CN116316607A - Combined optimization scheduling method, system, equipment and medium - Google Patents

Combined optimization scheduling method, system, equipment and medium Download PDF

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CN116316607A
CN116316607A CN202310397591.XA CN202310397591A CN116316607A CN 116316607 A CN116316607 A CN 116316607A CN 202310397591 A CN202310397591 A CN 202310397591A CN 116316607 A CN116316607 A CN 116316607A
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夏世威
佟明泽
李晨
胡致逸
孟祥龙
王鹏
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North China Electric Power University
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Abstract

The invention discloses a joint optimization scheduling method, a system, equipment and a medium, and relates to the field of power system optimization scheduling; the method comprises the following steps: acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; constructing an optimized scheduling model; solving the first objective function according to the first constraint condition to obtain a first solution value; inputting the first solving value into a correction model, and solving a second objective function by using a second constraint condition to obtain a first solving correction value; adjusting the power grid data of the power system by adopting the first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster; the invention realizes the recovery of power supply after disaster by jointly scheduling the schedulable resource configuration information and the photovoltaic prediction information, and reduces the loss caused by power failure.

Description

Combined optimization scheduling method, system, equipment and medium
Technical Field
The invention relates to the field of power system optimal scheduling, in particular to a joint optimal scheduling method, a system, equipment and a medium.
Background
Large-area power failure accidents generally cause that a power distribution network loses power of a main network, a plurality of lines in the network fail, and a considerable amount of important loads and common loads lose power. In order to reduce the damage caused by large-area power failure accidents of the power distribution network, a reasonable and effective post-disaster emergency scheduling method of the power distribution network needs to be formulated. In the current research of emergency dispatching after power distribution network disaster, generally, distributed power sources (distributed generation, DG) such as wind and light, mobile emergency resources such as repair workers (RC), mobile energy storage systems (mobile energy storage system, MESS) and the like are taken as objects, an emergency dispatching method is formulated, the uncertainty of DG output is less considered, and the intelligent soft point (SOP) is combined for optimal dispatching, so that the capability of the proposed dispatching method for coping with the uncertainty of DG output is weaker, and meanwhile, the capability of the SOP for quickly adjusting power and providing voltage reactive support is ignored. In addition, in the time dimension, considering the difference of response speeds of different schedulable resources and the difference of DG output prediction precision under a long-short time scale, performing emergency scheduling based on a single time scale is difficult to coordinate various emergency resources to participate in power supply recovery of a power distribution network, so that the power supply recovery speed is slow, and further loss caused by power failure is increased.
Disclosure of Invention
The invention aims to provide a joint optimization scheduling method, a system, equipment and a medium, which are used for realizing the recovery of power supply after disaster by joint scheduling of schedulable resource configuration information and photovoltaic prediction information, so as to reduce the loss caused by power failure.
In order to achieve the above object, the present invention provides the following solutions:
a joint optimization scheduling method, the method comprising:
acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource allocation information, the photovoltaic prediction information of the first time scale and the photovoltaic prediction information of the second time scale can be scheduled; the period of the first time scale is greater than the period of the second time scale; the photovoltaic prediction information includes: photovoltaic output and load requirements; the schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and the configuration of the converter;
constructing an optimized scheduling model;
wherein the optimized scheduling model comprises: an optimization model and a correction model; the optimization model comprises: a first objective function and a first constraint; the correction model includes: a second objective function and a second constraint;
The first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information of a first time scale and the schedulable resource configuration information; the first constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
the second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information of the second time scale and the schedulable resource configuration information; the second constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
solving the first objective function according to the first constraint condition to obtain a first solution value; the first solution value is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimum;
inputting the first solving value into the correction model, and solving the second objective function by using the second constraint condition to obtain a first solving correction value;
adjusting the power grid data of the power system by adopting a first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
Optionally, the schedulable resource configuration information constraint includes: mobile energy storage scheduling constraint, mobile energy storage operation constraint, mobile energy storage energy constraint, mobile energy storage charge state upper and lower limit constraint, mobile energy storage charge and discharge constraint, emergency repair personnel scheduling constraint, fault repair condition constraint and converter configuration operation constraint.
