CN114595891A - Power distribution network voltage and power flow boundary crossing risk assessment method, system and equipment - Google Patents

Power distribution network voltage and power flow boundary crossing risk assessment method, system and equipment Download PDF

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CN114595891A
CN114595891A CN202210235358.7A CN202210235358A CN114595891A CN 114595891 A CN114595891 A CN 114595891A CN 202210235358 A CN202210235358 A CN 202210235358A CN 114595891 A CN114595891 A CN 114595891A
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distribution network
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wind speed
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董昱
丁杰
董存
梁志峰
刘丽
杨文桢
陈丽娟
陈水耀
陈文进
夏俊荣
赵欣
张俊
王会超
彭琰
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State Grid Corp of China SGCC
Southeast University
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Southeast University
State Grid Zhejiang Electric Power Co Ltd
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

A method, a system and equipment for evaluating the voltage and power flow border-crossing risk of a power distribution network comprise: acquiring wind speed, illumination intensity and load requirements; combining a pre-constructed risk assessment model according to wind speed, illumination intensity and load requirements to obtain a minimum value of an objective function; obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the target function with a pre-constructed probability density function, and evaluating the risk of the power distribution network based on the risk comprehensive evaluation index value; the probability density function is obtained by fitting Gaussian distribution to deviations of historical data and predicted data of wind speed, illumination intensity and load requirements; the risk model is constructed by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and setting a constraint condition for the target. And obtaining a risk comprehensive evaluation index value based on the probability density function, wherein the risk comprehensive evaluation index value is suitable for a scene of distributed new energy large-scale access.

Description

Power distribution network voltage and power flow boundary crossing risk assessment method, system and equipment
Technical Field
The invention relates to the field of power grid optimized scheduling, in particular to a method, a system and equipment for evaluating the voltage and power flow out-of-range risk of a power distribution network.
Background
With the development of new energy power generation technology, the permeability of distributed new energy in a power grid is gradually improved, clean power generation can be realized by the new energy, and great environmental benefits can be brought to the operation of the power grid. However, the output of new energy has strong intermittency and uncertainty, such as distributed photovoltaic and distributed wind power, the output of the new energy is closely related to the geographical position and the weather condition, and the uncertainty factors bring certain threats to the safe and stable operation of the power grid. The traditional uncertainty-free power grid reliability analysis method cannot be applied to a distributed new energy large-scale access scene, and an operation safety evaluation method of a power distribution network is urgently needed to be constructed according to the output characteristics of new energy.
Disclosure of Invention
In order to solve the problem that the traditional uncertainty-based power grid reliability analysis method cannot be applied to a distributed new energy large-scale access scene, the invention provides a power distribution network voltage and power flow out-of-range risk assessment method, which comprises the following steps:
acquiring wind speed, illumination intensity and load requirements;
obtaining a minimum value of an objective function according to the wind speed, the illumination intensity and the load demand in combination with a pre-constructed risk evaluation model;
obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the target function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function which is constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
Preferably, the constructing of the risk assessment model comprises:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function, the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
Preferably, the objective function is represented by the following formula:
min OF＝{S1，S2，S3，S4，S5}；
in the formula, S1Risk of loss of load; s2The cost of abandoning light for the wind and the light of the distributed new energy is saved; s3The cost of operation; s4Transformer loss; s5Is the carbon emission.
Preferably, the constructing of the probability density function includes:
acquiring historical data of wind speed, illumination intensity and load demand;
dividing the historical data of the wind speed, the illumination intensity and the load demand into a training set and a test set according to a set proportion;
training a pre-constructed long and short term memory artificial neural network model according to the training concentrated wind speed, the illumination intensity and the load requirement to obtain a trained long and short term memory artificial neural network model;
inputting the wind speed, the illumination intensity and the load demand concentrated in the test into a trained long-short term memory artificial neural network model to obtain predicted values of the wind speed, the illumination intensity and the load demand;
calculating the difference value between the actual values of the wind speed, the illumination intensity and the load demand in the test set and the predicted value, and fitting the difference value by adopting Gaussian distribution to obtain a prediction error probability distribution function;
and superposing the prediction error probability distribution function and the actual value to obtain a probability density function.
Preferably, the obtaining of the risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function includes:
the wind speed, the illumination intensity and the load demand distribution corresponding to the minimum value of the objective function are brought into the probability density function to obtain the probability density function of the wind speed, the illumination intensity and the load demand;
the probability density function of the wind speed is brought into a relation between the photovoltaic output and the illumination intensity, and the probability density function of the illumination intensity is brought into a relation between the wind power output and the wind speed, so that the photovoltaic output probability density function and the wind power output probability density function are obtained;
substituting the probability density functions of the wind power output, the photovoltaic output and the load demand into each risk assessment index calculation formula to obtain each risk assessment index value;
and determining the weight of each risk assessment index based on a subjective and objective weighting method, and calculating to obtain a risk comprehensive assessment index value according to the weight occupied by each risk assessment index value.
Preferably, the risk comprehensive assessment index value is calculated according to the following formula:
Figure BDA0003541855740000031
in the formula, RtFor the comprehensive risk of the distribution network at time t,
Figure BDA0003541855740000032
are respectively as
Figure BDA0003541855740000033
Performing min-max normalization processing on the value;
Figure BDA0003541855740000034
the total risk of the uncertainty of the boundary crossing of the distribution network voltage at the moment t;
Figure BDA0003541855740000035
the total risk of uncertainty of power distribution network load flow boundary crossing at the moment t;
Figure BDA0003541855740000036
the total risk of the power distribution network load loss at the moment t; w is av，wL，wloadAnd the total risk of the voltage out-of-bound uncertainty of the power distribution network at the moment t, the total risk of the tide out-of-bound uncertainty of the power distribution network at the moment t and the total risk of the load loss of the power distribution network at the moment t are weighted values respectively.
Preferably, the total risk of uncertainty of power distribution network voltage boundary crossing at the moment t
Figure BDA0003541855740000037
Calculated as follows:
Figure BDA0003541855740000038
in the formula, N is the number of nodes of the power distribution network,
Figure BDA0003541855740000039
and (4) the total risk of uncertainty of the distribution network voltage out-of-range at the moment t of the distribution network node i.
Preferably, the power distribution network at the time tTotal risk of uncertainty of power flow crossing boundary
Figure BDA00035418557400000310
Calculated as follows:
Figure BDA00035418557400000311
in the formula, NL is the branch number of the distribution network,
Figure BDA00035418557400000312
and (4) the total uncertainty risk of the power distribution network load flow out-of-range at the moment t for the branch i'.
