Control method and system of combined cooling heating and power system

Abstract

The invention relates to the technical field of combined cooling heating and power control, and discloses a control method and system of a combined cooling heating and power system. The method comprises the following steps: acquiring real-time operation parameters; executing cooling and heating load demand prediction; performing energy flow optimization scheduling by adopting a cross energy deviation penalty model prediction control algorithm; constructing a thermal threshold self-adaptive compression mechanism to generate a hot water recovery reference threshold; the opening degree of the hot return water adjusting valve is controlled through a double-layer adjusting mechanism to complete dynamic recovery control of heat energy. In the prior art, heat recovery control is carried out based on a single heat load threshold value or a static setting mode, and especially in a scene of rapid fluctuation of cooling and heating loads, energy global coordination optimization is difficult to realize. Due to the fact that cold and heat coupling prediction, cross energy deviation punishment optimization scheduling and a nonlinear thermal threshold regulation and control mechanism are introduced, dynamic balance control and hot water recovery benefit maximization between the cooling, heating and power systems are achieved, and the comprehensive energy utilization efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of combined cooling, heating and power (CCHP) control technology, and in particular to a control method and system for a combined cooling, heating and power (CCHP) system. Background Technology

[0002] Currently, combined cooling, heating, and power (CCHP) systems have become one of the key infrastructures for building clean and efficient integrated energy systems, and are widely deployed in energy-consuming scenarios sensitive to fluctuations in heating and cooling loads, such as hospital campuses, data centers, commercial complexes, and industrial plants. Its core lies in achieving efficient coupling and conversion between electrical energy, heat energy (hot water), and cold energy (absorption refrigeration) through combined heat and power equipment such as gas-fired internal combustion engines or gas turbines, thereby maximizing the utilization rate of primary energy.

[0003] However, there are still many technical problems in the existing control strategies, mainly reflected in the following aspects: (1) The dynamic prediction capability of cold and heat load is weak and the energy flow configuration delay is serious. Most current CCHP systems use static load prediction based on time series or preset operating curves as the basis for load scheduling, which is difficult to adapt to the sudden change characteristics of cold and heat load under unstable operating scenarios. (2) The thermoelectric coupling regulation mechanism is crude and the regulating valve response lacks feedback adaptability. In traditional CCHP systems, the regulation between the hot water storage tank and the heating circuit usually relies on fixed opening degree or proportional integral (PI) control strategy to set threshold to adjust the opening degree of hot water valve. It fails to introduce feedback parameters such as heat storage state, inertial response and heat load lag. Especially under the condition of system response lag or frequent pump start and stop, there are problems such as unstable heating and low return water temperature, which reduces heat recovery efficiency. (3) There is a lack of a coordinated scheduling and recycling mechanism for heat storage units. Existing control systems generally treat the heat storage unit as a passive buffer and do not build a dynamic scheduling mechanism with the return water temperature difference as the feedback quantity. Especially during partial load operation (such as low load at night), the output of the heat storage unit cannot be effectively controlled, resulting in an imbalance in its energy storage strategy, and the return water temperature difference deviates from the set target, affecting the efficiency of the heat exchanger and the stability of downstream energy consumption.

[0004] Therefore, there is an urgent need for a control method that can still achieve multi-dimensional load forecasting, energy flow optimization scheduling, and precise control of hot water recovery under the conditions of alternating fluctuations in heating and cooling loads and dynamic changes in energy storage status, so as to improve the adaptive operation capability and overall energy utilization efficiency of the heating, cooling and power system. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the present invention aims to propose a control method for a combined cooling, heating, and power (CCHP) system. This method addresses the technical problem that existing technologies, which rely on a single heat load threshold or static setting for heat recovery control, struggle to achieve global energy coordination and optimization, especially in scenarios with rapid fluctuations in cooling and heating loads.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a control method for a combined cooling, heating and power (CCHP) system. The control method for the combined cooling, heating and power system includes: Step S10: Obtain the real-time operating parameters of the combined cooling, heating and power (CCHP) system, including power supply. Waste heat temperature Cooling output Heat output Cooling load demand Heat load demand Cold energy storage state and thermal energy storage state ; Step S20: Based on real-time operating parameters, a multi-window forecasting mechanism based on cold-heat coupling sensitivity is used to perform load demand forecasting, and the predicted cooling load demand value at future time t is output. Heat load demand forecast , Load Priority Weight With secondary weight of heat load ; Step S30: Based on the predicted cooling load demand Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit ; Step S40: Based on power supply load rate Absorption refrigeration power With power of electric refrigeration unit The dynamic hot water recovery control task is performed using a heat threshold compression regulation mechanism, and a hot water recovery reference threshold is output. ; Step S50: Based on the hot water recovery reference threshold It performs closed-loop feedback control tasks and outputs instructions on the opening degree of the hot water return regulating valve.

