Gas-fired boiler system coupling off-peak electricity solid heat storage and air compression waste heat recovery

Abstract

The invention discloses a gas-fired boiler system coupling off-peak electricity solid heat storage and air compression waste heat recovery, and relates to the technical field of heat supply systems.The system comprises an integrated gas-fired boiler, an off-peak electricity heat storage and air compression waste heat recovery device and a pipe network, and data are collected and heat loads are predicted through a multi-parameter sensing module; the self-adaptive regulation and control module optimizes energy distribution and controls the output of each device; the fault redundancy module keeps stable heat supply; the heat supply output and monitoring module accurately distributes heat and feeds back data to form closed-loop optimization; the multi-energy coupling heat supply system, the integrated gas-fired boiler and the off-peak electricity heat storage and air compression waste heat recovery device are constructed, through multi-parameter sensing and self-adaptive regulation and control, off-peak electricity and waste heat are fully utilized, gas dependence is reduced, and the cost is reduced; meanwhile, a fault redundancy emergency module is arranged to guarantee stable heat supply, a heat supply output and monitoring module achieves accurate distribution and closed-loop optimization, and the reliability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of heat supply system, in particular to a gas boiler system coupled with valley electricity solid heat storage and air compressor waste heat recovery. BACKGROUND

[0002] With the continuous improvement of energy structure adjustment and energy saving and emission reduction demand, as an important field of energy consumption, the energy utilization efficiency and operation cost control of heat supply system have become the focus of the industry. At present, gas boilers are widely used in industrial heat supply and civil heating due to their stable heat supply and fast start-up speed. However, the single dependence on gas energy supply mode is easily affected by gas price fluctuations, resulting in high operation cost. At the same time, the implementation of peak valley electricity price policy provides space for the use of low-cost electricity during valley electricity period. If the large amount of waste heat generated by the operation of air compressor in industrial production is not recovered, it will cause energy waste. In addition, the user heat load will change dynamically with factors such as season and production rhythm, which puts forward higher requirements for the load prediction accuracy and dynamic regulation and control ability of the heat supply system. It is urgent to build a multi-energy collaborative and intelligent regulation and control heat supply system to meet the development needs of the industry.

[0003] The traditional gas boiler heat supply system has obvious technical limitations. First, its energy source is relatively single, mainly relying on gas supply, which cannot effectively integrate valley electricity resources and industrial waste heat, not only leading to extensive energy utilization form, but also increasing the long-term operation burden due to the relatively high cost of gas. Second, the load prediction of traditional system is mainly based on experience or simple statistical method, which is difficult to accurately capture the dynamic changes of user heat load, and then leads to the lack of pertinence of gas boiler output adjustment, often resulting in mismatch between heat supply and actual demand, causing energy waste or insufficient heat supply. Thirdly, the traditional system lacks a perfect fault emergency mechanism, which is easy to cause heat supply interruption when the gas boiler or related equipment fails, affecting the normal production and life of users. In addition, the regulation and control mode of traditional system is passive, which cannot dynamically optimize energy distribution according to real-time electricity price, equipment running state and other parameters, and the overall operation efficiency is low. SUMMARY

[0004] The purpose of the present application is to make up for the shortcomings of the prior art, and to provide a gas boiler system coupled with valley electricity solid heat storage and air compressor waste heat recovery. The system integrates multi-energy heat supply modules, combines valley electricity heat storage, air compressor waste heat and gas boiler, obtains heat load, electricity price and equipment state data through multi-parameter sensing acquisition module, optimizes energy distribution through multi-parameter adaptive regulation and control module, realizes cost minimization operation, and at the same time, the system is equipped with fault redundancy emergency regulation and control module to ensure the continuity of heat supply. The heat output and state monitoring module accurately distributes heat and monitors the state of pipe network, improves the heat supply efficiency and reliability.

[0005] The application provides the following technical solutions to solve the above technical problems: a gas boiler system coupled with valley electricity solid heat storage and air pressure waste heat recovery, which comprises:

[0006] The multi-energy coupling heat supply module integrates a gas boiler, a valley electricity solid heat storage device, an air pressure waste heat recovery device and a heat supply pipe network, each device is connected through a pipeline and a valve, and is equipped with an electric regulating valve and a communication interface to receive a control instruction to adjust the path and proportion of heat output;

[0007] The multi-parameter sensing and collecting module deploys heat metering devices, sensors and communication units, collects user end heat load parameters, power grid electricity price period parameters and device operation state parameters, generates heat load prediction data through a dynamic weighted load prediction algorithm, and transmits the heat load prediction data to the multi-parameter adaptive control module;

