Intelligent building distributed energy management method based on V2B technology

Abstract

The invention relates to an intelligent building distributed energy management method based on a V2B technology, which is based on an intelligent building based on the V2B technology, and comprises the following steps of: setting an electricity-carbon dual excitation mechanism of an electric vehicle, establishing an intelligent building virtual energy storage model considering a heat storage characteristic, establishing an intelligent building distributed energy management model, and solving the models, so as to obtain the intelligent building distributed energy management model. And finally outputting the operation cost and the output plan of each unit. According to the method, the electricity price and the carbon transaction base price are combined, an electric vehicle electricity and carbon dual excitation mechanism is established, the electric vehicle is excited to charge and discharge in order, new energy consumption is promoted, and the electricity burden at the peak moment of a power grid is reduced; and the building air conditioners are abstracted into virtual energy storage, an intelligent building virtual energy storage model considering the heat storage characteristic is established, the building air conditioners are fully transferred to participate in regulation and control, load transfer is promoted, and the power utilization burden at the peak moment of a power grid is further reduced.

Description

Technical Field

[0001] The present invention relates to the field of power control of power systems, and in particular to a distributed energy management method for intelligent buildings based on "vehicle-to-building" technology. Background Art

[0002] As my country's urbanization level continues to improve, the urban population continues to increase, and the number and scale of urban buildings are also expanding rapidly. The "China Building Energy Conservation Annual Development Research Report" shows that my country's total building energy consumption accounts for about 21% of the total social energy consumption. At the same time, the Chinese government and enterprises are actively promoting sustainable energy investment and research in the construction field. Many newly built smart power buildings have deployed distributed new energy units to reduce electricity costs and enhance environmental friendliness.

[0003] The Implementation Plan on Promoting High-quality Development of New Energy in the New Era, released in 2022, recommends further promoting the application of new energy in the construction field. At the same time, the popularization and development of EV (Electric Vehicle) has brought new opportunities and challenges to energy conservation and emission reduction in the construction field. The Implementation Opinions of the National Development and Reform Commission and Other Departments on Strengthening the Integration and Interaction of New Energy Vehicles and Power Grids, released in 2023, proposes to actively explore the bidirectional charging and discharging application mode of efficient integration of new energy vehicles with parks, buildings, and family residences.

[0004] V2B (Vehicle to Building) technology also came into being. This technology applies bidirectional inverter charging and discharging technology to commercial buildings, further expanding the scope of bidirectional charging.

[0005] With the promotion of policies, the rapid development of EVs has provided strong support for building a low-carbon society, not only significantly reducing carbon emissions in the transportation sector, but also providing new impetus for energy transformation. However, with the increase in EV charging demand, the power system faces unprecedented challenges, especially in the parking lots of office or commercial buildings where there are relatively dense charging piles. These uncontrolled charging loads cause the electricity load of the buildings to rise sharply, leading to problems such as rising demand electricity charges and overloaded distribution transformers.

[0006] The existing technology has studied the above-mentioned problems, taking commercial buildings as the research object, analyzing the detailed architecture of the V2B model, and expanding the use scenarios of V2B technology by analyzing factors such as seasonal solar energy output, building supply and demand, grid electricity prices, and the impact of the number of EVs on V2B capacity configuration. EVs can be used as mobile power sources to achieve power sharing in multiple buildings. It can be seen that through V2B technology, EVs can be used as adjustable resources in buildings to participate in building energy management.

[0007] At the same time, the existing technology research on EV dispatching is carried out from the traditional dispatching level, mainly using EV to smooth out the peak and fill the valley, and improve economic benefits. A two-stage optimization dispatching strategy for EV-containing microgrids based on real-time electricity prices and an EV charging and discharging optimization dispatching strategy based on dynamic time-of-use electricity prices are proposed. Through these dispatching strategies, and using a flexible electricity price mechanism, EVs are guided to achieve peak smoothing and valley filling.

[0008] However, after implementation, it was found that a single incentive measure and inappropriate electricity price setting are difficult to cope with the random fluctuations of load, and battery loss will also occur when EV is charged and discharged, which reduces the enthusiasm of EV users to participate in optimized scheduling. In addition, considering the cost of purchasing electricity and the cost of battery loss, taking the charging / discharging power in each time period and its power fluctuation in adjacent time periods as influencing factors to formulate an energy exchange plan, it does not take into account how to promote electric vehicles to participate in the carbon trading market to reflect the carbon emission reduction characteristics of electric vehicles. Therefore, the existing technology is more about using compensation for the loss cost caused by EV battery discharge, conducting research on the dynamic economic dispatch model in the power system, and establishing a dynamic mechanism to compensate for the battery degradation cost with carbon trading income; or considering the complete absorption of new energy power generation, formulating tiered electricity prices for equivalent net load levels and coordinating incentives with carbon quotas to guide EVs to respond appropriately to charging and discharging when there is surplus or shortage of new energy power generation.

[0009] However, the above solutions lack consideration of smart buildings as an important carrier of energy management. Although their heat storage characteristics and the combination of various energy equipment provide more possibilities for energy management, in addition to EV charging loads, smart buildings themselves have other controllable loads of considerable size, such as building air conditioners. Currently, most of them are based on flexible loads and have not fully mobilized building air conditioners to participate in regulation. Therefore, how to effectively utilize the energy equipment of smart buildings to achieve efficient use of energy and reduce carbon emissions still requires further research and exploration.

[0010] In summary, there is a need for a new V2B smart building energy management strategy under the dual incentives of EV electricity and carbon, which can add a carbon-oriented and adaptive time-of-use electricity price model. This strategy can maximize the use of EV's energy storage capacity and guide it to participate in the carbon trading market. At the same time, it combines the heat storage characteristics of smart buildings and the optimal scheduling of energy equipment to achieve efficient energy management and reduce carbon emissions, providing new ideas and methods for the further development of the field of building energy management. Summary of the invention

[0011] In order to solve the above problems, the present invention provides a distributed energy management method for intelligent buildings based on V2B technology, which is specifically described as follows:

[0012] A smart building based on V2B technology, including a commercial building, characterized by:

[0013] The commercial building uses photovoltaic power generation and micro gas turbines as distributed power sources and relies on electric refrigeration equipment for cooling. The main loads of the commercial building include equipment load, temperature control load and electric vehicle charging load. The intelligent building energy management system is used to monitor all electrical equipment and power generation equipment in real time, and to centrally optimize and dispatch flexible controllable loads. The temperature control load in the building is uniformly set and controlled by the intelligent building energy management system to control the temperature of the building and the operating status of the refrigeration equipment, and adjust the cooling load and the heating load.

[0014] The commercial building is equipped with a V2B charging station, which not only provides electric vehicle charging services but also serves as a node of V2B technology. The V2B charging station is connected to the smart building energy management system. The commercial building and the V2B charging station constitute a smart building distributed energy management architecture based on V2B technology.

