CN113394776A - Power distribution network power supply capacity evaluation method based on flexible cold and hot demands - Google Patents

Power distribution network power supply capacity evaluation method based on flexible cold and hot demands Download PDF

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CN113394776A
CN113394776A CN202110766535.XA CN202110766535A CN113394776A CN 113394776 A CN113394776 A CN 113394776A CN 202110766535 A CN202110766535 A CN 202110766535A CN 113394776 A CN113394776 A CN 113394776A
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load
cold
heat
power supply
energy storage
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CN113394776B (en
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张章
迟福建
徐晶
张梁
刘晋辰
李桂鑫
夏冬
孙阔
祁彦鹏
李广敏
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F2111/04—Constraint-based CAD
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/08—Thermal analysis or thermal optimisation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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Abstract

The invention provides a power distribution network power supply capacity evaluation method based on flexible cold and heat requirements, relates to the technical field of power supply of power grids, and specifically comprises the following steps: constructing a cold and hot load virtual energy storage model, wherein the cold and hot load virtual energy storage model comprises an indoor air heat balance model, an envelope structure heat balance model and a building virtual energy storage model based on indoor air and envelope structure heat balance; and constructing a power supply capacity evaluation model considering the flexible cold and heat requirements of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model. By the method, the power supply capacity of the power distribution network can be evaluated under the condition that the influences of the comfort level of the cold and hot loads and the virtual energy storage characteristics of the building are fully considered, and the power supply potential of the network is further excavated.

Description

Power distribution network power supply capacity evaluation method based on flexible cold and hot demands
Technical Field
The invention relates to the technical field of power supply evaluation of a power grid, in particular to a power supply capacity evaluation method of a power distribution network based on flexible cold and heat requirements.
Background
By the comprehensive influence of the flexible cold and heat demands of the user and the thermal inertia of the building, once the superior power grid fails, the power consumption experience of the user on the cold/heat load cannot disappear immediately, so that the cold/heat load has greater elasticity compared with the electric load, and the energy storage system can be regarded as a flexible virtual energy storage resource. For the current big city, the land resources of the transformer substation are in shortage, the line corridor is difficult to obtain, the power supply potential of the power distribution network can be further excavated by reasonably utilizing the resources, the asset utilization rate is improved, and the equipment investment is reduced. Therefore, the method for evaluating the power supply capacity of the power distribution network based on the flexible cold and hot demands has great significance.
In the prior art, the evaluation of the power supply capacity of the power distribution network is divided into two categories, one category is a power supply capacity calculation method considering an N-1 safety criterion, the method focuses on load peak time with short duration, neglects the fluctuation of load on a longer time scale, and the result of the evaluated power supply capacity is too conservative. The other type is a reliability-based power supply capacity evaluation method, which reflects a balance relation between a load with continuous fluctuation characteristics and constant power grid capacity, but does not analyze the difference of the influence of an electrothermal cold load on the power supply reliability of a user side after a superior power grid fault, and ignores the virtual energy storage characteristic of the cold/hot load within certain user comfort level.
In summary, how to evaluate the power supply capacity of the power distribution network and further mine the power supply potential of the network under the condition of fully considering the influences of the comfort level of the cold and hot loads and the virtual energy storage characteristics of the building needs further research.
Disclosure of Invention
In view of the above, the present invention provides a power distribution network power supply capacity evaluation method based on flexible cold and hot requirements, so as to evaluate the power distribution network power supply capacity and further mine the power supply potential of the network under the condition of fully considering the cold and hot load comfort and the influence of the building virtual energy storage characteristics.
The invention provides a power distribution network power supply capacity evaluation method based on flexible cold and heat requirements, which specifically comprises the following steps:
constructing a cold and hot load virtual energy storage model, wherein the cold and hot load virtual energy storage model comprises an indoor air heat balance model, an envelope structure heat balance model and a building virtual energy storage model based on indoor air and envelope structure heat balance;
and constructing a power supply capacity evaluation model considering the flexible cold and heat requirements of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model.
