Heat supply system source network collaborative optimization scheduling method based on primary pipe network heat storage

Abstract

The invention discloses a heat supply system source network collaborative optimization scheduling method based on primary pipe network heat storage, which comprises the following steps: selecting a primary pipe network area connected with a heating station in a supply and demand imbalance state interval as a transformation object, and carrying out primary pipe network transformation by additionally arranging a communicating pipe and a communicating pipe adjusting device; a first-stage pipe network heat storage and release mechanism is established, and a transformed first-stage pipe network heat storage quantity model is established in combination with heat supply operation parameters, basic attributes, heat loss parameters and environmental parameters of the first-stage pipe network and the communicating pipe; the method comprises the following steps: setting a heating station time-sharing control target according to a heat load demand of a heating station, considering heat source heat load supply, heat supply network transmission and distribution capacity and heat storage capacity, combining a heating station valve characteristic identification model and a water pump characteristic identification model, and establishing an intelligent regulation and control model under the conditions of meeting the heating station time-sharing control target and pipe network hydraulic working condition balance; and a pipe network heat storage and release scheduling strategy and a time-phased regulation and control strategy of a communicating pipe adjusting device, a heating station valve and a water pump are output.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart heating systems, and specifically relates to a source-network collaborative optimization scheduling method for a heating system based on primary pipe network heat storage. Background Art

[0002] With the acceleration of urbanization and the transformation of energy structure, the district heating system has been widely used due to its high efficiency and environmental protection. District heating produces heat energy on a large scale and transmits it to multiple heating stations over long distances, which are then distributed to end users by the heating stations, achieving efficient use of energy and reducing pollution emissions.

[0003] The heat demand of thermal power stations is affected by many factors such as seasons, weather, and user habits, showing obvious peak and valley characteristics. Traditional heating systems often find it difficult to accurately match this dynamic change, and are prone to problems of mismatch between heat supply and demand, such as insufficient supply during peak hours of the heating season and oversupply during non-peak hours, resulting in energy waste and reduced user satisfaction. In order to cope with the contradiction between the heat supply of heat sources and the heat load demand of thermal power stations, thermal energy storage and release technology has become the key to smoothing supply and demand fluctuations. How to effectively utilize the heat supply of heat sources, the heat load demand of thermal power stations, and consider the heat storage characteristics of the primary pipeline network, and specifically regulate thermal power stations with imbalanced supply and demand to ensure accurate matching of the heat load demand of thermal power stations is an issue that needs to be urgently addressed.

[0004] Based on the above technical problems, it is necessary to design a new source-network collaborative optimization scheduling method for the heating system based on primary pipeline heat storage. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a source-network collaborative optimization scheduling method for a heating system based on primary pipe network heat storage, by transforming the primary pipe network area where the thermal power station with unbalanced supply and demand is located, adding connecting pipes and regulating devices, thereby enhancing the regulating capacity and adaptability of the heating network; and, in the period of oversupply of the heat source and heat load, the excess heat can be effectively stored in the primary network through the connecting pipe, thereby reducing energy waste, and releasing the stored heat when the heat source is insufficient, thereby ensuring stable heating of the thermal power station, improving energy utilization efficiency, and reducing operating costs; in addition, through the heat storage and release scheduling of the pipe network, and the time-sharing automatic control of the valves, water pumps and connecting pipe regulating devices of the thermal power station, the accuracy of the regulation is improved, thereby ensuring the precise matching of the thermal load demand of the thermal power station.

[0006] In order to solve the above technical problems, the technical solution of the present invention is:

[0007] The present invention provides a source-network collaborative optimization scheduling method for a heating system based on primary pipe network heat storage, which comprises:

[0008] S1. Analyze the historical supply and demand balance status of each heating station in the heating system, select the primary pipe network area connected to the heating station in the supply and demand imbalance range as the transformation object, and transform the primary pipe network by adding connecting pipes and connecting pipe regulating devices;

[0009] S2. Establish a heat storage and release mechanism for the modified primary pipe network: When the outdoor temperature is higher than the preset value and the heat source heat load is oversupplied, the inherent heat storage characteristics of the primary pipe network are used, and the interconnecting pipe and the interconnecting pipe regulating device are combined to store excess heat inside the primary network; when the outdoor temperature is lower than the reference value and the heat source heat load is undersupplied, the heat stored inside the primary network is released to meet the heat load demand of the thermal power station;

[0010] S3. Based on the heat storage and release mechanism of the transformed primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the interconnecting pipes, a heat storage model of the transformed primary pipe network is established; and, using graph theory to construct a physical model of the transformed primary pipe network, combined with the operation data to calculate the hydraulic condition of the transformed primary pipe network, and establish a hydraulic condition model of the transformed primary pipe network;

[0011] S4. Set the time-segment control target of the thermal power station according to the heat load demand of the thermal power station, coordinately consider the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in the time segment, and combine the valve characteristic identification model and the water pump characteristic identification model of the thermal power station. Under the condition of meeting the time-segment control target of the thermal power station and the hydraulic condition balance of the pipeline network, establish an intelligent control model, output the pipeline network heat storage and release scheduling strategy, and the time-segment control strategy of the interconnecting pipe regulating device, the thermal power station valve and the water pump.

