Heat supply pipe network cooperative control method and system based on heat load feedback

Abstract

The invention relates to the technical field of heat supply pipe networks, in particular to a heat supply pipe network cooperative control method and system based on heat load feedback. Comprising the steps that a plurality of equipment points are set according to structural parameters of the heat supply pipe network, and a linkage control model and a correction evaluation model are constructed according to all the equipment points; acquiring load feedback data, and setting a primary control strategy according to the load feedback data and the linkage control model; acquiring operation monitoring data of each equipment point, and judging whether a correction control strategy of each equipment point is generated or not according to the correction evaluation model and all the operation monitoring data; the intelligent balance valves are additionally arranged at key nodes (an initial station outlet, a partition heat exchange station inlet and the like) of the heat supply pipe network, the heat supply flow is dynamically adjusted in combination with the linkage control model, the problem of pipe network hydraulic unbalance caused by supply and return water temperature difference expansion is avoided, and the operation stability of the heat supply pipe network is improved.

Description

Technical Field

[0001] This application relates to the field of heating network technology, and in particular to a collaborative control method and system for heating networks based on heat load feedback. Background Technology

[0002] District heating systems are a vital infrastructure for ensuring people's livelihoods and industrial production. However, traditional heating systems have significant shortcomings in the coordinated control of the "source, network, and load" links, resulting in low system energy efficiency, poor heating quality, and challenges to operational stability.

[0003] Specifically, existing technologies suffer from the following main drawbacks: First, in the pipeline distribution stage, traditional methods rely on experience for hydraulic regulation or use valves with fixed openings, making it difficult to cope with dynamically changing heat loads. This leads to severe hydraulic imbalance across the entire network, with excessive heating for nearby users and insufficient flow for distant users, resulting in uneven heating and cooling. Simultaneously, to ensure the most unfavorable loop, the system often adopts a "high flow rate, small temperature difference" operating strategy, leading to persistently high power consumption of circulating water pumps.

[0004] Secondly, on the heat source side, especially in combined heat and power (CHP) units, the extraction steam pressure needs to be stabilized within a narrow safety range. However, changes in terminal heat load or adjustments to pipeline valves can cause fluctuations in primary pipeline pressure, which are then transmitted to the unit's heat exchangers, impacting the extraction steam pressure. Traditional control methods suffer from lag, and single feedback regulation is insufficient to withstand such rapid disturbances, threatening the safe and stable operation of the unit. Summary of the Invention

[0005] The purpose of this application is to provide a collaborative control method and system for heating pipe networks based on heat load feedback, in order to solve the above-mentioned technical problems and improve the operating efficiency and stability of heating pipe networks.

[0006] In some embodiments of this application, intelligent balancing valves are added to key nodes of the heating network (such as the outlet of the first station and the inlet of the zoned heat exchange station), and the heating flow is dynamically adjusted in conjunction with the linkage control model to avoid the problem of hydraulic imbalance in the network caused by the expansion of the temperature difference between the supply and return water, thereby improving the stability of the heating network operation.

[0007] In some embodiments of this application, a modified evaluation model is constructed to achieve pressure linkage control between the heat exchange station extraction steam unit and the heat exchange pipeline network. When the pressure of the heat exchange system fluctuates, the system pressure is stabilized by adjusting the speed of the circulating water pump or the opening of the extraction steam regulating valve of the unit, so as to avoid impacting the operation of the unit.

[0008] In some embodiments of this application, a collaborative control method for heating pipe networks based on heat load feedback is provided, including:

[0009] Multiple equipment points are set according to the structural parameters of the heating network, and a linkage control model and a correction evaluation model are constructed based on all equipment points.

[0010] Obtain load feedback data and set a primary control strategy based on the load feedback data and the linkage control model;

[0011] Acquire operational monitoring data from each equipment point, and determine whether to generate corrective control strategies for each equipment point based on the corrective evaluation model and all operational monitoring data.

[0012] This includes setting multiple device points, including:

[0013] Establish a sequence of device points A, A = (a1, a2, ..., a3) i …a n ), where a i Let be the i-th device point, and n be the number of device points.

