CN114612014A - Day-ahead interval optimization scheduling method, system, device and medium for building energy system - Google Patents

Day-ahead interval optimization scheduling method, system, device and medium for building energy system Download PDF

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CN114612014A
CN114612014A CN202210462453.0A CN202210462453A CN114612014A CN 114612014 A CN114612014 A CN 114612014A CN 202210462453 A CN202210462453 A CN 202210462453A CN 114612014 A CN114612014 A CN 114612014A
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energy storage
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程序
王超
胡宝玉
管华傲
郭泰龙
潘正阳
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State Grid Corp of China SGCC
State Grid Beijing Electric Power Co Ltd
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State Grid Beijing Electric Power Co Ltd
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Abstract

The invention discloses a day-ahead interval optimal scheduling method, a day-ahead interval optimal scheduling system, a day-ahead interval optimal scheduling device and a day-ahead interval optimal scheduling medium based on a building energy system, wherein the day-ahead interval optimal scheduling method comprises the following steps of: modeling a building energy system containing virtual energy storage based on the interval number to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models; constructing a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models; and solving the day-ahead interval optimization scheduling model to obtain an optimization scheme. The invention provides a virtual energy storage model comprising a virtual charge-discharge power interval variable, a virtual charge state interval number and a virtual energy storage heat capacity parameter based on outdoor temperature and illumination radiance interval number, so as to quantify the regulation potential provided by the building envelope structure influenced by uncertainty factors such as outdoor temperature and the like for the optimized scheduling of a building energy system.

Description

Day-ahead interval optimization scheduling method, system, device and medium for building energy system
Technical Field
The invention belongs to the field of optimization scheduling of building energy systems, and particularly relates to a method, a system, a device and a medium for optimization scheduling of a day-ahead interval of a building energy system.
Background
The rapid increase in building energy consumption increases global carbon emissions. The introduction of renewable clean energy in building energy systems is being energetically driven in order to reduce the carbon emissions of buildings. The photovoltaic power generation has the advantages of large specification span, easiness in installation, short installation period, simplicity in maintenance, no noise and the like, and combines the roof photovoltaic with the building, so that the photovoltaic power generation becomes a common form of building and photovoltaic power supply combination. However, photovoltaic output has a large uncertainty, and the peak-to-valley time and load power consumption of photovoltaic power generation may not be synchronized, which may lead to photovoltaic on-site consumption problems in building energy systems.
The problem of local consumption and photovoltaic electric energy utilization can be solved by equipping a building energy system with an electric energy storage system. However, the electrical energy storage system is still expensive to install, maintain, etc. and occupies a large space. The virtual energy storage system is established by utilizing the thermal inertia of the building envelope structure, a flexible power regulation capability can be provided for the operation optimization of the building energy system on the basis of not increasing additional investment and occupied space, the operation cost of the building energy system is reduced in an auxiliary manner, and the local consumption rate of the photovoltaic power generation in the building energy system is improved.
At present, the influence of uncertainty of outdoor temperature and illumination radiance on virtual energy storage of a building is not considered in the optimal scheduling of a building energy system considering the virtual energy storage, so that the accuracy of the virtual energy storage is reduced, the deviation between the subsequently formulated optimal scheduling result of the building energy system and the actual operation condition is caused, and the operation economy of the building energy system is reduced. Therefore, how to introduce the uncertain influences of the outdoor temperature and the illumination radiance into the virtual energy storage modeling and the subsequent optimization scheduling of the building energy system becomes a premise for supporting the economic operation of the building energy system.
Disclosure of Invention
Aiming at the defects of the existing building energy system day-ahead optimization scheduling technology considering virtual energy storage, the invention aims to provide a building energy system day-ahead interval optimization scheduling method, system, device and medium based on building envelope virtual energy storage, and aims to consider the influence of uncertainty of outdoor temperature and illumination radiance on building virtual energy storage, and propose the number of outdoor temperature and illumination radiance intervals based on an interval optimization theory, so as to quantify the regulation potential provided by the building envelope influenced by uncertainty factors such as outdoor temperature and the like for building energy system optimization scheduling, and guide the economic operation of the building energy system.
The first aspect of the embodiment of the invention provides a building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage, which comprises the following steps:
modeling a building energy system containing virtual energy storage based on the interval number to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models;
constructing a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and solving the day-ahead interval optimization scheduling model to obtain an optimization scheme.
