A centralized heating overall optimization control method and system

Abstract

This invention discloses a centralized heating overall optimization control method and system, comprising: adjusting the opening of dedicated balancing valves at other control points of the secondary network based on the hydraulic state indication value of the most unfavorable loop in the secondary network; adjusting the opening of the primary side electrically adjustable valve of the heat exchange station in real time through the flow control variable of the primary side electrically adjustable valve; realizing active variable flow operation of the secondary side of the heat exchange station through the circulation flow control variable of the circulating water pump on the secondary side of the heat exchange station; and calculating the setpoint of the heat source supply water temperature based on the basic setting and compensation value of the heat source supply water temperature, implementing supply water temperature control, and combining the minimum pressure difference on the primary side of the most unfavorable heat exchange station to achieve variable flow operation, thereby realizing the matching of heat supply and demand between the source and the network. This method is based on the hydraulic balance of the secondary network and gradually moves towards the heat source to achieve effective control of indoor temperature, meet the user's heating quality and thermal comfort requirements, and minimize the energy consumption and pollutant emissions of the heating system, actively responding to the national "dual carbon" strategic goal.

Description

Technical Field

[0001] This invention relates to the field of centralized heating system control technology, specifically to a centralized heating overall optimization control method and system. Background Technology

[0002] The ultimate goal of centralized heating systems is to meet users' indoor temperature needs in winter while minimizing energy consumption and pollutant emissions. To this end, professionals in the heating industry have conducted extensive research and practice from various perspectives. Undoubtedly, after decades of development, significant performance has been achieved. However, with rising urbanization rates, users' pursuit of heating quality, and the steady expansion of heating system scale, centralized heating systems have gradually exposed various problems. For conventional large-scale centralized heating systems, the overall system structure typically consists of three parts: heat source, heat network, and heat users; the heat source can be single or multiple; the heat network usually consists of a primary network, heat exchange stations, and a secondary network; and users include public buildings, residential buildings, industrial users, and commercial users. From the current operational perspective, the main bottlenecks restricting the optimization of centralized heating system operation stem from two aspects: firstly, heat source optimization, and secondly, the hydraulic balance state of the secondary network. The heat source is the main site for heat generation, exchange, and transmission, involving heat source type, heat sources, heat source distribution and matching, heat source control strategies, and control parameters. It is a very complex and crucial link directly related to the heat network. Hydraulic balance in the secondary network is essential for optimizing heat exchange stations and ensuring target indoor temperatures for users. When the secondary network is hydraulically imbalanced, it not only leads to a decline in heating quality but also severely impacts the economic operation of the heat exchange station, resulting in a significant increase in energy consumption of the heat exchange station, energy consumption of the heating system, and pollutant emissions from the heat source. Therefore, achieving overall operational optimization of the centralized heating system must start with the heat source and the secondary network. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for overall optimization control of centralized heating systems, so as to achieve overall operational optimization of centralized heating systems.

[0004] To achieve the above objectives, the present invention provides a centralized heating system overall optimization control method, comprising:

[0005] Step S1: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network to achieve hydraulic balance of the secondary network.

[0006] Step S2: Based on the indoor temperature setpoint of the heat user and the collected indoor temperature value of the heat user, and using the first calculation formula, the flow control variable of the primary side electric regulating valve of the heat exchange station is calculated. Then, the opening of the primary side electric regulating valve of the heat exchange station is adjusted in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user.

[0007] Step S3: Based on the acquired outdoor temperature and using the second calculation formula, the circulating flow control variable of the secondary circulating water pump of the heat exchange station is calculated, thereby realizing the active variable flow operation of the secondary side of the heat exchange station.

[0008] Step S4: Calculate the setpoint of the heat source water supply temperature based on the basic setting and compensation value, implement water supply temperature control, and combine the minimum pressure difference on the primary side of the most unfavorable heat exchange station to carry out variable flow operation, so as to achieve the matching of heat supply and demand between the source and the grid.

[0009] Optionally, in step S4,

[0010] The compensation value is calculated based on the heat exchange station's thermal characteristics, primary network transmission distance, and actual operating data of the primary network's thermal storage operation, using an intelligent AI algorithm.

[0011] The basic setting of the heat source water supply temperature is obtained through simulation using a pre-established dynamic mathematical model of the actual centralized heating system.

[0012] Optionally, in step S2, the first calculation formula is expressed as:

[0013]

[0014] f1(T z T zsp )=α1(T z -T zsp ) 2 +α2(T z -T zsp )+α3---(2)

[0015] f2(T z T zsp )=α4(T z -T zsp ) 2 +α5(T z -T zsp )+α6---(3)

[0016] f3(T z T zsp )=α7(T z -T zsp ) 2 +α8(T z -T zsp )+α9---(4)

[0017] Where uwv is the flow control variable of the primary side electrically controlled valve of the heat exchange station; T z T zspThese represent the collected indoor temperature values ​​and indoor temperature setpoints of heat users, respectively, both in °C; α1 to α9 are correlation coefficients obtained through intelligent AI algorithms based on the actual operating data of the primary side electric regulating valves of different heat exchange stations.

[0018] Optionally, in step S3, the second calculation formula is expressed as:

[0019] u wp p = β1T o +β2---(5)

[0020] Among them, u wp β1 and β2 are the control variables for the circulation flow rate of the secondary circulating water pump in the heat exchange station, To is the collected outdoor temperature in °C, and β1 and β2 are the correlation coefficients obtained by intelligent AI algorithm based on the actual operating data of the secondary circulating water pump in different heat exchange stations.

[0021] Optionally, step S1 specifically includes:

[0022] Step S11: Install dedicated balancing valves at the thermal inlet of each control point in the secondary network;

[0023] Step S12: Determine the most unfavorable loop in the secondary network;

[0024] Step S13: Calculate the actual required secondary network circulation flow based on the heating area, building type, user nature, heat dissipation device type, occupancy rate and building age of the heat users connected to the control point of the secondary network in the most unfavorable loop of the secondary network, and calculate the hydraulic state indication value of the most unfavorable loop of the secondary network by combining the actual measured circulation flow on site and using the intelligent AI algorithm.

[0025] Step S14: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network, so that the hydraulic state indication values ​​of the other control points of the secondary network are the same as the hydraulic state indication value of the most unfavorable loop of the secondary network, thereby achieving hydraulic balance of the secondary network.

[0026] Optionally, the intelligent AI algorithm employs a forward continuous rolling nonlinear regression method.

[0027] Optionally, the process of establishing the dynamic mathematical model of the actual centralized heating system is as follows:

[0028] A dynamic mathematical model of an ideal centralized heating system is created based on the first law of thermodynamics.

[0029] By combining actual operating data of the centralized heating system, the ideal centralized heating system dynamic mathematical model is transformed and verified into the actual centralized heating system dynamic mathematical model;

[0030] The ideal centralized heating system dynamic mathematical model consists of a heat source boiler dynamic mathematical model, a primary side dynamic mathematical model of the heat exchange station, a secondary side dynamic mathematical model of the heat exchange station, a radiator dynamic mathematical model, and an indoor air dynamic mathematical model.

