CN111612317B - Voltage out-of-limit risk assessment and prevention control method for active power distribution network - Google Patents
Voltage out-of-limit risk assessment and prevention control method for active power distribution network Download PDFInfo
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Abstract
The invention relates to a voltage out-of-limit risk assessment and prevention control method for an active power distribution network, which comprises the following steps of: establishing an ADN voltage out-of-limit risk assessment index based on the probability and the severity; acquiring basic data of power grid operation, and solving an ADN voltage out-of-limit risk evaluation index; and when the risk exceeds a safety threshold value, performing prevention control to reduce the ADN operation risk and improve the safety. The method can realize accurate identification of key weak nodes in operation of the power distribution network and effective management and control of risks.
Description
Technical Field
The invention relates to the technical field of safety of power distribution networks, in particular to a voltage out-of-limit risk assessment and prevention control method for an active power distribution network.
Background
The rapid development of the Distributed Generation (DG) of renewable energy sources brings great challenges to the regulation and control operation of the power distribution network, on one hand, the DG supplies electric energy at a user side to enable the power distribution network to be humanized, and on the other hand, the random intermittence of wind and light enables the network load flow and voltage distribution to have uncertainty. The proposal of an Active Distribution Network (ADN) concept provides an effective technical scheme for DG to be widely compatible with network access and distribution network operation level improvement. ADN is observable, sensible, controllable novel smart distribution network: based on an advanced measurement system, telemetering and remote signaling data such as voltage, power flow, switch position, on-load tap changer (OLTC) gear and the like of a network are observed in real time; through situation awareness, various key factors influencing network operation are effectively captured, and the safe operation situation of the power distribution network is accurately understood and predicted; and based on an optimized scheduling decision, performing Active Management (AM) on all controllable units integrated in the system, and promoting new energy consumption while the power distribution network operates economically and safely.
The operation risk assessment and early warning are the core content of the situation prediction link in ADN situation perception, and scholars have conducted certain research and exploration on the operation risk assessment of the power distribution network in the past. Document [1] (Wangyounan, Yangmuie, who is also general, etc.. initiative power distribution network operation risk assessment and early warning based on network flow transfer distribution factors [ J ]. power grid technology, 2017, 41 (02): 371-. Document [2] (liuqi. initiative power distribution system safety situation awareness modeling method research [ D ]. north China university of electric power (beijing), 2017) proposes an ADN safety situation early warning method, and fuzzy analytic hierarchy process is applied to determine weights of safety indexes such as power supply margin, main transformer load balance, voltage out-of-limit, and the like, but only severity and risk uncertainty are considered in risk assessment. Document [3] (Farshid Faghhi, Pierre-Etienne laboratory, Jean-Claude Maun, el al. A Net Balance-Based application in Risk Assessment of Distributed Generation Current [ C ].2015 IEEE Edhoven PowerTech, June 29-July 2, 2015, Eindhoven, Netherlands: 1-6) proposes a DG Risk reduction Assessment method Based on the Net difference value by using important samples to deal with the uncertainty of load and DG. Document [4] (murphy, consider, tang-mings, et al. distributed photovoltaic access distribution network operational risk assessment [ J/OL ] taking into account correlation). 1-10[2019-07-23] the influence of weather conditions on line faults and photovoltaic output is taken into consideration, and risk assessment such as voltage out-of-limit and load loss is carried out on a power distribution network in normal operation and single branch faults, but photovoltaic reactive power management is not considered in a model. Documents [1] to [4] only evaluate risks, and no effective preventive control measures are made for operating states with high risks and deteriorated safety situations.
Disclosure of Invention
In view of this, the present invention provides a voltage out-of-limit risk assessment and prevention control method for an active power distribution network, which can realize accurate identification of key weak nodes and effective risk control.
The invention is realized by adopting the following scheme: a voltage out-of-limit risk assessment and prevention control method for an active power distribution network specifically comprises the following steps:
establishing an ADN voltage out-of-limit risk assessment index based on the probability and the severity;
acquiring basic data of power grid operation, and solving an ADN voltage out-of-limit risk evaluation index;
and when the risk exceeds a safety threshold value, performing prevention control to reduce the ADN operation risk and improve the safety.
Further, the step of establishing the ADN voltage out-of-limit risk assessment index based on the probability and the severity specifically includes:
comprehensively considering the out-of-limit probability and the out-of-limit severity, and constructing voltage out-of-limit risk indexes as follows:
RVV,t,i＝PVV,t,i×SVV,t,i；
wherein the content of the first and second substances,
in the formula, Vt,iThe magnitude of the voltage at node i for time t, f (V)t,i) As a function of its probability density; vmax、VminThe upper limit and the lower limit of the node voltage are set; rVV,t,iVoltage out-of-limit risk indexes of the i node in a t period; pVV,t,iThe voltage out-of-limit probability of the i node is in a t period; sVV,t,iThe out-of-limit severity of the voltage of the i node is t time period;
V t,ithe higher limit voltage mean value and the lower limit voltage mean value of the i node in the t period are respectively.