[0015] According to a smart building based on V2B technology of the present invention, it is characterized in that each V2B charging pile of the V2B charging station supports two-way transmission of electric energy. When an electric vehicle is connected to the charging pile, the monitoring device records the access time and displays the V2B incentives and expected benefits on the charging pile screen. If the user chooses to obey the building regulation, it is necessary to upload the time of picking up the car and the expected charging power. The smart building energy management system optimizes and controls the charging and discharging process of the electric vehicle according to the electric vehicle information and charging demand information.

[0016] It should be noted that the electric energy returned by the V2B charging pile is only used within the range controlled by the smart building energy management system. There is only one-way energy transmission between the building and the power grid, and the return of building electric energy to the power grid is not supported.

[0017] A distributed energy management method for an intelligent building based on V2B technology, based on the above-mentioned intelligent building based on V2B technology, the specific steps are as follows:

[0018] 1) Establish a dual incentive mechanism for electric vehicles:

[0019] The incentive mechanism includes establishing a tiered carbon trading mechanism model and an adaptive time-of-use electricity price model. The tiered carbon trading mechanism model aggregates electric vehicle users, and the intelligent building energy management system acts as an agent to sell the carbon emission rights trading volume, thereby optimizing energy utilization and emission reduction effects on the load side. The adaptive time-of-use electricity price model dynamically adjusts the charging and discharging prices, and the model guides electric vehicle users to choose the appropriate time to charge, thereby achieving load balance and minimizing carbon emissions.

[0020] The tiered carbon trading mechanism is a policy tool for limiting and managing greenhouse gas emissions. It sets an emission cap for an enterprise or country and allocates a certain number of carbon quotas to achieve emission reduction targets. In order to optimize the load waveform of the distribution network, the present invention introduces the carbon trading mechanism into the distribution network system and allocates carbon quotas to EV users free of charge.

[0021] 2) Establish a virtual energy storage model for smart buildings that takes into account thermal storage characteristics:

[0022] The virtual energy storage model is based on the heat balance equation of the intelligent building, converts the heat storage characteristics of the building into energy storage characteristics, and establishes a virtual energy storage system model;

[0023] 3) Establish a distributed energy management model for smart buildings:

[0024] Integrate the virtual energy storage system model established in step 2) into the intelligent building distributed energy management model of this step. The management model realizes the effective operation of the intelligent building distributed energy management model by setting the objective function and setting the constraint conditions;

[0025] This step considers the acceptable temperature adjustment range of electric vehicles and users at the same time, and constructs a distributed energy management model for smart buildings. The main goal of this model is to minimize the overall operating cost of the building by comprehensively considering the electricity purchase cost, carbon trading cost, total user cost, and penalty cost for unsatisfied temperature adaptability.

[0026] 4) Solve the above model and finally output the operating cost and output plan of each unit.

[0027] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the establishment of a step-by-step carbon trading mechanism model in step 1) includes establishing a carbon quota model for electric vehicles, an actual carbon emission model for electric vehicles, and a step-by-step carbon emission trading model, which are specifically as follows:

[0028] 1.1) Establish a carbon quota model for electric vehicles:

[0029] This step uses the baseline method to calculate the carbon quota of electric vehicles. When the electric vehicle is charging, the carbon emissions of traditional fuel vehicles are used as the benchmark. By comparing the carbon emissions of electric vehicles with those of traditional fuel vehicles at the same driving distance, the initial carbon quota obtained by the electric vehicle user is determined. That is, the carbon quota of the electric vehicle at time t is the same as the carbon emissions of traditional fuel vehicles at the same driving distance. The specific formula is as follows:

[0030]

[0031] Where: P t EV,ch , Pt EV,dis are the charging and discharging power of the electric vehicle at time t;

[0032] μ ch , μ dis Respectively represent the charging and discharging status of electric vehicles;

[0033] is the initial carbon quota of electric vehicles at time t;

[0034] The distance that an electric vehicle can travel per unit of electricity;

[0035] The carbon emissions per unit distance traveled by traditional fuel vehicles;

[0036] E r The actual carbon emissions generated by each unit of electricity generated by non-renewable energy sources;

[0037] 1.2) Establishing the actual carbon emission model of electric vehicles:

[0038] This step uses the proportion of non-renewable energy and the proportion of the sum of the power generation system inside the building and the power purchased by the system to characterize the carbon emissions generated by the energy supply side when charging electric vehicles. In addition, this step assumes that the power purchased by the building from the upper power grid is generated by coal-fired units. The specific formula is as follows:

[0039]

[0040] P t non-res =P t buy +P t MT (36)

[0041]

[0042] Where: K r is the share of non-renewable energy generation in total electricity generation;

[0043] P t non-res is the non-renewable energy generation at time t;

[0044] P t res is the amount of renewable energy generated inside the building at time t;

[0045] P t buy The amount of electricity purchased by the building from the upper power grid at time t;

[0046] Pt MT is the amount of electricity generated by the gas turbine inside the building at time t;

[0047] The actual carbon emissions generated by charging an electric vehicle at time t;

[0048] In order to give full play to the low-carbon advantages of electric vehicles, it is necessary to ensure that their upstream energy supply mainly comes from renewable energy. If the electricity used by electric vehicles when charging comes entirely from the new energy output of the distribution network, the charging process of electric vehicles will not generate additional carbon emissions. Otherwise, the carbon emissions generated during the vehicle's driving process are simply transferred to the energy supply side. Therefore, in this step of establishing the actual carbon emission model of electric vehicles, the concept of the proportion of non-renewable energy is introduced to guide electric vehicles to choose to charge at the peak time of renewable energy output.

[0049] 1.3) Establish a tiered carbon emissions trading model:

[0050] In this step, when the actual carbon emissions of electric vehicles are greater than the free carbon quota, the intelligent building energy management system purchases excess carbon emission rights in the carbon trading market through the agent electric vehicle. Conversely, when the actual carbon emissions are less than the free carbon quota, the intelligent building energy management system sells the surplus carbon emission rights to obtain additional income. In the step-by-step carbon emission trading model of this step, carbon emissions are divided into several intervals, each of which corresponds to a different carbon price. The larger the carbon emissions, the higher the corresponding carbon trading price. The specific formula is as follows:

[0051]

[0052] Where: is the carbon emission trading amount of electric vehicles at time t;

[0053] C ev is the carbon trading price;

[0054] α and β are the carbon trading penalty and reward coefficients respectively;

[0055] C0 is the carbon trading base price;

[0056] L1 and L2 are respectively the positive and negative intervals of carbon emissions;

[0057] f carbon It is the tiered carbon trading cost.

[0058] The carbon emission trading amount of electric vehicles is the difference between the carbon emissions of fuel vehicles under the same driving distance and the carbon emissions generated by electric vehicles due to charging. In order to better manage and incentivize carbon emission trading of electric vehicles, this step adopts a stepped carbon emission trading model.

