Online operation method and system for household biogas combined heat and power generation based on behavior perception

Abstract

The present invention relates to a method and system for online operation of household biogas combined heat and power (CHP) systems based on behavioral perception. The method comprises the following steps: acquiring sensory parameters in a heated room, including the number of occupants in the room, indoor CO2 concentration, indoor illumination, the temperature difference between inside and outside the room, and historical total heating load values; inputting the acquired sensory parameters into a pre-built neural network model to obtain hourly heat load predictions; determining the heating mode of the household biogas combined heat and power system corresponding to the heated room based on the hourly heat load predictions; and then obtaining an operating plan for the household biogas combined heat and power system based on the operating constraints of the household biogas combined heat and power system. Compared with existing technologies, the present invention significantly reduces energy waste, improves farmers' comfort, enhances the stability of device operation, and better meets farmers' heating needs.

Description

Technical Field

[0001] The present invention relates to the field of household biogas combined heat and power systems, and in particular to a behavior-perception-based household biogas combined heat and power online operation method and system. Background Art

[0002] Household biogas combined heat and power systems use biogas as fuel, using a small-capacity engine to drive a generator to generate electricity. The waste heat is used for residential heating and domestic hot water. At the same time, farmers can also connect excess electricity to the grid and earn income from electricity sales. Compared to traditional distributed power systems, combined heat and power systems offer significant advantages in energy utilization, environmental protection, and economic and livelihood security.

[0003] 1. High energy efficiency;

[0004] The combined heat and power system effectively unifies the high-grade electricity demand with the low-grade heat (cold) demand, realizes the cascade utilization of energy, and the total efficiency can reach more than 90%.

[0005] 2. Zero carbon emissions;

[0006] The carbon content in biomass comes entirely from photosynthesis, absorbing carbon dioxide from the air. Therefore, biomass has zero carbon emissions overall. Addressing household energy needs based on resource endowments in rural areas is not only practical for short-term clean heating.

[0007] 3. The system cost is low, and farmers can obtain additional income from electricity sales;

[0008] The patent covers a household biogas cogeneration system that uses a traditional small internal combustion engine as its engine, modified with minor modifications to run on biogas. Due to the large scale and low cost of internal combustion engines, they are well-suited for rural household cogeneration. Farmers can also connect excess electricity to the grid and earn income from electricity sales, transforming themselves from mere energy end-consumers to energy "prosumers" who both consume and produce energy, thus encouraging farmers to utilize biomass.

[0009] 4. Make up for the shortcomings of imperfect rural power grids and increase the reliability of energy supply;

[0010] Numerous experiences, both domestically and internationally, have demonstrated that rather than investing significant resources in centralized energy to meet highly dispersed demand, it is more effective to vigorously develop distributed energy, integrating it with centralized energy to improve power quality and enhance environmental benefits. Traditional distributed power systems operate on a single-input, single-output basis: electricity is met by the grid, cooling loads are met by grid-driven electric chillers, and heating loads are met by coal- or gas-fired boilers. In the event of a grid failure or an unexpected disaster (such as a snowstorm, earthquake, or sabotage), energy supply is disrupted. Combined heat and power (CHP) systems, on the other hand, often feature multiple inputs and outputs. Electricity can be supplied by both the prime mover and the grid, cooling by both electric chillers and absorption chillers, and heating by both waste heat equipment and gas-fired boilers. In a CHP system, each energy supply branch has multiple safeguards, significantly improving energy supply reliability.

[0011] For large- and medium-sized combined heat and power (CHP) systems, the technology and market are relatively mature abroad, and their application is increasing in China. However, while developed countries have invested heavily in research and development of household micro-CHP systems in recent years, a few technologically advanced companies have launched their own household Micro-CHP brands, while no relevant application examples are available in China. Currently, most international Micro-CHP products are powered by internal combustion engines, external combustion engines, or fuel cells. Representative internal combustion engine-based models include the Honda Ecowill (1 kWe), the Remeha R-Gen (Sener Tec Dachs) (5.5 kWe), and the Vaillant Ecopower (4.7 kWe). These units operate in a "full-space" and "full-time" manner, lacking awareness of the user's usage behavior and intelligent operational optimization.

[0012] Numerous studies have shown that human behavior is a significant factor influencing building energy consumption and a crucial benchmark for evaluating building energy-saving technologies and measures. Different human behaviors lead to varying energy consumption levels, requiring corresponding measures. The randomness, diversity, and complexity of human behavior create ample room for energy savings in system operation.