Preferably, the step of constructing a cold and hot load virtual energy storage model, where the cold and hot load virtual energy storage model includes an indoor air heat balance model, an envelope heat balance model, and a building virtual energy storage model based on indoor air and envelope heat balance includes:
an indoor air heat balance model is constructed by adopting the following formula:
Q1(t)+Q2(t)+Q3(t)＝Q4(t)+Q5(t)；
Figure BDA0003151885700000021
Q1(t) the internal surface of the building envelope at time tHeat convection with air;
Q2(t) the heat consumption of the building door and window at the moment t;
Q3(t) building air sensible heat increment in unit time at the moment t;
Q4(t) the heat exchange power between the refrigerating and heating equipment and indoor air at the moment t;
Q5(t) the heat exchange power between indoor heat sources such as human bodies, cookers and lighting and indoor air at the moment t;
Tin(t) is the indoor temperature at time t;
Tout(t) is the outdoor temperature at time t;
T1(t) the temperature of the inner surface of the building enclosure at the moment t;
h is the convective heat transfer coefficient of the building envelope;
f is the internal surface area of the building envelope;
Qd(t) the heat consumption of the opening of the outer door at the moment t;
Qw(t) the external window is opened at time t to consume heat;
beta is the outdoor wind invasion addition rate;
Kcis the outer door heat transfer coefficient;
fcis the outer door area;
cwthe specific heat of outdoor air;
ρwis the outdoor air density;
Vois the air volume in the building;
n (t) is the number of ventilations in the period t;
cothe specific heat of indoor air;
ρois the indoor air density;
Qk(t) the heat exchange power between the refrigeration equipment and the indoor air at the moment t;
constructing a thermal balance model of the building envelope by adopting the following formula:
Figure BDA0003151885700000031
T1(t) the temperature of the inner surface of the building envelope at the moment t;
s is the area of the wall;
c is the heat capacity of the wall;
rho is the density of the wall body;
Δ x is the thickness of the wall;
lambda is the heat conductivity coefficient of the wall;
qcothe convection heat exchange quantity between the inner surface of the enclosure structure and air at the moment t;
qsothe inner surface receives the solar radiant heat penetrating through the outer window at the time t;
the building virtual energy storage model based on the heat balance of the indoor air and the building enclosure structure is constructed by adopting the following formula:
Figure BDA0003151885700000041
Qdyn(t) -output power of the cooling or heating equipment at time t.
Preferably, the step of constructing a power supply capacity evaluation model considering the flexible cold and heat demands of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model includes:
and constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition.
Preferably, the step of constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition includes:
the objective function of the cold and hot load virtual energy storage model is as follows:
Figure BDA0003151885700000042
Pnetthe power supply capacity of the power distribution network to be evaluated is obtained;
mpis the rated capacity of the distribution transformer p;
AE,pand AT,pThe load rate of the power supply load and the cold and hot load for the distribution transformer;
n is the total number of distribution transformers in the power distribution network;
the constraint conditions of the objective function of the cold and hot load virtual energy storage model are as follows:
equipment load rate constraint:
0≤AE,p+AT,p≤1；
user comfort constraints:
-0.5≤ΓPMV(t)≤0.5；
solving the average power supply availability of the power distribution network under the condition of meeting the objective function;
Figure BDA0003151885700000051
Figure BDA0003151885700000052
Figure BDA0003151885700000053
average power supply availability of an ASAI power distribution network;
SAIDIEpower off time of the electrical load after the fault;
SAIDITpower off time of cold and hot load after failure;
tkjthe power failure time of the jth user is the kth fault;
αkjthe percentage of the jth user electrical load in the kth fault in the total load is;
beta kj is the percentage of the j user cold and hot load in the total load during the k fault;
tsta, kj is the continuous operation time of the virtual energy storage resource corresponding to the j-th user cold and hot load during the k-th failure;
q is the total number of power failure users in the kth fault;
p is the total number of power failures within a specified time;
Talthe number of electricity-requiring hours of a user in a specified time;
Nalall the users in the area are evaluated.