As an alternative of the embodiment of the present invention, the building envelope virtual energy storage model includes:
Figure BDA0003622572590000021
Figure BDA0003622572590000022
wherein k is the kth scheduling time interval of the scheduling day, k is more than or equal to 1 and less than or equal to 48, and k belongs to N; pc/disVirtual charge and discharge power, kW, for building envelope virtual energy storage; pcomThe output is kW for the power distribution network; pPVPhotovoltaic power output for the roof, kW; potherThe energy consumption of indoor equipment except the variable frequency air conditioner in the building energy system is kW; pbaseThe reference power consumption for building envelope virtual energy storage is kW.
As an alternative to the embodiments of the present invention, the rooftop photovoltaic module includes:
Figure BDA0003622572590000023
in the formula, PPV_maxThe maximum output power of the roof photovoltaic is kW under the standard condition; sPV(k) For the actual illumination radiance of the photovoltaic surface in the kth scheduling period, 1000W/m2；SrefIs a reference value of the irradiance of the photovoltaic surface, 1000W/m2；TPV(k) The actual photovoltaic cell temperature in the kth scheduling period, DEG C; t isPV_refIs the reference value of the temperature of the photovoltaic cell, DEG C; pPV_fore(k) The maximum output power of the roof photovoltaic in the kth scheduling period is kW; a, b and c are compensation coefficients of the roof photovoltaic model, and values are respectively as follows: 0.0025 deg.C-1，0.0005m2/W，0.00288℃-1(ii) a e is the natural logarithm.
As an alternative of the embodiment of the present invention, the other electrical load models are:
[Pother(k)]＝[ηother(k)]×Pother.all
in the formula etaother(k) Building other electric load simultaneous rates of the energy system in the kth scheduling period; pother.allThe total power of all electric equipment except the variable frequency air conditioner in the building energy system is kW.
As an alternative of the embodiment of the present invention, the day-ahead interval optimized scheduling model is:
Figure BDA0003622572590000031
in the formula: pc/disVirtual charge and discharge power, kW, for building envelope virtual energy storage; pbaseThe reference power consumption for building envelope virtual energy storage, kW; potherThe energy consumption of indoor equipment except the variable frequency air conditioner in the building energy system is kW; pPVPhotovoltaic power output for the roof, kW.
As an alternative of the embodiment of the present invention, the constraint condition of the day-ahead interval optimization scheduling model is: the system comprises a user thermal comfort constraint, a virtual energy storage charge-discharge power constraint, a photovoltaic output power range constraint and a bus electric balance constraint.
As an alternative of the embodiment of the present invention, when solving the scheduling model for optimizing the interval before day, the objective functions min f are respectively solved-And objective function min f+Obtaining an optimal value interval result of the optimal scheduling of the building energy system by the corresponding optimal scheduling model;
objective function min f-And the corresponding constraints are shown below:
Figure BDA0003622572590000032
objective function min f+And the corresponding constraints are shown below:
Figure BDA0003622572590000041
solving objective function min f by calling CPLEX using interior point method-And min f+Obtaining the optimal value interval [ f ] of the optimization scheduling model of the day-ahead interval of the building energy systemopt -,fopt +]。
In a second aspect of the embodiments of the present invention, a system for the building energy system day-ahead interval optimization scheduling method based on the enclosure virtual energy storage is provided, including:
the first model building module is used for modeling a building energy system containing virtual energy storage based on the interval number so as to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models;
the second model building module is used for building a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and the model solving module is used for solving the day-ahead interval optimization scheduling model to obtain an optimization scheme.
In a third aspect of the embodiments of the present invention, a computer apparatus is provided, and includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the foregoing building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage when executing the computer program.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for optimizing and scheduling the day-ahead interval of the building energy system based on the enclosure virtual energy storage is implemented.
Compared with the closest prior art, the technical scheme provided by the invention has the following beneficial effects:
(1) according to the technical scheme provided by the invention, based on the outdoor temperature and the number of the illumination radiance intervals, a virtual energy storage model comprising a virtual charge-discharge power (VCDP) interval variable, the number of the virtual charge state (VSOC) intervals and a virtual energy storage thermal capacity (VHSC) parameter is provided, so that the adjustment potential provided by the building envelope structure influenced by uncertain factors such as the outdoor temperature for the optimized scheduling of the building energy system is quantified.
