CN109193643B - Method and system for calculating power distribution and distribution system network loss and reliability - Google Patents
Method and system for calculating power distribution and distribution system network loss and reliability Download PDFInfo
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- CN109193643B CN109193643B CN201811196743.5A CN201811196743A CN109193643B CN 109193643 B CN109193643 B CN 109193643B CN 201811196743 A CN201811196743 A CN 201811196743A CN 109193643 B CN109193643 B CN 109193643B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention provides a method and a system for calculating the network loss and reliability of a power distribution and utilization system, and the method specifically comprises the step of determining the predicted operation data of electric equipment to be added and distributed based on a power distribution and utilization mathematical model, wherein the predicted operation data of the electric equipment to be added and distributed changes along with time or load value, namely the predicted operation data is data obtained according to the service condition of newly-added electric equipment and accords with the operation state of the newly-added electric equipment, so that the calculated data such as the network loss, the reliability and the like accord with the actual condition of the intelligent power distribution and utilization system, and the accuracy of the calculated data such as the network loss, the reliability and the like is higher.
Description
Technical Field
The invention relates to the field of power distribution and utilization, in particular to a method and a system for calculating the network loss and reliability of a power distribution and utilization system.
Background
The intelligent power distribution and utilization system has great difference with the traditional power distribution network in the aspect of operation characteristics, and the main reason for generating the difference is that novel power distribution and utilization equipment is added in the intelligent power distribution and utilization system, such as a distributed power supply, an energy storage device, an electric automobile charging and discharging station and the like.
After novel distribution and utilization equipment is added in the intelligent distribution and utilization system, data such as the network loss and the reliability of the whole distribution and utilization system can be changed. In the prior art, when the novel power distribution and utilization equipment is to be increased, firstly, data such as network loss and reliability after the novel power distribution and utilization equipment is increased are calculated, when data such as network loss and reliability are calculated, the output or power consumption of the novel power distribution and utilization equipment to be increased can be set to be fixed and unchangeable, but actually, the output or power consumption of the novel power distribution and utilization equipment to be increased is constantly changed along with time or a load value, if the output of a distributed photovoltaic power supply is constantly changed along with time, and then when the novel power distribution and utilization equipment is to be increased, the data such as network loss and reliability after the novel power distribution and utilization equipment is increased are calculated and obtained.
Disclosure of Invention
In view of this, the present invention provides a method and a system for calculating the network loss and reliability of a power distribution and utilization system, so as to solve the problem that when an intelligent power distribution and utilization system in the prior art is to add a new type of power distribution and utilization equipment, the calculated data, such as the network loss and reliability, of the new type of power distribution and utilization equipment is inaccurate.
In order to solve the technical problems, the invention adopts the following technical scheme:
a method for calculating the network loss and reliability of a power distribution and utilization system comprises the following steps:
constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
acquiring historical operating data of existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be additionally distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
determining the predicted operation data of the electric equipment to be additionally distributed based on the power distribution and utilization mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
calculating the operation parameters of the power distribution and utilization model according to the power distribution and utilization model, the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added and distributed;
the operation parameters of the power distribution and utilization model comprise a load flow calculation result, a network loss value and a reliability coefficient.
Preferably, calculating the operation parameters of the power distribution and utilization model according to the power distribution and utilization model, the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added, and the calculating includes:
determining a plurality of time sections;
determining historical operation subdata in historical operation data of existing power distribution and utilization equipment corresponding to each time section and predicted operation subdata in predicted operation data of the power distribution and utilization equipment to be added;
and calculating the operation parameters of the power distribution and utilization model based on the power distribution and utilization model, the historical operation subdata corresponding to each time section and the predicted operation subdata.
Preferably, calculating the operation parameters of the power distribution and utilization model based on the power distribution and utilization model, the historical operation sub-data and the predicted operation sub-data corresponding to each time section includes:
constructing a circuit structure diagram corresponding to each time section based on the power distribution and utilization model, historical operation subdata corresponding to each time section and predicted operation subdata;
performing load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each time section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
and calculating the network loss value and the reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section.
Preferably, calculating the grid loss value and the reliability coefficient of the power distribution and utilization model based on the sub-grid loss value and the reliability sub-coefficient corresponding to each time section includes:
performing integral operation on the sub-grid loss value corresponding to each time section to obtain a grid loss value of the power distribution and utilization model;
and taking the reliability sub-coefficient with the minimum value in the reliability sub-coefficients corresponding to each time section as the reliability coefficient.
Preferably, after the step of calculating the grid loss value and the reliability coefficient of the power distribution and utilization model based on the sub-grid loss value and the reliability sub-coefficient corresponding to each time slice, the method further includes:
comparing the network loss value with a historical network loss value of the power distribution and utilization model to obtain a first comparison result;
comparing the reliability coefficient with a historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
and determining the result of increasing the advantages and disadvantages of the electric equipment to be additionally distributed according to the first comparison result and the second comparison result.
A system for calculating grid loss and reliability of a power distribution and utilization system, comprising:
the model construction module is used for constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
the information acquisition module is used for acquiring historical operating data of the existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be additionally distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
the data determining module is used for determining the predicted operation data of the to-be-increased and distributed electric equipment based on the power distribution and utilization mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
the parameter calculation module is used for calculating the operation parameters of the power distribution and utilization model according to the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added;
the operation parameters of the power distribution and utilization model comprise a load flow calculation result, a network loss value and a reliability coefficient.
Preferably, the parameter calculation module includes:
the time determination submodule is used for determining a plurality of time sections;
the data determining submodule is used for determining historical operation subdata in historical operation data of existing power distribution equipment corresponding to each time section and predicted operation subdata in predicted operation data of the power equipment to be additionally distributed;
and the parameter calculation submodule is used for calculating the operation parameters of the power distribution and utilization model based on the historical operation subdata and the predicted operation subdata corresponding to each time section.
