CN111339689A - Building comprehensive energy scheduling method, system, storage medium and computer equipment - Google Patents

Building comprehensive energy scheduling method, system, storage medium and computer equipment Download PDF

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CN111339689A
CN111339689A CN202010235539.0A CN202010235539A CN111339689A CN 111339689 A CN111339689 A CN 111339689A CN 202010235539 A CN202010235539 A CN 202010235539A CN 111339689 A CN111339689 A CN 111339689A
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黄佳畅
赖来利
李学聪
吴润基
王冬骁
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Guangdong University of Technology
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Abstract

The invention provides a building comprehensive energy scheduling method, a building comprehensive energy scheduling system, a building comprehensive energy scheduling storage medium and computer equipment, wherein the building comprehensive energy scheduling method is used for performing equivalent modeling on a photovoltaic power generation system, electric vehicle random charge and discharge behaviors and a building dynamic thermodynamic process and converting the modeling into a problem model based on mixed integer linear programming, and by utilizing a model prediction control method and a day-ahead scheduling control strategy, realizing the cooperative optimal control on a building comprehensive energy facility comprising the photovoltaic power generation system, the electric vehicle and a building load, improving the permeability of photovoltaic power generation in the power system, relieving the energy crisis, controlling the random charge and discharge behaviors of the electric vehicle, supplying power to the building load by utilizing the battery discharge of the electric vehicle, and reducing the peak load pressure of a power grid.

Description

Building comprehensive energy scheduling method, system, storage medium and computer equipment
Technical Field
The invention relates to the technical field of intelligent power grids, in particular to a building comprehensive energy scheduling method, a building comprehensive energy scheduling system, a storage medium and computer equipment.
Background
Among energy consumed by buildings, the energy consumption of an Air Conditioning (AC) system for adjusting the indoor temperature balance accounts for a large part of the proportion, and even accounts for 30% -40% of the total load amount in summer load peak period. Therefore, the air conditioning system can quickly respond to the dispatching of the power grid side by reasonably and effectively controlling the load of the air conditioner, and the method has important significance for improving the utilization efficiency of building energy and reducing the power demand and the load of a power distribution network in the load peak period.
Meanwhile, under the background that the photovoltaic power generation technology is rapidly developed and tends to mature, more and more building roofs, especially roofs of industrial plants, commercial buildings, civil buildings and agricultural facilities, are provided with photovoltaic power generation systems, and the Building Integrated Photovoltaic (BIPV) technology is also paid more and more attention. Building photovoltaic integration is an organism integrating future urbanization building forms and a distributed photovoltaic energy system under an intensive concept, can furthest realize self-circulation of energy in a building, and furthest reduces dependence on an external power system and burden of a power distribution network.
However, in recent years, with the access of a large number of electric vehicles to the power grid, the reliable operation of the power system brings new challenges: the concentrated charging of the electric vehicles may cause the load shortage in local areas, and the superposition of the charging time of the electric vehicles or the charging behavior in the peak time of the electric load will increase the burden of the power distribution network, and reduce the power quality of the power distribution network.
In view of the above problems, the prior art does not perform control optimization on a building integrated photovoltaic energy system accessed by an electric vehicle, and particularly performs optimal control on a building integrated energy facility including a photovoltaic power generation system, the electric vehicle and an air conditioner load. In addition, certain errors also exist in the prediction of the photovoltaic power generation by the current technical scheme, so that the control optimization of the whole system model is not accurate enough.
Disclosure of Invention
Aiming at the limitation of the prior art, the invention provides a building comprehensive energy scheduling method, a building comprehensive energy scheduling system, a building comprehensive energy scheduling storage medium and computer equipment, and the technical scheme adopted by the invention is as follows:
a building comprehensive energy scheduling method comprises the following steps:
building an equivalent model of the comprehensive energy facility of the building by using the photovoltaic power generation equivalent model, the electric automobile random charge-discharge model and the building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
converting the building integrated energy facility equivalent model into a problem model based on mixed integer linear programming by converting decision variables in the building integrated energy facility equivalent model into integer variables;
according to the using habit of an electric vehicle owner, modeling the randomness of the electric vehicle by using a probability density function, and generating a timetable for predicting the time of the electric vehicle accessing and leaving the building comprehensive energy facility;
acquiring day-ahead prediction data, and according to the day-ahead prediction data and the timetable, performing optimization solution on a problem model based on mixed integer linear programming by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
Compared with the prior art, the scheme realizes the cooperative optimal control of the building comprehensive energy facility comprising the photovoltaic power generation system, the electric automobile and the building load by performing equivalent modeling on the photovoltaic power generation system, the electric automobile random charge-discharge behavior and the building dynamic thermodynamic process and converting the equivalent modeling into a problem model based on mixed integer linear programming and utilizing a model prediction control method and a day-ahead scheduling control strategy, can improve the permeability of the photovoltaic power generation in the power system, can relieve the energy crisis, simultaneously controls the random charge-discharge behavior of the electric automobile, and utilizes the battery discharge of the electric automobile to supply power to the building load so as to reduce the peak pressure of the power grid load.