Further, the step of collecting basic data of power grid operation and solving the ADN voltage out-of-limit risk assessment index specifically includes:
step S11: inputting basic data including ADN network parameters, element parameters, load and short-term predicted values of wind-solar output;
step S12: solving the random power flow of the ADN by adopting a semi-invariant and Gram-Charlie series expansion method to obtain the voltage probability distribution of each node of the whole network and the voltage probability density distribution of each node of the whole network;
step S13: and calculating the corresponding voltage out-of-limit risk probability and severity by using the voltage probability density obtained in the step S12, and further obtaining a risk index value to realize quantitative risk evaluation.
Further, step S12 specifically includes the following steps:
step S121: solving the ground state trend by adopting a Newton method;
step S122: solving each order moment and a semi-invariant of the load injection power random variable by the probability distribution of the DG and the load injection power random variable;
step S123: calculating each-order semi-invariant of the node injection power random variable;
step S124: calculating each-order semi-invariant and center distance of the node voltage random variable;
step S125: solving the probability distribution of the node voltage random variable according to a Cram-Charlier series expansion formula;
step S126: and summing the expected value and the random disturbance to obtain the probability distribution of the node voltage.
Further, said performing a preventive control when the risk exceeds a safety threshold specifically comprises the steps of:
step S21: judging whether the risk assessment index of the current time interval exceeds a preset threshold value, if so, making n equal to 1, and entering a step S22, otherwise, entering the next time interval;
step S22: executing risk pre-control by adopting an nth-level active management means; wherein n has a value of 1, 2,3 or 4;
step S23: solving a risk pre-control model corresponding to the nth-level active management means by adopting a harmony search algorithm to obtain a prevention control optimization scheme;
step S24: and judging whether the optimized risk assessment index is still higher than a preset threshold value, if so, making n equal to n +1, returning to the step S22, and otherwise, returning to the step S21.
Further, when n is 1, a first-level active management means is adopted, and the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power;
when n is 1, the objective function of active management is:
in the formula, RVV,tThe sum of the voltage out-of-limit risk indexes of each node in the t period; delta is a risk penalty factor, NnodeThe number of nodes of the power distribution network.
Further, when n is 2, a first-level active management means and a second-level active management means are adopted simultaneously; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: and dispatching the controllable DG active power, the energy storage active power and the flexible load.
Furthermore, a first-level active management means, a second-level active management means and a third-level active management means are adopted at the same time; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: and performing active reduction on the wind and light DG.
Further, when n is 4, a first-stage active management means, a second-stage active management means, a third-stage active management means and a fourth-stage active management means are adopted at the same time; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: performing active reduction on the wind-solar DG; wherein, the fourth-level active management means is as follows: and carrying out OLTC gear shifting and shunt capacitor bank switching.
Further, when n is 2,3 or 4, the objective function of active management is:
in the formula,. DELTA.PtControlling the active modification cost of the ADN for the t period; n is a radical of hydrogenCDG、NESS、NFL、 NIDG、NOLTC、NCRespectively the total quantity of controllable DGs, stored energy, flexible loads, intermittent DGs, OLTCs and parallel capacitor banks in the power distribution network; pCDG,t,iThe active output of the ith controllable DG in the t period; p isESS,t,iThe active output of the ith energy storage in the period t, wherein the positive value is discharging and the negative value is charging; delta PFL,t,iThe active reduction amount of the ith flexible load in the t period is determined; pIDG,t,iThe active power of the ith intermittent DG in the t period; n isOLTC,t,iThe gear of the ith OLTC is t time period; n isC,t,iThe input group number of the ith set of parallel capacitors in the t period; pC'DG,t,i、PE'SS,t,i、ΔPF'L,t,i、PI'DG,t,i、n'OLTC,t,i、n'C,t,iRespectively determining and correcting values for risk pre-control of corresponding variables; lambda [ alpha ]III、λIVPenalty factor, λ, for three, four levels of active managementIV＞λIII＞1；Pload,tAnd the total active load of the power distribution network is in a period t.
Compared with the prior art, the invention has the following beneficial effects: the method establishes the ADN voltage out-of-limit risk assessment index based on probability and severity, and adopts a semi-invariant and Gram-Charlie series expansion method to solve the random load flow so as to realize the calculation of the risk index. And when the risk exceeds a safety threshold value, performing prevention control to reduce the ADN operation risk and improve the safety. Meanwhile, the invention provides a voltage out-of-limit risk pre-control model based on multi-level AM by considering AM means such as distributed energy active and reactive power, flexible load management, traditional reactive voltage equipment and the like. In addition, the method makes a model solving strategy on the basis of a harmony search algorithm to form a closed loop system of risk assessment and pre-control. Finally, the invention aims at the method for evaluating and pre-controlling the out-of-limit risk of the voltage by applying the invention to the calculation example, and realizes the accurate identification of the key weak node and the effective control of the risk.
