Building energy system optimization scheduling method based on active and passive energy storage efficient utilization

Abstract

The invention provides a building energy system optimization scheduling method based on active and passive energy storage efficient utilization, and belongs to the technical field of energy management and intelligent control. By integrating various flexible resources, efficient utilization of energy, cost optimization and thermal comfort guarantee are realized. And establishing a four-end prediction model, and accurately predicting energy supply and demand in future 24 hours by combining weather forecast and operation data. Double-stage optimization scheduling is adopted, the heat pump heating water temperature is optimized in the first stage, and equipment such as an energy storage battery is finely scheduled in the second stage. According to the method, multi-objective collaborative optimization is realized, flexible resource potential is fully excavated, calculation complexity is reduced, practical application is facilitated, energy utilization efficiency is effectively improved, operation cost is reduced, thermal comfort is guaranteed, intelligent and efficient development of a building energy management system is promoted, and the method has remarkable economic and social benefits.

Description

Technical Field

[0001] The present invention relates to the field of energy management and intelligent control technology, and in particular to a method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage. Background Art

[0002] With the rapid development of renewable energy technologies, the application of energy devices such as photovoltaic power generation, air-source heat pumps, energy storage batteries, and smart home appliances in buildings is becoming increasingly widespread. However, existing building energy systems still have many problems in energy management, especially the mismatch between photovoltaic power generation and system electricity supply and demand, which is particularly prominent. The specific manifestations are as follows:

[0003] (1) Lack of coordinated optimization and scheduling of multiple flexible resources: The existing system usually manages flexible resource equipment such as photovoltaic power generation, air source heat pumps, energy storage batteries, and reverse charging piles independently, lacks a unified scheduling optimization strategy, and it is difficult to achieve efficient energy utilization and cost minimization.

[0004] (2) Insufficient coordination and response between heat pump system scheduling and the power grid and photovoltaic power generation: The operation strategies of traditional heat pump systems mostly take into account unit efficiency and user thermal comfort experience, but fail to fully consider the volatility of photovoltaic power generation and the impact of time-of-use electricity prices, resulting in energy waste or increased operating costs. For example, during the peak period of photovoltaic power generation, the heat pump may not fully utilize photovoltaic power generation, while during the peak period of electricity consumption, the heat pump may increase the burden on the power grid. Therefore, in the multi-objective optimization process, there may be a contradiction between unit efficiency, cost-saving goals and user experience.

[0005] (3) Single energy storage battery scheduling strategy: Existing energy storage systems and electric vehicles mostly adopt fixed, user-defined charging and discharging modes, and fail to dynamically optimize scheduling based on photovoltaic power generation, time-of-use electricity prices, and user needs, resulting in low utilization of energy storage batteries and even shortening of their lifespan due to excessive charging and discharging.

[0006] (4) Lack of intelligence in the scheduling of transferable load appliances: The scheduling of transferable load appliances such as washing machines and water heaters is usually based on fixed time or manual control by users. It fails to combine photovoltaic power generation, time-of-use electricity prices and user living habits for intelligent optimization, which reduces energy utilization efficiency.

[0007] (5) Inefficient scheduling algorithms: Existing optimization scheduling methods typically use multi-parameter centralized solution algorithms to optimize multiple flexible resources simultaneously. This approach suffers from high computational complexity and low solution efficiency in actual engineering applications. It places high demands on computer computing power and is difficult to meet the real-time and practical requirements of building energy management systems.

[0008] Therefore, there is an urgent need for a building energy management system that can achieve the coordinated and optimized utilization of multiple flexible resources such as active and passive energy storage, take into account both thermal comfort and energy-saving goals, and be highly efficient, so as to improve energy utilization efficiency, reduce operating costs and ensure thermal comfort. Summary of the Invention

[0009] The present invention aims to provide a building energy system optimization and scheduling method based on the efficient utilization of active and passive energy storage. This method optimizes the scheduling of a building energy system that integrates photovoltaic power generation, air-source heat pumps, energy storage batteries, reverse electric vehicle charging stations, adjustable lighting fixtures, and smart appliances (such as washing machines and water heaters), achieving efficient energy utilization, cost optimization, and user thermal comfort. Using weather forecasts and building energy system operation data, a four-terminal prediction model for "building dynamic thermal environment, photovoltaic power generation, appliance load, and heat pump energy consumption" is established to accurately predict both the supply and demand sides of the energy system over the next 24 hours. A two-stage optimization and scheduling method is used. The first stage focuses on optimizing the air-source heat pump heating water temperature, combining photovoltaic power generation and time-of-use electricity prices to ensure thermal comfort. The second stage, based on the surplus photovoltaic power generation, finely schedules the energy storage batteries, reverse electric vehicle charging stations, adjustable lighting fixtures, and load-shifting appliances. This method achieves efficient utilization of multiple flexible resources in the energy system, such as building thermal inertia, active energy storage, and adjustable appliances, achieving multiple optimization goals, including maximizing energy efficiency, ensuring thermal comfort, and optimizing economic efficiency.

[0010] To achieve the above objectives, the present invention provides a method for optimizing the scheduling of a building energy system based on efficient utilization of active and passive energy storage, comprising the following steps:

[0011] Step S1: Collect building thermal information, heating system operation data, photovoltaic power generation system operation data, home appliance operation data, electric vehicle and energy storage system information;

[0012] Step S2: establishing a four-terminal prediction model, including: a building heat transfer dynamic prediction model, a heat pump energy consumption prediction model, a photovoltaic power generation power prediction model, and a household appliance load prediction model;

[0013] Step S3: Obtain the next 24h weather forecast data and time-of-use electricity prices;

[0014] Step S4: Input the data obtained in step S3 into the model of step S2 to obtain the predicted values ​​of indoor temperature, heat load, heat pump energy consumption, photovoltaic power generation power and household appliance load for the next 24 hours;

[0015] Step S5: Execute two-stage optimization scheduling.

[0016] Preferably, in step S1, the building thermal information includes the size, material and heat transfer coefficient of the enclosure structure; the heating system operation data includes the heating system water temperature, outdoor air temperature, solar radiation intensity and indoor air temperature and energy consumption; the photovoltaic power generation system operation data includes the outdoor air temperature, solar radiation intensity, outdoor air humidity, cloud cover and photovoltaic power generation power; the household appliance operation data includes the type of household appliance, the time range of household appliance use, the adjustable power and the total electrical load of the household appliance; the electric vehicle and energy storage system information includes the battery capacity, charge and discharge depth, charge and discharge power range, charge and discharge efficiency and initial state of charge.

[0017] Preferably, in step S2, a neural network is used to establish a heat pump energy consumption prediction model, the input parameters are outdoor temperature, supply water temperature and return water temperature, and the output parameter is the unit energy consumption;

[0018] A photovoltaic power generation prediction model is established using a neural network. The input parameters are solar radiation intensity, outdoor temperature, humidity, and cloud cover, and the output parameter is photovoltaic power generation.