[0013] Human behavior models can be further categorized into mobility models and energy usage models, both of which significantly impact residential building energy consumption. Research on human mobility characteristic models primarily focuses on fixed schedules, random schedules, time-autocorrelated random movement methods, event-based random movement methods, and Markov chain and event-based random movement models. Research on human energy behavior primarily focuses on lighting and air conditioning equipment usage habits. These models, all related to mathematical statistics, are relatively complex and highly personalized. However, due to the private nature of human behavior in buildings, the long-term impact of human behavioral preferences has not been fully studied, and there is a lack of effective means to collect behavioral data while protecting privacy. Summary of the Invention

[0014] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a behavior-based household biogas cogeneration online operation method and system that improves the stability of device operation and can better meet the heat needs of farmers.

[0015] The purpose of the present invention can be achieved by the following technical solutions:

[0016] A method for online operation of household biogas combined heat and power generation based on behavior perception includes the following steps:

[0017] Obtain the sensing parameters in the heating room, including the number of people in the room, indoor CO2 concentration, indoor illumination value, temperature difference between inside and outside the room, and historical total heating load value;

[0018] Inputting the acquired sensing parameters into a pre-built neural network model to obtain hourly heat load prediction values;

[0019] The heating mode of the household biogas cogeneration system corresponding to the heating room is determined according to the hourly heat load prediction value, and then an operation plan of the household biogas cogeneration system is obtained according to the operation constraints of the household biogas cogeneration system.

[0020] Furthermore, the process of acquiring the perception parameters in the heating room includes:

[0021] Use infrared sensors to determine the number of people in the room;

[0022] Obtain indoor CO2 concentration through carbon dioxide sensor;

[0023] Obtain indoor illuminance value through illuminance sensor;

[0024] The indoor temperature is sensed by the indoor thermometer, and the outdoor temperature is sensed by the outdoor temperature sensor, thereby measuring the temperature difference between the inside and outside of the room.

[0025] Further, determine the heating mode of the household biogas combined heat and power system corresponding to the heating room according to the hourly heat load prediction value, specifically as follows:

[0026] According to the hourly heat load prediction value Q1 f , f , f , , ,

[0031] , f , the rated heat supply Q2 of the micro combined heat and power unit in the household biogas combined heat and power system corresponding to the heating room, and the heat storage Q3 of the water tank, determine the states of the micro combined heat and power unit, the water tank and the heat supplement equipment in the biogas combined heat and power system, and form multiple heating modes.

[0027] Further, the process of selecting and judging the heating mode includes: according to the hourly heat load prediction value Q1 f Judge whether the load enters the peak range. If it enters the peak range, the micro combined heat and power unit supplies heat and the water tank stores heat until the water tank is full of heat or Q1 f > Q2 + Q3, then switch to combined heating of the micro combined heat and power unit and the water tank.

[0028] Further, the process of judging whether the load enters the peak range is specifically: obtain the heat load value Q1'max * α corresponding to the time when the heat supply load of the previous day reaches the heat supply limit of the unit minus the time H required for the water tank to be full of heat; compare the hourly heat load prediction value Q1 f with Q1'max * α to judge whether the load enters the peak range.

[0029] Further, if the load does not enter the peak range, then judge whether the load enters the valley range according to the hourly heat load prediction value Q1 f If it enters the valley range, the water tank supplies heat alone. If the water tank does not reach the minimum heat storage and release heat amount, the micro combined heat and power unit supplies heat and the water tank stores heat.

[0030] Further, the process of judging whether the load enters the valley range is specifically: obtain the load value Q1'min * β corresponding to the maximum heat storage time that can be satisfied after the water tank of the previous day is full of heat; compare the hourly heat load prediction value Q1 f with Q1'min * β to judge whether the load enters the valley range.

[0031] Further, if Q1 f that is, neither in the peak range nor in the valley range, compare Q1 f with Q2. If Q1 f < Q2, the micro combined heat and power unit supplies heat and the water tank stores heat. When the water tank is full of heat, the water tank supplies heat alone; if Q1 f > Q2, the micro combined heat and power unit and the water tank supply heat together. If the water tank does not reach the minimum heat release and storage heat amount, the micro combined heat and power unit and the heat supplement equipment supply heat at the same time.

[0032] Furthermore, according to the operation constraints of the household biogas combined heat and power system, an operation plan of the household biogas combined heat and power system is obtained, specifically:

[0033] After determining the heating mode, calculate the comprehensive optimization index IP under the existing plan. If the IP value reaches the optimal value, operate according to the current plan. If the IP value does not reach the optimal value, readjust the load rate under the heating mode and recalculate the IP value under the new plan until the IP value reaches the optimal value.

[0034] The calculation expression of the comprehensive optimization index IP is:

[0035] IP=ω1*ATC+ω2*PEC+ω3*CDE

[0036] In the formula, ω1+ω2+ω3=1, ATC is the economic comparison index, PEC is the energy efficiency comparison index, and CDE is the environmental comparison index.