Optionally, the expression of the first objective function is:
Figure BDA0004182726400000031
z is an integer decision variable output by the optimization model;
Figure BDA0004182726400000032
the photovoltaic output of the load node i at the moment t; omega shape T Is a set of time periods; i is a node set of the load; omega i,c The importance coefficient of the load node i; alpha i,t To represent the load shedding state at load node i at time t; />
Figure BDA0004182726400000033
Representing the active cut power of the load node i at the moment t; b is a branch set of the power distribution network; epsilon is the operation cost coefficient of the tie switch; alpha i-j,t The open or close state of the line (i-j) between the load node i and the adjacent coincidence node j at the moment t; w is a decision variable output by the optimization model.
Optionally, the expression of the second objective function is:
Figure BDA0004182726400000034
wherein Y is a decision variable output by the correction model; t (T) S The number of time periods in one period; k' is a time sequence number in the scheduling process; t' is a time within the period; i is a load node; i is a node set of the load; omega i,c The importance coefficient of the node i; Θ is the power grid data set; l represents the number of the power grid data; kappa represents a mobile energy storage and converter configuration optimization adjustment penalty coefficient;
Figure BDA0004182726400000035
representing the active cut power of the load node i at the time of k '+t'; />
Figure BDA0004182726400000036
The decision value of the k '+t' moment of the first power grid data in the optimization model is represented; ΔP l (k '+t') represents an output correction amount of the first grid data at the time k '+t'; p (P) 0,l (k '+t' -1) represents the initial value of the output of the first grid data at the time k '+t' -1.
A joint optimization scheduling system, the system comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource allocation information, the photovoltaic prediction information of the first time scale and the photovoltaic prediction information of the second time scale can be scheduled; the period of the first time scale is greater than the period of the second time scale; the photovoltaic prediction information includes: photovoltaic output and load requirements; the schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and the configuration of the converter;
the model construction module is used for constructing an optimal scheduling model;
Wherein the optimized scheduling model comprises: an optimization model and a correction model; the optimization model comprises: a first objective function and a first constraint; the correction model includes: a second objective function and a second constraint;
the first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information of a first time scale and the schedulable resource configuration information; the first constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
the second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information of the second time scale and the schedulable resource configuration information; the second constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
the first solving module is used for solving the first objective function according to the first constraint condition to obtain a first solving value; the first solution value is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimum;
The second solving module is used for inputting the first solving value into the correction model, and solving the second objective function by the second constraint condition to obtain a first solving correction value;
the determining module is used for adjusting the power grid data of the power system by adopting the first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
An apparatus comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic device to perform the joint optimization scheduling method described above.
A medium storing a computer program which when executed by a processor implements the joint optimization scheduling method described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a joint optimization scheduling method, a system, equipment and a medium, wherein a first objective function is constructed according to photovoltaic prediction information of a first time scale and schedulable resource configuration information, a second objective function is constructed according to photovoltaic prediction information of a second time scale and schedulable resource configuration information, then the first objective function is solved according to a first constraint condition, an obtained first solution value is input into a correction model, then the second objective function is solved according to a second constraint condition, and a scheduling scheme is determined; the invention solves the problem that a single time scale is difficult to coordinate by adopting the first time scale and the second time scale, and can improve the scheduling process of power supply recovery after disaster by carrying out joint scheduling according to the two dimensions of the schedulable resource configuration information and the photovoltaic prediction information.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a joint optimization scheduling method provided by an embodiment of the present invention;
fig. 2 is a block diagram of a joint optimization scheduling system according to an embodiment of the present invention.
Symbol description:
the system comprises an acquisition module-1, a model construction module-2, a first solving module-3, a second solving module-4 and a determination module-5.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the invention, the object and time dimensions are integrated, and the mobile emergency resource with uncertain output of distributed power sources (distributed generation, DG) such as wind, light and the like and the intelligent soft point (SOP) are considered to jointly optimize the scheduling method, so that the power supply recovery effect can be further improved, and the loss caused by large-scale power failure accidents of the power distribution network can be reduced.
The invention aims to provide a joint optimization scheduling method, a system, equipment and a medium, which are used for improving the scheduling process of power supply restoration after disaster by joint scheduling of schedulable resource configuration information and photovoltaic prediction information.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a joint optimization scheduling method, which includes:
step 100: acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource configuration information, the photovoltaic prediction information for the first time scale, and the photovoltaic prediction information for the second time scale may be scheduled. The period of the first time scale is greater than the period of the second time scale. Such as: solar photovoltaic forecast information and short-term photovoltaic forecast information 24 hours after disaster. The photovoltaic prediction information includes: photovoltaic output and load requirements; the road fault information includes: fault line and fault rush-repair time; specifically, the road fault information may further include: topology information of a power distribution network, a traffic network non-passable road set, and length information and traffic density information of each section of road. The schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and current transformer configuration. The current transformer configuration may be an intelligent soft switch configuration within the network.