[0008] The multi-parameter adaptive control module receives the heat load prediction value, the electricity price period and the device state parameter output by the multi-parameter sensing and collecting module, sequentially runs a multi-parameter cost optimization algorithm to determine the energy distribution direction, calculates the gas boiler output through a boiler gap compensation algorithm, controls the heat charging and discharging of the heat storage device through a heat storage period-state control algorithm, calculates the air pressure waste heat recovery amount through a waste heat quota utilization algorithm, and generates output control instructions for each device;

[0009] The fault redundancy emergency control module monitors device fault signals, determines an emergency compensation output through a fault multi-energy complementary algorithm, and generates an emergency control instruction to be sent to the heat supply output and state monitoring module;

[0010] The heat supply output and state monitoring module distributes heat to each user end through the electric flow regulating valve of the branch pipe network, monitors the actual heat load of the user end and the pipe network operation parameters, and feeds back the data to the multi-parameter sensing and collecting module for optimization.

[0011] Further, in the multi-energy coupling heat supply module, the water outlet end of the gas boiler, the heat release end of the valley electricity solid heat storage device and the water outlet end of the air pressure waste heat recovery device are connected to the main heat supply pipe network through an electric three-way valve; the water return end of the main heat supply pipe network is divided into three paths and is connected to the water inlet end of the gas boiler, the heat absorbing end of the valley electricity solid heat storage device and the water inlet end of the air pressure waste heat recovery device through electric regulating valves respectively, each connecting pipeline is made of seamless steel pipe, and a pressure gauge and a thermometer are arranged on each pipeline.

[0012] Further, in the multi-parameter perception acquisition module, the deployed acquisition devices include a 1st-level precision ultrasonic heat meter, an outdoor temperature sensor, a pressure sensor, a flow sensor, an RS485 communication unit and an industrial Ethernet unit; and the acquired parameters include user-side instantaneous heat load, cumulative heat load, supply and return water temperature and circulation flow, current electricity price, time period type and time period switching time of the power grid, multi-point temperature and heat storage capacity of the heat storage device, lubricating oil temperature, flow and temperature difference between inlet and outlet of the air compressor, real-time output and combustion state parameters of the gas boiler.

[0013] Further, in the multi-parameter perception acquisition module, the expression of the dynamic weighted load prediction algorithm is: wherein, is the predicted heat load at a future time, , , is a dynamic weight factor, satisfying , is the historical same-period heat load at the current time , is an outdoor temperature influence coefficient, is a reference temperature, is the current outdoor temperature, is a user basic heat load, is a user production fluctuation coefficient, is a random load fluctuation amount.

[0014] Further, in the multi-parameter adaptive control module, the expression of the multi-parameter cost optimization algorithm is: ; and the constraint condition is: ; ; ; ; ; wherein, is the system running cost per unit time, is the unit price of gas, is the gas boiler output, is the real-time combustion efficiency of the boiler, is the electricity price at time t, is the heat charging power of the heat storage device, is the heat charging efficiency of the heat storage device, is the remaining capacity of the heat storage device, is the benefit of replacing gas with unit waste heat, is the actual air compression waste heat recovery amount, is the heat release power of the heat storage device, is the predicted heat load at time t, is a heating standard rate coefficient, is the rated power of the gas boiler, The rated power of the thermal storage device, Let t be the maximum waste heat output of the air compressor.

[0015] Furthermore, in the multi-parameter adaptive control module, the expression for the boiler shortfall compensation algorithm is: ,in, Powering the gas-fired boiler The rated power of the gas-fired boiler. for Predict heat load in real time. This refers to the heat release power of the thermal storage device. The actual amount of waste heat recovered from the compressed air system, when hour, Pick .

[0016] Furthermore, in the multi-parameter adaptive control module, the expression for the thermal storage period-state control algorithm is: ,in, The power required for the thermal storage device to charge / discharge heat. The rated power of the thermal storage device, The current time is defined as follows: trough period is 23:00-7:00, flat period is 7:00-11:00 and 19:00-23:00, and peak period is 11:00-19:00. This represents the remaining thermal storage capacity.

[0017] Furthermore, in the multi-parameter adaptive control module, the expression for the waste heat quota utilization algorithm is: ,in, This represents the actual amount of waste heat recovered from the compressed air system. The heat exchange efficiency of the waste heat recovery device. The specific heat capacity of the air compressor lubricating oil. for Lubricating oil flow rate at all times for The temperature difference between the inlet and outlet of the lubricating oil should be monitored. for Predict heat load at all times.

[0018] Furthermore, in the fault redundancy emergency control module, the expression for the fault multi-energy complementary algorithm is: ,in, This is the total compensation output under fault conditions. This represents the total system output before the failure. As a fault influencing factor, The number of devices participating in the compensation. For the first The multi-energy complementarity coefficient of each device For the first The rated power of each device Set the total rated power for the system.