Drawings
FIG. 1 is a schematic voltage probability density graph according to an embodiment of the present invention.
Fig. 2 is a calculation flow of the voltage out-of-limit risk indicator according to the embodiment of the present invention.
Fig. 3 is a strategy solving flow of the HS-based risk pre-control model according to the embodiment of the present invention.
Fig. 4 is a diagram of an ADN network structure according to an embodiment of the present invention.
Figure 5 shows the daily operation of the ADN primary elements of an embodiment of the present invention.
FIG. 6 is a run-day ADN full net node voltage expectation according to an embodiment of the present invention.
Fig. 7 illustrates the risk of node voltage violations for the ADN full network on the operation day according to an embodiment of the present invention.
Fig. 8 is a voltage probability density curve of the nodes 15 and 16 before and after the 8-period risk pre-control according to the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiment is based on the predecessor achievement, researches on ADN operation risk assessment and pre-control are carried out, and node voltage out-of-limit risk caused by uncertainty of wind and light load in short-term operation is mainly considered. The embodiment provides a voltage out-of-limit risk assessment and prevention control method for an active power distribution network, which comprises the following steps:
establishing an ADN voltage out-of-limit risk assessment index based on the probability and the severity;
acquiring basic data of power grid operation, and solving an ADN voltage out-of-limit risk evaluation index;
and when the risk exceeds a safety threshold value, performing prevention control to reduce the ADN operation risk and improve the safety.
In this embodiment, the step of establishing the ADN voltage out-of-limit risk assessment index based on the probability and the severity specifically includes:
the power system operation risk assessment is defined as follows: for uncertainty factors faced by the system, a comprehensive measure of likelihood and severity is given. The operation risk of the power distribution network refers to potential threats brought to network safety power supply by forced outage of elements or severe power fluctuation, and the method mainly focuses on the risk of voltage out-of-limit research. The risk indicator should include both the likelihood of occurrence of a risk event and the severity of the outcome of the risk event. Assuming that the voltage probability density of an i node in a known t period is shown in fig. 1, comprehensively considering the out-of-limit probability and the out-of-limit severity, and constructing a voltage out-of-limit risk index as follows:
RVV,t,i＝PVV,t,i×SVV,t,i； (1)
wherein the content of the first and second substances,
in the formula, Vt,iThe magnitude of the voltage at node i for time t, f (V)t,i) As a function of its probability density; vmax、VminIn this embodiment, 1.05 and 0.95 times of rated voltage is taken as the upper limit and the lower limit of the node voltage; rVV,t,iVoltage out-of-limit risk indexes of the i node in a t period; pVV,t,iThe voltage out-of-limit probability of the i node is in a t period; sVV,t,iThe out-of-limit severity of the voltage of the i node is t time period;
V t,ithe calculation of the upper limit-exceeding voltage mean value and the lower limit-exceeding voltage mean value of the i node in the t period refers to a continuous random variable mathematical expectation algorithm, and the out-of-limit severity is described by the difference between the voltage limit value and the out-of-limit voltage mean value.
In this embodiment, the step of acquiring basic data of power grid operation and solving the ADN voltage out-of-limit risk assessment index is shown in fig. 2, and specifically includes:
step S11: inputting basic data including ADN network parameters, element parameters, load and short-term predicted values of wind-solar output; the uncertainty of wind-solar-load data in short-term operation can be described by adopting a normal probability model, the mean value of normal distribution is taken as a predicted value, and the standard deviation is taken as 10% of the predicted value.
Step S12: solving the random power flow of the ADN by adopting a semi-invariant and Gram-Charlie series expansion method to obtain the voltage probability distribution of each node of the whole network and the voltage probability density distribution of each node of the whole network;
step S13: and calculating the corresponding voltage out-of-limit risk probability and severity by using the voltage probability density obtained in the step S12, and further obtaining a risk index value to realize quantitative risk evaluation.
In this embodiment, step S12 specifically includes the following steps:
step S121: solving the ground state trend by adopting a Newton method;
step S122: solving each order moment and a semi-invariant of the load injection power random variable by the probability distribution of the DG and the load injection power random variable;
step S123: calculating each-order semi-invariant of the node injection power random variable;
step S124: calculating each-order semi-invariant and center distance of the node voltage random variable;
step S125: solving the probability distribution of the node voltage random variable according to a Cram-Charlier series expansion formula;
step S126: and summing the expected value and the random disturbance to obtain the probability distribution of the node voltage.