Preferably, the parameter calculation submodule includes:
the structure chart construction unit is used for constructing a circuit structure chart corresponding to each time section on the basis of the power distribution and utilization model, historical operation subdata corresponding to each time section and prediction operation subdata;
the first calculation unit is used for carrying out load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
the second calculation unit is used for calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each time section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
and the third calculating unit is used for calculating the network loss value and the reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section.
Preferably, the third calculation unit includes:
the calculating subunit is configured to perform integral operation on the sub-network loss value corresponding to each time section to obtain a network loss value of the power distribution and utilization model;
and the coefficient determining subunit is used for taking the reliability sub-coefficient with the minimum corresponding numerical value in the reliability sub-coefficients corresponding to each time section as the reliability coefficient.
Preferably, the method further comprises the following steps:
the first comparison subunit is used for comparing the network loss value with a historical network loss value of the power distribution and utilization model after the third calculation unit calculates the network loss value and the reliability coefficient of the power distribution and utilization model based on the network loss value and the reliability sub-coefficient corresponding to each time section to obtain a first comparison result;
the second comparison subunit is used for comparing the reliability coefficient with the historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
and the result determining subunit is used for determining the result of increasing the advantages and disadvantages of the electric equipment to be added and distributed according to the first comparison result and the second comparison result.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method and a system for calculating the network loss and reliability of a power distribution and utilization system, wherein the method and the system are used for determining the predicted operation data of electric equipment to be added and distributed based on a power distribution and utilization mathematical model, wherein the predicted operation data of the electric equipment to be added and distributed changes along with time or load values, namely the predicted operation data is data obtained according to the service condition of newly-added electric equipment and accords with the operation state of the newly-added electric equipment, the data such as the network loss, the reliability and the like obtained through calculation accord with the actual condition of the intelligent power distribution and utilization system, and the accuracy of the data such as the network loss, the reliability and the like obtained through calculation is higher.
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, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
Fig. 1 is a flowchart of a method for calculating network loss and reliability of a power distribution and distribution system according to an embodiment of the present invention;
FIG. 2 is a schematic view of a wind speed curve according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a fan power characteristic curve according to an embodiment of the present invention;
fig. 4 is a schematic circuit diagram of a non-linear general battery model according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a detailed process of step S4 in the method according to the embodiment of the present invention;
fig. 6 is a flowchart illustrating a detailed process of step S43 in the method according to the embodiment of the present invention;
FIG. 7 is a schematic diagram of a distributed power supply and load time trend and upper and lower regulation capacity boundaries provided by an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a system for calculating the grid loss and reliability of a power distribution and utilization system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
As shown in fig. 1, an embodiment of the present invention provides a method for calculating grid loss and reliability of a power distribution and distribution system, which may include:
s1, constructing a power distribution and utilization model;
the power distribution model comprises a circuit structure model of the existing power distribution equipment and a circuit structure model of the to-be-added power distribution equipment.
Specifically, the existing power distribution and utilization equipment can include the following three types:
(1) a network frame model of the power distribution network based on network line connection;
(2) time sequence models based on time sequence section analysis, such as fans, photovoltaics, etc.;
the time sequence model based on time sequence section analysis means that the active or reactive power curve of the power distribution and consumption equipment changes along with time. If the power distribution equipment is a photovoltaic in a distributed power supply, the active output of the photovoltaic is related to the illumination intensity, namely, the time, and the reactive power of the photovoltaic is related to the output voltage, namely, the time.
(3) And event models which influence the running state of the system by taking event occurrence as a unit, such as electric vehicle charging and discharging piles, energy storage devices, friendly loads and the like. Among them, friendly loads include interruptible loads and schedulable loads.
The event type model which influences the running state of the system by taking the event occurrence as a unit is that the power curve of the distribution electric equipment is related to the load characteristic. If the power distribution equipment is an energy storage device (such as flywheel energy storage, super capacitor and the like), the operation mode of the energy storage device comprises three states of charging, discharging and idling, and the energy storage device is related to whether a load exists or not.
The electric equipment to be additionally distributed comprises time sequence models such as photovoltaic models, fans and the like, and can also be event models such as electric automobile charging and discharging stations, energy storage devices and the like.
It should be noted that the number of the existing power distribution equipment and the power consumption equipment to be added is not limited, and may be one or more.
S2, acquiring historical operation data of the existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be added and distributed;
the power distribution and utilization mathematical model of the to-be-distributed power utilization equipment comprises an output mathematical model and a power utilization mathematical model; the power distribution and utilization mathematical model is a power output mathematical model when the power utilization equipment to be distributed is a time sequence model such as a photovoltaic model or a fan model, and the power distribution and utilization mathematical model is a power utilization mathematical model when the power utilization equipment to be distributed is an event model such as an energy storage device or an electric automobile charging and discharging station.
The construction of the power distribution and utilization mathematical model of the to-be-augmented power utilization equipment comprises the following steps:
when the electric equipment to be additionally distributed is different, the constructed mathematical models of the power distribution and utilization are different, and the specific method is as follows:
distributed power supply
1. Photovoltaic system
A photovoltaic output model is obtained by establishing a time sequence model of solar radiation intensity and utilizing a photoelectric conversion relation;
(1) solar radiation intensity model
For the latitude on the ground
For an observer with a longitude λ, the zenith angle θ (the complement of the solar altitude) and azimuth angle α of the sun observed at a certain time can be calculated by the following equations:
wherein, delta is the declination of the sun (the included angle between the connecting line of the sun and the earth center and the equatorial plane) and changes between +/-23 degrees 27'. Omega is a time angle, is the angle of the earth rotation after the circle of the observation point is superposed with the sun (namely, at noon in the local), and is from 0 degree to 360 degrees every day, and the time angle at noon is 0 degree.
