Intelligent cold and heat control method and system based on power coordination and power peak shaving

Abstract

The application discloses a wisdom cold and heat control method and system based on power cooperation and power peak regulation, and relates to the technical field of wisdom cold and heat control; the application determines the cold and heat cooperative control level of the current period based on the collected cold and heat control data set representing the supply side output fluctuation state and the cold and heat load coupling state of the energy consumption side, generates corresponding power peak regulation instructions according to the level result, and performs linkage control on the cold and heat adjusting equipment according to the power peak regulation instructions; the control result meeting the power grid peak regulation requirement and the cold and heat supply requirement is output based on the feedback result after the linkage control is executed, which is helpful to solve the problem that in the cold and heat combined supply scene of a park or the like, the energy fluctuation and intermittence are usually strong, the power load growth is superimposed, the contradiction between the power grid side power generation output change and the user side or power consumption end demand is intensified, and the dynamic balance of the power grid load is difficult to realize.

Description

Technical Field

[0001] This invention relates to the field of intelligent heating and cooling control technology, and in particular to an intelligent heating and cooling control method and system based on power coordination and power peak shaving. Background Technology

[0002] With the continuous growth of cooling and heating loads in park buildings, industrial stations, and integrated energy systems, the coupling relationship between cooling and heating loads and electricity loads is becoming increasingly stronger. Especially against the backdrop of the continuous improvement of peak-valley electricity pricing mechanisms, how to achieve orderly regulation of electricity load while meeting users' cooling and heating needs has become an important research direction in the field of smart energy control.

[0003] To achieve renewable energy consumption, coordinated regulation of cooling and heating loads, and improved grid peak-shaving capacity, existing technologies typically employ the following process: First, historical and real-time electrical load, cooling load, heating load, peak-valley electricity prices, meteorological information, and operating parameters of various equipment, such as heat pump units, electric chillers, electric heating equipment, cold and heat storage devices, circulating pumps, and regulating valves, are collected from the park or building side. A comprehensive energy coordination model is then constructed, including combined cooling, heating, and power units, heat pumps, electric chillers, electric heating equipment, and cold and heat storage devices. Subsequently, a day-ahead optimized scheduling plan is generated based on future load forecasts. During off-peak hours, electric chillers or electric heating equipment are prioritized for cold and heat storage, while the stored cold or heat is released during peak hours, reducing direct power consumption during peak hours. Based on this, intraday rolling corrections are performed in conjunction with real-time load deviations, and unit output, valve opening, circulating pump frequency, and energy storage charging and discharging status are adjusted in a coordinated manner. This ensures both cooling and heating demand while achieving peak shaving and valley filling and improving overall energy efficiency.

[0004] To address the adjustment needs arising from fluctuations in both supply and demand, existing technologies have proposed intelligent cooling and heating control schemes based on predictive analysis, module collaboration, and optimized scheduling. For example, Chinese invention patent application CN105676824B discloses a renewable energy cogeneration energy optimization scheduling system and method, which includes: an energy production control module, an energy storage control module, and a load control module, each connected to an energy management module; the energy production control module, energy storage control module, and load control module respectively collect information from on-site energy supply, energy storage, and load equipment, and exchange data with the energy management system via a fieldbus; the energy management module receives and stores the information uploaded by the energy production control module, energy storage control module, and load control module, and uses the information to achieve short-term and / or ultra-short-term forecasts of renewable energy or loads, and performs short-term and / or ultra-short-term optimizations of the operating status and power of cooling / heating, cooling / heating storage, power supply, energy storage, and dispatchable functional units.

[0005] Because new energy power generation is significantly affected by meteorological conditions, especially in combined cooling, heating, and power (CCHP) scenarios, the three energy sources—cooling, heating, and electricity—are not independent but exhibit significant energy conversion relationships, equipment coupling relationships, and operational constraints. For example, some cooling and heating source equipment generates waste heat during the cooling process, while some heating processes rely on electric drive devices for energy conversion. This leads to mutual influence and constraints between cooling regulation, heating regulation, and electricity consumption regulation. Furthermore, multiple types of equipment in CCHP often share infrastructure such as pipelines, heat exchange units, circulating pumps, and regulating valves. Different equipment have operational boundary couplings and regulation priority constraints. When one piece of equipment starts, stops, or adjusts its power, it may further affect the flow distribution, heat exchange efficiency, and system stability of other branches. Especially in integrated energy systems in industrial parks, multiple types of loads, such as office air conditioning, industrial process cooling, domestic hot water, and production heating, often coexist. Different loads have different requirements for temperature, continuity, and response speed, meaning that CCHP processes must not only meet energy balance but also consider end-user comfort, process stability, and peak-shaving economy, further increasing the difficulty of coordinated control.

[0006] New energy power generation is characterized by strong volatility, randomness, and intermittency. Meanwhile, the demand for cooling and heating energy at the electricity consumption end continues to grow, and there is a significant coupling relationship between cooling / heating loads and electricity loads. This leads to concentrated load fluctuations at different times, with supply-side output fluctuations and demand fluctuations at the consumption side overlapping, further increasing the regulatory pressure on the power grid in peak shaving, load balancing, and safe and stable operation. This, in turn, presents challenges to the cooling / heating control process. Specifically, the reasons are as follows: First, new energy output is greatly affected by weather, time of day, and environmental conditions, making it difficult to maintain continuous and stable output. Second, cooling / heating regulation devices such as heat pump units, electric chillers, and electric heating equipment are themselves high-power-consuming devices, and their operating status is frequently adjusted according to changes in cooling / heating demand, easily forming peak electricity consumption in local periods. Furthermore, factors such as the charging and discharging process of cold and heat storage devices, equipment start-up and shutdown constraints, and the lag in the response of cooling / heating terminals also lead to timing inconsistencies between cooling / heating balance regulation and power balance regulation.

[0007] Existing processes are typically based on load forecasting and day-ahead optimization scheduling, which cannot quickly and precisely coordinate adjustments to fluctuations in renewable energy output, changes in the coupling of heating and cooling loads, and the real-time operating status of equipment. They also cannot simultaneously meet the grid's peak-shaving needs, the continuity of heating and cooling supply, and the stability of system operation.