In another aspect, the present invention further provides a power distribution network voltage and power flow boundary crossing risk assessment system, including:
the acquisition module is used for acquiring wind speed, illumination intensity and load requirements;
the target calculation module is used for combining a pre-constructed risk assessment model according to the wind speed, the illumination intensity and the load demand to obtain a target function minimum value;
the evaluation module is used for obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the running cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
Preferably, the constructing of the risk assessment model comprises:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function and the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
In yet another aspect, the present invention also provides a computing device comprising: one or more processors;
a processor for executing one or more programs;
when the one or more programs are executed by the one or more processors, the method for evaluating the voltage and power flow out-of-range risk of the power distribution network is realized.
In still another aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed, implements a method for estimating voltage and load flow boundary crossing risk of a power distribution network as described above.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for evaluating the voltage and power flow boundary crossing risk of a power distribution network, which comprises the following steps: acquiring wind speed, illumination intensity and load requirements; obtaining a minimum value of an objective function according to the wind speed, the illumination intensity and the load demand in combination with a pre-constructed risk evaluation model; obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value; the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand; the risk model is constructed by an objective function constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the running cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function. The risk comprehensive evaluation index value is obtained based on the probability density function, and the method is suitable for a scene of distributed new energy large-scale access.
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FIG. 1 is a flow chart of a method for evaluating the voltage and power flow boundary crossing risk of a power distribution network according to the present invention;
FIG. 2 is a detailed flowchart of a method for evaluating the voltage and power flow boundary crossing risk of the power distribution network according to the present invention;
FIG. 3 is a diagram of a power system architecture in an embodiment of the present invention;
FIG. 4 is a wind speed and photovoltaic output prediction from a long-short term memory artificial neural network employed in the present invention;
FIG. 5 is a schematic diagram of voltage out-of-range uncertainty risk corresponding to each node obtained by a specific application of the method for evaluating voltage and power flow out-of-range risk of a power distribution network of the present invention;
FIG. 6 is a schematic view of a tidal current boundary crossing uncertainty risk corresponding to each node obtained by a specific application of the method for evaluating voltage and tidal current boundary crossing risks of a power distribution network of the present invention;
FIG. 7 is a comprehensive risk comparison schematic diagram of the distribution network at 21:00 nights corresponding to different wind power permeabilities.
Detailed Description
The invention provides a power distribution network voltage and power flow out-of-range risk assessment method, which is used for solving the problems of new energy output and load fluctuation in power distribution network risk assessment and adopts a long-short term memory artificial neural network and Gaussian distribution to construct a source charge random model. Meanwhile, a voltage out-of-range uncertain risk index and a power flow out-of-range uncertain risk index are provided, a load loss risk index is constructed based on the voltage and power flow out-of-range uncertainty, and comprehensive risk assessment of the power distribution network is achieved.
Example 1:
a method for evaluating a power distribution network voltage and power flow boundary crossing risk, as shown in fig. 1, includes:
step 1: acquiring wind speed, illumination intensity and load requirements;
step 2: obtaining a minimum value of an objective function according to the wind speed, the illumination intensity and the load demand in combination with a pre-constructed risk evaluation model;
and step 3: obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the target function with a pre-constructed probability density function, and evaluating the risk of the power distribution network based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function which is constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
The present invention is described in detail below:
the invention discloses a method for evaluating the voltage and power flow out-of-range risk of a power distribution network, which comprises the following steps of: (1) acquiring grid structure data, new energy output historical data and load demand data of a power grid; (2) constructing a wind, light and load output random model by adopting a long-short term memory artificial neural network and Gaussian distribution; (3) according to random power flow based on Latin hypercube sampling and a semi-invariant method, constructing uncertainty risk assessment indexes and assessment methods of power distribution network voltage boundary crossing and power flow boundary crossing; (4) establishing a multi-objective optimization objective function comprehensively considering the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission; (5) calculating out the voltage and power flow out-of-range uncertainty risk constraint, and establishing a multi-objective optimization constraint condition; (6) based on the target function and the constraint condition, acquiring the load loss risk of the power distribution network caused by the voltage and power flow out-of-range uncertainty; (7) and forming a comprehensive risk evaluation index of the operation of the power distribution network, which takes the voltage and load flow out-of-range uncertainty into account, based on subjective and objective weighting. The method can predict the new energy output in the power grid, form a new energy output random model, and evaluate the power grid risk by considering the voltage and tide out-of-range uncertainty on the basis.
The invention provides a power distribution network voltage and power flow boundary crossing risk assessment method which has the characteristics that the new energy output in a power grid can be predicted, and the randomness of the new energy output and the voltage and power flow boundary crossing uncertainty are calculated on the basis to realize the comprehensive risk assessment of the power grid.
A power distribution network voltage and power flow border crossing risk assessment method comprises the following steps:
before step 1, the method further comprises the following steps: and constructing a probability density function by adopting a long-short term memory artificial neural network and Gaussian distribution.
Training the long-term and short-term memory artificial neural network model based on historical data of wind speed, illumination intensity and load demand to obtain prediction data of the wind speed, the illumination intensity and the load demand, wherein the prediction data comprises the following specific steps:
acquiring historical data of wind speed, illumination intensity and load demand;
dividing historical data of wind speed, illumination intensity and load requirements into a training set and a test set according to a set proportion;
training the pre-constructed long and short term memory artificial neural network model according to the training concentrated wind speed, the illumination intensity and the load requirement to obtain the trained long and short term memory artificial neural network model.
And inputting the wind speed, the illumination intensity and the load demand into the trained long-short term memory artificial neural network model to obtain the predicted values of the wind speed, the illumination intensity and the load demand.
The photovoltaic output is related to the illumination intensity, the wind power output is related to the wind speed, and the relationship between the photovoltaic output and the wind power output can be represented by the following formula:
PPV＝Aηp (1)
Figure BDA0003541855740000071
in the formula, PPV、PWTActive power generated by photovoltaic power and wind power respectively; A. eta and p are respectively the area and the power of the photovoltaic panelElectrical efficiency and illumination intensity; v. ofin、vr、voutRespectively carrying out cut-in wind speed, rated wind speed and cut-out wind speed on the fan; prRated power for the fan; k is a radical of1When the wind speed value is between cut-in wind speed and rated wind speed, the primary term coefficient of a function of the output of the fan changing along with the wind speed is taken; k is a radical of2When the wind speed value is between cut-in wind speed and rated wind speed, the constant term of the function of the fan output changing along with the wind speed is taken as the wind speed value; v is the wind speed.
