Optimization adjustment method, device, equipment and storage medium for flexible energy use of buildings

Abstract

The present application is applicable to the field of electric power technology, and provides an optimization and regulation method, device, equipment and storage medium for flexible energy use in a building. By obtaining the day-ahead power purchase plan data of a building energy management system and the power generation power of the energy management system; inputting the day-ahead power purchase plan data and the power generation power into a pre-established mixed integer nonlinear programming model, a scheduling scheme for flexible loads in the building energy management system is obtained, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system; adjusting the building energy management system based on the scheduling scheme can reduce the building electricity cost and demand electricity charge while improving the efficiency of building energy use.

Description

Technical Field

[0001] The present application belongs to the field of electric power technology, and in particular, relates to an optimization regulation method, device, equipment and storage medium for flexible energy use in buildings. Background Art

[0002] In modern building management, building operators are usually faced with the dual challenges of high electricity costs and demand charges. Demand charges are calculated based on the maximum power consumption of a building in a billing cycle, and they have a significant impact on the building's electricity expenditure. With the continuous improvement of building intelligence and automation, the demand for power resources in various facilities in the building is also growing. However, the efficiency of power resource use and cost control are still issues that need to be urgently addressed during the operation of buildings. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment and storage medium for optimizing and regulating flexible energy use in a building, which can reduce the electricity cost and demand electricity charges of the building while improving the efficiency of energy use in the building.

[0004] In a first aspect, an embodiment of the present application provides a method for optimizing and adjusting flexible energy use of a building, comprising:

[0005] Obtaining day-ahead electricity purchase plan data of a building energy management system and power generation of the building energy management system;

[0006] Inputting the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system;

[0007] The building energy management system is adjusted based on the scheduling scheme.

[0008] In some embodiments, the total electricity fee includes: an electricity quantity fee plus a demand electricity fee, wherein the electricity quantity fee is equal to the power of the building energy management system at each time step multiplied by the length of the time step multiplied by the electricity price, and the demand electricity fee is equal to the maximum power of the building energy management system in a day multiplied by the demand electricity price.

[0009] In some embodiments, the building energy management system includes: a photovoltaic power generation system, a rigid load, and a flexible load, wherein the flexible load includes: a fully adjustable flexible load, a partially adjustable flexible load, and a flexible load with an energy storage device, and the objective function includes:

[0010] ;

[0011] in, ; ;

[0012]

[0013] is the total electricity cost, For electricity charges, is the demand charge, is the total energy consumption at moment t; represents the power of the rigid load at time t; represents the power of the fully adjustable flexible load at time t; represents the power of the partially adjustable flexible load at time t; and They represent the charging power and discharging power of the flexible load with energy storage device at time t, represents the power of the photovoltaic power generation system at time t, are binary variables, which are the switch state of the fully adjustable flexible load at time t, the switch state of the partially adjustable flexible load at time t, the charging state of the flexible load with energy storage device at time t, and the discharging state of the flexible load with energy storage device at time t. and They represent the electricity price and demand price of the day-ahead electricity purchase plan data at time t, N is the total number of time steps in a day, and T is the length of the time step.

[0014] In some embodiments, the constraints include: the total power consumption constraint of the building energy management system, the constraint of the fully adjustable flexible load, the constraint of the partially adjustable flexible load, the constraint of the flexible load with energy storage device, and the constraint of the maximum power, wherein the total power consumption constraint of the building energy management system is equal to the power consumption of the rigid load plus the power consumption of the fully adjustable flexible load plus the power consumption of the partially adjustable flexible load plus the charging amount of the flexible load with energy storage device minus the power generation of the photovoltaic power generation system minus the discharge amount of the flexible load with energy storage device, and the constraints of the fully adjustable flexible load include: the rated power constraint at each moment, the total power consumption constraint of a day, the minimum operating time constraint within a day, and the continuous operation time constraint. In the operation time constraints, the constraints of the partially adjustable flexible load include: power constraints at each moment, load regulation rate constraints, minimum operating time constraints within a day and continuous operation time constraints, and the constraints of the flexible load with energy storage device include: power constraints at each moment, the total power consumption of the flexible load with energy storage device in a day is greater than or equal to the total power consumption of the energy consumption plan on the day before, the flexible load with energy storage device completes at least one charge and discharge cycle in a day, the cumulative charge amount of the flexible load with energy storage device in each time step is greater than or equal to the discharge amount, the energy storage device corresponding to the flexible load with energy storage device can only be charged or discharged in each time step, the minimum operating time constraint within a day, and the continuous operation time constraint.

[0015] In some embodiments, the partially adjustable flexible loads include: building cooling and heating loads and equipment electricity consumption,

[0016] The method further includes: obtaining influencing factors of building cooling and heating loads and equipment power consumption, wherein the influencing factors include: weather data, personnel information, indoor environment setting value range, etc.;

[0017] The influencing factors are input into the machine learning model to determine the range of power constraints of the building's heating and cooling loads and equipment power consumption at each moment.

[0018] In some embodiments, obtaining the day-ahead power purchase plan data of the building energy management system and the power generation power of the building energy management system includes:

[0019] Obtaining historical electricity purchase plan data of the building energy management system;

[0020] Determining day-ahead electricity purchase plan data of the building energy management system based on the historical electricity purchase plan data;

[0021] Acquire weather data, and determine the power generation of the building energy management system based on the weather data and historical power generation data.

[0022] In some embodiments, the method further comprises:

[0023] Determining an optimized energy cost based on the scheduling scheme;

[0024] The dispatching scheme is evaluated based on the optimized energy cost and the energy cost corresponding to the day-ahead power purchase plan data.

[0025] In a second aspect, an embodiment of the present application provides an optimization and adjustment device for flexible energy use of a building, comprising:

[0026] An acquisition module, used to acquire the day-ahead electricity purchase plan data of the building energy management system and the power generation power of the building energy management system;

[0027] A calculation module, used for inputting the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system;

[0028] An adjustment module is used to adjust the building energy management system based on the scheduling plan.