[0019] A neural network is used to establish a household appliance load forecasting model, with the input parameters being outdoor temperature and humidity and historical electricity load.

[0020] Preferably, in step S2, a dynamic prediction model for building heat transfer is constructed based on the thermal resistance and heat capacity analogy principle as follows:

[0021]

[0022] Among them, C ai represents the indoor air heat capacity, T ai represents the predicted value of indoor air temperature, t represents time, T w_in represents the inner surface temperature of the non-transparent enclosure structure, R w_in represents the heat transfer resistance between the inner surface of the non-transparent envelope and the indoor air, T r_in represents the inner surface temperature of the roof of the non-transparent enclosure structure, R r_in represents the heat transfer resistance between the non-transparent envelope roof and the indoor air, T im Indicates the internal heat storage temperature, R im represents the heat exchange resistance between the internal heat storage body and the indoor air, T f represents the radiant floor temperature, R f represents the heat transfer resistance between the radiant floor and the indoor air, Q p The heat dissipated by people / appliances, Q win represents the heat transfer of the transparent enclosure structure, C w and C r are the heat capacities of the non-transparent enclosure wall and roof, T w_out and T r_out are the outer surface temperatures of the non-transparent enclosure wall and roof, Tw_in and T r_in are the inner surface temperatures of the non-transparent enclosure wall and roof, R w and R r are the heat transfer resistance of the non-transparent enclosure wall and roof, R w_out and R r_out They represent the heat transfer resistance between the non-transparent enclosure wall and roof outer surface and the outdoor air, R w_in and R r_in They represent the heat transfer resistance between the inner surface of the non-transparent enclosure wall and roof and the indoor air; T ao represents the outdoor air temperature, Q solar_w and Q solar_r They represent the solar radiation heat gain of the non-transparent enclosure wall and roof surface, C im Indicates the heat capacity of the internal heat storage body, Q solar_im It refers to the amount of heat gained by solar radiation through windows and exchange heat with internal heat storage bodies.

[0023] Preferably, in step S5, in the first stage, with the goal of minimizing system operating costs and minimizing indoor temperature deviation, a bionic algorithm is used to dynamically optimize the air source heat pump water supply temperature, and passive energy storage scheduling is achieved in combination with building thermal inertia. The objective function of the first stage optimization scheduling is:

[0024] J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 )=Min(a×C HVAC +ΔT ai );

[0025]

[0026] Among them, J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 ) represents the water temperature scheduling objective function value, T ws,set represents the optimal air source heat pump water temperature setting value, a represents the cost weight factor, which is used to adjust the importance of the heating system operating cost in the objective function, and Respectively represent the minimum and maximum allowable values ​​of water temperature setting, ΔT ai is the root mean square deviation of room temperature, which is used to characterize the indoor thermal comfort condition. HVAC The operating cost of the heating system for the next day is calculated as follows:

[0027] P HVAC (t) = P ASHP (t)+P PUMP (t);

[0028]

[0029] Among them, P HVAC (t) represents the system power consumption, which is determined by the heat pump power consumption P ASHP (t) and water pump power consumption P PUMP (t) composition, P HVAC_grid (t) represents the grid power consumption of the heat pump system, P PV (t) is the photovoltaic power generation at time t, y TOU (t) is the time-of-use electricity price at time t;

[0030] ΔT ai is the root mean square deviation of the room temperature, according to the indoor hourly temperature T ai (t) and the next day's indoor hourly set temperature T ai,set (t) is calculated as follows:

[0031]

[0032] Preferably, in step S5, in the second stage, based on the unabsorbed photovoltaic power generation surplus in the first stage, a mixed integer linear programming algorithm is used to optimize the energy storage battery charging and discharging power, the electric vehicle charging and discharging power, and the operating power of the adjustable household appliances. The objective function of the second stage optimization scheduling is:

[0033] J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j1,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 )=Min(C sys );

[0034] Among them, J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 ) represents the two-stage building energy system scheduling objective function, P c 、P dc Indicates the optimal battery charge and discharge power, P ev_c 、P ev_dcIndicates the optimal electric vehicle charging and discharging power, P ad,z represents the power consumption of the zth adjustable household appliance, P sh,j represents the power consumption of the household appliances of the jth type of transferred load;

[0035] C sys The operating cost of the building energy system for the next day is solved as follows:

[0036]

[0037] Among them, P grid (t) represents the amount of electricity that needs to be purchased from the power grid after optimized scheduling, and p(t) represents the time-of-use electricity price at time t. The solution process establishes a power balance model as follows:

[0038] P grid (t)+P dc (t)+P ev_dc (t)≥P HVAC (t);

[0039] P grid (t)+P dc (t)+P ev_dc (t)+P pv_unused (t)≥P app (t)+P app_fle (t)+P c (t)+P ev_c (t);

[0040] Among them, P pv_unused (t) represents the photovoltaic power that is not absorbed in one stage, P c (t), P ev_c (t) and P dc (t), P ev_dc (t) represents the charging and discharging power of the battery and the electric vehicle, P app (t) and P app_fle (t) represents the basic load and flexible load of other household appliances, P app (t) is derived from the prediction model.

[0041] Preferably, P app_fle The solution of (t) is mainly divided into two types: power-adjustable equipment flexibility and load-transferable equipment flexibility:

[0042] P app_fle (t) = P app_fle,ad (t)+P app_fle,sh (t);

[0043]

[0044] twork,s ≥t window,s t work,e ≤t window,e l work ≤l window ;

[0045]

[0046] Among them, N z (t) represents the number of adjustable power appliances of type z at time t, M j (t) represents the number of load-transferable household appliances of type j that can be adjusted at time t, P ad,z (t) represents the power that can be reduced by the zth power-adjustable household appliance at time t, P sh,j (t) represents the operating power of the jth load-transferable household appliance, U j (t) represents the flexible state of the jth load-transferable household appliance at time t, n represents the type of power-adjustable household appliance that can be adjusted at time t, m represents the type of load-transferable household appliance that can be adjusted at time t, P app_fle,ad (t) represents the flexible load that the adjustable household appliance can provide at time t, P app_fle,sh (t) represents the flexible load that the transferable household appliance can provide at time t, l work,shift Indicates the time period that can be transferred within the working window, t work,s Indicates the start time of equipment operation, t window,s Indicates the start time of the time window, t work,e Indicates the end time of equipment operation, t window,e Indicates the end time of the time window, l work Indicates working hours, l window,s Indicates the length of the time window;

[0047] The flexibility calculation formula is as follows:

[0048] Q app_fle (t) = Q app_fle,ad (t)+Q app_fle,sh (t);

[0049]

[0050] Among them, n z represents the heat dissipation coefficient of the zth power-adjustable household appliance, The cooling load coefficient of the zth power adjustable household appliance is C sh,jrepresents the operating power of the jth load-transferable household appliance, n1 represents the power consumption coefficient of the fluorescent lamp ballast, n2 represents the thermal insulation coefficient of the fluorescent lamp shade, n3 represents the motor utilization coefficient, n4 represents the motor load coefficient, n5 represents the heat dissipation coefficient of the electric heating equipment or electronic equipment, η represents the motor efficiency, Q app_fle (t) represents the total flexible load generated by the heat dissipation of the remaining appliances at time t, Q app_fle,ad (t) represents the total flexible load that the power-adjustable household appliances can provide due to heat dissipation at time t, Q app_fle,sh (t) represents the total flexible load that the power transferable household appliance can provide due to heat dissipation at time t, and COP represents the coefficient of performance of the household appliance.