In a preferred scheme, in the step of constructing the building integrated energy facility equivalent model by using the photovoltaic power generation equivalent model, the electric automobile random charge-discharge model and the building dynamic thermodynamic model, the photovoltaic power generation equivalent model is described according to the following formula:
PPV(t)＝APV·G(t)·ηPV(t)·ηinv；
ηPV(t)＝ηref·[1-β(TPV(t)-TPV，ref]；
Figure BDA0002430836180000031
0≤PPV(t)≤PPV，max；
wherein, PPV(t) represents the output power of the solar panel at the moment t, and the unit is kW; a. thePVRepresents the illuminated area of the solar panel in m2(ii) a G (t) represents the solar radiation intensity at the time t and has the unit of W/m2；ηPV(t) represents the energy conversion efficiency of the solar panel at time t ηinvIndicating the conversion efficiency of the grid-connected inverter ηrefRepresenting the reference energy conversion efficiency of the solar panel at standard temperature, β representing the influence coefficient of temperature on the energy conversion efficiency, TPV(t) represents the temperature of the solar panel at time t in ° c); t isPV，refRepresenting a reference standard temperature of the solar panel in units of ℃; t isamb(t) represents the ambient temperature at time t in units of; t isratedThe rated temperature of the solar panel is expressed in units of ℃; pPV，maxThe maximum output power of the solar panel is expressed in kW.
In a preferred scheme, in the step of constructing the equivalent model of the building comprehensive energy facility by using the photovoltaic power generation equivalent model, the electric automobile random charge-discharge model and the building dynamic thermodynamic model, the electric automobile random charge-discharge model is described according to the following formula:
Figure BDA0002430836180000032
Figure BDA0002430836180000041
wherein, PEV，ch，minAnd PEV，ch，maxRespectively representing the minimum charging power and the maximum charging power of the battery of the electric automobile, wherein the unit is kW;
Figure BDA0002430836180000042
in the form of a binary variable, the variable,
Figure BDA0002430836180000043
a value of 1 indicates that the ith electric vehicle is in a charged state at time t,
Figure BDA0002430836180000044
the value of (1) is 0, which indicates that the ith electric automobile is in a discharging state or an idle state at the time t;
Figure BDA0002430836180000045
the charging power of the battery of the ith electric automobile at the time t is represented and has the unit of kW; i is the total amount of the electric automobile;
Figure BDA0002430836180000046
indicating the time when the ith electric vehicle arrives at the building,
Figure BDA0002430836180000047
indicating the moment when the ith electric automobile leaves the building; pEV，dch，minAnd PEV，dch，maxRespectively representing the minimum discharge power and the maximum discharge power of the battery of the electric automobile, and the unit is kW);
Figure BDA0002430836180000048
in the form of a binary variable, the variable,
Figure BDA0002430836180000049
a value of 1 indicates that the ith electric vehicle is in a discharge state at time t,
Figure BDA00024308361800000410
the value of (1) is 0, which indicates that the ith electric automobile is in a charging state or an idle state at the time t;
Figure BDA00024308361800000411
representing the state of charge of the ith electric vehicle battery at time t ηEV，chAnd ηEV，dchRespectively representing the charging efficiency and the discharging efficiency of the electric vehicle battery; τ represents the length of each time node in units of s;
Figure BDA00024308361800000412
the battery capacity of the ith electric automobile is expressed in kWh;
Figure BDA00024308361800000413
indicating the state of charge of the ith electric vehicle battery arrival time,
Figure BDA00024308361800000414
indicating the state of charge demand of the battery when the ith electric vehicle leaves.
In a preferred scheme, in the step of constructing the building integrated energy facility equivalent model by using the photovoltaic power generation equivalent model, the electric automobile random charge-discharge model and the building dynamic thermodynamic model, the building dynamic thermodynamic model is described according to the following formula:
Figure BDA00024308361800000415
Sac(t)∈{0，1}；
Figure BDA00024308361800000416
wherein, Tr(t) represents the room air temperature at time t in units of; maAnd MwRespectively representing the indoor air quality and the building wall quality, and the unit is kg; cpaAnd CpwRespectively representing the heat capacity of air and the heat capacity of a wall body, and the unit is J/kg DEG C; t isamb(t) represents the outdoor ambient temperature at time t in ° c); t isw(t) represents the temperature of the building wall at the moment t, and the unit is; reqRepresenting the equivalent thermal resistance of the building wall; rwrRepresenting the thermal resistance between the building wall and the indoor air; rwaRepresenting the equivalent thermal resistance between the building wall and the outdoor environment; sac(t) represents the air conditioner on-off state at time t; qac(t) represents the cooling capacity of the air conditioner at the time t, and the unit is kWh; n represents the number of time nodes;
Figure BDA0002430836180000051
represents the lower limit of the indoor temperature requirement, and the unit is;
Figure BDA0002430836180000052
represents the upper limit of the indoor temperature requirement, and the unit is; pac(t) represents the air conditioner electric power at the time t, and the unit is kW; COP indicates the air-conditioning energy efficiency ratio.
In a preferred embodiment, the building integrated energy facility equivalent model is converted into a mixed integer linear programming based problem model by converting decision variables in the building integrated energy facility equivalent model into integer variables, and the power balance constraint condition of the mixed integer linear programming based problem model is described by the following formula:
PPV，build(t)+PPV，sell(t)＝PPV(t)；
Figure BDA0002430836180000053
PPV，build(t) the power of the photovoltaic power generation system for supplying power to the building at the moment t is represented in kW; pPV，sell(t) power sold to a power grid by the photovoltaic power generation system at the moment t is represented in kW;
Figure BDA0002430836180000054
the power of the ith electric automobile selling electricity to the power grid at the time t is represented, and the unit is kW;
Figure BDA0002430836180000055
the power of the ith electric automobile for supplying power to the building at the time t is represented by kW; pgrid，buy(t) the power of the building comprehensive energy facility for buying electricity from the power grid at the moment t is represented in kW; pbasic(t) represents the base load of the building in kW; pgrid，sell(t) power sold to the power grid by the building comprehensive energy facility at the moment t is represented in kW; epsilongrid(t) is a binary variable, εgrid(t) a value of 1 indicates that the building complex energy facility bought power from the grid at time t, εgrid(t) a value of 0 indicates that the building complex energy facility did not buy electricity from the grid at time t; pgrid，maxThe maximum power capacity of the power transmission line of the power distribution network is expressed in kW.