[0014] In some embodiments of this application, the construction of the linkage control model includes:

[0015] Based on the equipment point sequence A, set a sequentially. i For the target device point;

[0016] Based on the historical operation data of the heating network, a mapping sub-region for the target equipment point is set, and the mapping sub-region includes multiple load sensing points;

[0017] Generate a record data packet for the target device point;

[0018] Based on the recorded data packets, set the control sub-model for the target device point and the load fluctuation value b;

[0019] The control sub-models and load fluctuation values ​​for each equipment point are generated sequentially.

[0020] Construct an allocation sub-model based on all load fluctuation values;

[0021] A linkage control model is generated based on the allocation sub-model and all control sub-models.

[0022] In some embodiments of this application, generating the load fluctuation value b of the target device point includes:

[0023]

[0024] Where θ1 is the number of load sensing points within the mapping area of ​​the target device point; η i Let j be the influence factor of the i-th load sensing point; i It generates the fluctuation reference value of the i-th load sensing point based on the recorded data packet.

[0025] In some embodiments of this application, a primary control strategy is set, including:

[0026] Multiple control cycles can be preset;

[0027] Set the start time of each control cycle as the feedback time node;

[0028] Based on the equipment point sequence A, set a sequentially. i For the device to be controlled;

[0029] Obtain the load feedback data of the device to be controlled at the current feedback time point;

[0030] Generate the expected heat load of the device to be controlled at the current feedback time point;

[0031] The expected heat load of each node to be controlled at the current feedback time node is generated sequentially;

[0032] A primary allocation strategy is generated based on the expected heat load of each equipment point and the allocation sub-model.

[0033] Set the control sub-strategies for each device point according to the primary allocation strategy;

[0034] A first-level control policy is generated based on all control sub-policies.

[0035] In some embodiments of this application, determining whether to generate a correction control strategy for each device point includes:

[0036] Set multiple monitoring time points within the current control cycle;

[0037] Based on the equipment point sequence A, set a sequentially. i The equipment point to be evaluated;

[0038] Obtain the operational monitoring data of the device to be evaluated at the current monitoring time point;

[0039] A primary evaluation strategy for the equipment points to be evaluated is generated based on the modified evaluation model;

[0040] The operational risk value f of the equipment point to be evaluated is generated based on the operational monitoring data and the primary assessment model.

[0041] Preset operational risk threshold F1;

[0042] If f>F1, the current monitoring time node generates a correction control strategy for the device point to be evaluated.

[0043] In some embodiments of this application, generating the operational risk value f of the device point to be evaluated includes:

[0044]

[0045] Where θ2 represents the number of operational monitoring indicators; β i Let s be the influencing factor of the i-th operational monitoring indicator; i It is the real-time reference value of the i-th operational monitoring indicator of the equipment point to be evaluated; s' i It is the reference threshold value for the i-th operational monitoring indicator set according to the first-level evaluation strategy; Y(i) is the selection coefficient; if (s i -s' i If )>0, Y(i)=1; if (s i -s' i If ) < 0, Y(i) = 0.

[0046] Some embodiments of this application also include:

[0047] Obtain feedback data packets from all load sensing points at the current monitoring time point;

[0048] Generate the runtime deviation value d based on all feedback data packets;

[0049]

[0050] Where g is the deviation compensation coefficient; n is the number of equipment points; μ i c is the influence factor of the i-th equipment point; i It generates the actual heat load of the i-th device at the current monitoring time point based on all feedback data packets; c' i U1 is the expected heat load of the i-th device at the current monitoring time point; U2 is the preset first conversion coefficient; U1 is the preset second conversion coefficient; f i This represents the operational risk value of the i-th device at the current monitoring time point.

[0051] Preset operating deviation threshold D1;

[0052] If d > D1, the current monitoring time node generates a correction instruction for the first-level control strategy.

[0053] In some embodiments of this application, a heating network collaborative control system based on heat load feedback is provided, including:

[0054] The central control unit is used to set multiple equipment points according to the structural parameters of the heating network.

[0055] The central control unit is also used to construct a linkage control model and a correction evaluation model based on all equipment points;

[0056] The monitoring unit comprises multiple primary sub-modules and multiple secondary sub-modules;

[0057] The first-level submodule sets up each load sensing point, and the first-level submodule is used to collect load feedback data from each load sensing point.

