. The method includes: Several sets of first-class configuration parameters are determined, which represent the installed capacity of new energy power supply components and solar thermal collector components; For each set of first-class configuration parameters, it is input as a fixed boundary condition into the heating system simulation module. In the operating environment of the heating system simulation module, guided by the preset system economic indicators, the second-class configuration parameters are iteratively optimized to obtain the optimal second-class configuration parameters corresponding to the set of first-class configuration parameters. The second-class configuration parameters characterize the power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component. Based on the comprehensive evaluation results of all first-class configuration parameter sets and their corresponding optimal second-class configuration parameters, the target system configuration scheme is determined.

[0006] Furthermore, this application also proposes that the first set of configuration parameters includes wind power installed capacity, photovoltaic installed capacity, and collector area; The steps to determine several sets of first-class configuration parameters include: Obtain the upper and lower limits of the installed capacity of new energy power supply components and solar thermal collector components within the allowable range of the project; Set a discretization step size and discretize the wind power installed capacity, photovoltaic installed capacity and collector area between the upper and lower limits of the installed capacity. The discretized parameters are permuted and combined to generate several sets of first-class configuration parameters.

[0007] Furthermore, this application also proposes that, when iteratively optimizing the second type of configuration parameters based on preset system economic indicators, the heating system simulation module executes the following logic within each simulation time step: Calculate the power generation of the new energy power supply components and the heat collection power of the solar thermal collector components within the current time step; Determine whether the sum of power generation and heat collection power meets the current heat load demand; When the heat load demand is met and there is surplus energy, the thermal energy storage component is controlled to store heat until the capacity limit is reached. When the heat load demand is not met, the heat storage component is given priority to release heat. If the heat storage component still cannot meet the heat load demand after releasing heat, the electrothermal conversion component is controlled to consume grid power to supplement the heat supply.

[0008] Furthermore, this application also proposes to iteratively optimize the second type of configuration parameters to obtain the optimal second type of configuration parameters corresponding to the set of first type configuration parameters, including: Generate initial second-class configuration parameters; enter the optimization loop and input the current second-class configuration parameters into the heating system simulation module; Run full-time-cycle simulation and calculate preset system economic indicators based on economic settlement rules; Determine whether the preset system economic indicators meet the preset convergence conditions; if not, adjust the second type of configuration parameters according to the optimization algorithm, and return to the step of inputting the current second type of configuration parameters into the heating system simulation module; if they meet the conditions, determine the current second type of configuration parameters as the optimal second type of configuration parameters.

[0009] Furthermore, this application proposes that the preset system economic indicator is the total life cycle cost, which includes the initial investment cost, operation and maintenance cost, fuel cost, and electricity market transaction revenue; Based on the comprehensive evaluation results of all first-class configuration parameter sets and their corresponding optimal second-class configuration parameters, the target system configuration scheme is determined, including: Extract the optimal lifecycle cost corresponding to each set of first-class configuration parameters; Compare all extracted optimal lifecycle costs and determine their minimum value; The set of first-class configuration parameters corresponding to the minimum value and the optimal second-class configuration parameters corresponding to that set are used as the target system configuration scheme.

[0010] Furthermore, this application also proposes that the method further includes a multi-scenario weighted robust optimization process that considers environmental uncertainties, used to correct the operating efficiency of the solar thermal collector module and optimize the scale of the electrothermal conversion module and the thermal energy storage module. The steps of this process include: Based on environmental monitoring data from the industrial park, several different levels of air quality scenarios were defined, and an atmospheric attenuation coefficient was determined for each scenario. The baseline efficiency curve of the solar thermal collector is corrected based on the atmospheric attenuation coefficient to obtain the actual operating efficiency curve corresponding to each air quality scenario. The correction formula is: η_actual=η_base*(1-k_atm); where η_actual is the actual operating efficiency, η_base is the baseline efficiency, and k_atm is the atmospheric attenuation coefficient. Statistical analysis of historical environmental data is performed to determine the probability of occurrence for each air quality scenario, and the probability of occurrence is used as a weight.

[0011] Furthermore, this application also proposes that, in the operating environment of the heating system simulation module, the steps for iteratively optimizing the second type of configuration parameters, guided by preset system economic indicators, include: For each set of first-class configuration parameters, in the full-time-cycle simulation, for each time step, calculations for all defined air quality scenarios are run simultaneously. During the calculation process, the actual operating efficiency curve under the corresponding scenario is called to calculate the solar thermal contribution, the operating status of the electrothermal conversion module and the charging and discharging status of the thermal energy storage module. The optimization objective is to minimize the weighted expected total cost under all scenarios. During the iterative optimization process, it is also verified whether the system heating reliability meets the preset requirements under all scenarios.

[0012] Furthermore, this application also proposes that the operating logic of the digital model includes an active thermal storage strategy based on time-of-use pricing; when executing the operating logic, the heating system simulation module also includes the following steps within each simulation time step: Load the time-of-use electricity price data of the power grid and divide the entire time period into off-peak hours, flat hours and peak hours; When the current time step is in the off-peak period, before executing the step of judging whether the sum of power generation and heat collection power meets the current heat load demand, the prediction logic is executed first: check whether the current heat storage of the thermal energy storage component is lower than the preset peak period guaranteed heat storage. If the current heat storage capacity is below the peak level, the electrothermal conversion component will be forcibly activated to use grid power for heating and store the generated heat energy in the heat storage component until the current heat storage capacity reaches the peak level. When the current time step is during peak power hours, adjust the logic threshold for prioritizing the release of heat from the thermal energy storage component, and prohibit the electrothermal conversion component from consuming grid power, unless the current heat storage capacity of the thermal energy storage component has been exhausted.

[0013] Furthermore, this application proposes that peak-segment heat storage be dynamically determined through the following steps: When the simulation time step enters a valley power period, identify the duration range of the next peak power period immediately following the end of the valley power period; Extract the predicted heat load demand, predicted power generation of new energy power supply components, and predicted heat collection power of solar thermal collector components from the digital model within a time interval. The net heat load gap within the calculation period is the difference between the predicted heat load demand and the sum of the predicted heat collection power of the solar thermal collector and the predicted heat value of the power generation of the new energy power supply components after electrothermal conversion. The net heat load gap is set as the peak-segment guaranteed heat storage capacity, and adjusted according to the heat release efficiency of the thermal energy storage components.

[0014] Furthermore, this application proposes that the heating system simulation module consists of multiple independently configurable equipment simulation sub-modules; the steps for building and running the digital model of the heating system include parameter setting and simulation through the following sub-modules: The wind power module and the photovoltaic module are used to set the wind power installed capacity, the photovoltaic installed capacity and the corresponding annual ideal output characteristic curve, respectively; Solar thermal collector module, used to set the collector area and ideal output characteristic curve; The electrothermal conversion component module is used to set the maximum heating power and thermal efficiency; The thermal energy storage component module is used to set the maximum heat storage power, maximum heat release power, heat storage duration, heat release efficiency, and heat storage loss coefficient. The industrial park heat load module is used to set the heat load curve for 8760 hours throughout the year; The new energy surplus power grid connection module is used to set the annual surplus power grid connection load range and calculate the grid connection revenue in conjunction with spot electricity prices, long-term trading prices and electricity market transaction settlement rules.

[0015] As can be seen from the above, the present application provides a method for configuring the scale of an industrial park heating system based on local heat generation and transmission of new energy sources. This method includes determining a first set of configuration parameters, iteratively optimizing each set to obtain the optimal second set of parameters, determining the target configuration scheme based on comprehensive evaluation, and optimizing the scale of new energy and thermal energy storage components in an integrated and collaborative manner, while considering the system's economic indicators. This method solves the problem that existing technologies cannot be deeply adapted to heating scenarios, and has the advantages of improving the economy and reliability of industrial park heating systems, and reducing energy waste and heating gaps by collaboratively optimizing the scale of new energy and thermal energy storage components. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of the industrial park heating system large-scale configuration method based on local heat generation and transmission of new energy disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the heating system simulation module disclosed in the embodiment of the present invention. Detailed Implementation

[0017] The implementation details of the technical solution in this embodiment are described in detail below: In traditional, existing industrial park heating systems based on new energy sources, general-purpose production simulation software lacks dedicated models for local heat production and delivery scenarios, failing to effectively integrate flexible adjustment mechanisms for electricity market environments and periods of difficult heat absorption. Specifically, existing technologies primarily rely on historical load data from regional power grids for scale configuration, with limited consideration of the dynamic coupling between local new energy absorption and heating demand. This results in insufficient matching between the installed capacity of new energy power supply components and solar thermal collectors and the electrothermal conversion and thermal energy storage components. Furthermore, this configuration method fails to fully optimize system economic indicators, exposing projects to risks of reduced heating reliability and economic losses during operation. For example, in industrial park heating projects, wind and solar power output fluctuates significantly due to weather conditions, especially during winter periods of difficult heat absorption, when wind and solar output plummet while heat load demand increases. Existing models, when simulating such scenarios, cannot accurately reflect the dynamic response characteristics of parabolic trough concentrating solar collectors and spherical tank thermal storage systems, leading to frequent instances of insufficient or excessive heat supply during simulation operations. In some cases, when wind and solar power output is lower than expected, the system fails to activate the electrothermal conversion components in time to supplement the power supply, resulting in heating interruptions. Conversely, when wind and solar power output is sufficient, the lack of an effective heat storage strategy prevents the efficient utilization of excess energy. Consequently, the overall operating efficiency of the system decreases, and the stability of the heating supply is affected.