[0007] Preferably, in step S10, the combined cooling, heating and power system includes a power supply unit, a heat recovery unit, a cooling and heating energy storage unit, and a load monitoring unit.

[0008] Preferably, in step S20, a multi-window forecasting mechanism based on cold-heat coupling sensitivity is used to perform the load demand forecasting task based on real-time operating parameters, and the predicted value of cooling load demand at future time t is output. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The steps specifically include: Step S201: Construct a multi-scale cooling and heating load prediction model based on real-time operating parameters. The multi-scale cooling and heating load prediction model includes a short-term window cooling and heating load prediction model, a medium-term window cooling and heating load prediction model, and a long-term window cooling and heating load prediction model. Step S202: Use the real-time operating parameters as input to the multi-scale cooling and heating load prediction model. The multi-scale cooling and heating load prediction model outputs a set of cooling load prediction sequences for future time t. With heat load prediction sequence set ;in, This represents the short-term cooling load forecast for a future time t. The medium-term cooling load forecast for future time t; This represents the long-term cooling load forecast for a future time t. This represents the short-term heat load forecast for a future time t. This represents the short-term heat load forecast for a future time t. This represents the short-term heat load forecast for a future time t. Step S203: Based on the set of cooling load prediction sequences With heat load prediction sequence set The weighted residual inverse fusion principle is used to perform multi-time window fusion processing, and the predicted value of cooling load demand at future time t is output. Heat load demand forecast ; Step S204: Based on the set of cooling load prediction sequences A cooling load priority factor is constructed using the mean ratio of fluctuation amplitude, and a heating load priority factor is constructed using the mean of time-series rate of change analysis based on the set of heating load prediction sequences. The cooling and heating load priority factors are then normalized using the Softmax normalization method, and the output is... Load Priority Weight With secondary weight of heat load .

[0009] Preferably, in step S201, the short-term window heating and cooling load prediction model is established using a LightGBM network to identify load points with sudden increases or decreases; the medium-term window heating and cooling load prediction model is established using an LSTM time series network to identify load trends with periodic changes; and the long-term window heating and cooling load prediction model is established using a GRU-gated cyclic network to identify stable load cycle segments.

[0010] Preferably, in step S30, based on the predicted cooling load demand... Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit The steps specifically include: Step S301: Construct a set of energy flow balance constraints, which includes power balance constraints, cooling energy distribution balance constraints, thermal energy balance constraints, and equipment operation boundary constraints. Step S302: Obtain the power supply load rate, absorption cooling power, and electric cooling unit power. Use the power supply load rate, absorption cooling power, and electric cooling unit power as the optimized control variables for the MPC predictive control algorithm, combined with the predicted cooling load demand. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The MPC objective function J is constructed using a method based on cross-energy bias penalty. Step S303: Based on the MPC objective function J, the MPC predictive control algorithm is used to perform the optimal control solution extraction task based on the energy flow balance constraint set, and the current optimal control solution set is output. The current optimal control solution set includes the power supply load rate. Absorption refrigeration power With power of electric refrigeration unit .

[0011] Preferably, in step S40, the hot water recovery reference threshold is... The formula is expressed as:

[0012] in, The reference hot water recovery temperature; The heat recovery conversion coefficient is used to adjust the intensity of the influence of absorbed and mechanical energy on temperature rise; This is the disturbance attenuation coefficient, used to deduct the temperature effect caused by non-recoverable loads; The comprehensive factor for the energy conversion efficiency of the heat absorption unit; This is a comprehensive factor for mechanical energy recovery efficiency.

