Industrial park heat supply model and scale configuration method based on new energy green electric power direct connection

Abstract

The invention relates to the technical field of new energy heat supply, and provides an industrial park heat supply system based on new energy green electric power direct connection and a scale configuration method. Through the new energy power generation module, the electricity-heat conversion module and the heat energy storage module, conversion and storage of new energy are achieved, so that efficient and economical clean heat supply is supported, and the system has the advantages that direct connection heat supply of new energy green power can be achieved, the energy utilization efficiency is improved, and a basic framework is provided for scale configuration.

Description

Technical Field

[0001] This application relates to the field of new energy heating technology, specifically to an industrial park heating system based on direct connection of new energy green electricity, a method for large-scale configuration, and equipment for large-scale configuration. Background Technology

[0002] Against the backdrop of energy structure transformation, the demand for clean heating in industrial parks continues to grow, but the existing technological system has significant shortcomings. Currently, there is a lack of simulation models specifically for new energy heating systems using the green electricity direct connection model, which restricts the simulation and evaluation of this type of project. Here, green electricity direct connection mainly refers to the operation mode of new energy power generation systems (such as wind power and photovoltaics) supplying power to loads within the park through direct electrical connection, achieving local consumption, and determining the surplus electricity grid connection strategy according to the electricity market rules of different regions.

[0003] Existing general-purpose energy simulation software has not yet deeply integrated the collaborative operation mechanism of key units such as wind power, photovoltaics, electric heating systems, and thermal storage modules under the green electricity direct connection mode, making it difficult to accurately reflect the dynamic matching process of source and load. When determining the capacity of new energy power generation modules, electrothermal conversion modules, and thermal storage modules, traditional methods often rely on physical constraints such as land resources, fluctuations in new energy output, equipment utilization rates, and peak loads, failing to incorporate electricity market mechanisms into the optimization framework. Especially in green electricity direct connection projects, key price parameters such as transmission and distribution fees and system operating costs are not fully considered, resulting in an inability to accurately achieve the system operation goal of prioritizing clean heating and supplementing with surplus electricity grid connection. Furthermore, existing methods are insufficient in identifying the implicit heating gap formed by the coupling of electrothermal conversion power deviation and heat load temperature fluctuations, making it difficult for dispatch strategies to accurately respond to actual heat demand.

[0004] The aforementioned deficiencies often lead to system scale configurations deviating from optimal economic benefits, hindering the improvement of local renewable energy consumption and impeding the practical engineering application of integrated wind-solar-thermal-storage systems. It is particularly important to note that green electricity direct-connection projects should scientifically determine the type and installed capacity of renewable energy sources based on the principle of load-based source determination. In areas with continuously operating spot markets, projects should adopt a model primarily based on self-consumption with surplus electricity fed to the grid as a supplement; in areas without continuously operating spot markets, backfeeding to the public grid should not be permitted. The project's overall annual self-consumption of renewable energy should not be less than 60% of the total available power generation and not less than 30% of the total electricity consumption of the industrial park, and the self-consumption ratio should be gradually increased to ensure it is not less than 35% by 2030. The upper limit of the proportion of grid-connected electricity to total available power generation is generally no more than 20%, specifically determined by the provincial energy authorities based on actual conditions. Current technological systems have not effectively integrated the above operational requirements and proportional constraints, making it difficult to support the planning and design of green electricity direct-connection projects that conform to policy guidance.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this application is to provide an industrial park heating system based on direct connection of new energy green electricity, a method for large-scale configuration, and equipment for large-scale configuration, which can realize direct heating of new energy green electricity, improve energy utilization efficiency, and provide a basic framework for large-scale configuration.

[0007] In the first aspect, this application provides an industrial park heating system based on direct connection to new energy green electricity. The system includes at least a new energy power generation module, an electrothermal conversion module, and a thermal energy storage module; wherein: New energy power generation modules are used to convert new energy into electrical energy and connect it to the user-side power grid; The electrothermal conversion module is connected to the new energy power generation module to convert electrical energy into heat energy to meet heat load requirements; The thermal energy storage module is connected to the electrothermal conversion module and the heat load side to store or release thermal energy.

[0008] Furthermore, this application also proposes a method for the large-scale configuration of a heating system for an industrial park as described in the first aspect above, the method comprising: Acquire meteorological data, heat load demand data, and pricing mechanism parameters; the pricing mechanism parameters should at least include transmission and distribution fees and system operating costs. Set the initial capacity parameters for the new energy power generation module, electrothermal conversion module, and thermal energy storage module; Time series simulation is performed based on initial capacity parameters, meteorological data, heat load demand data, and price mechanism parameters. During the time series simulation, a scheduling strategy is executed. The scheduling strategy includes prioritizing the scheduling of the electricity to the electrothermal conversion module of the new energy power generation module to meet the heat load demand. When there is surplus electricity, the instantaneous grid connection revenue of the surplus electricity is calculated and compared with the expected future replacement value of storing it in the thermal energy storage module. The flow of the surplus electricity is determined based on the comparison results. The economic benefit index is derived from time series simulation, and the capacity parameters of the electrothermal conversion module and the thermal energy storage module are iteratively adjusted according to the economic benefit index until the economic benefit index meets the preset convergence condition.

[0009] Furthermore, this application also proposes calculating and comparing the immediate grid connection revenue of surplus electrical energy with the expected future replacement value of storing it in a thermal energy storage module, including: Obtain the current feed-in tariff, calculate the product of the remaining electricity and the feed-in tariff, and get the instantaneous feed-in revenue value; Based on meteorological data and heat load demand data, predict the electricity price trend in the future within a preset period and determine the alternative electricity price for the thermal energy storage module to release heat energy in the future to replace electric heating. The expected future substitution value is obtained by calculating the product of the remaining electrical energy, the electrothermal conversion efficiency of the electrothermal conversion module, and the substitution electricity price.

[0010] Furthermore, this application also proposes determining the flow of surplus electrical energy based on the comparison results, including: When the immediate grid connection benefit is greater than the expected future replacement value, a grid connection command is generated to control the transmission of surplus electrical energy to the user-side power grid. When the instant internet access revenue is less than or equal to the expected future replacement value, obtain the current thermal storage status of the thermal energy storage module. If the current heat storage state has not reached the preset heat storage limit, a heat storage command is generated to control the remaining electrical energy to be transmitted to the electrothermal conversion module to be converted into heat energy and stored in the heat energy storage module; if the current heat storage state has reached the preset heat storage limit, a grid connection command is generated to control the remaining electrical energy to be transmitted to the user-side power grid.

[0011] Furthermore, this application also proposes iteratively adjusting the capacity parameters of the electrothermal conversion module and the thermal energy storage module based on economic benefit indicators until the economic benefit indicators meet preset convergence conditions, including: Calculate the absolute value of the difference between the economic benefit index obtained in the current iteration and the economic benefit index obtained in the previous iteration; Determine if the absolute value of the difference is less than the preset convergence threshold; if the absolute value of the difference is greater than or equal to the convergence threshold, adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module according to the gradient of the change in economic benefit indicators, and enter the next round of time series simulation; if the absolute value of the difference is less than the convergence threshold, determine that the preset convergence condition is met, and output the current capacity parameters as the optimal configuration scheme.

[0012] Furthermore, this application also proposes that the economic benefit indicator is the system's net revenue; the steps for deriving the economic benefit indicator based on the time series simulation include: Within each time step of the time series simulation, calculate the electricity sales revenue, heating revenue, and national / local subsidies, and sum them up to obtain the net operating revenue for the whole year; Based on the initial capacity parameters and the preset unit capacity cost, calculate the initial investment cost of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; The system's net revenue is obtained by subtracting the initial investment cost, the preset annualized operation and maintenance cost, the cost of purchasing electricity from the public grid, the stable supply guarantee fee payable for grid-connected green electricity direct connection, operation and maintenance costs, and loan interest from the annual net operating revenue.

[0013] Furthermore, this application also proposes that, prior to executing a scheduling strategy during time series simulation, the method further includes a proactive identification and compensation step for implicit heating gaps; the proactive identification and compensation step for implicit heating gaps includes: The power deviation between the actual output power of the electrothermal conversion module and the command value is obtained, as well as the temperature deviation between the actual temperature and the set temperature of the key process points on the heat load side. Based on power deviation and temperature deviation, the implicit heating gap electricity is quantified; the implicit heating gap electricity is assigned an implicit gap value; the implicit gap value is set as a virtual price higher than the immediate internet access revenue and the expected future replacement value. The specific steps for prioritizing the dispatch of energy-to-heat conversion modules from renewable energy power generation modules to meet heat load demands include: Based on the value of the hidden heat gap, the electricity from the new energy power generation module is preferentially allocated to the electrothermal conversion module to meet the hidden heat supply gap; after the hidden heat supply gap is met, the electricity available for subsequent dispatch is then dispatched to meet the regular heat load demand.

[0014] Furthermore, this application also proposes to obtain the power deviation between the actual output power of the electrothermal conversion module and the commanded value, including: The actual output power is measured in real time by a high-precision sensor deployed at the output end of the electrothermal conversion module; The instantaneous difference between the actual output power and the control command value is calculated and continuously accumulated. When the accumulated difference reaches a preset threshold, a self-calibration process is initiated to generate a power correction amount, which is then fed back to the control logic to adjust subsequent command values, thereby eliminating power deviation.