Preferably, the step of constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition includes:
solving the objective function by adopting a Monte Carlo simulation method, which specifically comprises the following steps:
1) selecting a typical daily temperature change curve, calculating the output characteristic of the refrigeration/heating equipment maintaining the optimal comfort temperature (26 ℃) of a user at different moments as the load characteristic of the cold/heat load, and constructing a load time sequence model to reflect the time-varying characteristic of the load based on the load characteristics of the electric load and the cold/heat load;
2) initializing the analog clock to 0, randomly generating the time-to-failure TTF of each element, and finding the minimum TTFrGenerating a repair time TTR for the componentrAnd advance the analog clock to the TTFr；
3) Reading the numerical values of the electric load and the cold and hot load of the user carried by the power distribution network when the r-th element fails by using the load time sequence model, and calculating the proportion of the electric load and the cold and hot load to the total load;
4) determining the power failure time of each user based on the fault analysis process of the feeder line partition: wherein, the power failure time of the electric load is directly counted and recorded; the difference between the power failure time of the electric load and the virtual energy storage continuous operation time based on the comfort level of a user needs to be calculated for the cold and hot loads, and the value is counted and recorded as the power failure time of the cold and hot loads;
5) a new random number is generated and converted into a new runtime TTF of the element rr′；
6) Judging whether the analog clock spans years or not, respectively calculating SAIDI indexes of the electric load and the cold and hot load and accumulating if the analog clock does not span years, and calculating a terminal power supply reliability index ASAI if the analog clock spans years;
7) judging whether the simulation clock is advanced to the time length required by meeting the evaluation precision, finishing the simulation process, counting the reliability indexes of each simulation year, averaging, and returning to the step 2 if the reliability indexes of each simulation year are not reached;
(2) solving the power supply capacity by adopting a genetic algorithm, and respectively corresponding the single feeder load rate and the codes of all the feeder load rates of the network by adopting a binary coding rule and gene segments and chromosomes in a biological genetic concept; the individuals represent the load rates of all feeders, and the population is a collection of several individuals. And taking the power supply capacity of the power distribution network as a fitness function.
The embodiment of the invention has the following beneficial effects: the invention provides a power distribution network power supply capacity evaluation method based on flexible cold and heat requirements, which specifically comprises the following steps: constructing a cold and hot load virtual energy storage model, wherein the cold and hot load virtual energy storage model comprises an indoor air heat balance model, an envelope structure heat balance model and a building virtual energy storage model based on indoor air and envelope structure heat balance; and constructing a power supply capacity evaluation model considering the flexible cold and heat requirements of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model. By the method, the power supply capacity of the power distribution network can be evaluated under the condition that the influences of the comfort level of the cold and hot loads and the virtual energy storage characteristics of the building are fully considered, and the power supply potential of the network is further excavated.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a power distribution network power supply capacity evaluation method based on flexible cold and hot demand provided by the invention;
fig. 2 is a structural diagram of a power distribution network according to an example of a power distribution network power supply capacity evaluation method based on flexible cold and heat requirements according to an embodiment of the present invention;
fig. 3 is a calculated outdoor temperature curve and solar radiation curve graph of the power distribution network power supply capacity evaluation method based on flexible cold and heat requirements, provided by the embodiment of the invention;
fig. 4 is a power distribution network power supply capacity evaluation result of different schemes of a power distribution network power supply capacity evaluation method based on flexible cold and hot requirements provided by an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. 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.
At present, the existing electrified detector generally adopts a single detection mode for detection, and in some occasions, the detection precision and effectiveness are reduced due to factors such as external interference, and the like, so that the evaluation of a maintenance worker on the operation state of the dry-type reactor is not facilitated.
For the convenience of understanding the embodiment, a detailed description will be given to a dry-type reactor electrification detecting instrument disclosed in the embodiment of the present invention.
The first embodiment is as follows:
the invention provides a power distribution network power supply capacity evaluation method based on flexible cold and heat requirements, which specifically comprises the following steps:
constructing a cold and hot load virtual energy storage model, wherein the cold and hot load virtual energy storage model comprises an indoor air heat balance model, an envelope structure heat balance model and a building virtual energy storage model based on indoor air and envelope structure heat balance;
and constructing a power supply capacity evaluation model considering the flexible cold and heat requirements of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model.