[0008] It is evident that in scenarios such as combined cooling and heating systems in industrial parks, there is often a strong fluctuation and intermittency in energy supply, coupled with the superposition of increasing electricity load. This exacerbates the contradiction between changes in power generation output on the grid side and demand on the user side or at the electricity consumption end, making it difficult to achieve dynamic balance of grid load. Summary of the Invention

[0009] This invention provides a smart heating and cooling control method and system based on power coordination and peak shaving. The technical solution provided by this invention is as follows: On the one hand, a smart cooling and heating control method based on power coordination and power peak shaving is provided, including: S1, determining the cooling and heating coordination control level for the current period based on the collection of cooling and heating control data that characterizes the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side; S2, generating corresponding power peak shaving instructions based on the level results, and performing linkage control on cooling and heating regulation equipment according to the power peak shaving instructions; S3, correcting based on the feedback results after the linkage control is executed, and outputting control results that meet the grid peak shaving requirements and cooling and heating supply requirements.

[0010] On the other hand, a smart cooling and heating control system based on power coordination and power peak shaving is provided, including: a cooling and heating coordination determination module, a linkage control module, and a linkage feedback correction module; the cooling and heating coordination determination module is used to determine the cooling and heating coordination control level for the current period based on the collection of cooling and heating control data representing the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side; the linkage control module is used to generate corresponding power peak shaving instructions based on the level results, and to perform linkage control on the cooling and heating regulation equipment according to the power peak shaving instructions; the linkage feedback correction module is used to correct based on the feedback results after the linkage control is executed, and output control results that meet the grid peak shaving requirements and cooling and heating supply requirements.

[0011] The above technical solution has at least the following advantages compared with the existing technology: 1. Based on the collected data set representing the power output fluctuations on the supply side and the coupling state of heating and cooling loads on the energy consumption side, the current heating and cooling coordinated control level is determined, which helps to achieve accurate perception of supply and demand conditions and differentiated classification of control levels. Corresponding power consumption peak-shaving commands are generated based on the level results, and the heating and cooling regulation equipment is linked and controlled according to these commands. This helps to ensure that the power grid's peak-shaving demand and heating and cooling supply demand are simultaneously met, thereby achieving coordinated linkage among multiple links such as power sources, loads, and storage, and improving the accuracy of control execution. Based on the feedback results after the linkage control is executed, corrections are made to output control results that meet the power grid's peak-shaving requirements and heating and cooling supply requirements, which helps to eliminate operational deviations and continuously ensure the stable and efficient operation of the system.

[0012] 2. By responding to the prompts corresponding to the received cold and heat coordination control level, the cold and heat coordination control that is compatible with the cold and heat coordination control level is activated to obtain power peak-shaving instructions. Compared with the shortcomings of existing technologies, such as the inability to adapt to operating condition fluctuations and the disconnect between peak-shaving and power supply, this technology is more conducive to achieving hierarchical and precise regulation and a high degree of matching between regulation strategies and real-time operating conditions. This achieves the technical effect of balancing grid peak-shaving efficiency and cold and heat power supply stability, and maximizing the absorption of new energy.

[0013] 3. By monitoring the compliance of the linkage control and ensuring that the operating parameters of the heating and cooling equipment are within the preset target operating parameter range, load prediction is made for the future time period based on the corresponding operating parameters of the heating and cooling equipment. Conversely, linkage control optimization is performed based on the operating deviation results of the heating and cooling equipment. Compared with the shortcomings of existing methods, such as passive deviation handling and lack of foresight in regulation, this method helps to improve the system's adaptive optimization capability and rapid response capability to anomalies, thereby avoiding the accumulation of small deviations that may cause power supply failures and ensuring stable and controllable regulation throughout the entire process.

[0014] 4. Based on the operating status parameters of the cooling and heating regulation equipment, the model is input into a preset cooling and heating load prediction model and outputs the predicted cooling load value, predicted heating load value and corresponding charging and discharging time series data for the future time period. Compared with the shortcomings of existing methods, such as low load prediction accuracy and failure to combine with the real-time status of equipment, this method helps to improve the accuracy of load prediction and the rationality of forward planning, thereby planning the operation rhythm of energy storage devices in advance and avoiding the risks of energy supply gaps and peak shaving failures.

[0015] 5. In the event of a sudden increase or decrease in the output of new energy sources, the monitoring and linkage control status is qualified, and the abnormal classification results are obtained based on the parameters reflecting the abnormal supply of cold and heat. Compared with the shortcomings of existing technologies, such as the inability to distinguish between slight fluctuations and equipment failures and the one-size-fits-all approach to handling, this technology is more conducive to the classification and handling of abnormal energy supply and the classification and handling of faults and slight deviations. This enables rapid response to extreme working conditions and ensures uninterrupted supply of cold and heat. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 A flowchart illustrating the intelligent heating and cooling control method based on power coordination and peak shaving provided in this application embodiment; Figure 2 This is a schematic diagram of deep thermal synergistic control provided in an embodiment of this application; Figure 3 This is a schematic diagram of the optimized linkage control operation provided in the embodiments of this application; Figure 4 A schematic diagram of the structure of a smart heating and cooling control system based on power coordination and power peak shaving provided in an embodiment of this application; Figure 5 The controllable load response curve provided in the embodiments of this application; Figure 6 The diagram illustrates the total load decomposition and peak shaving effect provided in the embodiments of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0019] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms “first,” “second,” and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the word encompasses the element or object listed following the word and its equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0020] It should be noted that the terms "up", "down", "left", "right", "front", and "back" used in this invention are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0021] This invention provides a smart cooling and heating control method and system based on power coordination and power peak shaving.

[0022] Example 1, such as Figure 1 The flowchart shown is a process for a smart heating and cooling control method based on power coordination and peak shaving provided in this application embodiment. The smart heating and cooling control method based on power coordination and peak shaving includes: S1, in the set cooling and heating control scenario, determines the cooling and heating coordinated control level for the current period based on the collection of cooling and heating control data that characterizes the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side. This helps to improve the pertinence and scenario adaptability of the control decision, thereby achieving accurate perception of new energy fluctuations and cooling and heating load changes, and providing a reliable basis for subsequent coordinated peak shaving.

[0023] S2 generates corresponding peak-shaving instructions based on the grade results, and performs linkage control on the cooling and heating regulation equipment according to the peak-shaving instructions. This helps to achieve dynamic power matching between source, load and storage, thereby smoothing out peak and valley electricity consumption while ensuring stable cooling and heating supply. Cooling and heating regulation equipment includes, but is not limited to, cold storage devices, heat storage devices, heat pump units, electric chillers, electric heating equipment, circulating pumps and regulating valves. Among them, cold storage devices include cold water tanks, ice storage tanks, etc., and heat storage devices include heat exchangers, etc.