[0015] Furthermore, this application also proposes to obtain the temperature deviation between the actual temperature and the set temperature of key process points on the heat load side, and quantify the hidden heating shortfall in electricity consumption, including: Receive real-time temperature feedback from key process points and cross-validate it by combining flow and temperature data from the thermal pipeline network; Based on the actual temperature feedback at key process points, identify the real heat load gap caused by inaccurate heat load demand forecast data; By integrating the actual heat load gap with the gap caused by power deviation, the amount of electricity required to fill the integrated gap is calculated, thus obtaining the hidden heating gap electricity.

[0016] Thirdly, this application also proposes a large-scale configuration device for the industrial park heating system described in the first aspect above, the device comprising: The acquisition module is used to acquire meteorological data, heat load demand data, and pricing mechanism parameters; the pricing mechanism parameters include at least transmission and distribution fees and system operating fees. The setting module is used to set the initial capacity parameters of the new energy power generation module, the electrothermal conversion module, and the thermal energy storage module; The scheduling module is used to perform time series simulation based on initial capacity parameters, meteorological data, heat load demand data, and price mechanism parameters. During the time series simulation, the module executes scheduling strategies. The scheduling strategies include prioritizing the scheduling of electricity from new energy power generation modules to electrothermal conversion modules to meet heat load demand. When there is surplus electricity, the module calculates and compares the immediate grid connection revenue of the surplus electricity with the expected future replacement value of storing it in the thermal energy storage module, and determines the flow of surplus electricity based on the comparison results. The adjustment module is used to derive economic benefit indicators from time series simulation, and iteratively adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module based on the economic benefit indicators until the economic benefit indicators meet the preset convergence conditions.

[0017] As can be seen from the above, the industrial park heating system and its large-scale configuration method based on direct connection of new energy green electricity provided in this application realize the conversion and storage of new energy by including new energy power generation modules, electrothermal conversion modules and thermal energy storage modules, thereby supporting efficient and economical clean heating. It has the advantages of realizing direct connection heating of new energy green electricity, improving energy utilization efficiency, and providing a basic framework for large-scale configuration. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the operation simulation model of an industrial park heating system based on direct connection of new energy green electricity, as disclosed in an embodiment of the present invention. Figure 2 This is a flowchart of the steps of the method for large-scale configuration of the industrial park heating system disclosed in the embodiments of the present invention, applied to the first aspect. Figure 3 This is a schematic diagram of the large-scale configuration equipment structure of the industrial park heating system disclosed in the first aspect of the present invention. Detailed Implementation

[0019] The implementation details of the technical solution in this embodiment are described in detail below: Traditional industrial park heating systems lack the capability to construct heating models based on direct connections to renewable green electricity, hindering the ability to simulate operation and optimize scale configuration for 8760 hours annually. Current technologies primarily rely on parameters such as land resource constraints, renewable energy output characteristics, renewable energy utilization rates, and peak capacity during peak electricity load periods when configuring renewable energy power generation modules, electrothermal conversion modules, and thermal energy storage modules. However, they fail to consider factors such as the volatility of renewable energy market-based electricity prices, the stable supply guarantee fees under the green electricity direct connection model, and the price lock-in mechanism for heating revenue with users. This leads to deviations from optimal scale configuration schemes, consequently affecting key performance indicators such as system operational economy and the level of local renewable energy consumption.

[0020] For example, in a real-world application scenario in an industrial park, wind farms and photovoltaic arrays serve as new energy sources, providing heat energy to the heat load side through electric heating systems, and spherical tank thermal storage systems are configured to balance supply and demand fluctuations. During the large-scale configuration phase, the equipment capacity is determined solely based on historical meteorological data and heat load demand curves, without considering price mechanism parameters such as electricity market transaction prices, stable supply guarantee fees payable under the green electricity direct connection model, and heating revenue. When the output of the new energy power generation module exceeds the heat load demand, the surplus electricity is directly transmitted to the public grid via the user-side grid, without performing a comparative analysis of the immediate grid connection revenue and the expected future substitution value of storing the thermal energy in the thermal storage module. This results in a lack of economic optimization basis for the electricity flow decision, and a significant reduction in system operating efficiency.

[0021] If the above problems are not solved, the capacity for renewable energy absorption will be continuously constrained, the economic efficiency of system operation will further deteriorate, and the functional needs of industrial parks, which are mainly focused on clean heating, will not be stably met. At the same time, the revenue from the grid connection of surplus renewable energy power will not be maximized, thereby hindering the feasibility and large-scale promotion of integrated wind, solar, thermal and energy storage projects.

[0022] In response, this application proposes an industrial park heating system based on direct connection to new energy green electricity. This system includes at least a new energy power generation module, an electrothermal conversion module, and a thermal energy storage module; wherein: This new energy power generation module is used to convert new energy into electrical energy and connect it to the user-side power grid; The electrothermal conversion module is connected to the new energy power generation module and is used to convert electrical energy into heat energy to meet heat load requirements. The thermal energy storage module is connected to the electrothermal conversion module and the heat load side, and is used to store or release thermal energy.

[0023] For ease of understanding, the following explains some key terms in this embodiment: Direct green power connection from new energy sources refers to a power supply model that directly converts new energy sources such as wind and solar energy into electricity and supplies it directly to users within industrial parks to meet their electricity and heat needs. This model aims to improve the local consumption of green energy and reduce dependence on traditional fossil fuels. Industrial park heating systems are comprehensive systems that provide the necessary heat energy for production processes and buildings within industrial parks. These systems typically include heat sources, heat distribution networks, and end-user heating equipment, with the goal of ensuring a stable, reliable, and economical heat supply. New energy power generation modules are collections of equipment responsible for converting new energy sources (such as solar and wind energy) into electricity. These modules typically consist of photovoltaic arrays, wind turbine generators, and corresponding inverters and controllers, and their output electricity can be directly supplied to the park or connected to the power grid. Electrothermal conversion modules are devices that convert electrical energy into heat energy. These modules can take the form of electric boilers or electric heaters, and their main function is to efficiently convert electrical energy from new energy power generation modules or the power grid into heat energy to meet the heat load requirements of industrial parks. A thermal energy storage module refers to equipment used to store and release thermal energy. This module can take the form of a thermal storage tank, a phase change thermal storage device, etc. Its function is to store excess thermal energy when electricity is abundant or electricity prices are low, and release the thermal energy during peak heat load periods or when electricity prices are high, thereby achieving peak shaving and valley filling and optimized utilization of thermal energy. The user-side power grid refers to the power network within the industrial park, responsible for receiving electrical energy from renewable energy generation modules or the external power grid and distributing it to various electrical devices and electrothermal conversion modules within the park. Heat load demand refers to the total heat required for production processes, building heating, or domestic hot water within the industrial park. This demand typically fluctuates with time, season, and production plans, and is an important basis for the design and operation of the heating system.

[0024] The new energy power generation module is configured to convert new energy into electricity and connect the generated electricity to the user-side power grid. As one implementation, this module can consist of a series of photovoltaic panels that convert solar energy into direct current (DC), which is then converted into alternating current (AC) by an inverter. Alternatively, the module can be composed of wind turbine generators, using wind power to drive the generators and generate electricity. The generated electricity can be directly supplied to the industrial park or, when there is surplus, transmitted to the external power grid via the user-side grid. The electrothermal conversion module is connected to the new energy power generation module and its function is to convert electrical energy into heat energy to meet the industrial park's heat load requirements. Specifically, this module can be a large electric boiler that converts electrical energy into hot water or steam through resistance heating, and then transmits it to various heat-consuming areas through a heat pipe network. As another implementation, this module can consist of multiple distributed electric heaters installed directly near the heat-consuming equipment, performing electrothermal conversion according to local heat load requirements. The source of electricity can be direct power from the new energy power generation module or electricity from the user-side power grid; in cases where the supply of new energy power is insufficient, the public grid serves as an emergency supplement. The thermal energy storage module is connected to the electrothermal conversion module and the heat load side to store or release thermal energy. This module can be a large hot water storage tank. After the electrothermal conversion module converts electrical energy into heat energy, a portion of the heat energy is stored in the hot water storage tank. When heat load demand increases or renewable energy generation is insufficient, the heat energy in the storage tank can be released and transported to the heat load side through the heating network. For example, at night or on rainy days, renewable energy generation may be low; in such cases, the thermal energy storage module can release the heat energy stored during the day to maintain the continuity of heating. Alternatively, the module can employ phase change thermal energy storage materials, absorbing and releasing heat energy through the material's phase change process.

[0025] The following example will provide a more detailed explanation of the above technical solution: Suppose that an industrial park A has a continuous heat load demand, such as steam supply for production processes and winter heating for factory buildings. The park aims to utilize green energy to meet its heat load demand and reduce its reliance on traditional coal-fired boilers. Therefore, park A has introduced the industrial park heating system based on direct connection to new energy green electricity, as described in this embodiment.

[0026] like Figure 1The diagram shows a simulation model of an industrial park heating system based on direct connection to new energy green electricity, as described in this embodiment. Specifically, the system first deploys a new energy power generation module. This module consists of a photovoltaic array and several small wind turbines located on an open area within the park. Under conditions of ample sunlight and wind during the day, the photovoltaic array and wind turbines convert solar and wind energy into electrical energy. This electrical energy is transmitted through the user-side power grid within the park. When there is a demand for heat load within the park, some of the electricity generated by the new energy power generation module is sent to an electrothermal conversion module. This electrothermal conversion module is a high-efficiency electric boiler that rapidly converts the received electrical energy into high-temperature hot water. This high-temperature hot water is then transported through the park's heating network to various heat-consuming areas, such as heat exchangers in production workshops or radiators in office buildings, thereby meeting the park's heat load requirements.