[0059] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the establishment of an adaptive time-of-use electricity price model in the step 1) is specifically to introduce additional carbon guidance on the basis of time-of-use electricity prices, combine the dynamic changes of carbon trading prices, set the rising and falling ranges of electricity prices, and dynamically adjust the charging and discharging prices. The specific formula is as follows:

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066] Where: X t It is an adaptive time-of-use electricity price;

[0067] X t0 is the initial electricity price based on the carbon trading mechanism;

[0068] L3 and L4 are the power values ​​in the range of decreasing and increasing electricity prices, respectively;

[0069] X0 is the initial electricity price under the carbon-free trading mechanism;

[0070] C X0 is the initial carbon trading base price;

[0071] α' and β' are the growth and decline coefficients of electricity prices, respectively;

[0072] P t buy is the electric power of the interaction between the smart building and the distribution network at time t;

[0073] P av , P min , P max They are the average, minimum and maximum values ​​of the daily load of the smart building respectively.

[0074] The adaptive time-of-use electricity price model in this step can effectively guide electric vehicle users to choose appropriate times for charging, thereby achieving load balance and minimization of carbon emissions.

[0075] According to formulas (5) to (12), it can be seen that the adaptive time-of-use electricity price under the carbon trading mechanism is closely related to the traditional electricity price model. When the carbon trading base price C0 changes, the initial electricity price X t0 The carbon trading cost penalty coefficient α will affect the peak electricity price, while the reward coefficient β in the reward-penalty ladder carbon price model will affect the valley electricity price. This step constructs an adaptive time-of-use electricity price model by linking the ladder carbon trading mechanism with the dynamic time-of-use electricity price to achieve more flexible and economical energy management.

[0076] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the heat balance equation of the intelligent building in the step 2) is obtained based on the heat storage characteristics of the building and the energy conservation, and the specific formula is as follows:

[0077]

[0078] Where: m is the indoor air quality;

[0079] c p is the specific heat capacity of air;

[0080] T in is the indoor temperature;

[0081] Q gain , Q loss Represent heat gain and loss respectively;

[0082] In this step, it is also necessary to consider heat transfer through walls and windows, solar radiation heat, and internal heat sources, as follows:

[0083] 2.1) Heat transfer from walls and windows:

[0084] The heat transfer of walls and windows can be expressed as:

[0085] Q wall =U wall A wall (T out -T in ) (47)

[0086] Q window =U window A window (T out -T in ) (48)

[0087] Where: Q wall , Q window They are the heat transferred from the wall, window and the outside;

[0088] U wall , U window are the heat transfer coefficients of walls and windows respectively;

[0089] A wall , A window are the areas of walls and windows respectively;

[0090] T in , T out are indoor and outdoor temperatures respectively;

[0091] Heat transfer through walls and windows is the main way for buildings to exchange heat with the outside world.

[0092] 2.2) Solar radiation heat:

[0093] The solar radiation heat is transmitted into the room through the windows, which can be expressed as:

[0094] Q solar =I solar η solar A window (49)

[0095] Where: Q solar The heat transferred by solar radiation;

[0096] I solar is the solar radiation intensity;

[0097] η solar is the solar radiation transmittance of the window;

[0098] Solar radiation is one of the important sources of heat gain inside buildings.

[0099] 2.3) Heat from internal heat sources:

[0100] The heat sources inside the building include the heat generated by people, equipment, etc., which can be expressed as:

[0101] Q internal =Q people +Q equipment (50)

[0102] Where: Q internal The heat generation power of the building as internal heat source;

[0103] Q people , Q equipment heat generated by personnel and equipment respectively;

[0104] Combining the above-mentioned heat transfer of walls and windows, solar radiation heat, and internal heat sources, this step establishes a quantitative relationship between indoor temperature and heat change, and rewrites the heat balance equation as follows:

[0105]

[0106] +I solar η solar A window +Q internal -Q EC

[0107] The thermal storage characteristics of smart buildings refer to the use of the building's own thermal inertia and structural design to optimize energy management, achieve energy conservation and emission reduction, and improve comfort. The heat balance equation in this step is the basis for modeling the thermal storage characteristics of smart buildings.

[0108] According to a distributed energy management method for intelligent buildings based on V2B technology of the present invention, it is characterized in that the virtual energy storage system model is established in the step 2), and the charging and discharging strategy of the virtual energy storage system is based on the dynamic adjustment of the indoor temperature. The charging process corresponds to when the electricity price is low or the renewable energy is sufficient, by increasing the operating power of the refrigeration equipment, reducing the indoor temperature, thereby reducing power consumption, and the discharging process is when the electricity price is high or the energy supply is tight, by reducing the operating power of the refrigeration equipment, increasing the indoor temperature, to compensate for the power demand, and the specific formula is as follows:

[0109] P t VSS =P t EC -P t EC ' (52)

[0110] Where: P t VSS is the charge and discharge power of the virtual energy storage system at time t, positive for charging and negative for discharging;

[0111] P t EC The electrical power for cooling the building to regulate the indoor temperature within the temperature comfort range;

[0112] P t EC 'Electric power for cooling buildings without regulating indoor temperature.

[0113] The virtual energy storage system model converts the thermal storage characteristics of buildings into energy storage characteristics, thereby achieving flexible scheduling and management of energy. The core idea of ​​the virtual energy storage system is to use the thermal inertia and regulation capabilities of buildings to simulate the charging and discharging process of batteries to achieve energy storage and release.

[0114] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the objective function set in step 3) is specifically formulated as follows:

[0115] f total =f buy +f carbon +f user +f penalty (53)

[0116] Where: F total The overall operating cost of the smart building;

[0117] f buy The cost of purchasing electricity;

[0118] f carbon For carbon trading costs;

[0119] f user Cost to users;

[0120] f penalty is the penalty cost for not meeting the temperature adaptability;

[0121] The above electricity purchase cost f buy The electricity purchase cost of a smart building is the cost of purchasing electricity from the power grid. The calculation formula for the electricity purchase cost is:

[0122]

[0123] Where: ρ(t) is the electricity purchase price;

[0124] The above user cost f user The charging cost generated when electric vehicles participate in the optimized dispatching, the calculation formula of the user charging cost is:

[0125]

[0126] The penalty cost f for not satisfying the above temperature adaptability penalty The calculation formula is:

[0127]

[0128] Where: λ is the penalty factor, which can be regarded as the user's sensitivity to temperature comfort. The penalty factor λ can be selected according to the user's different sensitivity to temperature. The larger λ is, the greater the penalty brought by virtual energy storage will be, and vice versa. The penalty brought by virtual energy storage will be smaller.

[0129] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the setting constraint conditions in step 3) include electric power balance constraint, cold power balance constraint, heat balance constraint, equipment operation constraint and building temperature upper and lower limit constraint, a total of 5 constraint conditions, which are as follows:

[0130] 3.1) Electric power balance constraints:

[0131] The electrical power demand within a smart building must be balanced with the supply, i.e.:

[0132] P t buy +P t PV +P t MT +P t EV,dis =P t load +P t EV,ch +P t EC (57)

[0133] Where: P t load is the electrical load at time t;

[0134] 3.2) Cold power balance constraint:

[0135] The cooling power demand and supply inside the smart building must be balanced to ensure the appropriate indoor temperature, that is:

[0136]

[0137] Where: is the cooling load at time t; η is the cooling power;

[0138] 3.3) Thermal balance constraints:

[0139] The heat balance equation expressed by differential equation is discretized into difference equation for processing, as follows:

[0140]

[0141] The thermal balance constraints of smart buildings take into account the dynamic process of heat dissipation and temperature change. Since the heat dissipation and temperature change processes of buildings are relatively slow, the thermal balance equations represented by differential equations can be discretized into difference equations for processing. This processing method not only simplifies the computational complexity, but also makes the energy management optimization problem of smart buildings easier to solve.