[0051] Preferably, the battery charge and discharge power P c (t) and P dc(t) Subject to the following battery scheduling process constraints:

[0052]

[0053] Among them, SOC (t) represents the current energy state of the battery, SOC(t0) represents the energy state of the battery at the initial time t0, η c and η dc represents the battery charging and discharging efficiency, Δt represents the time interval, E Max is the rated capacity of the battery;

[0054] During the dispatch process, the battery is subject to the maximum and minimum capacity constraints allowed, specifically:

[0055] SOC min ≤SOC(t)≤SOC max ;

[0056] Among them, SOC max and SOC min Indicates the maximum and minimum capacity of the battery;

[0057] During the scheduling process, the battery is subject to the maximum and minimum power constraints allowed for charging and discharging, specifically:

[0058]

[0059] in, and Indicates the maximum charge and discharge power of the battery;

[0060] Since the battery cannot be charged and discharged at the same time, a binary variable is introduced to constrain the charge and discharge states to be mutually exclusive:

[0061] x c +xdc ≤1;

[0062] Among them, x c and x dc Indicates the battery charge and discharge status 0 and 1;

[0063] Charge and discharge cycle constraints:

[0064]

[0065] Where T represents the time range covered by the optimized scheduling.

[0066] Therefore, the present invention adopts the above-mentioned building energy system optimization scheduling method based on the efficient utilization of active and passive energy storage. By integrating multiple flexible resources such as photovoltaic power generation, air source heat pumps, energy storage batteries, reverse electric vehicle charging piles, adjustable lamps and smart appliances (such as washing machines and water heaters), it achieves multi-objective optimization of efficient energy utilization, cost optimization and user thermal comfort assurance. The beneficial technical effects are as follows:

[0067] This paper establishes a four-terminal prediction model, combining weather forecasts with building energy system operational data, to accurately predict both the supply and demand sides of the energy system over the next 24 hours. This prediction model fully accounts for building thermal inertia, photovoltaic power generation fluctuations, and variations in heat pump energy consumption, significantly improving prediction accuracy and enhancing the robustness of the scheduling strategy.

[0068] This invention utilizes a two-stage optimization scheduling approach. The first stage ensures indoor thermal comfort by optimizing the water temperature of air-source heat pumps, combined with photovoltaic power generation and time-of-use electricity pricing. The second stage fine-tunes the scheduling of energy storage batteries, reverse charging stations, adjustable lighting fixtures, and load-shifting appliances based on the remaining photovoltaic power generation. This approach achieves multi-objective coordinated optimization across economy, comfort, and energy efficiency, addressing the energy waste and increased costs associated with traditional single-scheduling strategies. It fully taps the potential of flexible resources such as building thermal inertia, active energy storage, and adjustable appliances.

[0069] This paper employs a two-stage optimization scheduling approach, breaking down the complex problem into two phases: heat pump water temperature scheduling and flexible resource scheduling. These two phases solve for system control parameters under multi-objective optimization. This approach effectively reduces computational complexity and reliance on computer power, facilitating rapid deployment and application in practical projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 This is a flow chart of an optimization scheduling method for building energy systems based on efficient utilization of active and passive energy storage;

[0071] Figure 2 This is a dynamic prediction model diagram for building heat transfer;

[0072] Figure 3 The relative relationship between the working hours of home appliances and the time window. DETAILED DESCRIPTION

[0073] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0074] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0075] Example 1

[0076] The present invention provides a building energy system optimization scheduling method based on the efficient utilization of active and passive energy storage. Based on indoor temperature and heat load forecasts, air source heat pump energy consumption forecasts, photovoltaic power generation power forecasts, and other household appliance power load forecasts, the method further dynamically plans the air source heat pump heating water temperature setpoint, battery and electric vehicle charging and discharging behaviors, and other flexible resource control parameters. While ensuring indoor thermal comfort, the method responds to time-of-use electricity price changes and photovoltaic power generation on a 24-hour cycle, alleviates the energy supply and demand mismatch problem, and reduces system operating costs. The method specifically includes the following steps:

[0077] Step S1, collecting data, including:

[0078] (1) Building thermal information: building envelope dimensions, materials, and heat transfer coefficients;

[0079] (2) Heating system operation data: heating system water temperature, outdoor air temperature, solar radiation intensity, indoor air temperature and energy consumption;

[0080] (3) Photovoltaic power generation system operating data: outdoor air temperature, solar radiation intensity, outdoor air humidity, cloud cover and photovoltaic power generation;

[0081] (4) Appliance operation data: appliance type, appliance usage time range, adjustable power, and total appliance power load;

[0082] (5) Electric vehicle and energy storage system information: battery capacity, charge and discharge depth, charge and discharge power range, charge and discharge efficiency, and initial state of charge (SOC).

[0083] Step S2: Based on the building thermal information and heating system operation data, the indoor temperature and heating load changes in the next 24 hours under different outdoor environments and different water supply temperatures are predicted to ensure that thermal comfort is not affected during the scheduling process. Based on the thermal resistance and heat capacity analogy principle, the present invention considers the thermal inertia brought by thermal storage bodies such as outdoor air temperature, solar radiation intensity, water supply temperature, building envelope, internal thermal storage body, radiant coil and radiant floor, and establishes a building dynamic thermal environment prediction model. The model is designed to predict dynamic indoor temperature changes as shown in formula (1) to formula (6).

[0084]

[0085] Among them, C ai represents the indoor air heat capacity, T ai represents the predicted value of indoor air temperature, t represents time, T w_in represents the inner surface temperature of the non-transparent enclosure structure, R w_in represents the heat transfer resistance between the inner surface of the non-transparent envelope and the indoor air, T r_in represents the inner surface temperature of the roof of the non-transparent enclosure structure, R r_in represents the heat transfer resistance between the non-transparent envelope roof and the indoor air, T im Indicates the internal heat storage temperature, R im represents the heat exchange resistance between the internal heat storage body and the indoor air, T f represents the radiant floor temperature, R f represents the heat transfer resistance between the radiant floor and the indoor air, Q p The heat dissipated by people / appliances, Q win Indicates the heat transfer of transparent envelope structure.