[0019] Furthermore, in the heating output and status monitoring module, heat distribution is achieved through an electric flow regulating valve with an adjustment accuracy of ±2%, and intelligent temperature controllers are installed at the inlet of each branch pipeline. The pipeline operation status monitoring includes the temperature, pressure, and flow parameters of the main water supply pipeline, the main return pipeline, and the branch nodes.

[0020] Compared with existing technologies, this gas-fired boiler system that couples off-peak electricity solid thermal storage with compressed air waste heat recovery has the following advantages:

[0021] I. This invention constructs a multi-energy coupled heating module that integrates a gas-fired boiler, off-peak electricity solid thermal storage device, and air compressor waste heat recovery device. It achieves coordinated operation of multiple devices through pipeline valves and electric regulating valves. Simultaneously, a multi-parameter sensing and acquisition module dynamically captures data on heat load, electricity price, and equipment operating status. Combined with a dynamic weighted load prediction algorithm, it generates accurate heat load prediction results. Based on this, a multi-parameter adaptive control module uses algorithms such as multi-parameter cost optimization, thermal storage time-state control, and waste heat quota utilization to rationally plan the energy allocation direction and the output ratio of each device. This fully utilizes the low-cost characteristics of off-peak electricity and air compressor waste heat resources, reducing dependence on gas, while flexibly adjusting heat output according to actual needs to avoid energy waste, significantly reducing system operating costs and improving energy utilization efficiency.

[0022] Second, this invention establishes a fault-redundant emergency control module to monitor equipment fault signals in real time and determine emergency compensation output using a fault multi-energy complementary algorithm. This ensures that the system can maintain stable heating even when a single device fails, effectively avoiding the risk of heating interruption. Simultaneously, the heating output and status monitoring module achieves precise heat distribution through an electric flow regulating valve. Intelligent thermostats are installed at the branch network inlets, and the actual heat load and network operating parameters at the user end are collected in real time. The data is fed back to the multi-parameter sensing and acquisition module to form a closed-loop optimization. This design not only ensures the stability and balance of heating quality at the user end but also further optimizes energy configuration through continuous feedback adjustments, enhancing the system's adaptability to different operating conditions and comprehensively improving overall operational reliability and user experience.

[0023] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0024] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0025] Figure 1 A working flow chart of a gas boiler system coupled with off-peak solid heat storage and air compressor waste heat recovery;

[0026] Figure 2 A device connection framework diagram of a gas boiler system coupled with off-peak solid heat storage and air compressor waste heat recovery. DETAILED DESCRIPTION

[0027] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object of the application, the specific embodiments, structures, features and effects according to the present application will be described in detail below in combination with the drawings and preferred embodiments.

[0028] Embodiment one:

[0029] A winter heating scene of a heavy machinery factory in the north.

[0030] The machinery factory is located in a severe cold region in the north, and needs to provide stable heating for three production workshops, two office buildings and one staff dormitory in winter. The daily heat load fluctuates obviously, the heat demand of the workshop increases sharply in the morning when the production starts, and only the on-duty heating load is kept in the dormitory at night. Two screw air compressors are matched in the factory area, which are continuously running and generating stable waste heat during daily production. The local power grid implements peak-valley time-of-use electricity price, and the unit price of gas is fixed. The system of the present application is used to meet the winter heating demand of the factory area, and at the same time, through the cooperation of multiple energy sources, the energy cost is reduced, and the waste heat of the air compressor is maximized.

[0031] The multi-parameter perception acquisition module works with a 1st level precision ultrasonic heat meter, an outdoor temperature sensor, a pressure sensor, a flow sensor, and an RS485 communication unit and an industrial Ethernet unit to comprehensively collect plant heating related parameters: covering the instantaneous heat load and cumulative heat load of each workshop, office building, and dormitory to ensure accurate grasp of real-time heat demand of each area; collecting heating pipe network supply and return water temperature and circulation flow to monitor the pipe network heat transmission state in real time; synchronously obtaining the current electricity price of the power grid, the type of the period it is in, and the period switching time to provide a basis for energy cost optimization; recording the multi-point temperature of the valley electricity solid heat storage device and the remaining capacity (SOC) of the heat storage to master the energy storage situation of the heat storage equipment; collecting the temperature, flow, and temperature difference between the inlet and outlet of the lubricating oil of the air compressor to judge the waste heat recycling potential; monitoring the real-time output and combustion state of the gas boiler to ensure the safety of equipment operation. After the collection is completed, a dynamic weighted load prediction algorithm is run to generate heat load prediction data for the next 1 hour in combination with historical same period heat load data, the influence of the current outdoor temperature on heat demand, user basic heat load, and random load fluctuation amount caused by production fluctuation. The expression of the dynamic weighted load prediction algorithm is: wherein, is the predicted heat load at the future moment, , , is the dynamic weight factor, satisfying , is the historical same period heat load at the current moment , is the outdoor temperature influence coefficient, is the base temperature, is the current outdoor temperature, is the user basic heat load, is the user production fluctuation coefficient, is the random load fluctuation amount, which provides a forward-looking basis for subsequent regulation and control. Finally, all collected parameters and prediction data are transmitted to the multi-parameter adaptive regulation and control module through the communication unit, as shown in Figure 1 .