(2) According to the technical scheme provided by the invention, based on an interval optimization theory, a method for optimizing and scheduling the day-ahead interval of the building energy system containing the virtual energy storage is provided by taking the adjustable range of the VCDP interval variable as constraint, and a method for solving a model for optimizing and scheduling the day-ahead interval is provided.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a day-ahead interval optimization scheduling method of a building energy system according to the present invention;
FIG. 2 is a schematic diagram of a typical and architectural energy system configuration of the present invention;
FIG. 3 is a schematic diagram of a grid-connected building energy system with virtual energy storage according to the present invention;
FIG. 4 is a schematic diagram of the VCDP definition of the present invention;
FIG. 5 shows that the optimal scheduling results under scenario I and scenario II are f respectively in this embodimentopt -、fopt +The corresponding indoor temperature; wherein, the optimized scheduling result under (a) scene I and scene II is fopt -Corresponding indoor temperature, (b) the optimal scheduling result under the scene I and the scene II is fopt +The corresponding indoor temperature;
FIG. 6 shows that the optimal scheduling results under scenario I and scenario II are f respectively in this embodimentopt -、fopt +The corresponding photovoltaic output power; wherein, the optimized scheduling result under (a) scene I and scene II is fopt -Corresponding photovoltaic output power, (b) the optimal scheduling result under the scene I and the scene II is fopt +The corresponding photovoltaic output power;
FIG. 7 is a view of the scene in this embodimentI. The optimized scheduling results under the scene II are respectively fopt -、fopt +The corresponding electricity purchasing power is obtained; wherein, the optimized scheduling result under (a) scene I and scene II is fopt -The corresponding electricity purchasing power in time, (b) the optimized scheduling result under the scene I and the scene II is fopt +The corresponding electricity purchasing power is obtained;
FIG. 8 shows that the optimized scheduling results under scenario I and scenario II in this embodiment are f respectivelyopt -、fopt +A total power consumption corresponding to time; wherein, the optimized scheduling result under (a) scene I and scene II is fopt -The time-corresponding total power consumption, (b) the optimized scheduling result under the scene I and the scene II is fopt +A total power consumption corresponding to time;
FIG. 9 shows that the optimal scheduling results under scenario I in this embodiment are fopt -、fopt +Time corresponding VCDP and its constraints; wherein, the optimized scheduling result in the scene I is foptTime-corresponding VCDP and its constraints, (b) optimal scheduling result under scenario I is fopt +Time corresponding VCDP and its constraints;
FIG. 10 shows that the optimal scheduling results under scenario I in this embodiment are fopt -、fopt +A time corresponding VSOC; wherein, the optimized scheduling result in the scene I is foptTime corresponding VSOC, (b) optimal scheduling result under scenario I is fopt +The corresponding VSOC.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The invention provides a building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage, which comprises the following steps:
step S1, modeling the building energy system containing virtual energy storage based on the interval number, including building envelope virtual energy storage model, roof photovoltaic model and other electric load model;
step S2, constructing a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and step S3, solving the optimization scheduling model in the daytime to obtain an optimization scheme, and performing optimization scheduling on the building energy system based on the optimization scheme.
Step S1 specifically includes:
a typical grid-connected building energy system configuration is shown in fig. 1 and includes rooftop photovoltaic, variable frequency air conditioning and other electrical loads.
Distribution network and roof photovoltaic provide the electric energy for the building. The photovoltaic output is influenced by the uncertainty factor of the irradiance, so that the interval number [ P ] is adoptedPV]Description of PV force, [ P ]PV]＝[PPV -，PPV +]In the superscript, "-" indicates a lower limit value of the interval, and "+" indicates an upper limit value of the interval. For the convenience of distinction, the invention will be followed by the unified symbol "[ 2 ]]"indicates the number of intervals or the interval variable, and the detailed description of each of the number of intervals or the interval variable will not be repeated.
The variable frequency air conditioner is used for building electric heating. In order to ensure that the temperature in the building is maintained within the thermal comfort range of the user, the power consumption and the heating power of the inverter air conditioner are uncertain due to the uncertainty of the outdoor temperature and the illumination radiation temperature, and therefore, the power consumption and the heating power need to be defined as the number of intervals. In FIG. 1, [ P ] is usedIAC]、[QIAC]The number of the power consumption intervals for heating of the building energy system and the number of the corresponding heating power intervals are represented.
Other electric loads refer to other indoor places except for variable frequency air conditioners in building energy systemsThe electrical load of the equipment, which is related to the user's behavior in the building's energy system, has uncertainty, and needs to be defined as the number of intervals, corresponding to [ P ] in FIG. 1other]。
In FIG. 1 [ Q ]diss,1]～[Qdiss,4]The heat dissipation power interval number corresponding to different building envelopes. The heat dissipation power is related to the outdoor temperature and the illumination radiance, and therefore is characterized by the number of intervals.
By considering the influence of the uncertainty of the outdoor temperature and the illumination radiance on the adjustment potential that the building envelope can provide for the building energy system, the building envelope and the variable frequency air conditioner can be modeled as virtual energy storage as shown in fig. 2.