Preferably, the step of constructing a cold and hot load virtual energy storage model, where the cold and hot load virtual energy storage model includes an indoor air heat balance model, an envelope heat balance model, and a building virtual energy storage model based on indoor air and envelope heat balance includes:
an indoor air heat balance model is constructed by adopting the following formula:
Q1(t)+Q2(t)+Q3(t)＝Q4(t)+Q5(t)；
Figure BDA0003151885700000091
Q1(t) carrying out heat convection on the inner surface of the enclosure structure and air at the moment t;
Q2(t) the heat consumption of the building door and window at the moment t;
Q3(t) building air sensible heat increment in unit time at the moment t;
Q4(t) the heat exchange power between the refrigerating and heating equipment and indoor air at the moment t;
Q5(t) the heat exchange power between indoor heat sources such as human bodies, cookers and lighting and indoor air at the moment t;
Tin(t) is the time t chamberInternal temperature;
Tout(t) is the outdoor temperature at time t;
T1(t) the temperature of the inner surface of the building enclosure at the moment t;
h is the convective heat transfer coefficient of the building envelope;
f is the internal surface area of the building envelope;
Qd(t) the heat consumption of the opening of the outer door at the moment t;
Qw(t) the external window is opened at time t to consume heat;
beta is the outdoor wind invasion addition rate;
Kcis the outer door heat transfer coefficient;
fcis the outer door area;
cwthe specific heat of outdoor air;
ρwis the outdoor air density;
Vois the air volume in the building;
n (t) is the number of ventilations in the period t;
cothe specific heat of indoor air;
ρois the indoor air density;
Qk(t) the heat exchange power between the refrigeration equipment and the indoor air at the moment t;
constructing a thermal balance model of the building envelope by adopting the following formula:
Figure BDA0003151885700000101
T1(t) the temperature of the inner surface of the building envelope at the moment t;
s is the area of the wall;
c is the heat capacity of the wall;
rho is the density of the wall body;
Δ x is the thickness of the wall;
lambda is the heat conductivity coefficient of the wall;
qcothe convection heat exchange quantity between the inner surface of the enclosure structure and air at the moment t;
qsothe inner surface receives the solar radiant heat penetrating through the outer window at the time t;
the building virtual energy storage model based on the heat balance of the indoor air and the building enclosure structure is constructed by adopting the following formula:
Figure BDA0003151885700000102
Qdyn(t) -output power of the cooling or heating equipment at time t.
Preferably, the step of constructing a power supply capacity evaluation model considering the flexible cold and heat demands of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model includes:
and constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition.
ρwTaking 1.4kg/m3(ii) a n (t) 0.5 times/h; qk(t) take 3.8W/m2；
The continuous operation time of the virtual energy storage resources after the superior power grid fails is closely related to the comfort level of a user, the comfort level requirement of the user on the indoor environment can be expressed by a thermal sensing average scale prediction index-prediction average ticket number (PMV), and the index comprehensively considers the factors such as the human body metabolism rate, the worn clothes, the indoor air temperature and the like. The simplified formula for the PMV index is:
Figure BDA0003151885700000111
in the formula: rPMV(t) PMV index value at time t; m is the human metabolism rate; i isclThe clothing thermal resistance is clothing thermal resistance of a human body;
preferably, the step of constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition includes:
the objective function of the cold and hot load virtual energy storage model is as follows:
Figure BDA0003151885700000112
Pnetthe power supply capacity of the power distribution network to be evaluated is obtained;
mpis the rated capacity of the distribution transformer p;
AE,pand AT,pThe load rate of the power supply load and the cold and hot load for the distribution transformer;
n is the total number of distribution transformers in the power distribution network;
the constraint conditions of the objective function of the cold and hot load virtual energy storage model are as follows:
equipment load rate constraint:
0≤AE,p+AT,p≤1；
user comfort constraints:
-0.5≤ΓPMV(t)≤0.5；
solving the average power supply availability of the power distribution network under the condition of meeting the objective function;
Figure BDA0003151885700000113
Figure BDA0003151885700000114
Figure BDA0003151885700000121
average power supply availability of an ASAI power distribution network;
SAIDIEpower off time of the electrical load after the fault;
SAIDITpower off time of cold and hot load after failure;
tkjthe power failure time of the jth user is the kth fault;
αkjis the k-th failureThe percentage of j user electrical loads in the total load;
beta kj is the percentage of the j user cold and hot load in the total load during the k fault;
tsta,kjthe continuous operation time of the virtual energy storage resource corresponding to the j user cold and hot load during the k fault;
q is the total number of power failure users in the kth fault;
p is the total number of power failures within a specified time;
Talthe number of electricity-requiring hours of a user in a specified time;
Nalall the users in the area are evaluated.