[0058] The secondary sub-modules are set at each device point, and the secondary sub-modules are used to collect the operation monitoring data of each device point;

[0059] The central control unit includes:

[0060] The first processing module is used to acquire load feedback data and set a primary control strategy based on the load feedback data and the linkage control model.

[0061] The second processing module is used to acquire the operation monitoring data of each equipment point, and determine whether to generate a correction control strategy for each equipment point based on the correction evaluation model and all operation monitoring data.

[0062] In some embodiments of this application, the central control unit further includes:

[0063] The third processing module is used to establish the equipment point sequence A, A = (a1, a2, ..., a...). i …a n ), where a i Let i be the i-th device point, and n be the number of device points;

[0064] Based on the equipment point sequence A, set a sequentially. i For the target device point;

[0065] Based on the historical operation data of the heating network, a mapping sub-region for the target equipment point is set, and the mapping sub-region includes multiple load sensing points;

[0066] Generate a record data packet for the target device point;

[0067] Based on the recorded data packets, set the control sub-model for the target device point and the load fluctuation value b;

[0068] The control sub-models and load fluctuation values ​​for each equipment point are generated sequentially.

[0069] Construct an allocation sub-model based on all load fluctuation values;

[0070] A linkage control model is generated based on the allocation sub-model and all control sub-models.

[0071] In some embodiments of this application, the second processing module is further configured to:

[0072] Multiple control cycles can be preset;

[0073] Set the start time of each control cycle as the feedback time node;

[0074] Based on the equipment point sequence A, set a sequentially.i For the device to be controlled;

[0075] Obtain the load feedback data of the device to be controlled at the current feedback time point;

[0076] Generate the expected heat load of the device to be controlled at the current feedback time point;

[0077] The expected heat load of each node to be controlled at the current feedback time node is generated sequentially;

[0078] A primary allocation strategy is generated based on the expected heat load of each equipment point and the allocation sub-model.

[0079] Set the control sub-strategies for each device point according to the primary allocation strategy;

[0080] A first-level control policy is generated based on all control sub-policies.

[0081] Compared with existing technologies, the advantages of the heating network collaborative control method and system based on heat load feedback proposed in this application are as follows:

[0082] By adding intelligent balancing valves at key nodes of the heating network (such as the outlet of the first station and the inlet of the zone heat exchange station), and combining them with a linkage control model to dynamically adjust the heating flow, the problem of hydraulic imbalance in the network caused by the widening temperature difference between the supply and return water can be avoided, thereby improving the stability of the heating network operation.

[0083] By constructing a modified evaluation model, pressure linkage control between the heat exchange station extraction steam unit and the heat exchange pipeline network can be achieved. When the pressure of the heat exchange system fluctuates, the system pressure can be stabilized by adjusting the speed of the circulating water pump or the opening of the extraction steam regulating valve of the unit, so as to avoid impacting the operation of the unit. Attached Figure Description

[0084] Figure 1 This is a flowchart illustrating a preferred embodiment of a heating network collaborative control method based on heat load feedback. Detailed Implementation

[0085] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0086] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0087] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0088] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0089] like Figure 1 As shown in the preferred embodiment of this application, a heating network collaborative control method based on heat load feedback includes:

[0090] S101: Set multiple equipment points according to the structural parameters of the heating network, and construct a linkage control model and a correction evaluation model based on all equipment points;

[0091] S102: Obtain load feedback data and set a primary control strategy based on the load feedback data and the linkage control model;

[0092] S103: Obtain the operation monitoring data of each equipment point, and determine whether to generate a correction control strategy for each equipment point based on the correction evaluation model and all operation monitoring data;

[0093] This includes setting multiple device points, including:

[0094] Establish a sequence of device points A, A = (a1, a2, ..., a3) i …a n ), where a i Let be the i-th device point, and n be the number of device points.

[0095] Specifically, by analyzing the structural parameters of the heating network, multiple key location nodes (such as the first station outlet, the inlet of the zone heat exchange station, and other flow control nodes) are selected, and multiple equipment points are set according to all key location nodes, where a single equipment point represents a key location node.

[0096] Specifically, intelligent balancing valves are added at various key locations to control the heat flow at each equipment point by dynamically adjusting the valve opening.