[0142] 3.4) Equipment operation constraints:

[0143] In this step, the equipment operation constraints include gas turbine output constraints, electric refrigerator output constraints, photovoltaic output constraints, and electric vehicle charging and discharging constraints, which are as follows:

[0144] 3.41) Gas turbine output constraints:

[0145]

[0146] Where: P t MT is the electrical power output of the gas turbine at time t;

[0147] η e is the electrical efficiency of the gas turbine;

[0148] P t gas is the natural gas power input to the gas turbine at time t;

[0149] are the upper and lower limits of the gas turbine output electric power respectively;

[0150] They are the upper and lower limits of the gas turbine's climbing output respectively;

[0151] 3.42) Electric refrigeration machine output constraints:

[0152]

[0153] Where: P t EC is the electric power input to the electric refrigerator at time t;

[0154] are the upper and lower limits of the input electric refrigerator power respectively;

[0155] 3.43) Photovoltaic output constraints:

[0156] 0≤P t PV ≤P t PV.pre (62)

[0157] Where: P t PV , P t PV.pre are the actual value and predicted value of photovoltaic output at time t respectively;

[0158] 3.44) Electric vehicle charging and discharging constraints:

[0159]

[0160] Where: P t EV,ch , P t EV,dis are the charging and discharging power of the electric vehicle at time t respectively;

[0161] are the charging and discharging power of the electric vehicle at time t respectively;

[0162] is the battery capacity of the electric vehicle at time t;

[0163] The upper and lower limits of the battery power of electric vehicles;

[0164] η ch , η dis The charging and discharging efficiency of electric vehicles;

[0165] E ESS is the total capacity of the electric vehicle battery;

[0166] μ ch and μ dis Respectively represent the charging and discharging status of electric vehicles;

[0167] 3.5) Building temperature upper and lower limit constraints:

[0168] In this step, the indoor temperature of the building needs to be maintained within the predetermined upper and lower limits, as follows:

[0169] T min ≤T in,t ≤T max (64)

[0170] Where: T min , T max are the minimum and maximum indoor temperatures respectively.

[0171] According to a V2B technology-based intelligent building distributed energy management method of the present invention, it is characterized in that the model solving process of step 4) is as follows:

[0172] 4.1) Collect new energy output and load forecast data and input them into the model;

[0173] 4.2) Input reference formula (1) to formula (13) into the simulation platform to establish the electric-carbon dual incentive mechanism model for electric vehicles;

[0174] 4.3) Input reference formula (14) to formula (20) into the simulation platform to establish a virtual energy storage model of the intelligent building taking into account the thermal storage characteristics;

[0175] 4.4) According to formula (21) to formula (32), the calculation formulas involved in the intelligent building distributed energy management model are input into the simulation platform. The various resource models and constraints refer to formula (25) to formula (32). It should be noted that if other types of resources need to be aggregated, the calculation formula can be directly added in the corresponding position;

[0176] 4.5) Based on the output and load forecast data of the new energy units, the objective function of the intelligent building distributed energy management model is input into the simulation platform according to formula (21) to determine the final unit output plan;

[0177] 4.6) Use the solver in the simulation platform or the programmed mixed integer linear programming algorithm to solve;

[0178] 4.7) Determine whether the convergence condition is met. If so, directly output the operating cost and the output plan of each unit. If not, return to step 4.5) to continue solving. In this step, the convergence condition can be formulated or changed by yourself.

[0179] The distributed energy management method for intelligent buildings based on V2B technology of the present invention achieves the following beneficial effects:

[0180] 1) The distributed energy management method of intelligent buildings based on V2B technology of the present invention combines the electricity price with the carbon trading base price to establish a dual incentive mechanism for electric vehicles and carbon, encourages electric vehicles to charge and discharge in an orderly manner, promotes the consumption of new energy, and reduces the power consumption burden of the power grid at peak times;

[0181] 2) The distributed energy management method for intelligent buildings based on V2B technology of the present invention abstracts the building air conditioner into virtual energy storage, establishes an intelligent building virtual energy storage model considering the heat storage characteristics, fully mobilizes the building air conditioner to participate in regulation, promotes load transfer, and further reduces the power consumption burden of the power grid during peak hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0182] Figure 1 A schematic diagram of an energy management architecture of a distributed energy management method for an intelligent building based on V2B technology of the present invention;

[0183] Figure 2 This is a model solving flow chart of step 4) of a distributed energy management method for an intelligent building based on V2B technology of the present invention. DETAILED DESCRIPTION

[0184] The following is a further description of the technical means, creative features, objectives and effects achieved by the V2B technology-based intelligent building distributed energy management method of the present invention in conjunction with the accompanying drawings and embodiments.

[0185] Example

[0186] like Figure 1As shown, a smart building based on V2B technology includes a commercial building. The commercial building uses photovoltaic power generation and micro gas turbines as distributed power sources and relies on electric refrigeration equipment for cooling. The main load of the commercial building includes equipment load, temperature control load and electric vehicle charging load. The smart building energy management system is used to monitor all power-consuming equipment and power generation equipment in real time, and to centrally optimize and dispatch flexible controllable loads. The temperature control load in the building is uniformly set and controlled by the smart building energy management system to control the temperature of the building and the operating status of the refrigeration equipment, and adjust the cooling load and the heating load.

[0187] Commercial buildings are equipped with V2B charging stations, which not only provide electric vehicle charging services but also serve as nodes of V2B technology. The V2B charging stations are connected to the smart building energy management system. The commercial buildings and V2B charging stations form a smart building distributed energy management architecture based on V2B technology.

[0188] Each V2B charging pile of the V2B charging station supports two-way transmission of electric energy. When an electric vehicle is connected to a charging pile, the monitoring device records the connection time and displays the V2B incentives and expected benefits on the charging pile screen. If the user chooses to obey the building regulation, he needs to upload the time of picking up the car and the expected charging power. The intelligent building energy management system optimizes and controls the charging and discharging process of the electric vehicle based on the electric vehicle information and charging demand information.

[0189] A distributed energy management method for an intelligent building based on V2B technology, based on the above-mentioned intelligent building based on V2B technology, the specific steps are as follows:

[0190] 1) Establish a dual incentive mechanism for electric vehicles:

[0191] The incentive mechanism includes establishing a tiered carbon trading mechanism model and an adaptive time-of-use electricity price model. The tiered carbon trading mechanism model aggregates electric vehicle users, and the intelligent building energy management system acts as an agent to sell the carbon emission rights trading volume, thereby optimizing energy utilization and emission reduction effects on the load side. The adaptive time-of-use electricity price model dynamically adjusts the charging and discharging prices, and the model guides electric vehicle users to choose the appropriate time to charge, thereby achieving load balance and minimizing carbon emissions.