[0012] Further, the S1 specifically includes:

[0013] Obtain the historical operation data of each thermal power station, including the supply and return water temperature and circulation flow of each thermal power station, and calculate the actual heat load of each thermal power station supplied by the heat source in different time periods;

[0014] Considering the heat load demand of the buildings under each heat station and the expected indoor temperature of heat users, set the expected heat load required by each heat station in different time periods;

[0015] Calculate the supply and demand balance indicators of each heating station in different time periods based on the actual heat load supplied to each heating station in different time periods and the expected heat load required by each heating station in different time periods;

[0016] When the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the first interval, the heating station is a heating station in an unbalanced supply and demand state interval, and the primary pipe network area connected to the heating station is selected as a priority renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the second interval, the heating station is a heating station in a critical supply and demand state interval, and the primary pipe network area connected to the heating station is selected as an alternative renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the third interval, the heating station is a heating station in a balanced supply and demand state interval and no renovation is performed;

[0017] In the primary pipeline area selected as the priority renovation object, the location layout and pipe diameter parameters of the newly added connecting pipes are designed, and corresponding regulating devices are configured for the connecting pipes. The heat flow direction and flow rate are dynamically adjusted according to demand, and the connecting pipes are interconnected with the primary pipeline network to carry out the primary pipeline network renovation.

[0018] Furthermore, the supply and demand balance state index of each heating station in different time periods is calculated as follows:

[0019]

[0020] δ s,t is the supply and demand balance indicator of the sth thermal power station in the tth period; Q s,t The actual heat load of the sth thermal power station in the tth period supplied by the heat source; Q s ' ,t is the expected heat load required by the sth thermal power station in the tth period.

[0021] Further, the S2 specifically includes:

[0022] Install temperature sensors, flow meters and pressure sensors at the key nodes of the transformed primary pipe network and interconnecting pipes to monitor the temperature, flow and pressure change data at the nodes and calculate the heat load data of the primary pipe network supplied by the heat source to the thermal power station;

[0023] When the deviation between the heat load data of the primary pipe network supplied by the heat source to the thermal power station and the heat load demand value of the thermal power station is greater than the set first threshold, it indicates that the heat source is in the period of heat load oversupply. At the same time, when it is monitored that the outdoor temperature is higher than the preset value and the heat load demand of the thermal power station is in the valley period, the modified primary pipe network enters the heat storage mode. On the basis of meeting the heat load demand of the thermal power station in the valley period, the excess heat is guided to be stored in the primary network through the interconnecting pipe by regulating the interconnecting pipe regulating device;

[0024] When the deviation between the heat load demand value of the thermal power station and the heat load data of the primary pipeline network supplied by the heat source to the thermal power station is greater than the set second threshold, it indicates that the heat source is in a period of heat load shortage. At the same time, when the outdoor temperature is monitored to be lower than the benchmark value and the heat load demand of the thermal power station is at its peak, the modified primary pipeline network enters the heat release mode, and the heat stored in the primary network is released through the interconnecting pipe by regulating the interconnecting pipe regulating device to meet the heat load demand of the thermal power station.

[0025] Further, in S3, based on the heat storage and release mechanism of the modified primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the interconnecting pipe, a heat storage model of the modified primary pipe network is established, specifically including:

[0026] Based on the heat storage and release mechanism of the modified primary pipe network, the time period of entering the heat storage mode is known, and the historical operating parameters and soft measurement parameters of the primary pipe network and the interconnecting pipe corresponding to the heat storage period are obtained, including the supply and return water temperature, circulation flow, heat load of the primary pipe network and the interconnecting pipe circulation flow, the operating parameters of the interconnecting pipe regulating device, and the water temperature in the interconnecting pipe; the soft measurement parameters are parameters that are difficult to measure in the actual operation process obtained by using the constructed modified primary pipe network soft measurement model;

[0027] Obtain the basic properties of the primary pipe network and the interconnecting pipe, including the length, diameter, material, and insulation thickness of the primary pipe network and the interconnecting pipe; obtain the environmental parameters during the heat storage period, including temperature, wind speed, and sunshine;

[0028] Based on the basic properties and environmental parameters of the primary pipe network and the interconnecting pipe, a heat loss model of the primary pipe network and the interconnecting pipe is established to obtain the heat loss parameters during the heat storage period;

[0029] According to the historical operating parameters, soft measurement parameters and heat loss parameters of the primary pipeline network and the interconnecting pipes, a heat storage capacity prediction model for the transformed primary pipeline network during the heat storage period is established.

[0030] Furthermore, in S3, the physical model of the transformed primary pipe network is constructed by using graph theory, and the hydraulic condition of the transformed primary pipe network is calculated in combination with the operation data to establish the hydraulic condition model of the transformed primary pipe network, which specifically includes:

[0031] Using graph theory, the heat source, heat station, pipeline branch connection, and interconnecting pipe connection in the modified primary pipe network are abstracted as connection nodes, and the supply and return pipe sections between the primary pipe network and the interconnecting pipe nodes are abstracted as edges. Through the relationship matrix between nodes and edges, the physical model of the modified primary pipe network is constructed.

[0032] Based on the physical model of the renovated primary pipeline network, the hydraulic condition calculation of the renovated primary pipeline network is carried out with the pipeline network supply and return water pressure, circulation flow, thermal load of the thermal power station, circulation flow of the thermal power station and heat source output as input, and the hydraulic condition model of the renovated primary pipeline network is established. The hydraulic condition parameters of each node of the renovated primary pipeline network under various conditions are obtained, including pressure, flow, temperature and flow velocity distribution information.