In other words, road fault information, photovoltaic output prediction information and schedulable resource configuration information after disaster are collected, traffic network topology paved along a power distribution network is obtained through abstraction, and time required by moving emergency resources at different moments and different road segments is further obtained.
Specifically, the time required for driving the mobile emergency resource is estimated according to the lengths of different road sections of the traffic network and the traffic flow densities of different road sections at different moments. The effect of this operation is to prepare the subsequent specific conforming emergency model.
Then, constructing a traffic network topology and estimating the running time of the mobile emergency resources.
And assuming that the traffic network is paved along the power distribution network, and equivalent intersections are nodes and road sections are connecting lines containing distance information, so that abstract traffic network topology is obtained. Obtaining the running speeds of the mobile emergency resources at different moments according to an improved Grignard model density-speed relation formula:
Figure BDA0004182726400000061
wherein d t The traffic density at the time t is represented; d, d max And d min Respectively representing the maximum value and the minimum value of the vehicle flow density; v (d) t ) Indicating that the vehicle is at d t Speed, v, of travel at traffic density free And v min The running speed in the free state and the running speed in the blocking state of the vehicle are respectively shown, and a and b are constants.
And obtaining an estimated value of the running time according to the ratio of the distance to the speed, and correcting the running time according to the influence of road damage on the running condition of the vehicle. Then, the estimated value T of the time required by the vehicle e to travel between any nodes i and j is obtained through weighted summation e,i,j 。
Figure BDA0004182726400000071
Figure BDA0004182726400000072
Wherein l i-j Representing the length of a road segment between adjacent nodes, t i-j At d for vehicle t Travel time at traffic density. Time t i-j The weight of the road section i-j under the moment t is considered, and the nodes i and j are adjacent nodes at the moment;
Figure BDA0004182726400000073
the expression is an upward integer. M is an arbitrarily large constant. T (T) e,i,j A predicted value of the running time of the vehicle e between the nodes i and j is obtained; c (i, j) represents a set of road segments traversed by the vehicle from node i to node j, where node i, j is not necessarily a neighboring node.
Step 200: constructing an optimized scheduling model; wherein, the optimal scheduling model comprises: an optimization model and a correction model; an optimization model comprising: a first objective function and a first constraint; a correction model, comprising: a second objective function and a second constraint.
The first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information and schedulable resource configuration information of a first time scale; a first constraint comprising: resource allocation information constraints, power distribution network topology constraints, and power distribution network power flow constraints can be scheduled.
The second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information and the schedulable resource configuration information of the second time scale; the second constraint includes: resource allocation information constraints, power distribution network topology constraints, and power distribution network power flow constraints can be scheduled.
In particular, the optimization model is based on long time scales, such as: the optimization was continued for 24 hours at 1 hour intervals. The optimization model takes a future predicted value of post-disaster photovoltaic output, namely a predicted value of a first time scale as input, and builds a mobile emergency resource and SOP combined optimization robust model considering uncertainty, and an optimization result is taken as a reference to guide a correction model. The two-stage robust optimization model is built in the optimization model, the uncertainty of photovoltaic output is dealt with, and the problems of low solving efficiency caused by the fact that the model contains more integer variables can be solved by the aid of the integer variables in the first stage of the model and the continuous variables in the second stage of the model.
The correction model is based on short time scales, such as: it may be performed every 15 minutes and continuously optimized for 2 hours at 15 minute intervals.
Considering that the predicted value of the first time scale and the predicted value of the second time scale of the photovoltaic output in the same period may be relatively close, in order to reduce the calculation time and prevent unnecessary frequent small adjustment of the mobile energy storage and the SOP, before executing the correction model each time, it is determined whether the index of the change of the two photovoltaic predicted values reaches a preset starting value, if so, the correction model is started, and the correction value is obtained according to the obtained correction values of the mobile energy storage output and the SOP transmission power in the future 15 minutes.