Preferably, the step of constructing an objective function of the cold and hot load virtual energy storage model, establishing a constraint condition, and solving an optimal solution of the objective function under the constraint condition includes:
solving the objective function by adopting a Monte Carlo simulation method, which specifically comprises the following steps:
1) selecting a typical daily temperature change curve, calculating the output characteristic of the refrigeration/heating equipment maintaining the optimal comfort temperature (26 ℃) of a user at different moments as the load characteristic of the cold/heat load, and constructing a load time sequence model to reflect the time-varying characteristic of the load based on the load characteristics of the electric load and the cold/heat load;
2) initializing the analog clock to 0, randomly generating the time-to-failure TTF of each element, and finding the minimum TTFrGenerating a repair time TTR for the componentrAnd advance the analog clock to the TTFr；
3) Reading the numerical values of the electric load and the cold and hot load of the user carried by the power distribution network when the r-th element fails by using the load time sequence model, and calculating the proportion of the electric load and the cold and hot load to the total load;
4) determining the power failure time of each user based on the fault analysis process of the feeder line partition: wherein, the power failure time of the electric load is directly counted and recorded; the difference between the power failure time of the electric load and the virtual energy storage continuous operation time based on the comfort level of a user needs to be calculated for the cold and hot loads, and the value is counted and recorded as the power failure time of the cold and hot loads;
5) a new random number is generated and converted into a new runtime TTF of the element rr′；
6) Judging whether the analog clock spans years or not, respectively calculating SAIDI indexes of the electric load and the cold and hot load and accumulating if the analog clock does not span years, and calculating a terminal power supply reliability index ASAI if the analog clock spans years;
7) and (3) judging whether the simulation clock is advanced to the time length required by meeting the evaluation precision, finishing the simulation process, counting the reliability indexes of each simulation year, averaging, and returning to the step 2 if the reliability indexes of each simulation year are not reached.
(2) Solving the power supply capacity by adopting a genetic algorithm, and respectively corresponding the single feeder load rate and the codes of all the feeder load rates of the network by adopting a binary coding rule and gene segments and chromosomes in a biological genetic concept; and the power supply capacity of the power distribution network is taken as a fitness function.
Example two:
the embodiment of the invention is illustrated by two pairs of embodiments I:
taking a distribution network structure in a certain area as an example, the total length of a distribution line is 10km, and the capacity is 6.91 MVA. In the reliability calculation process, the average fault rate of the distribution line is 0.065 times/(km.year), and the average repair time is 5 h; the average failure rate of the distribution transformer is 0.013 times/(meter year), and the average replacement time is 5 h; the average failure rate of the switching elements is 0.006 times/(year), and the average repair time is 5 h. The fault isolation time and the isolated transfer time are both 1 h. The building wall adopts a cement mortar wall, and relevant parameters are shown in tables 1 and 2;
TABLE 1 typical parameters table of building structure
Figure BDA0003151885700000131
Table 2 wall material parameter table
Figure BDA0003151885700000141
4.2 analysis of results
In order to show the effectiveness of the method provided by the invention, the following schemes are respectively set for comparative analysis:
scheme 1: only the electrical load is considered;
scheme 2: comprehensively considering the electric heating and cooling load, the comfortable temperature of a user is 26 ℃, and the deviation temperature is 1 ℃;
scheme 3: the comprehensive consideration of the electric heating and cooling load is that the comfortable temperature of a user is 26 ℃ and the deviation temperature is 2 ℃.
As can be seen from fig. 4, when the flexible cooling and heating requirements of the user are considered, the power supply capacity of the power distribution network is obviously improved under the same reliability. This is because, after the higher-level power grid fails, although the electric refrigeration (heating) equipment cannot normally operate, the virtual energy storage resource is released due to the influence of the virtual energy storage characteristic of the building, the power supply effect of the power grid cannot be instantly stopped, and the user experience of power consumption cannot be immediately lost. At this time, after the cold/hot load loses power, the virtual energy storage resource continues to supply energy for a period of time, so that the power failure time is reduced and the reliability index is improved.
