CN111446719B - Distribution network voltage stability control method and system - Google Patents
Distribution network voltage stability control method and system Download PDFInfo
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Abstract
The invention discloses a distribution network voltage stability control method and a distribution network voltage stability control system.A semi-invariant prediction model is adopted, and the real-time running state of a distribution network is taken as input to obtain the future running state of the distribution network; calculating to obtain a distribution network control sequence in a future operation state based on a convex optimization interior point method; and executing voltage stabilization control of the distribution network according to the distribution network control sequence.
Description
Technical Field
The invention belongs to the field of intelligent power distribution networks, and particularly relates to a distribution network voltage stability control method and a distribution network voltage stability control system.
Background
With the increasing occupation ratio of new energy, an energy storage system, an air conditioner/electric vehicle and other loads in a distribution network, the current direct current storage characteristic of the distribution network load source becomes more remarkable, and the traditional alternating current power distribution mode faces a serious challenge. The construction of a large-scale alternating current-direct current hybrid active power distribution network with high efficiency, low consumption and reliability is an important research and development direction for the construction of modern power grids. The voltage stability control of the distribution network is an important link for the distribution network control.
Disclosure of Invention
The invention provides a distribution network voltage stability control method and system for improving the consumption capacity of new energy, an energy storage system, an air conditioner/electric automobile and the like.
The technical scheme adopted by the invention is as follows: a distribution network voltage stability control method comprises the following steps:
step 1: adopting a semi-invariant prediction model to predict the current distribution network operation state to obtain the distribution network operation state of the next sampling time node; step 2: obtaining a distribution network control sequence in the distribution network running state of the next time node by adopting a convex optimization inner point method;
and step 3: and (4) performing stable control on the voltage of the distribution network according to the distribution network control sequence obtained in the step (2).
Further, the step 1 specifically includes the following substeps:
constructing a semi-invariant prediction model based on a semi-invariant method according to historical operation state data of the distribution network;
taking the current distribution network running state as the input of a semi-invariant prediction model to obtain a semi-invariant expanded Markov probability model of a next sampling time node;
a Markov probability model is developed based on the semi-invariant, and a probability prediction interval of the distribution network operation state is calculated;
calculating to obtain a corresponding probability prediction expectation based on the probability prediction interval of the distribution network operation state;
calculating to obtain a rolling prediction interval of the distribution network operation state based on the probability prediction expectation;
and representing the distribution network operation state of the next sampling time node through the rolling prediction interval of the distribution network operation state.
Furthermore, the semi-invariant prediction model comprises a new energy output semi-invariant prediction model, a charge and discharge semi-invariant prediction model of the energy storage system and a load semi-invariant prediction model.
Further, the prediction interval of the distribution network operation state comprises a probability prediction interval of output of new energy, charge and discharge of the energy storage system and load; the rolling prediction interval of the distribution network running state comprises a new energy output, an energy storage charging and discharging state and a load access state rolling prediction interval.
Further, the step 3 specifically includes the following sub-steps:
calculating to obtain a probability trend by utilizing a Monte Carlo-Markov method according to the probability prediction interval of the distribution network operation state;
and (3) checking whether the distribution network control sequence obtained in the step (2) meets the constraint or not based on the probability power flow, if so, executing voltage stability control of the distribution network according to the distribution network control sequence, and if not, calculating a new distribution network control sequence again according to the step (2) until an optimization target and the constraint are met.
Further, the distribution network comprises an alternating current-direct current hybrid active distribution network.
Further, the semi-invariant prediction model is described as follows:
wherein the content of the first and second substances,
phi (x) is the probability density function and cumulative distribution function of the natural distribution, f n (x) Probability density function representing the final prediction probability, F n (x) A cumulative distribution function representing the final prediction probability, i representing a cyclic variable; a. the i An ith order expansion representing the historical data;
an ith order expansion of a probability density function representing a natural distribution; phi (i) Representing the ith order expansion of the cumulative distribution function.