[0032] The multi-parameter adaptive regulation and control module works to receive the heat load prediction value, electricity price period information, and device operation state parameters output by the multi-parameter perception acquisition module, and sequentially executes a series of algorithms to achieve accurate regulation and control: first, a multi-parameter cost optimization algorithm is run to determine the energy distribution direction with the lowest system unit time operation cost as the target, in combination with the gas unit price, the current period electricity price, the heat storage charging efficiency, and the unit waste heat replacement gas benefit. The expression of the multi-parameter cost optimization algorithm is: ; the constraint condition is: ; ; ; ; ;in, The system's unit time operating cost, This refers to the unit price of gas. Powering the gas-fired boiler For the real-time combustion efficiency of the boiler, Let be the electricity price at time t. To provide heat charging power for the thermal storage device, To improve the efficiency of heat storage and charging, For the remaining thermal storage capacity, The revenue per unit of waste heat used to replace natural gas, This represents the actual amount of waste heat recovered from the compressed air system. Heat storage device heat release power, The predicted heat load is at time t. This is the heating compliance rate coefficient. The rated power of the gas-fired boiler. The rated power of the thermal storage device, To determine the maximum waste heat output of the air compressor at time t, priority should be given to utilizing low-priced off-peak electricity and free waste heat from the air compressor to reduce the consumption of high-priced gas during peak periods.

[0033] Next, the boiler deficit compensation algorithm is run. Based on the predicted heat load, the proposed heat release power of the thermal storage device, and the proposed recovery amount of waste heat from the air compressor, the output that the gas-fired boiler needs to bear is calculated. The expression for the boiler deficit compensation algorithm is as follows: ,in, Powering the gas-fired boiler The rated power of the gas-fired boiler. for Predict heat load in real time. This refers to the heat release power of the thermal storage device. The actual amount of waste heat recovered from the compressed air system, when hour, Pick This ensures that the output is maintained within 0.1-0.9 times the rated power, thus avoiding both inefficient operation of the boiler at low load and damage to the equipment caused by operation exceeding the rated power.

[0034] Then run the thermal storage period-state control algorithm again, and formulate a heat charging and discharging strategy based on the current period and the remaining thermal storage capacity. The expression of the thermal storage period-state control algorithm is: ,in, The power required for the thermal storage device to charge / discharge heat. The rated power of the thermal storage device, The current time is defined as follows: trough period is 23:00-7:00, flat period is 7:00-11:00 and 19:00-23:00, and peak period is 11:00-19:00. For the remaining capacity of heat storage, for example, valley segment 23:00-7:00 and SOC<90%, control the heat storage device to charge at rated power, make full use of low price valley electricity to store energy;

[0035] Finally, the waste heat limit utilization algorithm is run to calculate the actual recovery amount of air compressor waste heat according to real-time parameters of air compressor lubricating oil, and the expression of the waste heat limit utilization algorithm is: wherein, is the actual recovery amount of air compressor waste heat, is the heat exchange efficiency of the waste heat recovery device, is the specific heat capacity of air compressor lubricating oil, is is the lubricating oil flow at the moment, is is the lubricating oil inlet and outlet temperature difference at the moment, is is the predicted heat load at the moment, which ensures that the recovery amount does not exceed 60% of the predicted heat load value, avoiding excessive waste heat recovery leading to abnormal pipe network pressure or energy waste. The control instructions of the gas-fired boiler output, the heat charging and discharging power of the heat storage device, and the air compressor waste heat recovery amount are generated by comprehensively considering the results of all algorithms and sent to the multi-energy coupling heat supply module.