At sunrise and sunset times, θ is 90 °. From the above equation, it can be seen that:
wherein, ω is0And alpha0The solar time angle and azimuth angle at sunrise and sunset, respectively, with location
And season (δ). For example, for the spring (autumn) centuries in the northern hemisphere, δ is 0, ω0＝±90°，α0When the sun is at 90 degrees and 270 degrees, namely the sun is equal in length in day and night, the sun rises from the east and falls from the west; for summer solstice, δ is 23.5 °, in
Omega of0If it is 180 deg., the sun does not fall all day in the arctic circle.
Taking D as the number of days in a year (1 month and 1 day, D is equal to 1; 12 months and 31 days, D is 365) if 365 days correspond to the interval [0,2 pi ], then
Wherein, X is a date adjustment coefficient used for marking the influence of the revolution sun position of the earth on a certain day in a year.
Declination of the sun delta is:
δ＝0.006894-0.399512cos X+0.072075sin X-0.006799cos(2X)
+0.000896sin(2X)-0.002689cos(3X)+0.001516sin(3X) (6)
the distance between the day and the ground dm is:
dm＝1.000109+0.033494cos X+0.001472sin X
+0.000768cos(2X)+0.000079sin(2X) (7)
since the sun is at different heights at different times, the solar irradiance at the incident upper atmospheric level should be:
wherein S is0The integral irradiance of the sun on the vertical plane of the upper air boundary and sunlight, d is the distance between the sun and the earth and changes along with the revolution of the earth, d0Is the average distance of the day and the earth (1.496 multiplied by 10)8km, the average distance of day the earth reaches between 21 and 22 days 3 months and 22 to 23 days 9 months, near-day: 1.47X 108km, far-day: 1.52X 108km)，
Called solar constant, world meteorology organization
Has an optimum value of 1367 + -7W/m2。
The atmospheric transparency P is:
in the formula: p2Is the annual average atmospheric transparency.
The solar radiation intensity Pn after passing through the atmosphere is as follows:
Pn＝S0Pm (11)
wherein m is an atmospheric mass number.
(2) Output of photovoltaic system
PPV＝Pn*η*S*ηinv (12)
Wherein eta is the efficiency of the photovoltaic array, S is the illumination area of the photovoltaic array, etainvIs the efficiency of the inverter. Formula 12 can be a photovoltaic mathematical model for power distribution.
The photovoltaic power distribution and utilization mathematical model can obtain photovoltaic output curves of various time scales according to research purposes and requirements.
2. Fan blower
The output time sequence characteristics of the wind power generation equipment are directly related to wind power resources of a planning area, and the daily change of wind speeds in different seasons is obviously different. The curve of the output of the fan and the wind speed is dense and inseparable, and the active power output of the system depends on the wind speed. Firstly, a wind speed time sequence is generated by using a wind speed model, and then the wind power time sequence is obtained through a corresponding wind speed-wind power functional relation. Based on the real-time wind speed change curve, the established fan time sequence model is more accurate and practical.
(1) Wind speed curve
The spatial and temporal distribution of wind direction and wind speed is very complex, and extremely strong randomness is presented, the change curve of the wind speed Vwind is shown in figure 2, the change of the wind speed Vwind has no any rule, the frequency is very fast, and the wind speed is difficult to predict accurately. Scholars at home and abroad carry out a great deal of research on the probability distribution of the wind speed and establish various probability distribution models to describe the change of the wind speed. The Weibull distribution form is simple, fitting with actual wind speed statistical distribution is good, and the Weibull is widely applied. The invention adopts the distribution to establish a probability model of the wind speed, which comprises the following concrete steps:
the distribution function is:
the probability density function is:
in the formula, c and k are scale parameters and shape parameters of Weibull distribution respectively; the scale parameter c reflects the average wind speed of the wind farm, V being the given wind speed in m/s.
(2) Fan output model
The output of the fan changes along with the change of the wind speed due to the operation characteristics of the wind turbine, but the output power of the fan is not in a simple linear relation with the wind speed. When the actual wind speed is less than the cut-in wind speed or more than the cut-out wind speed of the wind turbine generator, the output power of the fan is zero due to insufficient wind energy and overlarge wind speed; when the actual wind speed is between the cut-in wind speed and the cut-out wind speed, the output of the fan continuously changes along with the wind speed under the limit of the rated power of the wind turbine generator.
In theory, a piecewise quadratic function is generally adopted to reflect the rule that the output of the fan changes along with the change of the wind speed, as shown in fig. 3.
In the formula: vci, Vr and Vco are respectively the cut-in wind speed, the rated wind speed and the cut-out wind speed of the wind turbine generator; pr is rated power of the wind turbine generator, and a constant A, B, C is an output power characteristic curve parameter of the fan, and is calculated by the following formula:
based on the wind speed curve changing along with time, a real-time fan output model can be obtained. Namely a power distribution and utilization mathematical model of the fan. By adopting the time sequence model of the fan output, the curve of the fan output changing along with the time can be obtained through simulation, and the randomness and the intermittence of the fan can be truly reflected.
Energy storage device
The charge-discharge model of the energy storage system is established as follows:
a charging model:
and (3) discharge model:
in the formula: pbat(t) represents the charging or discharging power of the energy storage system at the t hour, Ebat(t) represents stored energy for the energy storage system at the tth hour; pch-maxAnd Pdch-maxRespectively representing the maximum charging power and the maximum discharging power of the energy storage system; emaxAnd EminRepresenting the minimum and maximum capacity limits of the energy storage system, respectively.