The load power, the illumination intensity (or photovoltaic output) and the wind speed in the formulas (1) and (2) are predicted by adopting a long-short term memory artificial neural network, and the prediction method comprises the following steps:
It＝σ(WxIxt+WhIht-1+WcIct-1+bI) (3)
ft＝σ(Wxfxt+Whfht-1+Wcfct-1+bf) (4)
ct＝ftct-1+Ittanh(Wxcxt+Whcht-1+bc) (5)
ot＝σ(Wxoxt+Whoht-1+Wcoct-1+bo) (6)
ht＝ottanh(ct) (7)
in the formula, xtIs an input variable at the time t; h istIs the output variable at time t; sigma is a sigmod function; w and b are respectively a weight matrix and an offset vector; i istIs the output of the input gate; f. oftIs the output of the forgetting gate; c. CtIs the state of the memory cell; otIs the output of the output gate.
However, the distributed power output and load requirements are random, and the prediction itself cannot be completely accurate, so that a certain prediction error exists. Fitting deviations of actual values and predicted values of wind speed, illumination intensity and load demand historical data by adopting Gaussian distribution to obtain a following wind speed, illumination intensity and load demand prediction error probability distribution function shown in a formula (8); superposing the probability distribution function with the predicted actual value to obtain a probability distribution function of wind speed, illumination intensity and load demand, so that a source load model in risk assessment is more consistent with the actual operation while considering the operational randomness, as shown in formula (9):
Figure BDA0003541855740000081
Figure BDA0003541855740000082
in the formula, σWT、σPV、σLRespectively as standard deviation in the wind speed, illumination intensity, load demand prediction error probability density fitting function. Mu.sWT、μPV、μLRespectively predicting expected values, x, in the error probability density fitting function for wind speed, illumination intensity and load demandpvIs the intensity of the illumination; x is the number ofLIs the load demand; x is a radical of a fluorine atomwtIs the wind speed.
The step 1 of obtaining the wind speed, the illumination intensity and the load demand specifically comprises the following steps:
and acquiring grid structure data, new energy output historical data and load demand data of the power grid. The new energy output here includes wind speed and light intensity data.
In the step 2, combining a pre-constructed risk assessment model with the wind speed, the illumination intensity and the load demand to obtain a minimum value of an objective function, specifically comprising:
based on the random optimal power flow, establishing a multi-objective optimization objective function comprehensively considering the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission;
the objective function OF is as follows:
minOF＝{S1,S2,S3,S4,S5} (10)
Figure BDA0003541855740000091
in the formula, S1Risk of loss of load; s2The cost of abandoning wind and light for the distributed new energy is saved; s3For operating costs; s4Transformer loss; s5Carbon emissions; a. b and c are weighted values; p is a radical ofa,t、pb,t、pc,t、Pa,t、Pb,t、Pc,tRespectively a first-level load reduction value, a second-level load reduction value and a third-level load reduction value at the moment t and a first-level load reference value, a second-level load reference value and a third-level load reference value;
Figure BDA0003541855740000092
the cost coefficient of wind and light abandoning for the new energy generator set is obtained;
Figure BDA0003541855740000093
the air abandon quantity or the light abandon quantity of the new energy unit at the time t; pgt、PRES,tThe power purchased from the superior power grid at the moment t and the power sent by the new energy unit are respectively; cgt、CRES,tThe price of electricity sold by a superior power grid and the running cost of the new energy unit at the time t are respectively; c (r, y) is the capital recovery factor; cpcap、CecapInvestment cost factor for energy storage; cpom、CeomMaintaining a cost factor for the operation of stored energy; pbr、EbrThe rated power and the rated capacity of the battery are obtained;
Figure BDA0003541855740000094
the temperature of the hottest point of the transformer winding at the moment t; ccemisIs the average carbon emission rate at time t.
In the transformer loss cost expression, the winding hottest point temperature is calculated by the following formula:
Figure BDA0003541855740000095
Figure BDA0003541855740000096
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000097
is the ambient temperature at time t;
Figure BDA0003541855740000098
the values of the oil temperature at the top of the transformer exceeding the ambient temperature at the time t and the time t-1 are respectively;
Figure BDA0003541855740000099
the temperature of the hottest point of the transformer winding at the time t and the temperature of the hottest point of the transformer winding at the time t-1 respectively exceed the oil temperature at the top of the transformer; tau is0、τwIs a time constant; Δ t is the time interval;
Figure BDA0003541855740000101
the maximum value of the difference between the oil temperature at the top of the transformer and the ambient temperature at the moment t;
Figure BDA0003541855740000102
the maximum value of the difference between the hottest point temperature of the transformer winding and the ambient temperature at the moment t;
Figure BDA0003541855740000103
the maximum temperature rise of the top oil temperature and the winding hottest point temperature when the transformer is in a rated operation working condition is respectively. k is a radical oftR is the ratio of the transformer load to the rated power thereof at the time t and the ratio of the power loss when the transformer is in the rated full load and no load respectively; m and n are empirical cooling index parameters for the transformer windings and oil, respectively.
4) Calculating out the voltage and power flow out-of-range uncertainty risk constraint, and establishing a multi-objective optimization constraint condition;
considering power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-limit opportunity constraint, and obtaining the random optimal power flow optimization constraint conditions as follows:
Figure BDA0003541855740000104
Figure BDA0003541855740000105
Figure BDA0003541855740000106
p{HSTx≤HSTmax}≥γHST (17)
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000107
the maximum value of the voltage and the power flow boundary crossing risk which can be accepted at the moment t; pbc,t、Pbd,tRespectively charging and discharging the stored energy at the time t; etab,c、ηb,dEnergy storage charging efficiency and energy discharge efficiency at the time t are respectively obtained; SOCmax、SOCminThe upper and lower limits of the energy storage SOC; gamma is a self-discharge coefficient; cbatIs the battery capacity; HSTx、HSTmaxThe hottest point temperature and the upper temperature limit of the xth transformer winding in the power distribution network are respectively; gamma rayHSTIs a safety confidence in the temperature of the transformer windings. Pi,t、Qi,tRespectively injecting active power and reactive power of a node i at the time t; u shapei,t、Uj,tThe voltage amplitudes of the nodes i at the time t are respectively; deltaij,tIs the difference between the phase angles of the voltages at the node i and the node j at the time t; gij、BijRespectively the conductance and susceptance of the branches i-j.