[0013] Preferably, in step S50, based on the hot water recovery reference threshold The steps for executing a closed-loop feedback control task and outputting the opening command of the hot return water regulating valve specifically include: Step S501: Based on absorption cooling power With power of electric refrigeration unit The calculated value of the current thermal potential is obtained directly by using an explicit solution method based on the conservation of heat and work. And calculate the value based on the current thermal potential. Reference threshold for hot water recovery Constructing temperature difference error term , ; Step S502: Based on the temperature difference error term The valve opening controller function is constructed using a two-layer regulation mechanism combining a proportional-integral controller and a nonlinear gain regulator. Regulating valve opening controller function ; Control valve opening function The formula is expressed as:

[0014]

[0015] in, This is the normalization function; This is the unnormalized original control valve opening controller function after processing by a proportional-integral controller and a nonlinear gain regulator. The exponential activation function used for the nonlinear gain regulator; This refers to the nonlinear activation offset term of the nonlinear gain regulator. The slope factor of the nonlinear gain regulator; This refers to the gain coefficient of the proportional controller; The gain coefficient of the integral controller; For any time The corresponding temperature difference error term; Step S503: When the regulating valve opening controller function When the output is 0, the output hot return water regulating valve opening command is "hot return water regulating valve closing command"; When the regulating valve opening controller function When the output is 1, the output hot return water regulating valve opening command is "hot return water regulating valve fully open command"; when At this time, the output hot return water regulating valve opening command is the "hot return water regulating valve proportional opening command"; the "hot return water regulating valve proportional opening command" is used to instruct the valve controller to interact with the regulating valve opening controller function. Adjust the valve angle according to the corresponding ratio.

[0016] The present invention also provides a control system for a combined cooling, heating and power (CCHP) system, comprising: The real-time operating parameter acquisition module is used to acquire the real-time operating parameters of the combined cooling, heating and power (CCHP) system, including power supply capacity. Waste heat temperature Cooling output Heat output Cooling load demand Heat load demand Cold energy storage state and thermal energy storage state ; The cooling-heat coupling prediction and analysis module is used to perform load demand forecasting tasks based on real-time operating parameters and a multi-window forecasting mechanism based on cooling-heat coupling sensitivity, outputting the predicted cooling load demand value at future time t. Heat load demand forecast , Load Priority Weight With secondary weight of heat load ; The cross-flow optimization scheduling module is used to optimize the scheduling based on the predicted cooling load demand. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit ; The thermal threshold adaptive control module is used to adjust the power supply load rate. Absorption refrigeration power With power of electric refrigeration unit The dynamic hot water recovery control task is performed using a heat threshold compression regulation mechanism, and a hot water recovery reference threshold is output. ; The heat recovery closed-loop control module is used to control the heat recovery based on a hot water recovery reference threshold. It performs closed-loop feedback control tasks and outputs instructions on the opening degree of the hot water return regulating valve.

[0017] The present invention also provides a control device for a combined cooling, heating and power (CCHP) system, comprising: a memory, a processor, and a control program for the CCHP system stored in the memory and executable on the processor. When the control program for the CCHP system is executed by the processor, a control method for the CCHP system is implemented.

[0018] The present invention also provides a computer program product, including a control program for a combined cooling, heating and power (CCHP) system, wherein the control program for the CCHP system, when executed by a processor, implements the control method for the CCHP system.

[0019] The beneficial effects of this invention are as follows: By introducing a multi-window prediction mechanism based on the sensitivity of cold and heat coupling, this invention can accurately predict future load demand based on the dynamic change characteristics of cooling load and heating load in a combined cooling, heating and power system. It also combines load priority weights for differentiated scheduling, providing high-timeliness and high-resolution prior input for subsequent energy flow optimization. Compared with the traditional static threshold control method, it significantly improves response agility and scheduling foresight.

[0020] This invention innovatively integrates a model predictive control algorithm with cross-energy deviation penalty and a heat threshold compression regulation mechanism to construct a collaborative control architecture for energy flow scheduling and hot water recovery that covers the coupling of cold, heat, and electricity. It can adaptively output a reference threshold for hot water recovery in scenarios with drastic load fluctuations or dynamic adjustment of cold and heat priorities, and finely adjust the valve opening through a two-layer control function, effectively improving the heat recovery rate and supply-demand matching capability, and enhancing the overall operational stability and energy efficiency of the cooling, heating, and power system. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0022] Figure 1 This is a flowchart illustrating the first embodiment of a control method for a combined cooling, heating, and power (CCHP) system according to the present invention.

[0023] Figure 2 This is a schematic diagram of the cooling and heating load prediction curves for a first embodiment of the control method for a combined cooling, heating and power (CCHP) system of the present invention.