[0086] Among them, for the first term on the right side of formula (1), the heat transfer of the room through the non-transparent enclosure structure, the wall heat transfer is further calculated as shown in formulas (2) and (3), and the roof heat transfer is further calculated as shown in formulas (4) and (5).

[0087]

[0088] Among them, C w and C r are the heat capacities of the non-transparent enclosure wall and roof, T w_out and T r_out are the outer surface temperatures of the non-transparent enclosure wall and roof, T w_in and T r_in are the inner surface temperatures of the non-transparent enclosure wall and roof, R w and R r are the heat transfer resistance of the non-transparent enclosure wall and roof, R w_out and R r_outThey represent the heat transfer resistance between the non-transparent enclosure wall and roof outer surface and the outdoor air, R w_in and R r_in They represent the heat transfer resistance between the inner surface of the non-transparent enclosure wall and roof and the indoor air, T ao represents the outdoor air temperature, Q solar_w and Q solar_r They represent the solar radiation heat gain of the non-transparent enclosing structure wall and roof surfaces respectively.

[0089] The internal heat storage body of the building composed of interior walls, furniture, etc. increases its temperature continuously through convection heat transfer or solar radiation through windows. When the surface temperature of the internal heat storage body is higher than the indoor air temperature, the stored heat is gradually released into the indoor air in the form of convection to ensure indoor comfort.

[0090] For the heat storage and release characteristics of the heat storage body inside the building in formula (1), the calculation method is specifically shown in formula (6).

[0091]

[0092] Among them, C im Indicates the heat capacity of the internal heat storage body, Q solar_im It refers to the amount of heat gained by solar radiation through windows and exchange heat with internal heat storage bodies.

[0093] The solar radiation heat gain can be calculated by formula (7).

[0094] Q solar =v×I(7);

[0095] Among them, Q solar represents the amount of heat radiated by the sun, v represents the solar radiation absorption coefficient, which is affected by absorptivity, orientation, or angle, and I represents the predicted solar radiation intensity, which is obtained from the meteorological website;

[0096] For the transparent envelope structure, the heat transfer Q win , the calculation method is shown in formula (8).

[0097]

[0098] Among them, R win Thermal resistance of transparent enclosure structure.

[0099] There is heat exchange between the heat pump water system, the heating terminal and the indoor space. The calculation method is shown in formula (9).

[0100]

[0101] Among them, C f represents the floor heat capacity, T ws Indicates the heat pump supply water temperature.

[0102] Since some parameters in the model are affected by changes in the outdoor environment, such as increased wind speed leading to a decrease in the thermal resistance of the outer surface, a rolling identification parameter optimization method using a bionic algorithm or least squares method can be used to obtain accurate values ​​for each heat capacity, thermal resistance, and solar radiation coefficient. The genetic algorithm input parameter set is the model-predicted and actual measured values ​​of the indoor air temperature of the heating system for the last five consecutive days, with an interval of 1 hour. With the goal of minimizing the deviation between the model-predicted and actual indoor temperatures, a rolling optimization method is used to identify each parameter value, with a rolling frequency of once per day. The specific objective function F(.) is shown in Formula (10).

[0103]

[0104] Among them, t1 and t2 are the start and end time of identification and optimization respectively, with an interval of 120 hours. ai,test (t) is the indoor air temperature monitoring value.

[0105] After the building heat transfer dynamic prediction model is established and parameter identification and optimization are completed, the outdoor air temperature, solar radiation and air source heat pump water supply temperature in the next 24 hours are input into the above building heat transfer dynamic prediction model to predict the indoor air temperature in the next 24 hours. The model structure is as follows Figure 2 shown.

[0106] The system heating load depends primarily on the water supply temperature and the radiant floor temperature. Since the radiant floor has a large heat capacity and its temperature fluctuations are relatively stable, the present invention uses the radiant floor temperature calculated in the previous iteration and the current water supply temperature to calculate the system heating load, as shown in formula (11):

[0107]

[0108] Among them, Q ws Represents the system heating load, R pf It represents the heat transfer resistance between the radiant floor terminal and the indoor air.

[0109] Accurately predicting the energy consumption of air source heat pumps in the next 24 hours under different outdoor environments and different heating water temperatures, photovoltaic power generation power and the load of other household appliances is the basic data support for completing optimized scheduling. Using machine learning algorithms, we establish photovoltaic power generation prediction models, household appliance load prediction models and heat pump energy consumption prediction models to complete supply and demand forecasts.

[0110] The actual heating performance of the air source heat pump is affected by the water supply temperature and outdoor working conditions. ao ), water supply temperature (T ws ) and return water temperature (T wr) as input parameters, with unit energy consumption (P ASHP ) is the output parameter, and a neural network algorithm such as CNN or ANN is used to establish a heat pump energy consumption prediction model.

[0111] The distributed photovoltaic power generation power is mainly affected by the outdoor environment conditions. The present invention selects the solar radiation intensity (I) and outdoor temperature (T ao ), outdoor humidity (RH) and cloud cover (Cc) as input parameters, and photovoltaic power generation power (P pv ) is the output parameter, and a neural network algorithm such as CNN algorithm or LSTM is used to establish a photovoltaic power generation prediction model.

[0112] The energy consumption of other appliances in the building (such as lighting, television, refrigerator, etc.) is mainly affected by user behavior patterns, environmental parameters and user characteristics. In order to build an accurate energy consumption prediction model, this method is first used to cluster the historical data according to the time characteristics (working days and holidays in different seasons), and then select the electrical load (P t-24 ), outdoor temperature (T ao ), outdoor humidity (RH) as input parameters, and the total load of household appliances (P app ) is the output parameter, and a neural network algorithm such as CNN algorithm or LSTM is used to establish a household appliance load forecasting model.

[0113] Step S3: Collect future meteorological data and local grid electricity prices. Specific data include outdoor air temperature, solar radiation intensity, heating water temperature, indoor temperature, and time-of-use electricity prices.

[0114] Step S4: collect the future meteorological data (including solar radiation intensity (I), outdoor temperature (T ao ), outdoor humidity (RH) and cloud cover (Cc), and time-of-use electricity price information are respectively input into the building dynamic thermal environment prediction model, photovoltaic power generation prediction model, household appliance load prediction model, and heat pump energy consumption prediction model established in step S2 to obtain the indoor temperature changes, indoor heat load, photovoltaic power generation power, and air source heat pump heating system energy consumption under different working conditions and different heating water temperature settings.

[0115] Step S5: With the goal of minimizing the deviation between the system operating cost and the indoor temperature setting for the next 24 hours, the particle swarm algorithm or other biomimetic algorithms are used to dynamically plan the air source heat pump heating water temperature, and the MILP algorithm is combined to plan the battery, electric vehicle charging and discharging behavior, and household appliance electricity consumption behavior. The optimization scheduling is divided into two stages:

[0116] (1) The first stage (constructing the water temperature target optimization function) is to schedule the air source heat pump combined with the passive energy storage of the building's thermal inertia. The main adjustment parameter is the air source heat pump water temperature. The goal of the heating water temperature optimization scheduling is to maximize the power demand response, absorb photovoltaic power generation, and reduce operating costs while ensuring indoor thermal comfort. It is described as formulas (12) to (13).