5) Constructing a wind power risk assessment model based on a target function and constraint conditions, and obtaining a target function minimum value meeting the constraint conditions by the risk assessment model so as to obtain a power distribution network load loss risk caused by voltage and power flow out-of-range uncertainty;
in step 3, the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function are combined with a pre-constructed probability density function to obtain a comprehensive risk evaluation index value, and the power distribution network risk is evaluated based on the comprehensive risk evaluation index value, including:
substituting the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function into a probability density function to obtain the probability density function of the wind speed, the illumination intensity and the load demand, and substituting the probability density function of the wind speed and the illumination intensity into a relation between the photovoltaic output and the illumination intensity, wherein the relation between the wind power output and the wind speed is shown in formulas (1) and (2), so as to obtain the probability density functions of the photovoltaic output and the wind power output; randomly sampling wind power output, photovoltaic output and load requirements according to Latin hypercube sampling, calculating each-order origin moment and semi-invariant of the wind power, photovoltaic output and load requirements of each node in the power distribution network, calculating each-order semi-invariant of node voltage and branch power flow according to a Jacobian matrix used by last iteration of each node power in the Newton method power flow calculation of an expected value point, and finally substituting the semi-invariant of state quantity into Gram-Charlie series to obtain the probability density of the node voltage and the branch power flow, wherein the calculation method is as follows: 6) firstly, calculating each-order origin moment and semi-invariant of wind power, photovoltaic output and load requirements of each node in a power distribution network by adopting Latin hypercube sampling, then calculating each-order semi-invariant of node voltage and branch power flow according to a Jacobian matrix used by last iteration of each node power in the Newton method power flow calculation of an expected value point, and finally substituting the semi-invariant of state quantity into Gram-Charlier series to obtain the probability density of the node voltage and the branch power flow, wherein the calculation method is as follows:
Figure BDA0003541855740000111
Figure BDA0003541855740000112
in the formula, Pi νThe sampling value is the v power of the sampling value of wind power, photovoltaic or load requirements obtained by Latin hypercube sampling; a isν、κν+1For wind power, lightV-order origin moment and v + 1-order semi-invariant of the volt or load demand;
Figure BDA0003541855740000113
for a matrix formed by j powers of each element in the Jacobian matrix corresponding to the solution of the node voltage and the branch flow, Delta KWi (j)、
Figure BDA0003541855740000121
J-order semi-invariants respectively representing the injection power of the node i, the voltage of the node i and the power of the branch i; alpha is alpha1Is a first order origin moment; alpha (alpha) ("alpha")jIs the origin moment of j order;
Figure BDA0003541855740000122
is the number of combinations; k is a radical ofv-j+1Is a v-j +1 order semi-invariant.
According to the probability density function of the node voltage and the branch tide, the uncertainty risk assessment indexes of the node voltage boundary crossing and the branch tide boundary crossing of the power distribution network at the time t are constructed:
Figure BDA0003541855740000123
in the formula, vmax、vmin、pmax,i、qmax,iThe upper and lower limits of the node voltage and the active and reactive upper limits of the branch i are respectively; v. ofbase、pbase,i、qbase,iThe reference values of the node voltage and the active and reactive of the branch circuit i are respectively; f (v)i,t)、f(pi,t)、f(qi,t) The probability densities of the voltage of a node i at the time t, the active power of a branch i and the reactive power of the branch i are respectively;
Figure BDA0003541855740000124
evaluating an index of the voltage out-of-range uncertainty risk of the node i at the time t;
Figure BDA0003541855740000125
evaluating indexes of the uncertain risk of the power flow out-of-range of the branch i at the time t; v. ofi,tIs time tThe voltage of a node i; p is a radical ofi,tThe branch i has active power at the moment t; q. q.si,tAnd the branch i is idle at the moment t.
According to the Latin hypercube sampling and the semi-invariant method random power flow, a power distribution network voltage out-of-limit and power flow out-of-limit uncertainty risk assessment index and assessment method are established, loss load, distributed new energy consumption capability, operation cost, transformer loss and carbon emission are comprehensively considered on the basis of the voltage and power flow out-of-limit uncertainty risk, the loss load uncertainty risk assessment index and method caused by the power distribution network voltage and power flow out-of-limit uncertainty risk are established, and the assessment of the power distribution network comprehensive operation risk is achieved.
7) And determining the weight of each risk assessment index based on subjective and objective weighting, and forming a comprehensive risk assessment index of the operation of the power distribution network, which takes the voltage and load flow out-of-range uncertainty into account, by each risk assessment index and each risk assessment index weight.
And determining the weight value by adopting a subjective and objective weighting method, wherein the subjective weighting method adopts an analytic hierarchy process, and the objective weighting method adopts an entropy weight method. Subjective weighting by comparing the indicators
Figure BDA0003541855740000126
Compared with
Figure BDA0003541855740000127
Degree of importance and index
Figure BDA0003541855740000128
Compared with
Figure BDA0003541855740000129
The decision matrix can be obtained as follows:
C＝[cij]3×3 (21)；
in the formula, C is a judgment matrix with 3 rows and 3 columns; c. CijThe degree of importance of index i relative to index j.
And C, calculating the maximum characteristic root of C and the corresponding characteristic vector, wherein if the consistency check can be passed, the characteristic vector is the weight value of the subjective weighting method, otherwise, C is modified until the consistency check is met.
The method for acquiring the weight by the objective weighting method mainly comprises the following steps:
Figure BDA0003541855740000131
Figure BDA0003541855740000132
in the formula, RjIs the value of index j; z is a radical ofjIs the information entropy of the index j; omegajThe weight value of the index j determined by the objective weighting method.
Figure BDA0003541855740000133
In the formula, wAj、wEjRespectively determining the weight value of the index j by a subjective weighting method based on an analytic hierarchy process and the weight value of the index j by an objective weighting method based on an entropy weight method; w is ajThe weight value of the index j obtained by the subjective and objective weighting method.
A wind, light and load probability distribution model in the power distribution network is established by utilizing the long-short term memory artificial neural network and Gaussian distribution, so that the source load model in the risk assessment of the power distribution network gives consideration to source load operation uncertainty and operation actual conditions, and the refined modeling of a risk assessment basic model is realized.