[0027] To address the volatility of renewable energy generation and changes in heat load demand, the system is also equipped with a thermal energy storage module. This module is a large hot water storage tank. During periods when renewable energy generation is sufficient and heat load demand is relatively low (e.g., midday), the electrothermal conversion module converts excess electrical energy into heat energy and stores it in the hot water storage tank. When renewable energy generation decreases at night or on cloudy days, or when heat load demand increases during morning and evening peak hours, the heat energy in the storage tank is released and used to supplement heating through the heating network, ensuring a continuous and stable supply of heat load.

[0028] Furthermore, when the electricity generated by the new energy power generation module exceeds the park's internal power and heat demand, and the thermal energy storage module has reached its heat storage limit, the remaining green electricity can be connected to the external public power grid through the user-side grid. Thus, the system not only meets the park's own green heating needs but also achieves effective utilization of green electricity and improves economic benefits. In this way, the new energy power generation module, the electrothermal conversion module, and the thermal energy storage module work closely together to construct an efficient, flexible, and environmentally friendly industrial park heating solution, effectively solving the challenges of green heating and energy utilization in industrial parks.

[0029] Based on the above examples, the industrial park heating system provided in this embodiment demonstrates significant technological contributions. In the prior art, industrial park heating systems often rely on traditional fossil fuel boilers, or when introducing new energy sources, the coupling between electricity and heat is not high, and there is a lack of effective thermal energy storage mechanisms to mitigate the volatility of new energy sources.

[0030] Overall, the system in this embodiment provides a novel green heating solution for industrial parks. It not only meets the clean heating needs of industrial parks but also optimizes energy utilization efficiency, reduces operating costs, and improves the absorption of renewable energy through direct green electricity connection and thermal energy storage. This integrated design and operation mode provides a solid foundation for solving the problem of the lack of effective models and optimal configurations in existing green heating systems.

[0031] Secondly, this application proposes a method for the large-scale configuration of heating systems in industrial parks as described in the first aspect above, such as... Figure 2 As shown, the method includes: S201, acquire meteorological data, heat load demand data, and pricing mechanism parameters; the pricing mechanism parameters include at least transmission and distribution fees and system operating fees. S202, set the initial capacity parameters of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; S203, perform time series simulation based on the initial capacity parameters, meteorological data, heat load demand data, and price mechanism parameters, and execute a scheduling strategy during the time series simulation; the scheduling strategy includes prioritizing the scheduling of electrical energy from the new energy power generation module to the electrothermal conversion module to meet the heat load demand; when there is surplus electrical energy, calculate and compare the immediate grid connection revenue of the surplus electrical energy with the expected future replacement value stored in the thermal energy storage module, and determine the flow of the surplus electrical energy based on the comparison result; S204. Based on the time series simulation, the economic benefit index is obtained, and the capacity parameters of the electrothermal conversion module and the thermal energy storage module are iteratively adjusted according to the economic benefit index until the economic benefit index meets the preset convergence condition.

[0032] In the above methods, obtaining meteorological data, heat load demand data, and pricing mechanism parameters is fundamental for system-scale configuration and simulation analysis. Meteorological data affects the output of new energy power generation modules (e.g., solar irradiance, wind speed) and heat load demand (e.g., ambient temperature), and can be obtained through real-time data collection from weather stations, historical meteorological database queries, or third-party meteorological service interfaces. Heat load demand data is a core objective that the system needs to meet, and can be obtained through historical production data from industrial parks, process flow analysis, building energy consumption model prediction, or real-time sensor monitoring. Pricing mechanism parameters directly affect the calculation of the system's economic benefits and can be obtained through local electricity market trading rules, transmission and distribution price lists published by power grid companies, government subsidy policy documents, or system operation and maintenance contracts.

[0033] Setting the initial capacity parameters for the new energy power generation module, electrothermal conversion module, and thermal energy storage module is the starting point of the optimization process, providing an initial system configuration scheme for subsequent time series simulations. The setting of the initial capacity parameters affects the computational load and convergence speed of the simulation. This setting can be based on empirical values, historical project data, preliminary estimates, or industry standards. It can also be set through random generation, uniform distribution, or based on a heuristic algorithm to cover a wider search space.

[0034] Time-series simulation based on the initial capacity parameters, meteorological data, heat load demand data, and price mechanism parameters is a dynamic simulation method used to simulate the behavior and performance of a system under different operating conditions over a certain time span (e.g., one year, in hourly or minute increments) to evaluate its economic and technical feasibility. This simulation can be implemented by establishing a mathematical model and programming it using professional simulation software (such as MATLAB / Simulink, Python's PandaPower, EnergyPlus, etc.), or by constructing a rule-based or optimization algorithm-based simulation engine, combining historical or forecast data to calculate the system's power generation, power consumption, heat supply, heat storage, purchased and sold electricity, and related costs for each time period.

[0035] The core of the optimization process is to derive economic benefit indicators from the time-series simulation and iteratively adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module based on these indicators until the economic benefit indicators meet a preset convergence condition. This iterative adjustment of capacity parameters gradually approaches the optimal system configuration. Economic benefit indicators can include system net revenue, return on investment, cost per kilowatt-hour, annual operating cost, etc., calculated from simulation results. Iterative adjustments can employ optimization algorithms (such as gradient descent, genetic algorithms, particle swarm optimization, simulated annealing, etc.) to guide the direction and step size of capacity parameter adjustments. The convergence condition can be set as follows: the change in the economic benefit indicator is less than a certain threshold in several consecutive iterations, or a preset maximum number of iterations is reached, or the change in the capacity parameters is less than a certain threshold.

[0036] The following is a concrete example to illustrate this. First, a data acquisition system can be deployed within an industrial park to acquire real-time local meteorological data (such as hourly solar radiation intensity and ambient temperature obtained via a weather station API), heat load demand data (such as real-time heat consumption of each production line obtained via a connection to the park's SCADA system), and pricing mechanism parameters (such as real-time electricity prices obtained from an electricity trading platform and transmission and distribution fees and system operating costs read from a local database). In the initial capacity parameter setting phase, based on the park's preliminary plan, the capacity of the new energy power generation modules (such as photovoltaic power plants) can be set to 5 MW, the capacity of the electrothermal conversion modules (such as electric boilers) to 10 MW, and the capacity of the thermal energy storage modules (such as large hot water tanks) to 50 MWh. Subsequently, a Python-based simulation program will perform a time-series simulation, simulating the system's hourly operation over a year. Within each simulation time step, the scheduling strategy will prioritize the delivery of electricity generated by the photovoltaic power plant to the electric boiler to meet the current heat load demand. If the photovoltaic power generation exceeds the current heat load requirement, the system calculates the immediate grid connection revenue of the surplus electricity (e.g., if the current electricity price is 0.3 yuan / kWh, the revenue is 0.3 yuan / kWh), and calculates the expected future substitution value of converting it into heat energy and storing it in a hot water storage tank (e.g., considering the electrothermal conversion efficiency and the potential avoidance of high electricity purchase costs, the expected future substitution value is 0.7 yuan / kWh). If the expected future substitution value is higher, the surplus electricity will be sent to the electric boiler to be converted into heat energy and stored in the hot water storage tank; otherwise, it will be sold to the grid. After completing a year of simulation, the system calculates the economic benefit indicators under this configuration scheme, such as the annual net income. Then, an optimization algorithm (such as a genetic algorithm) will adjust the capacity parameters of the electric boiler and the hot water storage tank according to the changing trend of the annual net income. For example, if it is found that increasing the capacity of the hot water storage tank can significantly improve the net income, its capacity will be increased accordingly. This process will iterate repeatedly until the change in annual net income in several consecutive iterations is less than the preset convergence threshold (e.g., 0.1%). At this point, the current capacity parameter is output as the optimal configuration scheme.

[0037] Furthermore, this application proposes to calculate and compare the immediate grid connection revenue of the remaining electrical energy with the expected future substitution value stored in the thermal energy storage module, including: obtaining the current grid connection electricity price, calculating the product of the remaining electrical energy and the grid connection electricity price to obtain the immediate grid connection revenue; predicting the electricity price trend within a preset future period based on the meteorological data and heat load demand data, and determining the substitution electricity price when the thermal energy storage module releases heat energy to replace electric heating operation in the future; and calculating the product of the remaining electrical energy, the electrothermal conversion efficiency of the electrothermal conversion module, and the substitution electricity price to obtain the expected future substitution value.

[0038] The following is a concrete example to illustrate this. Assume that during the time-series simulation, the renewable energy generation module generates 100 kWh of surplus electricity. First, the system connects to the electricity market data interface to obtain the current real-time grid-connected electricity price, which is 0.45 yuan / kWh. Based on this, the immediate grid-connected revenue is calculated as 100 kWh multiplied by 0.45 yuan / kWh, or 45 yuan. Simultaneously, the system utilizes its built-in prediction module, which comprehensively analyzes current and future preset-period weather forecasts (e.g., a significant drop in temperature and weakening solar radiation in the next few hours) and industrial park heat load demand forecasts (e.g., a production line is about to start, requiring a large amount of heat energy), to predict that the electricity market price will reach its peak at a certain future time, for example, 1.1 yuan / kWh. This 1.1 yuan / kWh is determined as the replacement electricity price for the thermal energy storage module when it releases heat energy to replace electric heating. Assume the electrothermal conversion efficiency of the electrothermal conversion module is 98%. Therefore, the expected future substitution value is calculated as 100 kWh multiplied by 98% and then by 1.1 yuan / kWh, which equals 107.8 yuan. In this way, the system can clearly compare the 45 yuan revenue from immediate electricity sales with the 107.8 yuan value of future thermal storage substitution, thus providing a clear quantitative basis for subsequent dispatch decisions.