In a preferred scheme, the method comprises the following steps of obtaining day-ahead prediction data, and according to the day-ahead prediction data and the schedule, adopting a model prediction control method and a day-ahead optimization scheduling strategy to optimally solve a problem model based on mixed integer linear programming to obtain a control scheduling plan of the building comprehensive energy facility:
and acquiring the total operation cost of the control scheduling strategy in the scheduling period according to the control scheduling plan.
Further, in the step of obtaining the total operating cost of the control scheduling policy during the scheduling period according to the control scheduling plan, the total operating cost of the control scheduling policy during the scheduling period is obtained according to the following objective function:
Figure BDA0002430836180000061
wherein, Cbuy(t) shows the price of electricity bought (yuan/kWh) at time t, Csell(t) represents the electricity selling price (yuan/kWh) at time t; lambda [ alpha ]PVAnd λEVIs a predetermined positive number, λPV<λEV。
The present invention also provides the following:
a building integrated energy control and dispatching system comprises:
the building integrated energy facility equivalent model building module is used for building a building integrated energy facility equivalent model by using a photovoltaic power generation equivalent model, an electric automobile random charge-discharge model and a building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
the building integrated energy facility equivalent model conversion module is used for converting the building integrated energy facility equivalent model into a problem model based on mixed integer linear programming by converting decision variables in the building integrated energy facility equivalent model into integer variables;
the schedule generating module is used for modeling the randomness of the electric automobile by using a probability density function according to the automobile using habit of an electric automobile owner and generating a schedule for predicting the time of the electric automobile accessing and leaving the building comprehensive energy facility;
the control scheduling plan obtaining module is used for obtaining day-ahead prediction data, and according to the day-ahead prediction data and the timetable, performing optimization solution on a problem model based on mixed integer linear programming by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
In a preferred embodiment, the control scheduling plan obtaining module of the building integrated energy facility is further configured to obtain a total operating cost of the control scheduling policy during scheduling according to the control scheduling plan.
A storage medium having a computer program stored thereon, the computer program comprising: the computer program, when executed by a processor, implements the steps of the building integrated energy scheduling method as described above.
A computer device comprising a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, the computer program, when executed by the processor, implementing the steps of the building integrated energy scheduling method as described above.
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Fig. 1 is a flowchart 1 of a method for scheduling comprehensive energy of a building according to an embodiment of the present invention;
fig. 2 is a schematic flow diagram of power flow and information flow in the process of implementing the embodiment of the present invention;
fig. 3 is a flowchart 2 of a method for scheduling comprehensive energy of a building according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a framework of a building integrated energy control and dispatching system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and are used for illustration only, and should not be construed as limiting the patent. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Referring to fig. 1, a method for scheduling comprehensive energy of a building includes the following steps:
s101, constructing a building comprehensive energy facility equivalent model by using a photovoltaic power generation equivalent model, an electric automobile random charge-discharge model and a building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
s102, converting the equivalent model of the building integrated energy facility into a problem model based on mixed integer linear programming by converting decision variables in the equivalent model of the building integrated energy facility into integer variables;
s103, modeling the randomness of the electric automobile by using a probability density function according to the automobile using habits of an electric automobile owner, and generating a time schedule for predicting the time when the electric automobile is connected to and leaves the building comprehensive energy facility;
s104, acquiring day-ahead prediction data, and according to the day-ahead prediction data and the schedule, adopting a model prediction control method and a day-ahead optimization scheduling strategy to optimally solve a problem model based on mixed integer linear programming to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
Compared with the prior art, the scheme realizes the cooperative optimal control of the building comprehensive energy facility comprising the photovoltaic power generation system, the electric automobile and the building load by performing equivalent modeling on the photovoltaic power generation system, the electric automobile random charge-discharge behavior and the building dynamic thermodynamic process and converting the equivalent modeling into a problem model based on mixed integer linear programming and utilizing a model prediction control method and a day-ahead scheduling control strategy, can improve the permeability of the photovoltaic power generation in the power system, can relieve the energy crisis, simultaneously controls the random charge-discharge behavior of the electric automobile, and utilizes the battery discharge of the electric automobile to supply power to the building load so as to reduce the peak pressure of the power grid load.
Specifically, the loads in a general residential building or a commercial building can be classified into two types: one is basic load, such as lighting, power and the like, which is often necessary for life and work, and is greatly influenced by life habits and use habits of users, and is difficult to participate in the control of response of the demand side as directly controlled load; the other is a controllable load, such as an air conditioning load, and the like, and the load has certain heat storage and heat buffering capacity and can be used as the controlled load to participate in the control of the response of the demand side. In this embodiment, the controllable load mainly considered is the air conditioning load in the cooling mode, and the demand-side response type centralized control is performed on the air conditioning load group through the direct control technology, so that the goals of reducing the building energy consumption cost and reducing the peak pressure of the power grid load are achieved on the premise of ensuring the indoor temperature demand of the building.
In this embodiment, the target building is configured with a solar photovoltaic panel with a certain area size, and the net internet electricity price of solar photovoltaic power generation is lower than the electric power retail price. Therefore, it is economically advantageous to the building complex to use renewable energy in the local grid as much as possible.
The photovoltaic power generation equivalent model can be regarded as a model for studying the influence of factors such as illumination intensity, illumination effective area of the solar panel, environment temperature and the like on the output power of the solar panel and mathematically formulating the influence;
the electric automobile random Charge and discharge model can be considered as a model for researching the change of the SoC (State of Charge) of the battery along with the Charge and discharge power and time in the Charge and discharge process of the battery based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility;
the building dynamic thermodynamic model can be regarded as a model obtained by equating the heat transfer process among the outdoor environment temperature, the wall temperature and the indoor air temperature into a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor, and the model is obtained by researching the mathematical relationship in the circuit and linearizing the mathematical relationship based on the heat absorption physical process of an object.