[0029] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the methods described above when executing the computer program.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, any of the methods described above is implemented.

[0031] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a terminal device, enables the electronic device to execute any of the methods described above.

[0032] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0033] The embodiment of the present application provides an optimization and regulation method for flexible energy use in a building, which obtains the day-ahead power purchase plan data of a building energy management system and the power generation of the building energy management system; inputs the day-ahead power purchase plan data and the power generation into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system; adjusting the building energy management system based on the scheduling scheme can reduce the building electricity cost and demand electricity charge while improving the efficiency of building energy use.

[0034] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0036] Figure 1 A schematic diagram of the structure of an optimization and regulation system for flexible energy use of a building provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a process flow for implementing a method for optimizing and regulating flexible energy use in a building provided for the implementation of this application;

[0038] Figure 3 A schematic diagram of a process flow for implementing a method for optimizing and regulating flexible energy use in a building provided for the implementation of this application;

[0039] Figure 4 A schematic diagram of the structure of a building flexible energy optimization and regulation device provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0042] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0043] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0044] As used in the specification of this application and the appended claims, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrases "if it is determined" or "if it is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce detected" or "in response to detecting" depending on the context.

[0045] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0046] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the phrases "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appearing in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0047] Based on the technical problems of related technologies, the embodiment of the present application provides an optimization and adjustment method for flexible energy use in buildings, which can be applied to mobile phones, tablet computers, wearable devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA) and other electronic devices. The embodiment of the present application does not impose any restrictions on the specific type of electronic devices. The electronic device can be a server of a building energy management system, Figure 1 A structural diagram of an optimization and regulation system for building flexible energy use provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the optimization and regulation system for flexible energy use in buildings may include: a photovoltaic power generation system, a rigid load, a fully adjustable flexible load, a partially adjustable flexible load, a flexible load with an energy storage device, and a building energy management system, wherein the various subsystems in the optimization and regulation system for flexible energy use in buildings are connected through a power grid, and the building energy management system may be a building energy management system.

[0048] The functions implemented by the optimization and regulation method for flexible energy use of buildings provided in the embodiment of the present application can be realized by calling program codes by a processor of an electronic device, wherein the program codes can be stored in a computer storage medium.

[0049] The present application provides a method for optimizing and adjusting building flexible energy use. Figure 2 A schematic diagram of the implementation process of a method for optimizing the flexible energy use of a building provided for the implementation of this application is shown in FIG. Figure 2 As shown in the figure, the optimization adjustment methods of building flexible energy use include:

[0050] Step S101, obtaining day-ahead electricity purchase plan data of a building energy management system and the power generation power of the building energy management system.

[0051] In the embodiment of the present application, the building energy management system includes: a photovoltaic power generation system, a rigid load, and a flexible load. The photovoltaic power generation system is an unadjustable solar power generation system, and the power generation varies according to the lighting conditions. Rigid loads refer to those devices that must remain in operation under any circumstances and whose loads cannot be adjusted. For example: servers and data centers, constant temperature and humidity systems, key industrial plant production equipment, etc. Flexible loads refer to those power loads whose operating time and power can be flexibly adjusted. From another perspective, the adjustment of loads is manifested as translation, reduction, and transfer.

[0052] In the embodiment of the present application, the flexible load may include: a fully adjustable flexible load, a partially adjustable flexible load, and a flexible load with an energy storage device, wherein the fully adjustable flexible load means that the load can be translated and transferred, and the load reduced or increased at each moment can be adjusted arbitrarily within the range of 0 to the rated power. This load has a high degree of flexibility and can adjust its power output at any time according to actual needs. For example, without affecting normal operation, the load of certain non-critical equipment can be adjusted to the minimum or completely shut down. This flexibility makes it an important tool for optimizing electricity costs and coping with power load fluctuations. A partially adjustable flexible load means that the load adjustment range is subject to certain restrictions regardless of load translation, transfer or reduction. For example, in order to ensure the comfort of indoor personnel, the load of the air-conditioning system can only be adjusted within a certain reasonable range at each moment. Although this load has a certain adjustment capability, due to the high requirements for the comfort of the environment and users, its load adjustment range is relatively small. A flexible load with an energy storage device refers to the load regulation achieved by storing and releasing energy through energy storage devices (such as ice storage, electrochemical energy storage, etc.). This type of load stores energy during low electricity price periods and releases energy during peak electricity price periods, thereby shifting loads and reducing electricity costs. The addition of energy storage devices makes this type of load system more flexible and diversified in load regulation.

[0053] In the embodiment of the present application, the rigid load belongs to the basic load, which is the most basic load required for the operation of the building, such as power. The basic load is usually a rigid requirement for the operation of the building, so it cannot be directly adjusted to participate in the optimization control.

[0054] In the embodiment of the present application, the fully adjustable flexible load can flexibly adjust the load time and has strong time adjustability. It can adjust its own power consumption time according to the supply and demand situation of the power grid and changes in electricity prices, thereby providing support for energy optimization.

[0055] In an embodiment of the present application, for some adjustable loads, it is necessary to predict the load change range within the next day so as to make effective adjustments during the optimization process. First, collect specific dimensional data related to load changes, such as outdoor meteorological environment, predicted date type, and indoor personnel information. Then, the load adjustment range at each moment is generated according to a certain range of indoor temperature setting values ​​through the prediction model. For example, for the air-conditioning system (which is a partially adjustable load), by inputting information such as the outdoor temperature and date type of the future day, the model will output the predicted load change range according to the indoor set temperature range. This range of change will serve as a constraint for the optimization adjustment to ensure that the optimization result is within the load adjustment range, thereby achieving a reduction in electricity costs while ensuring stable system operation and user comfort.

[0056] In the embodiment of the present application, the flexible load with energy storage device has certain energy storage and buffering capacity, and can participate in the control of flexible load optimization by storing and releasing energy as a controlled load. In the embodiment of the present application, the flexible load with energy storage device is an electrochemical energy storage system.