[0033] Further, in S4, the time-division control target of the thermal power station is set according to the heat load demand of the thermal power station, specifically including:

[0034] According to the change trend of heat load demand of heat users under the heat station, and the temperature and flow regulation delay characteristics between the heat station and the heat users, the regulation period of the heat station is obtained;

[0035] According to the correlation analysis, the combined parameters influencing the predicted water supply temperature and circulation flow of the thermal power station are determined, including the heat load demand of the thermal power station, the supply and return water temperature of the primary side of the thermal power station, the primary side circulation flow and the supply and return water temperature of the secondary side of the thermal power station, the secondary side circulation flow, and the outdoor temperature;

[0036] Under the condition of meeting the changing trend of heat load demand of heat users under the jurisdiction of the thermal power station, the target combination parameters of secondary side water supply temperature and circulation flow rate in each adjustment period of the thermal power station are predicted and set as the time period control target of the thermal power station.

[0037] Furthermore, in S4, the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in different time periods are collaboratively considered, and the valve characteristic identification model of the thermal power station and the water pump characteristic identification model are combined to establish an intelligent control model under the condition of meeting the time period control target of the thermal power station and the hydraulic condition balance of the pipe network, output the pipe network heat storage and release scheduling strategy, and the time period control strategy of the connecting pipe regulating device, the thermal power station valve and the water pump, specifically including:

[0038] Obtain the heat source heat load supply data by time period, including the heat load that can be supplied by the heat source in each time period;

[0039] Obtain the transmission and distribution capacity parameters of the heat network, including the maximum flow and pressure parameters of the transformed primary pipe network and interconnecting pipes;

[0040] Based on the established heat storage prediction model of the modified primary pipe network, the heat storage of the primary pipe network in each period is obtained;

[0041] Based on the preset thermal power station valve characteristic identification model, the relationship characteristics between the valve opening and the resistance characteristics and flow rate in the thermal power station are obtained; based on the preset water pump characteristic identification model, the relationship characteristics between the thermal power station water pump operation frequency and the resistance characteristics and flow rate in the thermal power station are obtained;

[0042] The heat source heat load supply data of each time period, the transmission and distribution capacity parameters of the heat network, and the heat storage capacity are used as state variables; based on the relationship between the valve opening and the resistance characteristics and flow rate in the heat station, as well as the relationship between the operation frequency of the heat station water pump and the resistance characteristics and flow rate in the heat station, the operation parameters of the heat storage and release of the pipe network, the regulating and connecting pipe regulating device, the valves and water pumps of the heat station are used as control variables; the secondary side water supply temperature and the circulation flow target combination parameters of each regulation period of the heat station are used as output variables; and the environmental parameters are used as disturbance variables;

[0043] The MPC model predictive control method is adopted to establish state equations and output equations based on state variables, control variables, disturbance variables and output variables. Multi-step prediction is performed based on the state equations and output equations, and the objective function of the degree of deviation from the target output in the future prediction period is defined. The hydraulic condition balance constraint conditions of the pipeline network are set to solve and obtain the optimal control variable sequence.

[0044] Execute the first step to control the variable sequence and update the actual state variable information;

[0045] A feedback correction link is introduced to re-predict and optimize based on the latest state variables in the next control cycle, continuously update the control variable sequence, output the pipeline network heat storage and release scheduling strategy, and the time-sharing control strategy of the interconnecting pipe regulating device, thermal station valves and water pumps.

[0046] Furthermore, the setting of the hydraulic condition balance constraint conditions of the pipeline network includes: based on the established hydraulic condition model of the modified primary pipeline network, obtaining the hydraulic condition parameters of each node of the modified primary pipeline network under each condition, and setting the hydraulic condition balance constraint conditions of the pipeline network under each time period according to the hydraulic condition parameters.

[0047] The beneficial effects of the present invention are:

[0048] The present invention improves the regulating capacity and adaptability of the heat network by reconstructing the primary pipe network area where the heat station with unbalanced supply and demand is located, adding connecting pipes and regulating devices; the introduction of the heat storage and release mechanism enables the excess heat to be effectively stored in the primary network through the connecting pipes during the period of oversupply of the heat source and heat load, thereby reducing energy waste, and releasing the stored heat when the heat source is insufficient, thereby ensuring stable heating of the heat station, improving energy utilization efficiency, and reducing operating costs; establishing a heat storage model and a hydraulic condition model, which can accurately predict the heat storage amount, facilitate subsequent source network regulation, and meet the hydraulic balance of the entire pipe network during regulation; and realizing the pipe network heat storage and release scheduling, as well as the time-sharing automatic control of the valves, water pumps and connecting pipe regulating devices of the heat station, thereby improving the accuracy of regulation and ensuring the accurate matching of the heat load demand of the heat station.

[0049] Other features and advantages will be described in the following description, and partly become apparent from the description, or understood by practicing the invention. The purpose and other advantages of the invention are realized and obtained by the structures particularly pointed out in the description and the drawings.