Preferably, the semi-invariant and Gram-Charlier series expansion method is an effective means for solving random power flow, and the core idea is to utilize the linear relation between node injection and voltage and the property of the semi-invariant, take the semi-invariant as an intermediate bridge, and obtain the probability distribution of the state variables by the known node injection probability distribution.
The node injection power equation of the power flow calculation of the power system is as follows:
wherein, Pi、QiActive and reactive injection for a node i; vi、θiThe voltage amplitude and phase angle of the node i; gij、BijThe real and imaginary parts of the admittance matrix elements.
The above formula is simplified, node injection and state variables are substituted in a random variable mode, Taylor expansion is carried out, and high-order terms are omitted, so that the following can be obtained:
S0+ΔS＝f(X0+ΔX)＝f(X0)+J0ΔX； (5)
wherein: s0Injecting expected values of variables for the nodes; x0Is the desired value of the state variable (voltage amplitude and phase angle); Δ S and Δ X are corresponding random disturbances; s0＝f(X0) X is obtained by decomposition of ground state tide by the Czochralski method0，J0Is the jacobian matrix of the last iteration.
An approximately linear relationship between node injection and node voltage can further be obtained:
ΔX＝J0 -1ΔS； (6)
the semi-invariant is a digital characteristic of a random variable, and the semi-invariant and the central moment of each order can be easily obtained by using known moments of each order by using a conversion relation between the semi-invariant and the moments and between the semi-invariant and the central moments. The semi-invariant is provided with two special properties: if the random variables are independent, the r-order half invariant of the sum of the random variables is equal to the sum of the r-order half invariant of each random variable; ② the r-order semi-invariant of a times of the random variable is equal to a of the r-order semi-invariant of the random variablerAnd (4) multiplying. Based on the independence assumption of node injection and the nature of the semi-invariant, it can be derived from equation (6):
ΔX(r)＝(J0 -1)rΔS(r)； (7)
the superscript r in equation (7) represents the r-order semi-invariant of some random variable.
The semi-invariant of the node voltage can be obtained from the semi-invariant injected by the node according to equation (7), and then the probability density of the node voltage is obtained by using Gram-Charlier series expansion:
wherein the content of the first and second substances,
in order to be a normalized random variable,
μ and σ are the mean and standard deviation of x;
the probability density of the standard normal distribution is shown as a specific expression and each order derivative formula (9) and (10); c. CrIs a coefficient of each order of the series, and can be represented by the central moment beta of each order according to equation (11)rTo obtain; hr(x) Is Hermite polynomial.
After the voltage risk condition of each node of the whole network is obtained through evaluation calculation, weak nodes with overlarge risks can be identified, and risk pre-control is carried out by using the active management technology of the ADN, so that the risks are reduced to be within an acceptable safety range. The risk threshold for performing risk pre-control may be set by the control personnel according to the power supply region level and the risk resistance of the power distribution network, for example, the threshold is set to be the equivalent risk of a voltage out-of-limit risk event with a probability of 5% and a severity of 0.005, that is:
RVV,t,i,max＝5％×0.005＝2.5×10-4； (12)
in this embodiment, as shown in fig. 3, the executing of the preventive control when the risk exceeds the safety threshold specifically includes the following steps:
step S21: judging whether the risk assessment index of the current time interval exceeds a preset threshold value, if so, making n equal to 1, and entering a step S22, otherwise, entering the next time interval;
step S22: executing risk pre-control by adopting an nth-level active management means; wherein n has a value of 1, 2,3 or 4;
step S23: adopting a harmony search algorithm to solve a risk pre-control model corresponding to the nth-level active management means to obtain a prevention control optimization scheme;
step S24: and judging whether the optimized risk assessment index is still higher than a preset threshold value, if so, making n equal to n +1, returning to the step S22, and otherwise, returning to the step S21.
According to the method, a closed loop system is formed by risk assessment and pre-control, and the method is circulated and repeated, so that the voltage out-of-limit risk of the ADN is ensured to be in a controllable range.
The controllable resource of ADN is abundant, the initiative management means is diversified, and distributed energy source active and reactive power, flexible load management and traditional reactive voltage equipment etc. have been considered to this embodiment. In the risk pre-control decision, the execution priority level of each AM means is set as shown in the following table, a low-level (first-level) AM is executed first, and if the risk cannot be reduced to be within a safety threshold value by the level AM, the next level AM is started until the risk level reaches the standard.
Risk precontrol should control the risk within safe thresholds with as low a level of active management as possible, at the expense of minimal modifications to the operating regime.