[0037] The present invention also provides an operating system for the above-mentioned behavior-perception-based household biogas cogeneration online operation method, comprising a heating room, a heat storage tank, and a micro cogeneration unit, wherein the heating room is connected to a heating supplementary device, the heating room is provided with a terminal radiator, the heat storage tank is connected to the terminal radiator via a first heating circuit, the first heating circuit further comprising a third valve, a second pump body, and a fourth valve, the third valve, the second pump body, the fourth valve, the heat storage tank, and the terminal radiator being connected in sequence, the flow direction of the second pump body being toward the terminal radiator, a second valve being connected in parallel to the outer sides of the third valve, the second pump body, and the fourth valve, the flow direction of the second valve being away from the terminal radiator;

[0038] The micro-CHP unit is connected to the terminal radiator through the first heating circuit via a second heating branch. The second heating branch is connected in parallel to the third valve, the second pump body, the fourth valve, and the hot water storage tank. The second heating branch also includes a fifth valve, the first pump body, and the first valve. The fifth valve, the first pump body, the micro-CHP unit, and the first valve are connected in sequence. The flow direction of the first pump body is toward the terminal radiator.

[0039] The system is operated by adopting the above-mentioned behavior-aware household biogas combined heat and power online operation method.

[0040] Compared with the prior art, the present invention has the following advantages:

[0041] (1) Before the cogeneration unit is put into operation, the number of personnel, the correction of fresh air load and the switching of lighting equipment are added to the load forecast, and the change of the number of personnel is taken into account during the operation. Compared with the traditional method of only predicting the number of personnel and the switching of lighting equipment according to the model, it is more practical and can greatly reduce energy waste and improve farmers' comfort.

[0042] (2) In the operation of the cogeneration unit, the regularity of the heat load throughout the day is taken into account, allowing the equipment to respond to load peaks and valleys in advance, which improves the stability of the unit's operation and better meets the farmers' heat needs.

[0043] (3) It improves energy utilization efficiency, thereby indirectly reducing carbon emissions and helping to improve the rural living environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of the structure of a neural network model provided in an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of an online intelligent operation logic based on human behavior provided in an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of energy flow of a micro-cogeneration system provided in an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of a storage process based on data and human behavior data provided in an embodiment of the present invention;

[0048] Figure 5 Schematic diagram of an intelligent heating system provided in an embodiment of the present invention, which couples household cogeneration with a hot water storage tank and a supplementary heat system;

[0049] In the figure, 1. Heating room, 2. Heat storage tank, 3. Micro cogeneration unit, 4. Air source heat pump, 5. Terminal radiator, 6. Third valve, 7. Second pump body, 8. Fourth valve, 9. Second valve, 10. Fifth valve, 11. First pump body, 12. First valve, 13. Pressure differential bypass valve, 14. Main valve, 15. Lux meter, 16. Thermometer, 17. CO2 sensor, 18. Infrared sensor, 19. Thermometer, 20. Carbon dioxide sensor; T2. Unit supply water temperature sensor, F2. Unit flow sensor, T3. Heating return water temperature sensor, F3. Heating flow sensor, T4. Circulating return water temperature sensor, F4. Circulating flow sensor, T5. Unit return water temperature sensor, T6. Top temperature sensor, T7. Node temperature sensor, T8. Bottom temperature sensor. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations.

[0051] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention as claimed, but rather merely represents selected embodiments of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort shall fall within the scope of protection of the present invention.

[0052] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0053] In the description of the present invention, it should be noted that the terms "center", "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, or are the orientation or position relationship in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they should not be understood as limiting the present invention.

[0054] It should be noted that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0055] Furthermore, terms such as "horizontal" and "vertical" do not necessarily mean that a component must be absolutely horizontal or overhanging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but rather that it can be slightly tilted.

[0056] Example 1

[0057] This embodiment provides a method for online operation of household biogas combined heat and power generation based on behavior perception, comprising the following steps:

[0058] Obtain the sensing parameters in the heating room, including the number of people in the room, indoor CO2 concentration, indoor illumination value, temperature difference between inside and outside the room, and historical total heating load value;

[0059] Inputting the acquired sensing parameters into a pre-built neural network model to obtain hourly heat load prediction values;

[0060] The heating mode of the household biogas cogeneration system corresponding to the heating room is determined according to the hourly heat load prediction value, and then an operation plan of the household biogas cogeneration system is obtained according to the operation constraints of the household biogas cogeneration system.

[0061] The process of acquiring the perception parameters in the heating room includes:

[0062] Use infrared sensors to determine the number of people in the room;

[0063] Obtain indoor CO2 concentration through carbon dioxide sensor;

[0064] Obtain indoor illuminance value through illuminance sensor;

[0065] The indoor temperature is sensed by the indoor thermometer, and the outdoor temperature is sensed by the outdoor temperature sensor, thereby measuring the temperature difference between the inside and outside of the room.