[0018] In response, this application proposes a method for the large-scale configuration of an industrial park heating system based on local heat generation and transmission from new energy sources. This heating system includes at least new energy power supply components, solar thermal collector components, electrothermal conversion components, and thermal energy storage components. The method is implemented based on a heating system simulation module, which is used to construct and run a digital model of the heating system, as shown in the figure. The method includes: S101, determine several sets of first-class configuration parameters, which represent the installed capacity of the new energy power supply component and the solar thermal collector component; S102, for each set of the first type of configuration parameters, input them as fixed boundary conditions into the heating system simulation module; in the operating environment of the heating system simulation module, guided by the preset system economic indicators, iteratively optimize the second type of configuration parameters to obtain the optimal second type of configuration parameters corresponding to the set of the first type of configuration parameters. The second type of configuration parameters characterize the power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component. S103. Based on the comprehensive evaluation results of all the first type of configuration parameters and their corresponding optimal second type of configuration parameters, determine the target system configuration scheme.

[0019] For ease of understanding, the following explains some key terms in this embodiment: A new energy-based on-site heat generation and transmission industrial park heating system refers to a system that integrates multiple new energy sources (such as wind power, solar photovoltaic, and solar thermal energy) to produce and transmit heat energy within or near an industrial park. This system aims to meet the heat load demands of the industrial park while optimizing energy efficiency and economic benefits. New energy power supply components refer to a collection of devices capable of converting new energy sources (such as wind power and solar energy) into electrical energy, such as wind turbine generators and photovoltaic panel arrays. These components provide a clean power source for the heating system. Solar thermal collector components refer to devices capable of collecting solar radiation energy and converting it into heat energy, such as parabolic trough concentrators and flat-plate collectors. These components directly utilize solar energy to generate heat. Electrothermal conversion components refer to devices capable of converting electrical energy into heat energy, such as electric boilers and heat pumps. When electricity is abundant or prices are low, these components can convert electrical energy into heat energy to meet heating needs or for heat storage. Thermal energy storage components refer to devices capable of storing thermal energy and releasing it when needed, such as hot water storage tanks and molten salt storage tanks. This component is used to balance the production and demand of thermal energy, improving the flexibility and economy of system operation.

[0020] A heating system simulation module refers to a software or computing platform used to build and run a digital model of a heating system. This module can simulate the energy flow, equipment performance, and economic performance of the system under different operating conditions. A digital model is a computational model that abstracts and mathematically describes the physical heating system and its components. Through this model, the system can be virtually tested, its performance analyzed, and optimized in a computer environment. The first type of configuration parameter set refers to a combination of parameters characterizing the installed capacity of new energy power supply components and solar thermal collector components. Examples include wind power installed capacity, photovoltaic installed capacity, and the area of ​​the solar thermal collector field. The second type of configuration parameters refers to parameters characterizing the power scale of electrothermal conversion components and the capacity scale of thermal energy storage components. Examples include the maximum heating power of an electric boiler and the maximum thermal storage capacity of a hot water tank. System economic indicators refer to quantitative indicators used to evaluate the economic performance of a heating system, such as total life cycle cost, return on investment, and net present value. These indicators are the main guide in the optimization process. Iterative optimization refers to an optimization process that gradually approaches the optimal solution by repeatedly calculating and adjusting parameters. In this process, system economic indicators are used as evaluation criteria to guide the direction of parameter adjustments. The target system configuration scheme refers to the optimal combination of equipment size determined after optimization, which enables the system to achieve the preset economic or performance goals.

[0021] This embodiment provides a method for the large-scale configuration of an industrial park heating system based on local heat generation and transmission from new energy sources. This method, through systematic steps, aims to determine the optimal system configuration scheme to meet the heating needs of the industrial park and achieve good economic benefits.

[0022] First, the method involves determining several sets of first-class configuration parameters. These first-class configuration parameter sets characterize the installed capacity of the new energy power supply components and solar thermal collector components. In practice, several representative combinations of installed capacity can be manually set based on engineering experience or preliminary market research. For example, wind power installed capacity can be set to 10MW, 20MW, 30MW, etc., photovoltaic installed capacity to 5MW, 10MW, 15MW, etc., and solar thermal collector area to 10,000 square meters, 20,000 square meters, 30,000 square meters, etc., and then these values ​​can be combined in a limited number of ways. Alternatively, within a feasible installed capacity range, a certain number of parameter combinations can be randomly generated as the first-class configuration parameter set. These methods can all be used to preliminarily explore the impact of different new energy power generation and collector scales on system performance.

[0023] Secondly, for each set of the first type of configuration parameters, it is input as a fixed boundary condition into the heating system simulation module. Within the simulation module's operating environment, guided by preset system economic indicators, the second type of configuration parameters are iteratively optimized to obtain the optimal second type of configuration parameters corresponding to that set of the first type of configuration parameters. These second type of configuration parameters characterize the power scale of the electrothermal conversion components and the capacity scale of the thermal energy storage components. Specifically, for each determined set of new energy installed capacity (i.e., the first type of configuration parameter set), the initial scale of the electrothermal conversion components and thermal energy storage components can be set first. Then, these parameters are input into the simulation module for simulation operation, and the corresponding system economic indicators are calculated. Subsequently, based on experience or simple rules, the power scale of the electrothermal conversion components and the capacity scale of the thermal energy storage components can be adjusted, and simulation and calculation can be performed again. This process is repeated until a relatively optimal combination of second type of configuration parameters is found. For example, the power of the electrothermal conversion components can be increased to observe the impact on economic indicators; or the capacity of the thermal energy storage components can be adjusted to evaluate its improvement on system operating costs.

[0024] Finally, based on the comprehensive evaluation results of all the first-class configuration parameter sets and their corresponding optimal second-class configuration parameters, the target system configuration scheme is determined. After obtaining the optimal second-class configuration parameters corresponding to all the first-class configuration parameter sets, these results can be summarized. For example, the economic indicators of all schemes can be manually reviewed, and a scheme that seems more ideal can be selected as the target system configuration scheme. Alternatively, the economic indicators of all schemes can be simply ranked, and then the top-ranked schemes can be selected for further analysis and decision-making.

[0025] The following example will provide a more detailed explanation of the above technical solution: Suppose industrial park A plans to build a new energy-based heating system to replace traditional coal-fired heating, aiming to achieve optimal economic benefits while meeting heating demands. However, the industrial park faces the technical challenge of how to rationally allocate the scale of various equipment, including wind power, photovoltaics, solar thermal collectors, electrothermal conversion, and thermal energy storage. Especially when considering flexible adjustments during periods of electricity market fluctuations and difficulties in integrating new energy sources, existing general models struggle to provide optimal solutions.

[0026] To address this technical problem, the method of this embodiment is applied to the scale configuration of the heating system in Industrial Park A. First, based on the geographical location, available land area, and preliminary energy demand forecast of Industrial Park A, several sets of first-class configuration parameters are determined. For example, three sets of first-class configuration parameters are manually set: the first set is 10MW wind power installed capacity, 5MW photovoltaic installed capacity, and 20,000 square meters of solar thermal collector area; the second set is 15MW wind power installed capacity, 8MW photovoltaic installed capacity, and 30,000 square meters of solar thermal collector area; and the third set is 20MW wind power installed capacity, 10MW photovoltaic installed capacity, and 40,000 square meters of solar thermal collector area. These parameter sets characterize the installed scale of the new energy power supply components and the solar thermal collector components.

[0027] Subsequently, for each determined set of first-type configuration parameters, they are input as fixed boundary conditions into the heating system simulation module. For example, for the first set of parameters, the simulation module will simulate the output of wind power, photovoltaic power generation, and solar thermal collectors at this scale. In the operating environment of this simulation module, the second-type configuration parameters (i.e., the power scale of the electrothermal conversion components and the capacity scale of the thermal energy storage components) are iteratively optimized using the total lifecycle cost as a preset system economic indicator. Specifically, the simulation module starts with an initial set of electrothermal conversion power and thermal energy storage capacity, simulating the system's operation over 8760 hours throughout the year, including new energy power generation, thermal energy production, heat load fulfillment, grid power purchase and sale, and the charging and discharging of thermal energy storage. During the simulation, the corresponding total lifecycle cost is calculated. Then, the simulation module repeatedly performs simulations and calculations according to preset optimization logic (e.g., by gradually adjusting the electrothermal conversion power and thermal storage capacity and observing cost changes) until the optimal second-type configuration parameters that minimize the total lifecycle cost under the first-type configuration parameter set are found. For example, for the first set of parameters, the optimal electrothermal conversion power might be 5MW and the optimal thermal energy storage capacity might be 1000 cubic meters. Similarly, the same iterative optimization process is performed on the second and third sets of the first type of configuration parameters to obtain their corresponding optimal second type of configuration parameters.

[0028] Finally, based on the comprehensive evaluation results of all first-class configuration parameter sets and their corresponding optimal second-class configuration parameters, the target system configuration scheme is determined. For example, after optimizing all three sets of parameters, three complete system configuration schemes and their corresponding minimum life-cycle costs are obtained. By comparing the life-cycle costs of these three schemes, for example, the total cost of the first scheme is X yuan, the second scheme is Y yuan, and the third scheme is Z yuan. Assuming that Y yuan is the minimum value among the three, then the combination of the second set of first-class configuration parameters (15MW wind power, 8MW photovoltaic, 30,000 square meters of solar thermal collector) and the corresponding optimal second-class configuration parameters (e.g., 6MW electrothermal conversion power, 1200 cubic meters thermal energy storage capacity) will be determined as the target system configuration scheme for industrial park A. This scheme not only meets the heating needs of industrial park A but also achieves optimal economic efficiency.

[0029] Specifically, existing technologies, when configuring power generation capacity, typically rely on historical load data from the regional power grid, employing load curve fitting methods to determine basic power demand, and then combining factors such as land resources and renewable energy output characteristic curves to optimize the allocation of renewable energy installed capacity. This approach rarely considers the electricity market environment and flexible adjustments during periods of difficult power absorption, resulting in suboptimal capacity allocation and hindering the full realization of project economic benefits. In contrast, the method in this embodiment systematically determines several sets of first-type configuration parameters, and for each set, iteratively optimizes second-type configuration parameters in a simulation module, guided by preset system economic indicators, thereby obtaining the optimal second-type configuration parameters corresponding to that set of first-type configuration parameters. This two-stage, economic indicator-based iterative optimization process allows the system configuration scheme to fully consider renewable energy output characteristics, heat load demand, electrothermal conversion efficiency, and the flexibility of thermal energy storage, and to conduct economic evaluations under the electricity market environment, thus overcoming the shortcomings of existing technologies in comprehensive optimization.