Determining the weight of each evaluation index based on subjective and objective weighting, and further determining the comprehensive evaluation index of the operation risk of the power distribution network, wherein the specific calculation formula is as follows:
Figure BDA0003541855740000134
Figure BDA0003541855740000135
Figure BDA0003541855740000136
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000137
the total risk of the uncertainty of the out-of-range voltage of the distribution network at the time t,
Figure BDA0003541855740000138
The total risk of the power distribution network tide out-of-range uncertainty at the time t,
Figure BDA0003541855740000139
The total risk of the power distribution network load loss at the moment t;
Figure BDA00035418557400001310
are respectively as
Figure BDA0003541855740000141
Performing min-max normalization processing on the value; rtFor the comprehensive risk of the distribution network at time t, wv，wL，wloadRespectively weighing the total risk of power distribution network voltage, power flow out-of-range uncertainty and the total risk of power distribution network load loss;
Figure BDA0003541855740000142
the total risk of uncertainty of power distribution network voltage boundary crossing at the time t for the power distribution network node i;
Figure BDA0003541855740000143
and (4) the total uncertainty risk of the power distribution network load flow out-of-range at the moment t for the branch i'.
And analyzing the risk distribution condition and the risk size of the node or the branch by the risk comprehensive evaluation index value.
According to the method, the wind, light and load probability distribution model in the power distribution network is established by utilizing the long-term and short-term memory artificial neural network and the Gaussian distribution, so that the source load model in the risk assessment of the power distribution network gives consideration to the source load operation uncertainty and the actual operation condition, and the refined modeling of the risk assessment basic model is realized.
According to Latin hypercube sampling and semi-invariant method random power flow, the invention establishes risk assessment indexes and assessment methods for power distribution network voltage out-of-limit and power flow out-of-bound uncertainty.
The invention constructs the load loss uncertainty risk assessment index and method caused by the voltage and trend out-of-bounds uncertainty risk of the power distribution network based on the voltage and trend out-of-bounds uncertainty risk and comprehensively considering the load loss, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission, and realizes the assessment of the comprehensive operation risk of the power distribution network.
Example 2:
in order to more clearly illustrate the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings:
the invention provides a power distribution network voltage and power flow boundary crossing risk assessment method, which comprises the following steps:
s1 is as shown in fig. 1, acquiring grid structure data, new energy output historical data, and load demand data of the power grid;
s2, constructing a wind, light and load output random model by adopting a long-short term memory artificial neural network and Gaussian distribution;
the photovoltaic output is related to the illumination intensity, the wind power output is related to the wind speed, and the relationship between the photovoltaic output and the wind power output can be represented by the following formula:
PPV＝Aηp (1)
Figure BDA0003541855740000144
in the formula, PPV、PWTActive power generated by photovoltaic power and wind power respectively; A. eta and p are the area of the photovoltaic panel, the power generation efficiency and the illumination intensity respectively; v. ofin、vr、voutRespectively carrying out cut-in wind speed, rated wind speed and cut-out wind speed on the fan; prThe rated power of the fan.
The load power, the illumination intensity (or photovoltaic output) and the wind speed in the formulas (1) and (2) are predicted by adopting a long-short term memory artificial neural network, and the prediction method comprises the following steps:
It＝σ(WxIxt+WhIht-1+WcIct-1+bI) (3)
ft＝σ(Wxfxt+Whfht-1+Wcfct-1+bf) (4)
ct＝ftct-1+Ittanh(Wxcxt+Whcht-1+bc) (5)
ot＝σ(Wxoxt+Whoht-1+Wcoct-1+bo) (6)
ht＝ottanh(ct) (7)
in the formula, xtIs an input variable at the time t; h istIs the output variable at time t; sigma is a sigmod function; w and b are respectively a weight matrix and an offset vector; i istIs the output of the input gate; f. oftIs the output of the forgetting gate; c. CtIs the state of the memory cell; otIs the output of the output gate.
Training the long-term and short-term memory artificial neural network model based on historical data to obtain prediction data of wind speed, illumination intensity and load requirements. However, the distributed power output and load requirements are random, and the prediction itself cannot be completely accurate, so that a certain prediction error exists. Fitting deviations of actual values and predicted values of wind speed, illumination intensity and load demand historical data by adopting Gaussian distribution to obtain a following wind speed, illumination intensity and load demand prediction error probability distribution model as shown in a formula (8); superposing the probability distribution model with the predicted actual value to obtain a probability distribution model of wind speed, illumination intensity and load demand, so that the source load model in risk assessment is more consistent with the actual operation while considering the operational randomness, as shown in formula (9):
Figure BDA0003541855740000151
Figure BDA0003541855740000161
in the formula, KWT、KPVRespectively the number of normal distribution functions in the error fitting functions of the wind speed and the illumination intensity; mu.sk、ωkAre weight value, σWT、σPV、σLAnd respectively, the standard deviation in the probability density fitting function of the prediction error of the wind speed, the illumination intensity and the load demand. Mu.sWT、μPV、μLRespectively, the wind speed, the illumination intensity and the expected value in the load demand prediction error probability density fitting function.
S3, constructing uncertainty risk assessment indexes and assessment methods of power distribution network voltage boundary crossing and power flow boundary crossing according to random power flow based on Latin hypercube sampling and a semi-invariant method;
firstly, calculating each-order origin moment and semi-invariant of wind power, photovoltaic output and load requirements of each node in a power distribution network by adopting Latin hypercube sampling, then calculating each-order semi-invariant of node voltage and branch power flow according to a Jacobian matrix used by last iteration of each node power in the Newton method power flow calculation of an expected value point, and finally substituting the semi-invariant of state quantity into Gram-Charlier series to obtain the probability density of the node voltage and the branch power flow, wherein the calculation method is as follows:
Figure BDA0003541855740000162
Figure BDA0003541855740000163
in the formula, Pi νIs a pullingThe v power of a sampling value of wind power, photovoltaic or load requirements is obtained by T hypercube sampling; a isν、κν+1A v-order origin moment and a v + 1-order semi-invariant of wind power, photovoltaic or load requirements;
Figure BDA0003541855740000164
for a matrix formed by the j power of each element in the corresponding Jacobian matrix when the node voltage and the branch power flow are solved, delta KWi (j)、
Figure BDA0003541855740000165
Respectively, j-order semi-invariants representing the injection power of the node i, the voltage of the node i and the power of the branch i.