[0066] The heating mode of the household biogas combined heat and power system corresponding to the heating room is determined according to the hourly heat load prediction value, specifically:

[0067] According to the hourly heat load prediction value Q1 f , the rated heating capacity Q2 of the micro cogeneration unit in the household biogas cogeneration system corresponding to the heating room and the heat storage capacity Q3 of the water tank, determine the status of the micro cogeneration unit, water tank and heating equipment in the biogas cogeneration system, and form a variety of heating modes.

[0068] Preferably, the selection and judgment process of the heating mode includes: according to the hourly heat load prediction value Q1 f Determine whether the load has entered the peak range. If so, the micro cogeneration unit will supply heat and the water tank will store heat until the water tank is full of heat or Q1 f >Q2+Q3, the micro cogeneration unit and water tank are used for combined heating.

[0069] The specific process of judging whether the load has entered the peak range is as follows: obtain the heating load value Q1'max*α corresponding to the time when the heating load reaches the unit's heating limit on the previous day minus the time H required for the water tank to be fully heated; convert the hourly heat load forecast value Q1 f Compare with Q1'max*α to determine whether the load has entered the peak range.

[0070] If the load does not enter the peak range, then according to the hourly heat load prediction value Q1 f Judge whether the load enters the valley range. If it enters the valley range, the water tank supplies heat alone. If the water tank does not reach the minimum heat storage and release amount, the micro combined heat and power unit supplies heat and the water tank stores heat.

[0071] The process of judging whether the load enters the valley range is specifically as follows: Obtain the load value Q1'min*β corresponding to the maximum heat storage time that can be satisfied after the water tank is filled with heat on the previous day; Compare the hourly heat load prediction value Q1 f with Q1'min*β to judge whether the load enters the valley range.

[0072] If Q1 f That is, when it is neither in the peak range nor in the valley range, compare Q1 f with Q2. If Q1 f <Q2, the micro combined heat and power unit supplies heat and the water tank stores heat. When the water tank is filled with heat, the water tank supplies heat alone; if Q1 f >Q2, the micro combined heat and power unit and the water tank supply heat jointly. If the water tank does not reach the minimum heat release and storage amount, the micro combined heat and power unit and the heat supplement equipment supply heat simultaneously.

[0073] As Figure 5 shown, this embodiment also provides an operation system of a household biogas combined heat and power on-line operation method based on behavior perception as described above, including a heating room ①, a hot water storage tank ②, and a micro combined heat and power unit ③. The heating room ① is connected with a heat supplement equipment, and the heating room ① is provided with a terminal radiator ⑤. The hot water storage tank ② is connected to the terminal radiator ⑤ through a first heat supply loop. The first heat supply loop further includes a third valve ⑥, a second pump body ⑦, and a fourth valve ⑧. The third valve ⑥, the second pump body ⑦, the fourth valve ⑧, the hot water storage tank ②, and the terminal radiator ⑤ are connected in sequence. The flow direction of the second pump body ⑦ is towards the terminal radiator ⑤. A second valve ⑨ is connected in parallel on the whole outer side of the third valve ⑥, the second pump body ⑦, and the fourth valve ⑧. The flow direction of the second valve ⑨ is away from the terminal radiator ⑤;

[0074] The micro combined heat and power unit ③ is connected to the terminal radiator ⑤ through a second heat supply branch and via the first heat supply loop. The second heat supply branch is connected in parallel at both ends of the third valve ⑥, the second pump body ⑦, the fourth valve ⑧, and the hot water storage tank ②. The second heat supply branch further includes a fifth valve ⑩, a first pump body ⑪, and a first valve ⑫. The fifth valve ⑩, the first pump body ⑪, the micro combined heat and power unit ③, and the first valve ⑫ are connected in sequence. The flow direction of the first pump body ⑪ is towards the terminal radiator ⑤.

[0075] The above-mentioned household biogas cogeneration system can realize five heating modes, namely: in the first heating mode, the micro cogeneration unit, water tank and heating equipment provide heating at the same time, and the energy usage priority is water tank > unit > heating equipment; in the second heating mode, the micro cogeneration unit and heating equipment provide heating at the same time; in the third heating mode, the micro cogeneration unit provides heating and the water tank stores heat; in the fourth heating mode, the micro cogeneration unit and the water tank jointly provide heating; in the fifth heating mode, the water tank provides heating alone.

[0076] Specifically, in the first heating mode, when the micro cogeneration unit 3, the heat storage tank 2 and the heat supplement equipment are providing heat at the same time, the first valve 12 is opened, the second valve 9 is closed, the third valve 6 is opened, the fourth valve 8 is opened, the fifth valve 10 is opened, and the first pump body 11 and the second pump body 7 are opened.

[0077] In the second heating mode, when the micro cogeneration unit 3 and the heat supply equipment are supplying heat at the same time, the first valve 12 is opened, the second valve 9, the third valve 6 and the fourth valve 8 are all closed, the fifth valve 10 is opened, the first pump body 11 is opened, and the second pump body 7 is closed.