Further, the convex optimization interior point method in the step 2 specifically comprises the following steps:
s1: constructing distribution network penalty function
Wherein X represents the current control amount, r (k) Boundary value representing distribution network load flow calculation
S2: from X (k-1) Method for solving penalty function by using unconstrained optimization method for point issue
Extreme point X of * (r (k) ) (ii) a Wherein X (k-1) Representing the last step decision quantity of the current control quantity X;
s3: judging whether the formula (6) is satisfied, if so, stopping iterative calculation by X * (r (k) ) Is the optimal solution, otherwise r (k+1) ＝Cr (k) ,X (0) ＝X * (r (k) ) K is k +1, C is a decreasing coefficient, and the process goes to S2;
||X * (r (k) )-X * (r (k-1) )||≤ε (6)。
further, the step of calculating the probability load flow by using the monte carlo-markov method comprises:
constructing a distribution network Markov chain according to the probability prediction interval of the distribution network operation state, and selecting any point x of the distribution network Markov chain (t) Sampling simulation is carried out to obtain a sampling sequence X (1) ,…,X (n) Then for sequence X (1) ,…,X (n) Carrying out Monte Carlo integration to obtain probability trend:
E[f(t)]representing the probability trend, n, m representing the upper and lower limits of the integral, f (x) (t) ) Representing the sampling simulation results.
The invention also discloses a distribution network voltage stability control system, which comprises:
the semi-invariant prediction module is used for predicting the current distribution network operation state to obtain the distribution network operation state of the next time node;
the distribution network control sequence acquisition module is used for acquiring a distribution network control sequence in a distribution network running state of the next time node by adopting a convex optimization interior point method;
and the voltage stabilization control module is used for performing stable control on the distribution network voltage according to the distribution network control sequence.
Further, the semi-invariant prediction module comprises the following sub-modules:
the semi-invariant prediction model building module is used for building a semi-invariant prediction model based on a semi-invariant method according to the historical operation state data of the distribution network;
the probability prediction interval calculation module is used for calculating the probability according to the semi-invariant expansion probability output by the semi-invariant prediction model to obtain a probability prediction interval;
the probability prediction expectation calculation module is used for calculating to obtain corresponding probability prediction expectation according to the probability prediction interval;
and the rolling prediction interval calculation module is used for calculating the rolling prediction interval of the distribution network running state according to the probability prediction expectation.
Furthermore, the semi-invariant prediction model comprises a new energy output semi-invariant prediction model, a charge and discharge semi-invariant prediction model of the energy storage system and a load semi-invariant prediction model.
Furthermore, the distribution network is a large-scale alternating current-direct current hybrid active distribution network.
Further, the voltage stabilization control module comprises the following sub-modules:
the probability load flow calculation module is used for calculating the probability load flow by adopting a Monte Carlo-Markov method according to the probability prediction interval output by the probability prediction interval calculation module;
the distribution network control sequence optimization module is used for judging whether the probability tide meets the constraint condition or not, if not, the distribution network control sequence is adjusted until the constraint requirement is met, and the distribution network optimal control sequence is obtained; otherwise, the current distribution network control sequence is the optimal distribution network control sequence;
and the control module is used for executing the voltage control of the distribution network according to the optimal control sequence of the distribution network.
Has the advantages that: the invention has the following advantages:
1. the flexibility and robustness of distribution network control are improved;
2. the access capability of new energy, energy storage and other novel power supplies is improved.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a graph comparing loss of the entire network without model predictive control and with model predictive control;
fig. 3 is a graph of a comparison of the power of the main network link without model predictive control and with model predictive control.
Detailed Description
The invention is further illustrated below with reference to the figures and examples.
Example 1:
referring to fig. 1, a model-predicted voltage stabilization control method suitable for a large-scale ac/dc hybrid active distribution network according to this embodiment is described in detail below.
Step 1: based on historical operation data of a distribution network in a distribution automation system, an improved semi-invariant prediction model of the output of new energy, the charge and discharge of an energy storage system, the load of an air conditioner/electric vehicle and the like is constructed based on an improved semi-invariant method, and the expression of the improved semi-invariant prediction model is as follows:
wherein the content of the first and second substances,
phi (x) is the probability density function and cumulative distribution function of the natural distribution, f n (x) Probability density function representing the final prediction probability, F n (x) Representing cumulative scores representing the probability of a final predictionA function, i represents a cyclic variable; a. the i An ith order expansion representing the historical data;
an ith order expansion of a probability density function representing a natural distribution; phi (i) Representing the ith order expansion of the cumulative distribution function.