The virtual energy storage is used for quantifying and representing the uncertain adjustment potential provided by the building envelope structure for the optimized scheduling of the building energy system. In FIG. 2 [ P ]c/dis]、[Pbase]The VCDP interval variable and the reference power consumption interval number of the virtual energy storage are respectively indicated.
Firstly, a building envelope virtual energy storage model construction method based on interval number is provided.
According to the heat exchange principle of the building envelope structure, considering the influence of the number of the intervals of the outdoor temperature and the illumination radiance on the heat exchange between the building envelope structure and indoor air, and defining the interval number of the heat dissipation power of the building;
furthermore, a variable frequency air conditioner model is introduced to describe the relation between the power consumption power and the heating power in the building energy system;
based on the heat dissipation power and the variable-frequency air conditioner model, a virtual energy storage model containing VCDP interval variables is constructed, and the interval representation of the virtual energy storage with uncertainty is realized.
As shown in FIG. 1, in the winter heating period, when the inverter air conditioner is operated in the heating mode, the temperature in the interior of the building is higher than the temperature of the inner surface of the building enclosure, which may cause heat exchange between the air in the interior of the building and the building enclosure. Defining this heat exchange as dissipated power Q of the building envelopediss(t) is represented by the formula (1). Qdiss(t) is influenced by outdoor temperature and degree of irradiation of light, and thus it is defined asThe number of intervals. It should be noted that since the indoor temperature is affected by the interval variable VCDP, T in the formulain(t) is also described in terms of number of intervals.
Figure BDA0003622572590000071
In the formula, Qsolar(t) is the thermal power contributed to the indoor by the solar radiation transmission window at the moment t; fiIs the inner surface area, m, of the i-th building envelope2；αinIs the heat exchange coefficient of the inner surface of the building, W/(m)2·℃)；Tin(t) is the building indoor temperature at time t, DEG C; t isi sur(t) is the inner surface temperature of the i-th building envelope at the time t, DEG C. 4 Qdis, t (t) add up to the left of the equation: qdis (t).
Qsolar(t) is related to the illumination radiance, so the value is defined as the interval number, and the calculation formula is shown as the formula (2).
[Qsolar(t)]＝λwinFsolar[Swin(t)]Ks,win (2)
In the formula of lambdawinIs the glass transmission coefficient; fsolarIs the area of the window capable of projecting sunlight, m2；Swin(t) is the surface illumination radiance of the window at the time t, kW/m2；Ks,winAnd the window surface illumination radiance correction factor.
Ti sur(t) is affected by both the indoor temperature and the outdoor temperature, and is defined as the number of intervals, and the calculation formula is shown as formula (3).
Figure BDA00036225725900000814
In the formula (I), the compound is shown in the specification,
Figure BDA0003622572590000081
the daily average temperature of the inner surface of the i-th building envelope is obtained; delta Ti sur.out(t) is in the ith building enclosure at the time tThe amount of change in surface temperature due to the influence of outdoor temperature fluctuations; delta Ti sur.inAnd (t) is the change quantity of the temperature of the inner surface of the ith building envelope influenced by the indoor temperature fluctuation at the moment t.
Figure BDA0003622572590000082
The average indoor temperature and the average outdoor temperature are defined as interval numbers, as shown in formula (4).
Figure BDA0003622572590000083
In the formula (I), the compound is shown in the specification,
Figure BDA0003622572590000084
the average daily indoor temperature is DEG C. Since the indoor temperature of a building is generally maintained at a room temperature value set by a user,
Figure BDA0003622572590000085
and determining according to the indoor temperature scheme set by the user.
Figure BDA0003622572590000086
The daily average outdoor temperature is determined from the predicted outdoor temperature in deg.C. Since the outdoor temperature is expressed by the number of intervals,
Figure BDA0003622572590000087
and also expressed in terms of the number of intervals. RinThe heat resistance of the inner surface of the building envelope structure and the reciprocal of the heat exchange coefficient of the inner surface are calculated, (m)2·℃)/W；Ri 0For the heat transfer resistance of the i-th building enclosure, (m)2·℃)/W。
ΔTi sur.out(t) is related to outdoor temperature change, defined as interval number, and calculated as shown in formula (5).
Figure BDA0003622572590000088
ΔTi sur.in(t) is related to the indoor temperature change and is defined as the interval number, and the calculation formula is shown as the formula (6).