In step S102, the decision variables in the building integrated energy facility equivalent model refer to variables to be determined related to the constraint conditions and the objective function in the optimization problem. In this embodiment, the decision variables to be converted into integer variables include the on-off state quantity of the air conditioner at a certain time, the state quantity of the battery of the electric vehicle (charging, discharging or no operation), and the state quantity of the transaction between the building integrated energy facility and the external power grid (electricity buying or electricity selling); the decision variables which are not converted comprise indoor temperature variables, wall temperature variables, electric power variables of an air conditioner, SoC variables of each electric vehicle battery, power variables of photovoltaic power generation for supplying power to the building, power variables of electric vehicle battery discharge for supplying power to the building, and power variables (buying electric power and selling electric power) of the building comprehensive energy facility for trading with an external power grid.
Researchers of the invention find that after carrying out mathematical formula equivalence on a photovoltaic power generation process, an electric vehicle battery charging and discharging process and a building thermodynamic dynamic process, the mathematical formula equation is subjected to linearization processing and some decision variables are designated as Integer variables, and the definition of an MILP (Mixed Integer Linear Programming, MILP) problem is met, namely, constraints and objective functions contained in a problem model are Linear equations, and the variables have both continuous variables and Integer variables; by converting some variables in the linearized equations, such as the air conditioner on-off state, the electric vehicle charge-discharge state and the like, into integer variables, such as 0 for representing that the air conditioner state is off, 1 for representing that the air conditioner state is on, 1 for representing that the electric vehicle battery state is discharging, 0 for representing that the electric vehicle battery state is non-operational, and 1 for representing that the electric vehicle battery state is charging, the building integrated energy facility equivalent model is converted into a problem model based on mixed integer linear programming, so that the optimal solution of the problem can be obtained by calling a mathematical solver, and the building integrated energy facility in the next day can be effectively subjected to power planning.
In step S103, the probability density function may include chi-square distribution, uniform distribution, and the like; which consists in deriving the probability of occurrence of a certain independent variable. According to the invention, the time when each electric automobile arrives at the building and the probability of the stay time length in the building are researched, so that the time when each electric automobile arrives at and leaves the building is simulated, and the randomness of the electric automobiles is modeled.
In step S104, the day-ahead prediction data includes solar radiation intensity, ambient temperature, and electricity price data, and the former two may be predicted by acquiring historical data through a chinese meteorological department and using a neural network; the electricity price data may be predicted using a prediction method based on time series analysis based on historical electricity price data.
In step S104, a problem model based on the mixed integer linear programming is optimized and solved by using a model predictive control method and a day-ahead optimization scheduling strategy, so as to obtain a control scheduling plan of the building integrated energy facility, wherein,
model Predictive Control (MPC) consists of 3 main components: modeling, prediction, and control. From the aspect of the invention, the air conditioner switch in the building, the charging and discharging behaviors of the electric vehicle connected into the building comprehensive energy facility and the transaction of the building comprehensive energy facility and an external power grid are controlled based on the established building comprehensive energy facility equivalent model and day-ahead prediction data.
The invention relates to a strategy for optimizing and dispatching day by day, which is characterized in that day-ahead prediction data is input into a model, and dispatching data in the future day, including the on-off state of a building air conditioner, the charging and discharging behaviors of each electric automobile connected to a building comprehensive energy facility and data of transaction between the building comprehensive energy facility and an external power grid, are obtained by solving by a mathematical optimization solver.
The specific process of carrying out optimization solution on the problem model based on the mixed integer linear programming can adopt a branch-and-bound method to traverse the solution space of the programming problem so as to obtain an optimal solution, and the upper and lower boundaries are determined by the obtained feasible solution in the traversing process by utilizing the concept of 'pruning', so that the traversing range is further reduced.
Thus, in an alternative embodiment, the control schedule will include the following:
1. the on-off state of each time node set by the air conditioner in one day (in the embodiment, one time node is set to be taken every 15 min);
2. after the battery of the electric automobile is connected into the comprehensive energy facility of the building, the charging and discharging state of each time node is realized;
3. and at each time node in a day, the electricity buying and selling state of the building integrated energy facility to the power grid and the related electricity buying and selling power magnitude.
In an alternative embodiment, the facility equipment involved in implementing the present invention, including the building integrated energy facility referred to in the control scheduling plan of the building integrated energy facility in step S104, may include the following: the photovoltaic power generation end comprises a solar cell panel, a grid-connected inverter and a photovoltaic power generation meter; the building load control end comprises a building load controller, an air conditioner controllable load, a foundation load and a building load intelligent ammeter; the control end of the electric automobile comprises an electric automobile controller, an electric automobile and an electric automobile intelligent ammeter; referring to fig. 2, in which,
the solar cell panel is electrically connected with the grid-connected inverter, the grid-connected inverter is electrically connected with the photovoltaic power generation meter, and the photovoltaic power generation meter is electrically connected with a power transmission line of a building; the solar cell panel generates direct current by receiving sunlight irradiation, and the grid-connected inverter is responsible for converting the direct current output by the solar cell panel into alternating current;
the controllable load of the air conditioner and the basic load are respectively and electrically connected with the building load controller, the building load controller is connected with the building load intelligent ammeter, and the building load intelligent ammeter is electrically connected with a power transmission line of a building;
the electric automobile is electrically connected with the electric automobile controller, the electric automobile controller is electrically connected with the electric automobile intelligent ammeter, and the electric automobile intelligent ammeter is electrically connected with a power transmission line of a building;
the power transmission line of the building is electrically connected with an external power distribution network;
the photovoltaic power generation meter, the building load intelligent electric meter and the electric automobile intelligent electric meter are in communication connection with computer equipment in the building energy management center through signal lines respectively, and the building energy management center conducts optimized scheduling management on photovoltaic power generation, building load and an electric automobile through data transmitted by the three electric meters.