[0030] Through the above examples, it can be observed that this method can systematically find the optimal solution matching the scale of electrothermal conversion and thermal energy storage components from multiple combinations of new energy installed capacity, ultimately determining a system configuration that performs best in terms of total life cycle cost. This comprehensive evaluation and optimization capability enables Industrial Park A to obtain a truly optimal configuration, thereby maximizing the project's economic benefits and effectively addressing the challenges brought about by new energy consumption and electricity market fluctuations.

[0031] Furthermore, this application proposes the above-mentioned method for configuring the scale of industrial park heating systems based on on-site heating and transmission of new energy sources. The first set of configuration parameters includes wind power installed capacity, photovoltaic installed capacity, and collector area. The step of determining several sets of the first set of configuration parameters includes: obtaining the upper and lower limits of the installed capacity of the new energy power supply components and the solar thermal collector components within the allowable range of the project; setting a discretization step size, and discretizing the wind power installed capacity, photovoltaic installed capacity, and collector area within the upper and lower limits of the installed capacity; and performing full permutation and combination of the discretized parameters to generate the several sets of the first set of configuration parameters.

[0032] Specifically, the first set of configuration parameters characterizes the installed capacity of new energy power supply components and solar thermal collector components. Wind power installed capacity refers to the total power generation capacity of wind power equipment, usually measured in megawatts (MW); photovoltaic installed capacity refers to the total power generation capacity of solar photovoltaic equipment, also usually measured in megawatts (MW); collector area refers to the total area occupied by solar thermal collector components, usually measured in square meters (m²). These parameters are key factors affecting the initial investment of the system, the characteristics of new energy output, and the overall heating capacity of the system. When configuring the system scale, it is first necessary to clarify the actual engineering limitations on the installed capacity of new energy power supply components and solar thermal collector components. These upper and lower limits can be determined based on various factors, such as available land area, grid connection capacity limitations, local wind and solar energy resource potential, project budget constraints, and relevant regulatory and policy requirements. For example, the upper limit of photovoltaic and collector areas can be determined based on the actual usable roof area and open space area of ​​the industrial park, and the upper limit of wind power can be determined based on the grid absorption capacity and wind resource assessment results. To explore a broad configuration space with limited computing resources, it is necessary to discretize the continuous installed capacity parameters. The discretization step size refers to the interval between the upper and lower limits of the parameter values. For example, for wind power installed capacity, a step size of 5MW can be set; for photovoltaic installed capacity, a step size of 1MW can be set; and for collector area, a step size of 1000m² can be set. By setting an appropriate discretization step size, the continuous parameter space can be transformed into a finite, enumerable discrete point, which facilitates subsequent combination and evaluation. After discretizing the wind power installed capacity, photovoltaic installed capacity, and collector area, these discretized parameters need to be permuted and combined to generate all possible "first-class configuration parameter sets". For example, if wind power has N discrete values, photovoltaic has M discrete values, and collector area has P discrete values, then N*M*P different first-class configuration parameter sets will be generated. This full permutation and combination approach ensures that, within the set discretization accuracy, it can comprehensively cover various potential installation scale combinations of new energy power supply components and solar thermal collector components, providing comprehensive input for subsequent system economic evaluation.

[0033] The following is a concrete example to illustrate this. When determining the configuration method for the heating system scale of an industrial park, the first type of configuration parameter set can be generated by following these steps. Assume that the area available for wind power construction in an industrial park is limited, with an engineering allowable range of 10MW to 50MW for wind power capacity; the rooftop and open space available for photovoltaic (PV) installations is relatively large, with an engineering allowable range of 5MW to 30MW for PV capacity; and the area of ​​the solar thermal collector field is limited by available land, with an engineering allowable range of 10,000 square meters to 50,000 square meters. Based on this, a discretization step size can be set. For example, the discretization step size for wind power capacity can be set to 10MW, then its discrete values ​​may be {10MW, 20MW, 30MW, 40MW, 50MW}. The discretization step size for PV capacity can be set to 5MW, then its discrete values ​​may be {5MW, 10MW, 15MW, 20MW, 25MW, 30MW}. The discretization step size for the collector area can be set to 10,000 square meters, resulting in discrete values ​​of {10,000 m², 20,000 m², 30,000 m², 40,000 m², 50,000 m²}. Subsequently, these discretized wind power capacity, photovoltaic capacity, and collector area are subjected to a full permutation and combination. For example, one set of first-type configuration parameters might be (10MW wind power capacity, 5MW photovoltaic capacity, 10,000 m² collector area), while another might be (50MW wind power capacity, 30MW photovoltaic capacity, 50,000 m² collector area). In this way, all possible combinations can be generated, for example, 5 * 6 * 5 = 150 different sets of first-type configuration parameters, each representing a specific scale combination on the new energy side, providing comprehensive input for subsequent system simulation and optimization.

[0034] Through the above technical solution, this method can systematically and comprehensively explore the installed capacity configuration space of new energy power supply components and solar thermal collector components. By obtaining the upper and lower limits of the installed capacity within the allowable range of the project, setting a discretization step size for processing, and then performing full permutation and combination, it ensures that all reasonable and feasible sets of first-type configuration parameters are taken into consideration. This avoids local optima that may be caused by incomplete parameter settings in traditional methods, and significantly improves the possibility of finding the globally optimal or near-optimal system configuration scheme. This comprehensive exploration mechanism provides broader and more accurate boundary conditions for the subsequent iterative optimization of the second type of parameters (the power scale of the electrothermal conversion components and the capacity scale of the thermal energy storage components), thereby enabling the finally determined target system configuration scheme to have better performance in terms of economy and reliability.

[0035] This application further proposes that, in the operating environment of the heating system simulation module, when iteratively optimizing the second type of configuration parameters based on preset system economic indicators, the heating system simulation module executes the following logic within each simulation time step: calculating the power generation of the new energy power supply component and the heat collection power of the solar thermal collector component within the current time step; determining whether the sum of the power generation and the heat collection power meets the current heat load demand; when the heat load demand is met and there is surplus energy, controlling the thermal energy storage component to store heat until the capacity limit is reached; when the heat load demand is not met, prioritizing the release of heat from the thermal energy storage component; if the heat load demand still cannot be met after the thermal energy storage component releases heat, then controlling the electrothermal conversion component to consume grid power for supplementary heating.

[0036] Specifically, the step of calculating the power generation of the new energy power supply components and the heat collection power of the solar thermal collector components within the current time step aims to obtain the energy input from new energy sources that the system can utilize within a specific simulation time step, providing basic data for subsequent energy balance judgment. The power generation can be calculated using a mathematical model based on preset output characteristic curves of wind power and photovoltaic modules, combined with environmental conditions (such as wind speed and solar irradiance) at the current time step. The heat collection power can be estimated based on the ideal output characteristic curve set in the solar thermal collector component module, taking into account factors such as solar radiation intensity at the current time step. Alternatively, the simulation module can pre-load hourly or minute-by-minute new energy output data for the entire year, directly reading the corresponding power generation and heat collection power at each time step.

[0037] The step of determining whether the sum of the power generation and the heat collection power meets the current heat load demand is the core of system energy balance analysis. Its purpose is to determine whether the heat directly provided by new energy sources is sufficient to cover the heat load demand of the industrial park within the current time step. Specifically, the simulation module compares the calculated sum of the power generation (converted to heat after electrothermal conversion efficiency) and the heat collection power with the heat load demand set in the industrial park's heat load module for the current time step. If the sum is greater than or equal to the demand, it indicates that the new energy supply is sufficient; if it is less than the demand, it indicates a heat shortage.

[0038] When the heat load demand is met and there is surplus energy, the thermal energy storage component is controlled to store heat until its capacity limit is reached. This step aims to effectively utilize the excess renewable energy heat generated by the system and store it for later use, thereby improving the utilization rate of renewable energy and the operational flexibility of the system. When it is determined that the renewable energy supply is sufficient and there is surplus energy, the simulation module calculates the amount of heat that can be stored in the thermal energy storage component based on the amount of surplus energy and the maximum heat storage power and heat storage loss coefficient set in the thermal energy storage component module. The heat storage process will continue until the current heat storage capacity of the thermal energy storage component reaches its capacity limit.

[0039] When the heat load demand is not met, the thermal energy storage component is prioritized to release heat. If the heat release by the thermal energy storage component still cannot meet the heat load demand, the electrothermal conversion component is controlled to consume grid power to supplement the heat supply. This step is a key strategy to ensure that the heat load demand of the industrial park can still be reliably met when renewable energy heating is insufficient. Its function is to establish a priority mechanism, that is, to prioritize the use of stored thermal energy, and only consider consuming grid power. In specific implementation, when it is determined that renewable energy heating cannot meet the heat load demand, the simulation module will first check the current heat storage capacity of the thermal energy storage component and calculate the heat it can provide based on the maximum heat release power and heat release efficiency set in the thermal energy storage component module. If there is still a heat gap after the thermal energy storage component releases heat, the simulation module will start the electrothermal conversion component, calculate the required grid power consumption based on the maximum heating power and thermal efficiency set in the electrothermal conversion component module, and convert it into heat to make up for the remaining heat load gap.