[0024] It should be added that the embodiments of this invention provide a heat exchanger, including a shell for communicating with various types of energy host cold and heat source equipment and a heat exchange tube for waste heat recovery disposed in the shell. The various types of energy host cold and heat source equipment include electric chiller units, solar energy, air source heat pumps, water source heat pumps and electric boilers, etc. The various types of energy host cold and heat source equipment, energy storage devices or heat storage devices are connected to end users through valves, pipes and end users.

[0025] S3 corrects the output based on the feedback results after the execution of the linkage control, and outputs control results that meet the requirements of grid peak shaving and cooling and heating supply. This helps to improve the robustness of system operation and the accuracy of command execution, thereby helping to alleviate the grid regulation pressure and improve the renewable energy consumption rate.

[0026] In this embodiment, the interconnection and mutual support between S1-S3 helps to build a smart heating and cooling coordinated control system that integrates perception, decision-making, execution, and closed-loop optimization. Specifically, it is manifested as follows: First, the supply and demand fluctuations are quickly identified through level determination; then, equipment coordinated actions are achieved through tiered peak-shaving commands; and finally, operational deviations are eliminated through feedback correction. The three aspects are progressive and coordinated, which not only solves the control problems caused by the volatility of new energy sources and the randomness of heating and cooling loads, but also achieves a balance between the grid's peak-shaving needs and users' heating and cooling energy needs, significantly improving the safe and stable operation level and comprehensive utilization efficiency of the park's energy system.

[0027] For example, during the operation of the park's integrated energy system, when there are significant fluctuations in the output of new energy sources on the supply side, and the cooling or heating load on the energy consumption side changes simultaneously, the system first performs state identification in S1 based on the collected set of cooling and heating control data for the current period. This determines the degree of fluctuation in supply-side output, the degree of coupling between cooling and heating loads on the energy consumption side, and the adjustability of the equipment, and accordingly determines the corresponding cooling and heating coordinated control level. The purpose of this step is to quickly perceive and classify the current operating scenario, providing a basis for selecting subsequent control strategies.

[0028] After completing the level determination, the system enters S2, generating corresponding power peak-shaving commands based on the level results output from S1, and driving the cooling and heating regulation equipment to perform linkage control. If the determination result corresponds to a higher level of cooling and heating coordinated control, the system controls the cold storage device or heat storage device to participate in energy charging and discharging regulation, and simultaneously adjusts the operating status of equipment such as heat pump units, electric chillers, electric heating equipment, circulating pumps, and regulating valves to reduce direct power load and maintain the continuity of cooling and heating supply; if the determination result corresponds to a conventional level of cooling and heating coordinated control, the system maintains the initial operating parameter output of the cooling and heating source equipment and keeps the cold storage device and heat storage device in standby mode. The purpose of this step is to transform the determination result of S1 into specific execution actions, realizing coordinated matching between source, load, and storage.

[0029] After S2 is completed, the system enters S3, where the control effect is verified and corrected based on the feedback results of the linkage control execution. If the feedback results indicate that the grid peak-shaving effect, heating and cooling supply effect, or equipment execution status does not meet the expected requirements, the system optimizes and adjusts the corresponding control strategy and re-outputs the corrected control results; if the feedback results meet the preset requirements, the current control state is maintained and continues to operate. The purpose of this step is to perform closed-loop correction of the execution results of S2, avoiding the accumulation of control deviations due to equipment response lag, changes in end-load, or continued fluctuations in renewable energy.

[0030] It is evident that there is a clear hierarchical relationship between S1, S2, and S3: S1 is responsible for scene perception and level determination, S2 is responsible for executing linkage control based on the level results, and S3 is responsible for feedback correction of the linkage control results. Together, they form a closed-loop control link from perception, decision-making, execution to optimization, thereby enabling the intelligent heating and cooling system to respond collaboratively to fluctuations in new energy sources and changes in heating and cooling loads.

[0031] Preferably, the cooling and heating coordinated control level for the current period is determined based on the collection of cooling and heating control data representing the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side. Specifically, the cooling and heating control data collection is input into a preset coordinated and peak-shaving demand analysis model, and the cooling and heating coordinated control level corresponding to the current period is output. The cooling and heating coordinated control level includes a conventional cooling and heating coordinated control level and a deep cooling and heating coordinated control level. The cooling and heating control data collection includes the new energy power generation data corresponding to the supply side and the cooling and heating load data corresponding to the energy consumption side. The new energy power generation data includes at least the new energy power generation capacity and the power output fluctuation rate. The power output fluctuation rate refers to the ratio of the absolute value of the power difference between adjacent sampling times of the new energy power generation equipment monitored by the grid-connected power meter within a preset statistical time window to the corresponding reference power, which is used to reflect the strength of the fluctuation of the new energy power output. The reference power is either the rated power of the new energy power generation equipment or the average power generation capacity within the statistical time window. The new energy power generation capacity represents the actual power output value of the new energy power generation equipment monitored by the grid-connected power meter to the grid side or the load side at the current sampling time. Load data includes at least cooling load, heating load, cooling load fluctuation rate, and heating load fluctuation rate. Cooling load refers to the amount of cooling capacity monitored by a flow meter within a preset time period to maintain the target ambient temperature or process cooling requirements, usually measured in kW. Heating load refers to the amount of heating capacity monitored by a flow meter within a preset time period to maintain the target heating temperature or process heating requirements, usually measured in kW. When expressed in terms of electrical energy, cooling load can refer to the cumulative cooling energy consumed or converted accordingly within a preset time period to maintain the target ambient temperature or process cooling requirements, usually measured in kilowatt-hours. Heating load can refer to the cumulative heating energy consumed or converted accordingly within a preset time period to maintain the target heating temperature or process heating requirements. The cooling load fluctuation rate is expressed as the ratio of the absolute value of the difference between the cooling load value at the current sampling time and the cooling load value at the previous sampling time to the corresponding sampling time interval. The heating load fluctuation rate is expressed as the ratio of the absolute value of the difference between the heating load value at the current sampling time and the heating load value at the previous sampling time to the corresponding sampling time interval. The duration of the sampling time interval is monitored by a timer.