[0036] In the multi-energy coupling heat supply module, the water outlet end of the gas-fired boiler, the heat discharging end of the valley electricity solid heat storage device, and the water outlet end of the air compressor waste heat recovery device are connected to the main heat supply pipe network through an electric three-way valve, the water return end of the main heat supply pipe network is divided into three paths, and is connected to the water inlet end of the gas-fired boiler, the heat absorbing end of the heat storage device, and the water inlet end of the air compressor waste heat recovery device through electric regulating valves respectively. Seamless steel pipes are used for all connected pipelines to ensure stable heat transfer, and pressure gauges and thermometers are arranged on the pipelines to monitor the pipeline pressure and temperature in real time to prevent equipment overpressure or temperature abnormalities. After receiving the control instructions, the module adjusts the opening degrees of the electric three-way valve and each electric regulating valve to accurately control the heat output path and proportion: during the valley segment, the heat storage device is charged at rated power, and the air compressor waste heat recovery device continuously recovers waste heat according to the calculated recovery amount, and the heat generated by the two is preferentially supplied to the dormitory, and the remaining heat is stored in the heat storage device to reserve energy for the next day's peak segment; during the peak segment, the heat storage device is discharged at rated power, and the air compressor waste heat recovery device operates at full load. If the total heat of the two cannot meet the high load demand of the production workshop, the gas-fired boiler starts to operate according to the calculated output of the boiler gap compensation algorithm, and the expression of the fault multi-energy complementary algorithm is: wherein, is the total compensation output in the fault state, is the total output of the system before the fault, is the fault influence factor, is the number of devices participating in compensation, is the multi-energy complementary coefficient of the device, The rated power of the first The total rated power of the system, accurately supplementing the heat gap; during the flat section, preferentially using air compression waste heat, matching part of the heat storage device to release heat, only starting the gas boiler at low load when the heat is insufficient, further reducing gas consumption.

[0037] The heat output and state monitoring module works through the electric flow regulating valve at the branch of the main heating pipe network to accurately distribute heat to the three production workshops, two office buildings and one staff dormitory according to the actual demand of each area, ensuring that the heat supply and heat demand of each area match, avoiding the situation of local overheating or insufficient heating; at the inlet of each branch pipe network, an intelligent temperature controller is arranged to stabilize the indoor temperature of each area in real time, improving the heating experience of the plant personnel. At the same time, the module continuously monitors the actual heat load of the user end and the pipe network operation parameters, and feeds back these actual operation data to the multi-parameter sensing and collecting module in a timely manner, providing a real basis for the correction of the subsequent heat load prediction model and the optimization of the control instruction, forming a closed-loop operation mechanism of “collection-prediction-control-monitoring-optimization”.

[0038] The fault redundancy emergency control module works at 10:00 on a certain day, the system monitors that the air compression waste heat recovery device sends a fault signal, the fault redundancy emergency control module responds immediately: first, identify the fault device as “air compression waste heat recovery device”, and clearly the problem of current waste heat supply interruption; then run the fault multi-energy complementary algorithm, taking the total system output before the fault as the benchmark, combined with the fault influence factor, determine the devices participating in compensation as the gas boiler and the valley solid heat storage device; then according to the rated power of the two devices, the total rated power of the system and the multi-energy complementary coefficient, calculate the total compensation output, the heat storage device is adjusted from the original “partial heat release” state to “rated power heat release”, and the output of the gas boiler is increased from the original low load to 0.6 times the rated power. Generate emergency control instructions and send them to the heat output and state monitoring module to ensure that the heating of each area in the flat section plant area is not interrupted, the heat load standard rate meets the requirements, and the production and personnel heating experience are not affected by equipment failure.

[0039] Example two:

[0040] A winter heating scenario for a commercial complex in the south.

[0041] The commercial complex is located in the sub-cold region of the south, and needs to provide heating services for a 5-story shopping mall, two 20-story office buildings and one 15-story hotel in winter. The underground machine room of the complex is equipped with one centrifugal air compressor, which mainly supplies power for the elevators, fresh air systems of the office buildings and the hotel. The air compressor runs at a high frequency during the day and produces a lot of waste heat, and runs at a low frequency at night and produces little waste heat. The local power grid implements the same peak-valley time-of-use electricity price as in Example 1, and the gas price is fixed. The system of the present application is used to balance the heat load difference of different areas of the commercial complex, reduce the heating operation cost, and efficiently recover the waste heat of the air compressor.​

[0042] The multi-parameter sensing and acquisition module deploys Class 1 precision ultrasonic heat meters at the heating inlets of shopping malls, office buildings, and hotels to accurately collect independent heat load data for each area, adapting to the differentiated heating patterns of different regions. Outdoor temperature sensors are installed on the rooftop of the complex to capture the real-time impact of ambient temperature changes on heating demand. Pressure and flow sensors are installed on the main supply and return water pipelines and at each branch node to monitor the heat transfer status of the pipeline network. An RS485 communication unit connects the air compressor control system and the power grid price monitoring system to synchronously acquire the temperature, flow rate, and inlet / outlet temperature difference of the air compressor lubricating oil, as well as the current power grid price, time period type, and time period switching time. The module continuously monitors the real-time output and combustion status of the gas boiler to ensure safe equipment operation. Figure 2 As shown. After data collection, the dynamic weighted load forecasting algorithm is run, combining historical heat load patterns of commercial complexes during the same period, current outdoor temperature, basic heat load of each area, and random load fluctuations to generate heat load forecast data for the next 2 hours. The expression for the dynamic weighted load forecasting algorithm is: This provides a basis for subsequent targeted adjustments, and ultimately all collected parameters and prediction data are transmitted to the multi-parameter adaptive control module.