[0097] Specifically, constructing a linkage control model includes:

[0098] Based on the equipment point sequence A, set a sequentially. i For the target device point;

[0099] Based on the historical operation data of the heating network, a mapping sub-region for the target equipment point is set, and the mapping sub-region includes multiple load sensing points.

[0100] Generate a record data packet for the target device point;

[0101] Based on the recorded data packets, set the control sub-model for the target device point and the load fluctuation value b;

[0102] The control sub-models and load fluctuation values ​​for each equipment point are generated sequentially.

[0103] Construct an allocation sub-model based on all load fluctuation values;

[0104] A linkage control model is generated based on the allocation sub-model and all control sub-models.

[0105] Specifically, the mapped sub-region refers to the heating sub-region controlled by the location node corresponding to the target equipment point. Historical monitoring data of all heating users within the mapped sub-region of the target equipment point is acquired. Through analysis, several typical heating users (i.e., those with representative locations or representative heating habits) are selected, and all remaining unselected heating users are aggregated into these typical user groups. This generates multiple load sensing points, where each load sensing point represents a typical heating user. Load assessment models for each load sensing point are then constructed sequentially.

[0106] Specifically, the heating load of each load sensing point is predicted by the established load assessment model, and the heating load corresponding to each load sensing point is generated in sequence. The total sub-value of the load sensing point is generated by aggregating the number of typical heating users corresponding to each load sensing point (i.e., the product of the predicted heating load and the aggregated number of users). The expected heat load of the target equipment point is generated based on the total sub-value of all load sensing points and the total sub-value of all load sensing points.

[0107] Specifically, the record data packet of the target device point includes historical heating monitoring data of each load sensing point in the mapped sub-region of the target device point and historical operation data of the target device point.

[0108] Specifically, generating the load fluctuation value b of the target device point includes:

[0109]

[0110] Where θ1 is the number of load sensing points within the mapping area of ​​the target device point; η i Let j be the influence factor of the i-th load sensing point; i It generates the fluctuation reference value of the i-th load sensing point based on the recorded data packet.

[0111] Specifically, a corresponding fluctuation reference value is set based on the historical fluctuation of heating demand at the load sensing point. The greater the fluctuation, the larger the corresponding fluctuation reference value. The mapping relationship between the two can be set based on historical parameters.

[0112] Specifically, the influence factor of each load sensing point is set according to its participation in the mapping sub-region (i.e., the number of users aggregated by the corresponding typical heating users and the corresponding total sub-value; the larger the above two parameters, the greater the participation of the load sensing point in the mapping sub-region). The greater its participation, the larger the value of the corresponding influence factor.

[0113] Specifically, the larger the load fluctuation value, the greater the likelihood of fluctuations in the heating flow rate at the target equipment point.

[0114] It is understood that in the above embodiments, by adding intelligent balancing valves at key nodes of the heating network (such as the outlet of the first station and the inlet of the zone heat exchange station), and dynamically adjusting the heating flow rate in conjunction with the linkage control model, the problem of hydraulic imbalance in the network caused by the expansion of the temperature difference between the supply and return water is avoided, thereby improving the stability of the heating network operation.

[0115] In a preferred embodiment of this application, a primary control strategy is set, including:

[0116] Multiple control cycles can be preset;

[0117] Set the start time of each control cycle as the feedback time node;

[0118] Based on the equipment point sequence A, set a sequentially. i For the device to be controlled;

[0119] Obtain the load feedback data of the device to be controlled at the current feedback time point;

[0120] Generate the expected heat load of the device to be controlled at the current feedback time point;

[0121] The expected heat load of each node to be controlled at the current feedback time node is generated sequentially;

[0122] A primary allocation strategy is generated based on the expected heat load of each equipment point and the allocation sub-model.

[0123] Set the control sub-strategies for each device point according to the primary allocation strategy;

[0124] A first-level control policy is generated based on all control sub-policies.

[0125] Specifically, the control cycle can be set based on historical parameters. The greater the probability of load fluctuations in the heating network, the shorter the duration of a single control cycle.

[0126] Specifically, feedback data from each load sensing point within the mapping sub-region corresponding to the device to be controlled is obtained, thereby generating the corresponding expected heat load.