[0024] Figure 3This is a schematic diagram of the dynamic weight change of load priority in the first embodiment of the control method for a combined cooling, heating and power system of the present invention.

[0025] Figure 4 This is a schematic diagram of the energy flow optimization scheduling results of the first embodiment of the control method for a combined cooling, heating and power system of the present invention.

[0026] Figure 5 This is a schematic diagram of the equipment for the control method of a combined cooling, heating and power system according to the present invention. Detailed Implementation

[0027] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example 1: As Figure 1 The diagram shown is a flowchart of the first embodiment of the control method for the combined cooling, heating, and power (CCHP) system of the present invention, which presents the first embodiment of the control method for the CCHP system of the present invention.

[0029] In the first embodiment, the control method of the combined cooling, heating and power system includes: Step S10: Obtain the real-time operating parameters of the combined cooling, heating and power (CCHP) system, including power supply. Waste heat temperature Cooling output Heat output Cooling load demand Heat load demand Cold energy storage state and thermal energy storage state ; Understandably, the real-time operating parameters acquired in this step constitute the basic input information for the state identification and predictive control of the combined cooling, heating, and power (CCHP) system. Continuous monitoring of these parameters allows for the establishment of relationships between various energy flow paths within the system, and the identification of energy efficiency bottlenecks or non-cooperative operating phenomena, providing a precise basis for subsequent control logic decisions.

[0030] It should be understood that, compared with the traditional method of coarse scheduling based solely on a single indicator of cooling or heating load, this embodiment introduces multi-dimensional real-time operating parameters in step S10, especially integrating factors such as power output, actual cooling and heating load demand, and energy storage status, which can accurately reflect the supply and demand matching status and the dynamic characteristics of coupled energy.

[0031] Step S20: Based on real-time operating parameters, a multi-window forecasting mechanism based on cold-heat coupling sensitivity is used to perform load demand forecasting, and the predicted cooling load demand value at future time t is output. Heat load demand forecast , Load Priority Weight With secondary weight of heat load ; It should be noted that "cold-heat coupling sensitivity" refers to the degree of dynamic correlation between the changing trends of cold and heat loads. Specifically, it manifests as the impact of cold load changes on heat load changes within a certain time window, as well as the reverse constraint relationship between heat load and cold load scheduling strategies. The multi-window forecasting mechanism refers to constructing a forecasting model based on historical operating data at multiple different time scales (such as short-term minute-level, medium-term hour-level, and intraday cycle-level) to capture the evolution trends of cold and heat loads in different time domains, and dynamically adjusting the forecast weights and offset windows in conjunction with cold-heat coupling sensitivity.

[0032] Understandably, this step, by integrating the coupling characteristics of cooling and heating loads, enables a more accurate prediction of future energy demand trends. Compared to traditional prediction methods that treat cooling and heating loads as independent variables, the prediction mechanism of this invention fully considers the interconnected changes between the two. For example, in some scenarios, the air conditioning cooling process may trigger the heat recovery system to generate additional heat, thereby affecting the accuracy of heating load prediction. This mechanism, by introducing cooling-heat coupling sensitivity, effectively improves the synergy and responsiveness of cooling and heating load prediction.

[0033] It should be understood that, compared to traditional single-window forecasting methods or mechanisms with fixed model parameters, the multi-window forecasting mechanism adopted in this step can dynamically select the optimal window combination, adaptively switch between short-term and medium-to-long-term forecasting strategies under different load fluctuation modes, and adjust weights based on real-time cooling and heating load coupling relationships, making the forecast results more closely match actual operational needs. Furthermore, by outputting "load priority weights" and "heat load secondary weights," differentiated response control can be achieved under resource-constrained conditions, ensuring priority energy supply to core loads and improving supply-demand matching capabilities.

[0034] For example, such as Figure 2 and Figure 3As shown, a multi-window forecasting mechanism based on the sensitivity of cold and heat coupling was used to generate forecast curves for the cooling and heating loads for the next 24 hours. Simultaneously, corresponding load priority weight and heating load secondary weight curves were output. It can be observed that during the nighttime low-peak period of 0-8 hours, the fluctuation range of cooling and heating loads is small, with the cooling load priority weight close to 0.5, indicating a balanced response. As daytime load demand increases, the cooling load shows an upward trend, with the priority weight gradually rising to above 0.75, while the heating load secondary weight decreases accordingly. The forecast results can dynamically capture the co-evolution characteristics of cooling and heating loads, and the forecast curve closely follows the actual curve, indicating that the mechanism has high forecasting accuracy. Furthermore, the dynamic change in load priority weights can reflect the energy supply tendency under conditions of cooling and heating load conflict or resource scarcity, which is helpful for implementing differentiated control strategies in subsequent steps.