[0043] After receiving data, the multi-parameter adaptive control module executes precise control logic according to the algorithm flow: First, it runs the multi-parameter cost optimization algorithm, combining the gas unit price, the current electricity price, the thermal storage charging efficiency, and the revenue from replacing gas with waste heat per unit, to determine the energy allocation direction. The expression for the multi-parameter cost optimization algorithm is: The constraints are: ; ; ; ; During off-peak hours, low-priced off-peak electricity is prioritized to charge the thermal storage device. During the daytime, waste heat from the air compressor and heat released from the thermal storage device are prioritized. During peak hours, the gas boiler is only started when waste heat and thermal storage cannot meet the demand, so as to minimize the cost of energy use.

[0044] Next, the boiler deficit compensation algorithm is run. Based on the predicted heat load, the proposed heat release power of the thermal storage device, and the proposed recovery amount of waste heat from the compressed air, the output of the gas-fired boiler is calculated. The expression for the boiler deficit compensation algorithm is: This ensures that the output is maintained within 0.1-0.9 times the rated power, balancing equipment operating efficiency and safety;

[0045] Then run the thermal storage period-state control algorithm again, and formulate a strategy based on the current period and the remaining thermal storage capacity. The expression of the thermal storage period-state control algorithm is: , valley segment 23:00-7:00 and SOC < 90%, the heat storage device is charged at rated power to maximize the storage of low-cost energy; peak segment 11:00-19:00 and SOC > 30%, the heat storage device is discharged at rated power to supplement the heat load gap; flat segment 7:00-11:00, 19:00-23:00 and SOC ≥ 70%, the heat storage device suspends charging and discharging to avoid unnecessary energy consumption;

[0046] Finally, the residual heat limit utilization algorithm is run to adjust the recovery amount according to the air compressor operating state. The expression of the residual heat limit utilization algorithm is: When running at high frequency during the day, the lubricating oil temperature is high, the flow is large, and the temperature difference between the inlet and outlet is large. The calculated air compression residual heat recovery amount is close to 60% of the heat load prediction value. When running at low frequency at night, the lubricating oil parameters decrease, and the residual heat recovery amount is appropriately reduced to avoid insufficient or excessive waste of residual heat recovery. The control commands of the gas-fired boiler output, the heat storage device charging and discharging power, and the air compression residual heat recovery amount are generated by comprehensively considering the results of all algorithms and sent to the multi-energy coupling heating module.

[0047] The equipment in the multi-energy coupling heating module works according to the preset pipeline connection: the gas-fired boiler outlet, the valley electricity solid heat storage device heat release end, and the air compression residual heat recovery device outlet are connected to the main heating pipe network through an electric three-way valve. The main heating pipe network backwater end is divided into three paths and connected to the gas-fired boiler inlet, the heat storage device heat absorption end, and the air compression residual heat recovery device inlet through electric regulating valves. All connecting pipelines use seamless steel pipes to ensure heat transfer efficiency. Pressure gauges and thermometers are installed on the pipelines to monitor the pipeline pressure and temperature in real time to prevent overpressure or temperature abnormalities. After receiving the control commands, the module adjusts the opening of the electric three-way valve and each electric regulating valve to realize multi-energy collaborative energy supply: in the valley segment 23:00-7:00, the electric regulating valve is fully opened to the heat storage device inlet, the heat storage device is charged at rated power, and the air compression residual heat recovery device operates at low recovery amount at night to generate heat that is preferentially supplied to the hotel, and the excess heat is stored in the heat storage device; in the flat segment 7:00-11:00, 19:00-23:00, the electric three-way valve preferentially connects the air compression residual heat recovery device and the main heating pipe network, and the heat storage device partially discharges heat. If the heat still cannot meet the office demand of the office building, the gas-fired boiler inlet valve is adjusted to allow the boiler to operate at 0.2 times the rated power to supplement the heat; in the peak segment 11:00-19:00, the electric three-way valve simultaneously connects the heat storage device and the air compression residual heat recovery device. Only when the total heat of the two cannot meet the high load demand of the shopping mall during the day, the gas-fired boiler output is increased to 0.5 times the rated power to precisely fill the heat gap and ensure stable heating of the shopping mall and office building.