[0031] On the other hand, the present invention also provides a centralized heating overall optimization control system, comprising:

[0032] The secondary network control module is configured to adjust the opening of the dedicated balancing valves at other control points of the secondary network based on the hydraulic state indication value of the most unfavorable loop in the secondary network, so as to achieve hydraulic balance in the secondary network.

[0033] The primary side control module of the heat exchange station is configured to calculate the flow control variable of the primary side electric regulating valve of the heat exchange station based on the set value of the indoor temperature of the heat user and the collected indoor temperature value of the heat user, and then adjust the opening of the primary side electric regulating valve of the heat exchange station in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user.

[0034] The secondary side control module of the heat exchange station is configured to calculate the circulating flow control variable of the secondary side circulating water pump of the heat exchange station based on the acquired outdoor temperature and using the second calculation formula, thereby realizing the active variable flow operation of the secondary side of the heat exchange station.

[0035] Additionally, the heat source control module is configured to calculate the heat source water supply temperature setpoint based on the basic setting and compensation value, implement water supply temperature control, and perform variable flow operation in conjunction with the minimum pressure difference on the primary side of the most unfavorable heat exchange station to achieve heat supply and demand matching between the source and the grid.

[0036] In another aspect, the present invention also provides an electronic device, including: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the centralized heating overall optimization control method as described above is implemented.

[0037] In another aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the centralized heating overall optimization control method as described above.

[0038] The method of the present invention has the following advantages:

[0039] The centralized heating overall optimization control method of the present invention is based on the hydraulic balance of the secondary network and gradually moves towards the heat source to achieve effective control of the indoor temperature of heat users, meet the users' heating quality and thermal comfort requirements, and minimize the energy consumption and pollutant emissions of the heating system while ensuring heating quality, thus actively responding to the national "dual carbon" strategic goal. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of the centralized heating overall optimization control method of the present invention;

[0041] Figure 2 This is a schematic diagram of the process flow of a centralized heating system;

[0042] Figure 3 A schematic diagram of the response of an open-loop test of a dynamic mathematical model of an ideal central heating system;

[0043] Figure 4 This is a schematic diagram of the response of an open-loop test of a dynamic mathematical model of a real centralized heating system.

[0044] Figure 5 This is a schematic diagram of the dynamic response of the heating parameters of heat exchange station 1# in the dynamic mathematical model of an actual centralized heating system under hydraulic imbalance.

[0045] Figure 6 A schematic diagram of the dynamic response of the heating parameters of heat exchange station 1# in the dynamic mathematical model of the actual centralized heating system under hydraulic balance.

[0046] Figure 7 A schematic diagram illustrating the dynamic temperature response of the heat source, heat exchange stations 2 and 3 and their building complex under control strategy 1;

[0047] Figure 8 This is a schematic diagram of the dynamic temperature response of heat exchange station #1 and the building under control strategy 1;

[0048] Figure 9 A schematic diagram of the dynamic response of flow control variables for heat source, heat exchange station building 1, and heat exchange station building clusters 2 and 3 under control strategy 1;

[0049] Figure 10 A schematic diagram illustrating the dynamic temperature response of the heat source, heat exchange stations 2 and 3 and their building complex under control strategy 2;

[0050] Figure 11 This is a schematic diagram of the dynamic temperature response of heat exchange station #1 and the building under control strategy 2;

[0051] Figure 12 A schematic diagram of the dynamic response of flow control variables for heat source, heat exchange station building #1, and heat exchange station buildings #2 and #3 under control strategy 2;

[0052] Figure 13 This is a structural block diagram of the centralized heating overall optimization control system of the present invention. Detailed Implementation

[0053] The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the invention. 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.

[0054] See Figure 1 As shown in the figure, this embodiment of the invention provides a method for overall optimization control of centralized heating, the method comprising:

[0055] Step S1: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network to achieve hydraulic balance of the secondary network.

[0056] Step S2: Based on the indoor temperature setpoint of the heat user and the collected indoor temperature value of the heat user, and using the first calculation formula, the flow control variable of the primary side electric regulating valve of the heat exchange station is calculated. Then, the opening of the primary side electric regulating valve of the heat exchange station is adjusted in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user.

[0057] Step S3: Based on the acquired outdoor temperature and using the second calculation formula, the circulating flow control variable of the secondary circulating water pump of the heat exchange station is calculated, thereby realizing the active variable flow operation of the secondary side of the heat exchange station.

[0058] Step S4: Calculate the setpoint of the heat source water supply temperature based on the basic setting and compensation value, implement water supply temperature control, and combine the minimum pressure difference on the primary side of the most unfavorable heat exchange station to carry out variable flow operation, so as to achieve the matching of heat supply and demand between the source and the grid.

[0059] Optionally, in step S4 of the centralized heating overall optimization control method provided in this embodiment of the invention, the compensation value is calculated based on the thermal characteristics of the heat exchange station, the transmission distance of the primary network, and the actual operating data of the primary network heat storage operation, and is obtained through an intelligent AI algorithm.

[0060] The basic setting of the heat source water supply temperature is obtained through simulation using a pre-established dynamic mathematical model of the actual centralized heating system.

[0061] Specifically, in this embodiment, the strategy adopted for optimizing the control of the heat source portion of the centralized heating system is to operate the primary network circulation flow rate with variable flow to ensure the minimum pressure difference on the primary side of the most unfavorable heat exchange station. In this embodiment, intelligent AI compensation control is performed based on the thermal characteristics of the heat exchange station, the primary network transmission distance, and the actual operating data of the primary network heat storage operation (i.e., heat source water supply temperature data). The calculation formula for the heat source water supply temperature setpoint is as follows:

[0062] T bsp =T bspo +T bspc ---(6)

[0063] In the formula, T bsp T bsp0 T bspc These are the setpoint for the heat source water supply temperature, the basic setpoint for the heat source water supply temperature, and the compensation value for the heat source water supply temperature, all in °C. The basic setpoint for the heat source water supply temperature is derived from the dynamic mathematical model simulation of the actual centralized heating system; the compensation value for the heat source water supply temperature is derived from a large amount of actual operating data on the thermal characteristics of each heat exchange station, the primary network transmission distance, and the primary network heat storage operation, and is calculated using an intelligent AI algorithm. It can be described by the following formula (7):

[0064] T bspc =f(thermal characteristics of heat exchange station, primary network transmission distance, and increase in heat source water supply temperature)---(7)

[0065] Among them, the increase in heat source water supply temperature refers to the increase in heat source water supply temperature required based on the operating conditions of the primary network heat storage.