Step 2: the method comprises the following steps of obtaining a real-time running state of a current large-scale alternating current-direct current hybrid active distribution network based on a distribution automation system, wherein the real-time running state comprises the following steps: the method comprises the following steps that output information of a new energy machine set in a distribution network, charging and discharging information of an energy storage system in the distribution network, load input power information of an air conditioner/an electric vehicle and the like, measurement information of voltage and current phasors of each distribution network branch and node, power information of a distribution network direct current system, position information of a converter transformer contact, an alternating current system admittance matrix and direct current parameter information in a current topological state are obtained;
and 3, step 3: and predicting the state information of the large-scale alternating current-direct current hybrid active distribution network system in the next time period by using the model fitted in the step S1 and the actually measured information in the step S2, wherein the state information comprises the output of new energy, the charge and discharge of an energy storage system, the load of an air conditioner/electric vehicle and the like, and the method specifically comprises the following steps: calculating the output of new energy, the charge and discharge of an energy storage system, the semi-invariant development Markov probability model of the load such as an air conditioner/electric vehicle and the like in the state of the distribution network state at the next time point to obtain a probability prediction interval of the output of the new energy, the charge and discharge of the energy storage system, the load such as the air conditioner/electric vehicle and the like and an access state rolling prediction interval of the load such as the output of the new energy, the charge and discharge state of the energy storage system, the air conditioner/electric vehicle and the like expected by the probability prediction;
and 4, step 4: solving the optimal control sequence of the distribution network under the predicted load in the step 3 based on a convex optimization interior point method, wherein the solving process of the interior point method is as follows:
s4-1: taking an initial penalty factor r (0) Greater than 0, and the allowable error epsilon is greater than 0; r is (0) Is set as a boundary value of allowable power flow in the power flow calculation of the distribution network.
S4-2: taking an initial point X within a feasible region D (0) Let k equal to 1;
s4-3: constructing punishment function of AC/DC distribution network
From X (k-1) Method for solving penalty function by using unconstrained optimization method for point issue
Extreme point X of * (r (k) ) X represents a current control amount, r (k) Boundary threshold value for representing punishment, and in distribution network calculation, the boundary value of the power flow is generally set as r (k) ；X (k-1) Representing the last step decision quantity of the current control quantity X;
s4-4: check iteration termination criteria: if the following conditions are met:
||X * (r (k) )-X * (r (k-1) )||≤ε 1 ＝10 -5 -10 -7 (6)
or
The iterative calculation is stopped and X is used * (r (k) ) If the optimal solution is not constrained, the method goes to S4-5;
depending on the case, the termination criterion may also have the form:
||f(X (k) )-f(X (k-1) )||≤ε (8)
or
Or
S4-5: get r (k+1) ＝Cr (k) ,X (0) ＝X * (r (k) ),k＝k+1，Turning to S4-3.
The decreasing coefficient C is 0.1-0.5, usually 0.1, and may be 0.02.
Several issues should be noted with the interior point method:
(1) initial point X (0) Selecting:
initial point X (0) All constraints must be satisfied within a feasible domain, and are avoided as points on the constraint boundary.
If the constraint condition is simple, the constraint condition can be directly input manually; if the problem is complicated, the initial point X can be generated by random number (0) 。
(2) With respect to the initial penalty factor r (0) Selection of (2). Practical experience shows that the initial penalty factor r (0) If the selection is correct, the convergence rate of the interior point method and even the success or failure of solving the problem can be significantly influenced.
If r is (0) If the value is selected too small, then the penalty function is the new objective function
The effect of the medium penalty term is small, and then the calculation is carried out
Just like the unconstrained extreme of the original objective function f (x), this extreme is unlikely to approach the constrained extreme of f (x) and there is a risk of going out of the feasible region. On the contrary, if r (0) If the value is too large, the penalty function is constructed several times
The unconstrained extreme point of (a) will be far from the constrained boundary, which will reduce the computational efficiency. Can be taken out (0) About 1 ~ 50, but in most cases r is taken (0) ＝1。
Usually, when starting at point X (0) When it is a strict interior point, the penalty term should be made:
in the new objective function
The function of (2) and the original objective function f (X) (0) ) Has the equivalent effect of
Provided that the constraining region is non-convex and the initial point X (0) Nor close to the constraint boundary, then r (0) The value of (a) can be smaller, about 0.1 to 0.5 times the value calculated by the above formula.