Figure BDA0003622572590000089
υi outThe attenuation times of the temperature transmitted from the outdoor to the inner surface of the ith building envelope are obtained;
Figure BDA00036225725900000810
the delay coefficient when the temperature is transmitted to the inner surface of the i-th building envelope from the outside; upsilon isi inThe attenuation times of the temperature transmitted from the indoor to the inner surface of the i-th building envelope are obtained;
Figure BDA00036225725900000811
is the delay coefficient when the temperature is transmitted from the indoor to the inner surface of the i-th building envelope. Upsilon isi out、
Figure BDA00036225725900000812
υi inAnd
Figure BDA00036225725900000813
depending on the material of the building envelope.
Power consumption P of inverter air conditionerIAC(t) and heating Power QIAC(t) can be simplified to the linear relationship shown in the formula (7). It should be noted that, in order to ensure the thermal comfort of the user, the power consumption and the heating power of the inverter air conditioner are affected by the outdoor temperature and the illumination irradiance, and therefore, the number of the sections is defined.
[QIAC(t)]＝m[PIAC(t)]+d (7)
In the formula, PIACThe value range of (t) is shown as formula (8).
Pmin≤[PIAC(t)]≤Pmax (8)
In this embodiment, considering the influence of uncertainty of outdoor temperature and irradiance, a Virtual energy Storage model is constructed, which includes a variable of a Virtual Charge and Discharge Power (VCDP) interval of Virtual energy Storage, a Heat Capacity of Virtual energy Storage (VHSC) parameter of Virtual energy Storage, and a VSOC (Virtual State of Charge, VSOC) interval number of a ratio between actual stored Heat energy of Virtual energy Storage and VHSC.
The VCDP is the charge and discharge power of the virtual energy storage, and describes the power change before and after the virtual energy storage participates in the dispatching of the building energy system. The building energy system realizes the optimized scheduling of the building energy system by controlling the VCDP, and simultaneously, the VCDP is defined as an interval variable because the VCDP is related to the heat dissipation power of the building.
The VHSC is the heat capacity of the virtual energy storage, and describes the maximum heat capacity which can be provided by the virtual energy storage for the optimal scheduling of the building energy system.
VSOC is the ratio of virtual stored energy actual stored thermal energy to VHSC.
The virtual storage thermal energy is related to VCDP, and therefore defines VSOC as the number of intervals.
The relationship between the three parameters is shown in equation (9).
Figure BDA0003622572590000091
In the formula: ccaIs the virtual capacity of the virtual stored energy, kWh.
The calculation formulas of the three parameters are respectively given below.
(1) The virtual charge and discharge power calculation formula is as follows:
when the heating power and the heat dissipation power of the variable frequency air conditioner are equal, the indoor temperature can maintain the temperature set by the user unchanged. The heating power for maintaining the indoor temperature as the temperature set by the user is defined as the reference heating power. According to the formula (1), the reference heating power and the number of intervals [ Ti sur(t)]And [ Q ]solar(t)]Therefore, it is defined as the number of intervals, as shown in equation (10).
Figure BDA0003622572590000092
In the formula, Qbase(t) is the reference heating power, kW; t issetSetting indoor temperature, DEG C, for a user; fiIs the inner surface area, m, of the building envelope of the i-th class2；αinIs the heat exchange coefficient of the inner surface of the building, W/(m)2·℃)；Ti sur(t) the inner surface temperature of the i-th building envelope at the moment t, DEG C; qsolarAnd (t) is the heat power contributed to the room by the solar radiation transmitting the window at the moment t.
It is noted that, within a particular scheduling period k (k Δ t ≦ (k +1) Δ t), QbaseThe variation of (t) is small and can be regarded as a constant, as shown in formula (11).
Figure BDA0003622572590000101
By bringing formula (11) into formula (7), the reference heating power Q can be obtainedbase(k) The corresponding reference power consumption is expressed by equation (12).
Figure BDA0003622572590000102
As shown in FIG. 3, when P isIAC＝PbaseWhen, TinIs kept at Tset. If P isIAC≠PbaseAs shown at P' in FIG. 3, at PIACAnd PbaseThere is a power deviation therebetween, which is defined as VCDP, as shown in equation (13). When VCDP>When the energy consumption of the building is increased, the output power provided by the PV and the power distribution network for the operation of the building energy system is increased, and the virtual energy storage is in a charging state; otherwise, the building energy consumption is reduced, the output power provided by the PV and the power distribution network for the operation of the building energy system is reduced, and the virtual energy storage is in a discharge state.
Figure BDA0003622572590000103
It is assumed that the electricity purchasing power, the actual output power of the roof photovoltaic, the charge and discharge power of the electric energy storage device and other electric loads of the building energy system from the power distribution network are all kept unchanged in the kth scheduling period. Considering the electric power balance of the bus of the building energy system shown in fig. 1, the power consumption of the variable frequency air conditioner in the kth scheduling period is shown as the formula (14).