In addition, when only the electric load is considered, when the power supply capacity of the power distribution network is lower than the power supply capacity corresponding to the safety criterion meeting the 'N-1', the reliability index is not changed any more; after the electric heating and cooling loads are comprehensively considered, even if the power supply capacity of the power distribution network is lower than the power supply capacity corresponding to the safety criterion of meeting the 'N-1', the reliability index is still improved to a certain extent. In the scheme 1, if the power supply capacity of the power distribution network is lower than the power supply capacity corresponding to the safety criterion of meeting the 'N-1', even if the first section of the line breaks down, the load carried by the power distribution network can be completely transferred through other communication lines at the peak moment, the power failure time cannot be reduced, and the reliability index cannot be changed; in the scheme 2, the influence of the virtual energy storage resources on the user side is received, and the power failure time can be effectively shortened by discharging the virtual energy storage resources no matter the size of the load supplied by the power distribution network, so that the reliability index is improved to some extent.
Meanwhile, along with the relaxation of the limit of the user on the comfort degree interval, the power supply capacity of the power distribution network is improved to some extent under the same reliability constraint condition.
This is because the larger the temperature comfort interval range of the user, the looser the user's requirement for the supply of the cold and hot loads, i.e., the lower the user's sensitivity to the cold and hot loads. At the moment, after the upper-level power grid fails, the longer the duration of energy supplied by the virtual energy storage resources is, the shorter the power failure time of a user is, the stronger the power supply reliability of a power distribution network terminal user is, and the larger the total supply load of the power distribution network under the same reliability constraint is.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (5)

1. A power distribution network power supply capacity evaluation method based on flexible cold and hot demands is characterized by specifically comprising the following steps:
constructing a cold and hot load virtual energy storage model, wherein the cold and hot load virtual energy storage model comprises an indoor air heat balance model, an envelope structure heat balance model and a building virtual energy storage model based on indoor air and envelope structure heat balance;
and constructing a power supply capacity evaluation model considering the flexible cold and heat requirements of the user, and evaluating the power supply capacity of the power distribution network based on the cold and heat load virtual energy storage model.
2. The method of claim 1, wherein the step of constructing a cold and hot load virtual energy storage model, the cold and hot load virtual energy storage model comprising an indoor air heat balance model, an enclosure heat balance model, and a building virtual energy storage model based on indoor air and enclosure heat balance comprises:
an indoor air heat balance model is constructed by adopting the following formula:
Q1(t)+Q2(t)+Q3(t)＝Q4(t)+Q5(t)；
Figure FDA0003151885690000011
Q1(t) carrying out heat convection on the inner surface of the enclosure structure and air at the moment t;
Q2(t) the heat consumption of the building door and window at the moment t;
Q3(t) building air sensible heat increment in unit time at the moment t;
Q4(t) the heat exchange power between the refrigerating and heating equipment and indoor air at the moment t;
Q5(t) the heat exchange power between indoor heat sources such as human bodies, cookers and lighting and indoor air at the moment t;
Tin(t) is the indoor temperature at time t;
Tout(t) is the outdoor temperature at time t;
T1(t) the temperature of the inner surface of the building enclosure at the moment t;
h is the convective heat transfer coefficient of the building envelope;
f is the internal surface area of the building envelope;
Qd(t) the heat consumption of the opening of the outer door at the moment t;
Qw(t) the external window is opened at time t to consume heat;
beta is the outdoor wind invasion addition rate;
Kcis the outer door heat transfer coefficient;
fcis the outer door area;
cwthe specific heat of outdoor air;
ρwis the outdoor air density;
Vois the air volume in the building;
n (t) is the number of ventilations in the period t;
cothe specific heat of indoor air;
ρois the indoor air density;
Qk(t) the heat exchange power between the refrigeration equipment and the indoor air at the moment t;
constructing a thermal balance model of the building envelope by adopting the following formula:
Figure FDA0003151885690000021
T1(t) the temperature of the inner surface of the building envelope at the moment t;
s is the area of the wall;
c is the heat capacity of the wall;
rho is the density of the wall body;
Δ x is the thickness of the wall;
lambda is the heat conductivity coefficient of the wall;
qcothe convection heat exchange quantity between the inner surface of the enclosure structure and air at the moment t;
qsothe inner surface receives the solar radiant heat penetrating through the outer window at the time t;
the building virtual energy storage model based on the heat balance of the indoor air and the building enclosure structure is constructed by adopting the following formula:
Figure FDA0003151885690000031
Qdyn(t) -output power of the cooling or heating equipment at time t.