[0078] In the third heating mode, when the micro cogeneration unit 3 supplies heat and the heat storage tank 2 stores heat, the first valve 12 is opened, the second valve 9 is opened, the third valve 6 is closed, the fourth valve 8 is closed, the fifth valve 10 is opened, the first pump body 11 is opened, and the second pump body 7 is closed.

[0079] In the fourth heating mode, when the micro cogeneration unit 3 and the heat storage tank 2 jointly provide heat, the first valve 12 is opened, the second valve 9 is closed, the third valve 6 and the fourth valve 8 are opened, the fifth valve 10 is opened, the first pump body 11 is opened, and the second pump body 7 is opened.

[0080] In the fifth heating mode, when the hot water storage tank 2 provides heating alone, the first valve 12 is closed, the second valve 9 is closed, the third valve 6 and the fourth valve 8 are opened, the fifth valve 10 is closed, the first pump body 11 is closed, and the second pump body 7 is opened.

[0081] Optionally, the heat supplement equipment is an air source heat pump 4 .

[0082] Optionally, the hot water storage tank 2 is connected to a top temperature sensor T6, a node temperature sensor T7 and a bottom temperature sensor T8.

[0083] The micro cogeneration unit 3 is provided with a unit return water temperature sensor T5, a unit supply water temperature sensor T2 and a unit flow sensor F2 on both end pipes respectively. The micro cogeneration unit 3 is provided with a thermometer 19 and a carbon dioxide sensor 20.

[0084] A circulating return water temperature sensor T4 and a circulating flow sensor F4 are also provided in the first heating circuit. Both the circulating return water temperature sensor T4 and the circulating flow sensor F4 are located outside the second heating branch.

[0085] This enables perception of the operating status of the hot water storage tank 2 , the micro combined heat and power unit 3 and the heating circuit.

[0086] Optionally, the first heating circuit is further provided with a pressure differential bypass valve 13 connected in parallel at both ends of the terminal radiator 5, and the parallel circuit between the pressure differential bypass valve 13 and the terminal radiator 5 is connected to a main valve 14, a heating return water temperature sensor T3 and a heating flow sensor F3.

[0087] Optionally, the terminal radiator 5 is connected to a illuminance meter 15, a thermometer 16 and a CO2 sensor 17, and an infrared sensor 18 is provided in the heating room to realize the perception of the heating room.

[0088] Preferably, according to the operation constraints of the household biogas combined heat and power system, an operation plan of the household biogas combined heat and power system is obtained, specifically:

[0089] After determining the heating mode, calculate the comprehensive optimization index IP under the existing plan. If the IP value reaches the optimal value, operate according to the current plan. If the IP value does not reach the optimal value, readjust the load rate under the heating mode and recalculate the IP value under the new plan until the IP value reaches the optimal value.

[0090] The calculation expression of the comprehensive optimization index IP is:

[0091] IP=ω1*ATC+ω2*PEC+ω3*CDE

[0092] In the formula, ω1+ω2+ω3=1, ATC is the economic comparison index, PEC is the energy efficiency comparison index, and CDE is the environmental comparison index.

[0093] Any combination of the above preferred implementation modes can result in a better implementation mode. The following specifically describes an optimal implementation mode obtained by combining all the preferred implementation modes.

[0094] First, the real-time load prediction model based on usage behavior includes: real-time usage behavior perception, indoor and outdoor environmental parameter collection, actual operating load recording, and learning and optimization of the load prediction model based on the data obtained from the above perceptions.

[0095] Real-time usage behaviors that affect the load usually include people's preferences for starting the system (such as the deviation of a certain parameter from the normal value), the parameters that need to be maintained during system operation (set values), people's activity patterns in different rooms, the amount of fresh air in the room, window shading, lighting and equipment usage, etc.

[0096] The above behaviors are perceived through an integrated data collection system, which includes the following sensors and judgment principles:

[0097] Infrared sensors can determine the number of people entering and leaving a room, and at the same time, they can sense the patterns of people's activities in the room by combining time series;

[0098] The indoor thermometer senses the room temperature and, combined with the time series perception, determines the preference for turning on the system terminal. The outdoor temperature sensor measures the indoor and outdoor temperature difference.

[0099] Illumination sensors combined with time series can estimate the power of indoor lighting equipment and determine the use of curtain shading habits;

[0100] The carbon dioxide (CO2) sensor calculates the fresh air load infiltrating the room using the indoor and outdoor CO2 concentrations, the number of people in the room sensed by the infrared sensor (the amount of CO2 produced is known), and time series data using the following model:

[0101] Assuming that the room is composed of air with uniform temperature (i.e., treated according to the lumped parameter method), according to the principle of mass conservation, we can obtain:

[0102]

[0103] Combined with the initial conditions, we can solve:

[0104]

[0105] Among them, C a is the mass concentration of CO2 at the air inlet kg / m 3 , C i is the mass concentration of CO2 in the room kg / m 3 , q is the natural air intake volume m 3 / s, V is the air volume of the room m 3 , U is the amount of CO2 produced per unit time m 3 , n is the number of ventilation times n = q / V.