In this embodiment, when n is 1, the active management means adopted is to perform scheduling of controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power;
the first-stage active management only regulates and controls reactive resources without extra active correction, and when the first-stage active management is only executed to realize the pre-control target, the target function of the model is set as the minimum voltage out-of-limit risk index so as to promote the maximum reduction and even the zero-crossing of the risk. When n is 1, the objective function of active management is:
in the formula, RVV,tThe sum of the voltage out-of-limit risk indexes of each node in the t period; δ is a risk penalty if RVV,t,i>RVV,maxδ takes a very large value, otherwise it is 0; n is a radical ofnodeThe number of nodes of the power distribution network.
In this embodiment, when n is 2, the first-level active management means and the second-level active management means are adopted simultaneously; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: and dispatching the controllable DG active power, the energy storage active power and the flexible load.
In this embodiment, when n is 3, a first-level active management means, a second-level active management means and a third-level active management means are adopted at the same time; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: and performing active power reduction of the wind-light DG.
In this embodiment, when n is 4, a first-level active management means, a second-level active management means, a third-level active management means and a fourth-level active management means are adopted at the same time; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: performing active reduction on the wind-solar DG; wherein, the fourth-level active management means is as follows: and carrying out OLTC gear shifting and shunt capacitor bank switching.
In this embodiment, when n is 2,3 or 4, the objective function of active management is:
in the formula,. DELTA.PtPerforming active modification cost of risk control (ADN) for the time period t; n is a radical ofCDG、NESS、NFL、 NIDG、NOLTC、NCRespectively the total quantity of controllable DGs, stored energy, flexible loads, intermittent DGs, OLTCs and parallel capacitor banks in the power distribution network; pCDG,t,iThe active output of the ith controllable DG in the t period; pESS,t,iThe active power output of the ith energy storage in the t time period is positive, the positive value is discharging, and the negative value is charging; delta PFL,t,iThe active reduction amount of the ith flexible load in the t period is determined; pIDG,t,iThe active power of the ith intermittent DG in the t period; n isOLTC,t,iThe gear of the ith OLTC is the t period; n isC,t,iThe input group number of the ith set of parallel capacitors in the t period; p'CDG,t,i、P′ESS,t,i、ΔP′FL,t,i、P′IDG,t,i、n'OLTC,t,i、 n'C,t,iRespectively determining and correcting values for risk precontrol of corresponding variables; lambda [ alpha ]III、λIVPenalty factor, λ, for three, four levels of active managementIV＞λIII＞1；Pload,tAnd the total active load of the power distribution network in the period t. Considering the limitation of the service life and the operation times of the equipment, the OLTC and the capacitor are not suitable for frequent adjustment, so the product of the gear (number of groups) correction quantity and the whole network load is taken as the correction cost converted power of the equipment.
Preferably, the constraint conditions considered in this embodiment include:
Vmin≤Vt,i≤Vmax； (16)
St,j≤Smax； (17)
P′CDG,t,i≤PCDG,i,max； (18)
|P′CDG,t,i-PCDG,t-1,i|≤ΔPCDG,i,max； (19)
P′IDG,t,i≤PIDG,t,i； (20)
SOCmin≤SOC′t,i≤SOCmax； (23)
SOCt,i-ΔSOCmax≤SOC′t,i≤SOCt,i+ΔSOCmax； (24)
nOLTC,i,min≤n′OLTC,t,i≤nOLTC,i,max； (27)
|n'OLTC,t,i-nOLTC,t,i|≤ΔnOLTC,max； (28)
0≤n'C,t,i≤nC,i,max； (29)
|n'C,t,i-nC,t,i|≤ΔnC,max； (30)
wherein: pgrid,t、Qgrid,tActive power and reactive power injected into the power distribution network for a superior power grid in a period t; qload,tThe total reactive load of the power distribution network is obtained; ploss,t、Qloss,tThe total active and reactive network loss of the power distribution network; q'CDG,t,i、Q′IDG,t,i、Q'ESS,t,i、ΔQ'FL,t,iPre-controlling reactive power correction values for ith controllable DG, intermittent DG, energy storage and flexible load in a t period; st,jApparent power of jth branch, S, for t periodmaxIs its maximum capacity; pCDG,i,maxThe maximum active output of the ith controllable DG is obtained; equation (19) is the climbing constraint of controllable DG, Δ PCDG,i,maxThe maximum active power allowed to be adjusted for the controllable DG in the adjacent time period; equation (20) is the clipping constraint for intermittent DG;
for the operating power factor of the ith DG for period t,
is the upper and lower limit; SOCt,iIs the state of charge, SOC 'of the ith energy storage in the period of t't,iFor pre-controlling correction values, SOC, for their risksmax、SOCminTo its upper and lower limits, L is the period duration; eESS,iThe rated capacity of the energy storage battery; the equation (24) is the energy storage SOC correction limit, and since the time sequence of the energy storage operation is strong, the change of the charging and discharging behavior in a certain period of time will affect the subsequent operation, the correction amount should not be too large, and the Δ SOC is not largemaxThe maximum SOC allowed to be corrected for a single period is 10% in the present embodiment; sPCS,iIs the energy storage inverter capacity; pFL,t,iIs the initial value of the ith non-reduced flexible load in the t periodcurt,i,maxPercent of maximum load reduction; n isOLTC,i,max、nOLTC,i,minThe highest gear and the lowest gear of the ith OLTC are respectively; Δ nOLTC,maxIs OLTC (alkyl-vinyl-acetate copolymer) in a single time periodA large number of shifts; n isC,i,maxThe number of the ith set of capacitors; Δ nC,maxThe maximum switching times of the capacitor bank in a single time period.