[0117] J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 )=Min(a×C HVAC +ΔT ai ) (12);

[0118]

[0119] Among them, J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 ) represents the water temperature scheduling objective function value, T ws,set represents the optimal air source heat pump water temperature setting value, a represents the cost weight factor, which is used to adjust the importance of the heating system operating cost in the objective function, and Respectively represent the minimum and maximum allowable values ​​of water temperature setting, ΔT ai is the root mean square deviation of room temperature, which is used to characterize indoor thermal comfort conditions.

[0120] C HVAC represents the operating cost of the heating system for the next day, calculated according to formulas (14)-(16). HVAC (t) represents the system power consumption, which is determined by the heat pump power consumption P ASHP (t) and water pump power consumption P PUMP (t) composition, P HVAC_grid (t) represents the grid power consumption of the heat pump system. When the system power consumption is lower than the photovoltaic power generation, the grid power is not needed. On the contrary, the grid power is needed to supplement the insufficient system power consumption of the photovoltaic system. HVAC It is calculated based on the power consumption of the power grid and the electricity charges.

[0121] P HVAC (t) = P ASHP (t)+P PUMP (t)(14);

[0122]

[0123] Among them, P PV (t) is the photovoltaic power generation at time t, y TOU (t) is the time-of-use electricity price at time t.

[0124] ΔT ai is the root mean square deviation of the room temperature, which is used to characterize the indoor thermal comfort condition. ai (t) and the next day's indoor hourly set temperature T ai,set (t) is calculated as shown in formula (17).

[0125]

[0126] (2) The second stage (energy system optimization objective function) is to dispatch batteries, trams and other household appliances. The main adjustment parameters are the charging and discharging power of batteries and trams and the power consumption of other household appliances. Based on the optimization results of the first stage, photovoltaic power generation is further absorbed to achieve the reduction of operating costs of the entire building system. It is described as formula (18).

[0127] J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j1,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 )=Min(C sys ) (18);

[0128] Among them, J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 ) represents the two-stage building energy system scheduling objective function, P c 、P dc Indicates the optimal battery charge and discharge power, P ev_c 、P ev_dc Indicates the optimal electric vehicle charging and discharging power, P ad,z represents the power consumption of the zth adjustable household appliance, P sh,j It represents the power consumption of the household appliances of the jth type of transferred load.

[0129] C sys The operating cost of the building energy system for the next day is solved as follows:

[0130]

[0131] Among them, P grid (t) represents the amount of electricity that needs to be purchased from the grid after optimized scheduling, and p(t) represents the time-of-use electricity price at time t. The solution process requires the establishment of a power balance model, as follows:

[0132] P grid (t)+P dc (t)+P ev_dc (t)≥P HVAC (t)(20);

[0133] P grid (t)+P dc (t)+P ev_dc (t)+P pv_unused (t)≥P app (t)+P app_fle (t)+P c (t)+P ev_c (t) (21);

[0134] Among them, P pv_unused (t) represents the photovoltaic power that is not absorbed in one stage, P c (t), P ev_c (t) and P dc (t), P ev_dc (t) represents the charging and discharging power of the battery and the electric vehicle, P app (t) and P app_fle (t) represents the basic load and flexible load of other household appliances, P app (t) is derived from the prediction model.

[0135] P app_fle The solution of (t) is mainly divided into two types: power-adjustable device flexibility and load-transferable device flexibility. Among them, power-adjustable devices can provide flexibility by adjusting the power of the device, such as the adjustment of the power of lighting equipment; load-transferable devices can complete tasks at any time within the time window without affecting their own use functions, thereby bringing power flexibility, such as washing machines, dishwashers, coffee machines in buildings, etc. The time window refers to the acceptable working time period of household appliances, and the working time refers to the actual working time required for the equipment. Generally speaking, the time window and the required working time of each household appliance are constant values, and the length of the time window l window,s Not less than working hours l work , the starting time of equipment operation t work,s No earlier than the start time t of the time window window,s , the end time of equipment operation t work,e No later than the end time t of the time window window,eThe diagram of the relative relationship between working hours and time windows is as follows: Figure 3 shown.

[0136] Based on the correspondence between time windows and operating hours, the flexibility quantification model for household appliances is defined as formula (21), where household appliance flexibility includes power-adjustable equipment flexibility and load-transferable equipment flexibility, and the calculation formulas are shown in formulas (22) and (23), respectively. The flexibility of power-adjustable equipment mainly comes from the various lighting devices in the building. During the demand response period, the power of such equipment can be reduced according to a certain proportion, thereby reducing the total electricity load of the building. The flexibility of load-transferable equipment mainly comes from household appliances that can change their operating hours. By changing the operating hours of the equipment within the time window, the purpose of load transfer is achieved, which will not have a significant impact on the building and users, and does not require additional investment and operating costs.

[0137] P app_fle (t) = P app_fle,ad (t)+P app_fle,sh (t)(22);

[0138]

[0139] t work,s ≥t window,s t work,e ≤t window,e l work ≤l window (25);

[0140]

[0141] Among them, N z (t) represents the number of adjustable power appliances of type z at time t, M j (t) represents the number of load-transferable household appliances of type j that can be adjusted at time t, P ad,z (t) represents the power that can be reduced by the zth power-adjustable household appliance at time t, P sh,j (t) represents the operating power of the jth load-transferable household appliance, U j (t) represents the flexible state of the jth load-transferable household appliance at time t, n represents the type of power-adjustable household appliance that can be adjusted at time t, m represents the type of load-transferable household appliance that can be adjusted at time t, P app_fle,ad (t) represents the flexible load that the adjustable household appliance can provide at time t, P app_fle,sh (t) represents the flexible load that the transferable household appliance can provide at time t, l work,shift Indicates the time period during which transfers can be made within the work window.