[0127] Specifically, the prediction correction coefficient for the controlled equipment point is set according to the load fluctuation value of the controlled equipment point. The larger the load fluctuation value, the larger the corresponding correction coefficient. The mapping relationship between the two can be set according to historical parameters.

[0128] Specifically, the product of the prediction correction coefficient and the expected heat load of the controllable equipment point is set as the allocation value of the controllable equipment point. The allocation value of each equipment point is generated sequentially. The ratio of the allocation value of the controllable equipment point to the total allocation value is set as the heat flow ratio of the controllable equipment point. The heat flow ratio of each equipment point is generated sequentially. A first-level allocation strategy is generated based on the total heat flow ratio.

[0129] Specifically, the opening degree of the intelligent balancing valve corresponding to each equipment point is set according to the primary allocation strategy, and a primary control strategy is generated based on all opening parameters.

[0130] It is understood that in the above embodiments, by adding intelligent balancing valves at key nodes of the heating network (such as the outlet of the first station and the inlet of the zone heat exchange station), and dynamically adjusting the heating flow rate in conjunction with the linkage control model, the problem of hydraulic imbalance in the network caused by the expansion of the temperature difference between the supply and return water is avoided, thereby improving the stability of the heating network operation.

[0131] In a preferred embodiment of this application, determining whether to generate a correction control strategy for each device point includes:

[0132] Set multiple monitoring time points within the current control cycle;

[0133] Based on the equipment point sequence A, set a sequentially. i The equipment point to be evaluated;

[0134] Obtain the operational monitoring data of the device to be evaluated at the current monitoring time point;

[0135] A primary evaluation strategy for the equipment points to be evaluated is generated based on the modified evaluation model;

[0136] The operational risk value f of the equipment point to be evaluated is generated based on the operational monitoring data and the primary assessment model.

[0137] Preset operational risk threshold F1;

[0138] If f>F1, the current monitoring time node generates a correction control strategy for the device point to be evaluated.

[0139] Specifically, the operating risk threshold can be set based on historical parameters. When the operating risk value is greater than the preset risk threshold, it indicates that there is a risk of pressure fluctuation in the current equipment. It is necessary to adjust the speed of the circulating water pump or the opening of the unit's steam extraction regulating valve at the equipment point to be evaluated according to the modified control strategy, so as to maintain the stability of the system pressure and avoid impacting the operation of the heating pipeline.

[0140] Specifically, generating the operational risk value f for the equipment point to be evaluated includes:

[0141]

[0142] Where θ2 represents the number of operational monitoring indicators; β i Let s be the influencing factor of the i-th operational monitoring indicator; i It is the real-time reference value of the i-th operational monitoring indicator of the equipment point to be evaluated; s' i It is the reference threshold value for the i-th operational monitoring indicator set according to the first-level evaluation strategy; Y(i) is the selection coefficient; if (s i -s' i If )>0, Y(i)=1; if (s i -s' i If ) < 0, Y(i) = 0.

[0143] Specifically, the operational risk threshold can be set based on historical parameters.

[0144] Specifically, operational monitoring indicators include, but are not limited to, parameters reflecting the operational status of multiple equipment points, such as return water temperature, return water pipe pressure and pressure fluctuation trends, return water flow rate, and load fluctuations. Quantification ensures that the reference values ​​of each operational monitoring indicator are within the same range, and the higher the reference value of each indicator, the greater the likelihood of potential operational risks at that equipment point.

[0145] Specifically, the reference threshold values ​​for each operational risk indicator can be set based on historical parameters. The reference threshold value refers to the reference value corresponding to the operational risk indicator when the equipment point to be evaluated is at a safety critical point. When the real-time reference value of the operational risk indicator is greater than the preset reference threshold value, it indicates that the equipment point to be evaluated is in an abnormal operating state.

[0146] Specifically, by analyzing the recorded data packets of each device point, reference values ​​for each operation monitoring indicator at the safety critical point of different device points are generated, thereby generating evaluation sub-strategies for each device point, and constructing a modified evaluation model based on all evaluation sub-strategies.

[0147] Specifically, the influencing factors of each operational risk indicator can be set according to the degree of correlation between them and potential operational risks. The greater the degree of correlation, the larger the value of the corresponding influencing factor. The mapping relationship between the two can be set according to historical parameters.