And the charging and discharging model of the energy storage device is a power distribution and utilization mathematical model of the energy storage device.
The modeling method based on multiple space-time scales is combined with distributed energy sources, so that the charging and discharging power of the energy storage device can be controlled in real time, and the operation requirement of a power distribution network can be met.
Third, electric automobile
1. Battery model
The battery model can describe the external characteristics of the battery during operation, the adopted battery model is a controllable voltage source connected with a constant resistor in series, as shown in fig. 4, the model takes SoC as a state parameter, the SoC is related to the battery state and is used for indicating the state of charge of the battery, also called residual capacity, and the calculation method is as follows:
wherein Qr and Qc are the remaining capacity and the total capacity of the battery, respectively.
In fig. 4, E is the battery no-load voltage; e0Rated voltage for the battery; k is a polarization voltage; q is the battery capacity; r is the internal resistance of the battery; i is the battery discharge current; vbattIs the battery terminal voltage; a is an exponential amplitude; b is a time constant, U is a controllable voltage source, and R is an internal resistance. Terminal voltage V of batterybattAnd the charging power P can be obtained by a nonlinear equation related to the state of charge SoC:
wherein i is a battery discharge current; t is time; the model can simulate the charging power and SOC change of different types of electric automobiles.
2. Charging load of electric automobile
Initial SoC: before charging the electric vehicle, the remaining capacity of the battery is related to the distance traveled, and if the distance traveled by the electric vehicle per day is d and the maximum distance that can be traveled is dr, the SoC before charging can be obtained by equation (22):
wherein d can be obtained by statistical data of traffic departments; dr available battery capacity QbAnd calculating the ratio of the energy consumption Ce to the energy consumption Ce of the electric automobile per kilometer.
Battery capacity Q of different types of electric automobilesbIn contrast, the energy consumption Ce per kilometer is also different. The research shows that the battery capacity Q of each type of electric automobilebThe energy consumption Ce per kilometer is distributed dispersedly in a certain range, and the probability density function is shown in the formulas (23) and (24):
where μ is the mean, σ is the standard deviation, x1And x2Is QbX is the battery capacity; a and b are Ce ranges.
Charging time: the charging starting time of the electric automobile mainly depends on the traveling habit and the driving characteristic of an automobile owner, and is influenced by various uncertain factors, so that the charging starting time is random. The charging starting time of the electric automobile is directly influenced by the returning time of the last trip, and according to the survey statistical result of the United states department of transportation to the vehicles in the United states in 2001, the charging starting time meets the following distribution, namely, a probability density function, namely, a power distribution and utilization mathematical model is shown as a formula (25):
wherein, mus＝17.6h，σs＝3.4h。
Charge-discharge model under control of V2G
Assuming that the starting charging and discharging time of the schedulable electric automobile satisfies the uniform distribution in the charging and discharging time period of one day, the probability density function f of the starting charging timec(x) Probability density function f of the moment of starting dischargeD(x) Respectively as follows:
the probability density function of the daily mileage of the dispatchable electric automobile meets the normal distribution:
wherein muM＝16.58，σM＝6.57。
The charging and discharging power characteristics of the electric automobile realize numerical simulation by a Monte Carlo random sampling method.
Based on the time sequence modeling of the electric automobile, a 24h discharging and charging power curve can be obtained, the electric automobile is merged into a power distribution network, the voltage analysis and the network loss calculation of the time sequence are carried out, and a model basis is provided for researching the influence of the electric automobile grid connection on the reliability and the acceptance capability of the electric automobile.
S3, determining the predicted operation data of the electric equipment to be additionally distributed based on the power distribution and utilization mathematical model; and the predicted operation data of the electric equipment to be added and distributed changes along with time or load value.
Specifically, the power distribution and utilization mathematical model provides the change conditions of the output value along with time, the output value along with the load value and the power consumption along with time. The predicted operational data over a period of time may be predicted based on the power distribution mathematical model.
If the to-be-added power utilization equipment is photovoltaic, the power utilization mathematical model is an output model, the predicted operation data of the to-be-added power utilization equipment changes along with time, and the predicted operation data can be photovoltaic energy generated at different times.
And S4, calculating the operation parameters of the power distribution and utilization model according to the power distribution and utilization mathematical model, the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added. As shown in fig. 5, the method specifically includes the following steps:
step S4 may include:
s41, determining a plurality of time sections;
among them, a time series is divided by using a certain period of time T as a study object, m continuous time slices Ti (i is 0, 1, …, m-1) are extracted at the same time interval, and the m time slices are studied respectively. The defined time profile may be years, months, days, hours, minutes, etc. Preferably, the time section can be set to be hours, and the time section can be 8760 times in a year.
The determined time section is a time section determined based on a power distribution model. Namely, the distribution power model must include a circuit structure model of the power consumption equipment to be added.
S42, determining historical operation subdata in historical operation data of existing distribution electric equipment corresponding to each time section and predicted operation subdata in predicted operation data of the electric equipment to be additionally distributed;
specifically, after the time slices are determined, the data used to analyze each time slice needs to be determined. And taking the historical time section data corresponding to the time section in the historical operation data of the existing power distribution equipment as the historical operation subdata.
For example, assuming that the time profile is 12 points in a day of 2018, 6, and 20 days, if the acquired historical operation data is data between 2017, 1, and 1 day of 2018, 1 and 1 day of 2018, the data at 12 points in the day of 2018, 6, and 20 days is used as the historical operation sub data at 12 points in the day of 2018, 6, and 20 days.