[PIAC(k)]＝[Pcom(k)]+[PPV(k)]-[Pother(k)] (14)
In the formula, PcomThe output is kW for the power distribution network; pPVPhotovoltaic power output for the roof, kW; potherThe energy-saving system is the power load, kW, of other indoor equipment in a building energy system except for the variable frequency air conditioner.
Formula (15) can be obtained by substituting formula (14) for formula (13).
Figure BDA0003622572590000104
The VCDP is limited by the power consumption range of the inverter air conditioner, and the maximum discharge power and the maximum charge power of the virtual energy storage are shown in formulas (16) to (17). Maximum discharge power P of virtual energy storagedismax(k) And maximum charging power Pcmax(k) Number of average intervals [ Pbase(k)]In this case, the maximum discharge power and the maximum charge power of the virtual energy storage are defined as the number of intervals according to the number-of-intervals calculation rule.
[Pdismax(k)]＝[Pbase(k)]-Pmin (16)
[Pcmax(k)]＝Pmax-[Pbase(k)] (17)
In the formula, PminThe minimum power consumption is kW; pmaxThe maximum power consumption of the variable frequency air conditioner is kW.
(2) The calculation formula of the virtual energy storage heat capacity VHSC is as follows:
VHSC only has equivalent heat capacity with building enclosure and indoor temperature comfort range set by usermin,Tmax]Related to outdoor temperatureIndependent of illumination radiance, VHSC is therefore defined as a parameter rather than an interval number. According to the calculation formula of the heat storage quantity of the building envelope structure, VHSC can be calculated by the formula (18).
Cca＝C(Tmax-Tmin) (18)
Wherein C is the equivalent heat capacity of the building, kJ/DEG C.
(3) The virtual state of charge calculation formula is as follows:
VSOC is defined as the ratio of virtual stored energy actual stored heat energy to virtual stored energy heat capacity VHSC, and is used for describing the stored energy state of virtual stored energy, as shown in formula (19), and the range is 0 to 1. When T isin＝TminWhen VSOC is 0, when TinIs TmaxWhen VSOC is 1.
Figure BDA0003622572590000111
E (k) is virtual energy storage actual stored heat energy, and TinRelated, TinIs affected by the interval variable VCDP, and therefore e (k) is defined as the number of intervals. The calculation formula is shown in formula (20).
[E(k)]＝C([Tin(k)]-Tmin) (20)
Secondly, a roof photovoltaic model construction method based on the interval number is provided.
Because the photovoltaic output is influenced by uncertainty of the illumination radiance, assuming that the photovoltaic output power is kept unchanged in the kth scheduling period, a roof photovoltaic model based on the illumination radiance interval number shown in formula (21) is constructed.
Figure BDA0003622572590000112
In the formula, PPV_maxThe maximum output power of the roof photovoltaic is kW under the standard condition; sPV(k) The actual illumination radiance of the photovoltaic surface in the kth scheduling period is 1000W/m2；SrefIs a reference value of the irradiance of the photovoltaic surface, 1000W/m2；TPV(k)The actual photovoltaic cell temperature in the kth scheduling period, DEG C; t isPV_refIs the reference value of the temperature of the photovoltaic cell, DEG C; pPV_fore(k) The maximum output power of the roof photovoltaic in the kth scheduling period is kW; a, b and c are compensation coefficients of the roof photovoltaic model, and values are respectively as follows: 0.0025 deg.C-1，0.0005m2/W，0.00288℃-1。
The photovoltaic output in the building energy system is supposed to be only used for meeting the power utilization requirement of the building energy system, and the power is not transmitted to the upper-level distribution power grid.
When the electricity demand of the building energy system is small and the maximum output power of the roof photovoltaic is large, the actual output power of the roof photovoltaic is smaller than the maximum output power, and the problem of photovoltaic local consumption of the building energy system can be caused. Therefore, the photovoltaic local consumption rate eta shown in the formula (22) is introduced to describe the photovoltaic local consumption condition of the building energy system. The larger the eta is, the larger the photovoltaic local absorption amount is, and conversely, the smaller the photovoltaic local absorption amount is.
Figure BDA0003622572590000121
And finally, giving other electrical load models based on the interval number.
The energy consumption of other electrical loads is generally predicted and calculated by adopting the load concurrency rate, and because the load concurrency rate also has uncertainty, other electrical load prediction models are constructed by adopting the load concurrency rate interval number, and the models are shown as a formula (23).