[0192] 2) Establish a virtual energy storage model for smart buildings that takes into account thermal storage characteristics:

[0193] The virtual energy storage model is based on the heat balance equation of the intelligent building, converts the heat storage characteristics of the building into energy storage characteristics, and establishes a virtual energy storage system model;

[0194] 3) Establish a distributed energy management model for smart buildings:

[0195] Integrate the virtual energy storage system model established in step 2) into the intelligent building distributed energy management model of this step. The management model realizes the effective operation of the intelligent building distributed energy management model by setting the objective function and setting the constraint conditions;

[0196] 4) Solve the above model and finally output the operating cost and output plan of each unit.

[0197] The establishment of a tiered carbon trading mechanism model in step 1) includes establishing a carbon quota model for electric vehicles, an actual carbon emission model for electric vehicles, and a tiered carbon emission trading model, which are specifically as follows:

[0198] 1.1) Establish a carbon quota model for electric vehicles:

[0199] This step uses the baseline method to calculate the carbon quota of electric vehicles. When the electric vehicle is charging, the carbon emissions of traditional fuel vehicles are used as the benchmark. By comparing the carbon emissions of electric vehicles with those of traditional fuel vehicles at the same driving distance, the initial carbon quota obtained by the electric vehicle user is determined. That is, the carbon quota of the electric vehicle at time t is the same as the carbon emissions of traditional fuel vehicles at the same driving distance. The specific formula is as follows:

[0200]

[0201] Where: P t EV,ch , P t EV,dis are the charging and discharging power of the electric vehicle at time t;

[0202] μ ch , μ dis Respectively represent the charging and discharging status of electric vehicles;

[0203] is the initial carbon quota of electric vehicles at time t;

[0204] The distance that an electric vehicle can travel per unit of electricity;

[0205] The carbon emissions per unit distance traveled by traditional fuel vehicles;

[0206] E r The actual carbon emissions generated by each unit of electricity generated by non-renewable energy sources;

[0207] 1.2) Establishing the actual carbon emission model of electric vehicles:

[0208] This step uses the proportion of non-renewable energy and the proportion of the sum of the power generation system inside the building and the power purchased by the system to characterize the carbon emissions generated by the energy supply side when charging electric vehicles. In addition, this step assumes that the power purchased by the building from the upper power grid is generated by coal-fired units. The specific formula is as follows:

[0209]

[0210] P t non-res =P t buy +P t MT (68)

[0211]

[0212] Where: K r is the share of non-renewable energy generation in total electricity generation;

[0213] P t non-res is the non-renewable energy generation at time t;

[0214] P t res is the amount of renewable energy generated inside the building at time t;

[0215] P t buy The amount of electricity purchased by the building from the upper power grid at time t;

[0216] P t MT is the amount of electricity generated by the gas turbine inside the building at time t;

[0217] The actual carbon emissions generated by charging an electric vehicle at time t;

[0218] 1.3) Establish a tiered carbon emissions trading model:

[0219] In this step, when the actual carbon emissions of electric vehicles are greater than the free carbon quota, the intelligent building energy management system purchases excess carbon emission rights in the carbon trading market through the agent electric vehicle. Conversely, when the actual carbon emissions are less than the free carbon quota, the intelligent building energy management system sells the surplus carbon emission rights to obtain additional income. In the step-by-step carbon emission trading model of this step, carbon emissions are divided into several intervals, each of which corresponds to a different carbon price. The larger the carbon emissions, the higher the corresponding carbon trading price. The specific formula is as follows:

[0220]

[0221]

[0222] Where: is the carbon emission trading amount of electric vehicles at time t;

[0223] C ev is the carbon trading price;

[0224] α and β are the carbon trading penalty and reward coefficients respectively;

[0225] C0 is the carbon trading base price;

[0226] L1 and L2 are respectively the positive and negative intervals of carbon emissions;

[0227] f carbon It is the tiered carbon trading cost.

[0228] The establishment of an adaptive time-of-use electricity price model in step 1) is specifically to introduce additional carbon guidance on the basis of time-of-use electricity prices, combine the dynamic changes of carbon trading prices, set the rising and falling ranges of electricity prices, and dynamically adjust the charging and discharging prices. The specific formula is as follows:

[0229]

[0230]

[0231]

[0232]

[0233]

[0234]

[0235] Where: X t It is an adaptive time-of-use electricity price;

[0236] X t0 is the initial electricity price based on the carbon trading mechanism;

[0237] L3 and L4 are the power values ​​in the range of decreasing and increasing electricity prices, respectively;

[0238] X0 is the initial electricity price under the carbon-free trading mechanism;

[0239] C X0 is the initial carbon trading base price;

[0240] α' and β' are the growth and decline coefficients of electricity prices, respectively;

[0241] P t buy is the electric power of the interaction between the smart building and the distribution network at time t;

[0242] P av , P min , P max They are the average, minimum and maximum values ​​of the daily load of the smart building respectively.

[0243] The heat balance equation of the smart building in step 2) is obtained based on the heat storage characteristics of the building and the law of energy conservation. The specific formula is as follows:

[0244]

[0245] Where: m is the indoor air quality;

[0246] c p is the specific heat capacity of air;

[0247] T in is the indoor temperature;

[0248] Q gain , Q loss Represent heat gain and loss respectively;

[0249] In this step, it is also necessary to consider heat transfer through walls and windows, solar radiation heat, and internal heat sources, as follows:

[0250] 2.1) Heat transfer from walls and windows:

[0251] The heat transfer of walls and windows can be expressed as:

[0252] Q wall =U wall A wall (T out -T in ) (79)

[0253] Q window =U window A window (T out -T in ) (80)

[0254] Where: Q wall , Q window They are the heat transferred from the wall, window and the outside;

[0255] U wall , U window are the heat transfer coefficients of walls and windows respectively;

[0256] A wall , A window are the areas of walls and windows respectively;

[0257] T in , T out are indoor and outdoor temperatures respectively;

[0258] 2.2) Solar radiation heat:

[0259] The solar radiation heat is transmitted into the room through the windows, which can be expressed as:

[0260] Q solar =I solar η solar A window (81)

[0261] Where: Q solar The heat transferred by solar radiation;

[0262] I solar is the solar radiation intensity;

[0263] η solar is the solar radiation transmittance of the window;

[0264] 2.3) Heat from internal heat sources:

[0265] The heat sources inside the building include the heat generated by people, equipment, etc., which can be expressed as:

[0266] Q internal =Q people +Q equipment (82)

[0267] Where: Q internal The heat generation power of the building as internal heat source;

[0268] Q people , Q equipment heat generated by personnel and equipment respectively;

[0269] Combining the above-mentioned heat transfer of walls and windows, solar radiation heat, and internal heat sources, this step establishes a quantitative relationship between indoor temperature and heat change, and rewrites the heat balance equation as follows:

[0270]

[0271] In step 2), a virtual energy storage system model is established. The charging and discharging strategy of the virtual energy storage system is based on the dynamic adjustment of the indoor temperature. The charging process corresponds to reducing the power consumption by increasing the operating power of the refrigeration equipment when the electricity price is low or the renewable energy is sufficient. The discharging process is to reduce the operating power of the refrigeration equipment and increase the indoor temperature when the electricity price is high or the energy supply is tight, so as to compensate for the power demand. The specific formula is as follows:

[0272] P tVSS =P t EC -P t EC ' (84)

[0273] Where: P t VSS is the charge and discharge power of the virtual energy storage system at time t, positive for charging and negative for discharging;

[0274] P t EC The electrical power for cooling the building to regulate the indoor temperature within the temperature comfort range;

[0275] P t EC 'Electric power for cooling buildings without regulating indoor temperature.