[0032] Before implementation, the intelligent cooling and heating control method based on power coordination and power peak shaving provided by this invention constructs a series of models, including but not limited to a preset coordination and peak shaving demand analysis model, a data-driven cooling and heating source equipment power control model, a linkage control parameter configuration model, a cooling and heating load prediction model, and a supply anomaly level model.

[0033] The specific uses of the above model are as follows: It collects and aggregates the cooling and heating control data, inputs it into the collaborative and peak-shaving demand analysis model, and outputs the cooling and heating collaborative control level; it collects and aggregates the cooling and heating control data corresponding to the triggered peak-shaving actions, inputs them into the data cooling and heating source equipment power control model, and outputs the corresponding output power value of the cooling and heating source equipment; it collects and inputs the operating status parameters that are outside the preset target operating parameter range into the linkage control parameter configuration model, and outputs the linkage control parameter set; it collects and inputs the operating status parameters of the cooling and heating regulation equipment into the cooling and heating load prediction model, and outputs the predicted cooling load value, the predicted heating load value, and the corresponding charging and discharging time series data; it collects and inputs the parameters reflecting abnormal cooling and heating supply conditions into the supply anomaly level model, and outputs the anomaly classification results.

[0034] The training data for the above model includes: The training data for the collaborative and peak-shaving demand analysis model consists of a collection of cooling and heating control data and preset cooling and heating collaborative control levels; the training data for the data-driven cooling and heating source equipment power control model consists of a collection of cooling and heating control data corresponding to the peak-shaving action and preset output power values ​​of the cooling and heating source equipment; the training data for the linkage control parameter configuration model consists of operating status parameters outside the preset target operating parameter range and preset linkage control parameter sets; the training data for the cooling and heating load prediction model consists of operating status parameters of cooling and heating regulation equipment, preset predicted cooling load values, predicted heating load values, and corresponding charging and discharging time series data; and the training data for the supply anomaly level model consists of parameters reflecting abnormal cooling and heating supply conditions and preset anomaly classification results.

[0035] The aforementioned models are constructed using existing machine learning algorithms, such as support vector regression. Taking the cold and heat load prediction model as an example, the construction process is as follows: First, historical meteorological data, such as relative humidity, wind speed, solar radiation intensity, and operating status parameters of cold and heat regulation equipment, are collected. After data cleaning, normalization, and feature selection, the data is divided into training, validation, and test sets. Then, a radial basis function kernel is selected to build an SVR (Support Vector Regression) model. The model training is completed by optimizing hyperparameters such as penalty factors and kernel function parameters through cross-validation. Finally, a model that can predict cold load, heat load, and energy charging and discharging time series based on real-time operating status parameters is obtained, providing a reliable basis for subsequent cold and heat coordinated control and power peak shaving decisions.

[0036] Based on the level results, corresponding power consumption peak-shaving instructions are generated, and the cooling and heating regulation equipment is linked and controlled according to the power consumption peak-shaving instructions. The specific process is as follows: When the output level is the conventional cooling and heating coordinated control level, conventional cooling and heating coordinated control is started, and conventional level power consumption peak-shaving instructions are generated; when the output level is the deep cooling and heating coordinated control level, deep cooling and heating coordinated control is started, and deep level power consumption peak-shaving instructions are generated.

[0037] It needs to be further elaborated that conventional cold and heat coordinated control works as follows: a prompt to not trigger peak shaving actions is sent to the preset control center, maintaining the initial operating parameters of the cold and heat source equipment for output, while the cold storage and heat storage devices remain in standby mode and maintain normal power consumption. The initial operating parameters of the cold and heat source equipment include the initial operating power, initial supply water temperature, and initial outlet water flow rate of the heat pump unit, and the initial operating power, initial cooling capacity, and initial chilled water outlet temperature of the electric chiller. By implementing conventional cold and heat coordinated control—that is, maintaining the initial operating parameters of the cold and heat source equipment for output, while keeping the cold and heat storage devices in standby mode—it helps improve the basic operational stability of the system, reduces mechanical losses and additional energy consumption caused by frequent equipment start-ups and shutdowns and parameter fluctuations, and ensures constant user comfort in terms of cold and heat supply. Thus, under normal operating conditions with stable renewable energy output and small fluctuations in cold and heat loads, it achieves low-consumption, stable, and low-interference operation, balancing energy quality and the service life of the cold and heat regulation equipment.

[0038] like Figure 2 The diagram shown is a schematic of deep cold and heat coordinated control provided in an embodiment of the present invention: the grid load value is monitored, and when the grid load value is not greater than the preset grid load, a prompt not to trigger peak shaving action is sent to the preset control center, and the cold storage device and the heat storage device remain in standby state; otherwise, a prompt to trigger peak shaving action is sent, and the power of the corresponding cold and heat source device is adjusted to the output power value based on the obtained output power value of the cold and heat source device.

[0039] The specific process of deep cold and heat coordinated control is as follows: Monitor the grid load value and determine whether the grid load value is greater than the preset grid load; if the grid load value is not greater than the preset grid load, send a prompt not to trigger peak shaving action to the preset control center, and the cold storage device and the heat storage device remain in standby state. The grid load value refers to the total active power value actually obtained from the grid side of the target park, target energy supply area or target access node based on the power quality analyzer monitoring at the current sampling time or within the preset statistical time period; if the grid load value is greater than the preset grid load, perform the following operations: send a prompt to trigger peak shaving action, and input the corresponding cold and heat control data set as input data to the power control of the cold and heat source equipment. The model outputs the corresponding output power value of the cold and heat source equipment through the power control model of the cold and heat source equipment, and adjusts the power of the corresponding cold and heat source equipment to the output power value; it sends a prompt to the preset control center to switch the cold storage device and / or heat storage device from standby state to energy release state to reduce the direct power load of the cold and heat source equipment; after a preset time period, it monitors the grid load value to determine whether the grid load value is greater than the preset grid load. If it is still greater than the preset grid load, it sends a peak-shaving control alarm prompt to the preset control center; otherwise, it sends a prompt to the preset control center to switch the cold storage device and heat storage device to energy storage state, continues to execute the linkage control of the cold and heat regulation equipment, and monitors the qualification status of the linkage control.