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 The present invention is a flow chart of a method for coordinated optimization and scheduling of a heating system source network based on heat storage in a primary pipe network. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] like Figure 1 As shown, this embodiment 1 provides a source-network collaborative optimization scheduling method for a heating system based on primary pipe network heat storage, which includes:

[0056] S1. Analyze the historical supply and demand balance status of each heating station in the heating system, select the primary pipe network area connected to the heating station in the supply and demand imbalance range as the transformation object, and transform the primary pipe network by adding connecting pipes and connecting pipe regulating devices;

[0057] S2. Establish a heat storage and release mechanism for the modified primary pipe network: When the outdoor temperature is higher than the preset value and the heat source heat load is oversupplied, the inherent heat storage characteristics of the primary pipe network are used, and the interconnecting pipe and the interconnecting pipe regulating device are combined to store excess heat inside the primary network; when the outdoor temperature is lower than the reference value and the heat source heat load is undersupplied, the heat stored inside the primary network is released to meet the heat load demand of the thermal power station;

[0058] S3. Based on the heat storage and release mechanism of the transformed primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the interconnecting pipes, a heat storage model of the transformed primary pipe network is established; and, using graph theory to construct a physical model of the transformed primary pipe network, combined with the operation data to calculate the hydraulic condition of the transformed primary pipe network, and establish a hydraulic condition model of the transformed primary pipe network;

[0059] S4. Set the time-segment control target of the thermal power station according to the heat load demand of the thermal power station, coordinately consider the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in the time segment, and combine the valve characteristic identification model and the water pump characteristic identification model of the thermal power station. Under the condition of meeting the time-segment control target of the thermal power station and the hydraulic condition balance of the pipeline network, establish an intelligent control model, output the pipeline network heat storage and release scheduling strategy, and the time-segment control strategy of the interconnecting pipe regulating device, the thermal power station valve and the water pump.

[0060] In this embodiment, S1 specifically includes:

[0061] Obtain the historical operation data of each thermal power station, including the supply and return water temperature and circulation flow of each thermal power station, and calculate the actual heat load of each thermal power station supplied by the heat source in different time periods;

[0062] Considering the heat load demand of the buildings under each heat station and the expected indoor temperature of heat users, set the expected heat load required by each heat station in different time periods;

[0063] Calculate the supply and demand balance indicators of each heating station in different time periods based on the actual heat load supplied to each heating station in different time periods and the expected heat load required by each heating station in different time periods;

[0064] When the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the first interval, the heating station is a heating station in an unbalanced supply and demand state interval, and the primary pipe network area connected to the heating station is selected as a priority renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the second interval, the heating station is a heating station in a critical supply and demand state interval, and the primary pipe network area connected to the heating station is selected as an alternative renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the third interval, the heating station is a heating station in a balanced supply and demand state interval and no renovation is performed;

[0065] In the primary pipeline area selected as the priority renovation object, the location layout and pipe diameter parameters of the newly added connecting pipes are designed, and corresponding regulating devices are configured for the connecting pipes. The heat flow direction and flow rate are dynamically adjusted according to demand, and the connecting pipes are interconnected with the primary pipeline network to carry out the primary pipeline network renovation.

[0066] In actual applications, thermal power stations usually choose to adjust the main parameters every hour or every two hours, and adopt different control cycles on weekdays and weekends, daytime and nighttime; for example, when the thermal power station is controlled in a two-hour cycle, the control cycle of the day is divided into 0 o'clock, 2 o'clock, 4 o'clock, 6 o'clock, 8 o'clock, 10 o'clock, 12 o'clock, 14 o'clock, 16 o'clock, 18 o'clock, 20 o'clock, 22 o'clock and 24 o'clock, and the control period of the thermal power station is divided into 0 o'clock-2 o'clock, 2 o'clock-4 o'clock, 4 o'clock-6 o'clock, 6 o'clock-8 o'clock, 8 o'clock-10 o'clock, 10 o'clock-12 o'clock, 12 o'clock-14 o'clock, 14 o'clock-16 o'clock , 16:00-18:00, 18:00-20:00, 20:00-22:00, 22:00-24:00, a total of 12 time periods, calculate the expected heat load required by each thermal power station in each time period, and obtain the historical operation data of each thermal power station, calculate the actual heat load of the heat source supplying each thermal power station in each time period, and calculate the supply and demand balance status index of each thermal power station in each time period. When the number of time periods when the supply and demand balance index of the thermal power station is higher than the preset value is 11, it means that the supply and demand balance index of 11 time periods is higher than the preset value and is in the first interval (10-12), then the primary pipe network area connected to the thermal power station is selected as the priority transformation object. It should be noted that the settings of the first interval, the second interval and the third interval are dynamically set according to the total number of time periods in the thermal power station control cycle. In addition to the regulating valve, a pressure balancing valve, a flow meter and a temperature sensor need to be installed on the Unicom pipe.

[0067] In this embodiment, the supply and demand balance state index of each heating station in different time periods is calculated as follows:

[0068]

[0069] δ s,t is the supply and demand balance indicator of the sth thermal power station in the tth period; Q s,t The actual heat load of the sth thermal power station in the tth period supplied by the heat source; Q s ' ,t is the expected heat load required by the sth thermal power station in the tth period.

[0070] In this embodiment, S2 specifically includes:

[0071] Install temperature sensors, flow meters and pressure sensors at the key nodes of the transformed primary pipe network and interconnecting pipes to monitor the temperature, flow and pressure change data at the nodes and calculate the heat load data of the primary pipe network supplied by the heat source to the thermal power station;

[0072] When the deviation between the heat load data of the primary pipe network supplied by the heat source to the thermal power station and the heat load demand value of the thermal power station is greater than the set first threshold, it indicates that the heat source is in the period of heat load oversupply. At the same time, when it is monitored that the outdoor temperature is higher than the preset value and the heat load demand of the thermal power station is in the valley period, the modified primary pipe network enters the heat storage mode. On the basis of meeting the heat load demand of the thermal power station in the valley period, the excess heat is guided to be stored in the primary network through the interconnecting pipe by regulating the interconnecting pipe regulating device;

[0073] When the deviation between the heat load demand value of the thermal power station and the heat load data of the primary pipeline network supplied by the heat source to the thermal power station is greater than the set second threshold, it indicates that the heat source is in a period of heat load shortage. At the same time, when the outdoor temperature is monitored to be lower than the benchmark value and the heat load demand of the thermal power station is at its peak, the modified primary pipeline network enters the heat release mode, and the heat stored in the primary network is released through the interconnecting pipe by regulating the interconnecting pipe regulating device to meet the heat load demand of the thermal power station.