[0276] The specific formula for setting the objective function in step 3) is as follows:

[0277] f total =f buy +f carbon +f user +f penalty (85)

[0278] Where: F total The overall operating cost of smart buildings;

[0279] f buy The cost of purchasing electricity;

[0280] f carbon For carbon trading costs;

[0281] f user Cost to users;

[0282] f penalty is the penalty cost for not meeting the temperature adaptability;

[0283] The above electricity purchase cost f buy The electricity purchase cost of a smart building is the cost of purchasing electricity from the power grid. The calculation formula for the electricity purchase cost is:

[0284]

[0285] Where: ρ(t) is the electricity purchase price;

[0286] The above user cost f user The charging cost generated when electric vehicles participate in the optimized dispatching, the calculation formula of the user charging cost is:

[0287]

[0288] The penalty cost f for not satisfying the above temperature adaptability penalty The calculation formula is:

[0289]

[0290] Where: λ is the penalty factor, which can be regarded as the user's sensitivity to temperature comfort. The penalty factor λ can be selected according to the user's different sensitivity to temperature. The larger λ is, the greater the penalty brought by virtual energy storage will be, and vice versa. The penalty brought by virtual energy storage will be smaller.

[0291] The setting constraints in step 3) include electric power balance constraints, cooling power balance constraints, heat balance constraints, equipment operation constraints, and building temperature upper and lower limit constraints, a total of 5 constraints, as follows:

[0292] 3.1) Electric power balance constraints:

[0293] The electrical power demand within a smart building must be balanced with the supply, i.e.:

[0294] P t buy +P t PV +P t MT +P t EV,dis =P t load +P t EV,ch +P t EC (89)

[0295] Where: P t load is the electrical load at time t;

[0296] 3.2) Cold power balance constraint:

[0297] The cooling power demand and supply inside the smart building must be balanced to ensure the appropriate indoor temperature, that is:

[0298]

[0299] Where: is the cooling load at time t; η is the cooling power;

[0300] 3.3) Thermal balance constraints:

[0301] The heat balance equation expressed by differential equation is discretized into difference equation for processing, as follows:

[0302] △t[Uwall A wall (T out,t -T in,t )+U window A window (T out,t -T in,t )+I solar η solar A window (91)

[0303] +Q internal,t -Q EC,t ]-mc p (T in,t+1 -T in,t )=0

[0304] 3.4) Equipment operation constraints:

[0305] In this step, the equipment operation constraints include gas turbine output constraints, electric refrigerator output constraints, photovoltaic output constraints, and electric vehicle charging and discharging constraints, which are as follows:

[0306] 3.41) Gas turbine output constraints:

[0307]

[0308] Where: P t MT is the electrical power output of the gas turbine at time t;

[0309] η e is the electrical efficiency of the gas turbine;

[0310] P t gas is the natural gas power input to the gas turbine at time t;

[0311] are the upper and lower limits of the gas turbine output electric power respectively;

[0312] They are the upper and lower limits of the gas turbine's climbing output respectively;

[0313] 3.42) Electric refrigeration machine output constraints:

[0314]

[0315] Where: P t EC is the electric power input to the electric refrigerator at time t;

[0316] are the upper and lower limits of the input electric refrigerator power respectively;

[0317] 3.43) Photovoltaic output constraints:

[0318] 0≤P t PV ≤P t PV.pre (94)

[0319] Where: P t PV , P t PV.pre are the actual value and predicted value of photovoltaic output at time t respectively;

[0320] 3.44) Electric vehicle charging and discharging constraints:

[0321]

[0322] Where: P t EV,ch , P t EV,dis are the charging and discharging power of the electric vehicle at time t respectively;

[0323] are the charging and discharging power of the electric vehicle at time t respectively;

[0324] is the battery capacity of the electric vehicle at time t;

[0325] The upper and lower limits of the battery power of electric vehicles;

[0326] η ch , η dis The charging and discharging efficiency of electric vehicles;

[0327] E ESS is the total capacity of the electric vehicle battery;

[0328] μ ch and μ dis Respectively represent the charging and discharging status of electric vehicles;

[0329] 3.5) Building temperature upper and lower limit constraints:

[0330] In this step, the indoor temperature of the building needs to be maintained within the predetermined upper and lower limits, as follows:

[0331] T min ≤T in,t ≤T max (96)

[0332] Where: T min , T max are the minimum and maximum indoor temperatures respectively.

[0333] like Figure 2 As shown, the model solving process of step 4) is as follows:

[0334] 4.1) Collect new energy output and load forecast data and input them into the model;

[0335] 4.2) Input reference formula (1) to formula (13) into the simulation platform to establish the electric-carbon dual incentive mechanism model for electric vehicles;

[0336] 4.3) Input reference formula (14) to formula (20) into the simulation platform to establish a virtual energy storage model of the intelligent building taking into account the thermal storage characteristics;

[0337] 4.4) According to formula (21) to formula (32), the calculation formulas involved in the intelligent building distributed energy management model are input into the simulation platform. The various resource models and constraints refer to formula (25) to formula (32). It should be noted that if other types of resources need to be aggregated, the calculation formula can be directly added in the corresponding position;

[0338] 4.5) Based on the output and load forecast data of the new energy units, the objective function of the intelligent building distributed energy management model is input into the simulation platform according to formula (21) to determine the final unit output plan;

[0339] 4.6) Use the solver in the simulation platform or the programmed mixed integer linear programming algorithm to solve;

[0340] 4.7) Determine whether the convergence condition is met. If so, directly output the operating cost and the output plan of each unit. If not, return to step 4.5) to continue solving. In this step, the convergence condition can be formulated or changed by yourself.

[0341] The present invention provides a distributed energy management method for smart buildings based on V2B technology, which combines electricity prices with carbon trading base prices to establish a dual incentive mechanism for electric vehicles and carbon, thereby encouraging electric vehicles to charge and discharge in an orderly manner and promoting the consumption of new energy, thereby reducing the electricity consumption burden of the power grid during peak hours. The present invention also abstracts building air conditioners into virtual energy storage, establishes a virtual energy storage model for smart buildings that takes into account heat storage characteristics, fully mobilizes building air conditioners to participate in regulation, promotes load transfer, and further reduces the electricity consumption burden of the power grid during peak hours.