[0057] In the embodiment of the present application, the day-ahead power purchase plan data can be considered as predicted data, which is based on a time series type. The day-ahead power purchase plan data includes the power of the photovoltaic power generation system, rigid loads, and flexible loads. The power generation function of the building energy management system can be predicted in combination with weather forecasts and historical power generation data.

[0058] Step S102, inputting the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system.

[0059] In an embodiment of the present application, the mixed integer nonlinear programming model involves two types of decision variables: continuous variables and binary variables. The mixed integer nonlinear programming model can be a realistic model of a building energy management system based on a building, and the model mainly includes an objective function and constraints.

[0060] In the embodiment of the present application, the continuous variable refers to the power of the flexible load at each time step, wherein the flexible load with an energy storage device is divided into charging power and discharging power. The binary variable refers to the fact that in the actual management process of the building energy management system, there is usually a time constraint on the operation time of the flexible load, and the flexible load with an energy storage device is required to only perform charging or discharging operations at each time step.

[0061] In the embodiment of the present application, the binary variable can clearly indicate whether the flexible load is working at this time step and whether the energy storage device is discharging or charging.

[0062] In the embodiment of the present application, the objective function includes: minimizing the total electricity cost, and the constraint conditions include: actual operating restrictions of each component in the building energy management system.

[0063] In an embodiment of the present application, the total electricity fee includes: the electricity charge for electric quantity plus the electricity charge for demand quantity, wherein the electricity charge for electric quantity is equal to the sum of the power of the building energy management system at each time step multiplied by the length of the time step multiplied by the electricity price, and the electricity charge for demand quantity is equal to the maximum power of the building energy management system in a day multiplied by the electricity price for demand quantity.

[0064] In the embodiment of the present application, the length of the time step can be configured, and illustratively, can be configured to 1 hour.

[0065] In the embodiments of the present application, the maximum power may also be referred to as the maximum demand, which refers to the highest instantaneous power consumption of the user within the time period. The demand price is linked to the maximum demand of the user within a certain time period (such as a day). Different maximum demands usually correspond to different demand prices. The larger the maximum demand, the higher the demand price. The unit of maximum demand is kw, and the unit of demand price is yuan / kw. Electricity companies usually set demand prices for industrial users, commercial users, and large users. By setting demand prices, users can be guided to optimize their electricity consumption behavior and promote the rational use of power resources through economic means.

[0066] In the embodiment of the present application, the purpose of setting the objective function is to reduce the total electricity cost of the building energy management system (optimization of electricity consumption and electricity charges), while minimizing the peak load within a day (optimization of demand electricity charges), thereby helping to reduce the operating pressure of the building energy management system and improve the stability of the power grid.

[0067] In the embodiment of the present application, the building energy management system includes: a photovoltaic power generation system, a rigid load, and a flexible load. The flexible load includes: a fully adjustable flexible load, a partially adjustable flexible load, and a flexible load with an energy storage device. The objective function includes:

[0068] ;

[0069] in, , ;

[0070]

[0071] is the total electricity cost, For electricity charges, is the demand charge, is the total energy consumption at moment t; represents the power of the rigid load at time t; represents the power of the fully adjustable flexible load at time t; represents the power of the partially adjustable flexible load at time t; and They represent the charging power and discharging power of the flexible load with energy storage device at time t, represents the power of the photovoltaic power generation system at time t, are binary variables, which are the switch state of the fully adjustable flexible load at time t, the switch state of the partially adjustable flexible load at time t, the charging state of the flexible load with energy storage device at time t, and the discharging state of the flexible load with energy storage device at time t. and They represent the electricity cost and demand price of the day-ahead electricity purchase plan data at time t, N is the total number of time steps in a day, and T is the length of the time step.

[0072] In an embodiment of the present application, the constraints include: a total power consumption constraint of the building energy management system, a constraint on a fully adjustable flexible load, a constraint on a partially adjustable flexible load, a constraint on a flexible load with an energy storage device, and a maximum power constraint, wherein the total power consumption constraint of the building energy management system is equal to the power consumption of the rigid load plus the power consumption of the fully adjustable flexible load plus the power consumption of the partially adjustable flexible load plus the charging amount of the flexible load with an energy storage device minus the power generation of the photovoltaic power generation system minus the discharge amount of the flexible load with an energy storage device.

[0073] In the embodiment of the present application, the total power consumption of a real building in one day must be greater than or equal to 0, so the constraint target is that the total power consumption of the building energy management system is greater than or equal to 0. The total power consumption constraint of the building energy management system can be expressed by the following formula:

[0074] ;

[0075] in, .

[0076] In the embodiment of the present application, the constraints of the fully adjustable flexible load include: rated power constraints at each moment, total power consumption constraints for a day, minimum operating time constraints within a day, and continuous operating time constraints.

[0077] In the embodiment of the present application, in order to ensure the safe and stable operation of the building, the building energy management system requires to avoid any subsystem from using over-rated power as much as possible. For fully adjustable flexible loads, the rated power constraint at each moment can be expressed as follows: ;

[0078] ;

[0079] in, is the rated power of flexible load A.

[0080] In the embodiment of the present application, the purpose of the total power consumption constraint for one day is to require that the total power consumption of the fully adjustable flexible load constraint within one day is not less than the total predicted power consumption of the fully adjustable flexible load constraint, so as to meet the power supply and demand balance of the entire energy system and avoid power shortage. The total power consumption constraint for one day can be expressed by the following formula: ;

[0081] in, is the power of flexible load A in the day-ahead power purchase plan at time t.

[0082] In the embodiment of the present application, the goal of the minimum operating time constraint within a day is to ensure that the fully adjustable flexible load can run for a sufficient time within a day to meet the actual operation or life needs, and avoid the short operating time affecting the normal operation of the building and user experience. For the constraint of the working time, the constraint expression is as follows:

[0083] ;

[0084] in, It indicates the total number of time steps that the electrical equipment corresponding to the fully adjustable flexible load works at least every day.