Thus, in an alternative embodiment, the control schedule obtained in step S104 is executed according to the following operations: when the photovoltaic power generation system is used for supporting the building load, the surplus electric quantity is sold to the power grid to earn profit; when the photovoltaic power generation is not enough to support the building load, the building load is supplied with power by discharging the electric vehicle which arrives at the parking lot and is connected to the electric vehicle controller and buying power from a power grid, and on the premise of meeting the charge state requirement when each electric vehicle leaves, the electric vehicle is preferentially selected to discharge power to supply power to the building load, so that the operation cost of the building comprehensive energy facility is reduced; under the premise that photovoltaic power generation and electric automobile discharging are enough to support building load, the idle electric automobile can be scheduled to sell electricity to a power grid in a discharging mode so as to earn profits. The electric vehicle controller charges the battery about to leave the electric vehicle by buying electricity to the grid to meet its state of charge requirements.
In a preferred embodiment, in step S101, the photovoltaic power generation equivalent model is described by the following formula:
PPV(t)＝APV·G(t)·ηPV(t)·ηinv(1)
ηPV(t)＝ηref·[1-β(TPV(t)-TPV，ref](2)
Figure BDA0002430836180000121
0≤PPV(t)≤PPV，max(4)
wherein, PPV(t) represents the output power of the solar panel at the moment t, and the unit is kW; a. thePVRepresents the illuminated area of the solar panel in m2(ii) a G (t) represents the solar radiation intensity at the time t and has the unit of W/m2；ηPV(t) represents the energy conversion efficiency of the solar panel at time t ηinvIndicating the conversion efficiency of the grid-connected inverter ηrefRepresenting the reference energy conversion efficiency of the solar panel at standard temperature, β representing the influence coefficient of temperature on the energy conversion efficiency, TPV(t) represents the temperature of the solar panel at time t in ° c); t isPV，refTo representThe reference standard temperature of the solar panel is measured in units of; t isamb(t) represents the ambient temperature at time t in units of; t isratedThe rated temperature of the solar panel is expressed in units of ℃; pPV，maxThe maximum output power of the solar panel is expressed in kW.
In a preferred embodiment, in step S101, the electric vehicle stochastic charge-discharge model is described by the following formula:
Figure BDA0002430836180000122
Figure BDA0002430836180000131
wherein, PEV，ch，minAnd PEV，ch，maxRespectively representing the minimum charging power and the maximum charging power of the battery of the electric automobile, wherein the unit is kW;
Figure BDA0002430836180000132
in the form of a binary variable, the variable,
Figure BDA0002430836180000133
a value of 1 indicates that the ith electric vehicle is in a charged state at time t,
Figure BDA0002430836180000134
the value of (1) is 0, which indicates that the ith electric automobile is in a discharging state or an idle state at the time t;
Figure BDA0002430836180000135
the charging power of the battery of the ith electric automobile at the time t is represented and has the unit of kW; i is the total amount of the electric automobile;
Figure BDA0002430836180000136
indicating the time when the ith electric vehicle arrives at the building,
Figure BDA0002430836180000137
indicating the moment when the ith electric automobile leaves the building; pEV，dch，minAnd PEV，dch，maxRespectively representing the minimum discharge power and the maximum discharge power of the battery of the electric automobile, and the unit is kW);
Figure BDA0002430836180000138
in the form of a binary variable, the variable,
Figure BDA0002430836180000139
a value of 1 indicates that the ith electric vehicle is in a discharge state at time t,
Figure BDA00024308361800001310
the value of (1) is 0, which indicates that the ith electric automobile is in a charging state or an idle state at the time t;
Figure BDA00024308361800001311
representing the state of charge of the ith electric vehicle battery at time t ηEV，chAnd ηEV，dchRespectively representing the charging efficiency and the discharging efficiency of the electric vehicle battery; the length of each time node is expressed in the unit of s;
Figure BDA00024308361800001312
the battery capacity of the ith electric automobile is expressed in kWh;
Figure BDA00024308361800001313
indicating the state of charge of the ith electric vehicle battery arrival time,
Figure BDA00024308361800001314
indicating the state of charge demand of the battery when the ith electric vehicle leaves.
Inequality (7) ensures that the same electric vehicle cannot be charged and discharged at the same time t, and neither can be charged nor discharged; equation (8) is an equivalent model of the charging and discharging process of the battery of the electric vehicle, and inequality (10) is to prevent the battery of each electric vehicle from being overcharged or overdischarged.
Since the indoor air temperature is not only dependent on the difference between the indoor and outdoor ambient temperatures, but also affected by the heat exchange between the wall and the indoor and outdoor environment, in order to describe the building thermodynamic dynamic change process more precisely, in a preferred embodiment, in step S101, the building thermodynamic model is described by the following formula:
Figure BDA0002430836180000141
Sac(t)∈{0，1} (16
Figure BDA0002430836180000142
wherein, Tr(t) represents the room air temperature at time t in units of; maAnd MwRespectively representing the indoor air quality and the building wall quality, and the unit is kg; cpaAnd CpwRespectively representing the heat capacity of air and the heat capacity of a wall body, and the unit is J/kg DEG C; t isamb(t) represents the outdoor ambient temperature at time t in ° c); t isw(t) represents the temperature of the building wall at the moment t, and the unit is; reqRepresenting the equivalent thermal resistance of the building wall; rwrRepresenting the thermal resistance between the building wall and the indoor air; rwaRepresenting the equivalent thermal resistance between the building wall and the outdoor environment; sac(t) represents the air conditioner on-off state at time t; qac(t) represents the cooling capacity of the air conditioner at the time t, and the unit is kWh; n represents the number of time nodes;
Figure BDA0002430836180000143
represents the lower limit of the indoor temperature requirement, and the unit is;
Figure BDA0002430836180000144
represents the upper limit of the indoor temperature requirement, and the unit is; pac(t) represents the air conditioner electric power at the time t, and the unit is kW; COP indicates the air-conditioning energy efficiency ratio.