[0035] Step S30: Based on the predicted cooling load demand Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit ; It should be noted that the "MPC predictive control algorithm with cross-energy deviation penalty" in this step refers to introducing a cross-coupling error term between cold and hot energy flows as a component of the control cost function within the Model Predictive Control (MPC) framework, and applying a directional penalty to the adjustment response deviation of the cold and hot loads. Specifically, the priority weight of the cold load dominates the minimization of the cold load energy supply deviation, while the secondary weight of the hot load is used to adjust the amount of heat energy recovered from absorption refrigeration, thereby achieving overall coordination and minimum deviation operation of the three energy flow paths (cold, electricity, and heat).

[0036] Understandably, this control strategy not only considers the matching degree between the predicted values ​​of cooling and heating loads and their capacity, but also explicitly introduces a cooling load priority energy supply mechanism. Through weighted regulation, it adjusts the allocation ratio of various loads when power resources are limited or operating strategies are adjusted. During energy flow optimization, scheduling decisions are made among absorption chiller units, electric chiller units, and grid-connected units. The goal is to minimize energy flow deviations, avoid cooling-heating conflicts and energy waste, and improve overall efficiency while ensuring accurate cooling load supply.

[0037] For example, such as Figure 4The figure illustrates the intraday scheduling changes of the three energy flow modes (power supply load rate, absorption cooling, and electric cooling) in step S30. Darker colors indicate higher power output at that time. The figure shows that energy demand is concentrated during the morning peak (8-10 AM) and evening peak (5-8 PM), with all three energy flow paths showing increases. The power supply load rate and electric cooling power reach their peaks during the peak cooling load period. Absorption cooling power responds synchronously within the rising heat load range, reflecting a scheduling strategy of cross-coupling between cooling and heating. By dynamically adjusting the power output of the three paths in different time periods, energy coordination and efficiency improvement are achieved.

[0038] Step S40: Based on power supply load rate Absorption refrigeration power With power of electric refrigeration unit The dynamic hot water recovery control task is performed using a heat threshold compression regulation mechanism, and a hot water recovery reference threshold is output. ; It should be noted that the "heat threshold compression control mechanism" in this step refers to dynamically adjusting the reference threshold for hot water recovery by analyzing the energy conversion efficiency and heat redundancy exhibited by each energy flow path (power supply, absorption refrigeration, and electric refrigeration) at different time periods. This mechanism constructs a heat load pressure index curve based on multi-source power data, and sets a dynamic compression range on this basis. When the heat load fluctuates drastically or the energy efficiency is lower than the preset level, the hot water recovery trigger threshold is automatically tightened; conversely, it is appropriately relaxed, achieving dual protection for energy recovery efficiency and stability.

[0039] Understandably, the core of this mechanism lies in upgrading "hot water recovery control" from a passive response mode to a predictive proactive control mechanism. That is, instead of waiting until there is severe heat redundancy to initiate recovery, it activates the hot water recovery module in advance by narrowing the heat threshold trigger range when indicators such as power load rate and absorption cooling power are detected to be at a critical increase or in a high-energy-consumption operating state. In this way, not only is the efficiency reduction caused by heat accumulation avoided, but also precise timing matching of the building's hot water load is achieved.

[0040] Step S50: Based on the hot water recovery reference threshold It performs closed-loop feedback control tasks and outputs instructions on the opening degree of the hot water return regulating valve.