[0066] Optionally, in step S2 of the centralized heating overall optimization control method provided in this embodiment of the invention, the first calculation formula is expressed as:

[0067]

[0068] f1(T z T zsp )-α1(T z -T zsp ) 2 +α2 ( T z -T zsp )+α3---(2)

[0069] f2(T z T zsp )=α4(T z -T zsp ) 2 +α5(T z -T zsp )+α6---(3)

[0070] f3(T z T zsp )=α7(T z -T zsp ) 2 +α s (T z -T zsp )+α 9 ---(4)

[0071] Among them, u wv T is the flow control variable for the primary side electrically controlled valve of the heat exchange station. z T zsp These represent the collected indoor temperature values ​​and indoor temperature setpoints of heat users, respectively, both in °C; α1 to α9 are correlation coefficients obtained through intelligent AI algorithms based on the actual operating data of the primary side electric regulating valves of different heat exchange stations.

[0072] Specifically, in this embodiment, the strategy adopted for the optimized control of the primary side of the heat exchange station in the centralized heating system is to change the opening degree of the primary side electric regulating valve of the heat exchange station, thereby changing the heat supply by controlling the mass regulation of the heat exchange station, directly controlling the indoor temperature, and realizing the thermal balance of the primary network. Among them, the heat exchange station part directly controls the indoor temperature by adjusting the opening degree of the primary side electric regulating valve of the heat exchange station in real time through intelligent AI algorithm combined with edge computing. The flow control variables of the primary side electric regulating valve of each heat exchange station are calculated as shown in the above equations (1)-(4).

[0073] Optionally, in step S3 of the centralized heating overall optimization control method provided in this embodiment of the invention, the second calculation formula is expressed as:

[0074] u wp =β1T o +β2---(5)

[0075] Among them, u wp T is the control variable for the circulation flow rate of the secondary circulating water pump in the heat exchange station. o The outdoor temperature is measured in °C. β1 and β2 are correlation coefficients obtained using intelligent AI algorithms based on actual operating data of the secondary circulating water pumps at different heat exchange stations.

[0076] Specifically, in this embodiment, the strategy adopted for the optimization control of the secondary side of the heat exchange station in the centralized heating system is: the secondary network circulation flow rate is actively variable according to the thermal and hydraulic characteristics of each heat exchange station, and the calculation formula of the circulation flow rate control variable of the secondary side circulation pump of the heat exchange station is shown in the above formula (5).

[0077] Optionally, the intelligent AI algorithm in the centralized heating overall optimization control method provided in this embodiment of the invention adopts the forward continuous rolling nonlinear regression method.

[0078] Specifically, the intelligent AI algorithm for the flow control variables of the primary side electric regulating valve and the secondary side circulating water pump of the heat exchange station is described as follows: The continuously acquired indoor temperature values ​​(i.e., indoor temperature detection values), indoor temperature setpoints, outdoor temperatures (i.e., outdoor temperature detection values), secondary network circulating flow, and the operating data of the flow control variables of the primary side electric regulating valve and the secondary side circulating water pump of the heat exchange station are used to calculate the correlation coefficients in formulas (2) to (5) using a forward continuous rolling nonlinear regression method. The data interval for regression calculation is one month's worth of data, and a first-in-first-out (FIFO) approach is adopted. After obtaining the correlation coefficients through the intelligent AI algorithm, they are applied to edge computing control to execute real-time control operations. Edge computing based on heat exchange stations, predicated on the hydraulic balance of the secondary network, enables direct control of indoor temperatures for heat users. This improves the real-time performance of the overall control system and the accuracy of indoor temperature control targets. It avoids the lag and control deviations caused by various interferences during the control process inherent in existing host computer control systems (which acquire detection values, calculate control parameters, and then issue commands to regulate the heat exchange station). Furthermore, this edge computing-based sinking control method reduces the initial investment in the host computer control system and alleviates the computational constraints on the host computer control system in large-scale centralized heating systems.

[0079] Optionally, step S1 in the centralized heating overall optimization control method provided in this embodiment of the invention specifically includes:

[0080] Step S11: Install dedicated balancing valves at the thermal inlet of each control point in the secondary network;

[0081] Step S12: Determine the most unfavorable loop in the secondary network;

[0082] Step S13: Calculate the actual required secondary network circulation flow based on the heating area, building type, user nature, heat dissipation device type, occupancy rate and building age of the heat users connected to the control point of the secondary network in the most unfavorable loop of the secondary network, and calculate the hydraulic state indication value of the most unfavorable loop of the secondary network by combining the actual measured circulation flow on site and using the intelligent AI algorithm.

[0083] Step S14: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network, so that the hydraulic state indication values ​​of other control points of the secondary network are the same as the hydraulic state indication value of the most unfavorable loop of the secondary network, thereby achieving hydraulic balance of the secondary network.

[0084] Specifically, simulation analysis shows that the prerequisite for optimized control of the heat exchange station is the hydraulic balance of the secondary network. With advancements in heating technology, current secondary network hydraulic balancing techniques have evolved from manual adjustment based on experience and self-regulating flow / pressure differential adjustment to IoT-based methods. However, these methods all have drawbacks, such as consuming significant time during the hydraulic balancing process, hindering the operation of quality control methods, requiring substantial equipment investment, necessitating on-site power and communication, and yielding unsatisfactory adjustment results. To improve the efficiency of secondary network hydraulic balancing and achieve system energy conservation and consumption reduction, this embodiment adopts a rapid secondary network hydraulic balancing method. This method features low investment, minimal on-site requirements, simple and easy operation, rapid results, and significant effectiveness. The specific on-site operation steps are as follows:

[0085] (1) Install dedicated balancing valves at the thermal inlet of each control point in the secondary network;

[0086] (2) Identify the most unfavorable loop in the secondary network. Based on the heating area (i.e., building area), building type, user nature, type of heat dissipation device, occupancy rate, and building age of the users connected to the control point, calculate the actual required secondary network circulation flow. Combined with the on-site measured circulation flow (i.e., measured flow), calculate the hydraulic state indication value (r) of the control point using an intelligent AI algorithm. The calculation formula is as follows:

[0087] r = f(building area, building type, user type, heat dissipation device, occupancy rate, building age, actual flow) -- (8)

[0088] (3) Adjust the opening of the dedicated balancing valves of other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop, so that their hydraulic state indication values ​​are the same as those of the most unfavorable loop of the secondary network.

[0089] (4) After all control points have been debugged, the secondary network hydraulic balance can be achieved;

[0090] (5) Adjust the flow rate of the circulating water pump in the heat exchange station to match the design flow rate for the current year, and then the hydraulic balance operation of the secondary network can be realized quickly.