[0074] In actual applications, during the daytime when the outdoor temperature is high and the heat load demand is low, the supply load of the heat source is usually relatively high, which is greater than the heat load demand of the thermal power station. At this time, the excess heat is stored; during the nighttime when the outdoor temperature is low and the heat load demand is high, the supply load of the heat source is usually insufficient, which is less than the heat load demand of the thermal power station. At this time, the stored heat is released.

[0075] In this embodiment, in S3, based on the heat storage and release mechanism of the modified primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the connecting pipe, a heat storage model of the modified primary pipe network is established, specifically including:

[0076] Based on the heat storage and release mechanism of the modified primary pipe network, the time period of entering the heat storage mode is known, and the historical operating parameters and soft measurement parameters of the primary pipe network and the interconnecting pipe corresponding to the heat storage period are obtained, including the supply and return water temperature, circulation flow, heat load of the primary pipe network and the interconnecting pipe circulation flow, the operating parameters of the interconnecting pipe regulating device, and the water temperature in the interconnecting pipe; the soft measurement parameters are parameters that are difficult to measure in the actual operation process obtained by using the constructed modified primary pipe network soft measurement model;

[0077] Obtain the basic properties of the primary pipe network and the interconnecting pipe, including the length, diameter, material, and insulation thickness of the primary pipe network and the interconnecting pipe; obtain the environmental parameters during the heat storage period, including temperature, wind speed, and sunshine;

[0078] Based on the basic properties and environmental parameters of the primary pipe network and the interconnecting pipe, a heat loss model of the primary pipe network and the interconnecting pipe is established to obtain the heat loss parameters during the heat storage period;

[0079] According to the historical operating parameters, soft measurement parameters and heat loss parameters of the primary pipeline network and the interconnecting pipes, a heat storage capacity prediction model for the transformed primary pipeline network during the heat storage period is established.

[0080] In this embodiment, in S3, the physical model of the transformed primary pipe network is constructed by using graph theory, and the hydraulic condition of the transformed primary pipe network is calculated in combination with the operation data to establish the hydraulic condition model of the transformed primary pipe network, which specifically includes:

[0081] Using graph theory, the heat source, heat station, pipeline branch connection, and interconnecting pipe connection in the modified primary pipe network are abstracted as connection nodes, and the supply and return pipe sections between the primary pipe network and the interconnecting pipe nodes are abstracted as edges. Through the relationship matrix between nodes and edges, the physical model of the modified primary pipe network is constructed.

[0082] Based on the physical model of the renovated primary pipeline network, the hydraulic condition calculation of the renovated primary pipeline network is carried out with the pipeline network supply and return water pressure, circulation flow, thermal load of the thermal power station, circulation flow of the thermal power station and heat source output as input, and the hydraulic condition model of the renovated primary pipeline network is established. The hydraulic condition parameters of each node of the renovated primary pipeline network under various conditions are obtained, including pressure, flow, temperature and flow velocity distribution information.

[0083] It should be noted that the hydraulic operating condition model can obtain the hydraulic operating condition parameters of each node under each operating condition, and the hydraulic balance constraints of the pipeline network are set through the hydraulic operating condition parameters. Subsequent regulation needs to ensure the hydraulic balance of the pipeline network.

[0084] In this embodiment, in S4, the time-division control target of the thermal power station is set according to the heat load demand of the thermal power station, specifically including:

[0085] According to the change trend of heat load demand of heat users under the heat station, and the temperature and flow regulation delay characteristics between the heat station and the heat users, the regulation period of the heat station is obtained;

[0086] According to the correlation analysis, the combined parameters influencing the predicted water supply temperature and circulation flow of the thermal power station are determined, including the heat load demand of the thermal power station, the supply and return water temperature of the primary side of the thermal power station, the primary side circulation flow and the supply and return water temperature of the secondary side of the thermal power station, the secondary side circulation flow, and the outdoor temperature;

[0087] Under the condition of meeting the changing trend of heat load demand of heat users under the jurisdiction of the thermal power station, the target combination parameters of secondary side water supply temperature and circulation flow rate in each adjustment period of the thermal power station are predicted and set as the time period control target of the thermal power station.

[0088] It should be noted that the time-segment control target of the thermal power station changes dynamically with environmental parameters, heat load demand, etc., and the secondary water supply temperature and circulation flow of the thermal power station are the key parameters that determine the heating quality of the thermal power station, while the valve opening and water pump operation frequency are the control variables for adjusting the water supply temperature and circulation flow of the thermal power station. In addition, the thermal power station adjustment period needs to be set according to the temperature and flow adjustment delay characteristics between the thermal power station and the heat user. For example, when the temperature and flow of the thermal power station change, it will take a certain period of time for the heat user to respond to the change synchronously, so the thermal power station can be adjusted a certain time in advance.