[0085] In the embodiment of the present application, in the actual operation of the building, it is usually required that once the flexible load is running, the working time cannot be less than a certain time. Therefore, a continuous operation time constraint is set. The purpose of setting a continuous operation time constraint is to prevent frequent power on and off, reduce equipment wear and energy consumption fluctuations, and improve system stability and reliability. The duration can be 1 hour, 2 hours, etc. In order to ensure that the flexible load A can work continuously for at least a certain time each time it starts working, an auxiliary variable can be introduced. To indicate whether to start working at time step t and ensure that the subsequent time steps are also working. The continuous running time constraint can be expressed as:

[0086] ;

[0087] ;

[0088] ;

[0089] in, It indicates the total number of time steps that the flexible load A works continuously each time it starts.

[0090] In the embodiment of the present application, the constraints of the partially adjustable flexible load include: power constraints at each moment, load regulation rate constraints, minimum operating time constraints within a day, and continuous operating time constraints.

[0091] In the embodiment of the present application, at each moment, the constrained power of the partially adjustable flexible load can only be adjusted within a certain range. This is because the comfort level of the people in the building limits the amplitude of its power change. The power constraint at each moment can be expressed by the following formula:

[0092] ;

[0093] ;

[0094] in, and They are the upper and lower bounds of the power of flexible load B at time t respectively.

[0095] In some embodiments, the partially adjustable flexible loads include: building cooling and heating loads and equipment electricity consumption,

[0096] The method further includes: obtaining influencing factors of building cooling and heating loads and equipment power consumption, wherein the influencing factors include: weather data, personnel information, indoor environment setting value range, etc.;

[0097] The influencing factors are input into the machine learning model to determine the range of power constraints of the building's heating and cooling loads and equipment power consumption at each moment.

[0098] In the embodiment of the present application, the building cooling and heating loads and equipment electricity consumption may be HVAC.

[0099] In some embodiments, the influencing factors may also include: date type.

[0100] Inputting the influencing factors into the machine learning model to determine the range of the power constraint of the HVAC system at each moment may include:

[0101] Based on future weather data, indoor temperature setting range, date type and other factors To predict the upper and lower bounds of the power of the flexible load B at time t, the formula can be expressed as:

[0102] .

[0103] In the embodiment of the present application, in order to prevent the HVAC system from adjusting too quickly and affecting the temperature comfort in the building, the load adjustment rate is limited. The load adjustment rate constraint can be expressed by the following formula:

[0104] ;

[0105] ;

[0106] in, It is the maximum adjustment rate of partially adjustable flexible load.

[0107] In the embodiment of the present application, the purpose of the minimum operating time constraint within a day is to ensure that the electrical equipment corresponding to some adjustable flexible loads can run for a sufficient time within a day to meet the actual operation or life needs, and avoid the short operating time affecting the normal operation of the building and user experience. For the constraint of the working time, the constraint expression is as follows:

[0108] ;

[0109] in, It indicates the total number of time steps that the partially adjustable flexible load works at least every day.

[0110] In the embodiment of the present application, in the actual operation of the building, it is usually required that once the flexible load is running, the working time cannot be less than a certain time. The purpose is to prevent frequent power on and off, reduce equipment wear and energy consumption fluctuations, and improve system stability and reliability. Therefore, a continuous operation time constraint is set, and the duration is 1 hour, 2 hours, etc. In order to ensure that some adjustable flexible loads can work continuously for at least a certain time each time they start working, an auxiliary variable can be introduced To indicate whether to start working at time step t and ensure that the subsequent time steps are also working, where : Auxiliary variable (binary variable), indicating that work starts at time step t, the starting point of a new working period (1 means start, 0 means no start).

[0111] The constraints can be expressed as:

[0112] ;

[0113] ;

[0114] ;

[0115] in, It indicates the total number of time steps that the partially adjustable flexible load works continuously each time it is started.

[0116] In some embodiments, the constraints of the flexible load with an energy storage device include: power constraints at each moment, the total power consumption of the flexible load with an energy storage device in one day is greater than or equal to the total power consumption of the energy consumption plan on the previous day, the flexible load with an energy storage device completes at least one charge and discharge cycle in one day, the cumulative charge amount of the flexible load with an energy storage device in each time step is greater than or equal to the discharge amount, the energy storage device corresponding to the flexible load with an energy storage device can only be charged or discharged in each time step, the minimum operating time constraint within a day, and the continuous operating time constraint.

[0117] In the embodiments of the present application, the flexible load having an energy storage device refers to a system that can adjust the charging and discharging of an internal electrochemical energy storage device.

[0118] In the embodiment of the present application, in order to ensure the safe and stable operation of the system, the building energy management system requires to avoid any subsystem from using over-rated power as much as possible. Therefore, the power of charging and discharging of the flexible load with energy storage device at each moment is constrained, and the formula is as follows: ;

[0119] ;

[0120] .

[0121] In some embodiments, the total power consumption of the flexible load with energy storage device in one day is greater than or equal to the total power consumption of the day-ahead energy consumption plan. Through this constraint, the power supply and demand of the entire building energy management system is balanced to avoid power shortage. This constraint is described by the following formula:

[0122] ;

[0123] in, is the power of the flexible load C at time t in the day-ahead power consumption plan.

[0124] In some embodiments, the main purpose of the constraint that the flexible load with energy storage device completes at least one charge and discharge cycle in one day is to ensure that the flexible load with energy storage device achieves a certain energy storage and regulation capability and can effectively participate in demand response control. If the flexible load with energy storage device does not complete at least one complete charge and discharge cycle in one day, it is considered that it cannot play its due regulatory role. The constraint expression is as follows:

[0125] .

[0126] In the embodiment of the present application, the constraint that the cumulative charge amount of the flexible load with energy storage device is greater than or equal to the discharge amount in each time step ensures that the energy storage device of the flexible load with energy storage device maintains a certain energy reserve in each time step to avoid the situation where the discharge amount is greater than the charge amount. In order to ensure that the cumulative charge amount of the flexible load C is greater than or equal to the discharge amount in each time step, the following expression can be used to constrain:

[0127] ;

[0128] .