[0040] The embodiments of this invention provide a database that covers preset grid loads, preset target operating parameter ranges, etc. The construction of this database provides a standardized parameter reference benchmark, stable data decision support and efficient fast calling carrier for intelligent cooling and heating control methods based on power coordination and power peak shaving, which can effectively reduce the parameter calculation time in the real-time control process.

[0041] Taking the preset grid load as an example, the construction of this value is as follows: Based on the historical grid load data of the same period, the standard load baseline value of the target area during the non-peak period is extracted by the time series analysis method; the standard load baseline value is multiplied by the preset safety factor, that is, the preset safety multiple, such as 1.05 to 1.2 times, or dynamically determined as the preset grid load according to the critical threshold set in the day-ahead dispatch plan issued by the grid dispatch center.

[0042] Based on historical load data from a certain industrial park during non-peak hours in May 2025, such as 09:00–11:00 and 14:00–16:00 daily, a standard load baseline of 7.0MW was obtained after time series decomposition to remove outliers. Assuming a safety factor of 1.1, the preset grid load was calculated to be 7.7MW. When the park connects to the grid dispatch platform and receives a peak-shaving critical threshold of 8.2MW in the next day's dispatch plan, the system automatically updates the preset grid load to 8.2MW and uses this value as the basis for triggering peak-shaving actions the following day.

[0043] Taking the preset target operating parameter range as an example, the construction process of this range is as follows: First, collect the operating status parameters of various cooling and heating regulation equipment in the target park or combined cooling and heating scenario under historical stable operating conditions, historical high load conditions, historical low load conditions, and typical peak-shaving conditions. Taking an industrial park as an example, select its historical operating data for the annual cooling season and heating season as samples, and collect the operating status parameters of heat pump units, electric chillers, electric heating equipment, cold storage devices, heat storage devices, circulating pumps, and regulating valves under historical stable operating conditions, historical high load conditions, historical low load conditions, and typical peak-shaving conditions. Then, clean, classify, and divide the operating status parameters under different operating conditions, and combine the cooling and heating supply compliance status, power grid load changes, and equipment abnormal alarm status of the corresponding time period to retain the effective parameter data that meets the preset operating requirements. Further, perform statistical processing on the effective parameter data, extract the reasonable fluctuation range of each operating status parameter, and determine the reasonable fluctuation range as the corresponding target operating parameter range. Finally, store the target operating parameter range in the database for subsequent linkage control qualification judgment and linkage control optimization.

[0044] By implementing deep coordinated cooling and heating control—that is, first monitoring the grid load value, and when the grid load value exceeds the preset grid load, configuring the corresponding parameters based on the output power value of the cooling and heating source equipment, and switching the cold storage device and / or heat storage device from standby state to energy release state—it helps to improve the peak load shaving capacity of the grid during peak hours and the efficiency of surplus power absorption during off-peak hours of new energy sources. This simultaneously solves the core problem of mismatch between cooling and heating regulation and power peak shaving timing, thereby further ensuring the stable and balanced grid load, reducing the load pressure on grid lines, and continuously meeting the cooling and heating energy supply standards of the core area. This achieves the dual goals of power peak shaving and cooling and heating supply guarantee, and improves the economic efficiency and safety of the park's integrated energy system operation.

[0045] In this embodiment, based on the combined cold and heat control data set, namely the new energy power generation data corresponding to the supply side and the cold and heat load data corresponding to the energy consumption side, conventional cold and heat coordinated control level and deep cold and heat coordinated control level are obtained, and corresponding power consumption peak shaving instructions are generated according to the level results. This helps to improve the precision of control decision-making and the accuracy of operating condition adaptation, and achieve precise matching between peak shaving instructions and supply and demand fluctuations. In this way, it can ensure the efficient implementation of grid peak shaving and valley filling needs, continuous and stable cold and heat supply at the user end, and efficient local consumption of new energy power generation. This is conducive to comprehensively avoiding the risks of grid load fluctuations and power supply interruptions caused by supply and demand imbalances.

[0046] When the output level is the conventional cooling and heating coordinated control level, conventional cooling and heating coordinated control is activated; when the output level is the deep cooling and heating coordinated control level, deep cooling and heating coordinated control is activated. This helps to distinguish the control priorities under different operating conditions, differentiate the control logic between stable operating conditions and complex fluctuating operating conditions, and thus effectively weaken the grid regulation pressure caused by the randomness of new energy output and the coupling fluctuation of cooling and heating loads, and alleviate the load imbalance problem caused by the superposition of supply-side and energy-consuming side fluctuations.

[0047] Preferably, the specific process for monitoring the compliance of the linkage control is as follows: Obtain the operating status parameters of the cooling and heating regulating equipment, and compare these parameters with the preset target operating parameter range; if all operating status parameters of the cooling and heating regulating equipment are within the preset target operating parameter range set by designated personnel, perform load forecasting for the future time period based on the corresponding operating status parameters of the cooling and heating regulating equipment, to output control results that meet the grid peak-shaving requirements and cooling and heating supply requirements, i.e., the predicted cooling load value, predicted heating load value, and corresponding charging and discharging time sequence data for the future time period; if the operating status parameters of the cooling and heating regulating equipment are not within the preset target operating parameter range, obtain the corresponding operating deviation results of the cooling and heating regulating equipment, and perform linkage control optimization operations based on the operating deviation results of the cooling and heating regulating equipment; the operating deviation results of the cooling and heating regulating equipment are represented by the difference between the operating status parameters of the cooling and heating regulating equipment and the boundary values ​​of the preset target operating parameter range.

[0048] Specifically, when the operating status parameters of the heating and cooling equipment are higher than the upper limit of the preset target operating parameter range, the difference between the operating status parameters and the upper limit is used as the operating deviation result. When the operating status parameters are lower than the lower limit of the preset target operating parameter range, the difference between the operating status parameters and the lower limit is used as the operating deviation result. The operating status parameters of the heating and cooling equipment include the average cooling temperature and the average heating temperature. The average cooling temperature represents the average temperature of the refrigerant, chilled water, or chilled air output by the heating and cooling equipment to the cooling load side, monitored by temperature sensors, within a preset statistical time period. The average heating temperature represents the average temperature of the heat medium, hot water, or hot air output by the heating and cooling equipment to the heat load side, monitored by temperature sensors, within a preset statistical time period. The linkage control optimization operation is used to coordinately correct the operating power, charging and discharging status, and flow regulation parameters of each heating and cooling equipment based on the operating deviation results, so as to reduce the deviation between the actual operating status and the target operating status of the equipment, improve the stability of heating and cooling supply, the linkage matching of equipment, and the power peak shaving execution effect.