[Pother(k)]＝[ηother(k)]×Pother.all (23)
In the formula etaother(k) Building other electric load simultaneous rates of the energy system in the kth scheduling period; pother.allThe energy-saving air conditioner is the sum of the powers of all electric equipment except the variable frequency air conditioner in a building energy system, namely kW.
Step S2 specifically includes:
firstly, a day-ahead optimization scheduling model is constructed, and determination of an objective function and constraint conditions is included.
The aim of optimizing the dispatching in the day ahead is to further improve photovoltaic local consumption on the basis of minimizing the electricity purchasing cost of users of the building energy system, and an interval objective function is shown as a formula (24). It should be noted that although the objective function calculation formula of the day-ahead interval optimization scheduling model is fixed, the building envelope structure can provide uncertainty for the virtual energy storage for the optimization scheduling of the building energy system, so that the final optimization scheduling result of the building energy system is also the interval number (i.e., the objective function is in the form of [ f ]).
Figure BDA0003622572590000122
Wherein p (k) is the power purchase price of the distribution network in the kth scheduling period.
The constraints are as follows:
1) virtual energy storage constraints
(1) VSOC constraints
The VSOC constraint corresponds to the user thermal comfort constraint and is required to satisfy equation (25). When VSOC is 1, the actual room temperature is equal to the upper limit of the user thermal comfort range; when VSOC is 0, the actual room temperature is equal to the user thermal comfort range lower limit.
0≤VSOC(k)≤1 (25)
(2) VCDP constraints
The VCDP constraint is affected by the virtual tank maximum charge power and the maximum discharge power, as shown in equation (26).
-[Pdismax(k)]≤[Pc/dis(k)]≤[Pcmax(k)] (26)
2) Photovoltaic outputable power range constraints
The photovoltaic output power is constrained by the photovoltaic maximum output power, as shown in equation (27).
0≤[PPV(k)]≤[PPV_fore(k)] (27)
3) Building energy system electrical balance constraint
As shown in fig. 2, the bus balance constraint shown in equation (28) should be satisfied in the optimized scheduling process.
[Pcom(k)]+[PPV(k)]＝[Pc/dis(k)]+[Pbase(k)]+[Pother(k)] (28)
Secondly, a model solving method is provided.
According to the interval optimization theory, aiming at the day-ahead optimization scheduling model of the building energy system provided by the invention, the objective functions min f can be respectively solved-And objective function min f+And obtaining an optimal value interval result (electricity purchasing cost of building energy system users) of the optimized scheduling of the building energy system by the corresponding optimized scheduling model.
The result of the optimal value interval can be expressed as [ fopt -,fopt +]. Meanwhile, the optimal scheduling scheme corresponding to the upper and lower boundaries of the optimal value interval can provide reference for the establishment of the optimal scheduling scheme of the building energy system.
Wherein the objective function min f-And its corresponding constraint is shown as equation (29).
Figure BDA0003622572590000131
Objective function min f+And its corresponding constraint is shown as equation (30).
Figure BDA0003622572590000141
S3 obtaining the optimal value range [ f ] of the building energy system day-ahead range optimization scheduling model provided by the invention by calling CPLEX and solving two functions shown in formula (29) and formula (30) by using an interior point methodopt-,fopt +]。
As shown in fig. 4 to 9, comparing the method (scene I) provided by the present invention with the current result of performing the day-ahead interval optimization scheduling (scene II) on the building energy system without considering the enclosure virtual energy storage, the electricity purchasing cost can be reduced and the photovoltaic local absorption rate can be improved under the influence of uncertainty factors of outdoor temperature and illumination radiance on the basis of ensuring the thermal comfort of the user.
Based on the same inventive concept, a second aspect of the embodiments of the present invention provides a system for implementing the building energy system day-ahead interval optimized scheduling method based on the building envelope virtual energy storage, including:
the first model building module is used for modeling a building energy system containing virtual energy storage based on the interval number so as to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models;
the second model building module is used for building a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and the model solving module is used for solving the day-ahead interval optimization scheduling model to obtain an optimization scheme.
Based on the same inventive concept, in a third aspect of the embodiments of the present invention, there is provided a computer apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the above building energy system day-ahead interval optimization scheduling method based on enclosure virtual energy storage.
Based on the same inventive concept, in a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, where a computer program is stored, and when the computer program is executed by a processor, the method for optimizing and scheduling a building energy system day-ahead interval based on building envelope virtual energy storage is implemented.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or are equivalent to the scope of the invention are intended to be embraced therein.

Claims (10)

1. A building energy system day-ahead interval optimal scheduling method based on building envelope virtual energy storage is characterized by comprising the following steps:
modeling a building energy system containing virtual energy storage based on the interval number to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models;
constructing a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and solving the day-ahead interval optimization scheduling model to obtain an optimization scheme.
2. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage of claim 1, wherein the building envelope virtual energy storage model comprises:
Figure FDA0003622572580000011
Figure FDA0003622572580000012
wherein k is the kth scheduling time interval of the scheduling day, k is more than or equal to 1 and less than or equal to 48, and k belongs to N; pc/disVirtual charge and discharge power for building envelope virtual energy storage; pcomOutputting power to the power distribution network; pPVPhotovoltaic power is output for the roof; potherThe energy consumption of other indoor equipment except the variable frequency air conditioner in the building energy system is reduced; p isbaseAnd the reference power consumption for the virtual energy storage of the building envelope structure.
3. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage of claim 2, wherein the rooftop photovoltaic model comprises:
Figure FDA0003622572580000013
in the formula, PPV_maxUnder the standard condition, the maximum output power of the roof photovoltaic is obtained; sPV(k) For the actual illumination radiance of the photovoltaic surface in the kth scheduling period, 1000W/m2；SrefIs a reference value of the irradiance of the photovoltaic surface, 1000W/m2；TPV(k) Scheduling the actual photovoltaic cell temperature within the time period k; t isPV_refIs a photovoltaic cell temperature reference value; pPV_fore(k) The maximum output power of the roof photovoltaic in the kth scheduling period is obtained; a, b and c are compensation coefficients of the roof photovoltaic model, and values are respectively as follows: 0.0025 deg.C-1，0.0005m2/W，0.00288℃-1(ii) a e is the natural logarithm.
4. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage of claim 3, wherein the other electrical load models are:
[Pother(k)]＝[ηother(k)]×Pother.all
in the formula etaother(k) Building other electric load simultaneous rates of the energy system in the kth scheduling period; pother.allThe power of all electric equipment except the variable frequency air conditioner in the building energy system is the sum.
5. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage of claim 4, wherein the day-ahead interval optimization scheduling model is:
Figure FDA0003622572580000021
in the formula: pc/disVirtual charge and discharge power for building envelope virtual energy storage; pbaseThe reference power consumption for building envelope virtual energy storage; potherFor the electricity utilization of other indoor equipment except the variable frequency air conditioner in the building energy systemA load; pPVAnd photovoltaic output is provided for the roof.
6. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage of claim 5, wherein the constraint conditions of the day-ahead interval optimization scheduling model are as follows: user thermal comfort constraint, virtual energy storage charge and discharge power constraint, photovoltaic output power range constraint and bus electric balance constraint.
7. The building energy system day-ahead interval optimization scheduling method based on building envelope virtual energy storage as claimed in claim 6, wherein when solving the day-ahead interval optimization scheduling model, an objective function min f-and an objective function min f are solved respectively+Obtaining an optimal value interval result of the optimal scheduling of the building energy system by the corresponding optimal scheduling model;
objective function min f-And the corresponding constraints are shown below:
Figure FDA0003622572580000022
objective function min f+And the corresponding constraints are shown below:
Figure FDA0003622572580000031
solving objective function min f by calling CPLEX using interior point method-And min f+Obtaining the optimal value range [ f ] of the optimization scheduling model of the day-ahead interval of the building energy systemopt -,fopt +]。
8. A system for the building energy system day-ahead interval optimization scheduling method based on the building envelope virtual energy storage is characterized by comprising the following steps:
the first model building module is used for modeling a building energy system containing virtual energy storage based on the interval number so as to obtain a building envelope virtual energy storage model, a roof photovoltaic model and other electric load models;
the second model building module is used for building a day-ahead interval optimization scheduling model based on the building envelope virtual energy storage model, the roof photovoltaic model and other electric load models;
and the model solving module is used for solving the day-ahead interval optimization scheduling model to obtain an optimization scheme.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the building energy system day-ahead interval optimization scheduling method based on enclosure virtual energy storage according to any one of claims 1 to 7.
10. A computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method for optimizing scheduling of building energy system day-ahead interval based on enclosure virtual energy storage according to any one of claims 1 to 7.
CN202210462453.0A 2022-04-28 2022-04-28 Day-ahead interval optimization scheduling method, system, device and medium for building energy system Pending CN114612014A (en)

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Publication number Priority date Publication date Assignee Title
WO2012145563A1 (en) * 2011-04-19 2012-10-26 Viridity Energy, Inc. Methods, apparatus and systems for managing energy assets
CN114362158A (en) * 2022-01-10 2022-04-15 天津大学 Day-ahead optimization control method of building micro-grid system based on time-varying virtual energy storage

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Title
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