The present embodiment adopts the air conditioning system coefficient of performance (air conditioning core) shown by the calculation formula (15)Impact of Performance, COP, defined as the ratio of the power of air conditioning cooling or heating to the electric power consumed by the air conditioner) to improve the Performance of the model, and the external ambient temperature T predicted each time point T in the day ahead is input using the model predictive control method and the control strategy of the day ahead scheduleamb(t), where t is chosen to be 15 minutes, for each time point t in the mixed integer linear programming based problem model described above, the air conditioning state SacAnd (t) optimizing and solving the value so as to carry out centralized control on the building air conditioning load.
In a preferred embodiment, in step S102, the power balance constraint of the problem model based on mixed integer linear programming is described by the following formula:
PPV，build(t)+PPV，sell(t)＝PPV(t) (18
Figure BDA0002430836180000151
PPV，build(t) the power of the photovoltaic power generation system for supplying power to the building at the moment t is represented in kW; pPV，sell(t) power sold to a power grid by the photovoltaic power generation system at the moment t is represented in kW;
Figure BDA0002430836180000152
the power of the ith electric automobile selling electricity to the power grid at the time t is represented, and the unit is kW;
Figure BDA0002430836180000153
the power of the ith electric automobile for supplying power to the building at the time t is represented by kW; pgrid，buy(t) the power of the building comprehensive energy facility for buying electricity from the power grid at the moment t is represented in kW; pbasic(t) represents the base load of the building in kW; pgrid，sell(t) power sold to the power grid by the building comprehensive energy facility at the moment t is represented in kW; epsilongrid(t) is a binary variable, εgrid(t) a value of 1 indicates that the building complex energy facility bought power from the grid at time t, εgrid(t) has a value of0 represents that the building comprehensive energy facility does not buy electricity from the power grid at the moment t; pgrid,maxThe maximum power capacity of the power transmission line of the power distribution network is expressed in kW.
Equation (18) ensures that the remaining electricity is resold to the grid in addition to the photovoltaic power generation supplying power to the building; equation (19) ensures that each electric vehicle can participate in supplying power to the building and selling power to the grid; equation (20) is a power balance equation for a building complex energy facility; equation (21) indicates that the source of electricity sold to the power grid by the building integrated energy facility is only photovoltaic power generation and electric vehicle discharge; inequalities (22) and (23) give the upper and lower power limits of the integrated energy facility of the building for buying and selling electricity to the power grid.
In a preferred embodiment, referring to fig. 3, a method for scheduling integrated energy of a building includes the following steps:
s201, constructing a building comprehensive energy facility equivalent model by using a photovoltaic power generation equivalent model, an electric automobile random charge-discharge model and a building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
s202, converting the equivalent model of the building integrated energy facility into a problem model based on mixed integer linear programming by converting decision variables in the equivalent model of the building integrated energy facility into integer variables;
s203, modeling the randomness of the electric automobile by using a probability density function according to the automobile using habits of an electric automobile owner, and generating a time schedule for predicting the time when the electric automobile is connected to and leaves the building comprehensive energy facility;
s2041, acquiring day-ahead prediction data, and according to the day-ahead prediction data and the schedule, performing optimization solution on a problem model based on mixed integer linear programming by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data;
s2042, according to the control scheduling plan, obtaining the total operation cost of the control scheduling strategy in the scheduling period.
Further, in step S2042, the total operating cost of the control scheduling policy during scheduling is obtained according to the following objective function:
Figure BDA0002430836180000161
wherein, Cbuy(t) shows the price of electricity bought (yuan/kWh) at time t, Csell(t) represents the electricity selling price (yuan/kWh) at time t; lambda [ alpha ]PVAnd λEVIs a predetermined positive number, λPV<λEV. In particular, λPVAnd λEVAre all very small positive numbers to ensure that the effect on the objective function is as small as possible, and lambda is setPV<λEVSo that the building energy management center preferentially selects the photovoltaic power generation to supply power to the building load to improve the permeability of the photovoltaic power generation.
The present invention also provides the following:
a building integrated energy control and dispatching system comprises:
the building integrated energy facility equivalent model building module 1 is used for building a building integrated energy facility equivalent model by using a photovoltaic power generation equivalent model, an electric automobile random charge-discharge model and a building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
the building integrated energy facility equivalent model conversion module 2 is used for converting the building integrated energy facility equivalent model into a problem model based on mixed integer linear programming by converting decision variables in the building integrated energy facility equivalent model into integer variables;
the schedule generating module 3 is used for modeling the randomness of the electric automobile by using a probability density function according to the automobile using habit of an electric automobile owner and generating a schedule for predicting the time of the electric automobile accessing and leaving the building comprehensive energy facility;
the control scheduling plan obtaining module 4 of the building integrated energy facility is used for obtaining day-ahead prediction data, and according to the day-ahead prediction data and the timetable, a problem model based on mixed integer linear programming is optimized and solved by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building integrated energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
In a preferred embodiment, the control scheduling plan obtaining module 4 of the building integrated energy facility is further configured to obtain a total operating cost of the control scheduling policy during scheduling according to the control scheduling plan.
A storage medium having a computer program stored thereon, the computer program comprising: the computer program, when executed by a processor, implements the steps of the building integrated energy scheduling method as described above.