Preferably, the Harmony Search (HS) algorithm is an intelligent algorithm for simulating harmony creation during music playing, and has the advantages of few control parameters, strong optimizing capability and higher calculation efficiency. The core search concept of HS is as follows: simulating potential solutions of the optimization problem by harmony, storing the existing harmony by using a harmony memory library HM, generating new generation harmony by three modes of in-library inheritance, out-library random generation and tone fine adjustment, replacing the original inferior harmony by the new superior harmony and storing the new superior harmony into the HM, and repeatedly generating new harmony for iteration until the algorithm converges.
The form of HM is:
wherein HMS is the total number of harmony within HM; x is the number ofiIs the ith harmonic;
a j-th dimensional component being an ith harmony; f (x)i) The objective function value for the ith harmonic.
New harmony xnewEach dimensional component of
The method can be generated in three modes of inside-library inheritance, outside-library random generation and tone fine adjustment.
Selecting within HM by probability of HMCR
Any one of the above is randomly generated outside the HM according to the probability of 1-HMCR, and the specific formula is as follows:
if it is
Selected from within the HM, which will perform pitch trimming operations with a probability of PAR as follows:
wherein, XjIs a feasible solution space; r is0Is (0,1) uniformly distributed random numbers; r is a radical of hydrogen1Is (-1,1) uniformly distributed random numbers; bjThe bandwidth is adjusted for the pitch of the j-th dimension component.
Next, the active power distribution network shown in fig. 4 is used as an example in the embodiment, and voltage out-of-limit risk assessment and pre-control simulation analysis are performed. The ADN supplies power to a new coastal area: the feeder line A is a coastal residential area and is integrated with a large amount of tidal flat wind power; the feeder B is a business tourist area; feeder C is photovoltaic industry garden. OLTC is 17 gears in total, and the variable range of the variable ratio is +/-8 x 1.25%. The compensation capacity of a single group of parallel capacitors is 0.4MVar, 10 groups of C1 and 5 groups of C2. The rated capacity of the controllable micro-gas turbine is 0.5 MW; the rated capacity of each wind-light DG is 0.38MW, and the wind-light permeability reaches 60%; the rated capacity of the energy storage battery pack is 2MW & h, the maximum charge-discharge power is 0.5MW, the interface capacity of the inverter is 0.5MVA, and the lower limit of SOC is 10%. Nodes 4-6, 20-22, 28-31 are compliant loads with a percent maximum load reduction of 20%. Assuming that the same kind of DG of the same feeder operates at the same power factor, the flexible loads of the same feeder are managed at the same reduction ratio.
The daily operation of each element of the ADN is known (calculated from economic dispatch), and the load, DG and stored energy are shown in fig. 5, where wind-solar-load is a short-term prediction curve. The T1 gear is 3 in the period of 1-6, 1 in the period of 7, 3 in the period of 8-22 and 2 in the period of 23-24; the T2 gear is 0 for periods 1-6, -3 for periods 7-20, and-1 for periods 21-24. C1 is put into groups, wherein the time interval is 2 from 1 to 9, the time interval is 5 from 10 to 15 and the time interval is 4 from 16 to 24; the number of C2 groups was 3 in 1 hour, 0 in 2-9 hours, and 3 in 10-24 hours. In terms of flexible load, the feed line A is cut down by 18.66 and 5.25 (%) in 20 and 21 periods respectively; the feed line B respectively cuts down 18.10 percent, 13.54 percent, 11.52 percent and 15.45 percent in 17-20 periods; the feed line C is cut by 13.15 (%) for 17 periods.
In the embodiment, node voltage out-of-limit risks of the power distribution network in 24 periods of the operation day are evaluated and calculated one by using i5-6200U CPU and Matlab2014a on an 8G RAM computer. Three-dimensional bar graphs of node voltage expectation and node voltage out-of-limit risks obtained by random load flow calculation and risk assessment are shown in fig. 6 and 7. The evaluation calculation takes 15.12s in total, and the average time of a single calculation takes 0.63 s.