[0040] The following example illustrates this. Assume that within a certain simulation time step, the industrial park's heat load demand is 100 units of heat. The simulation module first calculates that the power generation of the renewable energy power supply components is equivalent to 20 units of heat, and the heat collection power of the solar thermal collector components is 40 units of heat. At this point, the total renewable energy heat supply is 60 units of heat. Since 60 units of heat is less than the 100 units of heat load demand, the system determines that the renewable energy heat supply does not meet the demand. Next, the simulation module checks the thermal energy storage components. Assume that the thermal energy storage components currently have 50 units of heat storage, and their maximum heat release power allows them to provide all 50 units of heat within the current time step. The system will prioritize controlling the thermal energy storage components to release 50 units of heat. At this point, the heat load gap becomes 100 - 60 - 50 = -10 units of heat, meaning there is still a 10-unit heat gap. In this situation, the simulation module will activate the electrothermal conversion components, consuming grid power to generate 10 units of heat to supplement the heat supply, thus fully meeting the current heat load demand. Conversely, if in another time step, the total heat supply from new energy sources is 150 units of heat, and the heat load demand remains at 100 units of heat, then the new energy supply meets the demand with a surplus of 50 units of heat. If the thermal energy storage component has not yet reached its capacity limit (e.g., its maximum capacity is 200 units of heat, and the current stored heat is 100 units of heat), the system will control the thermal energy storage component to store the remaining 50 units of heat, bringing its total stored heat to 150 units of heat.

[0041] Through the aforementioned technical solution, this refined energy scheduling logic ensures that the system can make intelligent decisions according to preset priorities in energy balance assessment, surplus energy utilization, and heat load gap replenishment. For example, it prioritizes the use of new energy sources and energy storage, and only draws power from the grid last. This significantly improves the accuracy and reliability of simulation results, enabling the iterative optimization of the second type of configuration parameters based on the simulation module to more realistically reflect the economic performance of the system. This effectively avoids the suboptimal configuration problem caused by inaccurate simulation, ultimately resulting in a more economical and efficient large-scale configuration scheme for industrial park heating systems.

[0042] This application further proposes specific steps for iteratively optimizing the second type of configuration parameters to obtain the optimal second type of configuration parameters corresponding to the set of first type configuration parameters. These steps include: generating initial second type of configuration parameters; entering an optimization loop and inputting the current second type of configuration parameters into the heating system simulation module; running a full-time-series simulation and calculating the preset system economic index based on the economic settlement rules; determining whether the preset system economic index meets the preset convergence condition; if not, adjusting the second type of configuration parameters according to the optimization algorithm and returning to the step of inputting the current second type of configuration parameters into the heating system simulation module; if the condition is met, determining the current second type of configuration parameters as the optimal second type of configuration parameters.

[0043] Specifically, generating initial second-class configuration parameters aims to provide a starting point for subsequent optimization processes. These initial parameters can be set based on empirical values, historical data, or industry best practices to ensure that the optimization process begins its search from a relatively reasonable region, thereby improving convergence speed. Alternatively, random generation can be used to increase the breadth of the solution space explored. Entering the optimization loop, the current second-class configuration parameters are input into the heating system simulation module. This step establishes an interface between the optimization algorithm and the simulation module, ensuring that each iteration can evaluate system performance based on the new parameter combination. This can be achieved through automatic parameter passing via a programming interface (API) or by loading parameters into the simulation module by reading a configuration file. Running a full-time-cycle simulation calculates preset system economic indicators based on economic settlement rules. Its purpose is to comprehensively evaluate the performance and economic benefits of the heating system under the current parameter combination throughout its entire operating cycle (e.g., 8760 hours per year). The simulation module simulates the system's operating state based on the input power scale of the electrothermal conversion components and the capacity scale of the thermal energy storage components, combined with the installed capacity of the new energy power supply components and solar thermal collector components, as well as external conditions (such as heat load, electricity price, etc.). It then calculates the corresponding economic indicators according to preset economic settlement rules (such as total life cycle cost, return on investment, etc.). The module determines whether the preset system economic indicators meet preset convergence conditions; this step is the termination criterion of the optimization process. Convergence conditions can be set as follows: the change in the economic indicator is less than a preset threshold in several consecutive iterations; the preset maximum number of iterations is reached; or the economic indicator reaches a preset absolute optimal value. If these conditions are not met, the second type of configuration parameters are adjusted according to the optimization algorithm, and the process returns to inputting the current second type of configuration parameters into the heating system simulation module, indicating that the optimization process will continue. The optimization algorithm can be a heuristic algorithm, such as a genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, etc., or a gradient-based optimization algorithm. These algorithms adjust the second type of configuration parameters according to their internal logic based on the evaluation results of the previous iteration, aiming to obtain better economic indicators in the next iteration. If satisfied, the current second-type configuration parameters are determined as the optimal second-type configuration parameters. This means that the optimization process has reached the preset convergence criterion, and the current parameter combination is considered the optimal solution under the first-type configuration parameter set.

[0044] This application's solution systematically explores the solution space of the second type of configuration parameters (power scale of the electrothermal conversion component and capacity scale of the thermal energy storage component) by introducing a structured optimization loop. First, an initial second type of configuration parameter is generated as the starting point for optimization. Then, an iterative loop is entered. In each iteration, the current second type of configuration parameter is input into the heating system simulation module, and a full-time cycle simulation is run to evaluate the system performance under this parameter combination. A preset system economic index is calculated based on economic settlement rules. Next, it is determined whether the economic index meets the preset convergence condition. If not, the second type of configuration parameter is adjusted according to a preset optimization algorithm (such as a genetic algorithm or particle swarm optimization algorithm), and the loop returns to the beginning to continue the next round of simulation and evaluation. When the economic index meets the convergence condition, the current second type of configuration parameter is determined as the optimal second type of configuration parameter. This explicit optimization process ensures that, given the installed capacity of new energy power supply components and solar thermal collector components, the optimal configuration of electrothermal conversion components and thermal energy storage components can be found efficiently and reliably, thus overcoming the problems of low optimization efficiency, difficulty in convergence, or inability to determine the timing of optimization termination that may exist in traditional methods.

[0045] The following is a concrete example to illustrate this. Suppose that after determining the first set of configuration parameters, such as wind power capacity, photovoltaic capacity, and collector area, it is necessary to optimize the power scale of the electrothermal conversion module and the capacity scale of the thermal energy storage module. First, an initial set of second-type configuration parameters can be generated; for example, the initial power scale of the electrothermal conversion module is set to 5MW, and the initial capacity scale of the thermal energy storage module is set to 10MWh. Then, this set of parameters is input into the heating system simulation module, and a one-year full-time simulation is run to calculate the corresponding life-cycle cost. If the preset convergence condition is that the change in life-cycle cost is less than 0.1% in five consecutive iterations, and the current cost change is greater than this threshold, then according to the preset optimization algorithm (e.g., using a genetic algorithm), the power scale of the electrothermal conversion module and the capacity scale of the thermal energy storage module are adjusted based on the current simulation results, generating new second-type configuration parameters. For example, a genetic algorithm might select lower-cost parameter combinations for "crossover" and "mutation" based on the principle of "survival of the fittest," generating new parameter combinations, such as adjusting the power scale of the electrothermal conversion component to 6MW and the capacity scale of the thermal energy storage component to 12MWh. Subsequently, the new parameters are input into the simulation module for evaluation. This process continues until the change in the total lifecycle cost is less than 0.1% over five consecutive iterations. At this point, the second-class configuration parameters from the last iteration are determined as the optimal second-class configuration parameters.

[0046] Through the above technical solution, this application provides a structured and systematic iterative optimization method for a second type of configuration parameters. This method, through explicit initialization, cyclic simulation, economic evaluation, convergence judgment, and parameter adjustment mechanisms, ensures that, given a set of first-type configuration parameters, the optimal power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component can be found efficiently and reliably. This not only improves the efficiency and accuracy of the optimization process, avoiding blind trial and error and local optima, but also provides a solid foundation for the overall economic optimization of industrial park heating systems, thus making the final determined target system configuration scheme more economical and reliable.

[0047] This application further proposes setting the preset system economic indicator as the total life cycle cost, whereby the total life cycle cost includes initial investment cost, operation and maintenance cost, fuel cost, and electricity market transaction revenue. Simultaneously, the step of determining the target system configuration scheme based on the comprehensive evaluation results of all first-type configuration parameter sets and their corresponding optimal second-type configuration parameters includes: extracting the optimal total life cycle cost corresponding to each set of first-type configuration parameters; comparing all extracted optimal total life cycle costs to determine their minimum value; and combining the first-type configuration parameter set corresponding to the minimum value with the corresponding optimal second-type configuration parameters to obtain the target system configuration scheme.

[0048] The preset economic indicator for the system is the total life cycle cost (TLC). TLC is a comprehensive economic evaluation indicator that aims to comprehensively measure all costs and benefits incurred throughout the entire life cycle of a heating system, from planning, design, construction, operation, maintenance to eventual decommissioning. By using TLC as the optimization target, decision-making biases caused by considering only short-term costs or single cost items can be avoided, thus ensuring the long-term economic viability of the selected configuration. For example, a detailed cost model can be established to quantify various costs and benefits and compare them at the same point in time; alternatively, financial indicators such as net present value (NPV) or internal rate of return (IRR) can be used to indirectly reflect the merits of the TLC.

[0049] The total lifecycle cost specifically includes initial investment cost, operation and maintenance cost, fuel cost, and electricity market transaction revenue. Initial investment cost refers to all investments required during the initial system construction phase, such as equipment purchase, installation, and civil engineering costs. Operation and maintenance cost refers to daily expenses incurred during system operation, such as personnel salaries, equipment maintenance, spare parts replacement, and water, electricity, and gas consumption. Fuel cost refers to fuel costs consumed during system operation, such as the cost of electricity consumed by the electrothermal conversion components. Electricity market transaction revenue refers to the income obtained by the system from selling excess electricity to the electricity market during operation. Clearly defining the composition of these costs and revenues helps ensure the comprehensiveness and accuracy of cost accounting, providing a clear calculation basis for subsequent economic assessments. Various costs and revenues can be obtained through historical data, market research, equipment supplier quotations, etc., or estimated by establishing a parametric cost model based on factors such as system scale and equipment type.