According to the probability density function of the node voltage and the branch tide, the uncertainty risk assessment indexes of the node voltage boundary crossing and the branch tide boundary crossing of the power distribution network at the time t are constructed:
Figure BDA0003541855740000171
in the formula, vmax、vmin、pmax,i、qmax,iThe upper and lower limits of the node voltage and the active and reactive upper limits of the branch i are respectively set; v. ofbase、pbase,i、qbase,iThe reference values of the node voltage and the active and reactive of the branch circuit i are respectively; f (v)i,t)、f(pi,t)、f(qi,t) The probability densities of the voltage of the node i, the active power of the branch i and the reactive power of the branch i at the moment t are respectively.
S4: establishing a multi-objective optimization objective function comprehensively considering the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission;
the objective function OF is as follows:
minOF＝{S1,S2,S3,S4,S5} (10)
Figure BDA0003541855740000172
in the formula, a, b and c are weighted values; p is a radical ofa,t、pb,t、pc,t、Pa,t、Pb,t、Pc,tRespectively a first-level load reduction value, a second-level load reduction value and a third-level load reduction value at the moment t and a first-level load reference value, a second-level load reference value and a third-level load reference value;
Figure BDA0003541855740000173
the cost coefficient of wind and light abandoning for the new energy generator set is obtained;
Figure BDA0003541855740000174
the air abandon quantity or the light abandon quantity of the new energy unit at the time t; pgt、PRES,tThe power purchased from the superior power grid at the moment t and the power sent by the new energy unit are respectively; cgt、CRES,tThe price of electricity sold by a superior power grid and the running cost of the new energy unit at the time t are respectively; c (r, y) is the capital recovery factor; cpcap、CecapInvestment cost factor for energy storage; cpom、CeomMaintaining a cost factor for the operation of stored energy; pbr、EbrThe rated power and the rated capacity of the battery are obtained;
Figure BDA0003541855740000181
the temperature of the hottest point of the transformer winding at the moment t; ccemisIs the average carbon emission rate at time t.
In the transformer loss cost expression, the winding hottest point temperature is calculated by the following formula:
Figure BDA0003541855740000182
Figure BDA0003541855740000183
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000184
respectively representing the ambient temperature at the moment t and the oil temperature at the top of the transformer;
Figure BDA0003541855740000185
the maximum values of the oil temperature at the top of the transformer and the temperature of the hottest point of the winding at the time t are respectively; tau is0、τwIs a time constant;
Figure BDA0003541855740000186
the maximum temperature rise of the top oil temperature and the winding hottest point temperature when the transformer is in a rated operation working condition is respectively. k is a radical oftR is the ratio of the transformer load to the rated power thereof at the time t and the ratio of the power loss when the transformer is in the rated full load and no load respectively; m and n are empirical cooling index parameters for the transformer windings and oil, respectively.
S5: calculating out the voltage and power flow out-of-range uncertainty risk constraint, and establishing a multi-objective optimization constraint condition;
considering power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-limit opportunity constraint, and obtaining the random optimal power flow optimization constraint conditions as follows:
Figure BDA0003541855740000187
Figure BDA0003541855740000188
Figure BDA0003541855740000189
p{HSTx≤HSTmax}≥γHST (17)
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000191
the maximum value of the voltage and the power flow boundary crossing risk which can be accepted at the moment t; pbc,t、Pbd,tRespectively charging and discharging the stored energy at the time t; etab,c、ηb,dEnergy storage charging efficiency and energy discharge efficiency at the time t are respectively obtained; SOCmax、SOCminThe upper and lower limits of the energy storage SOC; gamma is a self-discharge coefficient; cbatIs the battery capacity; HSTx、HSTmaxThe hottest point temperature and the upper temperature limit of the xth transformer winding in the power distribution network are respectively; gamma rayHSTIs a safety confidence of the transformer winding temperature.
S6: based on the target function and the constraint condition, acquiring the load loss risk of the power distribution network caused by the voltage and power flow out-of-range uncertainty;
s7: and forming a comprehensive risk evaluation index of the operation of the power distribution network, which takes the voltage and load flow out-of-range uncertainty into account, based on subjective and objective weighting.
The comprehensive evaluation indexes of the operation risk of the power distribution network are as follows:
Figure BDA0003541855740000192
Figure BDA0003541855740000193
Figure BDA0003541855740000194
in the formula (I), the compound is shown in the specification,
Figure BDA0003541855740000195
the total risk of the uncertainty of the out-of-range voltage of the distribution network at the time t,
Figure BDA0003541855740000196
The total risk of the power distribution network tide out-of-range uncertainty at the time t,
Figure BDA0003541855740000197
The total risk of the power distribution network load loss at the moment t;
Figure BDA0003541855740000198
are respectively as
Figure BDA0003541855740000199
Performing min-max normalization processing on the value; rtIs the comprehensive risk of the distribution network at time t, wv，wL，wloadRespectively weighing the total risk of power distribution network voltage, power flow out-of-range uncertainty and the total risk of power distribution network load loss;
Figure BDA00035418557400001910
the total uncertainty risk of the power distribution network voltage out-of-range at the moment t is the power distribution network node i;
Figure BDA00035418557400001911
and (4) the total uncertainty risk of the power distribution network load flow out-of-range at the moment t for the branch i'.
Figure BDA00035418557400001912
In the formula, wAj、wEjRespectively determining the weight value of the index j by a subjective weighting method based on an analytic hierarchy process and the weight value of the index j by an objective weighting method based on an entropy weight method; w is ajThe weight value of the index j obtained by the subjective and objective weighting method.
In the embodiment of the present invention, the IEEE33 power distribution system shown in fig. 3 is used, and the system node 1 is connected to the upper grid through a transformer. The node 11 and the node 31 are respectively connected with a distributed wind generating set with rated power of 1.5MW, the node 18 is connected with a photovoltaic generating set with rated power of 1MW, and the node 7 is connected with an energy storage device. The reference active load of the system is 3.715MW, and the reference reactive load is 2.3 Mvar. Next, the simulation results of the embodiment of the present invention will be explained.