[0091] To improve the overall control speed (i.e., timeliness) and accuracy of centralized heating systems, and to enhance system safety, reliability, and scalability, edge computing control of heat exchange stations, based on the secondary network hydraulic balance, is adopted according to the functions of each component of the centralized heating system. This approach decomposes the functions of the large-scale centralized heating system and implements optimized control based on intelligent AI computing at the heat exchange stations, thereby achieving a better user experience and energy-saving effects. The main reason for not extending edge computing to the heat inlets of each heat user in a building is their sheer number, which would significantly increase system investment and maintenance costs, reducing the system's cost-effectiveness. Furthermore, the actual effect would be similar to that under the secondary network hydraulic balance condition at the building / unit heat inlet. Therefore, by combining secondary network hydraulic balance and typical indoor temperature data acquisition, and with the assistance of intelligent AI algorithms, a comprehensive control strategy for the heating system based on edge computing at heat exchange stations can achieve significant cost-effectiveness advantages and energy-saving and emission-reduction effects.

[0092] Optionally, the process of establishing the dynamic mathematical model of the actual centralized heating system in the centralized heating overall optimization control method provided in this embodiment of the invention is as follows:

[0093] A dynamic mathematical model of an ideal centralized heating system is created based on the first law of thermodynamics.

[0094] By combining actual operating data of the centralized heating system, the dynamic mathematical model of the ideal centralized heating system is transformed and verified into a dynamic mathematical model of the actual centralized heating system;

[0095] The ideal centralized heating system dynamic mathematical model consists of a heat source boiler dynamic mathematical model, a primary side dynamic mathematical model of the heat exchange station, a secondary side dynamic mathematical model of the heat exchange station, a radiator dynamic mathematical model, and an indoor air dynamic mathematical model.

[0096] Specifically, in this embodiment, to analyze and compare the overall control strategies of a centralized heating system, a complete dynamic mathematical model of the centralized heating system is created using the first law of thermodynamics for simulation analysis and research. The dynamic mathematical model of the heating system consists of heat sources, a heat network (primary network, heat exchange stations, and secondary network), and heat users. First, a physical model of a conventional centralized heating system is selected as the research object; second, an ideal dynamic mathematical model of the centralized heating system is created using the first law of thermodynamics, and combined with actual operating data of the centralized heating system, the ideal model is modified into a dynamic mathematical model of the actual centralized heating system; third, the actual dynamic mathematical model of the centralized heating system is used to simulate the operation control strategy in order to obtain the optimal control path for the centralized heating system.

[0097] Physical model of a central heating system

[0098] The total heating area of ​​a certain centralized heating system is 155,410 m². 2The system is designed with a heat load of 6.12MW and consists of a single heat source (gas-fired boiler), three heat exchange stations, and heat users (buildings and residential clusters). The primary network heating radius is 749m. Branching pipe networks connect the heat source and heat exchange stations, as well as the heat exchange stations and heat users. The system's process flow diagram is shown below. Figure 2 As shown in the figure. The symbols and their meanings in the figure: u f T is the boiler fuel control variable; b T r The boiler supply and return water temperatures, in °C; u w11 u w12 u w13 For the primary side circulation flow control variables of heat exchange stations #1, #2, and #3; T r11 T r12 T r13 The primary return water temperatures of heat exchange stations #1, #2, and #3 are in °C; T s21 T s22 T s23 The secondary water supply temperatures for heat exchange stations #1, #2, and #3 are in °C; T r21 T r22 T r23 The secondary return water temperatures of heat exchange stations #1, #2, and #3 are in °C; u w211 u w212 uw 213 u w22 u w23 The control variable for the secondary network circulation flow of buildings 1, 2, and 3 of heat exchange station 1, and buildings 2 and 3 of heat exchange station 2; T r211 T r212 T r213 The secondary network return water temperature of buildings 1#, 2#, and 3# in heat exchange station 1# is given in °C; T z11 T z12 T z13 T z2 T z3 The indoor temperature (°C) is the temperature of buildings 1, 2, and 3 of heat exchange station 1, and the building clusters of heat exchange station 2 and heat exchange station 3.

[0099] To simplify the cumbersome derivation process of the mathematical model without losing the main characteristics of the heating system, some parameters of the heating system are treated as lumped parameters. For example, the multiple buildings of heat exchange station 2# and 3# are displayed by building clusters 2# and 3#, and their physical parameters are also represented in a lumped manner.

[0100] The system design parameters are shown in Table 1 below.

[0101] Table 1 Design parameters of the heating system

[0102]

[0103]

[0104] Ideal dynamic mathematical model:

[0105] (1) Control body

[0106] Based on the heat transfer process of the heating system and the selection of objects with large heat capacity in the system, the control volume in the dynamic mathematical model of the system is determined as follows: heat source boiler, primary and secondary network side of heat exchange station, building terminal heat dissipation device, and indoor air of building.

[0107] (2) Dynamic mathematical model of heat source boiler

[0108]

[0109] In the formula, t is time, in seconds; C b The boiler body heat capacity is expressed in W / ℃; G fd The rated flow rate of boiler fuel, in m 3 / s; HV is the lower heating value of boiler fuel, J / m 3 η b For boiler efficiency; c w G represents the specific heat of water, in J / (kg·℃); 11d G 12d G 13d The design circulation flow rate for the primary side of heat exchange stations 1#, 2# and 3# is kg / s. Equation (9) illustrates that the net heat stored in the heat source boiler is related to the heat released by boiler combustion and the heat output from the primary network.

[0110] (3) Dynamic mathematical model of the primary side of the heat exchange station

[0111]

[0112] In the formula, C ex11 C ex12 C ex13 The primary heat capacity of heat exchangers #1, #2, and #3 in heat exchange stations is given in W / ℃; G 11d G 12d G 13d The design circulation flow rate for the primary side of heat exchange stations #1, #2, and #3 is kg / s; f ex1 f ex2 f ex3 U represents the excess heat transfer area coefficient for heat exchangers #1, #2, and #3 in heat exchange stations. ex1 U ex2 U ex3is the comprehensive heat transfer coefficient of heat exchangers 1#, 2# and 3# in heat exchange stations, W / ℃; LMTD1, LMTD2 and LMTD3 are the logarithmic mean temperature differences of heat exchangers 1#, 2# and 3# in heat exchange stations, ℃. Equation (10) illustrates the difference between the net heat stored on the primary side of the heat exchangers in the heat exchange stations and the heat provided by the primary network to the heat exchangers and the heat transferred to the low-temperature side by the heat exchangers.

[0113] (4) Dynamic mathematical model of the secondary side of the heat exchange station

[0114]

[0115] In the formula, C ex21 C ex22 C ex23 The secondary side heat capacity of heat exchangers #1, #2, and #3 in heat exchange stations is given in W / ℃; G 21d1 G 21d2 G 21d3 G 22d G 23d The design circulation flow rate for the secondary network of buildings 1#, 2#, and 3# of heat exchange station 1# and building clusters 2# and 3# of heat exchange station 1# is kg / s. Equation (11) describes that the net heat stored on the secondary side of the heat exchangers of heat exchange stations 1#, 2#, and 3# is related to the heat transfer of the heat exchangers and the heat carried out by the circulating water of the secondary network.