3. The method according to claim 1, wherein the step of constructing a power supply capacity evaluation model considering flexible cold and hot demands of users and evaluating the power supply capacity of the power distribution network based on the cold and hot load virtual energy storage model comprises the following steps:
and constructing an objective function considering the cold and hot load virtual energy storage and power supply capacity model, establishing constraint conditions, and solving the optimal solution of the objective function under the constraint conditions.
4. The method according to claim 3, wherein the steps of constructing an objective function of the cold and hot load virtual energy storage model and establishing constraint conditions, and solving an optimal solution of the objective function under the constraint conditions comprise:
the objective function of the cold and hot load virtual energy storage model is as follows:
Figure FDA0003151885690000032
Pnetthe power supply capacity of the power distribution network to be evaluated is obtained;
mpis the rated capacity of the distribution transformer p;
AE,pand AT,pThe load rate of the power supply load and the cold and hot load for the distribution transformer;
n is the total number of distribution transformers in the power distribution network;
the constraint conditions of the objective function of the cold and hot load virtual energy storage model are as follows:
equipment load rate constraint:
0≤AE,p+AT,p≤1；
user comfort constraints:
-0.5≤ΓPMV(t)≤0.5；
solving the average power supply availability of the power distribution network under the condition of meeting the objective function;
Figure FDA0003151885690000041
Figure FDA0003151885690000042
Figure FDA0003151885690000043
average power supply availability of an ASAI power distribution network;
SAIDIEpower off time of the electrical load after the fault;
SAIDITpower off time of cold and hot load after failure;
tkjthe power failure time of the jth user is the kth fault;
αkjthe percentage of the jth user electrical load in the kth fault in the total load is;
beta kj is the percentage of the j user cold and hot load in the total load during the k fault;
tsta,kjthe continuous operation time of the virtual energy storage resource corresponding to the j user cold and hot load during the k fault;
q is the total number of power failure users in the kth fault;
p is the total number of power failures within a specified time;
Talthe number of electricity-requiring hours of a user in a specified time;
Nalall the users in the area are evaluated.
5. The method according to claim 3, wherein the step of constructing an objective function considering a cold and hot load virtual energy storage and supply capacity model and establishing constraint conditions, and solving an optimal solution of the objective function under the constraint conditions comprises:
(1) the method for solving the average power supply availability ASAI of the power distribution network by adopting a Monte Carlo simulation method specifically comprises the following steps:
1) selecting a typical daily temperature change curve, calculating the output characteristic of the refrigeration/heating equipment maintaining the optimal comfort temperature (26 ℃) of a user at different moments as the load characteristic of the cold/heat load, and constructing a load time sequence model to reflect the time-varying characteristic of the load based on the load characteristics of the electric load and the cold/heat load;
2) initializing the analog clock to 0, randomly generating the time-to-failure TTF of each element, and finding the minimum TTFrGenerating a repair time TTR for the componentrAnd advance the analog clock to the TTFr；
3) Reading the numerical values of the electric load and the cold and hot load of the user carried by the power distribution network when the r-th element fails by using the load time sequence model, and calculating the proportion of the electric load and the cold and hot load to the total load;
4) determining the power failure time of each user based on the fault analysis process of the feeder line partition: wherein, the power failure time of the electric load is directly counted and recorded; the difference between the power failure time of the electric load and the virtual energy storage continuous operation time based on the comfort level of a user needs to be calculated for the cold and hot loads, and the value is counted and recorded as the power failure time of the cold and hot loads;
5) a new random number is generated and converted into a new runtime TTF of the element rr′；
6) Judging whether the analog clock spans years or not, respectively calculating SAIDI indexes of the electric load and the cold and hot load and accumulating if the analog clock does not span years, and calculating a terminal power supply reliability index ASAI if the analog clock spans years;
7) and (3) judging whether the simulation clock is advanced to the time length required by meeting the evaluation precision, finishing the simulation process, counting the reliability indexes of each simulation year, averaging, and returning to the step 2 if the reliability indexes of each simulation year are not reached.
(2) Solving the power supply capacity by adopting a genetic algorithm, and respectively corresponding the single feeder load rate and the codes of all the feeder load rates of the network by adopting a binary coding rule and gene segments and chromosomes in a biological genetic concept; and the power supply capacity of the power distribution network is taken as a fitness function.
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