[0048] The heat supply output and state monitoring module works through the electric flow regulating valve at the branch of the main heat supply pipe network to accurately distribute heat according to the time period demand of each area: the flow is increased during 12:00-14:00 and 18:00-20:00 to match the high heat demand caused by high passenger flow; the flow is kept stable during 9:00-18:00 to meet the demand of the office period; the flow is evenly distributed for 24 hours to maintain stable heating for the hotel; the intelligent temperature controller is arranged at the inlet of each branch pipe network to stabilize the temperature of the shopping mall at 20℃, the office building at 18℃ and the hotel at 22℃, thereby improving the heating experience of users in different areas. At the same time, the module continuously monitors the actual heat load of the user end and the pipe network operation parameters, and feeds back these actual operation data to the multi-parameter sensing and collecting module in time for correcting the next round of heat load prediction model and control instruction, so as to ensure that the control strategy always fits the actual heat demand of the commercial complex and avoid energy waste or insufficient heating.

[0049] The fault redundancy emergency control module works at 15:00 on a certain day. The system monitors that the valley solid heat storage device sends a fault signal, and the fault redundancy emergency control module starts immediately: firstly, the fault signal is captured to confirm that the heat storage device cannot participate in the peak period heat release, and it is clear that there is a large heat gap in the current system; then, the fault multi-energy complementary algorithm is run to determine the devices participating in compensation as the air compressor waste heat recovery device and the gas boiler based on the total system output before the fault and the fault influence factor. The expression of the fault multi-energy complementary algorithm is: ; then, the total compensation output is calculated according to the rated power of the two devices, the total rated power of the system and the multi-energy complementary coefficient. The air compressor waste heat recovery device is adjusted to full load operation, and the output of the gas boiler is increased from the original 0.2 times the rated power to 0.8 times the rated power. The emergency control instruction is generated and sent to the heat supply output and state monitoring module to ensure that the heating of the shopping mall, office building and hotel in the peak period is not affected, the heat load standard rate meets the requirements, and the normal operation and customer experience of the commercial complex are not affected by the device failure.

[0050] In summary, under this scenario, the system adapts to the different regional differentiated heat load demand of the commercial complex. The multi-parameter sensing and collecting module collects the load and device parameters of each area, generates prediction data by combining the dynamic weighted load prediction algorithm; the multi-parameter adaptive control module relies on four core algorithms to preferentially use low-price valley electricity and air compressor waste heat to reduce the use of gas in the peak period; the multi-energy coupled heat supply module adjusts the energy supply state of the device according to the time period and demand; the heat supply output and state monitoring module improves the heating experience through accurate temperature control and flow regulation, and feeds back data for optimization control; the fault redundancy emergency control module runs the fault multi-energy complementary algorithm to fill the heat gap when the heat storage device fails. The system effectively balances the load difference of the shopping mall, office building and hotel, realizes the dual goals of stable heating and cost optimization.

[0051] The above merely describes the preferred embodiments of the present application, and is not intended to limit the present application in any form. Although the present application has been disclosed with the preferred embodiments as above, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, as long as the changes or modifications do not deviate from the technical solution of the present application. Any modification, change, equivalent change and modification of the above embodiments made according to the technical essence of the present application still belong to the scope of the technical solution of the present application.

Claims

1. A gas boiler system coupled with valley electricity solid heat storage and air pressure waste heat recovery, characterized in that, The system comprises: Multi-energy coupling heat supply module: integrated gas boiler, off-peak solid heat storage device, air pressure waste heat recovery device and heat supply pipe network, each device is connected through pipeline and valve, and is equipped with electric regulating valve and communication interface to receive control instruction to adjust the path and proportion of heat output; Multi-parameter sensing and collecting module: deploy heat metering device, sensor and communication unit, collect user end heat load parameter, power grid electricity price time period parameter and equipment operation state parameter, generate heat load prediction data through dynamic weighted load prediction algorithm, and transmit to multi-parameter adaptive control module; Multi-parameter adaptive control module: receives heat load prediction value, electricity price time period and equipment state parameter output by multi-parameter sensing and collecting module, sequentially runs multi-parameter cost optimization algorithm to determine energy distribution direction, calculates gas boiler output through boiler gap compensation algorithm, controls heat charging and discharging of heat storage device through heat storage time period-state control algorithm, calculates air pressure waste heat recovery amount through waste heat quota utilization algorithm, and generates output control instruction of each device; Fault redundancy emergency control module: monitors equipment fault signal, determines emergency compensation output through fault multi-energy complementary algorithm, and generates emergency control instruction to send to heat supply output and state monitoring module; Heat supply output and state monitoring module: distributes heat to each user end through electric flow regulating valve of branch pipe network, monitors actual heat load and pipe network operation parameter of user end, and feeds back data to multi-parameter sensing and collecting module for optimization.