[0050] When determining the target system configuration scheme, the first step is to extract the optimal total lifecycle cost corresponding to each set of first-class configuration parameters. This step summarizes and organizes the results of iterative optimization, extracting the lowest total lifecycle cost value corresponding to the optimal second-class configuration parameters found under each first-class configuration parameter set, preparing for subsequent comprehensive evaluation. For example, after the simulation module completes the optimization of the second-class parameters for a certain first-class configuration parameter set, the lowest total lifecycle cost value obtained through optimization is associated with and stored with that first-class configuration parameter set; alternatively, this associated data can be stored using data structures (such as lists, dictionaries, or databases) for convenient subsequent querying and comparison.

[0051] Next, all extracted optimal lifecycle costs are compared to determine their minimum. This step is crucial for the final decision, identifying the most economically viable option by comparing the economics of all candidate solutions. For example, this can be achieved by iterating through all stored optimal lifecycle cost values ​​and using a comparison algorithm to find the minimum; alternatively, a sorting algorithm can be used to sort all cost values ​​in ascending order, with the first element being the minimum.

[0052] Finally, the set of first-class configuration parameters corresponding to the minimum value is combined with the corresponding optimal second-class configuration parameters to form the target system configuration scheme. This step is the final output of the optimization result, clearly presenting the most economical configuration scheme as a guiding scheme for the construction of industrial park heating systems. For example, once the minimum life cycle cost is determined, the associated set of first-class configuration parameters and the optimal second-class configuration parameters can be traced back to combine them into a complete configuration scheme; alternatively, this final scheme can be output in the form of a report, data file, or visualization chart.

[0053] The following is a concrete example to illustrate this. Suppose an industrial park needs to configure its renewable energy heating system and wants to find the most cost-effective solution. First, based on the allowable engineering scope and discretization step size, multiple sets of first-type configuration parameters are generated. For example, one set includes a wind power installed capacity of 5MW, a photovoltaic installed capacity of 10MW, and a collector area of ​​2000 square meters. For this set of first-type configuration parameters, the heating system simulation module begins iterative optimization of second-type configuration parameters. In this process, the preset system economic index is explicitly set as the total life cycle cost. The calculation of this cost comprehensively considers the initial investment costs of wind power, photovoltaic, solar thermal collectors, electrothermal conversion components, and thermal energy storage components; daily operation and maintenance costs; fuel costs generated by the electrothermal conversion components consuming grid electricity; and electricity market transaction revenue obtained from selling excess renewable energy generation to the grid. The simulation module continuously adjusts the power output of the electrothermal conversion components and the capacity of the thermal energy storage components. For example, it adjusts the electrothermal conversion power from 2MW and thermal energy storage capacity from 500MWh to 3MW and 600MWh. After each adjustment, a full-time-cycle simulation is performed, and the corresponding lifecycle cost is calculated. This process continues until a second type of configuration parameter combination that minimizes the lifecycle cost is found. Once all first-type configuration parameter sets have completed the above optimization process and obtained their respective optimal lifecycle costs, the system extracts these optimal cost values ​​uniformly. For example, if the optimal lifecycle cost corresponding to the first set of parameter combinations is 10 million yuan, the second set is 9.5 million yuan, and the third set is 11 million yuan, then the system will identify 9.5 million yuan as the minimum value. Finally, the set of first-class configuration parameters corresponding to the minimum value of 9.5 million yuan (e.g., wind power installed capacity of 5MW, photovoltaic installed capacity of 10MW, and collector area of ​​2000 square meters) is combined with the optimal second-class configuration parameters corresponding to this set (e.g., electrothermal conversion component power of 3MW and thermal energy storage capacity of 600MWh) to form the final recommended target system configuration scheme.

[0054] By defining the preset system economic indicators as total life cycle costs and specifying their components in detail, this application enables a comprehensive and accurate assessment of the economics of industrial park heating systems, avoiding decision-making biases that may result from single-indicator or short-term cost evaluations. Furthermore, by selecting the scheme with the lowest total life cycle cost from all first-class configuration parameter sets and their corresponding optimal second-class configuration parameters as the target system configuration scheme, this application ensures that the selected scheme has the best economic benefits throughout its entire life cycle. This allows industrial parks to obtain an optimized configuration that meets heating needs while also considering long-term economic efficiency when investing in and constructing heating systems, thereby effectively reducing operational risks and total cost of ownership, and improving return on investment.

[0055] Furthermore, this application proposes a multi-scenario weighted robust optimization process that considers environmental uncertainties to correct the operating efficiency of solar thermal collector modules and optimize the scale of electrothermal conversion modules and thermal energy storage modules. The process includes the following steps: Based on environmental monitoring data from the industrial park, several air quality scenarios of different levels are defined, and an atmospheric attenuation coefficient is determined for each scenario. The baseline efficiency curve of the solar thermal collector is corrected according to the atmospheric attenuation coefficient to obtain the actual operating efficiency curve corresponding to each air quality scenario. The correction formula is: η_actual=η_base*(1-k_atm); where η_actual is the actual operating efficiency, η_base is the baseline efficiency, and k_atm is the atmospheric attenuation coefficient. Statistical analysis is performed on historical environmental data to determine the probability of occurrence of each air quality scenario, and the probability of occurrence is used as a weight.

[0056] This application's approach introduces environmental uncertainties, particularly the impact of air quality on the efficiency of solar thermal collectors, and quantifies this impact into a multi-scenario weighted robust optimization process. The process first defines different air quality scenarios based on industrial park environmental monitoring data and assigns a corresponding atmospheric attenuation coefficient to each scenario. These attenuation coefficients are used to correct the baseline efficiency curve of the solar thermal collectors, thereby obtaining actual operating efficiency curves under different air quality scenarios. This allows the simulation model to more realistically reflect the performance of the components in the actual environment. Simultaneously, statistical analysis of historical environmental data determines the probability of occurrence for each air quality scenario, and these probabilities are used as weights. In subsequent optimization, the evaluation of system economic indicators comprehensively considers these weighted scenarios; for example, the optimal configuration is determined by calculating the minimum weighted expected total cost across all scenarios. This method ensures that the final determined heating system configuration not only performs well under ideal conditions but also maintains good robustness and economy in the face of actual environmental fluctuations, thus avoiding system performance degradation or economic losses due to environmental factors.

[0057] The following example illustrates this. Suppose an industrial park is located in an area with fluctuating air quality. To optimize its renewable energy heating system configuration, we can first collect air quality monitoring data from the past three years and classify the air quality scenarios into three categories based on PM2.5 concentration: "Excellent" (PM2.5 < 50 μg / m³), "Lightly Polluted" (50 ≤ PM2.5 < 100 μg / m³), and "Moderately Polluted" (PM2.5 ≥ 100 μg / m³). Statistical analysis of historical data shows that the probability of occurrence for these three scenarios is 60%, 30%, and 10%, respectively. Simultaneously, through field testing or empirical models, atmospheric attenuation coefficients k_atm can be determined for each of these three scenarios; for example, k_atm is 0.05 for the excellent scenario, 0.15 for the lightly polluted scenario, and 0.30 for the moderately polluted scenario. In the heating system simulation module, a baseline efficiency curve η_base for the solar thermal collector components is preset. During optimization iterations, for each air quality scenario, the actual operating efficiency curve under that scenario is calculated using the correction formula η_actual=η_base*(1-k_atm) based on its corresponding atmospheric attenuation coefficient k_atm. For example, if the baseline efficiency is 0.7, the actual efficiency is 0.7*(1-0.05)=0.665 under the excellent scenario, 0.7*(1-0.15)=0.595 under the light pollution scenario, and 0.7*(1-0.30)=0.49 under the moderate pollution scenario. When iteratively optimizing the second type of configuration parameters (the power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component), the heating system simulation module calculates the life-cycle cost of each candidate configuration scheme under the three air quality scenarios, and then weights these three costs according to their respective probabilities of occurrence to obtain a weighted expected life-cycle cost. The optimization algorithm will iterate with the goal of minimizing this weighted expected life-cycle cost, and finally select the optimal power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component under the consideration of environmental uncertainties.

[0058] Through the above technical solution, this application effectively solves the problem of system performance not matching expectations due to neglecting environmental uncertainties in traditional optimization methods. By incorporating different air quality scenarios and their probabilities into the optimization considerations and correcting the operating efficiency of solar thermal collectors, the final determined heating system configuration scheme is more robust. This not only improves the reliability and stability of the system in actual operation but also makes the assessment of the total life cycle cost more accurate, thereby avoiding the risk of additional operation and maintenance costs or insufficient heating caused by environmental factors, and ensuring the long-term economic efficiency and sustainability of the industrial park heating system.

[0059] Furthermore, this application proposes a step-by-step approach in the operating environment of the heating system simulation module, guided by preset system economic indicators, to iteratively optimize the second type of configuration parameters. This step includes: for each set of first type configuration parameters, performing full-time-cycle simulation, and simultaneously running calculations for all defined air quality scenarios at each time step; during the calculation process, calling the actual operating efficiency curves under the corresponding scenarios to calculate the solar thermal contribution, the operating status of the electrothermal conversion components, and the charging and discharging status of the thermal energy storage components; performing iterative optimization with the goal of minimizing the weighted expected total cost under all scenarios; and simultaneously verifying whether the system heating reliability meets the preset requirements under all scenarios during the iterative optimization process.