The predicted operation data of the electric equipment to be added and distributed is predicted future data, such as predicted operation data between 1 month and 1 day of 2018 and 1 month and 1 day of 2019, and if the time section is 12 points in the day of 6 months and 20 days of 2018, the data of 12 points in the day of 6 months and 20 days of 2018 in the predicted operation data of the electric equipment to be added and distributed is used as predicted operation subdata.
Here, some data in the historical operating data may be used as the data of the existing distribution equipment corresponding to the time section, or the data of the time section may be predicted as the data of the existing distribution equipment corresponding to the time section by using the distribution model of the existing distribution equipment.
And S43, calculating the operation parameters of the power distribution and utilization model based on the power distribution and utilization mathematical model, the historical operation subdata corresponding to each time section and the predicted operation subdata. The operation parameters of the power distribution and utilization model comprise a load flow calculation result, a network loss value and a reliability coefficient.
As shown in fig. 6, step S43 specifically includes the following steps:
s431, constructing a circuit structure diagram corresponding to each time section based on the power distribution and utilization mathematical model, historical operation subdata corresponding to each time section and prediction operation subdata;
specifically, after the historical operation subdata and the predicted operation subdata corresponding to each time section are known, the circuit structure of the existing power distribution equipment can be determined according to the historical operation subdata, and the circuit structure of the power consumption equipment to be additionally distributed can be determined according to the predicted operation subdata, wherein the circuit structure can be composed of electronic elements such as a resistor, an inductor and a reactor.
After the circuit structure of the existing power distribution and utilization equipment and the circuit structure of the power utilization equipment to be added are determined, the circuit structure diagram corresponding to the time section can be determined according to the connection structure of the existing power distribution and utilization equipment and the power utilization equipment to be added.
S432, carrying out load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
load flow calculation, which means that under the conditions of network topology, element parameters, power generation and load parameters of a given power system, the distribution of active power, reactive power and voltage in a power network is calculated;
specifically, the specific process of the power flow calculation may include:
in an intelligent power distribution and utilization system, PV and PI type nodes are increased due to the introduction of a distributed power supply, the PV node type load processing is difficult for a forward-backward substitution method, the PV node needs to be processed, an algorithm of the intelligent power distribution and utilization system trend is solved, and the goal of trend convergence is achieved.
First, a DG (distributed generator) of the PV node type can be considered as a voltage-controlled current source. In order to keep the voltage amplitude of the PV node type DG constant, appropriate reactive power and reactive current injection need to be determined, so the problem is translated into finding the reactive injection current for each PV type DG node, so that the voltage amplitude of each node is equal to a rated value, and only positive sequence current and positive sequence voltage exist in the DG for the symmetric operation of the synchronous generator. In the embodiment, the positive sequence current amplitude injected into the PV node is obtained by calculating the difference between the positive sequence component amplitude of the voltage of the PV node and the rated amplitude, and reactive compensation is performed on the PV node, so that the DG is converted into the PQ node operation model from the PV node operation model. The method comprises the following specific steps:
setting the DG initial three-phase total active power P and the terminal voltage positive sequence component amplitude Usc as a certain value, setting the initial reactive power Q as zero, calculating the terminal voltage positive sequence component amplitude and the rated voltage amplitude of the PV node after convergence according to the power flow algorithm, and judging whether the difference value is within an allowable error range. If the amplitude difference is within the allowable error range, the voltage of the PV node is converged to an initial set value; if the amplitude difference exceeds the allowable error range, the PV node compensates by injecting reactive current to maintain the voltage within the allowable range, and the reactive injection positive sequence current is calculated according to the following formula:
ZvΔIq＝ΔUv (29)
in the formula: delta IqInjecting a positive sequence current vector for reactive power; zvThe positive sequence sensitivity impedance matrix is a positive sequence sensitivity impedance matrix, the dimension of the positive sequence sensitivity impedance matrix is equal to the number of PV nodes, diagonal elements are all positive sequence impedance sums of branches from each PV node to a root node, and non-diagonal elements are all positive sequence impedance sums of the same branches from PV node i and PV node j to the root node; delta UvThe vector of the amplitude difference between the positive sequence voltage of the PV node and the rated voltage is shown.
And adding each phase of reactive injection current into the initial injection current of the ith node, then carrying out load flow calculation again, and checking a new voltage amplitude difference. And if the reactive injection power of the PV node DG in the iterative calculation exceeds the specified limit, limiting the reactive injection power to be the rated minimum value or the rated maximum value in order to ensure the safe operation of the power supply equipment.
In conclusion, a forward-backward substitution method based on current compensation is used as a power flow algorithm for solving the intelligent power distribution and utilization system, the defect that the PV nodes are difficult to process by the forward-backward substitution method is overcome, and the purpose of power flow convergence is achieved.
For the novel business of other PQ nodes, the trend analysis idea is as follows:
the distributed power supply, the energy storage device, the electric automobile charging and discharging station, the controllable load and other novel services of the smart power grid have the greatest characteristics that the command of system operation scheduling can be received to a certain extent, the process of response of a system demand side is participated, and under the premise that the power utilization comfort level of a user is guaranteed as much as possible, the active and reactive optimal regulation of the load is realized. The mathematical relationship between the active and the reactive power of the load can be determined by the power factor, relating to the physical properties of the different consumers and their power electronic interface controllers. Because the load flow calculation reflects the system characteristics of a time section, the output of the energy storage device and the output of the distributed power supply can be comprehensively regarded as an output source, namely a special PQ node. However, unlike the conventional load, the PQ node has certain capacity up-regulation and capacity down-regulation capabilities, and has strong coupling relation with time and space, so that the PQ node is suitable for the application of time-series load flow calculation, as shown in fig. 7.