The correction model is a short-time rolling optimization model based on Model Predictive Control (MPC), takes a photovoltaic output short-time predicted value, namely a predicted value of a second time scale as input, takes an actual measured value of mobile energy storage and SOP output as an initial value, takes correction amounts of mobile energy storage output and SOP transmission power in a future limited time domain as decision variables, and carries out rolling optimization in the limited time domain, and mainly comprises three parts of model prediction, rolling optimization and feedback correction. The provided correction model has the function of combining more accurate photovoltaic output short-time predicted values on the basis of the output result of the optimization model, carrying out optimization decision on correction amounts of mobile energy storage output and SOP transmission power, and maximally improving the power supply recovery effect while preventing short-time rolling optimization from frequently carrying out great adjustment on the mobile energy storage and SOP.
Judging whether the change index of the predicted value of the photovoltaic output reaches the starting value D before each execution of the correction model start ：
Figure BDA0004182726400000081
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004182726400000082
the photovoltaic predicted value change index at the moment k'; f represents a set of photovoltaic access nodes; t 'represents a T' th period in a lower layer scheduling period (2 hours), T S Represents the number of time periods within one lower layer scheduling period (2 hours/15 minutes=8); / >
Figure BDA0004182726400000083
For the predicted value before the day of the photovoltaic output, +.>
Figure BDA0004182726400000084
Is a photovoltaic output short-time predicted value.
Setting a start value D in a correction model start Judgment was made every 15 minutes. At time k' of the correction model, if the predicted value changes index
Figure BDA0004182726400000091
Greater than the starting value D start Performing corresponding scheduling processing of the correction model; otherwise, judging the k' +1 moment.
And the decision variables are solved through short-time rolling optimization, so that the mobile energy storage output and SOP transmission power in a limited future domain are predicted, and the prediction model is as follows:
Figure BDA0004182726400000092
wherein: p (k '+τ|k') is the k 'time to predict the future k' +τ time mobile energy storage capacity and SOP transmission power; p (P) 0 The initial value of the movable energy storage and SOP output at the moment k' is obtained by actual measurement; ΔP (k ' +t ' |k ') is the future [ k ' + (t ' -1), k ' +t ' at time k ' determined by rolling optimization ']The energy storage output correction quantity and the SOP transmission power correction quantity are moved in a period of time, and are optimized decision variables; τ is the sequence number of the period within one lower layer scheduling period.
Step 300: solving the first objective function according to the first constraint condition to obtain a first solution value; the first solution is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimal.
Taking uncertainty of a solar photovoltaic predicted value into consideration, and establishing a two-stage robust optimization objective function, namely a first objective function, by taking the minimum sum of power failure loss and operation cost of a tie switch in a worst photovoltaic output scene as a target.
Specifically, the expression of the first objective function is:
Figure BDA0004182726400000093
z is an integer decision variable output by the optimization model;
Figure BDA0004182726400000094
the photovoltaic output of the load node i at the moment t; omega shape T Is a set of time periods; i is a node set of the load; omega i,c The importance coefficient of the load node i; alpha i,t To represent the load shedding state at load node i at time t; />
Figure BDA0004182726400000096
Representing the active cut power of the load node i at the moment t; b is a branch set of the power distribution network; epsilon is the operation cost coefficient of the tie switch; alpha i-j,t The open or close state of the line (i-j) between the load node i and the adjacent coincidence node j at the moment t; w is a decision variable output by the optimization model.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004182726400000095
Figure BDA0004182726400000101
Figure BDA0004182726400000102
respectively represent the active power of the load node i at the moment tReducing power and reactive power; />
Figure BDA0004182726400000103
And (3) with
Figure BDA0004182726400000104
Respectively representing the charging power and the discharging power of the kth mobile energy storage vehicle at the time t; />
Figure BDA0004182726400000105
The active power and the reactive power input by the SOP to the load node i at the time t by the node i side converter are respectively shown.
Figure BDA0004182726400000106
To indicate whether the failed node i' was repaired by a serviceman at time t, the 0-1 variable, if the repair is complete,
Figure BDA0004182726400000107
X k,i,t to represent whether the kth mobile energy storage vehicle is at 0-1 variable of the load node i at the time t, if yes, X k,i,t ＝1；X k,m-n,t For a 0-1 variable representing whether the kth mobile energy storage vehicle is driving on the road section m-n at the time t, if yes, X k,m-n,t ＝1；Y r,i′,t A 0-1 variable for indicating whether the t moment of the r group of repair personnel is positioned at the fault node i'; y is Y r,i′,，j′,t A 0-1 variable representing whether the rush-repair person t runs between the fault nodes i 'and j' at the moment.