[0060] In this application, the proposed solution for large-scale configuration of industrial park heating systems addresses the potential robustness issues of single-scenario optimization by deeply integrating environmental uncertainties (especially the impact of air quality on solar thermal collector efficiency) into the iterative optimization process of the second type of configuration parameters. Specifically, after determining the first type of configuration parameter set, the simulation module no longer runs a single full-time-cycle simulation but simultaneously initiates and executes calculations for all defined air quality scenarios at each time step. This means that at any given moment, the system simulates the actual output of the solar thermal collector under different air quality conditions, such as sunny, cloudy, and hazy conditions. During these parallel calculations, the simulation module calls the corresponding actual operating efficiency curve based on the atmospheric attenuation coefficient for each specific scenario, thereby accurately calculating the thermal contribution of the solar thermal collector under that scenario. Based on this, the system further simulates the operating status of the electrothermal conversion components and the charging and discharging status of the thermal energy storage components to meet the current heat load requirements. In this way, the system can obtain detailed operating data and cost information corresponding to different combinations of the second type of configuration parameters under various environmental scenarios. Subsequently, the optimization algorithm iteratively seeks the lowest possible cost across all scenarios. This means that the optimizer no longer focuses solely on minimizing cost under a specific scenario, but comprehensively considers all possible scenarios and their probabilities, striving to find a configuration scheme with the lowest average cost and effective response to environmental changes in the long run. Simultaneously, at each step of the iterative optimization, the system verifies in parallel whether the heating reliability meets preset requirements under all scenarios. This dual-constraint optimization mechanism (optimal economy and reliability compliance) ensures that the final determined second-type configuration parameters are not only economically competitive but also reliably provide heat to the industrial park in the face of various environmental uncertainties. Through this comprehensive multi-scenario weighted robust optimization, the proposed scheme generates a more adaptable and resilient heating system configuration scheme, significantly improving the system's performance and risk resistance in actual operation.

[0061] As a specific implementation method, when configuring a heating system for an industrial park on a large scale, it is assumed that three air quality scenarios have been defined: Scenario A (sunny, probability 0.5), Scenario B (cloudy, probability 0.3), and Scenario C (haze, probability 0.2). In the simulation module's operating environment, when iteratively optimizing the second type of configuration parameters (power scale of the electrothermal conversion components and capacity scale of the thermal energy storage components) for a set of first-type configuration parameters (e.g., wind power capacity 10MW, photovoltaic capacity 5MW, collector area 10000 square meters), the simulation process will proceed as follows: Within each simulation time step (e.g., 1 hour) of the full time-series cycle (e.g., 8760 hours per year), the simulation module will simultaneously launch three independent calculation threads or processes, corresponding to Scenario A, Scenario B, and Scenario C respectively. Each thread, during calculation, will call the corrected actual operating efficiency curve of the solar thermal collector components under its respective scenario. For example, scenario A might invoke the efficiency curve η_actual_A, scenario B might invoke η_actual_B, and scenario C might invoke η_actual_C. Based on these efficiency curves, each thread independently calculates the thermal contribution of the solar thermal collector within the current time step. Subsequently, according to the current heat load demand, each thread further simulates the operating state of the electrothermal conversion component and the charging and discharging state of the thermal energy storage component. After completing the simulation for all time steps, each scenario will obtain a corresponding total system cost and heating reliability index. The optimization algorithm will iteratively search for the objective function "0.5 * total cost of scenario A + 0.3 * total cost of scenario B + 0.2 * total cost of scenario C" to minimize the weighted expected total cost. Simultaneously, in each iteration, the algorithm will check whether the heating satisfaction rate under all scenarios is higher than the preset 98% requirement. If a certain combination of second-type configuration parameters causes the heating satisfaction rate of any scenario to be lower than 98%, then that combination will be considered infeasible, or a penalty term will be added to guide the optimization direction. In this way, the optimization process will eventually converge to a power scale of the electrothermal conversion components and a capacity scale of the thermal energy storage components that can both ensure heating reliability under various air quality scenarios and achieve optimal long-term operating economy.

[0062] Furthermore, in some embodiments, regarding the weighted expected total cost, this embodiment comprehensively considers the operational performance under all "air quality scenarios" when evaluating the scale configuration of the electric heating system and the thermal storage system. For each auxiliary system configuration and each air quality scenario s, this embodiment calculates its annual operating cost (Cost_s). Cost_s mainly includes: the electricity consumed by the electric heating system multiplied by the electricity price for the corresponding period (considering peak and off-peak electricity prices), the maintenance cost of the thermal storage system, and the production losses that may result from insufficient heating. Taking all scenarios into account, the weighted total expected annual cost (Total_Expected_Cost) is calculated as follows: M: Represents the total number of defined environmental operating scenarios reflecting different atmospheric transparency; s: Represents the s-th environmental operating scenario, s=1,2,...,M; P s Cost represents the probability of the s-th environmental operating scenario occurring within the entire simulation period; s This represents the estimated annual operating cost of the heating system under the s-th environmental operating scenario, when the heating system is configured with the scale parameters to be evaluated, within the simulation model; C cap It is the annualized investment cost of the auxiliary systems (electric heaters and thermal storage tanks).

[0063] This embodiment also evaluates the heating reliability of each scheme under different scenarios, for example, calculating the annual heat load fulfillment rate. The optimization objective of this embodiment is to find a combination of electric heating system capacity (Cap_EH) and thermal storage system capacity (Cap_ST) that minimizes Total_Expected_Cost. Simultaneously, certain heating reliability requirements must be met; for example, the heat load fulfillment rate must be greater than or equal to 98% in all scenarios. Heuristic optimization algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO) can be used. The algorithm flow includes: first, randomly generating an initial set of auxiliary system capacity combinations; then, for each combination, performing the third step of "system performance simulation under multiple scenarios" and calculating its Total_Expected_Cost and heat load fulfillment rate; next, based on Total_Expected_Cost and reliability constraints, selecting combinations with better performance for "breeding" (crossover and mutation) to generate new combinations; finally, repeating the above steps until a preset number of iterations is reached or the optimal combination that meets the conditions is found. Through this optimized framework, this embodiment can ensure that the auxiliary system can maintain stable operation and good economic efficiency even when faced with minor fluctuations in solar energy supply caused by endogenous emissions from industrial parks.

[0064] Through the aforementioned technical solution, this application further deepens the consideration of environmental uncertainties based on the fundamental method for configuring large-scale heating systems. By simultaneously running calculations for all defined air quality scenarios at each time step in a full-time-cycle simulation and calling the actual operating efficiency curves under the corresponding scenarios, the true performance of solar thermal collectors under different environmental conditions can be simulated more accurately, thereby obtaining system operation data that is closer to reality. With minimizing the weighted expected total cost under all scenarios as the optimization objective, and simultaneously verifying the system's heating reliability, the optimization results not only have economic advantages but also ensure the stability and reliability of heating in the face of various environmental changes. This significantly improves the robustness and adaptability of the configured heating system scheme, effectively avoiding system performance degradation or economic losses caused by environmental uncertainties, and providing a more reliable and economical heating guarantee for industrial parks.

[0065] Furthermore, this application also proposes that the operating logic of the digital model includes an active thermal storage strategy based on time-of-use pricing. When the heating system simulation module executes the operating logic, it further includes the following steps within each simulation time step: loading grid time-of-use pricing data and dividing the entire time-series cycle into off-peak, flat, and peak periods; when the current time step is in the off-peak period, before executing the step of determining whether the sum of the power generation and the heat collection power meets the current heat load demand, a prediction logic is first executed: detecting whether the current heat storage capacity of the thermal energy storage component is lower than the preset peak-segment guaranteed heat storage capacity; if it is lower than the peak-segment guaranteed heat storage capacity, the electrothermal conversion component is forcibly activated to use grid power for heating and store the generated heat energy in the thermal energy storage component until the current heat storage capacity reaches the peak-segment guaranteed heat storage capacity; when the current time step is in the peak period, the logic threshold for prioritizing the heat release of the thermal energy storage component is adjusted to prohibit the electrothermal conversion component from consuming grid power, unless the current heat storage capacity of the thermal energy storage component has been exhausted.

[0066] Among them, the active heat storage strategy based on time-of-use pricing refers to the heating system actively adjusting the heat storage behavior of its thermal energy storage components according to the differences in electricity prices during different periods of the power grid, in order to reduce system operating costs. Its core lies in utilizing electricity during low-price periods for heat storage and prioritizing the use of stored thermal energy during high-price periods, reducing dependence on high-priced electricity. This strategy can significantly improve the system's economic efficiency and promote the consumption of renewable energy. Loading time-of-use pricing data from the power grid divides the entire time series into off-peak, flat, and peak periods to provide the system with electricity price information and clarify the division of different price ranges. Time-of-use pricing data is usually published by power companies and includes electricity price standards for different time periods within a day or week. Loading this data can be achieved by reading a pre-set electricity price table file, obtaining it in real-time from the electricity market interface, or through API calls. The entire time-series cycle is divided into off-peak, flat, and peak electricity periods based on electricity prices. For example, off-peak electricity periods typically refer to the nighttime hours when electricity consumption is low, with the lowest prices; peak electricity periods refer to the daytime hours when electricity consumption is high, with the highest prices; and flat electricity periods fall in between. This division forms the basis for implementing active thermal storage strategies.

[0067] When the current time step falls within the off-peak electricity period, before executing the step of determining whether the sum of the power generation and the heat collection power meets the current heat load demand, a pre-judgment logic is first executed: detecting whether the current heat storage capacity of the thermal energy storage component is lower than the preset peak-period guaranteed heat storage capacity. This step introduces a forward-looking judgment mechanism, aiming to proactively assess the heat storage status of the thermal energy storage component during off-peak electricity periods. The execution of the pre-judgment logic takes precedence over the conventional supply and demand balance judgment, reflecting the proactive nature of the strategy. The current heat storage capacity of the thermal energy storage component can be detected through real-time monitoring by sensors or obtained from the internal state variables of a simulation model. The preset peak-period guaranteed heat storage capacity is a key parameter, representing the minimum heat required to meet the heat load demand of subsequent peak electricity periods. Its setting can be based on historical load data, prediction models, or optimization algorithms. If it is lower than the peak-period guaranteed heat storage capacity, the electrothermal conversion component is forcibly activated to use grid power for heating, and the generated heat energy is stored in the thermal energy storage component until the current heat storage capacity reaches the peak-period guaranteed heat storage capacity. This step describes the mandatory thermal storage measures taken by the system during off-peak hours when thermal energy reserves are insufficient to meet peak-peak demand. Forced activation of the electrothermal conversion unit means that even if the current heat load is met, the system will actively consume low-priced grid electricity for heating. The electrothermal conversion unit efficiently converts electrical energy into heat energy and stores it in the thermal energy storage unit. This process continues until the thermal energy storage unit reaches the preset peak-peak guarantee heat storage capacity, ensuring sufficient thermal energy reserves during peak hours when electricity prices are high.