[0049] like Figure 3The diagram shown is a schematic of the linkage control optimization operation provided in the embodiment of the present invention: Based on the operating state parameters that are not within the preset target operating parameter range, a linkage control parameter set is obtained, and the target heating and cooling equipment, the energy charging and discharging state of the cold storage device, the energy charging and discharging state of the heat storage device, the opening degree of the regulating valve and the frequency of the circulating pump are coordinated and corrected respectively.

[0050] The linkage control optimization operation involves matching a corresponding linkage control optimization strategy based on the operating deviation results of the heating and cooling equipment, performing coordinated corrections based on the matched linkage control optimization strategy, and outputting the corrected operating status parameters of the heating and cooling equipment for load forecasting in the future time period. The coordinated correction based on the matched linkage control optimization strategy includes coordinated corrections of the target heating and cooling equipment, the energy storage device's charging and discharging status, the energy storage device's charging and discharging status, the regulating valve opening degree, and the circulating pump frequency. The specific process of the coordinated correction based on the matched linkage control optimization strategy is as follows: The system uses operating status parameters that are outside the preset target operating parameter range as input parameters to the linkage control parameter configuration model, and outputs the corresponding linkage control parameter set. The linkage control parameter set represents a dataset consisting of the configuration parameters of the target heating and cooling equipment, the energy storage device charging and discharging configuration parameters, the energy storage device charging and discharging configuration parameters, the regulating valve opening degree, and the circulating pump frequency. Based on the output linkage control parameter set, the corresponding parameters are configured accordingly, and after a preset time period after the configuration is completed, the operating status parameters of the heating and cooling equipment are obtained to predict the load for the future time period.

[0051] By performing coordinated control optimization operations, namely, coordinating the correction of the target heating and cooling regulation equipment, the energy storage and discharging status of the cold storage device, the energy storage and discharging status of the heat storage device, the opening degree of the regulating valve, and the frequency of the circulating pump, and configuring them accordingly based on the output set of coordinated control parameters, this method helps to improve the coordination matching of the entire heating and cooling regulation equipment, the accuracy of parameter correction, and the stability of system operation, compared to the shortcomings of existing methods such as incomplete parameter correction and incomplete elimination of operating deviations. This can quickly reduce the deviation between the actual operating state and the target operating state of the heating and cooling regulation equipment, balance power peak-shaving efficiency and heating and cooling supply quality, extend the overall service life of the equipment, and reduce the overall energy consumption of the system.

[0052] In addition to the above, the configuration parameters of the target heating and cooling equipment include the operating power and operating frequency of the heating and cooling equipment; the energy storage device's charging and discharging configuration parameters include the energy storage device's charging rate, energy storage device's discharging rate, energy storage device's charging power, and energy storage device's discharging power; and the energy storage device's charging and discharging configuration parameters include the energy storage device's charging rate, energy storage device's discharging rate, energy storage device's charging power, and energy storage device's discharging power.

[0053] In this embodiment, by monitoring the compliance of the linkage control, and when the operating status parameters of the cooling and heating regulating equipment are all within the preset target operating parameter range, load prediction for the future time period is made based on the corresponding operating status parameters of the cooling and heating regulating equipment. Conversely, linkage control optimization is performed based on the operating deviation results of the cooling and heating regulating equipment. Compared with the shortcomings of the existing technology, such as the lack of control closed loop, difficulty in timely correction of operating deviation, lack of real-time data support for load prediction, and passive response to power supply anomalies and peak shaving deviations, this method is conducive to improving the closed-loop control capability, anomaly response speed and predictive foresight of the entire cooling and heating coordinated control. This effectively ensures that the cooling and heating supply parameters are continuously and stably up to standard, and that the power peak shaving command is accurately implemented, avoiding the accumulation of small deviations that may cause power supply failures or peak shaving failures.

[0054] It is worth mentioning that by monitoring the correlation between the compliance status of linkage control and the load forecast in the future period, it is more conducive to improving the forward adaptability and dynamic iteration capability of the control strategy, breaking the passive control mode of execution first and adjustment later, thereby predicting the future fluctuation trend of heating and cooling load and power load in advance, avoiding potential abnormal risks in advance, and making subsequent peak shaving and energy supply control more targeted.

[0055] Preferably, load prediction for the future time period is performed based on the operating status parameters of the corresponding heating and cooling regulation equipment. The specific process is as follows: The operating status parameters of the heating and cooling regulation equipment obtained in the current time period are input into the preset heating and cooling load prediction model, and the predicted cooling load value, predicted heating load value, and corresponding charging and releasing time sequence data for the future time period are output. The charging and releasing time sequence data includes the energy release time sequence of the thermal storage device, the energy release time sequence of the cold storage device, the energy charging time sequence of the thermal storage device, and the energy charging time sequence of the cold storage device. Taking the energy release time sequence of the thermal storage device as an example, this data refers to the time sequence data composed of the energy release start time, energy release end time, and heat release power of the thermal storage device at each predicted time in the future time period, which is used to reflect the heat release scheduling sequence and heat release change law of the thermal storage device in the future time period.

[0056] like Figure 4The diagram shown is a structural schematic of a smart heating and cooling control system for power coordination and peak shaving provided in an embodiment of this invention. The system includes: a heating and cooling coordination determination module, a linkage control module, and a linkage feedback correction module. The heating and cooling coordination determination module determines the heating and cooling coordination control level for the current period based on a set of collected heating and cooling control data representing the power supply fluctuation state and the coupling state of the energy consumption load. The linkage control module generates corresponding peak shaving commands based on the level results and performs linkage control on the heating and cooling adjustment equipment according to the commands. The heating and cooling adjustment equipment includes, but is not limited to, cold storage devices, heat storage devices, heat pump units, electric chillers, electric heating equipment, circulating pumps, and regulating valves. The linkage feedback correction module corrects the system based on the feedback results after the linkage control is executed, outputting control results that meet the grid peak shaving requirements and heating and cooling supply requirements.