And 5: and (4) sampling by using a Monte Carlo-Markov method according to the output of the new energy, the charging and discharging of the energy storage system, the load probability prediction interval of the air conditioner/electric vehicle and the like obtained in the step (3) to obtain a prediction curve, and calculating the active power flow and the reactive power flow of the distribution network system and the voltage curve of each node under the control strategy according to the distribution network optimal control sequence obtained in the step (4). The Monte Carlo-Markov method sampling process comprises the following steps:
firstly, constructing a distribution network system Markov chain according to a probability prediction interval of a distribution network operation state, wherein the distribution of the distribution network system Markov chain in a stable state is pi (x), and x is any point x of the distribution network system Markov chain (0) Sampling simulation is carried out to obtain a sampling sequence X (1) ,…,X (n) Then aligning the sequence X (1) ,…,X (n) Performing a monte carlo integration:
E[f(t)]expressing integral quantity, namely probability power flow, n and m respectively express upper and lower limits of integral, and f (x) (t) ) Representing the sampling simulation result;
step 6: and (5) verifying whether the active power flow and the reactive power flow of the distribution network system calculated in the step (5) and the voltage curve of each node meet the constraint requirement, and if not, adjusting the optimal control sequence of the solution distribution network until the optimal target and the constraint are met.
For the same distribution network system, after a 24h power curve of system energy and energy storage is added, model predictive control is not considered, and a comparison graph of the loss of the whole network is controlled by considering model predictive control is shown in a figure 2; the main network link power pair is for example figure 3. As can be seen from fig. 2 and 3, under the same distribution network operation condition, the main tie line power fluctuation and the network loss of the model prediction control method are considered to be significantly lower than the levels of the model prediction.
Example 2:
on the basis of embodiment 1, this embodiment discloses a distribution network voltage stabilization control system, including:
the semi-invariant prediction module is used for predicting the current distribution network operation state by adopting a semi-invariant prediction model to obtain the distribution network operation state of the next time node; the semi-invariant prediction model comprises a new energy output semi-invariant prediction model, a charge and discharge semi-invariant prediction model of the energy storage system and a load semi-invariant prediction model;
the distribution network control sequence acquisition module is used for acquiring a distribution network control sequence in a distribution network running state of the next time node by adopting a convex optimization interior point method;
and the voltage stabilization control module is used for performing stable control on the distribution network voltage according to the distribution network control sequence.
The semi-invariant prediction module comprises the following sub-modules:
the semi-invariant prediction model building module is used for building a semi-invariant prediction model based on a semi-invariant method according to the historical operation state data of the distribution network;
the probability prediction interval calculation module is used for calculating the probability according to the semi-invariant expansion probability output by the semi-invariant prediction model to obtain a probability prediction interval;
the probability prediction expectation calculation module is used for calculating to obtain corresponding probability prediction expectation according to the probability prediction interval;
and the rolling prediction interval calculation module is used for calculating the rolling prediction interval of the distribution network running state according to the probability prediction expectation.
The voltage stabilization control module comprises the following sub-modules:
the probability load flow calculation module is used for calculating the probability load flow by adopting a Monte Carlo-Markov method according to the probability prediction interval output by the probability prediction interval calculation module;
the distribution network control sequence optimization module is used for judging whether the probability power flow meets the constraint condition or not, and if not, adjusting the distribution network control sequence until the constraint requirement is met to obtain a distribution network optimal control sequence; otherwise, the current distribution network control sequence is the optimal distribution network control sequence;
and the control module is used for executing the voltage control of the distribution network according to the optimal control sequence of the distribution network.