[0142] For power-adjustable and load-transferable devices, when they participate in demand response and bring about direct power flexibility, the power changes and transfers of home appliances will cause changes in the heat dissipation of the devices, which in turn indirectly causes fluctuations in the cooling and heating loads. Finally, the fluctuations in the cooling and heating loads will change the energy consumption of the air-conditioning system, further leading to changes in the electrical load. Therefore, the heat dissipation of home appliances also has a certain degree of electricity flexibility. The flexibility of home appliances due to changes in heat dissipation can be divided into the heat dissipation flexibility of power-adjustable devices and the heat dissipation flexibility of load-transferable devices. The flexibility calculation formula is:

[0143] Q app_fle (t) = Q app_fle,ad (t)+Q app_fle,sh (t)(27);

[0144]

[0145] Among them, n z represents the heat dissipation coefficient of the zth power-adjustable household appliance, The cooling load coefficient of the zth power adjustable household appliance is C sh,j represents the operating power of the jth load-transferable household appliance, n1 represents the power consumption coefficient of the fluorescent lamp ballast, which is generally 1.2, n2 represents the thermal insulation coefficient of the fluorescent lamp shade, which is 0.5-0.6 when the shade is perforated and 0.6-0.8 when the shade is not perforated, n3 represents the motor utilization coefficient, which is the ratio of the maximum actual power of the motor to the installed power, which is generally 0.7-0.9, n4 represents the motor load coefficient, which is the ratio of the average actual power of the motor to the maximum actual power, which is generally 0.15-0.5, n5 represents the heat dissipation coefficient of electric heating equipment or electronic equipment, which is 1 for electric fans, computers, etc. and 0.5-0.9 for other equipment, η represents the motor efficiency, which is 1 for electric fans, computers, etc. and 0.5-0.9 for other equipment, Q app_fle (t) represents the total flexible load generated by the heat dissipation of the remaining appliances at time t, Q app_fle,ad (t) represents the total flexible load that the power-adjustable household appliances can provide due to heat dissipation at time t, Q app_fle,sh (t) represents the total flexible load that the power-transferable household appliance can provide due to heat dissipation at time t, and COP (Coefficient of Performance) represents the operating performance coefficient of the household appliance.

[0146] Secondly, the battery charge and discharge power P c (t) and P dc(t) Subject to the following battery scheduling process constraints:

[0147]

[0148] Among them, SOC (t)represents the current energy state of the battery, SOC(t0) represents the energy state of the battery at the initial time t0, η c and η dc Indicates the battery charging and discharging efficiency, usually a value between 0 and 1, indicating the energy conversion efficiency of the battery during the charging process. Δt represents the time interval, E Max is the rated capacity of the battery.

[0149] During the dispatch process, the battery is subject to the maximum and minimum capacity constraints allowed, specifically:

[0150] SOC min ≤SOC(t)≤SOC max (32);

[0151] Among them, SOC max and SOC min Indicates the maximum and minimum capacity of the battery.

[0152] During the scheduling process, the battery is subject to the maximum and minimum power constraints allowed for charging and discharging, specifically:

[0153]

[0154] in, and Indicates the maximum charge and discharge power of the battery.

[0155] Since the battery cannot be charged and discharged at the same time, a binary variable is introduced to constrain the charge and discharge states to be mutually exclusive:

[0156] x c +x dc ≤1(35);

[0157] Among them, x c and x dc Indicates the battery's charge and discharge states 0 and 1, thus ensuring that the battery can only be in the charge or discharge state at any time.

[0158] To ensure battery health, set charge and discharge cycle constraints:

[0159]

[0160] Wherein, T represents the time range covered by the optimized scheduling, which is 24 hours in this embodiment.

[0161] The constraints and scheduling models for electric vehicles are the same as those for batteries, but they need to be charged and discharged within a fixed time window.

[0162] Taking a certain residential building in a certain city as an example, the specific implementation method of the present invention is further described with reference to the accompanying drawings.

[0163] The residential building area is 198m 2 The exterior walls are made of double-sided thick color steel plates with polyurethane insulation panels, and the exterior windows are single-layer Low-E glass windows. The specific enclosure structure information is shown in Table 1.

[0164] Table 1 Enclosure structure information table

[0165] Enclosure structure <![CDATA[Heat transfer coefficient (W / (m 2 ·K))]]> Polyurethane insulation panels 0.024 Color-coated steel plate 0.25 Low-E glass windows 3

[0166] The building's installed photovoltaic capacity is 12 kWp. The heating system primarily consists of an air-cooled chiller with a variable-frequency heat pump, a circulating water pump, a buffer water tank, and radiant flooring. Other adjustable household appliances include a television, an induction cooker, a dishwasher, a microwave, a rice cooker, a coffee maker, a laptop, and three adjustable fluorescent lamps. The heat pump unit has a nominal heating capacity of 18 kW and a heating power of 5.77 kW. The electric vehicle has a capacity of 70 kWh, a maximum charge and discharge rate of 0.5C, and a charge and discharge efficiency of 0.9. The battery capacity is 5 kWh. The electricity price is based on the residential electricity price during the heating season in a certain province, as shown in Table 2.

[0167] Table 2 Time-of-use electricity price list

[0168] time Electricity price period Electricity price (yuan / kWh) 8：00-20：00 Peak section 0.8769 20：00-8：00 Valley Section 0.6469

[0169] The building energy system is centrally controlled by a cloud server, based on the Linux system and real-time communication technology, and the control hardware is built. The server mainly includes the following modules:

[0170] (1) Data acquisition module.

[0171] The data acquisition module acquires the data required by the model and algorithm through online collection and manual input, and stores it in the database. MySQL is used as its backend database storage.

[0172] Meteorological parameters: Based on the weather forecast website, online API technology is used to obtain hourly weather conditions for the next 24 hours, outdoor air dry-bulb temperature, solar radiation intensity, relative humidity, air quality, wind speed, wind direction, cloud cover rate, etc.

[0173] Host information: Get the unit model, etc. according to the unit nameplate.

[0174] Building information: Obtain exterior wall area, exterior wall thickness, door and window area, roof area, roof thickness, heat exchange terminal type, etc. based on building drawings or on-site measurements.

[0175] Time-of-use electricity prices: Visit the State Grid and obtain time-of-use electricity prices for residents in the project area through user inquiries.

[0176] (2) Each prediction module.

[0177] The prediction models proposed in this paper were established using Python. The photovoltaic power prediction model and the heat pump energy consumption prediction model used convolutional neural network algorithms, and the household appliance load prediction model used the long short-term memory algorithm.

[0178] Each prediction model was trained and validated. Using 720 sets of actual building operating data from November 15 to 20, 2024, a particle swarm optimization algorithm was used to identify the parameters of the RC model. The specific parameter identification results are shown in Table 3. Using 2880 sets of operating data from the 2024 heating season, 70% of which was used for training and 30% for validation, the photovoltaic power generation prediction model, the heat pump energy consumption prediction model, and the remaining appliance load prediction models were trained and validated, respectively. The predicted MAPEs were 2.1%, 4.3%, and 1.3%, respectively.

[0179] Table 3 RC model parameter identification results

[0180]

[0181] (3) Optimize the scheduling module.

[0182] Based on the scheduling method proposed by the present invention (i.e. Figure 1 ), choose Python language to develop the optimization strategy, choose particle swarm optimization algorithm for the first stage, and use MILP algorithm for the second stage.

[0183] For the first-stage objective function, considering the indoor temperature requirement of ±1°C, the cost weight factor a is selected as 20. In the process of solving the objective function, considering computational efficiency and accuracy, the population size and maximum number of iterations of the particle swarm algorithm are set to 200 and 1000 respectively.