[0039] This application further proposes a step for determining the flow of surplus electrical energy based on the comparison results, specifically including: when the immediate grid connection benefit is greater than the expected future replacement value, generating a grid connection instruction to control the transmission of surplus electrical energy to the user-side power grid; when the immediate grid connection benefit is less than or equal to the expected future replacement value, obtaining the current thermal storage state of the thermal energy storage module; if the current thermal storage state has not reached the preset thermal storage limit, generating a thermal storage instruction to control the transmission of surplus electrical energy to the electrothermal conversion module for conversion into thermal energy and storage in the thermal energy storage module; if the current thermal storage state has reached the preset thermal storage limit, generating a grid connection instruction to control the transmission of surplus electrical energy to the user-side power grid.

[0040] In this system, when the immediate grid connection benefit exceeds the expected future replacement value, a grid connection command is generated to control the transmission of surplus electricity to the user-side grid. This aims to ensure that when the economic benefits of directly connecting surplus electricity to the grid are higher than the expected future benefits of converting it into thermal energy storage, the system prioritizes selling the electricity to the grid. This can be executed by a smart controller or energy management system. The controller receives the economic evaluation results and, when it determines that the grid connection benefit is higher, sends a command to the inverter or grid connection switch to directly connect the surplus electricity generated by the renewable energy generation module to the user-side grid. Another implementation method is to use a rule-based expert system with a set of preset decision logic. Once the condition of "the immediate grid connection benefit exceeding the expected future replacement value" is met, the system automatically triggers the grid connection operation and sends the command to the grid interface device via the communication interface. When the immediate grid connection benefit is less than or equal to the expected future replacement value, the current thermal energy storage module's thermal storage status is obtained. This is to further evaluate the feasibility of thermal storage when the economic benefits of thermal storage are better, and to avoid ineffective thermal storage operations. This can be achieved by deploying temperature and level sensors within the thermal energy storage module to monitor the temperature and volume of its internal medium in real time, thereby calculating the current heat storage capacity. Alternatively, the control unit of the thermal energy storage module can periodically report its current heat storage percentage or remaining capacity to the central energy management system. This information can be estimated based on internal sensor data or a preset heat capacity model. If the current heat storage status has not reached the preset heat storage limit, a heat storage command is generated to control the transfer of remaining electrical energy to the electrothermal conversion module, converting it into heat energy and storing it in the thermal energy storage module. This ensures that the remaining electrical energy can be effectively utilized and converted into heat energy for future use. When the energy management system receives the current heat storage status information of the thermal energy storage module and confirms that it has not reached the limit, the system sends a start command to the electrothermal conversion module and adjusts its power to convert the remaining electrical energy from the new energy power generation module into heat energy. Simultaneously, the system coordinates the valves or pumps of the thermal energy storage module to ensure that the heat energy is stored smoothly. If the current thermal storage capacity has reached the preset upper limit, a grid connection command is generated to control the transmission of remaining electrical energy to the user-side grid. This addresses the special case where thermal storage is economically advantageous but physical conditions do not permit it. In this situation, the system will revert to a grid connection strategy to avoid energy waste and maximize economic benefits. When the energy management system determines that the thermal energy storage module is fully loaded, even if the previous economic assessment favored thermal storage, the system will immediately change its decision and generate a grid connection command. This command will be sent to the inverter or grid-connected equipment via the communication network to integrate the remaining electrical energy into the user-side grid.

[0041] The proposed solution compares the immediate grid connection benefit with the expected future substitution value when surplus electricity is generated by the renewable energy generation module. If the immediate grid connection benefit is higher, the electricity is directly transmitted to the user-side grid. If the expected future substitution value is higher, the current thermal energy storage status of the thermal energy storage module is further assessed. Only when the thermal energy storage module still has available capacity and thermal storage is more economically viable, is the surplus electricity transmitted to the electrothermal conversion module to be converted into thermal energy and stored in the thermal energy storage module. If the thermal energy storage module has reached its thermal storage limit, even if thermal storage is slightly economically advantageous, the system will intelligently choose to transmit the electricity to the user-side grid. This decision-making mechanism combines economic benefit assessment with physical storage capacity limitations, forming a more comprehensive and practical scheduling strategy, avoiding ineffective thermal storage or electricity waste caused by blindly pursuing economic benefits. In this way, the system can utilize renewable energy more flexibly and efficiently, ensuring optimal power flow under different operating conditions, thereby improving the operating efficiency and economy of the entire industrial park's heating system.

[0042] The following is a concrete example to illustrate this. Suppose that at a certain time, the renewable energy generation module generates surplus electricity exceeding the current heat load demand. The system first calculates that the immediate revenue from feeding this electricity into the grid is 0.3 yuan / kWh, while the expected value of converting it into thermal energy for storage and future release to replace electric heating is 0.4 yuan / kWh. Since the expected future replacement value is higher than the immediate grid connection revenue, the system tends to prioritize thermal energy storage. At this point, the system queries the current thermal energy storage module's storage status. If the thermal energy storage module's storage capacity is 70% of its total capacity, not yet reaching the preset 95% storage limit, the system generates a thermal energy storage command to transfer the surplus electricity to the electrothermal conversion module, converting it into thermal energy and storing it in the thermal energy storage module. Conversely, if the thermal energy storage module's storage capacity has reached 98%, i.e., reached the preset storage limit, then even if thermal energy storage is slightly more economical, the system will still generate a grid connection command to transmit the surplus electricity to the user-side grid to avoid energy waste due to insufficient storage space.

[0043] Furthermore, this application proposes a specific mechanism for iteratively adjusting the capacity parameters of the electrothermal conversion module and the thermal energy storage module until the economic benefit index meets a preset convergence condition. This mechanism includes calculating the absolute value of the difference between the economic benefit index obtained in the current iteration and the economic benefit index obtained in the previous iteration; determining whether the absolute value of the difference is less than a preset convergence threshold; if the absolute value of the difference is greater than or equal to the convergence threshold, adjusting the capacity parameters of the electrothermal conversion module and the thermal energy storage module according to the gradient of the change in the economic benefit index, and entering the next round of time series simulation; if the absolute value of the difference is less than the convergence threshold, determining that the preset convergence condition is met, and outputting the current capacity parameters as the optimal configuration scheme.

[0044] The following example illustrates this. Suppose that in a certain iteration, the system calculates an economic benefit indicator (e.g., annual net income) of 10 million yuan. In the next iteration, after adjusting the capacity parameters and conducting time series simulation, the new economic benefit indicator becomes 10.05 million yuan. At this point, the absolute value of the difference between the economic benefit indicators calculated in the two iterations is 50,000 yuan. If the preset convergence threshold is 10,000 yuan, since 50,000 yuan is greater than 10,000 yuan, the system will determine that convergence has not yet occurred. Next, the system will analyze how the improvement in economic benefit from 10 million yuan to 10.05 million yuan is related to the changes in the capacity parameters of the electrothermal conversion module and the thermal energy storage module, thereby calculating the gradient of the economic benefit indicator. For example, it may be found that slightly increasing the capacity of the electrothermal conversion module contributes the most to the improvement in economic benefit. Based on this gradient information, the system will further adjust the capacity parameters of these two modules and start a new round of time series simulation. This process continues until, in a certain iteration, for example, the economic benefit indicator changes from 10.12 million yuan to 10.125 million yuan. At this point, the absolute value of the difference is 0.5 million yuan, which is less than the preset convergence threshold of 1 million yuan. The system will then determine that the convergence condition has been met and output the capacity parameters of the current electrothermal conversion module and thermal energy storage module as the final optimal configuration scheme.

[0045] Through the above technical solution, this application provides a clear, efficient, and reliable iterative optimization mechanism, solving the technical problems of how to determine convergence and how to effectively adjust capacity parameters in the scalable configuration method. This solution ensures that the optimization process automatically terminates after reaching a preset accuracy, avoiding unnecessary consumption of computational resources, and guides the capacity parameters to converge rapidly towards the optimal configuration, thereby significantly improving the efficiency and accuracy of scalable configuration of industrial park heating systems.

[0046] In this embodiment, the economic benefit indicator is further proposed to be the system net revenue; the step of deriving the economic benefit indicator based on the time series simulation includes: Within each time step of the time series simulation, calculate the electricity sales revenue, heating revenue, and national / local subsidies, and sum them up to obtain the net operating revenue for the whole year; Based on the initial capacity parameters and the preset unit capacity cost, calculate the initial investment cost of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; The system's net revenue is obtained by subtracting the initial investment cost, the preset annualized operation and maintenance cost, the cost of purchasing electricity from the public grid, the stable supply guarantee fee payable for grid-connected green electricity direct connection, operation and maintenance costs, and loan interest from the annual net operating revenue.