[0148] It is understandable that in the above embodiments, by constructing a modified evaluation model, the pressure linkage control between the heat exchange station extraction steam unit and the heat exchange pipeline network is realized. When the pressure of the heat exchange system fluctuates, the system pressure is kept stable by adjusting the speed of the circulating water pump or the opening of the unit extraction steam regulating valve, so as to avoid impacting the operation of the unit.

[0149] In a preferred embodiment of this application, it further includes:

[0150] Obtain feedback data packets from all load sensing points at the current monitoring time point;

[0151] Generate the runtime deviation value d based on all feedback data packets;

[0152]

[0153] Where g is the deviation compensation coefficient; n is the number of equipment points; μ i c is the influence factor of the i-th equipment point; i It generates the actual heat load of the i-th device at the current monitoring time point based on all feedback data packets; c' i U1 is the expected heat load of the i-th device at the current monitoring time point; U2 is the preset first conversion coefficient; U1 is the preset second conversion coefficient; f i This represents the operational risk value of the i-th device at the current monitoring time point.

[0154] Preset operating deviation threshold D1;

[0155] If d > D1, the current monitoring time node generates a correction instruction for the first-level control strategy.

[0156] Specifically, the operating deviation threshold can be set based on historical parameters. When the real-time operating deviation value is greater than the preset operating deviation threshold, it indicates that the heating efficiency corresponding to the current heating flow allocation is poor, and the primary control strategy needs to be adjusted in time to meet the heating demand of each mapped sub-region.

[0157] Specifically, by setting a first conversion coefficient, the operating deviation value is kept within a preset range, and The larger the value, the greater the corresponding operational deviation.

[0158] Specifically, by presetting a second conversion coefficient, the deviation compensation coefficient g is made to be within a preset value range, and The larger the value of , the larger the corresponding deviation compensation coefficient g. The mapping relationship between the two can be set according to historical parameters.

[0159] Specifically, the operating deviation value of each equipment point is set according to its corresponding load fluctuation value. The larger the load fluctuation value, the smaller the reference value of the corresponding influencing factor. The mapping relationship between the two can be set according to historical parameters.

[0160] In another preferred embodiment of the heating network collaborative control method based on heat load feedback based on any of the above preferred embodiments, this preferred embodiment provides a heating network collaborative control method based on heat load feedback, comprising:

[0161] The central control unit is used to set multiple equipment points according to the structural parameters of the heating network.

[0162] The central control unit is also used to build a linkage control model and correct the evaluation model based on all equipment points;

[0163] The monitoring unit comprises multiple primary sub-modules and multiple secondary sub-modules;

[0164] The first-level submodule sets up each load sensing point, and the first-level submodule is used to collect load feedback data from each load sensing point.

[0165] The secondary sub-modules are set up at each device point, and the secondary sub-modules are used to collect the operation monitoring data of each device point;

[0166] The central control unit includes:

[0167] The first processing module is used to acquire load feedback data and set a primary control strategy based on the load feedback data and the linkage control model.

[0168] The second processing module is used to acquire the operation monitoring data of each equipment point, and determine whether to generate a correction control strategy for each equipment point based on the correction evaluation model and all operation monitoring data.

[0169] In a preferred embodiment of this application, the central control unit further includes:

[0170] The third processing module is used to establish the equipment point sequence A, A = (a1, a2, ..., a...). i …a n ), where a i Let i be the i-th device point, and n be the number of device points;

[0171] Based on the equipment point sequence A, set a sequentially. i For the target device point;

[0172] Based on the historical operation data of the heating network, a mapping sub-region for the target equipment point is set, and the mapping sub-region includes multiple load sensing points.

[0173] Generate a record data packet for the target device point;

[0174] Based on the recorded data packets, set the control sub-model for the target device point and the load fluctuation value b;

[0175] The control sub-models and load fluctuation values ​​for each equipment point are generated sequentially.

[0176] Construct an allocation sub-model based on all load fluctuation values;

[0177] A linkage control model is generated based on the allocation sub-model and all control sub-models.