[0184] The optimization strategy resets the heat pump water supply temperature. The heat pump automatically adjusts the operating frequency using a PID algorithm based on the deviation between the supply water temperature and the set temperature.

[0185] The first-stage optimization strategy resets the heat pump's water supply temperature. The heat pump automatically adjusts its operating frequency using a PID algorithm based on the deviation between the water supply temperature and the set temperature.

[0186] The two-stage optimization strategy adjusts the charging and discharging power of the battery and tram and the power consumption of the rest of the appliances, with the working time windows of the rest of the appliances and the arrival time windows of the tram trips as constraints.

[0187] All modules and databases described above are deployed on a cloud platform server. Furthermore, the server is equipped with an external data acquisition API that automatically retrieves future outdoor weather parameters from weather websites. Furthermore, the server can interact with the actual system to collect system operating data and issue scheduling commands.

[0188] The indoor thermal environment prediction model and the heat pump energy consumption prediction model are run daily, with model updates based on training results to continuously improve model accuracy. Meteorological data is collected daily or hourly. Time-of-use electricity price data is obtained from the State Grid Corporation of China and updated daily. The photovoltaic power generation prediction model is randomly run daily, with model training updated daily. The household appliance energy load prediction model runs at 10:30 PM daily, with model training updated daily. The scheduling algorithm is divided into a day-ahead scheduling program and an intraday rolling optimization program, with the day-ahead scheduling program starting at 11:00 PM daily. System operation data monitoring and database updates are conducted every five minutes.

[0189] Ultimately, the operating costs of the building in this case during the heating season can be reduced by 15-20%, the operating energy consumption can be reduced by 20-25%, the photovoltaic absorption rate can be increased by 18-25%, and the heat pump COP can be increased by about 5%. In addition, the solution speed of this method is improved by about 10 minutes compared with the existing algorithm under the same level of computer configuration.

[0190] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0191] Therefore, the present invention adopts the above-mentioned building energy system optimization scheduling method based on the efficient utilization of active and passive energy storage to optimize the scheduling method of the building energy system that integrates photovoltaic power generation, air source heat pumps, energy storage batteries, reverse electric vehicle charging piles, adjustable lamps and smart appliances (such as washing machines, water heaters, etc.), so as to achieve efficient energy utilization, cost optimization and user thermal comfort guarantee. Through weather forecasts and building energy system operation data, a four-terminal prediction model of "building dynamic thermal environment, photovoltaic power generation, appliance load and heat pump energy consumption" is established to achieve accurate prediction of both the supply and demand sides of the energy system in the next 24 hours. Combined with a two-stage optimization scheduling method, the first stage focuses on optimizing the water temperature of heat pump heating, combining photovoltaic power generation and time-of-use electricity prices to ensure thermal comfort; the second stage, based on the surplus photovoltaic power generation, finely schedules the energy storage batteries, reverse charging piles, adjustable lamps and load-transferable appliances, thereby achieving efficient utilization of multiple flexible resources such as building thermal inertia, active energy storage and adjustable appliances in the energy system, and achieving multiple optimization goals such as maximizing energy efficiency, ensuring thermal comfort and optimizing economic efficiency.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A building energy system optimization scheduling method based on efficient utilization of active and passive energy storage, characterized in that In, the following steps are included: Step S1: Collect building thermal information, heating system operation data, photovoltaic power generation system operation data, home appliance operation data, electric vehicle and energy storage system information; Step S2: establishing a four-terminal prediction model, including: a building heat transfer dynamic prediction model, a heat pump energy consumption prediction model, a photovoltaic power generation power prediction model, and a household appliance load prediction model; Step S3: Obtain the next 24h weather forecast data and time-of-use electricity prices; Step S4: Input the data obtained in step S3 into the model of step S2 to obtain the predicted values ​​of indoor temperature, heat load, heat pump energy consumption, photovoltaic power generation power and household appliance load for the next 24 hours; Step S5: Execute two-stage optimization scheduling.

2. A building energy system optimization scheduling method based on efficient utilization of active and passive energy storage according to claim 1, characterized in that: In step S1, the building thermal information includes the size, material and heat transfer coefficient of the enclosure structure; the heating system operation data includes the heating system water temperature, outdoor air temperature, solar radiation intensity and indoor air temperature and energy consumption; the photovoltaic power generation system operation data includes the outdoor air temperature, solar radiation intensity, outdoor air humidity, cloud cover and photovoltaic power generation power; the household appliance operation data includes the type of household appliance, the time range of household appliance use, the adjustable power and the total electrical load of the household appliance; the electric vehicle and energy storage system information includes the battery capacity, charge and discharge depth, charge and discharge power range, charge and discharge efficiency and initial state of charge.

3. The method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage according to claim 1, characterized in that: In step S2, a neural network is used to establish a heat pump energy consumption prediction model, with the input parameters being the outdoor temperature, the supply water temperature, and the return water temperature, and the output parameter being the unit energy consumption; A photovoltaic power generation prediction model is established using a neural network. The input parameters are solar radiation intensity, outdoor temperature, humidity, and cloud cover, and the output parameter is photovoltaic power generation. A neural network is used to establish a household appliance load forecasting model, with the input parameters being outdoor temperature and humidity and historical electricity load.

4. The method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage according to claim 1, characterized in that: In step S2, based on the thermal resistance and heat capacity analogy principle, a dynamic prediction model for building heat transfer is constructed as follows: Among them, C ai represents the indoor air heat capacity, T ai represents the predicted value of indoor air temperature, t represents time, T w_in represents the inner surface temperature of the non-transparent enclosure structure, R w_in represents the heat transfer resistance between the inner surface of the non-transparent envelope and the indoor air, T r_in represents the inner surface temperature of the roof of the non-transparent enclosure structure, R r_in represents the heat transfer resistance between the non-transparent envelope roof and the indoor air, T im Indicates the internal heat storage temperature, R im represents the heat exchange resistance between the internal heat storage body and the indoor air, T f represents the radiant floor temperature, R f represents the heat transfer resistance between the radiant floor and the indoor air, Q p is the heat dissipation of people / appliances, Q win represents the heat transfer of the transparent enclosure structure, C w and C r are the heat capacities of the non-transparent enclosure wall and roof, T w_out and T r_out are the outer surface temperatures of the non-transparent enclosure wall and roof, T w_in and T r_in are the inner surface temperatures of the non-transparent enclosure wall and roof, R w and R r are the heat transfer resistance of the non-transparent enclosure wall and roof, R w_out and R r_out They represent the heat transfer resistance between the non-transparent enclosure wall and roof outer surface and the outdoor air, R w_in and R r_in They represent the heat transfer resistance between the inner surface of the non-transparent enclosure wall and roof and the indoor air; T ao represents the outdoor air temperature, Q solar_w and Q solar_r They represent the solar radiation heat gain of the non-transparent enclosure wall and roof surface, C im Indicates the heat capacity of the internal heat storage body, Q solar_im It refers to the amount of heat gained by solar radiation through windows and exchange heat with internal heat storage bodies.