[0041] It should be noted that in step S50, the reference threshold for hot water recovery is used. The steps for executing a closed-loop feedback control task and outputting the opening command of the hot return water regulating valve specifically include: Step S501: Based on absorption cooling power With power of electric refrigeration unit The calculated value of the current thermal potential is obtained directly by using an explicit solution method based on the conservation of heat and work. And calculate the value based on the current thermal potential. Reference threshold for hot water recovery Constructing temperature difference error term , ; Step S502: Based on the temperature difference error term The valve opening controller function is constructed using a two-layer regulation mechanism combining a proportional-integral controller and a nonlinear gain regulator. Regulating valve opening controller function ; Control valve opening function The formula is expressed as:

[0042]

[0043] in, This is the normalization function; This is the unnormalized original control valve opening controller function after processing by a proportional-integral controller and a nonlinear gain regulator. The exponential activation function used for the nonlinear gain regulator; This refers to the nonlinear activation offset term of the nonlinear gain regulator. The slope factor of the nonlinear gain regulator; This refers to the gain coefficient of the proportional controller; The gain coefficient of the integral controller; For any time The corresponding temperature difference error term; Step S503: When the regulating valve opening controller function When the output is 0, the output hot return water regulating valve opening command is "hot return water regulating valve closing command"; When the regulating valve opening controller function When the output is 1, the output hot return water regulating valve opening command is "hot return water regulating valve fully open command"; when At this time, the output hot return water regulating valve opening command is the "hot return water regulating valve proportional opening command"; the "hot return water regulating valve proportional opening command" is used to instruct the valve controller to interact with the regulating valve opening controller function. Adjust the valve angle according to the corresponding ratio.

[0044] Understandably, the "closed-loop feedback control task" in this step is not only based on a static comparison of the hot water recovery reference threshold, but also incorporates the real-time thermal potential calculation value under the current state. By constructing a "temperature difference error term" as the controller input variable, the adjustment process achieves continuity, real-time performance, and error adaptability. Simultaneously, the controller employs a two-layer control structure, combining the steady-state error suppression capability of a traditional proportional-integral (PI) controller with the rapid response capability of a nonlinear gain regulator to sharp load fluctuations, thereby achieving dynamic control of the valve opening.

[0045] It should be understood that traditional hot water recovery strategies often employ a fixed threshold control mode, neglecting the real-time interaction between the heating and cooling systems. This can easily lead to problems such as "valve dead zone," "over-adjustment lag," or "inability to adjust sensitively" during switching, load fluctuations, or energy efficiency optimization scenarios. The dual-layer control structure and temperature difference-driven mechanism introduced in this invention not only enhance adjustment sensitivity but also improve the synergy between the hot water return path and the heating and cooling systems. This represents a highly efficient thermal energy regulation and control strategy for heating and cooling coupling scenarios. For example, in a commercial building's combined cooling and heating system, a surge in air conditioning cooling load one afternoon leads to a significant increase in the output heat energy of the absorption chiller. However, since the building's hot water demand is low, if the hot water return path is not adjusted in time, the heat energy will accumulate, resulting in energy waste. Through the control mechanism in this step, the increase in absorption chiller power is monitored in real time, and the current heat potential is calculated to be significantly higher, while the hot water recovery reference threshold is relatively low. At this time, a temperature difference error term is formed, triggering the proportional-integral controller to quickly generate a response command. After correction by the nonlinear regulator, a control command of "partial opening of the hot water return regulating valve at 73%" is output. The command acts directly on the valve drive unit, quickly opening the return water path so that heat energy can be partially recovered for subsequent bathing, heating and other uses, avoiding heat accumulation that could cause high temperature alarms, and also improving the dynamic energy efficiency of the entire energy system.

[0046] Example 2: Furthermore, the control system for a combined cooling, heating, and power (CCHP) system provided by this invention employs a control method for a CCHP system as described in the above embodiments, thereby solving the technical problem of controlling a CCHP system. Compared with the prior art, the beneficial effects of the control system for a CCHP system provided by this invention are the same as those of the control method for a CCHP system provided in the above embodiments, and other technical features in the control system for a CCHP system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0047] Example 3: This invention provides a control device for a combined cooling, heating, and power (CCHP) system. Please refer to... Figure 5A control device for a combined cooling, heating, and power (CCHP) system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform the control method for a CCHP system as described in Embodiment 1 above. The control device for a CCHP system in this embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and vehicle terminals (e.g., vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. The control device for a CCHP system is merely an example and should not limit the functionality or scope of the embodiments of this invention. The control device for a CCHP system may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. Random access memory 1004 also stores various programs and data required for the operation of a control device for a combined cooling, heating, and power (CCHP) system. Processing device 1001, read-only memory 1002, and random access memory 1004 are interconnected via bus 1005. I / O interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the control device of a CCHP system to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a control device for a CCHP system with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems may be implemented or possessed alternatively.