[0106] In this way, the amount of carbon dioxide produced U can be indirectly known through the known number of people. Combined with the time-varying carbon dioxide concentration Ci, the ambient carbon dioxide concentration Ca, and the volume V of the room, the number of fresh air ventilation times n can be obtained. Combined with the indoor and outdoor temperatures, the fresh air load can be obtained.

[0107] The inlet and outlet of the household biogas cogeneration unit are equipped with sensors to measure the temperature difference, and a flow sensor is used to measure the water flow. The total heating load is calculated according to the following formula:

[0108] Q=cmΔt

[0109] Where Q is the total heating load, kW; c is the specific heat of water, kJ / kg.℃; m is the water mass flow rate, kg / s; Δt is the water supply temperature difference, ℃.

[0110] The load forecasting model of this application adopts a three-layer neural network model (such as Figure 1 ), the input layer contains the five perceived parameters mentioned above, and the output layer is the predicted value of the total heating load;

[0111] The hourly data columns generated during the operation of the above five input layer parameters can be used as training data sets, validation sets or test sets of the prediction model;

[0112] This patented load forecasting model has a data interface with the public service APP of weather forecast, which serves as the input parameter of the hourly indoor and outdoor temperature difference when forecasting load.

[0113] Secondly, the online intelligent operation method and system for household biogas combined heat and power generation is based on the load forecast results obtained above, combined with hardware system parameter constraints, local energy prices, system operation and maintenance costs and energy efficiency, environmental impact (mainly carbon emissions), etc. to form an online intelligent operation method. Its overall logic is as follows: Figure 2 .

[0114] The economic comparison index ATC, energy efficiency comparison index PEC, and environmental comparison index CDE, each with a certain weight, constitute the comprehensive operation optimization index IP:

[0115] IP=ω1*ATC+ω2*PEC+ω3*CDE

[0116] Where, ω1+ω2+ω3=1;

[0117] Economic indicators OC is the daily operating cost, that is, OC = (total heating load at a certain time of the day (kW.h) / fuel lower calorific value or electric heat pump heating cop) * real-time energy price + real-time power consumption * electricity price - real-time on-grid power * on-grid electricity price. For a certain time, the choice of CHP heating and heat pump heating should be based on the economic efficiency of unit kW heat consumption, that is, comparison Among them, γ1 and γ2 are the unit kW heat economy of CHP and heat pump at a certain moment, Q fj is the load forecast value at a certain moment, Q2 is the heating capacity of CHP, and Q4 is the heating capacity of heat pump.

[0118] Energy efficiency index PEC uses primary energy utilization rate PEC = F pgu *k f +max{E grid ,0}*k e +min{E grid ,0}*Δk, where k fand k e are the conversion coefficients of gas and grid electricity into primary energy consumption, respectively; Δk is the difference between the primary energy consumption per kilowatt-hour of the CHP system and the primary energy consumption per kilowatt-hour of grid electricity.

[0119] Environmental indicator CDE uses CO2 emissions, CDE = F pgu *μ f +max{E grid ,0}*μ e +min{E grid ,0}*Δμ, where μ f and μ e are the conversion coefficients of natural gas and grid electricity into CO2 emissions, respectively; Δμ is the difference between the CO2 emissions per kWh of CHP system and the CO2 emissions per kWh of grid electricity.

[0120] The hourly heat load prediction value Q1 is obtained by the neural network model f .

[0121] By modifying the hourly heat load forecast value Q1 f Adjust the load rate of the mCHP unit, and then determine the operation plan of the mCHP system based on the internal constraints, external constraints and energy conservation principle of the unit.

[0122] Optionally, the internal constraints are the number of unit starts and stops per unit time and the maximum temperature of the unit.

[0123] Optionally, the external constraints are the maximum indoor temperature and the indoor set temperature.

[0124] The economic indicators, environmental indicators and energy efficiency indicators of the mCHP system operation plan are calculated to obtain the comprehensive indicator IP.

[0125] Determine whether the comprehensive indicators have reached the optimal level. If so, end the process and run according to the original operation plan. If not, adjust the load rate and go through the process again.

[0126] Optional, wherein the mCHP system operation plan includes: calibrating the load value of the current day's peak load to the load value Q1'max*α at the previous day's maximum load moment, wherein α is a variable, satisfying α*Q1'max equals the heating load value corresponding to the time corresponding to the previous day's heating load reaching the unit's heating limit minus the time H required for the water tank to be fully heated; calibrating the load value of the current day's estimated load to the load value Q1'min*β at the previous day's minimum load moment, wherein β is a variable, satisfying β*Q1'min equals the load value corresponding to the maximum heat storage time that can be satisfied after the water tank is fully heated on the previous day. f Compare with the load value Q1'max*α at the maximum load moment of the previous day: When Q1 fWhen Q1 > Q1'max * α, it indicates that the heating load enters the peak range. At this time, the water tank stores heat to cope with the upcoming heating peak; the predicted load value Q1 f is compared with the load value Q1'min * β at the maximum load moment of the previous day: when Q1 f < Q1'min * β, it indicates that the heating load enters the valley range. At this time, the water tank stores heat to cope with the upcoming heating valley.