Considering that the photovoltaic power supply output has uncertainty, the uncertainty increases the error of the photovoltaic output predicted value before the day, so that the photovoltaic output uncertainty is characterized by adopting a polyhedron uncertainty set U on the basis of the photovoltaic output predicted value:
Figure BDA0004182726400000108
wherein:
Figure BDA0004182726400000109
and the upper limit of the fluctuation value and the fluctuation value of the photovoltaic output at the node i at the moment t respectively. />
Figure BDA00041827264000001010
The predicted value is the predicted value before the photovoltaic output day, namely the predicted value of the first time scale. Gamma ray i,t Is a random variable, and takes the value of [ -1,1]. Γ represents an uncertain limit value of photovoltaic output fluctuation, the total fluctuation amount can be controlled, and the conservation degree of the model is adjusted. When Γ takes 0, the photovoltaic output is considered to be equal to the predicted value, and the total fluctuation amount is 0; the larger the Γ value, the higher the degree of conservation of the model.
Step 400: and inputting the first solving value into the correction model, and solving the second objective function by using the second constraint condition to obtain a first solving correction value.
Step 500: adjusting the power grid data of the power system by adopting the first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
The correction model is performed on the basis of the optimization model, but the scheduling of mobile emergency resources in the traffic network and the network reconstruction are not performed. The lower-layer short-time rolling optimization model is a correction model, the movable energy storage and SOP output values determined by the upper-layer scheduling optimization model are used as references, 15 minutes are used as time intervals, the power failure loss is minimum, the movable energy storage and SOP are compared with the upper-layer optimization result, the correction quantity is minimum, and the power supply recovery effect is improved to the greatest extent while the movable energy storage and SOP are prevented from being greatly adjusted by short-time rolling optimization.
Specifically, the expression of the second objective function is:
Figure BDA0004182726400000111
wherein Y is a decision variable output by the correction model; t (T) S The number of time periods in one period; k' is a time sequence number in the scheduling process; t' is a period A time within; i is a load node; i is a node set of the load; omega i,c The importance coefficient of the node i; Θ is the power grid data set; l represents the number of the power grid data; kappa represents a mobile energy storage and converter configuration optimization adjustment penalty coefficient;
Figure BDA0004182726400000112
representing the active cut power of the load node i at the time of k '+t'; />
Figure BDA0004182726400000113
The decision value of the k '+t' moment of the first power grid data in the optimization model is represented; ΔP l (k '+t') represents an output correction amount of the first grid data at the time k '+t'; p (P) 0,l (k '+t' -1) represents the initial value of the output of the first grid data at the time k '+t' -1. Wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BDA0004182726400000114
Figure BDA0004182726400000115
respectively moving the energy storage charge and discharge power correction amount; />
Figure BDA0004182726400000116
And respectively, correction amounts of active and reactive power transmitted by the SOP.
Wherein the schedulable resource configuration information constraint includes: the method comprises the steps of mobile energy storage scheduling constraint, mobile energy storage operation constraint, mobile energy storage energy constraint, mobile energy storage charge state upper limit and lower limit constraint, mobile energy storage charge and discharge constraint, emergency repair personnel scheduling constraint, fault repair condition constraint and converter configuration operation constraint.
Specifically, based on the movement characteristics of the mobile energy storage in the traffic network, the mobile energy storage scheduling constraint is constructed.
And (5) moving the energy storage running continuity constraint. When the mobile energy storage enters a certain node at the time t or is already at the node (corresponding to the right side of the following formula), the mobile energy storage can select to leave the node or stay at the node at the time t+1 (corresponding to the left side of the following formula):
Figure BDA0004182726400000121
Wherein:
Figure BDA0004182726400000122
representing the set of all possible endpoints of the mobile energy store starting from node j; />
Figure BDA0004182726400000123
Representing the set of all possible starting points that the mobile energy store ends with node j. X is X k,i,j,t For a 0-1 variable representing whether the mobile energy storage k runs between the nodes i and j at the moment t, if so, the value is 1; x is X k,i,t To indicate whether the mobile energy store k is located at the 0-1 variable of the i node at the time t, if so, the value is 1.
The same mobile energy storage single moment can only be in two states of running or staying at a certain node:
Figure BDA0004182726400000124
the mobile energy storage is considered to not reach the destination during the running between nodes and the preparation period before the operation, namely, the charging and discharging operation can not be performed:
Figure BDA00041827264000001211
wherein: t is t σ The job preparation time after the travel of the mobile energy storage to the destination is shown as a constant.