[0178] In a preferred embodiment of this application, the second processing module is further configured to:

[0179] Multiple control cycles can be preset;

[0180] Set the start time of each control cycle as the feedback time node;

[0181] Based on the equipment point sequence A, set a sequentially. i For the device to be controlled;

[0182] Obtain the load feedback data of the device to be controlled at the current feedback time point;

[0183] Generate the expected heat load of the device to be controlled at the current feedback time point;

[0184] The expected heat load of each node to be controlled at the current feedback time node is generated sequentially;

[0185] A primary allocation strategy is generated based on the expected heat load of each equipment point and the allocation sub-model.

[0186] Set the control sub-strategies for each device point according to the primary allocation strategy;

[0187] A first-level control policy is generated based on all control sub-policies.

[0188] According to the first concept of this application, by adding intelligent balancing valves at key nodes of the heating network (such as the outlet of the first station and the inlet of the zone heat exchange station), and combining them with a linkage control model to dynamically adjust the heating flow rate, the problem of hydraulic imbalance in the network caused by the expansion of the temperature difference between the supply and return water can be avoided, thereby improving the stability of the heating network operation.

[0189] According to the second concept of this application, by constructing a modified evaluation model, the pressure linkage control between the heat exchange station extraction steam unit and the heat exchange pipeline network can be realized. When the pressure of the heat exchange system fluctuates, the system pressure is stabilized by adjusting the speed of the circulating water pump or the opening of the unit extraction steam regulating valve, so as to avoid impacting the operation of the unit.

[0190] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A heat supply network collaborative control method based on heat load feedback, characterized in that, The application relates to a heat supply pipeline network control method and device. According to the structure parameters of the heat supply pipeline network, a plurality of equipment points are set, a linkage control model and a correction evaluation model are constructed according to all the equipment points, load feedback data are acquired, and a primary control strategy is set according to the load feedback data and the linkage control model. According to the correction evaluation model and all the operation monitoring data, it is judged whether to generate a correction control strategy of each equipment point. The setting of the plurality of equipment points comprises: The construction of the linkage control model comprises: Establishing a device point number sequence A, A=(a1, a2…a i …a n ), wherein a i is the i-th device point, and n is the number of device points.

2. The heat load feedback based heating network coordinated control method of claim 1, wherein, According to historical operation data of the heat supply pipeline network, a mapping sub-region of a target equipment point is set, and the mapping sub-region comprises a plurality of load sensing points. According to the device point number sequence A, a is set in turn i Target device point; A record data packet of the target equipment point is generated. According to the record data packet, a control sub-model of the target equipment point and a load fluctuation value b are set. The control sub-models and the load fluctuation values of all the equipment points are generated in sequence. An allocation sub-model is constructed according to all the load fluctuation values. The linkage control model is generated according to the allocation sub-model and all the control sub-models. The generation of the load fluctuation value b of the target equipment point comprises:

3. The heat load feedback based heating network coordinated control method of claim 2, wherein, The setting of the primary control strategy comprises: Wherein θ1 is the number of load-aware points in the mapping area of the target device point; η i is the influence factor of the i th load-aware point; j i is the fluctuation reference value of the i th load-aware point generated according to the record data packet.

4. The heat load feedback-based heating network cooperative control method according to claim 2, wherein, A plurality of control periods are preset. A starting time node of each control period is set as a feedback time node. Load feedback data of a to-be-controlled equipment point at a current feedback time node are acquired. According to the device point number sequence A, a is set in turn i is the device point to be controlled; An expected heat load of the to-be-controlled equipment point at the current feedback time node is generated. Expected heat loads of all the to-be-controlled nodes at the current feedback time node are generated in sequence. A primary allocation strategy is generated according to the expected heat loads of all the equipment points and the allocation sub-model. Control sub-strategies of all the equipment points are set according to the primary allocation strategy. The primary control strategy is generated according to all the control sub-strategies. The judgment of whether to generate the correction control strategy of each equipment point comprises:

5. The heat load feedback based heating network coordinated control method of claim 4, wherein, A plurality of monitoring time nodes are set in a current control period. Operation monitoring data of a to-be-evaluated equipment point at a current monitoring time node are acquired. According to the device point number sequence A, a is set in turn i is the device point to be evaluated; A primary evaluation strategy of the to-be-evaluated equipment point is generated according to the correction evaluation model. An operation risk value f of the to-be-evaluated equipment point is generated according to the operation monitoring data and the primary evaluation model. An operation risk value threshold F1 is preset. If f>F1, the correction control strategy of the to-be-evaluated equipment point is generated at the current monitoring time node. The generation of the operation risk value f of the to-be-evaluated equipment point comprises:

6. The heat load feedback based heating network coordinated control method of claim 5, wherein, Further comprising: Wherein, θ2 is the number of operation monitoring indexes; β i is the influence factor of the i th operation monitoring index; s i is the real-time reference value of the i th operation monitoring index of the equipment point to be evaluated; s' i is the reference value threshold of the i th operation monitoring index set according to the first-level evaluation strategy; Y(i) is a selection coefficient; if (s i -s' i )>0, Y(i) = 1; if (s i -s' i )<0, Y(i) = 0.

7. The heat load feedback based heating network coordinated control method of claim 6, wherein, Feedback data packets of all the load sensing points at a current monitoring time node are acquired. An operation deviation value d is generated according to all the feedback data packets. An operation deviation value threshold D1 is preset. wherein g is a deviation compensation coefficient; n is the number of equipment points; μ i is the influence factor of the i-th equipment point; c i is the actual thermal load of the i-th equipment point at the current monitoring time node generated according to all feedback data packets; c' i is the expected thermal load of the i-th equipment point at the current monitoring time node; U1 is a preset first conversion coefficient; U2 is a preset second conversion coefficient; f i is the operation risk value of the i-th equipment point at the current monitoring time node; If d>D1, a correction instruction of the primary control strategy is generated at the current monitoring time node. The application further relates to a heat supply pipeline network control device.

8. A heat-supply network collaborative control system based on heat load feedback, adopting the heat-supply network collaborative control method based on heat load feedback according to any one of claims 1-7, characterized in that, The central control unit is used for setting a plurality of equipment points according to the structure parameters of the heat supply pipeline network. The central control unit is further used for constructing a linkage control model and a correction evaluation model according to all the equipment points. The monitoring unit comprises a plurality of primary sub-modules and a plurality of secondary sub-modules. The primary sub-module sets each load sensing point, and the primary sub-module is used for collecting load feedback data of each load sensing point. The secondary sub-module is arranged at each equipment point, and the secondary sub-module is used for collecting operation monitoring data of each equipment point. The central control unit comprises: A first processing module is used for acquiring load feedback data, and setting a primary control strategy according to the load feedback data and a linkage control model. ​ The second processing module is configured to acquire operation monitoring data of each device point, and determine whether to generate a correction control strategy for each device point according to a correction evaluation model and all the operation monitoring data.

9. The heat load feedback based district heating network coordinated control system of claim 8, wherein, The central control unit further comprises: The third processing module is configured to establish a device point sequence A, A=(a1, a2,..., an), wherein ai is the ith device point, and n is the number of device points. i …a n ), wherein ai is the ith device point, and n is the number of device points. i The third processing module is configured to establish a device point sequence A, A=(a1, a2,..., an), wherein ai is the ith device point, and n is the number of device According to the device point number series A, a is set in turn i Target device point; According to the historical operation data of the heat supply pipe network, a mapping sub-region of the target device point is set, and the mapping sub-region comprises a plurality of load sensing points; A record data packet of the target device point is generated; According to the record data packet, a control sub-model and a load fluctuation value b of the target device point are set; The control sub-model and the load fluctuation value of each device point are sequentially generated; According to all the load fluctuation values, a distribution sub-model is constructed; According to the distribution sub-model and all the control sub-models, a linkage control model is generated.

10. The heat load feedback based district heating network coordinated control system of claim 9, wherein, The second processing module is further configured to: A plurality of control periods are preset; A start time node of each control period is set as a feedback time node; According to the device point number sequence A, a is set in turn i is the device point to be controlled; Load feedback data of a to-be-controlled device point at a current feedback time node is acquired; An expected heat load of the to-be-controlled device point at the current feedback time node is generated; The expected heat load of each to-be-controlled node at the current feedback time node is sequentially generated; According to the expected heat load of each device point and the distribution sub-model, a first distribution strategy is generated; According to the first distribution strategy, a control sub-strategy of each device point is set; According to all the control sub-strategies, a first control strategy is generated.

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