As can be seen from fig. 4, the wind speed and the photovoltaic output can be predicted by using the method of the long-term and short-term memory artificial neural network, wherein the abscissa in the graph is the time, and the ordinate is the predicted value of the wind speed or the photovoltaic output, so that a data basis is provided for the risk assessment of the power distribution network considering the voltage and power flow out-of-range uncertainty. Fig. 5 shows a risk condition index of the node 33 in 24 hours a day, wherein the abscissa of the index is 1 to 24 hours, and the ordinate of the index is a voltage out-of-range uncertainty risk value, fig. 6 shows a load flow out-of-range uncertainty risk index of the branch 3-23, and the result shows that the voltage of the node 33 has a large out-of-range uncertainty risk in 3:00-6:00 and 20:00-21:00, and fig. 6 shows that the load flow of the branch 3-23 has a large out-of-range uncertainty risk in 20:00-21: 00. The reason is that the wind power resources are sufficient from 3:00 to 6:00, the load is in a light load state at night, the risk of voltage exceeding the upper limit exists, the load requirement is large from 20:00 to 21:00, the risk of voltage exceeding the lower limit exists, and the uncertainty risk of tide crossing the boundary exists. Fig. 7 shows the combined risk of the distribution network at 21:00 for 4 cases, with wind penetration of 30%, 40%, 60% and 70%, respectively. With the improvement of the wind power permeability, the lower limit risk of the voltage and the uncertain risk of the load flow out of bound caused by heavy load are improved, and the load loss risk caused by the uncertain voltage and load flow out of bound is reduced, so that the comprehensive risk in the period of time is in a descending trend. Therefore, the power distribution network risk assessment method considering the voltage and power flow out-of-bound uncertainty can predict the new energy output in the power grid, form a new energy output random model, and evaluate the power grid risk by considering the voltage and power flow out-of-bound uncertainty on the basis.
The foregoing describes specific embodiments of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the embodiments and descriptions described above are merely illustrative of the method of the present invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Example 3:
the invention based on the same invention concept also provides a power distribution network voltage and power flow boundary-crossing risk assessment system, which comprises:
the acquisition module is used for acquiring wind speed, illumination intensity and load requirements;
the target calculation module is used for combining a pre-constructed risk assessment model according to the wind speed, the illumination intensity and the load demand to obtain a target function minimum value;
the evaluation module is used for obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function which is constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
The construction of the risk assessment model comprises the following steps:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function and the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
The risk assessment model building module is used for:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function and the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
The system also comprises a probability density function building module used for:
acquiring historical data of wind speed, illumination intensity and load demand;
dividing the historical data of the wind speed, the illumination intensity and the load demand into a training set and a test set according to a set proportion;
training a pre-constructed long and short term memory artificial neural network model according to the training concentrated wind speed, the illumination intensity and the load requirement to obtain a trained long and short term memory artificial neural network model;
inputting the wind speed, the illumination intensity and the load demand concentrated in the test into a trained long-short term memory artificial neural network model to obtain predicted values of the wind speed, the illumination intensity and the load demand;
calculating the difference value between the actual values of the wind speed, the illumination intensity and the load demand in the test set and the predicted value, and fitting the difference value by adopting Gaussian distribution to obtain a prediction error probability distribution function;
and superposing the prediction error probability distribution function and the actual value to obtain a probability density function.
The evaluation module is specifically configured to:
the wind speed, the illumination intensity and the load demand distribution corresponding to the minimum value of the objective function are brought into the probability density function to obtain the probability density function of the wind speed, the illumination intensity and the load demand;
the probability density function of the wind speed is brought into a relation between the photovoltaic output and the illumination intensity, and the probability density function of the illumination intensity is brought into a relation between the wind power output and the wind speed, so that the photovoltaic output probability density function and the wind power output probability density function are obtained;
substituting the probability density functions of the wind power output, the photovoltaic output and the load demand into each risk assessment index calculation formula to obtain each risk assessment index value;
and determining the weight of each risk assessment index based on a subjective and objective weighting method, and calculating to obtain a risk comprehensive assessment index value according to the weight occupied by each risk assessment index value.
The risk comprehensive evaluation index value is calculated according to the following formula:
Figure BDA0003541855740000221
in the formula, RtFor the comprehensive risk of the distribution network at time t,
Figure BDA0003541855740000222
are respectively as
Figure BDA0003541855740000223
Performing min-max normalization processing on the value;
Figure BDA0003541855740000224
the total risk of uncertainty of the out-of-range voltage of the power distribution network at the moment t is determined;
Figure BDA0003541855740000225
the total risk of uncertainty of power distribution network load flow boundary crossing at the moment t;
Figure BDA0003541855740000226
the total risk of the power distribution network load loss at the moment t; w is av，wL，wloadAnd the total risk of the voltage out-of-bound uncertainty of the power distribution network at the moment t, the total risk of the tide out-of-bound uncertainty of the power distribution network at the moment t and the total risk of the load loss of the power distribution network at the moment t are weighted values respectively.
Total risk of uncertainty of out-of-range distribution network voltage at time t
Figure BDA0003541855740000227
Calculated as follows:
Figure BDA0003541855740000228
in the formula, N is the number of nodes of the power distribution network,
Figure BDA0003541855740000229
and i is a node number, and the total risk of uncertainty of the power distribution network voltage out-of-range at the moment t of the power distribution network node i is represented by i.
Total uncertainty risk of power flow out-of-bounds of distribution network at time t
Figure BDA0003541855740000231
Calculated as follows:
Figure BDA0003541855740000232
in the formula, NL represents the number of branches of the power distribution network,
Figure BDA0003541855740000233
and i ' is the total uncertainty risk of the power distribution network power flow boundary crossing at the moment t of the branch i ', and i ' is the branch number.
For convenience of description, each part of the above apparatus is separately described as each module or unit by dividing the function. Of course, the functionality of the various modules or units may be implemented in the same one or more pieces of software or hardware in practicing the invention.
Based on the same inventive concept, in yet another embodiment of the present invention, a computing device is provided, which includes a processor and a memory, the memory storing a computer program, the computer program including program instructions, the processor executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for executing the steps of the power distribution network voltage and power flow boundary-crossing risk assessment method.
Based on the same inventive concept, in yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the power distribution network voltage and power flow boundary-crossing risk assessment method in the above embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention.

Claims (12)

1. A power distribution network voltage and power flow boundary crossing risk assessment method is characterized by comprising the following steps:
acquiring wind speed, illumination intensity and load requirements;
obtaining a minimum value of an objective function according to the wind speed, the illumination intensity and the load demand in combination with a pre-constructed risk evaluation model;
obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the target function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function which is constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
2. The method of claim 1, wherein the constructing of the risk assessment model comprises:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function and the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
3. The method of claim 1, wherein the objective function is expressed by the following equation:
min OF＝{S1，S2，S3，S4，S5}；
in the formula, S1Risk of loss of load; s2The cost of abandoning wind and light for the distributed new energy is saved; s3For operating costs; s4Transformer loss; s5Is the carbon emission.