[0129] In the embodiment of the present application, the battery energy storage device can only perform charging or discharging operations at each moment. Moreover, performing charging and discharging operations simultaneously in the same time step will reduce the overall efficiency of the energy storage device. Charging and discharging should be performed separately to avoid conflicts. Therefore, the action of the flexible load with energy storage device at each time step is constrained, and the constraint expression is:

[0130] ;

[0131] .

[0132] In the embodiment of the present application, the minimum operating time constraint within a day is to ensure that the electrical equipment corresponding to the flexible load with the energy storage device can continue to operate for a certain period of time within a day to meet the actual operation or life needs, and avoid the short operating time affecting the normal operation of the building and user experience. For the constraint of the working time, the present invention restricts the binary variable, and the constraint expression is as follows:

[0133] ;

[0134] in, It indicates the total number of time steps that the electrical equipment corresponding to the flexible load with energy storage device works at least every day.

[0135] In the embodiment of the present application, in the actual operation of the building, it is usually required that once the flexible load is running, the corresponding electrical equipment must work for a certain period of time. The purpose is to prevent frequent power on and off, reduce equipment wear and energy consumption fluctuations, and improve system stability and reliability. The duration is 1 hour, 2 hours, etc. In order to ensure that the flexible load with an energy storage device can work continuously for at least a certain period of time each time it starts working, an auxiliary variable can be introduced To indicate whether to start working at time step t and ensure that the subsequent time steps are also working. Definition:

[0136] : Auxiliary variable (binary variable), indicating the start of work at time step t, the starting point of the new working period (1 means start, 0 means no start) The constraint condition can be expressed as:

[0137] ;

[0138] ;

[0139] ;

[0140] in, It indicates the total number of time steps that the flexible load with energy storage device works continuously each time it starts.

[0141] In the embodiment of the present application, the demand electricity fee is obtained by multiplying the maximum power of the building energy management system in one day by the demand electricity price. Therefore, the maximum power is limited, and the constraint of the maximum power is expressed by the following description:

[0142] ;

[0143] .

[0144] Step S103: adjusting the building energy management system based on the scheduling scheme.

[0145] In the embodiment of the present application, the adjustment plan may include: the optimal energy utilization strategy including the output and operating status of various energy equipment.

[0146] In the embodiment of the present application, the solved optimal energy utilization strategy is deployed and implemented in the actual system.

[0147] The embodiment of the present application provides an optimization and regulation method for flexible energy use in a building, which obtains the day-ahead power purchase plan data of a building energy management system and the power generation of the building energy management system; inputs the day-ahead power purchase plan data and the power generation into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system; adjusting the building energy management system based on the scheduling scheme can reduce electricity costs and demand electricity charges while improving the efficiency of power resource use.

[0148] In some embodiments, step S101 may be implemented by the following steps:

[0149] Obtaining historical electricity purchase plan data of the building energy management system;

[0150] Determining day-ahead electricity purchase plan data of the building energy management system based on the historical electricity purchase plan data;

[0151] Acquire weather data, and determine the power generation of the building energy management system based on the weather data and historical power generation data.

[0152] In some embodiments, after step S102, the method further includes:

[0153] Determining an optimized energy cost based on the scheduling scheme;

[0154] The dispatching scheme is evaluated based on the optimized energy cost and the energy cost corresponding to the day-ahead power purchase plan data.

[0155] In the embodiment of the present application, the evaluation formula can be expressed as:

[0156] ;

[0157] in, The energy cost corresponding to the day-ahead electricity purchase plan data; is the optimized energy cost.

[0158] The energy-saving effect can be evaluated by this formula.

[0159] Based on the above-mentioned embodiments, the embodiment of the present application further provides an optimization and adjustment method for flexible energy use in a building, which optimizes the electricity demand of flexible loads during the operation of the building through a mixed integer nonlinear programming (MINLP) model. The method achieves the economic optimization of electricity cost by adjusting the energy use strategy of the flexible load under the premise of meeting the actual operation constraints. The MINLP model comprehensively considers the rigid load, photovoltaic power generation system, and flexible load in the building energy management system, and realizes the efficient utilization of power resources through system collaborative optimization. The constraints of different load systems are set in detail in the model, including power constraints, total power consumption constraints, working time constraints, charge and discharge balance constraints, etc., to ensure the stability and operability of the operation of the building energy management system and adapt to various constraints in the actual building operation environment. The objective function design not only considers the electricity cost of electricity, but also incorporates the demand electricity cost. By optimizing the total electricity cost of the sum of the two, the cost is minimized. The method provided in the embodiment of the present application can effectively alleviate the problem of high electricity costs in the operation of the building, and provide a reliable technical solution for enterprises to save electricity costs.

[0160] Figure 3 A schematic diagram of the implementation process of a method for adjusting the flexible energy use of a building provided in an embodiment of the present application is shown in FIG. Figure 3 As shown, including:

[0161] Step 1: Data optimization requires relevant data.

[0162] In the embodiment of the present application, in order to reasonably optimize the operation of the building energy management system, the first step is to obtain the corresponding data. The relevant data include: the day-ahead power purchase scheduling plan data of each component of the building energy management system and the power generation power of the photovoltaic system. Simply put, it is the data predicted for the future. These data are of time series type. Including the power of photovoltaic power generation systems, rigid loads and flexible loads. Among them, rigid loads and flexible loads can be predicted by machine learning methods based on historical data. The power generation power of the photovoltaic system can be predicted in combination with weather forecasts and historical power generation data. In addition, the relevant data also include: the time-of-use electricity price of the electricity charge and the demand price of the demand charge.

[0163] Step 2: With the goal of minimizing the electricity cost and demand cost of the building energy management system, a mixed integer nonlinear programming model is established.

[0164] In the embodiment of the present application, the above-mentioned real model of the building energy management system can be equivalently established into a mathematical model. According to the cost minimization and real condition constraints, in the embodiment of the present application, the equivalent model is converted into a mixed integer nonlinear programming (MINLP) model.