By such a tunable PQ model, the following advantages are achieved:
the convergence of load flow calculation can be realized: when the power flow is large and convergence cannot be achieved, the goal of power flow convergence is achieved by optimizing P, Q values in the range of up-and-down adjustment capacity;
in view of the time distribution characteristics of the loads, the method is very suitable for being combined with time sequence load flow and probability load flow to achieve the purpose of evaluating the safety of the power grid.
S433, calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each intermittent section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
specifically, for each time section, a sub-network loss value and a reliability sub-coefficient are calculated.
The power distribution model comprises photovoltaic power, an electric vehicle charging and discharging station and a power distribution network, and the electric vehicle charging and discharging station is to-be-augmented power utilization equipment.
The photovoltaic historical operation subdata can be an output curve, and the electric vehicle charging and discharging station predicted operation subdata can be load data, such as charging times, charging time and the like. The historical operation subdata of the power distribution network can be data such as voltage grade, transformer existence and the like.
The calculation of the sub-grid loss value can adopt a node equivalent power method, which comprises the following specific steps:
and calculating the operation load flow of the power distribution network aiming at different discontinuous surfaces by considering the randomness and the intermittence of the output of the distributed power supply and aiming at the intelligent power distribution and utilization system with the grid structure determined on the basis of a time sequence load flow calculation model by combining the theory of time sequence load flow. And calculating the network loss under each time section, and traversing each time section to summarize to obtain the system time sequence network loss distribution. And summarizing the network loss under each time section to obtain a network loss result belonging to the time interval. On the basis of time sequence flow, the node equivalent power method can make up for the defect of poor data synchronism and is suitable for network loss calculation of the intelligent power distribution network.
The reliability sub-coefficients can be calculated by a Monte Carlo simulation method and a failure mode influence analysis method. The method comprises the following specific steps:
the Monte Carlo simulation method is used as an analysis method for state selection of the intelligent power distribution and utilization system. The monte carlo simulation method is to sample the states of elements by random numbers generated by a computer and then combine the sampled states to obtain the state of the whole system. The intelligent power distribution and utilization system for novel service access has a plurality of advantages by adopting a Monte Carlo simulation method to evaluate the reliability: firstly, the Monte Carlo simulation method is easy to simulate random factors such as random fluctuation of load, random fault of elements, random change of climate and the like and correction control strategies of the system, and the calculation result is closer to reality. Secondly, under the condition of meeting the requirement of certain calculation precision, the sampling times of the Monte Carlo simulation method are irrelevant to the scale of the system, so the method is particularly suitable for the reliability evaluation of a novel complex system. Thirdly, besides the index representing the average performance of the system, the Monte Carlo simulation method can also obtain the probability distribution of the reliability index, and the evaluation result is more comprehensive. Fourth, the simulation process of the Monte Carlo method is very simple and intuitive and is easily understood and mastered by the engineer. In conclusion, the Monte Carlo simulation method is adopted as the analysis method for state selection of the intelligent power distribution and utilization system.
For the intelligent power distribution and utilization system with novel service access, the fault mode influence analysis method is adopted to evaluate the reliability of the system state, so that the method has a plurality of advantages: firstly, the types and load points of elements in a power distribution system are more, and the fault influence of different elements can be different; even if the same element fails, the load points at different positions have different influences, and detailed analysis on the fault influence is necessary; secondly, only the first-order fault influence is generally considered for the radiation type network, the fault mode influence table may be only a simple fault analysis matrix or an increment value of a certain fault to the reliability index, and if some fast search technologies are adopted for analysis, such as fault traversal, fault diffusion and the like, too much calculation amount is not increased; thirdly, the FMEA method is the basis of other fault analysis methods, such as a minimal cut set method, a reliability block diagram and the like, which all include a process of influence analysis. In conclusion, the fault mode influence analysis method is adopted as the analysis method for state evaluation of the intelligent power distribution and utilization system.
S434, calculating a network loss value and a reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section;
optionally, on the basis of this embodiment, step S434 may include:
1) performing integral operation on the sub-grid loss value corresponding to each time section to obtain a grid loss value of the power distribution and utilization model;
specifically, the sub-grid loss values of different discontinuities can form a sub-grid loss value change curve, and integral calculation is performed according to the curve to obtain a total grid loss value, namely the grid loss value of the power distribution and utilization model.
2) And taking the reliability sub-coefficient with the minimum value in the reliability sub-coefficients corresponding to each time section as the reliability coefficient.
Specifically, after the reliability sub-coefficient corresponding to each time section is obtained, the minimum reliability sub-coefficient is selected as the reliability coefficient of the power distribution and utilization model.
It should be noted that, in this embodiment, only one method for calculating the network loss value and the reliability coefficient is given, and in addition, the network loss value and the reliability coefficient may also be calculated by using other methods.
Optionally, after step S434 is executed, the method may further include:
1) comparing the network loss value with a historical network loss value of the power distribution and utilization model to obtain a first comparison result;
2) comparing the reliability coefficient with a historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
3) and determining the result of increasing the advantages and disadvantages of the electric equipment to be additionally distributed according to the first comparison result and the second comparison result.
The historical grid loss value and the historical reliability coefficient are calculated based on historical operation data of a power distribution and utilization model without adding power utilization equipment to be added.
If the network loss value is larger than the historical network loss value, the network loss is increased after the power utilization equipment to be additionally distributed is added; if the network loss value is smaller than the historical network loss value, the network loss is reduced after the power utilization equipment to be added and distributed is added.
If the reliability coefficient is larger than the historical reliability coefficient, the reliability is enhanced after the electric equipment to be additionally distributed is added; if the reliability coefficient is smaller than the historical reliability coefficient, the reliability is reduced after the electric equipment to be additionally distributed is added.