[0127] Set the minimum heat release and storage amount of the water tank, that is, when T6 < C, if the water tank is not in the heat release state at this time, the water tank does not release heat.

[0128] Set the maximum heat storage amount of the water tank, that is, when T7 > A, the water tank stops storing heat.

[0129] The corresponding energy flow diagram of the micro - combined heat and power device of the present invention is shown in Figure 3 .

[0130] In the embodiment of the present invention, first, the human behavior perception data is detected by the end - point sensor, including: personnel displacement, indoor illuminance value, indoor carbon dioxide concentration, indoor and outdoor temperature values.

[0131] In the embodiment of the present invention, the personnel movement is detected by an infrared sensor to detect the personnel entry and exit situation of each room, so as to determine the number of personnel n in each room.

[0132] In the embodiment of the present invention, the fresh air load of outdoor air infiltrating into the room is calculated from the indoor and outdoor CO2 concentrations Ci, Ca and the number of personnel n in the room.

[0133] In the embodiment of the present invention, the power of the lighting equipment in the room and the usage habit of the shading system are obtained from the indoor illuminance value E and the time series.

[0134] Optionally, within the time period of 16:00 - 24:00 - 4:00, if the illuminance value E shows a sudden change, it can be regarded as the start of the lighting equipment, that is, the indoor equipment heat dissipation is corrected. Within the time period of 4:()0 - 16:00, it is usually regarded as the off - state of the lighting equipment.

[0135] In the embodiment of the present invention, the indoor and outdoor thermometers sense the indoor and outdoor temperatures T1, T2, and at the same time, the preference for opening the end of the system is sensed in combination with the time series.

[0136] In the embodiment of the present invention, the number of personnel n in the room; the indoor CO2 concentration Ci; the indoor illuminance value E; the indoor and outdoor temperature difference ΔT; the historical total heating load value Q are input into the neural network model.

[0137] In the embodiment of the present invention, the predicted value Q1 of the total heating load is output from the neural network model. f .

[0138] In the embodiment of the present invention, as Figure 4 shown, by comparing the predicted heat load value Q1 f , the rated heat supply of the micro combined heat and power unit Q2, and the heat storage quantity Q3 of the water tank at this moment, the states of the micro combined heat and power unit, the water tank and the heat supplement equipment are determined. There are 5 heating modes in total, which are: the first heating mode, the micro combined heat and power unit, the water tank and the heat supplement equipment supply heat simultaneously, and the energy usage priority is water tank > unit > heat supplement equipment; the second heating mode, the micro combined heat and power unit and the heat supplement equipment supply heat simultaneously; the third heating mode, the micro combined heat and power unit supplies heat and the water tank stores heat; the fourth heating mode, the micro combined heat and power unit and the water tank supply heat jointly; the fifth heating mode, the water tank supplies heat alone.

[0139] Compare Q1 f with Q1'max*α to judge whether the load enters the peak range. If it enters the peak range, the third heating mode is adopted until the water tank is full of heat or Q1 f >Q2+Q3, then change to the fourth heating mode.

[0140] Preferably, Q1'max*α is the heat supply load value corresponding to the time - H after the heat supply load of the previous day reaches the heat supply limit of the unit. If the heat supply load of the previous day fails to reach the heat supply limit of the unit, the comparison of the magnitudes of Q1'max*α and Q1 f is cancelled.

[0141] Preferably, H is the time required for the water tank to be full of heat.

[0142] If it does not enter the peak range, then compare Q1 f with Q1'min*β to judge whether the load enters the valley range. If it enters the valley range, the fifth heating mode is adopted. If the water tank does not reach the minimum heat storage and release quantity, the third heating mode is adopted. If the heat supply load of the previous day is greater than the heat supply limit of the unit, the comparison of the magnitudes of Q1 f and Q1'min*β is cancelled.

[0143] Optionally, Q1'min*β is the load value corresponding to the maximum heat storage time that can be satisfied after the water tank is full of heat on the previous day.

[0144] If Q1 f , that is, neither in the peak range nor in the valley range, compare Q1 f with Q2. If Q1 f <Q2, then adopt the third heating mode. When the water tank is full of heat, adopt the fifth heating mode. If Q1 f >Q2, then adopt the fourth heating method. If the water tank does not reach the minimum heat release and storage quantity, adopt the second heating mode.