[0047] Specifically, performing full-time simulation and settling operational revenue and expenditure refers to progressively advancing the simulation process within a complete simulation year at a preset time resolution (e.g., 15 minutes or 1 hour). Within each step, based on the system operating status determined by the scheduling strategy (e.g., renewable energy generation capacity, grid-connected electricity, thermal storage status, etc.) and external market environment parameters (e.g., real-time electricity prices, subsidy policies), all revenue and costs for that period are meticulously calculated. The settlement of operational revenue and expenditure for this step is primarily based on the policies and market rules under the green electricity direct connection model. This mainly includes: Electricity sales revenue: Not all surplus electricity can be sold. First, based on the principle of "source determined by load" and project access policies (such as a self-consumption ratio of no less than 60% and a grid connection ratio generally not exceeding 20%), the allowed grid connection electricity limit must be determined. During settlement, the actual grid connection electricity volume determined within this step is multiplied by the current grid connection price, ensuring that the cumulative grid connection electricity volume does not exceed the policy ceiling for the total annual power generation. Any excess is recorded as zero revenue in the model, thus reflecting policy constraints.

[0048] Heating revenue: Settlement is based on the total amount of heat energy actually supplied to the industrial park by the system within this phase (including direct heating and heat storage release), combined with the heat price stipulated in the heating contract. This portion of revenue reflects the system's ability to meet the core needs of clean heating.

[0049] National / Local Subsidies: Based on the electricity generated and consumed by the new energy power generation modules within the project's scope, as well as potential low-carbon behaviors such as thermal storage and electric heating to replace traditional heating, the financial incentives that can be obtained are calculated according to the subsidy standards stipulated in policy documents. This benefit item is directly related to the realization of the project's green value.

[0050] Public grid electricity purchase cost: When the output of new energy sources and thermal storage cannot meet the full electricity and heat load, the cost of purchasing electricity from the public grid is calculated based on the amount of electricity purchased and the current market electricity purchase price.

[0051] Stable supply guarantee costs, etc.: As a grid-connected green electricity direct connection project, the system standby and ancillary service fees that need to be paid according to relevant regulations are included in the cost based on the capacity or electricity billing model.

[0052] The following numerical case illustrates the calculation process of the economic benefit indicator (system net revenue) in this scale configuration method. First, a green electricity direct-connection heating system is planned for an industrial park. In the first iteration, the initial capacity parameters are set as follows: photovoltaic power generation module installed capacity P_pv is 10 MW, electrothermal conversion module (electrode boiler) rated power P_eh is 8 MW, and thermal energy storage module (atmospheric pressure hot water tank) effective capacity E_tes is 40 MWh. The simulation calculation is performed on a one-year cycle with a time step of 1 hour. Within each hourly step, the system executes scheduling strategies and performs financial settlements based on real-time data.

[0053] Let's take a typical daytime period as an example. Assume that between 12:00 PM and 1:00 PM on a certain day, meteorological data indicates an actual photovoltaic output of 8.5 MW, a park heat load demand of 6 MW (thermal power), and a current real-time grid-connected electricity price of 0.35 yuan per kilowatt-hour. According to forecasts, the grid purchase price (i.e., the substitution price) during the peak nighttime heat load period will be 0.65 yuan per kilowatt-hour, and the electrothermal conversion efficiency η of the electrode boiler is 98%. The dispatching process is as follows: The system first allocates the electricity corresponding to the 6 MW of thermal power to the electrode boiler, consuming approximately 6.12 MW of electricity to meet the immediate heat demand. At this time, the remaining available photovoltaic power is 2.38 MW. Next, the system needs to decide where this remaining electricity should go: immediately connect to the grid or store it as heat energy for later use. The decision is based on an economic value comparison. The benefit of immediately connecting to the grid is 2.38 MW of electricity multiplied by the electricity price of 0.35 yuan, approximately 833 yuan. If used for thermal energy storage, considering the electrothermal conversion efficiency, the stored thermal energy can replace the more valuable electricity purchased from the grid when released in the future. Its expected substitution value is 2.38 MWh multiplied by 98% efficiency and a substitution price of 0.65 yuan, totaling approximately 1516 yuan. Since the expected value of thermal energy storage is higher than the immediate grid connection revenue, and assuming the storage tank is not full, the system instructs to convert this 2.38 MWh of electrical energy into thermal energy and store it in the thermal energy storage module. Therefore, the revenue from selling electricity during this period is zero, but future heating capacity is accumulated.

[0054] Let's take another nighttime period as an example. Assume that from 8 PM to 9 PM on the same night, photovoltaic output is zero, the heat load demand is 7 MW, and the current real-time electricity purchase price is 0.70 yuan per kilowatt-hour. The dispatch strategy at this time is to prioritize the use of energy storage. Assume the system releases 5 MW of heat energy from the thermal storage module to meet part of the load. The remaining 2 MW of heat load is provided by the electrode boiler, which requires approximately 2.04 MW of electricity. Since there is no photovoltaic power generation, this part of the electricity must be purchased entirely from the public grid, incurring an electricity purchase cost of approximately 1428 yuan. The heating revenue for this period is calculated based on the heating contract price, assuming it is 100 yuan per kilowatt-hour, then the revenue from 7 MW of heat energy is 700 yuan.

[0055] Following the above rules, simulations and financial settlements are performed for each of the 8760 hours throughout the year. By summing up the electricity sales revenue, heating revenue, and various subsidies for all time steps, and then subtracting all electricity purchase costs, system operating expenses, etc., the simulated "annual net operating revenue" under this configuration scheme can be obtained. Assume that the annual net operating revenue R_op calculated in this simulation is 5.2 million yuan.

[0056] Next, we calculate the initial investment cost. Assuming the unit cost of a photovoltaic module is 3.5 yuan per watt, the investment for 10 MW would be 35 million yuan. The unit cost of an electrothermal conversion module is 0.8 yuan per watt, so the investment for 8 MW would be 6.4 million yuan. The unit cost of a thermal energy storage module is 0.3 yuan per watt-hour, so the investment for 40 MWh would be 12 million yuan. The total initial investment cost, C_inv, is 53.4 million yuan. To assess the annual economic benefits, the initial investment cost needs to be amortized. Assuming a project lifespan of 20 years, using the straight-line depreciation method, the annualized initial investment is 2.67 million yuan. Assuming the annualized operation and maintenance cost is 2% of the initial investment, i.e., 1.068 million yuan, we also consider annual financial costs (such as loan interest) of 800,000 yuan.

[0057] Ultimately, the "annual system net profit" N of this configuration scheme can be calculated using the formula: N = Annual operating net profit - Annualized initial investment - Annual operation and maintenance cost - Financial cost. Substituting the values, N = 520 - 267 - 106.8 - 80 = 662,000 yuan. This result of 662,000 yuan is the key economic benefit indicator for the current iteration. Based on this, the optimization algorithm will automatically adjust the capacity parameters of the electrothermal conversion and thermal storage modules, repeatedly perform simulations and net profit calculations until the net profit indicator tends to be maximized and stabilized, and finally output the economically optimal scale configuration scheme.

[0058] Furthermore, the method also includes: formulating capacity schemes for M groups of new energy power generation modules based on new energy resource endowment, land conditions, and policy requirements for source-load matching of grid-connected green electricity direct connection projects, where M is a positive integer; for each group of the new energy power generation module capacity schemes, the following steps are performed respectively: setting initial capacity parameters for the electrothermal conversion module and thermal energy storage module under the capacity scheme; performing time series simulation based on the initial capacity parameters, the meteorological data, the heat load demand data, and the price mechanism parameters, and executing the scheduling strategy during the time series simulation; obtaining economic benefit indicators based on the time series simulation, and iteratively adjusting the capacity parameters of the electrothermal conversion module and thermal energy storage module based on the economic benefit indicators until the economic benefit indicators meet the preset convergence conditions, thereby obtaining the optimal electrothermal conversion module capacity and thermal energy storage module capacity corresponding to the capacity scheme; after obtaining the optimal electrothermal conversion module capacity and thermal energy storage module capacity corresponding to all M group capacity schemes, comprehensively comparing the economic benefit indicators, new energy heating ratio, and new energy utilization rate of each scheme, and selecting the final project scale configuration scheme.

[0059] Specifically, in this embodiment, proposing a capacity scheme for M groups of new energy power generation modules means, before conducting detailed economic optimization, first planning multiple candidate schemes for the installation capacity of new energy sources (such as photovoltaics and wind power) based on the objective constraints and policy guidance of the project. This usually requires a comprehensive design that combines on-site surveys, resource assessment reports (such as annual irradiance and average wind speed), available land area, investment budget limits, and the source-load matching principles required by local energy management departments for "grid-connected green electricity direct connection" projects (i.e., encouraging self-consumption and restricting large-scale backfeeding). For example, for an industrial park, based on different investment intensities and land use planning, M=3 schemes may be proposed: Scheme A (10MW photovoltaic), Scheme B (15MW photovoltaic), and Scheme C (20MW photovoltaic). The purpose of this step is to ensure that subsequent refined optimization is carried out within a set of feasible macro-level options that comply with the policy framework.