A computer device comprising a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, the computer program, when executed by the processor, implementing the steps of the building integrated energy scheduling method as described above.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.

Claims (10)

1. A building comprehensive energy scheduling method is characterized by comprising the following steps:
building an equivalent model of the comprehensive energy facility of the building by using the photovoltaic power generation equivalent model, the electric automobile random charge-discharge model and the building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
converting the building integrated energy facility equivalent model into a problem model based on mixed integer linear programming by converting decision variables in the building integrated energy facility equivalent model into integer variables;
according to the using habit of an electric vehicle owner, modeling the randomness of the electric vehicle by using a probability density function, and generating a timetable for predicting the time of the electric vehicle accessing and leaving the building comprehensive energy facility;
acquiring day-ahead prediction data, and according to the day-ahead prediction data and the timetable, performing optimization solution on a problem model based on mixed integer linear programming by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
2. The method for dispatching the building comprehensive energy resource of claim 1, wherein in the step of constructing the building comprehensive energy resource facility equivalent model by using a photovoltaic power generation equivalent model, an electric vehicle random charge-discharge model and a building dynamic thermodynamic model, the photovoltaic power generation equivalent model is described by the following formula:
PPV(t)＝APV·G(t)·ηPV(t)·ηinv；
ηPV(t)＝ηref·[1-β(TPV(t)-TPV，ref]；
Figure FDA0002430836170000011
0≤PPV(t)≤PPV，max；
wherein, PPV(t) represents the output power of the solar panel at the moment t, and the unit is kW; a. thePVRepresents the illuminated area of the solar panel in m2(ii) a G (t) represents the solar radiation intensity at the time t and has the unit of W/m2；ηPV(t) represents the energy conversion efficiency of the solar panel at time t ηinvIndicating the conversion efficiency of the grid-connected inverter ηrefRepresenting the reference energy conversion efficiency of the solar panel at standard temperature, β representing the influence coefficient of temperature on the energy conversion efficiency, TPV(t) represents the temperature of the solar panel at time t in ° c); t isPV，refRepresenting a reference standard temperature of the solar panel in units of ℃; t isamb(t) represents the ambient temperature at time t in units of; t isratedThe rated temperature of the solar panel is expressed in units of ℃; pPV，maxRepresenting the sunThe maximum output power of the energy cell panel is kW.
3. The method for dispatching the building comprehensive energy resource of claim 1, wherein in the step of constructing the building comprehensive energy resource facility equivalent model by using a photovoltaic power generation equivalent model, an electric vehicle random charge-discharge model and a building dynamic thermodynamic model, the electric vehicle random charge-discharge model is described by the following formula:
Figure FDA0002430836170000021
Figure FDA0002430836170000022
Figure FDA0002430836170000023
Figure FDA0002430836170000024
Figure FDA0002430836170000025
Figure FDA0002430836170000026
Figure FDA0002430836170000027
Figure FDA0002430836170000028
wherein, PEV，ch，minAnd PEV，ch，maxRespectively representing the minimum charging power and the maximum charging power of the battery of the electric automobile, wherein the unit is kW;
Figure FDA0002430836170000029
in the form of a binary variable, the variable,
Figure FDA00024308361700000210
a value of 1 indicates that the ith electric vehicle is in a charged state at time t,
Figure FDA00024308361700000211
the value of (1) is 0, which indicates that the ith electric automobile is in a discharging state or an idle state at the time t; pi EV，ch(t) represents the charging power of the battery of the ith electric automobile at the time t, and the unit is kW; i is the total amount of the electric automobile;
Figure FDA0002430836170000031
indicating the time when the ith electric vehicle arrives at the building,
Figure FDA0002430836170000032
indicating the moment when the ith electric automobile leaves the building; pEV，dch，minAnd PEV，dch，maxRespectively representing the minimum discharge power and the maximum discharge power of the battery of the electric automobile, and the unit is kW);
Figure FDA0002430836170000033
in the form of a binary variable, the variable,
Figure FDA0002430836170000034
a value of 1 indicates that the ith electric vehicle is in a discharge state at time t,
Figure FDA0002430836170000035
the value of (1) is 0, which indicates that the ith electric automobile is in a charging state or an idle state at the time t;
Figure FDA0002430836170000036
indicating the charging state of the ith electric vehicle battery at the time tState ηEV，chAnd ηEV，dchRespectively representing the charging efficiency and the discharging efficiency of the electric vehicle battery; τ represents the length of each time node in units of s;
Figure FDA0002430836170000037
the battery capacity of the ith electric automobile is expressed in kWh;
Figure FDA0002430836170000038
indicating the state of charge of the ith electric vehicle battery arrival time,
Figure FDA0002430836170000039
indicating the state of charge demand of the battery when the ith electric vehicle leaves.
4. The method for dispatching the building comprehensive energy resource of claim 1, wherein in the step of constructing the building comprehensive energy resource facility equivalent model by using a photovoltaic power generation equivalent model, an electric vehicle random charge-discharge model and a building dynamic thermodynamic model, the building dynamic thermodynamic model is described by the following formula:
Figure FDA00024308361700000310
Figure FDA00024308361700000311
Figure FDA00024308361700000312
Figure FDA00024308361700000313
Figure FDA00024308361700000314
Sac(t)∈{0，1}；
Figure FDA00024308361700000315
wherein, Tr(t) represents the room air temperature at time t in units of; maAnd MwRespectively representing the indoor air quality and the building wall quality, and the unit is kg;
Figure FDA00024308361700000316
and
Figure FDA00024308361700000317
respectively representing the heat capacity of air and the heat capacity of a wall body, and the unit is J/kg DEG C; t isamb(t) represents the outdoor ambient temperature at time t in ° c); t isw(t) represents the temperature of the building wall at the moment t, and the unit is; reqRepresenting the equivalent thermal resistance of the building wall; rwrRepresenting the thermal resistance between the building wall and the indoor air; rwaRepresenting the equivalent thermal resistance between the building wall and the outdoor environment; sac(t) represents the air conditioner on-off state at time t; qac(t) represents the cooling capacity of the air conditioner at the time t, and the unit is kWh; n represents the number of time nodes;
Figure FDA0002430836170000041
represents the lower limit of the indoor temperature requirement, and the unit is;
Figure FDA0002430836170000042
represents the upper limit of the indoor temperature requirement, and the unit is; pac(t) represents the air conditioner electric power at the time t, and the unit is kW; COP indicates the air-conditioning energy efficiency ratio.