5. The method for optimizing and scheduling building energy systems based on efficient utilization of active and passive energy storage according to claim 4 is characterized in that: In step S5, in the first stage, with the goal of minimizing system operating costs and minimizing indoor temperature deviation, a bionic algorithm is used to dynamically optimize the air source heat pump water supply temperature, and passive energy storage scheduling is achieved by combining building thermal inertia. The objective function of the first stage optimization scheduling is: J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 )=Min(a×C HVAC +ΔT ai ); Among them, J(T ws,set,0 ,T ws,set,2 ,...,T ws,set,23 ) represents the water temperature scheduling objective function value, T ws,set represents the optimal air source heat pump water temperature setting value, a represents the cost weight factor, and Respectively represent the minimum and maximum allowable values ​​of water temperature setting, ΔT ai is the root mean square deviation of room temperature, C HVAC The operating cost of the heating system for the next day is calculated as follows: P HVAC (t)=P ASHP (t)+P PUMP (t); Among them, P HVAC (t) represents the system power consumption, which is determined by the heat pump power consumption P ASHP (t) and water pump power consumption P PUMP (t) composition, P HVAC_grid (t) represents the grid power consumption of the heat pump system, P PV (t) is the photovoltaic power generation at time t, y TOU (t) is the time-of-use electricity price at time t; ΔT ai is the root mean square deviation of the room temperature, according to the indoor hourly temperature T ai (t) and the next day's indoor hourly set temperature T ai,set (t) is calculated as follows:

6. The method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage according to claim 5, characterized in that: In step S5, in the second stage, based on the unabsorbed photovoltaic power generation surplus in the first stage, a mixed integer linear programming algorithm is used to optimize the energy storage battery charging and discharging power, electric vehicle charging and discharging power, and adjustable household appliance operating power. The objective function of the second stage optimization scheduling is: J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j1,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 )=Min(C sys ); Among them, J(P c,0 ,P dc,0 ,P ev_c,0 ,P ev_dc,0 ,P ad,z,0 ,P sh,j,0 ...,P c,23 ,P dc,23 ,P ev_c,23 ,P ev_dc,23 ,P ad,z,23 ,P sh,j,23 ) represents the two-stage building energy system scheduling objective function, P c 、P dc Indicates the optimal battery charge and discharge power, P ev_c 、P ev_dc Indicates the optimal electric vehicle charging and discharging power, P ad,z represents the power consumption of the zth adjustable household appliance, P sh,j represents the power consumption of the household appliances of the jth type of transferred load; C sys The operating cost of the building energy system for the next day is solved as follows: Among them, P grid (t) represents the amount of electricity that needs to be purchased from the power grid after optimized scheduling, and p(t) represents the time-of-use electricity price at time t. The solution process establishes a power balance model as follows: P grid (t)+P dc (t)+P ev_dc (t)≥P HVAC (t); P grid (t)+P dc (t)+P ev_dc (t)+P pv_unused (t)≥P app (t)+P app_fle (t)+P c (t)+P ev_c (t); Among them, P pv_unused (t) represents the photovoltaic power that is not absorbed in one stage, P c (t), P ev_c (t) and P dc (t), P ev_dc (t) represents the charging and discharging power of the battery and the electric vehicle, P app (t) and P app_fle (t) represents the basic load and flexible load of other home appliances.

7. The method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage according to claim 6, characterized in that: P app_fle The solution of (t) is mainly divided into two types: power-adjustable equipment flexibility and load-transferable equipment flexibility: P app_fle (t)=P app_fle,ad (t)+P app_fle,sh (t); t work,s ≥t window,s t work,e ≤t window,e l work ≤l window ; Among them, N z (t) represents the number of adjustable power appliances of type z at time t, M j (t) represents the number of load-transferable household appliances of type j that can be adjusted at time t, P ad,z (t) represents the power that can be reduced by the zth power-adjustable household appliance at time t, P sh,j (t) represents the operating power of the jth load-transferable household appliance, U j (t) represents the flexible state of the jth load-transferable household appliance at time t, n represents the type of power-adjustable household appliance that can be adjusted at time t, m represents the type of load-transferable household appliance that can be adjusted at time t, P app_fle,ad (t) represents the flexible load that the adjustable household appliance can provide at time t, P app_fle,sh (t) represents the flexible load that the transferable household appliance can provide at time t, l work,shift Indicates the time period that can be transferred within the working window, t work,s Indicates the start time of equipment operation, t window,s Indicates the start time of the time window, t work,e Indicates the end time of equipment operation, t window,e Indicates the end time of the time window, l work Indicates working hours, l window,s Indicates the length of the time window; The flexibility calculation formula is as follows: Q app_fle (t)=Q app_fle,ad (t)+Q app_fle,sh (t); Among them, n z represents the heat dissipation coefficient of the zth power-adjustable household appliance, The cooling load coefficient of the zth power adjustable household appliance is C sh,j represents the operating power of the jth load-transferable household appliance, n1 represents the power consumption coefficient of the fluorescent lamp ballast, n2 represents the thermal insulation coefficient of the fluorescent lamp shade, n3 represents the motor utilization coefficient, n4 represents the motor load coefficient, n5 represents the heat dissipation coefficient of the electric heating equipment or electronic equipment, η represents the motor efficiency, Q app_fle (t) represents the total flexible load generated by the heat dissipation of the remaining appliances at time t, Q app_fle,ad (t) represents the total flexible load that the power-adjustable household appliances can provide due to heat dissipation at time t, Q app_fle,sh (t) represents the total flexible load that the power transferable household appliance can provide due to heat dissipation at time t, and COP represents the coefficient of performance of the household appliance.

8. The method for optimizing and scheduling a building energy system based on efficient utilization of active and passive energy storage according to claim 7, characterized in that: Battery charge and discharge power P c (t) and P dc(t) Subject to the following battery scheduling process constraints: Among them, SOC (t) represents the current energy state of the battery, SOC(t0) represents the energy state of the battery at the initial time t0, η c and η dc represents the battery charging and discharging efficiency, Δt represents the time interval, E Max is the rated capacity of the battery; During the dispatch process, the battery is subject to the maximum and minimum capacity constraints allowed, specifically: SOC min ≤SOC(t)≤SOC max ; Among them, SOC max and SOC min Indicates the maximum and minimum capacity of the battery; During the scheduling process, the battery is subject to the maximum and minimum power constraints allowed for charging and discharging, specifically: in, and Indicates the maximum charge and discharge power of the battery; Since the battery cannot be charged and discharged at the same time, a binary variable is introduced to constrain the charge and discharge states to be mutually exclusive: x c +x dc ≤1; Among them, x c and x dc Indicates the battery charge and discharge status 0 and 1; Charge and discharge cycle constraints: Where T represents the time range covered by the optimized scheduling.

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