[0060] The core of this method is to perform time-series simulations with iterative optimization for each new energy capacity scheme. Taking Scheme A (10MW photovoltaic) as an example, an initial capacity estimate needs to be set for the associated electrothermal conversion module (such as an electric boiler) and thermal energy storage module (such as a hot water storage tank). For example, the initial electric heating power is set to 8MW, and the initial thermal storage capacity is set to 100MWh. Subsequently, this set of initial capacity parameters, along with 8760 hours of meteorological data throughout the year (determining photovoltaic output), heat load demand data (determining heat demand), and price mechanism parameters (such as time-of-use pricing, grid connection pricing, and transmission and distribution fees), are input into the simulation model. The model simulates the entire year's operation hourly with a 1-hour step size: priority is given to using photovoltaic power for electric heating to meet the immediate heat load, and the remaining power is allocated according to the dispatch strategy (comparing the immediate grid connection revenue with the future value of thermal storage); when photovoltaic power is insufficient, thermal storage or electricity purchased from the public grid is used to supplement it. After completing one year-long simulation, an economic benefit indicator such as the system's net revenue is calculated. Next, based on this revenue indicator, optimization algorithms such as gradient descent are used to automatically adjust the parameters of the electric heating power and thermal storage capacity (for example, adjusting the electric heating power to 8.5MW and the thermal storage capacity to 120MWh), and a full-year simulation is performed again to calculate the new revenue. This process is repeated until the revenue difference between two adjacent iterations is less than a preset minimum value (e.g., 1000 yuan), at which point convergence is considered achieved. The electric heating power (e.g., 9MW) and thermal storage capacity (e.g., 150MWh) obtained at this point represent the optimal configuration for Scheme A. Repeating this complete process for Schemes B and C yields three sets of optimal coupling configurations for different photovoltaic scales.

[0061] In practical applications, after obtaining the optimal configuration for all M groups of schemes, the final scheme is selected by comprehensively comparing the economic benefit indicators, renewable energy heating ratio, and renewable energy utilization rate of each scheme. At this point, the decision-maker is no longer facing a single scheme, but multiple internally optimized complete techno-economic packages. For example, Scheme A (10MW photovoltaic + 9MW electric heating + 150MWh thermal storage) may have an annualized net income of 5 million yuan, with a renewable energy heating ratio of 65%; Scheme B (15MW photovoltaic + 12MW electric heating + 200MWh thermal storage) has an annualized net income of 5.8 million yuan, with a renewable energy heating ratio of 78%; Scheme C (20MW photovoltaic + 14MW electric heating + 250MWh thermal storage) has an annualized net income of 6 million yuan, but the renewable energy heating ratio is as high as 85%, and some electricity may be forced to be abandoned due to exceeding the back-feed limit allowed by the source-load matching policy, leading to a decrease in renewable energy utilization rate. Decision-makers need to weigh the options: Option C offers the highest returns and strongest green attributes, but requires the largest investment and may violate policy red lines; Option A offers the most stable investment but lower returns and green benefits; Option B may be the best balance between returns, green indicators, and policy compliance. This three-stage approach—generating multiple options, deeply optimizing each option separately, and then comprehensively comparing them horizontally—systematically avoids the local optimum trap that single-path optimization may fall into, ensuring that the final selected project scale is globally optimal in terms of technology, economics, and policy.

[0062] This application further proposes an active identification and compensation step for implicit heating gaps before executing the scheduling strategy during time-series simulation. This step specifically includes: obtaining the power deviation between the actual output power of the electrothermal conversion module and the command value, and the temperature deviation between the actual temperature and the set temperature of key process points on the heat load side; quantifying the implicit heating gap electricity volume based on the power deviation and the temperature deviation; assigning an implicit gap value to the implicit heating gap electricity volume; setting the implicit gap value as a virtual price higher than the immediate grid connection revenue and the expected future substitution value; prioritizing the scheduling of electricity from the new energy power generation module to the electrothermal conversion module to meet the implicit heating gap electricity volume; and after meeting the implicit heating gap electricity volume, scheduling electricity available for subsequent scheduling to meet the regular heat load demand.

[0063] The proactive identification and compensation step for hidden heating gaps aims to detect and resolve deviations that are not easily detected during the heating process and are not explicitly included in routine scheduling considerations, thereby ensuring that the system can maintain operational stability and reliability while pursuing economic benefits. This step can be implemented by deploying an independent real-time monitoring and early warning system to continuously collect and analyze key operational data, or by integrating it into an existing energy management system as part of an advanced scheduling module. Obtaining the power deviation between the actual output power and the command value of the electrothermal conversion module refers to measuring the actual electrical energy consumed or the heat energy generated by the electrothermal conversion module and comparing it with the command value issued by the control system to determine whether its operating status meets expectations. This can be achieved by installing high-precision energy metering equipment at the input end of the electrothermal conversion module or installing a heat meter at its output end for real-time monitoring, and comparing the measured data with the control commands. Obtaining the temperature deviation between the actual temperature and the set temperature of key process points on the heat load side refers to monitoring the difference between the actual temperature and the set temperature required by the process in critical links with strict temperature requirements in industrial production. This can be achieved by deploying high-precision temperature sensors (such as thermocouples and resistance temperature detectors) at key process points and transmitting real-time temperature data to the central control system for analysis. Quantifying the implicit heating deficit involves converting the aforementioned power and temperature deviations into an equivalent electrical energy value, representing the additional electrical energy required to compensate for these deviations. This can be achieved through a pre-defined mathematical model or empirical formula. For example, based on the magnitude of the temperature deviation and the characteristics of the heat load, the required heat can be calculated, and then, combined with the efficiency of the electrothermal conversion module, the required electrical energy can be converted. Assigning an implicit deficit value to the implicit heating deficit means setting a high virtual price for this portion of the electricity used to compensate for the implicit deficit to ensure it receives the highest priority in scheduling decisions. This virtual price can be a fixed value, such as a punitive price much higher than the market electricity price, or it can be a dynamically adjusted price based on the severity of the deviation and its impact on production. The value of the implicit heating gap is set as a virtual price higher than the immediate grid connection revenue and the expected future replacement value. This setting is the core of the dispatch strategy, ensuring that resolving the implicit heating gap is always more economically "attractive" than selling electricity to the grid or storing energy, thus forcing the system to prioritize operational stability issues. Prioritizing the dispatch of electricity from renewable energy generation modules to electrothermal conversion modules to meet the implicit heating gap means that once an implicit heating gap is identified, the energy management system will immediately allocate the electricity generated by the renewable energy generation modules to the electrothermal conversion modules to make up for this gap, regardless of other economic benefits.After meeting the hidden heating shortage electricity demand, the remaining renewable energy will be dispatched to meet the regular heating load demand. This means that only after ensuring the stable operation of key process points will the remaining renewable energy be dispatched according to the original economic optimization principle, such as to meet daily heating demand, sell to the grid, or for energy storage.

[0064] The following is a concrete example to illustrate this. Suppose there is a production line in an industrial park that is extremely sensitive to temperature control, and its heat load is provided by an electrothermal conversion module. During the time-series simulation of system-scale configuration, the system continuously simulates operation and executes scheduling strategies. At a certain moment, the system detects through sensors deployed on the electrothermal conversion module that its actual output power is slightly lower than the commanded value. At the same time, the actual temperature reported by the temperature sensors at key process points on the production line is also slightly lower than the set value. These subtle deviations may not immediately attract attention under traditional economic scheduling strategies because their impact on overall economic benefits may be insignificant. However, this solution immediately quantifies these power and temperature deviations into, for example, a hidden heating gap of 100kW. The system assigns this 100kW of electricity a very high hidden gap value, such as a virtual price of 10 yuan per kilowatt-hour, far exceeding the current grid-connected electricity price of 0.5 yuan per kilowatt-hour and the energy storage substitution value of 0.8 yuan per kilowatt-hour. At this point, even if the new energy power generation module has sufficient electricity to sell to the grid or for energy storage, the dispatch strategy will prioritize allocating this 100kW of electricity to the electrothermal conversion module to quickly make up for the implicit heating gap and ensure that the temperature at key process points on the production line returns to normal. Only after confirming that the implicit heating gap has been fully compensated will the remaining electricity generated by the new energy power generation module be directed based on a comparison between the immediate grid-connected revenue and the expected future substitution value.

[0065] Through the aforementioned technical solutions, the industrial park heating system can proactively identify and compensate for hidden deviations in the heating process, effectively preventing problems such as production interruptions, product quality degradation, or equipment damage caused by minor heating shortages or over-heating. This scheduling mechanism, which prioritizes operational stability, significantly improves the reliability, safety, and support capabilities of the industrial park heating system for industrial production processes, ensuring the stable operation of key process points and thus providing a solid energy guarantee for the continuous and efficient production of the industrial park.

[0066] Furthermore, this application proposes a step for obtaining the power deviation between the actual output power of the electrothermal conversion module and the command value, including: measuring the actual output power in real time using a high-precision sensor deployed at the output end of the electrothermal conversion module; calculating the instantaneous difference between the actual output power and the control command value, and continuously accumulating the instantaneous difference; when the accumulated difference reaches a preset threshold, initiating a self-calibration process to generate a power correction amount, and feeding the power correction amount back to the control logic to adjust subsequent command values, thereby eliminating the power deviation.

[0067] The real-time measurement of actual output power using high-precision sensors deployed at the output end of the electrothermal conversion module refers to installing sensing devices with high measurement accuracy and fast response capabilities at the energy output port of the electrothermal conversion module to continuously and instantly acquire its current actual output power data. High-precision sensors can employ, for example, a combination of current and voltage sensors based on the Hall effect to calculate instantaneous power, or integrated intelligent power sensors to directly output the power value. Real-time measurement means that the sensor can continuously collect data at a sufficiently high sampling frequency to ensure the capture of dynamic changes in power; for example, it can be set to collect data tens or even hundreds of times per second.