[0342] The present invention is applicable to the field of distributed energy management of various intelligent buildings.

[0343] At the same time, those skilled in the art should recognize that the above embodiments are only used to illustrate the present application, and are not used to limit the present application. As long as they are within the spirit of the present application, any changes or modifications to the above embodiments will fall within the scope of the claims of the present application.

Claims

1. A smart building based on V2B technology, including a commercial building, characterized by: The commercial building uses photovoltaic power generation and micro gas turbines as distributed power sources and relies on electric refrigeration equipment for cooling. The main loads of the commercial building include equipment load, temperature control load and electric vehicle charging load. The intelligent building energy management system is used to monitor all electrical equipment and power generation equipment in real time, and to centrally optimize and dispatch flexible controllable loads. The temperature control load in the building is uniformly set and controlled by the intelligent building energy management system to control the temperature of the building and the operating status of the refrigeration equipment, and adjust the cooling load and the heating load. The commercial building is equipped with a V2B charging station, which not only provides electric vehicle charging services but also serves as a node of V2B technology. The V2B charging station is connected to the smart building energy management system. The commercial building and the V2B charging station constitute a smart building distributed energy management architecture based on V2B technology.

2. The intelligent building based on V2B technology as claimed in claim 1, characterized in that: Each V2B charging pile of the V2B charging station supports two-way transmission of electric energy. When an electric vehicle is connected to a charging pile, a monitoring device records the connection time and displays V2B incentives and expected benefits on the charging pile screen. If the user chooses to obey the building regulation, he needs to upload the time of picking up the car and the expected charging power. The intelligent building energy management system optimizes and controls the charging and discharging process of the electric vehicle based on the electric vehicle information and charging demand information.

3. A distributed energy management method for an intelligent building based on V2B technology, based on the intelligent building based on V2B technology of claim 1 or claim 2, wherein the specific steps are as follows: 1) Establish a dual incentive mechanism for electric vehicles: The incentive mechanism includes establishing a tiered carbon trading mechanism model and an adaptive time-of-use electricity price model, among which: The tiered carbon trading mechanism model aggregates electric vehicle users, and the intelligent building energy management system acts as an agent to sell the carbon emission rights trading volume, thereby optimizing energy utilization and emission reduction effects on the load side. The adaptive time-of-use electricity price model dynamically adjusts the charging and discharging prices, and the model guides electric vehicle users to choose the appropriate time to charge, thereby achieving load balance and minimizing carbon emissions. 2) Establish a virtual energy storage model for smart buildings that takes into account thermal storage characteristics: The virtual energy storage model is based on the heat balance equation of the intelligent building, converts the heat storage characteristics of the building into energy storage characteristics, and establishes a virtual energy storage system model; 3) Establish a distributed energy management model for smart buildings: Integrate the virtual energy storage system model established in step 2) into the intelligent building distributed energy management model of this step. The management model realizes the effective operation of the intelligent building distributed energy management model by setting the objective function and setting the constraint conditions; 4) Solve the above model and finally output the operating cost and output plan of each unit.

4. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The step 1) of establishing a step-by-step carbon trading mechanism model includes establishing a carbon quota model for electric vehicles, an actual carbon emission model for electric vehicles, and a step-by-step carbon emission trading model, which are specifically as follows: 1.1) Establish a carbon quota model for electric vehicles: This step uses the baseline method to calculate the carbon quota of electric vehicles. When the electric vehicle is charging, the carbon emissions of traditional fuel vehicles are used as the benchmark. By comparing the carbon emissions of electric vehicles with those of traditional fuel vehicles at the same driving distance, the initial carbon quota obtained by the electric vehicle user is determined. That is, the carbon quota of the electric vehicle at time t is the same as the carbon emissions of traditional fuel vehicles at the same driving distance. The specific formula is as follows: Where: P t EV,ch , P t EV,dis are the charging and discharging power of the electric vehicle at time t; μ ch , μ dis Respectively represent the charging and discharging status of electric vehicles; is the initial carbon quota of electric vehicles at time t; The distance that an electric vehicle can travel per unit of electricity; The carbon emissions per unit distance traveled by traditional fuel vehicles; E r The actual carbon emissions generated by each unit of electricity generated by non-renewable energy sources; 1.2) Establishing the actual carbon emission model of electric vehicles: This step uses the proportion of non-renewable energy and the proportion of the sum of the power generation system inside the building and the power purchased by the system to characterize the carbon emissions generated by the energy supply side when charging electric vehicles. In addition, this step assumes that the power purchased by the building from the upper power grid is generated by coal-fired units. The specific formula is as follows: P t non-res =P t buy +P t MT (4) Where: K r is the share of non-renewable energy generation in total electricity generation; P t non-res is the non-renewable energy generation at time t; P t res is the amount of renewable energy generated inside the building at time t; P t buy The amount of electricity purchased by the building from the upper power grid at time t; P t MT is the amount of electricity generated by the gas turbine inside the building at time t; The actual carbon emissions generated by charging an electric vehicle at time t; 1.3) Establish a tiered carbon emissions trading model: In this step, when the actual carbon emissions of electric vehicles are greater than the free carbon quota, the intelligent building energy management system purchases excess carbon emission rights in the carbon trading market through the agent electric vehicle. Conversely, when the actual carbon emissions are less than the free carbon quota, the intelligent building energy management system sells the surplus carbon emission rights to obtain additional income. In the step-by-step carbon emission trading model of this step, carbon emissions are divided into several intervals, each of which corresponds to a different carbon price. The larger the carbon emissions, the higher the corresponding carbon trading price. The specific formula is as follows: Where: is the carbon emission trading amount of electric vehicles at time t; C ev is the carbon trading price; α and β are the carbon trading penalty and reward coefficients respectively; C0 is the carbon trading base price; L1 and L2 are respectively the positive and negative intervals of carbon emissions; f carbon It is the tiered carbon trading cost.

5. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The establishment of an adaptive time-of-use electricity price model in step 1) is specifically to introduce additional carbon guidance on the basis of time-of-use electricity prices, combine the dynamic changes of carbon trading prices, set the rising and falling ranges of electricity prices, and dynamically adjust the charging and discharging prices. The specific formula is as follows: Where: X t It is an adaptive time-of-use electricity price; X t0 is the initial electricity price based on the carbon trading mechanism; L3 and L4 are the power values ​​in the range of decreasing and increasing electricity prices, respectively; X0 is the initial electricity price under the carbon-free trading mechanism; C X0 is the initial carbon trading base price; α' and β' are the growth and decline coefficients of electricity prices, respectively; P t buy is the electric power of the interaction between the smart building and the distribution network at time t; P av , P min , P max They are the average, minimum and maximum values ​​of the daily load of the smart building respectively.

6. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The heat balance equation of the smart building in step 2) is obtained based on the heat storage characteristics of the building and the law of energy conservation. The specific formula is as follows: Where: m is the indoor air quality; c p is the specific heat capacity of air; T in is the indoor temperature; Q gain , Q loss Represent heat gain and loss respectively; In this step, it is also necessary to consider heat transfer through walls and windows, solar radiation heat, and internal heat sources, as follows: 2.1) Heat transfer from walls and windows: The heat transfer of walls and windows can be expressed as: Q wall =U wall A wall (T out -T in ) (15) Q window =U window A window (T out -T in ) (16) Where: Q wall , Q window They are the heat transferred from the wall, window and the outside; U wall , U window are the heat transfer coefficients of walls and windows respectively; A wall , A window are the areas of walls and windows respectively; T in , T out are indoor and outdoor temperatures respectively; 2.2) Solar radiation heat: The solar radiation heat is transmitted into the room through the windows, which can be expressed as: Q solar =I solar or solar A window (17) Where: Q solar The heat transferred by solar radiation; I solar is the solar radiation intensity; η solar is the solar radiation transmittance of the window; 2.3) Heat from internal heat sources: The heat sources inside the building include the heat generated by people, equipment, etc., which can be expressed as: Q internal =Q people +Q equipment (18) Where: Q internal The heat generation power of the building as internal heat source; Q people , Q equipment heat generated by personnel and equipment respectively; Combining the above-mentioned heat transfer of walls and windows, solar radiation heat, and internal heat sources, this step establishes a quantitative relationship between indoor temperature and heat change, and rewrites the heat balance equation as follows:

7. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The virtual energy storage system model is established in step 2), and the charging and discharging strategy of the virtual energy storage system is based on the dynamic adjustment of the indoor temperature. The charging process corresponds to reducing the power consumption by increasing the operating power of the refrigeration equipment when the electricity price is low or the renewable energy is sufficient, while the discharging process is to reduce the operating power of the refrigeration equipment and increase the indoor temperature when the electricity price is high or the energy supply is tight, so as to compensate for the power demand. The specific formula is as follows: P t VSS =P t EC -P t EC ' (20) Where: P t VSS is the charge and discharge power of the virtual energy storage system at time t, positive for charging and negative for discharging; P t EC The electrical power for cooling the building to regulate the indoor temperature within the temperature comfort range; P t EC 'Electric power for cooling buildings without regulating indoor temperature.

8. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The specific formula for setting the objective function in step 3) is as follows: min F total =f buy +f carbon +f user +f penalty (21) Where: F total The overall operating cost of smart buildings; f buy The cost of purchasing electricity; f carbon For carbon trading costs; f user Cost to users; f penalty is the penalty cost for not meeting the temperature adaptability; The above electricity purchase cost f buy The electricity purchase cost of a smart building is the cost of purchasing electricity from the power grid. The calculation formula for the electricity purchase cost is: Where: ρ(t) is the electricity purchase price; The above user cost f user The charging cost generated when electric vehicles participate in the optimized dispatching, the calculation formula of the user charging cost is: The penalty cost f for not satisfying the above temperature adaptability penalty The calculation formula is: Where: λ is the penalty factor, which can be regarded as the user's sensitivity to temperature comfort. The penalty factor λ can be selected according to the user's different sensitivity to temperature. The larger λ is, the greater the penalty brought by virtual energy storage will be, and vice versa. The penalty brought by virtual energy storage will be smaller.

9. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The setting constraints in step 3) include electric power balance constraints, cooling power balance constraints, heat balance constraints, equipment operation constraints, and building temperature upper and lower limit constraints, a total of 5 constraints, as follows: 3.1) Electric power balance constraints: The electrical power demand within a smart building must be balanced with the supply, i.e.: P t buy +P t PV +P t MT +P t EV,dis =P t load +P t EV,ch +P t EC (25) Where: P t load is the electrical load at time t; 3.2) Cold power balance constraint: The cooling power demand and supply inside the smart building must be balanced to ensure the appropriate indoor temperature, that is: Where: is the cooling load at time t; η is the cooling power; 3.3) Thermal balance constraints: The heat balance equation expressed by differential equation is discretized into difference equation for processing, as follows: △t[U wall A wall (T out,t -T in,t )+U window A window (T out,t -T in,t )+I solar η solar A window (27) +Q internal,t -Q EC,t ]-mc p (T in,t+1 -T in,t )=0 3.4) Equipment operation constraints: In this step, the equipment operation constraints include gas turbine output constraints, electric refrigerator output constraints, photovoltaic output constraints, and electric vehicle charging and discharging constraints, which are as follows: 3.41) Gas turbine output constraints: Where: P t MT is the electrical power output of the gas turbine at time t; η e is the electrical efficiency of the gas turbine; P t gas is the natural gas power input to the gas turbine at time t; are the upper and lower limits of the gas turbine output electric power respectively; They are the upper and lower limits of the gas turbine's climbing output respectively; 3.42) Electric refrigeration machine output constraints: Where: P t EC is the electric power input to the electric refrigerator at time t; are the upper and lower limits of the input electric refrigerator power respectively; 3.43) Photovoltaic output constraints: 0≤P t PV ≤P t PV.pre (30) Where: P t PV , P t PV.pre are the actual value and predicted value of photovoltaic output at time t respectively; 3.44) Electric vehicle charging and discharging constraints: Where: P t EV,ch , P t EV,dis are the charging and discharging power of the electric vehicle at time t respectively; are the charging and discharging power of the electric vehicle at time t respectively; is the battery capacity of the electric vehicle at time t; The upper and lower limits of the battery power of electric vehicles; η ch , η dis The charging and discharging efficiency of electric vehicles; E ESS is the total capacity of the electric vehicle battery; μ ch and μ dis Respectively represent the charging and discharging status of electric vehicles; 3.5) Building temperature upper and lower limit constraints: In this step, the indoor temperature of the building needs to be maintained within the predetermined upper and lower limits, as follows: T min ≤T in,t ≤T max (32) Where: T min , T max are the minimum and maximum indoor temperatures respectively.

10. The V2B-based intelligent building distributed energy management method according to claim 3, characterized in that: The model solving process of step 4) is as follows: 4.1) Collect new energy output and load forecast data and input them into the model; 4.2) Input reference formula (1) to formula (13) into the simulation platform to establish the electric-carbon dual incentive mechanism model for electric vehicles; 4.3) Input reference formula (14) to formula (20) into the simulation platform to establish a virtual energy storage model of the intelligent building taking into account the thermal storage characteristics; 4.4) According to formula (21) to formula (32), the calculation formulas involved in the intelligent building distributed energy management model are input into the simulation platform. The various resource models and constraints refer to formula (25) to formula (32). It should be noted that if other types of resources need to be aggregated, the calculation formula can be directly added in the corresponding position; 4.5) Based on the output and load forecast data of the new energy units, the objective function of the intelligent building distributed energy management model is input into the simulation platform according to formula (21) to determine the final unit output plan; 4.6) Use the solver in the simulation platform or the programmed mixed integer linear programming algorithm to solve; 4.7) Determine whether the convergence condition is met. If so, directly output the operating cost and the output plan of each unit. If not, return to step 4.5) to continue solving. In this step, the convergence condition can be formulated or changed by yourself.

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