Under most operating conditions, the voltage out-of-limit risk value is in a safe range, and the ADN can run economically and safely. The risk exceeds the threshold, the key weak nodes to be pre-controlled and their time periods are shown in the following table. Due to the fact that the wind power permeability of the feeder line A is high, when the load does not reach a peak value, the wind power output is high, and the power is redundant (such as 8 and 14 time periods in a table), the voltage lifting effect is obvious, certain upper limit-exceeding risks exist at the tail end of the feeder line, and the problem is one of common problems of a power distribution network with a high permeability DG.
Taking the weak nodes 15 and 16 in the 8-period as an example, risk pre-control is performed. By calculation, the out-of-limit risk can be effectively controlled by adopting the first-stage active management, and a specific pre-control decision scheme is shown in the table below. The risk pre-control optimization calculation takes 24.06 s.
In the above table, the positive values are load and absorbed power.
The voltage probability density curves of the nodes 15 and 16 before and after the risk pre-control is performed in the 8 th period are shown in fig. 8, wherein the dotted line is the pre-control curve. According to the curve, when the pre-control is not executed, although the planned voltage value (expected value) is in a reasonable range, the wind-solar load uncertainty is considered, and then certain risk of exceeding the upper limit is still faced, and if the actual wind-solar output ratio is higher than the pre-estimated value or the actual load is lower in 8 time periods, the voltage exceeding is most likely to occur; after risk pre-control is executed, the voltage probability density of the nodes 15 and 16 is separated from the out-of-limit area, and risk zero control is realized. On the other hand, the risk pre-control measures only transfer ADN reactive resources, high-level active management such as abandoning wind and light, adjusting long-time scale elements and the like is not implemented, and the risk control effect is ensured on the basis of adjusting the system operation mode as small as possible.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (6)
1. A voltage out-of-limit risk assessment and prevention control method for an active power distribution network is characterized by comprising the following steps:
establishing an ADN voltage out-of-limit risk assessment index based on the probability and the severity;
acquiring basic data of power grid operation, and solving an ADN voltage out-of-limit risk evaluation index;
when the risk exceeds a safety threshold, executing prevention control to reduce the ADN operation risk and improve the safety;
the step of establishing the ADN voltage out-of-limit risk assessment index based on the probability and the severity specifically comprises the following steps:
comprehensively considering the out-of-limit probability and the out-of-limit severity, and constructing voltage out-of-limit risk indexes as follows:
RVV,t,i＝PVV,t,i×SVV,t,i；
wherein the content of the first and second substances,
in the formula, Vt,iThe magnitude of the voltage at node i for time t, f (V)t,i) Is its probability density function; vmax、VminThe upper limit and the lower limit of the node voltage are set; r isVV,t,iVoltage out-of-limit risk indexes of the i node in a t period; p isVV,t,iThe voltage out-of-limit probability of the i node is in a t period; sVV,t,iThe out-of-limit severity of the voltage of the i node is t time period;
V t,irespectively an upper limit voltage average value and a lower limit voltage average value of the i node in the t period;
the step of acquiring basic data of power grid operation and solving the ADN voltage out-of-limit risk assessment index specifically comprises the following steps:
step S11: inputting basic data including ADN network parameters, element parameters, load and short-term predicted values of wind-solar output;
step S12: solving the random power flow of the ADN by adopting a semi-invariant and Gram-Charlie series expansion method to obtain the voltage probability distribution of each node of the whole network and the voltage probability density distribution of each node of the whole network;
step S13: calculating the corresponding voltage out-of-limit risk probability and severity by using the voltage probability density obtained in the step S12, further obtaining a risk index value, and realizing quantitative risk evaluation;
step S12 specifically includes the following steps:
step S121: solving the ground state trend by adopting a Newton method;
step S122: solving each order moment and a semi-invariant of the load injection power random variable by the probability distribution of the DG and the load injection power random variable;
step S123: calculating semi-invariants of each order of the node injection power random variables;
step S124: calculating each-order semi-invariant and center distance of the node voltage random variable;
step S125: solving the probability distribution of the node voltage random variable according to a Cram-Charlier series expansion formula;
step S126: summing the expected value and the random disturbance to obtain the probability distribution of the node voltage;
the performing of the preventive control when the risk exceeds the safety threshold comprises in particular the steps of:
step S21: judging whether the risk assessment index of the current time interval exceeds a preset threshold value, if so, making n equal to 1, and entering a step S22, otherwise, entering the next time interval;
step S22: executing risk pre-control by adopting an nth-level active management means; wherein n has a value of 1, 2,3 or 4;
step S23: solving a risk pre-control model corresponding to the nth-level active management means by adopting a harmony search algorithm to obtain a prevention control optimization scheme;
step S24: and judging whether the optimized risk assessment index is still higher than a preset threshold value, if so, making n equal to n +1, returning to the step S22, and otherwise, returning to the step S21.