And determining the advantages and disadvantages of the electric equipment to be added based on the first comparison result of the network loss value and the historical network loss value and the second comparison result of the reliability coefficient and the historical reliability coefficient, and further determining whether the electric equipment to be added is required to be added.
It should be noted that whether to add the to-be-added electric equipment may also be determined according to the electricity utilization condition of the local user after adding the to-be-added electric equipment and the influence degree of adding the to-be-added electric equipment on the local economic benefit.
The embodiment provides a method for calculating a network loss value and a reliability coefficient, and then the network loss value and the reliability coefficient can be calculated according to the method in the embodiment, so as to analyze whether the to-be-added power utilization equipment needs to be added or not.
In this embodiment, after the time sections are determined, the operation parameters of the power distribution and utilization model may be calculated according to the data corresponding to each time section, so as to determine whether the operation state of the power distribution and utilization model is normal.
In this embodiment, based on the power distribution mathematical model, the predicted operation data of the to-be-added power equipment is determined, where the predicted operation data of the to-be-added power equipment changes with time or load value, that is, the predicted operation data is data obtained according to the use condition of the newly-added power equipment, and conforms to the operation state of the newly-added power equipment, so that the calculated data such as the network loss and the reliability conforms to the actual condition of the intelligent power distribution system, and the accuracy of the calculated data such as the network loss and the reliability is higher.
Optionally, corresponding to the above method, another embodiment of the present invention provides a system for calculating grid loss and reliability of a power distribution and distribution system, as shown in fig. 8, where the system may include:
the model construction module 101 is used for constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
the information acquisition module 102 is configured to acquire historical operating data of existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be augmented and distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
the data determining module 103 is configured to determine predicted operation data of the to-be-augmented and-distributed electric equipment based on the power distribution and utilization mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
and the parameter calculation module 104 is configured to calculate an operation parameter of the power distribution and utilization model according to the power distribution and utilization model, the historical operation data of the existing power distribution and utilization equipment, and the predicted operation data of the to-be-augmented power distribution and utilization equipment, where the operation parameter of the power distribution and utilization model includes a load flow calculation result, a network loss value, and a reliability coefficient.
In this embodiment, based on the power distribution mathematical model, the predicted operation data of the to-be-added power equipment is determined, where the predicted operation data of the to-be-added power equipment changes with time or load value, that is, the predicted operation data is data obtained according to the use condition of the newly-added power equipment, and conforms to the operation state of the newly-added power equipment, so that the calculated data such as the network loss and the reliability conforms to the actual condition of the intelligent power distribution system, and the accuracy of the calculated data such as the network loss and the reliability is higher.
It should be noted that, for the working process of each module in this embodiment, please refer to the corresponding description in the above embodiments, which is not described herein again.
Optionally, on the basis of the above embodiment of the parameter calculating device, the parameter calculating module includes:
the time determination submodule is used for determining a plurality of time sections;
the data determining submodule is used for determining historical operation subdata in historical operation data of existing power distribution equipment corresponding to each time section and predicted operation subdata in predicted operation data of the power equipment to be additionally distributed;
and the parameter calculation submodule is used for calculating the operation parameters of the power distribution and utilization model based on the power distribution and utilization model, the historical operation subdata corresponding to each time section and the prediction operation subdata.
In this embodiment, after the time sections are determined, the operation parameters of the power distribution and utilization model may be calculated according to the data corresponding to each time section, so as to determine whether the operation state of the power distribution and utilization model is normal.
It should be noted that, for the working processes of each module and sub-module in this embodiment, please refer to the corresponding description in the above embodiments, which is not described herein again.
Optionally, on the basis of the previous embodiment, the parameter calculation sub-module further includes:
the structure chart construction unit is used for constructing a circuit structure chart corresponding to each time section on the basis of the power distribution and utilization model, historical operation subdata corresponding to each time section and prediction operation subdata;
the first calculation unit is used for carrying out load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
the second calculation unit is used for calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each time section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
the third calculating unit is used for calculating the network loss value and the reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section;
further, the third calculation unit includes:
the calculating subunit is configured to perform integral operation on the sub-network loss value corresponding to each time section to obtain a network loss value of the power distribution and utilization model;
and the coefficient determining subunit is used for taking the reliability sub-coefficient with the minimum corresponding numerical value in the reliability sub-coefficients corresponding to each time section as the reliability coefficient.
Further, the third calculation unit further includes:
the first comparison subunit is used for comparing the network loss value with a historical network loss value of the power distribution and utilization model after the third calculation unit calculates the network loss value and the reliability coefficient of the power distribution and utilization model based on the network loss value and the reliability sub-coefficient corresponding to each time section to obtain a first comparison result;
the second comparison subunit is used for comparing the reliability coefficient with the historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
and the result determining subunit is used for determining the result of increasing the advantages and disadvantages of the electric equipment to be added and distributed according to the first comparison result and the second comparison result.
The embodiment provides a method for calculating a network loss value and a reliability coefficient, and then the network loss value and the reliability coefficient can be calculated according to the method in the embodiment, so as to analyze whether the to-be-added power utilization equipment needs to be added or not.
It should be noted that, for the working processes of each module, sub-module, unit, and sub-unit in this embodiment, please refer to the corresponding description in the above embodiments, which is not repeated herein.
Optionally, on the basis of the above embodiment of the method and system for calculating the grid loss value and the reliability coefficient, another embodiment of the present invention provides an electronic device for calculating the grid loss and reliability of a power distribution and distribution system, including: a memory and a processor;
wherein the memory is used for storing programs;
the processor calls a program and is used to:
constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
acquiring historical operating data of existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be additionally distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
determining the predicted operation data of the electric equipment to be additionally distributed based on the power distribution and utilization mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
and calculating the operation parameters of the power distribution and utilization model according to the power distribution and utilization model, the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added.