[0068] Calculating the instantaneous difference between the actual output power and the control command value involves comparing the actual output power measured in real time by a high-precision sensor with the control command value currently issued by the system to the electrothermal conversion module point by point, and obtaining the difference between the two at a certain moment. This calculation of instantaneous difference is usually performed by the processor or microcontroller in the control system, and can be obtained through simple subtraction.

[0069] Continuous accumulation of instantaneous differences refers to the summation of instantaneous differences calculated over a continuous period of time. This accumulation operation can be implemented in various ways, such as integrating over a fixed time window, using a moving average, or simply summing all instantaneous difference values ​​over a period of time. The purpose of accumulation is to identify persistent, non-random power deviations, rather than transient, acceptable fluctuations.

[0070] When the accumulated difference reaches a preset threshold, the self-calibration process is initiated. This means that when the absolute value of the continuously accumulating instantaneous difference exceeds a pre-set allowable range, an automated program is triggered to calibrate the electrothermal conversion module or its control system. The preset threshold setting needs to comprehensively consider system stability requirements, measurement errors, and allowable power fluctuation ranges. The self-calibration process may include re-evaluating the efficiency parameters of the electrothermal conversion module, adjusting the gain of the internal control algorithm, or comparing with external reference standards.

[0071] The generated power correction amount refers to a value calculated during the self-calibration process based on the detected cumulative power deviation to correct future control commands. The power correction amount can be positive or negative, indicating how much power needs to be increased or decreased to match the commanded value with the actual output. This correction amount can be generated using a PID (Proportional-Integral-Derivative) controller, a fuzzy logic controller, or a model-based prediction algorithm.

[0072] Feeding the power correction value back to the control logic to adjust subsequent command values ​​means sending the calculated power correction value as input back to the control system of the electrothermal conversion module. The control logic will then adjust the power command values ​​sent to the electrothermal conversion module in real time based on this correction value; for example, if the correction value is positive, the subsequent command value will be increased; if the correction value is negative, the subsequent command value will be decreased.

[0073] Eliminating power deviation means that through the closed-loop feedback control mechanism described above, the actual output power of the electrothermal conversion module can continuously and accurately track the control command value, and ultimately reduce the deviation between the actual output power and the command value to an acceptable range, or even eliminate it completely.

[0074] The following is a concrete example. In a specific implementation, within an industrial park heating system, the electrothermal conversion module can be a high-power electric boiler. At the boiler's output, a high-precision three-phase smart energy meter is deployed. This meter has a measurement accuracy of 0.2S and can upload the boiler's actual output power data to the central control unit in real time at a 100-millisecond sampling interval. The central control unit (e.g., an industrial-grade PLC) receives the boiler's control command value (e.g., 500kW) and continuously receives the actual output power from the smart energy meter (e.g., 495kW at a certain moment). The PLC calculates the instantaneous difference between the two values ​​in real time (500kW - 495kW = 5kW). These instantaneous differences are continuously accumulated, for example, integrated over a 5-minute sliding time window. If the accumulated difference (e.g., the integrated value reaches 100kW·min, equivalent to a 5kW deviation over 20 minutes) exceeds a preset threshold, the PLC initiates a self-calibration process. This self-calibration process may involve rapidly detecting the resistance of the heating elements inside the electric boiler, or comparing it with a reference power meter that has been calibrated periodically. Based on the self-calibration result, the PLC generates a power correction (e.g., +5kW). This +5kW correction is then fed back into the electric boiler's control logic to adjust subsequent power command values. For example, if the next command is still 500kW, the PLC will adjust it to 505kW and send it to the electric boiler to ensure that the actual output power of the electric boiler can more accurately reach 500kW, thereby effectively eliminating power deviation.

[0075] In some embodiments described above in this application, although the power deviation of the electrothermal conversion module is used to identify implicit heating gaps, relying solely on the power deviation may not fully reflect the actual heating insufficiency on the heat load side. This is especially true when heat load demand forecasts are inaccurate, potentially leading to the heating system's inability to respond promptly and accurately to actual heat load changes, affecting the stable operation of key process points. To address this, this application further proposes obtaining the temperature deviation between the actual temperature and the set temperature of the key process points on the heat load side, and quantifying the implicit heating gap electricity consumption. This includes: receiving real-time temperature feedback from the key process points and cross-validating it with flow and temperature data from the heating network; using the actual temperature feedback from the key process points as a benchmark, identifying the actual heat load gap caused by inaccurate heat load demand forecast data; integrating the actual heat load gap with the gap caused by the power deviation, calculating the electricity required to fill the integrated gap, and obtaining the implicit heating gap electricity consumption.

[0076] The process involves receiving real-time temperature feedback from key process points and cross-validating it with flow and temperature data from the heating network. This aims to obtain the most direct and accurate heating effect information from the heat load side. Real-time temperature feedback refers to continuously monitoring the operating temperature of key production links or equipment (i.e., key process points) within the industrial park that are sensitive to heat demand. Cross-validation with flow and temperature data from the heating network involves comparing and analyzing the temperature data from the key process points with the flow and temperature data of the medium in the heating network that transports heat energy. For example, the accuracy of the temperature feedback and the losses during heat transmission can be determined by comparing the difference between the network outlet temperature and the temperature at the key process point, or by analyzing the relationship between changes in network flow and the temperature response at the process point. This cross-validation effectively eliminates the impact of sensor malfunctions, abnormal data transmission, or localized heat loss on the accuracy of the temperature feedback, ensuring the reliability of subsequent gap identification. Another approach is to use distributed temperature sensing technology (such as fiber optic temperature measurement) to continuously monitor the temperature of key process points and their surrounding areas. At the same time, combined with the flow and temperature data obtained from the pipeline SCADA system (monitoring and data acquisition system), the data fusion algorithm is used to perform real-time verification to improve data reliability.

[0077] Using the actual temperature feedback at the key process points as a benchmark, the system identifies the actual heat load gap caused by inaccurate heat load demand forecasts. The core of this step lies in using actual operating data to correct or supplement any deviations that may exist based on the forecast data. Heat load demand forecasts are typically based on historical data, production plans, and weather forecasts, but in actual operation, unforeseen circumstances, process adjustments, or errors in the forecast model may lead to discrepancies with the actual demand. Using the actual temperature feedback at the key process points as a benchmark means that when the actual temperature deviates from the set temperature, the system considers there to be insufficient or excessive heating. For example, if the actual temperature is lower than the set temperature, it indicates a real heat load gap. This identification can be triggered by setting a temperature threshold and duration; for example, when the actual temperature is continuously lower than the set temperature by X degrees, a real heat load gap is determined. Another identification method is to establish a heat balance model for the key process points, substitute the actual temperature feedback into the model, deduce the actual heat load demand at the current moment, and compare it with the predicted heat load demand. The difference is the real heat load gap caused by inaccurate forecasting.

[0078] The hidden heating gap is obtained by integrating the actual heat load gap with the gap caused by the power deviation and calculating the amount of electricity required to fill the integrated gap. This step aims to comprehensively and accurately quantify the total gap of the heating system. The actual heat load gap reflects the difference between the actual demand and the forecast on the heat load side, while the gap caused by the power deviation (as described in the above implementation, it may stem from the discrepancy between the actual output and the command value of the electrothermal conversion module) reflects the operating efficiency or control accuracy problem of the heating equipment itself. Integrating these two types of gaps means superimposing them to form a comprehensive heating deficiency. For example, the actual heat load gap (expressed in heat units) can be converted into an equivalent electricity demand and then directly added to the electricity gap caused by the power deviation. Calculating the amount of electricity required to fill the integrated gap means converting the integrated heat gap into the total amount of additional electrical energy required based on the efficiency of the electrothermal conversion module. For example, if the integrated gap is X joules of heat and the efficiency of the electrothermal conversion module is Y, then the required electricity is X / Y. Another integration approach is to merge the two gaps using a weighted average or rules based on expert experience to form a unified gap index, and then calculate the required electricity consumption based on this index. The resulting implicit heating gap electricity consumption is a comprehensive and more accurate quantitative indicator of insufficient heating, used to guide subsequent power dispatching.

[0079] As a specific implementation method, high-precision platinum resistance temperature sensors or thermocouples can be deployed in industrial parks, such as at critical process points requiring precise temperature control, like chemical reactors, semiconductor production lines, or food processing equipment, to collect real-time internal or surface temperature data. This temperature data is transmitted to the central control system via industrial Ethernet or wireless sensor networks. Simultaneously, ultrasonic flow meters and high-precision temperature sensors are installed on the main and branch pipelines of the heating network to monitor the flow and temperature of the heating medium (such as steam or hot water). After receiving real-time temperature feedback from the critical process points, the central control system compares it with the flow and temperature data of the heating network. For example, if the temperature at a critical process point drops, while the flow and temperature of its corresponding branch in the heating network are normal, it may indicate heat loss or increased demand at that process point; if the network flow or temperature is also abnormal, it may indicate a problem with upstream heating. This cross-validation eliminates data anomalies and confirms the authenticity of the temperature feedback. Once it is confirmed that the actual temperature at the critical process point is consistently lower than its set temperature, the system determines that a real heat load shortfall exists. For example, if the set temperature is 150℃ and the actual temperature drops to 148℃ and remains there for 5 minutes, a gap identification is triggered. The system will estimate the heat required to compensate for this temperature deviation, i.e., the actual heat load gap, based on this temperature deviation and the heat capacity and heat loss model of the process point. Simultaneously, as mentioned earlier, there may be a power deviation between the actual output power of the electrothermal conversion module and the commanded value; this deviation will also be quantified as a power gap. Subsequently, the system will superimpose and integrate these two gaps (the actual heat load gap converted into equivalent power, and the power gap caused by the power deviation) to calculate the total implicit heating gap power. For example, if the actual heat load gap is equivalent to 100kWh of power, and the power deviation results in a 20kWh power gap, then the integrated implicit heating gap power is 120kWh. This 120kWh power demand will be given the highest scheduling priority, guiding the electricity from the new energy power generation module to flow preferentially to the electrothermal conversion module to quickly fill this gap.