2. The method according to claim 1, wherein when n is 1, a first-stage active management means is adopted, and the first-stage active management means is: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power;
when n is 1, the objective function of active management is:
in the formula, RVV,tThe sum of the voltage out-of-limit risk indexes of each node in the t period; delta is a risk penalty factor, NnodeThe number of nodes of the power distribution network.
3. The method for assessing, preventing and controlling the voltage threshold risk of the active power distribution network according to claim 1, wherein when n is 2, a first-stage active management means and a second-stage active management means are adopted simultaneously; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: and dispatching the active power, the energy storage active power and the flexible load of the controllable DG.
4. The method according to claim 1, wherein when n is 3, a first-level active management means, a second-level active management means and a third-level active management means are adopted at the same time; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: and performing active reduction on the wind and light DG.
5. The method according to claim 1, wherein when n is 4, a first-level active management means, a second-level active management means, a third-level active management means and a fourth-level active management means are simultaneously adopted; wherein, the first-level active management means is as follows: dispatching controllable DG reactive power, wind-solar DG reactive power and energy storage reactive power; wherein, the second level active management means is as follows: dispatching active power, energy storage active power and flexible load of the controllable DG; wherein, the third level active management means is as follows: performing active reduction on the wind-solar DG; wherein, the fourth-level active management means is as follows: and carrying out OLTC gear shifting and shunt capacitor bank switching.
6. The method for voltage out-of-limit risk assessment and prevention control of an active power distribution network according to any one of claims 3-5, wherein when n is 2,3 or 4, the objective function of active management is:
in the formula,. DELTA.PtPerforming active modification cost of risk control (ADN) for the time period t; n is a radical ofCDG、NESS、NFL、NIDG、NOLTC、NCRespectively the total quantity of controllable DGs, stored energy, flexible loads, intermittent DGs, OLTCs and parallel capacitor banks in the power distribution network; pCDG,t,iThe active output of the ith controllable DG in the t period; pESS,t,iThe active power output of the ith energy storage in the t time period is positive, the positive value is discharging, and the negative value is charging; delta PFL,t,iThe active reduction amount of the ith flexible load in the t period; pIDG,t,iThe active power of the ith intermittent DG in the t period; n isOLTC,t,iThe gear of the ith OLTC is t time period; n is a radical of an alkyl radicalC,t,iThe input group number of the ith set of parallel capacitors in the t period; p'CDG,t,i、P′ESS,t,i、ΔP′FL,t,i、P′IDG,t,i、n'OLTC,t,i、n'C,t,iRespectively determining and correcting values for risk pre-control of corresponding variables; lambdaIII、λIVPenalty factor, λ, for three, four levels of active managementIV＞λIII＞1；Pload,tAnd the total active load of the power distribution network in the period t.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103138269A (en) * | 2013-02-06 | 2013-06-05 | 上海交通大学 | Layered and distributed network voltage regulator control system and method based on active mechanism |
| CN103972985A (en) * | 2014-05-26 | 2014-08-06 | 湖南大学 | Online safety precaution and prevention control method of power distribution network |
| WO2015048737A1 (en) * | 2013-09-30 | 2015-04-02 | Do Rosario Jackseario Antonio Dionisio | Power quality of service optimization for microgrids |
| CN107565576A (en) * | 2017-09-21 | 2018-01-09 | 国网福建省电力有限公司 | A kind of active distribution network reactive Voltage Optimum method that more active management means are mutually coordinated |
-
2020
- 2020-05-09 CN CN202010388181.5A patent/CN111612317B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103138269A (en) * | 2013-02-06 | 2013-06-05 | 上海交通大学 | Layered and distributed network voltage regulator control system and method based on active mechanism |
| WO2015048737A1 (en) * | 2013-09-30 | 2015-04-02 | Do Rosario Jackseario Antonio Dionisio | Power quality of service optimization for microgrids |
| CN103972985A (en) * | 2014-05-26 | 2014-08-06 | 湖南大学 | Online safety precaution and prevention control method of power distribution network |
| CN107565576A (en) * | 2017-09-21 | 2018-01-09 | 国网福建省电力有限公司 | A kind of active distribution network reactive Voltage Optimum method that more active management means are mutually coordinated |
Non-Patent Citations (2)
| Title |
|---|
| "计及不确定性的主动配电网电压越限风险评估及预控";廖剑波 等;《福建省电机工程学会2020年学术年会获奖论文集》;20211130;全文 * |
| 基于Cornish-Fisher展开的配电网电压越限风险评估;姬广龙等;《太阳能学报》;20200128(第01期);第359-364页 * |
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