The same mobile energy storage single moment can only stay at one node:
Figure BDA0004182726400000125
regarding mobile energy storage operation constraints, the following is specific:
and (5) moving the constraint of the charge and discharge states of the energy storage. The mobile energy storage can be charged and discharged only after reaching the destination node, and the charging state and the discharging state are mutually exclusive.
Figure BDA0004182726400000126
Wherein:
Figure BDA0004182726400000127
and->
Figure BDA0004182726400000128
Respectively 0-1 variable representing the charge and discharge states of the kth mobile energy storage vehicle at the node i at the time t, if the mobile energy storage vehicle is in the charge state +. >
Figure BDA0004182726400000129
If in a discharge state
Figure BDA00041827264000001210
Mobile stored energy constraint:
Figure BDA0004182726400000131
wherein: e (E) k,t The capacity of a battery of the kth mobile energy storage vehicle at the time t;
Figure BDA0004182726400000132
and->
Figure BDA0004182726400000133
Respectively representing the charge and discharge power of the kth mobile energy storage vehicle at the time t; η (eta) ch And eta dch Respectively representing the charge and discharge efficiency of the mobile energy storage vehicle; Δt is the time interval.
Upper and lower limit constraint of mobile energy storage charge state:
Figure BDA0004182726400000134
wherein: s c max And s c min Respectively representing the maximum value and the minimum value of the charge state of the mobile energy storage vehicle; e (E) c For mobile energy storage rated capacities.
Mobile energy storage charge-discharge constraint:
Figure BDA0004182726400000135
Figure BDA0004182726400000136
wherein:
Figure BDA0004182726400000137
and->
Figure BDA0004182726400000138
Respectively representing the charge and discharge power of the kth mobile energy storage vehicle at the time t; />
Figure BDA0004182726400000139
And (3) with
Figure BDA00041827264000001310
And respectively representing the upper limit of the charge and discharge power of the kth mobile energy storage vehicle.
Regarding the dispatch constraints of the rush repair personnel, the following are specific:
the emergency repair personnel drive the continuity constraint. When the rush-repair personnel drive into a certain node at the time t or are already at the node (corresponding to the right side of the following formula), the rush-repair team can choose to drive away from the node or stay at the node at the time t+1 (corresponding to the left side of the following formula):
Figure BDA00041827264000001311
wherein: i' is a fault node in the network, I fault To contain the set of all failed nodes in the network,
Figure BDA00041827264000001312
r represents the r group of rush repair personnel; y is Y r,i′,t A 0-1 variable for indicating whether the t moment of the r group of repair personnel is positioned at the fault node i'; y is Y r,i′,j′,t A 0-1 variable representing whether the rush-repair person t runs between the fault nodes i 'and j' at the moment.
The same group of rush-repair personnel can only drive or stay at a certain node at a single moment to perform the rush-repair in two states:
Figure BDA0004182726400000141
when a rush-repair person drives between two nodes or does not finish the rush-repair of the previous node, the rush-repair person cannot appear in the next node:
Figure BDA0004182726400000142
wherein:
Figure BDA0004182726400000143
the time required by the r-th group of rush repair personnel to repair the fault i' is a certain constant.
The same group of repair workers can only stay at one fault node at a single moment:
Figure BDA0004182726400000144
regarding the fault repair case constraints, the following are specific:
if the time required for repairing the fault i 'is not reached, the fault i' is considered to be unrepaired:
Figure BDA0004182726400000145
wherein:
Figure BDA0004182726400000146
to indicate whether the repair of the failed node i' is completed 0-1 variable, if the repair is completed,/-if yes>
Figure BDA0004182726400000147
The failed node is considered to not fail again after repair is completed:
Figure BDA0004182726400000148
it is believed that if and only if the repair of the fault at both ends of the faulty line is completed, the line can be closed:
Figure BDA0004182726400000149
wherein: b (B) fault For a set of fault branches, (i ' -j ') represents a faulty line between adjacent fault nodes i ' and jv; alpha i-j,t To represent the 0-1 variable of the opening or closing of the line (i-j) between adjacent nodes at time t, alpha if the line (i-j) is closed i-j,t ＝1。
Any fault can only be salvaged once:
Figure BDA00041827264000001410
regarding intelligent soft-switch SOP operational constraints:
the soft Start (SOP) of the embodiment of the invention is a double-end back-to-back voltage source converter (B2B-VSC), which is a fully-controlled power electronic device, and has the advantages of continuously adjusting power, providing reactive voltage support and the like. In the event of an accident, the SOP usually works in a VdcQ-Vf control mode, namely the electrified normal side works in a VdcQ mode, and voltage and frequency support are provided by a power supply with self-starting capability; the power failure fault side works in the Vf control mode, at the moment, the fault side VSC can provide voltage and frequency support, active power and reactive power are input into a fault side power grid, the power supply recovery effect is further improved, and loss caused by large-scale power failure accidents of the power distribution network is reduced.