[0080] Thirdly, this embodiment also proposes a large-scale configuration device for the industrial park heating system described in the first aspect, such as... Figure 3 As shown, the device includes: The acquisition module 301 is used to acquire meteorological data, heat load demand data, and price mechanism parameters; the price mechanism parameters include at least transmission and distribution fees and system operating fees. The setting module 302 is used to set the initial capacity parameters of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; The scheduling module 303 is used to perform time series simulation based on the initial capacity parameters, the meteorological data, the heat load demand data, and the price mechanism parameters, and to execute a scheduling strategy during the time series simulation. The scheduling strategy includes prioritizing the scheduling of electrical energy from the new energy power generation module to the electrothermal conversion module to meet the heat load demand. When there is surplus electrical energy, the module calculates and compares the immediate grid connection revenue of the surplus electrical energy with the expected future replacement value stored in the thermal energy storage module, and determines the flow of the surplus electrical energy based on the comparison result. The adjustment module 304 is used to derive economic benefit indicators based on the time series simulation, and iteratively adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module based on the economic benefit indicators until the economic benefit indicators meet the preset convergence conditions.

[0081] 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 heating system for industrial parks based on direct connection to new energy green electricity, characterized in that, The system includes at least a new energy power generation module, an electrothermal conversion module, and a thermal energy storage module; wherein: The new energy power generation module is used to convert new energy into electrical energy and connect it to the user-side power grid; The electrothermal conversion module is connected to the new energy power generation module and is used to convert electrical energy into heat energy to meet heat load requirements; The thermal energy storage module is connected to the electrothermal conversion module and the heat load side, and is used to store or release thermal energy.

2. A method for the large-scale configuration of a heating system for an industrial park as described in claim 1, characterized in that, The method includes: Acquire meteorological data, heat load demand data, and pricing mechanism parameters; the pricing mechanism parameters include at least transmission and distribution fees and system operating fees. Set the initial capacity parameters of the new energy power generation module, the electrothermal conversion module, and the thermal energy storage module; Based on the initial capacity parameters, meteorological data, heat load demand data, and price mechanism parameters, a time series simulation is performed, and a scheduling strategy is executed during the time series simulation. The scheduling strategy includes prioritizing the scheduling of electricity from the new energy power generation module to the electrothermal conversion module to meet the heat load demand. When there is surplus electricity, the immediate grid connection revenue of the surplus electricity is calculated and compared with the expected future replacement value of storing it in the thermal energy storage module, and the flow of the surplus electricity is determined based on the comparison result. The economic benefit index is derived from the time series simulation, and the capacity parameters of the electrothermal conversion module and the thermal energy storage module are iteratively adjusted according to the economic benefit index until the economic benefit index meets the preset convergence condition.

3. The scale configuration method according to claim 2, characterized in that, The calculation and comparison of the immediate grid connection revenue of the remaining electrical energy with the expected future replacement value of storing it in the thermal energy storage module includes: Obtain the current grid connection electricity price, calculate the product of the remaining electricity and the grid connection electricity price, and obtain the instant grid connection revenue value; Based on the meteorological data and heat load demand data, predict the electricity price trend within a preset period in the future, and determine the alternative electricity price for the thermal energy storage module to release heat energy in the future to replace electric heating. The expected future substitution value is obtained by multiplying the remaining electrical energy, the electrothermal conversion efficiency of the electrothermal conversion module, and the substitution electricity price.

4. The scale configuration method according to claim 3, characterized in that, The step of determining the flow of the remaining electrical energy based on the comparison results includes: When the instant internet access benefit value is greater than the expected future replacement value, an internet access command is generated to control the transmission of the remaining electrical energy from the user-side power grid to the public power grid. When the instant internet access benefit value is less than or equal to the expected future replacement value, obtain the current heat storage status of the thermal energy storage module; If the current heat storage state has not reached the preset heat storage limit, a heat storage command is generated to control the remaining electrical energy to be transmitted to the electrothermal conversion module to be converted into heat energy and stored in the heat energy storage module; if the current heat storage state has reached the preset heat storage limit, a grid connection command is generated to control the remaining electrical energy to be transmitted to the public grid through the user-side grid.

5. The scale configuration method according to claim 2, characterized in that, The step of iteratively adjusting the capacity parameters of the electrothermal conversion module and the thermal energy storage module according to the economic benefit index until the economic benefit index meets the preset convergence condition includes: Calculate the absolute value of the difference between the economic benefit index obtained in the current iteration and the economic benefit index obtained in the previous iteration; Determine whether the absolute value of the difference is less than a preset convergence threshold; if the absolute value of the difference is greater than or equal to the convergence threshold, adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module according to the change gradient of the economic benefit index, and enter the next round of time series simulation; if the absolute value of the difference is less than the convergence threshold, determine that the preset convergence condition is met, and output the current capacity parameters as the optimal configuration scheme.

6. The scale configuration method according to claim 2, characterized in that, The economic benefit indicator is the system's net revenue; the step of deriving the economic benefit indicator based on the time series simulation includes: Within each time step of the time series simulation, calculate the electricity sales revenue, heating revenue, and national / local subsidies, and sum them up to obtain the net operating revenue for the whole year; Based on the initial capacity parameters and the preset unit capacity cost, calculate the initial investment cost of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; The system's net revenue is obtained by subtracting the initial investment cost, the preset annualized operation and maintenance cost, the cost of purchasing electricity from the public grid, the stable supply guarantee fee payable for grid-connected green electricity direct connection, operation and maintenance costs, and loan interest from the annual net operating revenue.

7. The scale configuration method according to claim 6, characterized in that, Before executing the scheduling strategy during the time series simulation, the method further includes a step of actively identifying and compensating for implicit heating gaps; the step of actively identifying and compensating for implicit heating gaps includes: The power deviation between the actual output power of the electrothermal conversion module and the command value is obtained, as well as the temperature deviation between the actual temperature and the set temperature of the key process point on the heat load side. Based on the power deviation and the temperature deviation, the implicit heating gap electricity is quantified; an implicit gap value is assigned to the implicit heating gap electricity; the implicit gap value is set as a virtual price higher than the instant internet access revenue value and the expected future replacement value. The step of prioritizing the dispatch of electrical energy from the new energy power generation module to the electrothermal conversion module to meet the heat load demand specifically includes: Based on the value of the hidden heat gap, the electrical energy of the new energy power generation module is preferentially allocated to the electrothermal conversion module to meet the hidden heat supply gap; after the hidden heat supply gap is met, the electrical energy available for subsequent dispatch is then dispatched to meet the regular heat load demand.

8. The scale configuration method according to claim 7, characterized in that, Obtaining the power deviation between the actual output power of the electrothermal conversion module and the command value includes: The actual output power is measured in real time by a high-precision sensor deployed at the output end of the electrothermal conversion module. The instantaneous difference between the actual output power and the control command value is calculated and continuously accumulated. When the accumulated difference reaches a preset threshold, a self-calibration process is initiated to generate a power correction amount, and the power correction amount is fed back to the control logic to adjust subsequent command values, thereby eliminating the power deviation.

9. The scale configuration method according to claim 8, characterized in that, The process of obtaining the temperature deviation between the actual temperature and the set temperature of the key process point on the heat load side, and quantifying the hidden heating shortfall in electricity consumption, includes: Receive real-time temperature feedback from the key process points and perform cross-validation by combining the flow and temperature data of the thermal pipeline network; Based on the actual temperature feedback of the key process points, identify the real heat load gap caused by inaccurate heat load demand forecast data; By integrating the actual heat load gap with the gap caused by the power deviation, the amount of electricity required to fill the integrated gap is calculated to obtain the hidden heating gap electricity.

10. A large-scale configuration device applied to the industrial park heating system of claim 1, characterized in that, The device includes: The acquisition module is used to acquire meteorological data, heat load demand data, and pricing mechanism parameters; the pricing mechanism parameters include at least transmission and distribution fees and system operating fees. The setting module is used to set the initial capacity parameters of the new energy power generation module, the electrothermal conversion module and the thermal energy storage module; The scheduling module is used to perform time series simulation based on the initial capacity parameters, the meteorological data, the heat load demand data, and the price mechanism parameters, and to execute a scheduling strategy during the time series simulation. The scheduling strategy includes prioritizing the scheduling of electrical energy from the new energy power generation module to the electrothermal conversion module to meet the heat load demand. When there is surplus electrical energy, the module calculates and compares the immediate grid connection revenue of the surplus electrical energy with the expected future replacement value of storing it in the thermal energy storage module, and determines the flow of the surplus electrical energy based on the comparison result. The adjustment module is used to derive economic benefit indicators based on the time series simulation, and iteratively adjust the capacity parameters of the electrothermal conversion module and the thermal energy storage module based on the economic benefit indicators until the economic benefit indicators meet the preset convergence conditions.

Images