4. The method of claim 1, wherein the constructing of the probability density function comprises:
acquiring historical data of wind speed, illumination intensity and load demand;
dividing the historical data of the wind speed, the illumination intensity and the load demand into a training set and a test set according to a set proportion;
training a pre-constructed long and short term memory artificial neural network model according to the training concentrated wind speed, the illumination intensity and the load requirement to obtain a trained long and short term memory artificial neural network model;
inputting the wind speed, the illumination intensity and the load demand concentrated in the test into a trained long-short term memory artificial neural network model to obtain predicted values of the wind speed, the illumination intensity and the load demand;
calculating the difference value between the actual values of the wind speed, the illumination intensity and the load demand in the test set and the predicted value, and fitting the difference value by adopting Gaussian distribution to obtain a prediction error probability distribution function;
and superposing the prediction error probability distribution function and the actual value to obtain a probability density function.
5. The method of claim 1, wherein the obtaining of the risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function comprises:
the wind speed, the illumination intensity and the load demand distribution corresponding to the minimum value of the objective function are brought into the probability density function to obtain the probability density function of the wind speed, the illumination intensity and the load demand;
the probability density function of the wind speed is brought into a relation between the photovoltaic output and the illumination intensity, and the probability density function of the illumination intensity is brought into a relation between the wind power output and the wind speed, so that the photovoltaic output probability density function and the wind power output probability density function are obtained;
substituting the probability density functions of the wind power output, the photovoltaic output and the load demand into each risk evaluation index calculation formula to obtain each risk evaluation index value;
and determining the weight of each risk assessment index based on a subjective and objective weighting method, and calculating to obtain a risk comprehensive assessment index value according to the weight occupied by each risk assessment index value.
6. The method of claim 5, wherein the risk-integrated assessment index value is calculated as:
Figure FDA0003541855730000021
in the formula, RtFor the comprehensive risk of the distribution network at time t,
Figure FDA0003541855730000022
are respectively as
Figure FDA0003541855730000023
Performing min-max normalization processing on the value;
Figure FDA0003541855730000024
the total risk of uncertainty of the out-of-range voltage of the power distribution network at the moment t is determined;
Figure FDA0003541855730000025
the total risk of uncertainty of power distribution network load flow boundary crossing at the moment t;
Figure FDA0003541855730000026
the total risk of the power distribution network load loss at the moment t; w is av，wL，wloadUncertain voltage out-of-bounds for distribution network at time tAnd the total risk of sex, the total risk of uncertainty of power distribution network load flow boundary crossing at the moment t and the total risk of load loss of the power distribution network at the moment t.
7. The method of claim 6, wherein the total risk of distribution network voltage out-of-bounds uncertainty at time t
Figure FDA0003541855730000027
Calculated as follows:
Figure FDA0003541855730000031
in the formula, N is the number of nodes of the power distribution network,
Figure FDA0003541855730000032
and (4) the total risk of uncertainty of the distribution network voltage out-of-range at the moment t of the distribution network node i.
8. The method of claim 6, wherein the total risk of power flow out-of-bounds uncertainty of the power distribution network at time t
Figure FDA0003541855730000033
Calculated as follows:
Figure FDA0003541855730000034
in the formula, NL represents the number of branches of the power distribution network,
Figure FDA0003541855730000035
and (4) the total uncertainty risk of the power distribution network load flow out-of-range at the moment t for the branch i'.
9. The utility model provides a distribution network voltage and trend risk assessment system that borders on cross, its characterized in that includes:
the acquisition module is used for acquiring wind speed, illumination intensity and load requirements;
the target calculation module is used for combining a pre-constructed risk assessment model according to the wind speed, the illumination intensity and the load demand to obtain a target function minimum value;
the evaluation module is used for obtaining a risk comprehensive evaluation index value by combining the wind speed, the illumination intensity and the load demand corresponding to the minimum value of the objective function with a pre-constructed probability density function, and evaluating the power distribution network risk based on the risk comprehensive evaluation index value;
the probability density function is obtained by fitting Gaussian distribution on deviations of historical data and predicted data of wind speed, illumination intensity and load demand;
the risk model is constructed by an objective function which is constructed by taking the minimum sum of the loss load risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target and a constraint condition set for the objective function.
10. The system of claim 9, wherein the construction of the risk assessment model comprises:
constructing an objective function by taking the minimum sum of the load loss risk, the distributed new energy consumption capacity, the operation cost, the transformer loss and the carbon emission as a target;
setting power distribution network power flow constraint, voltage and power flow out-of-range uncertainty risk constraint, energy storage operation constraint and transformer winding hottest point temperature out-of-range opportunity constraint for the objective function;
and constructing a risk evaluation model by the objective function and the power distribution network power flow constraint, the voltage and power flow out-of-range uncertainty risk constraint, the energy storage operation constraint and the transformer winding hottest point temperature out-of-range opportunity constraint.
11. A computer device, comprising:
one or more processors;
a processor for executing one or more programs;
the one or more programs, when executed by the one or more processors, implement a method for power distribution network voltage and power flow boundary crossing risk assessment as recited in any of claims 1-8.
12. A computer-readable storage medium, having a computer program stored thereon, which, when executed, implements a method for assessing voltage and load flow boundary crossing risk in a power distribution network according to any one of claims 1 to 8.
CN202210235358.7A 2022-03-11 2022-03-11 Power distribution network voltage and power flow boundary crossing risk assessment method, system and equipment Pending CN114595891A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099590A (en) * 2022-06-14 2022-09-23 国网浙江省电力有限公司电力科学研究院 Active power distribution network economic optimization scheduling method and system considering light load uncertainty
CN117436706A (en) * 2023-12-18 2024-01-23 国网天津市电力公司电力科学研究院 Distribution area security risk assessment method and system considering photovoltaic and electric vehicles
CN118074237A (en) * 2024-04-17 2024-05-24 南京邮电大学 Distributed power distribution network-containing risk assessment method and device considering line faults

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115099590A (en) * 2022-06-14 2022-09-23 国网浙江省电力有限公司电力科学研究院 Active power distribution network economic optimization scheduling method and system considering light load uncertainty
CN117436706A (en) * 2023-12-18 2024-01-23 国网天津市电力公司电力科学研究院 Distribution area security risk assessment method and system considering photovoltaic and electric vehicles
CN118074237A (en) * 2024-04-17 2024-05-24 南京邮电大学 Distributed power distribution network-containing risk assessment method and device considering line faults

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