[0116] (5) Dynamic mathematical model of radiator

[0117]

[0118] In the formula, C ht11 C ht12 C ht13 C ht2 C ht3 The heat capacity of the radiators in buildings 1, 2, and 3 of heat exchange station 1, and in building clusters 2 and 3 of heat exchange station 1, is expressed in W / ℃; f ht11 f ht12 f ht13 f ht2 f ht3 U represents the excess heat transfer area coefficient of the radiators in buildings 1, 2, and 3 of heat exchange station 1, and in building clusters 2 and 3 of heat exchange station 1; ht11 U ht12 U ht13 U ht2 U ht3 The comprehensive heat transfer coefficient (W / ℃) of the radiators in buildings 1, 2, and 3 of heat exchange station 1, and in building clusters 2 and 3 of heat exchange station 1; T z11 T z12 T z13 T z2 Tz3 The indoor temperatures of heat users in buildings 1#, 2# and 3# of heat exchange station 1#, and building groups 2# and 3# of heat exchange station 1# are ℃, and c is a coefficient related to the heat transfer coefficient test of the radiator. Equation (12) indicates that the net heat stored inside the radiator is related to the heat supplied by its secondary network and the amount of heat dissipated by the radiator to the room.

[0119] (6) Indoor air dynamic mathematical model

[0120]

[0121] In the formula, C a11 C a12 C a13 C a2 C a3 The indoor air heat capacity (W / ℃) of buildings 1, 2, and 3 in heat exchange station 1, and the building clusters 2 and 3 in heat exchange station 2; F 1wins1 F 1wins2 F 1wins3 F 2wins F 3wins The area of ​​the south-facing windows in m² is the area of ​​buildings 1, 2, and 3 in heat exchange station 1, and the area of ​​building clusters 2 and 3 in heat exchange station 1. 2 ;q sols The intensity of south-facing solar radiation, W / m 2 U en11 U en12 U en13 U en2 U en3 -Comprehensive heat transfer coefficients (W / ℃) of the building envelopes of buildings 1#, 2#, and 3# in heat exchange station 1#, and the building clusters of heat exchange station 2# and 3#. o Let be the outdoor temperature, ℃. Equation (13) describes the net heat stored in the indoor air of a building as the difference between the heat carried by its radiators and the heat released from the indoor air to the outside.

[0122] (7) Dynamic mathematical model of ideal centralized heating system

[0123] The ideal dynamic mathematical model of a centralized heating system, based on the laws of thermodynamics, consists of equations (9) to (13), including 17 equations describing the dynamic changes in heat within the control system. After converting and verifying the ideal dynamic mathematical model into a practical centralized heating system dynamic mathematical model using actual operating data, it is used for heating system characteristic analysis, dynamic simulation, control strategy simulation, and energy consumption analysis.

[0124] Open-loop test of dynamic mathematical model of centralized heating system:

[0125] (1) Ideal dynamic model open-loop test

[0126] An ideal centralized heating system dynamic mathematical model refers to a mathematical model where the heat transfer area surplus coefficient of heat exchangers in the heat exchange station and radiators in the building is 1, the actual circulation flow rates of the primary and secondary networks are the design circulation flow rates, and solar radiation is not considered. This model is used to verify the dynamic response of the heating system under different outdoor temperatures. Taking the design operating condition of the heating system as an example, when the outdoor temperature is -15℃, the control variable (u) of the fuel in the heat source boiler is adjusted. f This ensures that the indoor temperature of heat users reaches the design temperature (20℃). The system dynamic response is shown in [link to system dynamic response]. Figure 3 As shown in the figure, the secondary supply and return water temperatures and indoor temperature of heat exchange station #3 were arbitrarily selected in the simulation. Under the design outdoor temperature, when the fuel control variable was adjusted to 0.874, the indoor temperature (building complex of heat exchange station #3) reached 20℃, and the heat source supply and return water temperatures were 110℃ and 50℃, respectively. Observing the dynamic response results of other heat exchange stations, their supply and return water temperatures and indoor temperatures all reached the design parameters. The response results suggest that the ideal dynamic model has sufficient accuracy.

[0127] (2) Open-loop test of actual dynamic model

[0128] When the heat transfer area margin of the heat exchangers in the heat exchange station and the radiators in the building of the actual heating system is not 1, and the circulation flow rates of the primary and secondary networks are the actual circulation flow rates (expressed as the circulation flow rate ratio, which is the actual circulation flow rate / design circulation flow rate), by inputting different parameters and comparing them with the actual operating parameters, if the error between the model output parameters and the actual parameters is less than the error limit (error less than 8%), the dynamic model at this time can be considered as the actual dynamic model, and its response is basically consistent with the actual operating conditions. It can be used for obtaining the characteristics of the heating system and dynamic simulation. The heat transfer area margin and circulation flow rate of the heating system in the case are as follows:

[0129] (a) The heat transfer area surplus coefficients of heat exchangers in heat exchange stations 1#, 2# and 3# are 1.36, 1.58 and 1.45, respectively;

[0130] (b) The heat transfer area surplus coefficients of the radiators of buildings 1, 2 and 3 of heat exchange station 1, and the building groups of heat exchange station 2 and 3 are 1.38, 1.52, 1.3, 1.39 and 1.45, respectively;

[0131] (c) The primary network circulation flow ratios of heat exchange stations 1, 2 and 3 are 1.15, 1.12 and 1.07, respectively;

[0132] (d) The secondary network circulation flow ratios of buildings 1#, 2# and 3# of heat exchange station 1#, and building groups of heat exchange station 2# and 3# are 1.37, 1.13, 0.88, 1.39 and 1.22, respectively.

[0133] Without considering solar radiation, with an outdoor temperature of -15℃, and to maintain an average indoor temperature of 20℃, the system dynamic response is as follows when the fuel control variable is adjusted to 0.903: Figure 4 The steady-state temperatures of the heat source supply and return water, the secondary network supply and return water temperatures of heat exchange station #3, and the indoor temperatures are 88.4℃, 33.5℃, 48.1℃, 31.6℃, and 20.1℃, respectively. Compared with actual operating data, the errors of the above steady-state values ​​are all less than 8%. Therefore, the actual dynamic mathematical model has sufficient accuracy to meet the requirements of dynamic simulation. Figure 3 visible, Figure 4 The actual dynamic model shows that the supply and return water temperatures of the heat source are lower than those of the ideal dynamic model. This is mainly due to the excess heat transfer area coefficient (greater than 1) of the heat exchanger and radiator and the excessive circulation flow of the heating network.