[0089] In practical applications, the correlation analysis is used to determine the influencing factors of the combined parameters of the predicted water supply temperature and circulation flow of the thermal power station. The variance inflation factor is an indicator used to evaluate the multicollinearity between features and is expressed as:

[0090]

[0091] is the coefficient of determination between the ith feature and other features; the larger the VIF value, the more serious the multicollinearity between the features, and the closer it is to 1, the less multicollinearity there is. Generally, the boundary value of VIF is set to 5. When the VIF of a feature is greater than 5, it means that there is a high multicollinearity between the feature and other features, and the feature with the highest VIF needs to be eliminated. By looping through all features until the VIF values ​​of the remaining features are all less than 5, the multicollinearity between features is processed.

[0092] A deep neural network (DNN) is used to predict the target combination parameters of the secondary water supply temperature and circulation flow rate in each regulation period of the thermal power station. The DNN model structure contains different numbers of hidden layers. The neurons between adjacent layers are connected in pairs. Each hidden layer has m neurons and the output neuron is 2.

[0093] In this embodiment, in S4, the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in different time periods are collaboratively considered, and the valve characteristic identification model of the thermal power station and the water pump characteristic identification model are combined. Under the condition of meeting the time period control target of the thermal power station and the hydraulic condition balance of the pipe network, an intelligent control model is established to output the pipe network heat storage and release scheduling strategy, as well as the time period control strategy of the communication pipe regulating device, the thermal power station valve and the water pump, which specifically includes:

[0094] Obtain the heat source heat load supply data by time period, including the heat load that can be supplied by the heat source in each time period;

[0095] Obtain the transmission and distribution capacity parameters of the heat network, including the maximum flow and pressure parameters of the transformed primary pipe network and interconnecting pipes;

[0096] Based on the established heat storage prediction model of the modified primary pipe network, the heat storage of the primary pipe network in each period is obtained;

[0097] Based on the preset thermal power station valve characteristic identification model, the relationship characteristics between the valve opening and the resistance characteristics and flow rate in the thermal power station are obtained; based on the preset water pump characteristic identification model, the relationship characteristics between the thermal power station water pump operation frequency and the resistance characteristics and flow rate in the thermal power station are obtained;

[0098] The heat source heat load supply data of each time period, the transmission and distribution capacity parameters of the heat network, and the heat storage capacity are used as state variables; based on the relationship between the valve opening and the resistance characteristics and flow rate in the heat station, as well as the relationship between the operation frequency of the heat station water pump and the resistance characteristics and flow rate in the heat station, the operation parameters of the heat storage and release of the pipe network, the regulating and connecting pipe regulating device, the valves and water pumps of the heat station are used as control variables; the secondary side water supply temperature and the circulation flow target combination parameters of each regulation period of the heat station are used as output variables; and the environmental parameters are used as disturbance variables;

[0099] The MPC model predictive control method is adopted to establish state equations and output equations based on state variables, control variables, disturbance variables and output variables. Multi-step prediction is performed based on the state equations and output equations, and the objective function of the degree of deviation from the target output in the future prediction period is defined. The hydraulic condition balance constraint conditions of the pipeline network are set to solve and obtain the optimal control variable sequence.

[0100] Execute the first step to control the variable sequence and update the actual state variable information;

[0101] A feedback correction link is introduced to re-predict and optimize based on the latest state variables in the next control cycle, continuously update the control variable sequence, output the pipeline network heat storage and release scheduling strategy, and the time-sharing control strategy of the interconnecting pipe regulating device, thermal station valves and water pumps.

[0102] In practical applications, the state equation is expressed as:

[0103] x k+1 =f(x k ,u k ,d k );

[0104] x k is the state variable at the current moment; u k is the control variable at the current moment; d k is the disturbance variable at the current moment; f is the mathematical model of the system's dynamic behavior;

[0105] The output equation is expressed as:

[0106] y k =g(x k ,u k );

[0107] Let the number of forecast periods be N p , then calculate the future N p The state and output of each time step are expressed as:

[0108]

[0109] Define the objective function, expressed as:

[0110]

[0111] r k+i is the target output for the k+i period, and is the target combined parameter of water supply temperature and circulation flow rate; w 1 、w 2 They correspond to the weight factors of output tracking error and control action change rate respectively.

[0112] It should be noted that, taking the identification of valve characteristics of thermal power stations as an example, an in-depth analysis of the relationship between valve opening and resistance characteristics and flow rate in thermal power stations can provide reliable theoretical support for valve regulation. The flow characteristics of the valve are expressed as:

[0113]

[0114] Q is the flow rate of the valve at a certain opening; Q max is the flow rate of the valve when it is fully open; l is the stroke of the valve core at a certain opening; l max It is the stroke of the valve core when the valve is fully open;

[0115] The valve flow rate Q is expressed as:

[0116]

[0117] F is the cross-sectional area of ​​the valve; ζ is the valve resistance coefficient; ΔP is the pressure drop across the valve; ρ is the density of the fluid in the tube.

[0118] In this embodiment, the setting of the hydraulic condition balance constraint conditions of the pipeline network includes: based on the established hydraulic condition model of the modified primary pipeline network, obtaining the hydraulic condition parameters of each node of the modified primary pipeline network under each condition, and setting the hydraulic condition balance constraint conditions of the pipeline network under each time period according to the hydraulic condition parameters.

[0119] In several embodiments provided in the present application, it should be understood that the disclosed system and method can also be implemented in other ways. The system embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and a part of the module, program segment or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0120] In addition, the functional modules in each embodiment of the present invention can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part. If the function is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), disk or optical disk and other media that can store program codes.