5. The method for dispatching building integrated energy resources according to claim 1, wherein the building integrated energy resources equivalent model is transformed into a problem model based on mixed integer linear programming in the step of transforming the decision variables in the building integrated energy resources equivalent model into integer variables, and the power balance constraint condition of the problem model based on mixed integer linear programming is described by the following formula:
PPV，build(t)+PPV，sell(t)＝PPV(t)；
Figure FDA0002430836170000043
PPV，build(t)+∑iPi EV，build(t)+Pgrid，buy(t)＝Pac(t)+Pbasic(t)+∑iPi EV，ch(t)；
Pgrid，sell(t)＝PPV，sell(t)+Pi EV，sell(t)；
Figure FDA0002430836170000044
Figure FDA0002430836170000045
PPV，build(t) the power of the photovoltaic power generation system for supplying power to the building at the moment t is represented in kW; pPV，sell(t) power sold to a power grid by the photovoltaic power generation system at the moment t is represented in kW; pi EV，sell(t) represents the power sold to the power grid by the ith electric automobile at the time t, and the unit is kW; pi EV，build(t) the power of the ith electric automobile for supplying power to the building at the moment t is represented by kW; pgrid，buy(t) the power of the building comprehensive energy facility for buying electricity from the power grid at the moment t is represented in kW; pbasic(t) represents the base load of the building in kW; pgrid，sell(t) power sold to the power grid by the building comprehensive energy facility at the moment t is represented in kW; epsilongrid(t) is a binary variable, εgrid(t) a value of 1 indicates that the building complex energy facility has started at time tPower buying in the grid, epsilongrid(t) a value of 0 indicates that the building complex energy facility did not buy electricity from the grid at time t; pgrid，maxThe maximum power capacity of the power transmission line of the power distribution network is expressed in kW.
6. The method for scheduling integrated energy resources of a building according to claim 1, wherein the method for scheduling integrated energy resources of a building includes the steps of obtaining forecast data in the day-ahead, and performing optimal solution on a problem model based on mixed integer linear programming according to the forecast data in the day-ahead and the schedule by using a model predictive control method and a day-ahead optimal scheduling strategy to obtain a control scheduling plan of integrated energy resources of a building, and further includes the steps of:
and acquiring the total operation cost of the control scheduling strategy in the scheduling period according to the control scheduling plan.
7. The method according to claim 6, wherein in the step of obtaining the total operating cost of the control scheduling policy during scheduling according to the control scheduling plan, the total operating cost of the control scheduling policy during scheduling is obtained according to the following objective function:
Figure FDA0002430836170000051
wherein, Cbuy(t) shows the price of electricity bought (yuan/kWh) at time t, Csell(t) represents the electricity selling price (yuan/kWh) at time t; lambda [ alpha ]PVAnd λEVIs a predetermined positive number, λPV<λEV。
8. A building comprehensive energy control and dispatching system is characterized by comprising
The building integrated energy facility equivalent model building module is used for building a building integrated energy facility equivalent model by using a photovoltaic power generation equivalent model, an electric automobile random charge-discharge model and a building dynamic thermodynamic model; the photovoltaic power generation equivalent model simulates the influence of illumination intensity, illumination effective area of the solar panel and environment temperature on the output power of the solar panel; the electric automobile random charge-discharge model simulates the change of the battery charge state of the electric automobile along with charge-discharge power and time in the charge-discharge process based on the characteristic that the electric automobile is randomly accessed to a building comprehensive energy facility; the heat transfer process among the outdoor environment temperature, the wall body temperature and the indoor air temperature in the building dynamic thermodynamic model is equivalent to a circuit comprising a resistor and a capacitor, wherein the temperature is equivalent to voltage, the specific heat capacity is equivalent to the capacitor, and the thermal resistance is equivalent to the resistor;
the building integrated energy facility equivalent model conversion module is used for converting the building integrated energy facility equivalent model into a problem model based on mixed integer linear programming by converting decision variables in the building integrated energy facility equivalent model into integer variables;
the schedule generating module is used for modeling the randomness of the electric automobile by using a probability density function according to the automobile using habit of an electric automobile owner and generating a schedule for predicting the time of the electric automobile accessing and leaving the building comprehensive energy facility;
the control scheduling plan obtaining module is used for obtaining day-ahead prediction data, and according to the day-ahead prediction data and the timetable, performing optimization solution on a problem model based on mixed integer linear programming by adopting a model prediction control method and a day-ahead optimization scheduling strategy to obtain a control scheduling plan of the building comprehensive energy facility; wherein the day-ahead prediction data comprises solar radiation intensity data, environmental temperature data and electricity price data.
9. A storage medium having a computer program stored thereon, the computer program comprising: the computer program when being executed by a processor realizes the steps of the building integrated energy scheduling method according to any one of claims 1 to 7.
10. A computer device, characterized by: comprising a storage medium, a processor and a computer program stored in the storage medium and executable by the processor, the computer program, when executed by the processor, implementing the steps of the method for scheduling integrated energy for buildings according to any of claims 1 to 7.
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