[0134] When the secondary network hydraulics are imbalanced, the indoor temperature will inevitably deviate. This situation can be demonstrated through dynamic simulation. When the secondary circulation flow control variables for buildings 1#, 2#, and 3# in heat exchange station 1# are 1.37, 1.13, and 0.88 respectively, the dynamic response of the secondary network return water temperature and indoor temperature for buildings 1#, 2#, and 3# in heat exchange station 1# is shown in [the figure]. Figure 5 .like Figure 5 As shown, the secondary network return water temperatures and indoor temperatures of buildings 1#, 2#, and 3# in heat exchange station #1 are 32.7℃, 32.7℃, 34.5℃, 19.4℃, 21.5℃, and 18.7℃, respectively. Therefore, when the secondary network hydraulics are imbalanced, the return water temperature and indoor temperature of the buildings are inconsistent. The simulation results suggest that achieving hydraulic balance in the secondary network is a prerequisite for optimizing the control (mass regulation) of the heat exchange station. This can be achieved by controlling the secondary network circulation flow variable (u) of each building in heat exchange station #1. w211 u w212 and u w213 ), ensuring the flow control variable u in the most unfavorable loop (building #3 with the lowest indoor temperature) w213 When the value is 1, the indoor temperature of all buildings within heat exchange station #1 is maintained uniformly (the evaluation criterion for the hydraulic balance of the secondary network is the uniformity of indoor temperature). The dynamic response of heat exchange station #1 is shown in [reference needed]. Figure 6 At this time, the secondary circulation flow control variables for buildings 1#, 2#, and 3# in heat exchange station 1# are 0.83, 0.67, and 1, respectively. From Figure 6 As can be seen, when the indoor temperature reaches 20.7℃, the return water temperature of each building is not the same, and the maximum difference is 10℃. This suggests that the hydraulic balance adjustment of the secondary network based on the consistency of the return water temperature may lead to a large indoor temperature deviation, thus losing the essential meaning of the hydraulic balance of the secondary network.

[0135] In order to analyze and obtain the optimized control strategy in the overall optimization control method of centralized heating in this embodiment, two control strategies were compared and analyzed under the basic operating condition and the optimized operating condition. The basic operating condition is control strategy 1, and the optimized operating condition is control strategy 2 (that is, the optimized control strategy adopted in the overall optimization control method of centralized heating in this embodiment).

[0136] The simulation conditions are as follows: (1) The simulation time is two consecutive days; (2) Heat dissipation and water replenishment losses of the heating network are ignored; (3) The outdoor temperature range is -4℃ to -15℃; (4) The solar radiation range is 0W / m 2 ~160W / m 2 (5) Simulation is performed using a real dynamic mathematical model; (6) The indoor temperature control target is 20℃.

[0137] Control Strategy 1 - Basic Operating Conditions

[0138] The control strategies commonly used in centralized heating systems are adopted as the basic operating condition for analysis and comparison. The control strategies for each key component of the heating system are as follows:

[0139] Secondary network section: The secondary network operates at a constant flow rate and is in a state of hydraulic imbalance.

[0140] For heat users: a low proportion of indoor temperature data is collected, but the installation location is not modified and it does not directly participate in system control.

[0141] Heat exchange station section: The secondary network water supply temperature is adjusted by controlling the opening of the primary side electric regulating valve of the heat exchange station. The water supply temperature setpoint is obtained based on long-term operating experience.

[0142] Primary network section: The primary network operates passively with variable traffic.

[0143] Heat source section: The temperature of the heat source water supply is controlled by adjusting the boiler fuel, and the set value of the water supply temperature is derived from operating experience.

[0144] Control Strategy 2 - Optimized Operating Conditions

[0145] Secondary network section: A rapid hydraulic balancing method is used to achieve hydraulic balance of the secondary network of each heat exchange station.

[0146] For heat users: Each heat exchange station collects indoor temperatures of typical heat users (20-30 points per heat exchange station, depending on the actual heating area and number of users). For each temperature measurement point, the actual indoor temperature reading is corrected for its specific installation location. The correction method involves comparing the indoor temperature reading at the installation point with a standard indoor temperature reading (standard indoor temperature reading: the indoor temperature value after stabilizing for 15 minutes at a point 1.5m above the geometric center of the main room (usually bedrooms and living room) of the heat user) to obtain the true indoor temperature value. Simultaneously, the true indoor temperature value directly participates in the edge calculation and control of the heat exchange station.

[0147] Heat exchange station section: The indoor temperature is directly controlled by adjusting the opening of the primary side electric regulating valve of the heat exchange station in real time through intelligent AI-assisted edge computing.

[0148] The secondary network circulation flow rate is actively variable based on the thermal and hydraulic characteristics of each heat exchange station.

[0149] Primary network section: By changing the opening of the primary side electric regulating valve of the heat exchange station, the heat supply is changed by controlling the quality regulation of the heat exchange station, directly controlling the indoor temperature, and achieving the thermal balance of the primary network.

[0150] Heat source section: Intelligent AI compensation control based on the thermal characteristics of the heat exchange station, the transmission distance of the primary network, and the heat source water temperature of the primary network heat storage operation; the primary network adopts variable flow operation to ensure the minimum pressure difference on the primary side of the most unfavorable terminal heat exchange station.

[0151] The following is a simulation analysis of the two control strategies:

[0152] (1) Control Strategy 1

[0153] Based on the control strategies for the heat source and heat exchange station in Control Strategy 1 (Basic Operating Condition) and the hydraulic imbalance of the secondary network, the system dynamic simulations for two consecutive days are shown below. Figures 7-9 The following data analysis and comparisons ignore the influence of the initial values ​​of the dynamic mathematical model on the dynamic response (i.e., ignore simulation data within 3 hours).

[0154] Figure 7 In (a), the return water temperature of the heat source changes relatively steadily, ranging from 33.6℃ to 37.6℃, while the supply water temperature of the heat source changes over a larger range. Figure 7 (b) and (c) show the changes in the supply and return water temperatures on the secondary side of heat exchange stations #2 and #3. The average temperatures of the secondary network return water are 33.3℃ and 32.1℃, respectively, with a difference of 1.2℃. Figure 7 (d) The indoor temperature range and average value of building clusters 2# and 3# of heat exchange station are 21.7℃~25.4℃, 22.4℃~26.3℃, 20.5℃ and 21.1℃, respectively.

[0155] Figure 8 In (a), the average secondary network return water temperatures of buildings 1, 2, and 3 within heat exchange station 1 are 34.6℃, 31.6℃, and 29.8℃, respectively. The maximum deviation in the average return water temperature (4.8℃) originates from differences in the thermal characteristics of the buildings and hydraulic imbalances in the secondary network. Observation Figure 8 (b) It can be seen that the average indoor temperature (T) of all buildings within heat exchange station #1 is... z1arg The average indoor temperature was 20.6℃, ranging from 21.9℃ to 25.8℃; the average indoor temperatures of buildings 1, 2, and 3 were 21.2℃, 21.9℃, and 18.7℃, respectively. The dynamic response of indoor temperature indicates poor thermal comfort for heat users, and necessary measures should be taken to improve the heating quality of the system.