[0048] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the control method for a combined cooling, heating, and power (CCHP) system as described above. The computer program product provided by this invention can solve the technical problem of controlling a CCHP system. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as the beneficial effects of the control method for a CCHP system provided in the above embodiments, and will not be repeated here.

[0049] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0050] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0051] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A control method for a combined cooling, heating, and power (CCHP) system, characterized in that, The methods include: Step S10: Obtain the real-time operating parameters of the combined cooling, heating and power (CCHP) system, including power supply. Waste heat temperature Cooling output Heat output Cooling load demand Heat load demand Cold energy storage state and thermal energy storage state ; Step S20: Based on real-time operating parameters, a multi-window forecasting mechanism based on cold-heat coupling sensitivity is used to perform load demand forecasting, and the predicted cooling load demand value at future time t is output. Heat load demand forecast , Load Priority Weight With secondary weight of heat load ; Step S30: Based on the predicted cooling load demand Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit ; Step S40: Based on power supply load rate Absorption refrigeration power With power of electric refrigeration unit The dynamic hot water recovery control task is performed using a heat threshold compression regulation mechanism, and a hot water recovery reference threshold is output. ; Step S50: Based on the hot water recovery reference threshold It performs closed-loop feedback control tasks and outputs instructions on the opening degree of the hot water return regulating valve.

2. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 1, characterized in that, In step S10, the combined cooling, heating and power (CCHP) system includes a power supply unit, a heat recovery unit, a cooling and heating energy storage unit, and a load monitoring unit.

3. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 1, characterized in that, In step S20, a multi-window forecasting mechanism based on cold-heat coupling sensitivity is used to perform load demand forecasting based on real-time operating parameters, and outputs the predicted cooling load demand value at future time t. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The steps specifically include: Step S201: Construct a multi-scale cooling and heating load prediction model based on real-time operating parameters. The multi-scale cooling and heating load prediction model includes a short-term window cooling and heating load prediction model, a medium-term window cooling and heating load prediction model, and a long-term window cooling and heating load prediction model. Step S202: Use the real-time operating parameters as input to the multi-scale cooling and heating load prediction model. The multi-scale cooling and heating load prediction model outputs a set of cooling load prediction sequences for future time t. With heat load prediction sequence set ;in, This represents the short-term cooling load forecast for a future time t. The medium-term cooling load forecast for future time t; This represents the long-term cooling load forecast for a future time t. This represents the short-term heat load forecast for a future time t. This represents the short-term heat load forecast for a future time t. This represents the short-term heat load forecast for a future time t. Step S203: Based on the set of cooling load prediction sequences With heat load prediction sequence set The weighted residual inverse fusion principle is used to perform multi-time window fusion processing, and the predicted value of cooling load demand at future time t is output. Heat load demand forecast ; Step S204: Based on the set of cooling load prediction sequences A cooling load priority factor is constructed using the mean ratio of fluctuation amplitude, and a heating load priority factor is constructed using the mean of time-series rate of change analysis based on the set of heating load prediction sequences. The cooling and heating load priority factors are then normalized using the Softmax normalization method, and the output is... Load Priority Weight With secondary weight of heat load .

4. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 3, characterized in that, In step S201, the short-term window heating and cooling load prediction model is established using a LightGBM network to identify load points with sudden increases or decreases; the medium-term window heating and cooling load prediction model is established using an LSTM time series network to identify periodically changing load trends. The long-term window heating and cooling load prediction model is established based on a GRU gated cyclic network to identify stable load cycle segments.

5. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 1, characterized in that, In step S30, based on the predicted cooling load demand... Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit The steps specifically include: Step S301: Construct a set of energy flow balance constraints, which includes power balance constraints, cooling energy distribution balance constraints, thermal energy balance constraints, and equipment operation boundary constraints. Step S302: Obtain the power supply load rate, absorption cooling power, and electric cooling unit power. Use the power supply load rate, absorption cooling power, and electric cooling unit power as the optimized control variables for the MPC predictive control algorithm, combined with the predicted cooling load demand. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The MPC objective function J is constructed using a method based on cross-energy bias penalty. Step S303: Based on the MPC objective function J, the MPC predictive control algorithm is used to perform the optimal control solution extraction task based on the energy flow balance constraint set, and the current optimal control solution set is output. The current optimal control solution set includes the power supply load rate. Absorption refrigeration power With power of electric refrigeration unit .

6. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 1, characterized in that, In step S40, the reference threshold for hot water recovery is... The formula is expressed as: ； in, The reference hot water recovery temperature; The heat recovery conversion coefficient is used to adjust the intensity of the influence of absorbed and mechanical energy on temperature rise; This is the disturbance attenuation coefficient, used to deduct the temperature effect caused by non-recoverable loads; The comprehensive factor for the energy conversion efficiency of the heat absorption unit; This is a comprehensive factor for mechanical energy recovery efficiency.

7. The control method for a combined cooling, heating, and power (CCHP) system as described in claim 1, characterized in that, In step S50, based on the hot water recovery reference threshold The steps for executing a closed-loop feedback control task and outputting the opening command of the hot return water regulating valve specifically include: Step S501: Based on absorption cooling power With power of electric refrigeration unit The calculated value of the current thermal potential is obtained directly by using an explicit solution method based on the conservation of heat and work. And calculate the value based on the current thermal potential. Reference threshold for hot water recovery Constructing temperature difference error term , ; Step S502: Based on the temperature difference error term The valve opening controller function is constructed using a two-layer regulation mechanism combining a proportional-integral controller and a nonlinear gain regulator. Regulating valve opening controller function ; Control valve opening function The formula is expressed as: ； ； in, This is the normalization function; This is the unnormalized original control valve opening controller function after processing by a proportional-integral controller and a nonlinear gain regulator. The exponential activation function used for the nonlinear gain regulator; This refers to the nonlinear activation offset term of the nonlinear gain regulator. The slope factor of the nonlinear gain regulator; This refers to the gain coefficient of the proportional controller; The gain coefficient of the integral controller; For any time The corresponding temperature difference error term; Step S503: When the regulating valve opening controller function When the output is 0, the output hot return water regulating valve opening command is "hot return water regulating valve closing command"; When the regulating valve opening controller function When the output is 1, the output hot return water regulating valve opening command is "hot return water regulating valve fully open command"; when At this time, the output hot return water regulating valve opening command is "hot return water regulating valve proportional opening command"; the "hot return water regulating valve proportional opening command" is used to instruct the valve controller to interact with the regulating valve opening controller function. Adjust the valve angle according to the corresponding ratio.

8. A control system for a combined cooling, heating, and power (CCHP) system, applied to the control method for a CCHP system according to any one of claims 1 to 7, characterized in that, The control system of the combined cooling, heating and power system includes: The real-time operating parameter acquisition module is used to acquire the real-time operating parameters of the combined cooling, heating and power (CCHP) system, including power supply capacity. Waste heat temperature Cooling output Heat output Cooling load demand Heat load demand Cold energy storage state and thermal energy storage state ; The cooling-heat coupling prediction and analysis module is used to perform load demand forecasting tasks based on real-time operating parameters and a multi-window forecasting mechanism based on cooling-heat coupling sensitivity, outputting the predicted cooling load demand value at future time t. Heat load demand forecast , Load Priority Weight With secondary weight of heat load ; The cross-flow optimization scheduling module is used to optimize the scheduling based on the predicted cooling load demand. Heat load demand forecast , Load Priority Weight With secondary weight of heat load The power flow optimization scheduling task is performed using an MPC predictive control algorithm based on cross-energy deviation penalty, and the output power load rate is determined. Absorption refrigeration power With power of electric refrigeration unit ; The thermal threshold adaptive control module is used to adjust the power supply load rate. Absorption refrigeration power With power of electric refrigeration unit The dynamic hot water recovery control task is performed using a heat threshold compression regulation mechanism, and a hot water recovery reference threshold is output. ; The heat recovery closed-loop control module is used to control the heat recovery based on a hot water recovery reference threshold. It performs closed-loop feedback control tasks and outputs instructions on the opening degree of the hot water return regulating valve.

9. A control device for a combined cooling, heating, and power (CCHP) system, characterized in that, The control device of the combined cooling, heating and power system includes: a memory, a processor, and a control program for the combined cooling, heating and power system stored in the memory and executable on the processor. When the control program for the combined cooling, heating and power system is executed by the processor, it implements a control method for a combined cooling, heating and power system according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a control program for a combined cooling, heating, and power (CCHP) system, which, when executed by a processor, implements a control method for a CCHP system according to any one of claims 1 to 7.

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