[0057] In this embodiment, load forecasting for future time periods is performed based on the operating status parameters of the cooling and heating regulation equipment. Specifically, the predicted cooling load value, predicted heating load value, and corresponding charging and discharging time sequence data for future time periods are output based on the operating status parameters of the cooling and heating regulation equipment. This helps to achieve early perception of cooling and heating load fluctuation trends and advance planning of the charging and discharging rhythm of energy storage devices. It also connects real-time control with forward-looking prediction, thereby enabling early matching of new energy output changes and grid peak-shaving periods. This avoids energy supply gaps or peak-shaving failures caused by blind charging and discharging of energy storage devices, further improving the stability of cooling and heating supply and the efficiency of power peak-shaving execution.

[0058] Example 2, based on the method provided in Example 1, in the event of a sudden increase or decrease in renewable energy output, if the change in renewable energy output exceeds the preset maximum output change threshold, the change in renewable energy output is represented by the difference between the power output of the renewable energy equipment at the current sampling time and the power output of the renewable energy equipment at the previous sampling time. This short-term mismatch between cooling / heating load and power load may result in slight or severe anomalies in the actual cooling / heating supply values ​​deviating from the set target values. The specific process for monitoring and controlling the compliance status is as follows: Monitor the cooling / heating supply status and classify the supply anomaly level based on parameters reflecting abnormal cooling / heating supply conditions. The parameters reflecting abnormal cooling and heating supply include one or more of the following: the absolute value of the difference between the cooling capacity and the preset target cooling capacity, the absolute value of the difference between the heating capacity and the preset target heating capacity, and the duration of the cooling and heating supply interruption. The specific process for classifying the supply anomaly level is as follows: input the parameters reflecting abnormal cooling and heating supply into the supply anomaly level model and output the anomaly classification result; when the output anomaly classification level is high anomaly level, send a maintenance reminder for the heat storage device or cold storage device to the preset personnel; when the output anomaly classification level is low anomaly level, perform load forecasting for the future time period based on the operating status parameters of the cooling and heating regulation equipment.

[0059] In this embodiment, when there is a sudden increase or decrease in the output of new energy sources, the monitoring and linkage control status is monitored. That is, the supply anomaly level is divided according to the parameters reflecting the abnormality of the heating and cooling supply. When the abnormality level is high, the maintenance prompt of the heat storage device or cold storage device is sent to the preset personnel. When the abnormality level is low, the load forecast for the future time period is made based on the operating status parameters of the heating and cooling regulation equipment. Compared with the shortcomings of existing methods, such as the lag in anomaly identification, passive fault handling, and easy mis-control or over-handling, this method helps to achieve a combination of accurate hierarchical identification of energy supply anomalies, rapid fault alarm and proactive handling of minor deviations. This can effectively avoid the expansion of energy supply instability caused by drastic fluctuations in new energy sources and ensure the continuous and reliable supply of heating and cooling.

[0060] like Figure 5 The figure shown is a controllable load response curve provided by an embodiment of the present invention: the horizontal axis is the load, and the vertical axis is the time, such as from the 19th to the 26th of a certain month. This figure shows the controllable load adjustment effect of the system on different dates. The blue area represents the reference load, such as the cooling load or the heating load. The red curve represents the load after adjustment by the system. It can achieve peak shaving and valley filling of the load while maintaining indoor comfort, respond to the peak and valley electricity price signal of the power grid, and improve the power coordination efficiency.

[0061] like Figure 6 The figure shown is a diagram illustrating the total load decomposition and peak shaving effect provided in an embodiment of this invention. This diagram shows the total load distribution and composition of the system on different dates, including HVAC (Heating, Ventilation, and Air Conditioning) load, controllable load, and non-reducible load. The horizontal axis represents the load, and the vertical axis represents time. For example, from the 19th to the 25th of a certain month, the controllable load is reduced during peak hours and reasonably adjusted during off-peak hours to achieve overall load peak shaving and valley filling, optimize power coordination efficiency, and at the same time ensure indoor comfort and a stable supply of uncontrollable load.

[0062] The following points need to be explained: (1) The accompanying drawings of the embodiments of the present invention only involve the structures involved in the embodiments of the present invention. Other structures can refer to the general design.

[0063] (2) For clarity, the thickness of layers or regions is enlarged or reduced in the drawings used to describe embodiments of the invention, i.e., these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being “above” or “below” another element, the element may be “directly” located “above” or “below” the other element or there may be intermediate elements.

[0064] (3) Where there is no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other to obtain new embodiments.

[0065] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A smart heating and cooling control method based on power coordination and peak shaving, characterized in that, include: S1. Based on the collection of cooling and heating control data that characterizes the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side, determine the cooling and heating coordinated control level for the current period. S2, Generate corresponding power consumption peak-shaving instructions based on the grade results, and perform linkage control on the cooling and heating regulation equipment according to the power consumption peak-shaving instructions; S3, based on the feedback results after the execution of the linkage control, makes corrections and outputs control results that meet the grid peak-shaving requirements and cooling and heating supply requirements; The set of cooling and heating control data, which is based on the collected data representing the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side, determines the cooling and heating coordination control level for the current period. Specifically, the set of cooling and heating control data is input into a preset coordination and peak-shaving demand analysis model, and the cooling and heating coordination control level corresponding to the current period is output. The cooling and heating synergistic control levels include conventional cooling and heating synergistic control level and deep cooling and heating synergistic control level; The collection of cooling and heating control data includes new energy power generation data on the supply side and cooling and heating load data on the energy consumption side. The process of generating corresponding peak-shaving commands based on the grade results, and then performing linkage control on the heating and cooling regulation equipment according to the peak-shaving commands, is as follows: When the output level is the conventional cooling and heating coordinated control level, the conventional cooling and heating coordinated control is activated, and a conventional level power consumption peak shaving command is generated. When the output level is deep cooling and heating coordinated control level, deep cooling and heating coordinated control is activated, and deep-level power consumption peak-shaving command is generated. The conventional combined cooling and heating control is as follows: Send a notification that peak shaving action is not triggered to the preset control center, maintain the initial operating parameters of the cold and heat source equipment for output, and keep the cold storage device and heat storage device in standby mode to maintain normal power consumption. The specific process of the deep heating and cooling coordinated control is as follows: Monitor the power grid load value and determine whether the power grid load value is greater than the preset power grid load; If the grid load value is not greater than the preset grid load, a prompt not to trigger peak shaving action is sent to the preset control center, and the cold storage device and the heat storage device remain in standby mode. If the grid load value is greater than the preset grid load, perform the following operations: Send a prompt to trigger peak shaving action, input the corresponding set of cold and heat control data as input data into the data cold and heat source equipment power control model, and output the corresponding output power value of the cold and heat source equipment through the data cold and heat source equipment power control model to adjust the power of the corresponding cold and heat source equipment to the output power value; Send a prompt to the preset control center to switch the cold storage device and / or heat storage device from standby mode to energy release mode; Monitor the grid load value and determine whether the grid load value is greater than the preset grid load. If it is still greater than the preset grid load, send a peak-shaving control alarm to the preset control center. Otherwise, send a prompt to the preset control center to switch the cold storage device and the heat storage device to the energy storage state, continue to execute the linkage control of the cold and heat regulation equipment, and monitor the qualification status of the linkage control. The training data corresponding to the collaborative and peak-shaving demand analysis model is a collection of cold and hot control data and a preset cold and hot collaborative control level. The training data corresponding to the power control model of the data cold and heat source equipment consists of a collection of cold and heat control data corresponding to the triggering of peak shaving actions and the preset output power values ​​of the cold and heat source equipment.