[0121] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A method for coordinated optimization and scheduling of source and network of a heating system based on heat storage of a primary pipe network, characterized in that: It includes: S1. Analyze the historical supply and demand balance status of each heating station in the heating system, select the primary pipe network area connected to the heating station in the supply and demand imbalance range as the transformation object, and transform the primary pipe network by adding connecting pipes and connecting pipe regulating devices; S2. Establish a heat storage and release mechanism for the modified primary pipe network: When the outdoor temperature is higher than the preset value and the heat source heat load is oversupplied, the inherent heat storage characteristics of the primary pipe network are used, and the interconnecting pipe and the interconnecting pipe regulating device are combined to store excess heat inside the primary network; when the outdoor temperature is lower than the reference value and the heat source heat load is undersupplied, the heat stored inside the primary network is released to meet the heat load demand of the thermal power station; S3. Based on the heat storage and release mechanism of the transformed primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the interconnecting pipes, a heat storage model of the transformed primary pipe network is established; and, using graph theory to construct a physical model of the transformed primary pipe network, combined with the operation data to calculate the hydraulic condition of the transformed primary pipe network, and establish a hydraulic condition model of the transformed primary pipe network; S4. Set the time-segment control target of the thermal power station according to the heat load demand of the thermal power station, coordinately consider the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in the time segment, and combine the valve characteristic identification model and the water pump characteristic identification model of the thermal power station. Under the condition of meeting the time-segment control target of the thermal power station and the hydraulic condition balance of the pipeline network, establish an intelligent control model, output the pipeline network heat storage and release scheduling strategy, and the time-segment control strategy of the interconnecting pipe regulating device, the thermal power station valve and the water pump.

2. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: The S1 specifically includes: Obtain the historical operation data of each thermal power station, including the supply and return water temperature and circulation flow of each thermal power station, and calculate the actual heat load of each thermal power station supplied by the heat source in different time periods; Considering the heat load demand of the buildings under each heat station and the expected indoor temperature of heat users, set the expected heat load required by each heat station in different time periods; Calculate the supply and demand balance indicators of each heating station in different time periods based on the actual heat load supplied to each heating station in different time periods and the expected heat load required by each heating station in different time periods; When the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the first interval, the heating station is a heating station in an unbalanced supply and demand state interval, and the primary pipe network area connected to the heating station is selected as a priority renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the second interval, the heating station is a heating station in a critical supply and demand state interval, and the primary pipe network area connected to the heating station is selected as an alternative renovation object; when the number of time periods in which the supply and demand balance index of a heating station is higher than a preset value is in the third interval, the heating station is a heating station in a balanced supply and demand state interval and no renovation is performed; In the primary pipeline area selected as the priority renovation object, the location layout and pipe diameter parameters of the newly added connecting pipes are designed, and corresponding regulating devices are configured for the connecting pipes. The heat flow direction and flow rate are dynamically adjusted according to demand, and the connecting pipes are interconnected with the primary pipeline network to carry out the primary pipeline network renovation.

3. The method for coordinated optimization and scheduling of heating system source and network according to claim 2 is characterized in that: The supply and demand balance state index of each heating station in different time periods is calculated as follows: δ s,t is the supply and demand balance indicator of the sth thermal power station in the tth period; Q s,t The actual heat load of the sth thermal power station in the tth period supplied by the heat source; Q s ' ,t is the expected heat load required by the sth thermal power station in the tth period.

4. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: The S2 specifically includes: Install temperature sensors, flow meters and pressure sensors at the key nodes of the transformed primary pipe network and interconnecting pipes to monitor the temperature, flow and pressure change data at the nodes and calculate the heat load data of the primary pipe network supplied by the heat source to the thermal power station; When the deviation between the heat load data of the primary pipe network supplied by the heat source to the thermal power station and the heat load demand value of the thermal power station is greater than the set first threshold, it indicates that the heat source is in the period of heat load oversupply. At the same time, when it is monitored that the outdoor temperature is higher than the preset value and the heat load demand of the thermal power station is in the valley period, the modified primary pipe network enters the heat storage mode. On the basis of meeting the heat load demand of the thermal power station in the valley period, the excess heat is guided to be stored in the primary network through the interconnecting pipe by regulating the interconnecting pipe regulating device; When the deviation between the heat load demand value of the thermal power station and the heat load data of the primary pipeline network supplied by the heat source to the thermal power station is greater than the set second threshold, it indicates that the heat source is in a period of heat load shortage. At the same time, when the outdoor temperature is monitored to be lower than the benchmark value and the heat load demand of the thermal power station is at its peak, the modified primary pipeline network enters the heat release mode, and the heat stored in the primary network is released through the interconnecting pipe by regulating the interconnecting pipe regulating device to meet the heat load demand of the thermal power station.

5. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: In S3, based on the heat storage and release mechanism of the modified primary pipe network, combined with the heating operation parameters, basic properties, heat loss parameters and environmental parameters of the primary pipe network and the interconnecting pipe, a heat storage model of the modified primary pipe network is established, specifically including: Based on the heat storage and release mechanism of the modified primary pipe network, the time period of entering the heat storage mode is known, and the historical operating parameters and soft measurement parameters of the primary pipe network and the interconnecting pipe corresponding to the heat storage period are obtained, including the supply and return water temperature, circulation flow, heat load of the primary pipe network and the interconnecting pipe circulation flow, the operating parameters of the interconnecting pipe regulating device, and the water temperature in the interconnecting pipe; the soft measurement parameters are parameters that are difficult to measure in the actual operation process obtained by using the constructed modified primary pipe network soft measurement model; Obtain the basic properties of the primary pipe network and the interconnecting pipe, including the length, diameter, material, and insulation thickness of the primary pipe network and the interconnecting pipe; obtain the environmental parameters during the heat storage period, including temperature, wind speed, and sunshine; Based on the basic properties and environmental parameters of the primary pipe network and the interconnecting pipe, a heat loss model of the primary pipe network and the interconnecting pipe is established to obtain the heat loss parameters during the heat storage period; According to the historical operating parameters, soft measurement parameters and heat loss parameters of the primary pipeline network and the interconnecting pipes, a heat storage capacity prediction model for the transformed primary pipeline network during the heat storage period is established.

6. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: In S3, the physical model of the transformed primary pipe network is constructed by using graph theory, and the hydraulic condition of the transformed primary pipe network is calculated in combination with the operation data to establish the hydraulic condition model of the transformed primary pipe network, which specifically includes: Using graph theory, the heat source, heat station, pipeline branch connection, and interconnecting pipe connection in the modified primary pipe network are abstracted as connection nodes, and the supply and return pipe sections between the primary pipe network and the interconnecting pipe nodes are abstracted as edges. Through the relationship matrix between nodes and edges, the physical model of the modified primary pipe network is constructed. Based on the physical model of the renovated primary pipeline network, the hydraulic condition calculation of the renovated primary pipeline network is carried out with the pipeline network supply and return water pressure, circulation flow, thermal load of the thermal power station, circulation flow of the thermal power station and heat source output as input, and the hydraulic condition model of the renovated primary pipeline network is established. The hydraulic condition parameters of each node of the renovated primary pipeline network under various conditions are obtained, including pressure, flow, temperature and flow velocity distribution information.

7. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: In S4, the time-division control target of the thermal power station is set according to the heat load demand of the thermal power station, specifically including: According to the change trend of heat load demand of heat users under the heat station, and the temperature and flow regulation delay characteristics between the heat station and the heat users, the regulation period of the heat station is obtained; According to the correlation analysis, the combined parameters influencing the predicted water supply temperature and circulation flow of the thermal power station are determined, including the heat load demand of the thermal power station, the supply and return water temperature of the primary side of the thermal power station, the primary side circulation flow and the supply and return water temperature of the secondary side of the thermal power station, the secondary side circulation flow, and the outdoor temperature; Under the condition of meeting the changing trend of heat load demand of heat users under the jurisdiction of the thermal power station, the target combination parameters of secondary side water supply temperature and circulation flow rate in each adjustment period of the thermal power station are predicted and set as the time period control target of the thermal power station.

8. The method for coordinated optimization and scheduling of heating system source and network according to claim 1, characterized in that: In S4, the heat source heat load supply, the transmission and distribution capacity of the heat network and the heat storage capacity in different time periods are collaboratively considered, and the valve characteristic identification model and the water pump characteristic identification model of the thermal power station are combined. Under the condition of meeting the time-sharing control target of the thermal power station and the hydraulic condition balance of the pipe network, an intelligent control model is established to output the pipe network heat storage and release scheduling strategy, as well as the time-sharing control strategy of the interconnecting pipe regulating device, the thermal power station valve and the water pump, which specifically includes: Obtain the heat source heat load supply data by time period, including the heat load that can be supplied by the heat source in each time period; Obtain the transmission and distribution capacity parameters of the heat network, including the maximum flow and pressure parameters of the transformed primary pipe network and interconnecting pipes; Based on the established heat storage prediction model of the modified primary pipe network, the heat storage of the primary pipe network in each period is obtained; Based on the preset thermal power station valve characteristic identification model, the relationship characteristics between the valve opening and the resistance characteristics and flow rate in the thermal power station are obtained; based on the preset water pump characteristic identification model, the relationship characteristics between the thermal power station water pump operation frequency and the resistance characteristics and flow rate in the thermal power station are obtained; The heat source heat load supply data of each time period, the transmission and distribution capacity parameters of the heat network, and the heat storage capacity are used as state variables; based on the relationship between the valve opening and the resistance characteristics and flow rate in the heat station, as well as the relationship between the operation frequency of the heat station water pump and the resistance characteristics and flow rate in the heat station, the operation parameters of the heat storage and release of the pipe network, the regulating and connecting pipe regulating device, the valves and water pumps of the heat station are used as control variables; the secondary side water supply temperature and the circulation flow target combination parameters of each regulation period of the heat station are used as output variables; and the environmental parameters are used as disturbance variables; The MPC model predictive control method is adopted to establish state equations and output equations based on state variables, control variables, disturbance variables and output variables. Multi-step prediction is performed based on the state equations and output equations, and the objective function of the degree of deviation from the target output in the future prediction period is defined. The hydraulic condition balance constraint conditions of the pipeline network are set to solve and obtain the optimal control variable sequence. Execute the first step to control the variable sequence and update the actual state variable information; A feedback correction link is introduced to re-predict and optimize based on the latest state variables in the next control cycle, continuously update the control variable sequence, output the pipeline network heat storage and release scheduling strategy, and the time-sharing control strategy of the interconnecting pipe regulating device, thermal station valves and water pumps.

9. The method for coordinated optimization and scheduling of heating system source and network according to claim 8, characterized in that: The setting of the hydraulic condition balance constraint conditions of the pipeline network includes: based on the established hydraulic condition model of the modified primary pipeline network, obtaining the hydraulic condition parameters of each node of the modified primary pipeline network under each condition, and setting the hydraulic condition balance constraint conditions of the pipeline network under each time period according to the hydraulic condition parameters.

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