[0165] In the embodiment of the present application, with the goal of minimizing the electricity cost and demand cost of the building energy management system, establishing a mixed integer nonlinear programming model can be achieved by the following steps:

[0166] Step 201: Establish the objective function of the mixed integer nonlinear programming model. The objective function mainly includes two types of variables: continuous variables and binary variables. Continuous variables: the power of the flexible load at each time step, where the flexible load with energy storage device is divided into charging power and discharging power. Binary variables: Since the building energy management system usually has a time constraint on the running time of the flexible load in the actual management process, and the flexible load with energy storage device is required to only perform charging or discharging operations at each time step. Therefore, setting a binary variable can clearly indicate whether the flexible load is working at this time step, and whether the energy storage device is discharging or charging.

[0167] After determining the variable type, the entire objective function consists of two parts:

[0168] First: electricity consumption and electricity charges. The electricity consumption and electricity charges are equal to the sum of the total power of the building energy management system multiplied by the electricity price at each time step.

[0169] Second: Demand electricity charges. The demand electricity charges are the maximum power of the building energy management system in a day multiplied by the demand electricity price.

[0170] The direction of the objective function is minimization. The purpose of setting the objective function in this way is to reduce the total electricity cost of the building energy management system (optimization of electricity consumption and electricity charges), while minimizing the peak load within a day (optimization of demand electricity charges), thereby helping to reduce the operating pressure of the building energy management system and improve the stability of the power grid.

[0171] Step 202: Establish constraints of the mixed integer nonlinear programming model.

[0172] When establishing a mixed integer nonlinear programming (MINLP) model, it is necessary to set corresponding constraints based on the operating characteristics and realistic constraints of the actual building energy management system. These constraints include: the total power consumption of the building energy management system, the constraints of flexible load operation, and the maximum power constraints of the energy system guided by demand charges.

[0173] Step 203: Constraints on the total power consumption of the building energy management system.

[0174] The purpose of this constraint is that after the optimization scheme proposed in the present invention is deployed, the total power consumption of the building energy management system in one day must be greater than the total power consumption of the system predicted in the day-ahead power purchase plan, ensuring that the power consumption in the next day meets the building operation requirements.

[0175] Step 204: Constraints for fully adjustable flexible loads. The constraints are as follows: (1) power constraints at each moment; (2) total power consumption constraints within a day; (3) minimum operating time constraints within a day; and (4) continuous operating time constraints.

[0176] Step 205: Constraints for partially adjustable flexible loads include the following: (1) power constraints at each moment; (2) load regulation rate constraints; (3) minimum operating time constraints within a day; and (4) continuous operating time constraints.

[0177] Step 206: Constraint of flexible load with energy storage device.

[0178] The constraints are as follows: (1) power constraint at each moment; (2) total power consumption constraint within a day; (3) the energy storage device in the flexible load C completes at least one energy storage and discharge cycle within a day; (4) the accumulated storage energy of the energy storage device in the flexible load C is greater than the discharge energy in each time step; (5) minimum operating time constraint within a day; (6) continuous operating time constraint.

[0179] Step 207: Maximum power constraint.

[0180] The maximum power constraint is achieved by introducing a variable representing the maximum power in a day into the objective function and ensuring that the variable is always greater than or equal to the total power at each time of the day in the constraint conditions. In this way, the maximum power in a day is reflected in the objective function through the constraint on the maximum power variable, so that the demand charge is minimized in the optimization process.

[0181] Step 3: Solve the moderate integer nonlinear programming model constructed in step S2 to obtain the optimization result, and evaluate the energy saving rate and economic benefit of the result.

[0182] In the embodiment of the present application, after the MINLP optimization model is established, an optimization solution is performed to determine the final energy optimization result. By optimizing and solving the MINLP model, the optimal energy strategy including the output of various energy equipment and operating status can be obtained. Then the optimization results are analyzed to evaluate the energy-saving effect and economy of the scheme, and to confirm whether the established optimization goals and constraints are met. The solved optimal energy strategy is deployed and implemented in the actual system, and the energy consumption data before and after the deployment are compared, the actual energy saving rate is calculated, and the effectiveness of the optimization scheme is verified.

[0183] The method provided in the embodiment of the present application can improve the energy efficiency of the building energy management system and reduce the electricity cost of the system operation.

[0184] According to the aforementioned embodiments, the embodiments of the present application provide an optimization and regulation device for flexible energy use in buildings. The modules included in the device and the units included in the modules can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU, Central Processing Unit), a microprocessor (MPU, Microprocessor Unit), a digital signal processor (DSP, Digital Signal Processing) or a field programmable gate array (FPGA, Field Programmable Gate Array), etc.

[0185] The embodiment of the present application provides an optimization and adjustment device for flexible energy use of a building. Figure 4 A schematic diagram of a structural diagram of an optimization and adjustment device for building flexible energy use provided in an embodiment of the present application, such as Figure 4 As shown, the optimization and adjustment device 400 for building flexible energy use includes:

[0186] An acquisition module 401 is used to acquire the day-ahead power purchase plan data of the building energy management system and the power generation power of the building energy management system;

[0187] The calculation module 402 is used to input the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for the flexible load in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system;

[0188] The adjustment module 403 is used to adjust the building energy management system based on the scheduling scheme.

[0189] In some embodiments, the total electricity fee includes: an electricity quantity fee plus a demand electricity fee, wherein the electricity quantity fee is equal to the power of the building energy management system at each time step multiplied by the length of the time step multiplied by the electricity price, and the demand electricity fee is equal to the maximum power of the building energy management system in a day multiplied by the demand electricity price.