[0156] Figure 9 The dynamic responses of the flow control variables of the heat source, each building of heat exchange station 1, and the building clusters of heat exchange stations 2 and 3 are given. Figure 9 (a) shows the dynamic changes of the heat source fuel control variable, which ranges from 0.683 to 0.943 and has an average value of 0.831. Figure 9 (b) Displaying the heat source circulation flow ratio (r) G1 The control variables for the actual circulating flow rate of the heat source (the actual circulating flow rate of the heat source / the design circulating flow rate of the heat source) and the primary circulating flow rate of each heat exchange station are both 1, indicating that the actual circulating flow rate of the primary network is operating according to the design circulating flow rate. Figure 9 (c) Give the circulation flow ratio (r) of the secondary network of heat exchange station #1. G2 - The ratio of the actual circulating flow rate of the secondary network at heat exchange station #1 to its design circulating flow rate is 1.15. From Figure 9 As can be seen in (c) and (d), the circulating flow control variables for buildings 1#, 2#, and 3# of heat exchange station 1#, and heat exchange stations 2# and 3# are 1.37, 1.13, 0.88, 1.39, and 1.22, respectively, all of which are constant flow operation.

[0157] (2) Control Strategy 2

[0158] Based on the control strategies for the heat source and heat exchange station in control strategy 2 (optimized operating condition), and the two-day system dynamic simulation when the secondary network achieves hydraulic balance, see [see...]. Figures 10-12 .

[0159] Figure 10 (a) Provide the dynamic changes in the supply and return water temperatures of the heat source, and compare them. Figure 7 (a) It can be seen that the temperature of the heat source water supply increases by 2℃~3℃. Figure 10 (b) and (c) show the dynamic response of the secondary network supply and return water temperatures of heat exchange stations #2 and #3. (Comparison) Figure 7As shown in (b) and (c), the secondary water supply and return water temperatures of control strategy 2 are 2℃~3℃ and 6℃~7℃ lower than those of control strategy 1, respectively. Figure 10 (d) It shows that the indoor temperature dynamics of building clusters in heat exchange stations 2# and 3# are similar, and the range of variation is within 20±0.5℃.

[0160] Figure 11 (a) Display the return water temperature of the secondary network of each building in heat exchange station 1#. The average return water temperatures of buildings 1#, 2# and 3# are 26.8℃, 22.1℃ and 30.1℃, respectively, and the maximum difference between the average return water temperatures is 8℃. Figure 11 (b) The dynamic changes in the average indoor temperature of each building in heat exchange station #1 and the average indoor temperature of all buildings in heat exchange station #1 are given. The average indoor temperatures of buildings #1, #2, and #3, and the total average indoor temperature of heat exchange station #1 are 19.9℃, 20℃, 20.2℃, and 20.1℃, respectively. This indicates that the indoor temperatures are consistent after the secondary network achieves hydraulic balance (an inevitable result of hydraulic balance), and there is a significant deviation in the return water temperature of the secondary network in the buildings. (Comparison) Figure 8 It can be seen that the return water temperature of each building in heat exchange station 1# under control strategy 2 is lower than the corresponding return water temperature under control strategy 1, and the indoor temperature of heat exchange station 1# building under control strategy 2 is more stable (the indoor temperature fluctuation range is smaller, and its average value is closer to the control target).

[0161] Figure 12 (a) The dynamic changes of the control variables for the heat source fuel are given, with an average value of 0.76. Figure 12 (b) The dynamic response of the heat source circulation flow ratio and the primary side circulation flow control variable of each heat exchange station is given. As shown in the figure, the dynamic response of the primary network circulation flow control variable is similar. When the solar radiation intensity is high, the heat supply of the heat exchange station can be reduced by reducing the primary network circulation flow, so as to meet the heat supply and demand matching requirements. Figure 12 (c) Display the dynamic changes of the circulation flow ratio of the secondary network of heat exchange station 1 and the circulation flow control variables of each building. Compared with control strategy 1, it can be seen that the secondary network of control strategy 2 is operating with variable flow. Figure 12 (d) Display the dynamic response of the circulating flow control variables of the secondary networks of heat exchange station 2# and 3#. Both secondary networks are in variable flow operation mode.

[0162] Comparative analysis of energy consumption of different control strategies:

[0163] Simulation results of different control strategies for the centralized heating system were observed. Control strategy 2 effectively controlled the indoor temperature for users, well meeting their heating quality and thermal comfort requirements. Minimizing energy consumption of the heating system while ensuring heating quality is also one of the goals of optimized control. Through dynamic simulation, the energy-saving performance of each component of the centralized heating system under different control strategies was analyzed, and the comparison results are shown in Table 2. Note that the power consumption analysis uses the cubic relationship between flow rate and power for calculation.

[0164] Table 2 Energy consumption ratio of centralized heating system under different control strategies

[0165]

[0166]

[0167] As shown in Table 2, the heat consumption of the heat source under control strategy 2 is reduced by nearly 9%. If indoor heat gain compensation is considered, the system's free heat can be utilized more effectively, typically increasing the heat saving rate by another 5-8%. The electricity consumption results show that the electricity saving rate of both the heat source and the heat exchange station under control strategy 2 exceeds 50%. Therefore, control strategy 2 has significant energy-saving and emission-reduction benefits while meeting the heating quality requirements of users, and has great promotional value and potential.

[0168] In summary, the overall optimization control strategy adopted by the centralized heating system optimization control method of this invention involves controlling the heat source by acquiring the setpoint of the heat source supply water temperature, and operating the heat source circulation flow rate using a variable flow rate based on the minimum pressure difference on the primary side of the most unfavorable heat exchange station. The opening of the primary side electrically controlled valve of the heat exchange station is adjusted in real time through the flow control variable to achieve direct control of the indoor temperature of heat users. Active variable flow rate operation is achieved on the secondary side of the heat exchange station through the circulation flow control variable of the secondary side circulating water pump. Furthermore, the opening of the dedicated balancing valves at other control points of the secondary network is adjusted based on the hydraulic state indication value of the most unfavorable loop in the secondary network to achieve hydraulic balance in the secondary network (a prerequisite for overall system control). This centralized heating system optimization control method of this invention effectively controls the indoor temperature of heat users by addressing both the heat source and the secondary network, effectively meeting the users' heating quality and thermal comfort requirements. While ensuring heating quality, it also minimizes energy consumption and pollutant emissions from the heating system.

[0169] On the other hand, see Figure 13 As shown, this embodiment of the invention also provides a centralized heating overall optimization control system 1, comprising:

[0170] The secondary network control module 10 is configured to adjust the opening of the dedicated balancing valves at other control points of the secondary network based on the hydraulic state indication value of the most unfavorable loop of the secondary network, so as to achieve hydraulic balance of the secondary network.

[0171] The primary side control module 20 of the heat exchange station is configured to calculate the flow control variable of the primary side electric regulating valve of the heat exchange station based on the set value of the indoor temperature of the heat user and the collected indoor temperature value of the heat user, and then adjust the opening of the primary side electric regulating valve of the heat exchange station in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user.