In this embodiment, based on the power distribution mathematical model, the predicted operation data of the to-be-added power equipment is determined, where the predicted operation data of the to-be-added power equipment changes with time or load value, that is, the predicted operation data is data obtained according to the use condition of the newly-added power equipment, and conforms to the operation state of the newly-added power equipment, so that the calculated data such as the network loss and the reliability conforms to the actual condition of the intelligent power distribution system, and the accuracy of the calculated data such as the network loss and the reliability is higher.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (4)
1. A method for calculating the network loss and reliability of a power distribution and utilization system is characterized by comprising the following steps:
a1, constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
a2, acquiring historical operation data of existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be added and distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
a3, determining the predicted operation data of the electric equipment to be added and distributed based on the power distribution and distribution mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
a4, calculating the operation parameters of the power distribution and utilization model according to the power distribution and utilization model, the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added; the method comprises the following steps:
a41, determining a plurality of time sections;
a42, determining historical operation subdata in historical operation data of existing power distribution and utilization equipment corresponding to each time section and predicted operation subdata in predicted operation data of the power distribution and utilization equipment to be added;
a43, calculating operation parameters of the power distribution model based on the power distribution model, historical operation subdata and predicted operation subdata corresponding to each time section; the method comprises the following steps:
a431, constructing a circuit structure diagram corresponding to each time section based on the power distribution and utilization model, historical operation subdata corresponding to each time section and prediction operation subdata;
a432, carrying out load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
a433, calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each time section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
a434, calculating a network loss value and a reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section; performing integral operation on the sub-grid loss value corresponding to each time section to obtain a grid loss value of the power distribution and utilization model; taking the reliability sub-coefficient with the minimum corresponding numerical value in the reliability sub-coefficients corresponding to each time section as the reliability coefficient;
the operation parameters of the power distribution and utilization model comprise a load flow calculation result, a network loss value and a reliability coefficient.
2. The method of claim 1, wherein step a434 further comprises:
comparing the network loss value with a historical network loss value of the power distribution and utilization model to obtain a first comparison result;
comparing the reliability coefficient with a historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
and determining the result of increasing the advantages and disadvantages of the electric equipment to be additionally distributed according to the first comparison result and the second comparison result.
3. A system for calculating power distribution and distribution system network loss and reliability, comprising:
the model construction module is used for constructing a power distribution and utilization model; the power distribution and utilization model comprises a circuit structure model of the existing power distribution and utilization equipment and a circuit structure model of the to-be-added power distribution and utilization equipment;
the information acquisition module is used for acquiring historical operating data of the existing power distribution and utilization equipment and a power distribution and utilization mathematical model of the power distribution and utilization equipment to be additionally distributed; the power distribution and utilization mathematical model is an output mathematical model or a power utilization mathematical model of the to-be-increased power utilization equipment;
the data determining module is used for determining the predicted operation data of the to-be-increased and distributed electric equipment based on the power distribution and utilization mathematical model; the method comprises the following steps that prediction operation data of the to-be-added and distributed electric equipment change along with time or load value;
the parameter calculation module is used for calculating the operation parameters of the power distribution and utilization model according to the historical operation data of the existing power distribution and utilization equipment and the predicted operation data of the power distribution and utilization equipment to be added, and comprises the following steps: the time determination submodule is used for determining a plurality of time sections;
the data determining submodule is used for determining historical operation subdata in historical operation data of existing power distribution equipment corresponding to each time section and predicted operation subdata in predicted operation data of the power equipment to be additionally distributed;
the parameter calculation submodule is used for calculating the operation parameters of the power distribution and utilization model based on the historical operation subdata and the predicted operation subdata corresponding to each time section;
the parameter calculation submodule includes:
the structure chart construction unit is used for constructing a circuit structure chart corresponding to each time section on the basis of the power distribution and utilization model, historical operation subdata corresponding to each time section and prediction operation subdata;
the first calculation unit is used for carrying out load flow calculation according to the circuit structure diagram corresponding to each time section to obtain a load flow calculation result;
the second calculation unit is used for calculating to obtain a sub-network loss value and a reliability sub-coefficient corresponding to each time section according to the load flow calculation result, the historical operation sub-data and the prediction operation sub-data corresponding to each time section;
the third calculating unit is used for calculating the network loss value and the reliability coefficient of the power distribution and utilization model based on the sub-network loss value and the reliability sub-coefficient corresponding to each time section;
the third calculation unit includes:
the calculating subunit is configured to perform integral operation on the sub-network loss value corresponding to each time section to obtain a network loss value of the power distribution and utilization model;
a coefficient determining subunit, configured to use, as the reliability coefficient, a reliability sub-coefficient with a smallest corresponding value in reliability sub-coefficients corresponding to each time slice;
the operation parameters of the power distribution and utilization model comprise a load flow calculation result, a network loss value and a reliability coefficient.
4. The system of claim 3, wherein the third computing unit further comprises:
the first comparison subunit is used for comparing the network loss value with a historical network loss value of the power distribution and utilization model after the third calculation unit calculates the network loss value and the reliability coefficient of the power distribution and utilization model based on the network loss value and the reliability sub-coefficient corresponding to each time section to obtain a first comparison result;
the second comparison subunit is used for comparing the reliability coefficient with the historical reliability coefficient of the power distribution and utilization model to obtain a second comparison result;
and the result determining subunit is used for determining the result of increasing the advantages and disadvantages of the electric equipment to be added and distributed according to the first comparison result and the second comparison result.
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