[0068] When the current time step falls within the peak electricity period, the logic threshold for prioritizing the heat release of the thermal energy storage component is adjusted to prohibit the electrothermal conversion component from consuming grid power, unless the current heat storage capacity of the thermal energy storage component is exhausted. This step aims to maximize the utilization of stored thermal energy during peak electricity periods and avoid consuming high-priced grid power. Adjusting the logic threshold for prioritizing the heat release of the thermal energy storage component means that the system will be more inclined to draw heat from the thermal energy storage component, even if its heat storage capacity is low. Prohibiting the electrothermal conversion component from consuming grid power is a mandatory economic control measure, except in extreme cases, i.e., when the current heat storage capacity of the thermal energy storage component is completely exhausted, in which case the system allows the electrothermal conversion component to intervene to ensure heating reliability. This ensures that system operating costs are strictly controlled during periods of highest electricity prices.

[0069] As a specific implementation method, the heating system simulation module can be implemented as a software program developed using the Python language, containing multiple sub-modules, such as a data loading module, a simulation calculation module, and an optimization control module. The time-of-use electricity price data can be stored in a CSV file, recording the electricity price information for each 24-hour period. For example, 00:00-08:00 is the off-peak period, 08:00-12:00 is the flat period, 12:00-18:00 is the peak period, 18:00-22:00 is the flat period, and 22:00-24:00 is the off-peak period. At the beginning of each time step (e.g., 15 minutes), the simulation program reads the electricity price data corresponding to the current time and determines its time period. When the simulation enters an off-peak period, such as 3:00 AM, the simulation module first checks the current heat storage capacity of the thermal energy storage components. Assuming the preset peak-period guaranteed heat storage capacity is 1000 kWh, and the current heat storage capacity of the thermal energy storage components is 500 kWh, it is lower than the guaranteed amount. Even if current renewable energy generation and thermal collection can meet the immediate heat load, the simulation module will immediately trigger the operation command of the electrothermal conversion component, causing it to consume grid power at maximum power (e.g., 500kW) for heating. The generated heat energy is sent to the thermal energy storage component until its heat storage reaches 1000kWh. When the simulation enters peak electricity hours, such as 2 PM, the simulation module will adjust the heat release priority of the thermal energy storage component. At this time, the system will prioritize extracting heat from the thermal energy storage component to meet the heat load demand. Simultaneously, the operation permission of the electrothermal conversion component will be temporarily locked unless the heat storage of the thermal energy storage component drops to zero and can no longer provide any heat. Only under such extreme circumstances will the electrothermal conversion component be allowed to consume grid power for supplementary heating to avoid heat interruption. This control logic ensures that during periods of highest electricity prices, the system utilizes low-cost stored thermal energy as much as possible, minimizing the consumption of high-priced electricity.

[0070] By introducing an active heat storage strategy based on time-of-use pricing, the proposed solution effectively addresses the problem of traditional heating systems failing to fully utilize the advantages of time-of-use pricing during operation. During off-peak hours, the system actively identifies and utilizes low-priced electricity for heat storage, reserving sufficient heat energy for subsequent high-price periods, thus avoiding the forced use of high-priced electricity due to insufficient heat during peak hours. During peak hours, the system prioritizes the consumption of stored low-cost heat energy and strictly limits the use of high-price grid electricity by the electrothermal conversion components, significantly reducing system operating costs. This strategic management of the heat storage components' charging and discharging behavior enables the heating system to meet the heat load demands of industrial parks while significantly improving operational economics, thus achieving a more optimized and cost-effective overall solution during the system scale-up configuration phase.

[0071] Furthermore, this application proposes to dynamically determine the peak-segment guaranteed heat storage through the following steps: when the simulation time step enters the off-peak period, identify the duration interval of the next peak period immediately following the end of the off-peak period; extract the predicted heat load demand, predicted power generation of the new energy power supply components, and predicted heat collection power of the solar thermal collector components from the digital model within the duration interval; calculate the net heat load gap within the duration interval, where the net heat load gap is the difference between the predicted heat load demand and the sum of the predicted heat collection power of the solar thermal collector components and the predicted power generation of the new energy power supply components after electrothermal conversion; set the net heat load gap as the peak-segment guaranteed heat storage, and correct it according to the heat release efficiency of the thermal energy storage components.

[0072] Specifically, when the simulation time step enters a valley electricity period, the system needs to anticipate the upcoming high electricity price period to provide a clear time window for active thermal storage. To this end, the method identifies the duration of the next peak electricity period immediately following the end of the valley electricity period. This can be achieved by preloading the annual time-of-use electricity price table and automatically determining the start and end times of the peak electricity period based on the current time step and the price table; alternatively, the system can interact in real-time with the power grid dispatch system or electricity market data interface to obtain future electricity price forecast information, thereby dynamically identifying peak electricity periods. To accurately calculate the peak-segment guaranteed heat storage capacity, the method extracts the predicted heat load demand, predicted power generation of new energy power supply components, and predicted heat collection power of solar thermal collector components from the digital model within the specified duration. These predicted data are the basis for calculating the required heat storage capacity, and their accuracy is crucial. The digital model can integrate historical data analysis and prediction algorithm modules to predict the heat load, power generation and heat collection power in a specific time period in the future based on historical heat load data, meteorological data (such as sunshine, wind speed and temperature) and the characteristic curves of new energy components; or, through external data interfaces, it can obtain prediction data from meteorological forecast services, industrial park production planning systems, etc., and input them into the digital model for processing.

[0073] Based on this, the method calculates the net heat load gap within the specified time interval. This net heat load gap precisely quantifies the difference between the heat provided by the system's own renewable energy sources and solar thermal collectors and the actual heat load demand during peak power periods, without consuming grid power. In the digital model, the predicted renewable energy power generation is converted into equivalent calorific value using a preset electrothermal conversion efficiency. This is then added to the predicted solar thermal collector power and subtracted from the predicted heat load demand to obtain the net heat load gap. This calculation can be performed using integration or accumulation, accumulating the power and demand over the entire time interval to obtain the total net heat load gap. Finally, the method sets the net heat load gap as the peak-segment guaranteed heat storage capacity and corrects it based on the heat release efficiency of the thermal energy storage components. This correction step ensures that the thermal energy storage components can provide sufficient heat to compensate for the net heat load gap during peak power periods, while also considering energy losses during storage and release. For example, the calculated net heat load gap can be directly divided by the heat release efficiency of the thermal energy storage component (e.g., if the heat release efficiency is 0.9, then the gap is divided by 0.9) to obtain the actual energy that needs to be stored; or, when setting the peak period to ensure the amount of heat storage, a certain safety margin can be introduced, that is, on the basis of considering the heat release efficiency, an additional percentage of heat storage can be added to cope with prediction errors or sudden situations.

[0074] The following is a concrete example to illustrate this. Assume that during the simulation, the current time step enters a low-electricity period, and the system identifies that the next peak electricity period after this low-electricity period ends is from 8:00 AM to 12:00 PM, a duration of 4 hours. Based on historical data and prediction algorithms, the digital model predicts that the total heat load demand of the industrial park during this 4-hour peak electricity period will be 1000 kWh. Simultaneously, it predicts that renewable energy power generation components (such as wind and solar power) can generate 300 kWh of electricity during this period, producing 300 kWh of calorific value after electrothermal conversion (assuming an electrothermal conversion efficiency of 100%), while solar thermal collectors can provide 200 kWh of collector power. At this point, the system calculates the net heat load gap for this duration as: 1000 kWh (predicted heat load demand) - (300 kWh (predicted renewable energy power generation calorific value) + 200 kWh (predicted solar thermal collector power)) = 500 kWh. If the heat dissipation efficiency of the thermal energy storage component is 90%, the net heat load gap is adjusted to: 500 kWh / 0.9 = 555.56 kWh. Ultimately, 555.56 kWh is set as the peak-period guaranteed heat storage capacity. During off-peak hours, the system will forcibly activate the electrothermal conversion component, using grid power to heat the thermal energy storage component to 555.56 kWh, ensuring that the heat load demand can be fully met during the upcoming peak period without consuming expensive peak electricity.

[0075] Furthermore, this application also proposes that the heating system simulation module consists of multiple independently configurable equipment simulation sub-modules; the step of constructing and running the digital model of the heating system includes parameter setting and simulation through the following sub-modules. Among them, such as... Figure 2 The diagram shown is a structural schematic of the heating system simulation module in this embodiment.

[0076] The wind power module and photovoltaic module 201 are used to set the wind power installed capacity, photovoltaic installed capacity and corresponding annual ideal output characteristic curves, respectively; Solar thermal collector module 202 is used to set the collector area and ideal output characteristic curve; Electrothermal conversion module 203 is used to set the maximum heating power and thermal efficiency; The thermal energy storage component module 204 is used to set the maximum heat storage power, maximum heat release power, heat storage duration, heat release efficiency and heat storage loss coefficient. Industrial park heat load module 205 is used to set the heat load curve for 8760 hours throughout the year; The new energy surplus power grid connection module 206 is used to set the annual surplus power grid connection load range and calculate the grid connection revenue in combination with spot electricity price, long-term transaction electricity price and electricity market transaction settlement rules.