2. The intelligent heating and cooling control method based on power coordination and peak shaving as described in claim 1, characterized in that, The specific procedure for verifying the compliance of the monitoring and linkage control is as follows: Obtain the operating status parameters of the heating and cooling equipment, and compare the operating status parameters of the heating and cooling equipment with a preset target operating parameter range; If the operating status parameters of the heating and cooling regulation equipment are all within the preset target operating parameter range, load forecasting for the future time period is performed based on the corresponding operating status parameters of the heating and cooling regulation equipment, so as to output control results that meet the grid peak shaving requirements and heating and cooling supply requirements. If the operating status parameters of the heating and cooling equipment are not within the preset target operating parameter range, obtain the corresponding operating deviation results of the heating and cooling equipment, and perform linkage control optimization operations based on the operating deviation results of the heating and cooling equipment. The operating deviation of the heating and cooling equipment is represented by the difference between the operating status parameters of the heating and cooling equipment and the boundary values ​​of the preset target operating parameter range; The operating parameters of the heating and cooling equipment include the average cooling temperature and the average heating temperature.

3. The intelligent heating and cooling control method based on power coordination and peak shaving as described in claim 2, characterized in that, The linkage control optimization operation means matching the corresponding linkage control optimization strategy based on the operating deviation of the cooling and heating equipment, performing collaborative correction based on the matched linkage control optimization strategy, and outputting the corrected operating status parameters of the cooling and heating equipment to predict the load in the future time period. The coordinated correction based on the linkage control optimization strategy obtained by matching includes coordinated correction of the target heating and cooling equipment, the energy charging and discharging state of the cold storage device, the energy charging and discharging state of the heat storage device, the opening degree of the regulating valve and the frequency of the circulating pump. The coordinated correction based on the matched linkage control optimization strategy is performed as follows: Based on the operating status parameters that are not within the preset target operating parameter range, the parameters are input to the linkage control parameter configuration model, and the corresponding linkage control parameter set is output. The linkage control parameter set represents a dataset consisting of the configuration parameters of the target heating and cooling equipment, the energy storage device charging and discharging configuration parameters, the energy storage device charging and discharging configuration parameters, the regulating valve opening degree, and the circulating pump frequency. Based on the output set of linkage control parameters, the corresponding parameters are configured accordingly, and after a preset time period following the configuration, the operating status parameters of the heating and cooling equipment are obtained to predict the load for the future time period.

4. The intelligent heating and cooling control method based on power coordination and peak shaving as described in claim 3, characterized in that, The configuration parameters of the target heating and cooling control equipment include the operating power and operating frequency of the heating and cooling control equipment; The energy storage device's charging and discharging configuration parameters include the energy storage device's charging rate, energy storage device's discharging rate, energy storage device's charging power, and energy storage device's discharging power. The energy storage device's charging and discharging configuration parameters include the energy storage device's charging rate, energy storage device's discharging rate, energy storage device's charging power, and energy storage device's discharging power.

5. The intelligent heating and cooling control method based on power coordination and peak shaving according to claim 2, characterized in that, The specific process for predicting the load for a future time period based on the operating status parameters of the corresponding heating and cooling control equipment is as follows: The operating status parameters of the cooling and heating regulation equipment obtained in the current time period are input into the preset cooling and heating load prediction model, and the predicted cooling load value, predicted heating load value and corresponding charging and discharging time series data for the future time period are output. The energy release timing data includes the energy release timing of the thermal storage device, the energy release timing of the cold storage device, the energy release timing of the thermal storage device, and the energy release timing of the cold storage device.

6. The intelligent heating and cooling control method based on power coordination and peak shaving as described in claim 1, characterized in that, The specific procedure for verifying the compliance of the monitoring and linkage control is as follows: Monitor the status of cold and heat supply, and classify the supply anomaly level based on parameters reflecting abnormal cold and heat supply conditions; The parameters reflecting abnormal cooling and heating supply include one or more of the following: the absolute value of the difference between the cooling capacity and the preset target cooling capacity, the absolute value of the difference between the heating capacity and the preset target heating capacity, and the duration of the cooling and heating supply interruption. The specific process for classifying supply anomaly levels is as follows: Input the parameters reflecting abnormal cold and heat supply into the supply anomaly level model and output the anomaly classification results; When the output anomaly classification level is high, a maintenance reminder for the thermal storage device or cold storage device is sent to the preset personnel. When the output anomaly classification level is low, load forecasting for the future time period is performed based on the operating status parameters of the heating and cooling regulation equipment.

7. A smart heating and cooling control system based on power coordination and peak shaving, used to implement the smart heating and cooling control method based on power coordination and peak shaving as described in any one of claims 1-6, characterized in that, include: The module includes a cold / heat coordination determination module, a linkage control module, and a linkage feedback correction module. The cooling and heating coordination determination module is used to determine the cooling and heating coordination control level for the current period based on the collection of cooling and heating control data that characterizes the power output fluctuation state on the supply side and the cooling and heating load coupling state on the energy consumption side. The linkage control module is used to generate corresponding power consumption peak-shaving instructions based on the level results, and to perform linkage control on the cooling and heating regulation equipment according to the power consumption peak-shaving instructions. The linkage feedback correction module is used to correct the control based on the feedback results after the linkage control is executed, and output the control results that meet the power grid peak shaving requirements and cooling and heating supply requirements.

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