[0172] The secondary side control module 30 of the heat exchange station is configured to calculate the circulating flow control variable of the secondary side circulating water pump of the heat exchange station based on the acquired outdoor temperature and using the second calculation formula, thereby realizing the active variable flow operation of the secondary side of the heat exchange station.

[0173] Additionally, the heat source control module 40 is configured to calculate the heat source water supply temperature setpoint based on the basic setting and compensation value, implement water supply temperature control, and perform variable flow operation in conjunction with the minimum pressure difference on the primary side of the most unfavorable heat exchange station to achieve heat supply and demand matching between the source and the grid.

[0174] The specific details of each module in the above-mentioned centralized heating overall optimization control system have been described in detail in the corresponding centralized heating overall optimization control method, so they will not be repeated here.

[0175] In another aspect, embodiments of the present invention also provide an electronic device, including: a processor and a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the centralized heating overall optimization control method as described in the above embodiments is implemented.

[0176] Specifically, the aforementioned memory and processor can be general-purpose memory and processor, without any specific limitations. When the processor executes computer-readable instructions stored in the memory, it can perform the centralized heating overall optimization control method described in the above embodiments.

[0177] In another aspect, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the centralized heating overall optimization control method as described in the above embodiments.

[0178] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0179] Although the present invention has been described in detail above with general descriptions and specific embodiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for overall optimization control of centralized heating, characterized in that, include: Step S1: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network to achieve hydraulic balance of the secondary network. Step S2: Based on the indoor temperature setpoint of the heat user and the collected indoor temperature value of the heat user, and using the first calculation formula, the flow control variable of the primary side electric regulating valve of the heat exchange station is calculated. Then, the opening of the primary side electric regulating valve of the heat exchange station is adjusted in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user. Step S3: Based on the acquired outdoor temperature and using the second calculation formula, the circulating flow control variable of the secondary circulating water pump of the heat exchange station is calculated, thereby realizing the active variable flow operation of the secondary side of the heat exchange station. Step S4: Calculate the setpoint of the heat source water supply temperature based on the basic setting and compensation value, implement water supply temperature control, and combine the minimum pressure difference on the primary side of the most unfavorable heat exchange station to carry out variable flow operation to achieve heat supply and demand matching between the source and the grid. The compensation value is calculated based on the actual operating data of the heat exchange station's thermal characteristics, the primary network transmission distance, and the primary network's heat storage operation, and is obtained through an intelligent AI algorithm. The basic setting of the heat source water supply temperature is obtained through simulation using a pre-established dynamic mathematical model of the actual centralized heating system.

2. The centralized heating overall optimization control method according to claim 1, characterized in that, In step S2, the first calculation formula is expressed as: （1） - --（2） - --（3） - --（4） Among them, u wv T is the flow control variable for the primary side electrically controlled valve of the heat exchange station. z , T zsp These represent the collected indoor temperature values ​​and indoor temperature setpoints of heat users, respectively, both in °C; α1 to α9 are correlation coefficients obtained through intelligent AI algorithms based on the actual operating data of the primary side electric regulating valves of different heat exchange stations.

3. The centralized heating overall optimization control method according to claim 1, characterized in that, In step S3, the second calculation formula is expressed as: ---（5） Among them, u wp T is the control variable for the circulation flow rate of the secondary circulating water pump in the heat exchange station. o The outdoor temperature is ℃, and β1 and β2 are the correlation coefficients obtained by intelligent AI algorithm based on the actual operating data of the secondary circulating water pumps of different heat exchange stations.

4. The centralized heating overall optimization control method according to claim 1, characterized in that, Step S1 specifically includes: Step S11: Install dedicated balancing valves at the thermal inlet of each control point in the secondary network; Step S12: Determine the most unfavorable loop in the secondary network; Step S13: Calculate the actual required secondary network circulation flow based on the heating area, building type, user nature, heat dissipation device type, occupancy rate and building age of the heat users connected to the control point of the secondary network in the most unfavorable loop of the secondary network, and calculate the hydraulic state indication value of the most unfavorable loop of the secondary network by combining the actual measured circulation flow on site and using the intelligent AI algorithm. Step S14: Adjust the opening of the dedicated balancing valves at other control points of the secondary network according to the hydraulic state indication value of the most unfavorable loop of the secondary network, so that the hydraulic state indication values ​​of the other control points of the secondary network are the same as the hydraulic state indication value of the most unfavorable loop of the secondary network, thereby achieving hydraulic balance of the secondary network.

5. The centralized heating overall optimization control method according to any one of claims 1-4, characterized in that, The intelligent AI algorithm employs a forward continuous rolling nonlinear regression method.

6. The centralized heating overall optimization control method according to claim 1, characterized in that, The process of establishing the dynamic mathematical model of the actual centralized heating system is as follows: A dynamic mathematical model of an ideal centralized heating system is created based on the first law of thermodynamics. By combining actual operating data of the centralized heating system, the ideal centralized heating system dynamic mathematical model is transformed and verified into the actual centralized heating system dynamic mathematical model; The ideal centralized heating system dynamic mathematical model consists of a heat source boiler dynamic mathematical model, a primary side dynamic mathematical model of the heat exchange station, a secondary side dynamic mathematical model of the heat exchange station, a radiator dynamic mathematical model, and an indoor air dynamic mathematical model.

7. A centralized heating overall optimization control system, characterized in that, include: The secondary network control module is configured to adjust the opening of the dedicated balancing valves at other control points of the secondary network based on the hydraulic state indication value of the most unfavorable loop in the secondary network, so as to achieve hydraulic balance in the secondary network. The primary side control module of the heat exchange station is configured to calculate the flow control variable of the primary side electric regulating valve of the heat exchange station based on the set value of the indoor temperature of the heat user and the collected indoor temperature value of the heat user, and then adjust the opening of the primary side electric regulating valve of the heat exchange station in real time through the flow control variable of the primary side electric regulating valve of the heat exchange station to achieve direct control of the indoor temperature of the heat user. The secondary side control module of the heat exchange station is configured to calculate the circulating flow control variable of the secondary side circulating water pump of the heat exchange station based on the acquired outdoor temperature and using the second calculation formula, thereby realizing the active variable flow operation of the secondary side of the heat exchange station. In addition, the heat source control module is configured to calculate the heat source water supply temperature setpoint based on the basic setting and compensation value, implement water supply temperature control, and perform variable flow operation in combination with the minimum pressure difference on the primary side of the most unfavorable heat exchange station to achieve heat supply and demand matching between the source and the grid. The compensation value is calculated based on the actual operating data of the heat exchange station's thermal characteristics, the primary network transmission distance, and the primary network's heat storage operation, and is obtained through an intelligent AI algorithm. The basic setting of the heat source water supply temperature is obtained through simulation using a pre-established dynamic mathematical model of the actual centralized heating system.

8. An electronic device, characterized in that, include: A processor and a memory, wherein the memory stores computer-readable instructions that, when executed by the processor, implement the centralized heating overall optimization control method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the centralized heating overall optimization control method as described in any one of claims 1-6.

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