[0190] In some embodiments, the building energy management system includes: a photovoltaic power generation system, a rigid load, and a flexible load, wherein the flexible load includes: a fully adjustable flexible load, a partially adjustable flexible load, and a flexible load with an energy storage device, and the objective function includes:

[0191] ;

[0192] in, T, ;

[0193]

[0194] is the total electricity cost, For electricity charges, is the demand charge, is the total energy consumption at moment t; represents the power of the rigid load at time t; represents the power of the fully adjustable flexible load at time t; represents the power of the partially adjustable flexible load at time t; and They represent the charging power and discharging power of the flexible load with energy storage device at time t, represents the power of the photovoltaic power generation system at time t, are binary variables, which are the switch state of the fully adjustable flexible load at time t, the switch state of the partially adjustable flexible load at time t, the charging state of the flexible load with energy storage device at time t, and the discharging state of the flexible load with energy storage device at time t. and They represent the electricity cost and demand price of the day-ahead electricity purchase plan data at time t, N is the total number of time steps in a day, and T is the length of the time step.

[0195] In some embodiments, the constraints include: total power consumption constraints of the building energy management system, constraints on fully adjustable flexible loads, constraints on partially adjustable flexible loads, constraints on flexible loads with energy storage devices, and maximum power constraints, wherein the total power consumption constraints of the building energy management system are equal to the power consumption of the rigid load plus the power consumption of the fully adjustable flexible load plus the power consumption of the partially adjustable flexible load plus the charging amount of the flexible load with energy storage device minus the power generation of the photovoltaic power generation system minus the discharge amount of the flexible load with energy storage device, and the constraints of the fully adjustable flexible load include: rated power constraints at each moment, total power consumption constraints for one day, minimum operating time constraints within one day, and continuous operation constraints. The constraints of the partially adjustable flexible load include: power constraints at each moment, load regulation rate constraints, minimum operating time constraints within a day and continuous operating time constraints. The constraints of the flexible load with energy storage device include: power constraints at each moment, the total power consumption of the flexible load with energy storage device in a day is greater than or equal to the total power consumption of the energy consumption plan on the previous day, the flexible load with energy storage device completes at least one charge and discharge cycle in a day, the cumulative charge amount of the flexible load with energy storage device in each time step is greater than or equal to the discharge amount, the energy storage device corresponding to the flexible load with energy storage device can only be charged or discharged in each time step, the minimum operating time constraint within a day, and the continuous operating time constraint.

[0196] In some embodiments, the partially adjustable flexible loads include: a HVAC system,

[0197] The optimization and adjustment device of building flexible energy use also includes:

[0198] An influencing factor acquisition module is used to obtain influencing factors of building cooling and heating loads and equipment power consumption, wherein the influencing factors include: weather data, personnel information, indoor environment setting value range, etc.;

[0199] A determination module is used to input the influencing factors into a machine learning model to determine the range of power constraints of the building's cooling and heating loads and equipment power consumption at each moment.

[0200] In some embodiments, the acquisition module includes:

[0201] A first acquisition unit, configured to acquire historical electricity purchase plan data of the building energy management system;

[0202] A determination unit, configured to determine the day-ahead electricity purchase plan data of the building energy management system based on the historical electricity purchase plan data;

[0203] The second acquisition unit is used to acquire weather data, and determine the power generation power of the building energy management system based on the weather data and historical power generation data.

[0204] In some embodiments, the optimization and regulation device for building flexible energy use further includes:

[0205] An energy cost determination module, used to determine the optimized energy cost based on the scheduling scheme;

[0206] An evaluation module is used to evaluate the scheduling plan based on the optimized energy cost and the energy cost corresponding to the day-ahead power purchase plan data.

[0207] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0208] in addition, Figure 4 The optimization and adjustment device for flexible building energy use shown can be a software unit, a hardware unit, or a combination of software and hardware units built into existing electronic equipment, or it can be integrated into the electronic equipment as an independent pendant, or it can exist as an independent terminal device.

[0209] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0210] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 5 As shown, the electronic device 3 of this embodiment may include: at least one processor 30 ( Figure 5 Only one processor 30 is shown in the figure), a memory 31, and a computer program 32 stored in the memory 31 and executable on at least one processor 30. When the processor 30 executes the computer program 32, the steps in any of the above-mentioned method embodiments are implemented, for example Figure 2Steps S101 to S103 in the illustrated embodiment. Alternatively, when the processor 30 executes the computer program 32, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Figure 4 Functions of modules 401 to 403 are shown.

[0211] Exemplarily, the computer program 32 may be divided into one or more modules / units, one or more modules / units are stored in the memory 31, and are executed by the processor 30 to complete the present application. One or more modules / units may be a series of computer program 32 instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3.

[0212] The embodiment of the present application further provides a computer-readable storage medium, which stores a computer program 32. When the computer program 32 is executed by the processor 30, the steps in the above-mentioned method embodiments can be implemented.