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 so forth) 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (12)
1. A distribution network voltage stability control method is characterized in that: the method comprises the following steps:
step 1: predicting the current distribution network operation state by adopting a semi-invariant prediction model to obtain the distribution network operation state of the next sampling time node;
step 2: obtaining a distribution network control sequence in the distribution network running state of the next time node by adopting a convex optimization inner point method;
and step 3: according to the distribution network control sequence obtained in the step 2, carrying out stable control on the distribution network voltage;
the semi-invariant prediction model is described as follows:
wherein the content of the first and second substances,
phi (x) is the probability density function and cumulative distribution function of the natural distribution, f n (x) Probability density function representing the final prediction probability, F n (x) A cumulative distribution function representing the final prediction probability, i representing a cyclic variable; a. the i An ith order expansion representing the historical data;
an ith order expansion of a probability density function representing a natural distribution; phi (phi) of (i) An ith order expansion representing a cumulative distribution function;
the convex optimization interior point method in the step 2 specifically comprises the following steps:
s1: constructing distribution network penalty function
Wherein X represents the current control amount, r (k) Boundary values representing distribution network load flow calculations;
s2: from X (k-1) Method for solving penalty function by using unconstrained optimization method for point issue
Extreme point X of * (r (k) ) (ii) a Wherein X (k-1) Representing the last step decision quantity of the current control quantity X;
s3: judging whether the formula (6) is satisfied, if so, stopping iterative calculation by X * (r (k) ) Is the optimal solution, otherwise r (k+1) ＝Cr (k) ,X (0) ＝X * (r (k) ) K is k +1, C is a decreasing coefficient, and the process goes to S2;
||X * (r (k) )-X * (r (k-1) )||≤ε (6)。
2. the distribution network voltage stabilization control method according to claim 1, characterized in that: the step 1 specifically comprises the following substeps:
constructing a semi-invariant prediction model based on a semi-invariant method according to historical operation state data of the distribution network;
taking the current distribution network running state as the input of a semi-invariant prediction model to obtain a semi-invariant expanded Markov probability model of a next sampling time node;
a Markov probability model is developed based on the semi-invariant, and a probability prediction interval of the distribution network operation state is calculated;
calculating to obtain a corresponding probability prediction expectation based on the probability prediction interval of the distribution network operation state;
calculating to obtain a rolling prediction interval of the distribution network operation state based on the probability prediction expectation;
and representing the distribution network operation state of the next sampling time node through the rolling prediction interval of the distribution network operation state.
3. The distribution network voltage stabilization control method according to claim 1 or 2, characterized in that: the semi-invariant prediction model comprises a new energy output semi-invariant prediction model, a charge and discharge semi-invariant prediction model of the energy storage system and a load semi-invariant prediction model.
4. The distribution network voltage stabilization control method according to claim 3, characterized in that: the prediction interval of the distribution network operation state comprises a probability prediction interval of the output of new energy, the charge and discharge of the energy storage system and the load; the rolling prediction interval of the distribution network running state comprises a new energy output, an energy storage charging and discharging state and a load access state rolling prediction interval.
5. The distribution network voltage stabilization control method according to claim 2, characterized in that: the step 3 specifically comprises the following substeps:
calculating to obtain a probability trend by utilizing a Monte Carlo-Markov method according to the probability prediction interval of the distribution network operation state;
and (3) checking whether the distribution network control sequence obtained in the step (2) meets the constraint or not based on the probability power flow, if so, executing voltage stability control of the distribution network according to the distribution network control sequence, and if not, calculating again according to the step (2) to obtain a new distribution network control series until the optimization target and the constraint are met.
6. The distribution network voltage stabilization control method according to claim 1, characterized in that: the distribution network comprises an alternating current-direct current hybrid active distribution network.
7. The distribution network voltage stabilization control method according to claim 5, characterized in that: the step of calculating to obtain the probability load flow by using the Monte Carlo-Markov method comprises the following steps:
constructing a distribution network Markov chain according to a probability prediction interval of a distribution network operation state, and selecting any point x of the distribution network Markov chain (t) Sampling simulation is carried out to obtain a sampling sequence X (1) ,…,X (n) Then aligning the sequence X (1) ,…,X (n) Carrying out Monte Carlo integration to obtain the probability trend:
E[f(t)]representing the probability trend, n, m representing the upper and lower limits of the integral, f (x) (t) ) Showing the results of the sampling simulation.