2. The gas boiler system of claim 1, wherein, In the multi-energy coupling heat supply module, the water outlet end of the gas boiler, the heat release end of the off-peak solid heat storage device and the water outlet end of the air pressure waste heat recovery device are connected to the main heat supply pipe network through an electric three-way valve; the water return end of the main heat supply pipe network is divided into three paths and connected to the water inlet end of the gas boiler, the heat absorption end of the off-peak solid heat storage device and the water inlet end of the air pressure waste heat recovery device through electric regulating valves respectively; seamless steel pipes are used for the connecting pipelines, and pressure gauges and thermometers are arranged on the pipelines.

3. The gas boiler system of claim 1, wherein, In the multi-parameter sensing and collecting module, the collecting devices arranged include 1st precision ultrasonic heat meter, outdoor temperature sensor, pressure sensor, flow sensor, RS485 communication unit and industrial Ethernet unit; the parameters collected include user end instantaneous heat load, cumulative heat load, supply and return water temperature and circulation flow, current electricity price, time period type and time period switching time of the power grid, multi-point temperature and heat storage capacity of the heat storage device, lubricating oil temperature, flow and temperature difference between inlet and outlet of the air compressor, real-time output and combustion state parameter of the gas boiler.

4. The gas boiler system of claim 1, wherein, The expression of the dynamic weighting load prediction algorithm in the multi-parameter perception acquisition module is: Wherein, is the predicted heat load of a future time, , , is a dynamic weight factor, satisfying , is the historical same period heat load of a current time , is an outdoor temperature influence coefficient, is a reference temperature, is a current outdoor temperature, is a user basic heat load, is a user production fluctuation coefficient, is a random load fluctuation amount.

5. The gas boiler system of claim 1, wherein, The expression of the multi-parameter cost optimization algorithm in the multi-parameter adaptive regulation module is: The constraint condition is: ; ; ; ; ; is the system running cost per unit time, is the gas unit price, is the gas boiler output, is the real-time combustion efficiency of the boiler, is the electricity price at time t, is the charging power of the heat storage device, is the heat storage charging efficiency, is the remaining capacity of the heat storage, is the benefit of replacing gas with unit waste heat, is the actual air pressure waste heat recovery amount, is the heat storage device heat release power, is the predicted heat load at time t, is the heating standard rate coefficient, is the rated power of the gas boiler, is the rated power of the heat storage device, is the maximum waste heat production of the air compressor at time t.

6. The gas boiler system of claim 5, wherein, The expression of the boiler gap compensation algorithm in the multi-parameter adaptive regulation module is: Wherein, is the output of the gas boiler, is the rated power of the gas boiler, is is the predicted heat load at the moment, is the heat release power of the heat storage device, is the actual recovery amount of air pressure waste heat, when , take .

7. The gas boiler system of claim 6, wherein, The expression of the heat storage period-state regulation algorithm in the multi-parameter adaptive regulation module is: wherein, is the heat charging / discharging power of the heat storage device, is the rated power of the heat storage device, is the current time, the valley section is 23:00-7:00, the flat section is 7:00-11:00 and 19:00-23:00, and the peak section is 11:00-19:00, is the residual capacity of the heat storage.

8. The gas boiler system of claim 7, wherein, In the multi-parameter adaptive control module, the expression for the waste heat quota utilization algorithm is: ,in, This represents the actual amount of waste heat recovered from the compressed air system. The heat exchange efficiency of the waste heat recovery device. The specific heat capacity of the air compressor lubricating oil. for Lubricating oil flow rate at all times for The temperature difference between the inlet and outlet of the lubricating oil should be monitored. for Predict heat load at all times.

9. The gas boiler system of claim 1, wherein, In the fault redundancy emergency control module, the expression for the fault multi-energy complementarity algorithm is: ,in, This is the total compensation output under fault conditions. This represents the total system output before the failure. As a fault influencing factor, The number of devices participating in the compensation. For the first The multi-energy complementarity coefficient of each device For the first The rated power of each device Set the total rated power for the system.

10. The gas boiler system of claim 1, wherein, In the heat supply output and state monitoring module, heat distribution is realized through electric flow regulating valve, the regulating accuracy is ±2%, and intelligent temperature controllers are arranged at the entrances of each branch pipe network; the pipe network operation state monitoring includes temperature, pressure and flow parameters of the main water supply pipeline, main return water pipeline and branch node.

Images