[0145] In an embodiment of the present invention, after determining the heating mode, the comprehensive optimization index IP is calculated under the existing scheme. If the IP value reaches the optimal value, the scheme is operated; if the IP value does not reach the optimal value, the load rate under the heating mode is readjusted and the IP value under the new scheme is recalculated until the IP value reaches the optimal value.

[0146] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A method for online operation of household biogas combined heat and power generation based on behavior perception, characterized in that: A household biogas combined heat and power system comprises a heating room (1), a heat storage tank (2) and a micro combined heat and power unit (3), wherein the heating room (1) is connected to a heat supplement device, the heating room (1) is provided with a terminal radiator (5), the heat storage tank (2) is connected to the terminal radiator (5) via a first heating circuit; the micro combined heat and power unit (3) is connected to the terminal radiator (5) via a second heating branch and the first heating circuit, and the method comprises the following steps: Obtain the sensing parameters in the heating room, including the number of people in the room, indoor CO2 concentration, indoor illumination value, temperature difference between inside and outside the room, and historical total heating load value; Inputting the acquired sensing parameters into a pre-built neural network model to obtain hourly heat load prediction values; determining a heating mode of the household biogas combined heat and power system corresponding to the heating room according to the hourly heat load forecast value, and then obtaining an operation plan of the household biogas combined heat and power system according to the operation constraints of the household biogas combined heat and power system; The heating mode of the household biogas combined heat and power system corresponding to the heating room is determined according to the hourly heat load prediction value, specifically: According to the hourly heat load prediction value Q1 f 2. The rated heat supply Q2 of the micro-CHP unit in the household biogas CHP system corresponding to the heating room and the heat storage Q3 of the water tank are used to determine the status of the micro-CHP unit, water tank and heating equipment in the biogas CHP system to form multiple heating modes; The selection and judgment process of the heating mode includes: according to the hourly heat load prediction value Q1 f Determine whether the load has entered the peak range. If so, the micro cogeneration unit will supply heat and the water tank will store heat until the water tank is full of heat or Q1 f >Q2+Q3, the micro cogeneration unit and the water tank are used for combined heating; If the load does not enter the peak range, then according to the hourly heat load prediction value Q1 f Determine whether the load has entered the valley range. If so, the water tank will provide heat alone. If the water tank has not reached the minimum heat storage and release capacity, the micro-CHP unit will provide heat and the water tank will store heat. If Q1 f is neither in the peak range nor in the valley range, compare Q1 f with Q2. If Q1 f < Q2, the micro combined heat and power unit supplies heat and the water tank stores heat. When the water tank is full of heat, the water tank supplies heat alone; if Q1 f > Q2, the micro combined heat and power unit and the water tank supply heat jointly. If the water tank does not reach the minimum heat release storage capacity, the micro combined heat and power unit and the heat supplement equipment supply heat simultaneously; According to the operation constraints of the household biogas combined heat and power system, the operation plan of the household biogas combined heat and power system is obtained, which is as follows: After determining the heating mode, calculate the comprehensive optimization index IP under the existing plan. If the IP value reaches the optimal value, operate according to the current plan. If the IP value does not reach the optimal value, readjust the load rate under the heating mode and recalculate the IP value under the new plan until the IP value reaches the optimal value. The calculation expression of the comprehensive optimization index IP is: Where, , ATC is the economic comparison index, PEC is the energy efficiency comparison index, and CDE is the environmental comparison index.

2. The method for online operation of household biogas combined heat and power generation based on behavior perception according to claim 1, characterized in that: The process of acquiring the perception parameters in the heating room includes: Use infrared sensors to determine the number of people in the room; Obtain indoor CO2 concentration through carbon dioxide sensor; Obtain indoor illuminance value through illuminance sensor; The indoor temperature is sensed by the indoor thermometer, and the outdoor temperature is sensed by the outdoor temperature sensor, thereby measuring the temperature difference between the inside and outside of the room.

3. The method for online operation of household biogas combined heat and power generation based on behavior perception according to claim 1, characterized in that: The first heating circuit further comprises a third valve (6), a second pump body (7), and a fourth valve (8); the third valve (6), the second pump body (7), the fourth valve (8), the heat storage tank (2), and the terminal radiator (5) are connected in sequence; the flow direction of the second pump body (7) is toward the terminal radiator (5); a second valve (9) is connected in parallel to the outer side of the third valve (6), the second pump body (7), and the fourth valve (8); the flow direction of the second valve (9) is away from the terminal radiator (5); The second heating branch is connected in parallel to the third valve (6), the second pump body (7), the fourth valve (8) and the two ends of the heat storage tank (2). The second heating branch also includes a fifth valve (10), the first pump body (11) and the first valve (12). The fifth valve (10), the first pump body (11), the micro cogeneration unit (3) and the first valve (12) are connected in sequence. The flow direction of the first pump body (11) is toward the terminal radiator (5).

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