[0077] The following is a specific example to illustrate this. When constructing a digital model of an industrial park's heating system, the following methods can be used for parameter setting and simulation: First, for the wind power module, its installed capacity can be set to 50MW, and an 8760-hour ideal output characteristic curve generated based on local annual wind speed data and turbine power curves can be imported. For the photovoltaic module, its installed capacity can be set to 30MW, and an 8760-hour ideal output characteristic curve generated based on local annual solar radiation data and photovoltaic panel efficiency can be imported. Second, in the solar thermal collector module, the collector area can be set to 10,000 square meters, and its annual ideal output characteristic curve can be generated based on the selected trough solar collector type, combined with local solar radiation intensity and ambient temperature. Next, the electrothermal conversion module can be set to a maximum heating power of 20MW and a thermal efficiency of 98%. The thermal energy storage module can be set to a maximum heat storage power of 15MW, a maximum heat release power of 18MW, a heat storage duration of 8 hours, a heat release efficiency of 95%, and a heat storage loss coefficient of 0.005 / hour. Meanwhile, the industrial park's heat load module will load a detailed 8760-hour annual heat load curve reflecting the seasonal changes and diurnal patterns of production in the park. For example, the heat load is higher during the winter heating season and lower in summer, and the heat load is higher during daytime production periods than at night. Finally, the renewable energy surplus power grid connection module can be set to a surplus power grid connection load range of 0-80MW and load local electricity market spot price data, long-term transaction price contract information, and specific transaction settlement rules to calculate the revenue from renewable energy surplus power grid connection in real time during the simulation. During simulation, these sub-modules will calculate independently according to their respective set parameters and logic, and interact with each other through the simulation platform. For example, the output data of wind power and photovoltaic modules will be aggregated and participate in the heat load satisfaction process together with the heat output of solar thermal collectors. Thermal energy storage components will charge and release heat according to supply and demand balance, electrothermal conversion components will be activated when necessary, and excess electricity will be used for revenue calculation through the renewable energy surplus power grid connection module.

[0078] The above description is merely an embodiment of this application and is not intended to limit the scope of protection 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 protection of this application.

Claims

1. A method for large-scale configuration of an industrial park heating system based on local heat generation and transmission from new energy sources, wherein the heating system includes at least new energy power supply components, solar thermal collector components, electrothermal conversion components, and thermal energy storage components, the method being implemented based on a heating system simulation module, wherein the heating system simulation module is used to construct and run a digital model of the heating system, characterized in that, The method includes: Several sets of first-class configuration parameters are determined, and the first-class configuration parameter sets represent the installed capacity of the new energy power supply components and the solar thermal collector components; For each set of the first type of configuration parameters, it is input as a fixed boundary condition into the heating system simulation module; in the operating environment of the heating system simulation module, guided by the preset system economic indicators, the second type of configuration parameters are iteratively optimized to obtain the optimal second type of configuration parameters corresponding to the set of the first type of configuration parameters. The second type of configuration parameters characterize the power scale of the electrothermal conversion component and the capacity scale of the thermal energy storage component. Based on the comprehensive evaluation results of all the first type of configuration parameter sets and their corresponding optimal second type of configuration parameters, the target system configuration scheme is determined.

2. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources, as described in claim 1, is characterized in that... The first set of configuration parameters includes wind power installed capacity, photovoltaic installed capacity, and collector area; The step of determining several sets of first-class configuration parameters includes: Obtain the upper and lower limits of the installed capacity of the new energy power supply components and the solar thermal collector components within the allowable range of the project; Set a discretization step size and discretize the wind power installed capacity, photovoltaic installed capacity and collector area between the upper and lower limits of the installed capacity. The discretized parameters are permuted and combined to generate the several sets of first-class configuration parameters.

3. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources, as described in claim 1, is characterized in that... When iteratively optimizing the second type of configuration parameters based on preset system economic indicators, the heating system simulation module executes the following logic within each simulation time step: Calculate the power generation of the new energy power supply component and the heat collection power of the solar thermal collector component within the current time step; Determine whether the sum of the power generation and the heat collection power meets the current heat load demand; When the heat load requirement is met and there is surplus energy, the thermal energy storage component is controlled to store heat until the capacity limit is reached. When the heat load demand is not met, the heat storage component is controlled to release heat first. If the heat storage component still cannot meet the heat load demand after releasing heat, the electrothermal conversion component is controlled to consume grid power to supplement the heat supply.

4. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources, as described in claim 1, is characterized in that... The iterative optimization of the second type of configuration parameters to obtain the optimal second type of configuration parameters corresponding to the set of first type configuration parameters includes: Generate initial second-type configuration parameters; enter the optimization loop and input the current second-type configuration parameters into the heating system simulation module; Run a full-time-cycle simulation and calculate the preset system economic indicators based on the economic settlement rules; Determine whether the preset system economic indicators meet the preset convergence conditions; if not, adjust the second type of configuration parameters according to the optimization algorithm, and return to the step of inputting the current second type of configuration parameters into the heating system simulation module; if they meet the conditions, determine the current second type of configuration parameters as the optimal second type of configuration parameters.

5. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources according to claim 1, characterized in that, The preset system economic indicator is the total life cycle cost, which includes initial investment cost, operation and maintenance cost, fuel cost, and electricity market transaction revenue. The step of determining the target system configuration scheme based on the comprehensive evaluation results of all the first type of configuration parameter sets and their corresponding optimal second type of configuration parameters includes: Extract the optimal lifecycle cost corresponding to each set of first-class configuration parameters; Compare all extracted optimal lifecycle costs and determine their minimum value; The first set of configuration parameters corresponding to the minimum value and the optimal second set of configuration parameters corresponding to that set are combined as the target system configuration scheme.

6. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission of new energy sources according to claim 5, characterized in that, The method also includes a multi-scenario weighted robust optimization process that considers environmental uncertainties to correct the operating efficiency of the solar thermal collector and optimize the scale of the electrothermal conversion module and the thermal energy storage module. The steps of this process include: Based on environmental monitoring data from the industrial park, several different levels of air quality scenarios were defined, and an atmospheric attenuation coefficient was determined for each scenario. The baseline efficiency curve of the solar thermal collector is corrected based on the atmospheric attenuation coefficient to obtain the actual operating efficiency curve corresponding to each air quality scenario. The correction formula is: η_actual=η_base*(1-k_atm); where η_actual is the actual operating efficiency, η_base is the baseline efficiency, and k_atm is the atmospheric attenuation coefficient. Statistical analysis of historical environmental data is performed to determine the probability of occurrence of each air quality scenario, and the probability of occurrence is used as a weight.

7. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission of new energy sources according to claim 6, characterized in that, The step of iteratively optimizing the second type of configuration parameters in the operating environment of the heating system simulation module, guided by preset system economic indicators, includes: For each set of first-class configuration parameters, in the full-time-cycle simulation, for each time step, calculations for all defined air quality scenarios are run simultaneously. During the calculation process, the actual operating efficiency curve under the corresponding scenario is called to calculate the solar thermal contribution, the operating status of the electrothermal conversion component and the charging and discharging status of the thermal energy storage component. The optimization objective is to minimize the weighted expected total cost under all scenarios. During the iterative optimization process, it is also verified whether the system heating reliability meets the preset requirements under all scenarios.

8. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission of new energy sources according to claim 3, characterized in that, The operational logic of the digital model includes an active thermal storage strategy based on time-of-use electricity pricing; when executing the operational logic, the heating system simulation module further includes the following steps within each simulation time step: Load the time-of-use electricity price data of the power grid and divide the entire time period into off-peak hours, flat hours and peak hours; When the current time step is in the off-peak period, before executing the step of determining whether the sum of the power generation and the heat collection power meets the current heat load demand, the prediction logic is executed first: detect whether the current heat storage of the thermal energy storage component is lower than the preset peak period guaranteed heat storage. If the current heat storage capacity is lower than the peak range guaranteed heat storage capacity, the electrothermal conversion component will be forcibly activated to use grid power for heating and store the generated heat energy in the heat storage component until the current heat storage capacity reaches the peak range guaranteed heat storage capacity. When the current time step is within the peak power period, the logic threshold for prioritizing the heat release of the thermal energy storage component is adjusted to prevent the electrothermal conversion component from consuming grid power, unless the current heat storage capacity of the thermal energy storage component has been exhausted.

9. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources, as described in claim 8, is characterized in that... The peak segment's guaranteed heat storage capacity is dynamically determined through the following steps: When the simulation time step enters the valley power period, the duration interval of the next peak power period immediately following the end of the valley power period is identified; Extract the predicted heat load demand, predicted power generation of new energy power supply components, and predicted heat collection power of solar thermal collector components from the digital model within the time interval. Calculate the net heat load gap within the time interval. The net heat load gap is the difference between the predicted heat load demand and the sum of the predicted solar thermal collector power and the predicted renewable energy power generation power after electrothermal conversion. The net heat load gap is set as the peak-segment guaranteed heat storage capacity, and is adjusted according to the heat release efficiency of the thermal energy storage component.

10. The method for large-scale configuration of industrial park heating systems based on local heat generation and transmission from new energy sources according to claim 1, characterized in that, The heating system simulation module consists of multiple independently configurable equipment simulation sub-modules; the steps of building and running the digital model of the heating system include parameter setting and simulation through the following sub-modules: The wind power module and the photovoltaic module are used to set the wind power installed capacity, the photovoltaic installed capacity and the corresponding annual ideal output characteristic curve, respectively; Solar thermal collector module, used to set the collector area and ideal output characteristic curve; The electrothermal conversion component module is used to set the maximum heating power and thermal efficiency; The thermal energy storage component module is used to set the maximum heat storage power, maximum heat release power, heat storage duration, heat release efficiency, and heat storage loss coefficient. The industrial park heat load module is used to set the heat load curve for 8760 hours throughout the year; The new energy surplus power grid connection module is used to set the annual surplus power grid connection load range and calculate the grid connection revenue in conjunction with spot electricity prices, long-term trading prices and electricity market transaction settlement rules.

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