8. The utility model provides a join in marriage net voltage stability control system which characterized in that: comprises that
The semi-invariant prediction module is used for predicting the current distribution network operation state to obtain the distribution network operation state of the next time node; the description is as follows:
wherein, the first and the second end of the pipe are connected with each other,
phi (x) is the probability density function and cumulative distribution function of the natural distribution, f n (x) Probability density function representing the final prediction probability, F n (x) A cumulative distribution function representing the final prediction probability, i representing a cyclic variable; a. the i An ith order expansion representing the historical data;
an ith order expansion of a probability density function representing a natural distribution; phi (i) An ith order expansion representing a cumulative distribution function;
the distribution network control sequence acquisition module is used for acquiring a distribution network control sequence in a distribution network running state of the next time node by adopting a convex optimization interior point method;
the voltage stabilization control module is used for performing stabilization control on the distribution network voltage according to the distribution network control sequence;
the convex optimization interior point method specifically comprises the following steps:
s1: constructing distribution network penalty function
Wherein X represents the current control amount, r (k) Boundary values representing distribution network load flow calculations;
s2: from X (k-1) Method for solving penalty function by using unconstrained optimization method for point issue
Extreme point X of * (r (k) ) (ii) a Wherein X (k-1) Representing the last step decision quantity of the current control quantity X;
s3: judging whether the formula (6) is satisfied, if so, stopping iterative calculation by X * (r (k) ) Is the optimal solution, otherwise r (k+1) ＝Cr (k) ,X (0) ＝X * (r (k) ) K is k +1, C is a decreasing coefficient, and the process goes to S2;
||X * (r (k) )-X * (r (k-1) )||≤ε (6)。
9. the distribution network voltage stabilization control system of claim 8, wherein: the semi-invariant prediction module comprises the following sub-modules:
the semi-invariant prediction model building module is used for building a semi-invariant prediction model based on a semi-invariant method according to the historical operation state data of the distribution network;
the probability prediction interval calculation module is used for calculating the probability according to the semi-invariant expansion probability output by the semi-invariant prediction model to obtain a probability prediction interval;
the probability prediction expectation calculation module is used for calculating to obtain corresponding probability prediction expectation according to the probability prediction interval;
and the rolling prediction interval calculation module is used for calculating the rolling prediction interval of the distribution network operation state according to the probability prediction expectation.
10. The distribution network voltage stabilization control system of claim 8, wherein: the semi-invariant prediction model comprises a new energy output semi-invariant prediction model, a charge and discharge semi-invariant prediction model of the energy storage system and a load semi-invariant prediction model.
11. The distribution network voltage stabilization control system of claim 8, wherein: the distribution network is a large-scale alternating current-direct current hybrid active distribution network.
12. The distribution network voltage stabilization control system of claim 9, wherein: the voltage stabilization control module comprises the following sub-modules:
the probability power flow calculation module is used for calculating to obtain probability power flow by adopting a Monte Carlo-Markov method according to the probability prediction interval output by the probability prediction interval calculation module;
the distribution network control sequence optimization module is used for judging whether the probability power flow meets the constraint condition or not, and if not, adjusting the distribution network control sequence until the constraint requirement is met to obtain a distribution network optimal control sequence; otherwise, the current distribution network control sequence is the optimal distribution network control sequence;
and the control module is used for executing the voltage control of the distribution network according to the optimal control sequence of the distribution network.
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| CN106786806A (en) * | 2016-12-15 | 2017-05-31 | 国网江苏省电力公司南京供电公司 | A kind of power distribution network active reactive based on Model Predictive Control coordinates regulation and control method |
| CN108711846A (en) * | 2018-04-28 | 2018-10-26 | 国网山东省电力公司电力科学研究院 | A kind of ac and dc systems long-term voltage stability model predictive control method |
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| CN106786806A (en) * | 2016-12-15 | 2017-05-31 | 国网江苏省电力公司南京供电公司 | A kind of power distribution network active reactive based on Model Predictive Control coordinates regulation and control method |
| CN108711846A (en) * | 2018-04-28 | 2018-10-26 | 国网山东省电力公司电力科学研究院 | A kind of ac and dc systems long-term voltage stability model predictive control method |
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