[0213] An embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0214] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. According to this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program 32, and the computer program 32 can be stored in a computer-readable storage medium. When the computer program 32 is executed by the processor 30, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program 32 includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the terminal, a recording medium, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), an electric carrier signal, a telecommunication signal, and a software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0215] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0216] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0217] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0218] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0219] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for optimizing and regulating flexible energy use of a building, characterized in that: include: Obtaining day-ahead electricity purchase plan data of a building energy management system and power generation of the building energy management system; Inputting the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the building energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the building energy management system; The building energy management system is adjusted based on the scheduling scheme; the total electricity fee includes: the electricity fee for electric quantity plus the electricity fee for demand quantity, the electricity fee for electric quantity is equal to the sum of the power of the building energy management system in each time step multiplied by the length of the time step and then multiplied by the electricity price, the demand electricity fee is equal to the maximum power of the building energy management system in one day multiplied by the demand electricity price; the building energy management system includes: photovoltaic power generation system, rigid load, flexible load, flexible load includes: fully adjustable flexible load, partially adjustable flexible load, flexible load with energy storage device, the objective function includes: ； in, ， ； is the total electricity cost, For electricity charges, is the demand charge, is the total energy consumption at moment t; represents the power of the rigid load at time t; represents the power of the fully adjustable flexible load at time t; represents the power of the partially adjustable flexible load at time t; and They represent the charging power and discharging power of the flexible load with energy storage device at time t, represents the power of the photovoltaic power generation system at time t, are binary variables, which are the switch state of the fully adjustable flexible load at time t, the switch state of the partially adjustable flexible load at time t, the charging state of the flexible load with energy storage device at time t, and the discharging state of the flexible load with energy storage device at time t. and They respectively represent the electricity charges and demand prices of the day-ahead electricity purchase plan data at time t, N is the total number of time steps in a day, and T is the length of the time step; the constraints include: the total power consumption constraint of the building energy management system, the constraint of the fully adjustable flexible load, the constraint of the partially adjustable flexible load, the constraint of the flexible load with energy storage device, and the maximum power constraint, wherein the total power consumption constraint of the building energy management system is equal to the power consumption of the rigid load plus the power consumption of the fully adjustable flexible load plus the power consumption of the partially adjustable flexible load plus the charging amount of the flexible load with energy storage device minus the power generation of the photovoltaic power generation system minus the discharge amount of the flexible load with energy storage device, and the constraints of the fully adjustable flexible load include: the rated power constraint at each moment, the total power consumption of the day Constraints, minimum operating time constraints and continuous operating time constraints within a day. The constraints of the partially adjustable flexible load include: power constraints at each moment, load regulation rate constraints, minimum operating time constraints and continuous operating time constraints within a day. The constraints of the flexible load with energy storage device include: power constraints at each moment, the total power consumption of the flexible load with energy storage device in a day is greater than or equal to the total power consumption of the energy consumption plan on the previous day, the flexible load with energy storage device completes at least one charging and discharging cycle within a day, the cumulative charging amount of the flexible load with energy storage device in each time step is greater than or equal to the discharge amount, the energy storage device corresponding to the flexible load with energy storage device can only be charged or discharged within each time step, the minimum operating time constraint within a day, and the continuous operating time constraint.

2. The method according to claim 1, characterized in that The partially adjustable flexible loads include: building cooling and heating loads and equipment electricity consumption, The method further includes: obtaining influencing factors of building cooling and heating loads and equipment power consumption, wherein the influencing factors include: weather data, personnel information, and indoor environment setting value range; The influencing factors are input into the machine learning model to determine the range of power constraints of the building's heating and cooling loads and equipment power consumption at each moment.

3. The method according to any one of claims 1 to 2, characterized in that: The obtaining of the day-ahead electricity purchase plan data of the building energy management system and the power generation power of the building energy management system includes: Obtaining historical electricity purchase plan data of the building energy management system; Determining day-ahead electricity purchase plan data of the building energy management system based on the historical electricity purchase plan data; Acquire weather data, and determine the power generation of the building energy management system based on the weather data and historical power generation data.

4. The method according to claim 3, characterized in that The method further comprises: Determining an optimized energy cost based on the scheduling scheme; The dispatching scheme is evaluated based on the optimized energy cost and the energy cost corresponding to the day-ahead power purchase plan data.

5. An optimization and adjustment device for flexible energy use of buildings, characterized in that: include: An acquisition module, used to acquire the day-ahead power purchase plan data of the energy management system and the power generation power of the energy management system; A calculation module, used for inputting the day-ahead power purchase plan data and the generated power into a pre-established mixed integer nonlinear programming model to obtain a scheduling scheme for flexible loads in the energy management system, wherein the mixed integer nonlinear programming model includes: an objective function and constraints, the objective function includes: minimizing the total electricity cost, and the constraints include: actual operating restrictions of each component in the energy management system; The adjustment module is used to adjust the energy management system based on the scheduling scheme. The total electricity fee includes: the electricity fee for electric quantity plus the electricity fee for demand quantity. The electricity fee for electric quantity is equal to the sum of the power of the building energy management system in each time step multiplied by the length of the time step and then multiplied by the electricity price. The demand electricity fee is equal to the maximum power of the building energy management system in one day multiplied by the demand electricity price. The building energy management system includes: a photovoltaic power generation system, a rigid load, and a flexible load. The flexible load includes: a fully adjustable flexible load, a partially adjustable flexible load, and a flexible load with an energy storage device. The objective function includes: ； in, ， ； is the total electricity cost, For electricity charges, is the demand charge, is the total energy consumption at moment t; represents the power of the rigid load at time t; represents the power of the fully adjustable flexible load at time t; represents the power of the partially adjustable flexible load at time t; and They represent the charging power and discharging power of the flexible load with energy storage device at time t, represents the power of the photovoltaic power generation system at time t, are binary variables, which are the switch state of the fully adjustable flexible load at time t, the switch state of the partially adjustable flexible load at time t, the charging state of the flexible load with energy storage device at time t, and the discharging state of the flexible load with energy storage device at time t. and They respectively represent the electricity charges and demand prices of the day-ahead electricity purchase plan data at time t, N is the total number of time steps in a day, and T is the length of the time step; the constraints include: the total power consumption constraint of the building energy management system, the constraint of the fully adjustable flexible load, the constraint of the partially adjustable flexible load, the constraint of the flexible load with energy storage device, and the maximum power constraint, wherein the total power consumption constraint of the building energy management system is equal to the power consumption of the rigid load plus the power consumption of the fully adjustable flexible load plus the power consumption of the partially adjustable flexible load plus the charging amount of the flexible load with energy storage device minus the power generation of the photovoltaic power generation system minus the discharge amount of the flexible load with energy storage device, and the constraints of the fully adjustable flexible load include: the rated power constraint at each moment, the total power consumption of the day Constraints, minimum operating time constraints and continuous operating time constraints within a day. The constraints of the partially adjustable flexible load include: power constraints at each moment, load regulation rate constraints, minimum operating time constraints and continuous operating time constraints within a day. The constraints of the flexible load with energy storage device include: power constraints at each moment, the total power consumption of the flexible load with energy storage device in a day is greater than or equal to the total power consumption of the energy consumption plan on the previous day, the flexible load with energy storage device completes at least one charging and discharging cycle within a day, the cumulative charging amount of the flexible load with energy storage device in each time step is greater than or equal to the discharge amount, the energy storage device corresponding to the flexible load with energy storage device can only be charged or discharged within each time step, the minimum operating time constraint within a day, and the continuous operating time constraint.

6. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

Images