Intelligent soft switching active balancing constraints:
Figure BDA0004182726400000151
Figure BDA0004182726400000152
wherein:
Figure BDA0004182726400000153
representing active power injected into a node i by SOP at a time t; />
Figure BDA0004182726400000154
Representing reactive power injected by SOP to a node i at a time t; h represents a set of SOP access nodes; />
Figure BDA0004182726400000155
Active loss for the i-side VSC; />
Figure BDA0004182726400000156
Is the i-side VSC active loss coefficient. SOP reactive output and access capacity constraint:
Figure BDA0004182726400000157
Figure BDA0004182726400000158
Wherein:
Figure BDA0004182726400000159
injecting upper and lower limits of reactive power to the node i for SOP respectively; />
Figure BDA00041827264000001510
The capacity of the SOP installed between nodes i, j is shown.
For the nonlinear relationship existing in the SOP operation constraint, the nonlinear relationship can be converted into a second order cone constraint by a second order cone relaxation method:
Figure BDA00041827264000001511
for network topology constraints, the following are specific:
island division constraint: the subnetwork at node i where photovoltaic or mobile energy storage is present to supply power is called an island.
Judging whether each node in the network has a power supply for supplying power, namely judging a source node:
Figure BDA0004182726400000161
/>
Φ j ＝1,j∈F
wherein: phi j Indicating whether node j has power or not, phi j =1 means that node j is connected to a mobile energy storage or photovoltaic power source and is referred to as source node; g is a node set which can be accessed by mobile energy storage; f represents a set of photovoltaic access nodes.
If any node i has a power supply for supplying power, the node i must belong to a certain island, otherwise, the node i is not necessarily:
Figure BDA0004182726400000162
wherein: phi (phi) i,s,t Dividing variables for island, and when t is time, nodeWhen i belongs to island s, phi i,s,t =1, noted i e s; m is a relatively large constant.
Judging that when a father node of a certain node belongs to an island, a child node of the certain node can belong to the island:
Figure BDA0004182726400000163
wherein: x-shaped articles j-i,t To describe 0-1 variable of parent-child relationship between adjacent nodes, χ is given that j is the parent node of i j-i,t ＝1；α i-j,t To represent the 0-1 variable of the opening or closing of the line (i-j) between adjacent nodes at time t, alpha if the line (i-j) is closed i-j,t ＝1。
Radial network topology constraints:
ensuring that any pair of adjacent nodes i-j only has one parent-child relationship:
χ i-j,t +χ j-i,t ＝α i-j,t ；
except the source node, each node has only one parent node at any time:
∑ i∈δ(j) χ i-j,t ＝1,j∈{Φ j ＝0}；
the source node has no parent node:
∑ i∈δ(j) χ i-j,t ＝0,j∈{Φ j ＝1}；
regarding power flow constraints of the distribution network, the following is specific:
considering the safe operation of the power distribution network, a DistFlow equation is adopted to restrict the power flow. The following constraints are satisfied at any time, so the subscript t is ignored.
Figure BDA0004182726400000171
Figure BDA0004182726400000172
Figure BDA0004182726400000173
Figure BDA0004182726400000174
Figure BDA0004182726400000175
/>
Figure BDA0004182726400000176
Figure BDA0004182726400000177
Wherein: r is R i-j 、X i-j Branch resistances and branch reactances respectively;
Figure BDA0004182726400000178
the square of the amplitude of the branch current and the square of the amplitude of the node voltage are distinguished; />
Figure BDA0004182726400000179
Figure BDA00041827264000001710
The upper and lower limit constraints of the square of the voltage amplitude and the upper limit constraint of the square of the current amplitude are respectively given.
For the trend constraint, a nonlinear relation exists among voltage, current and power in the last formula, and the nonlinear relation is converted into a second order cone constraint by a second order cone relaxation method:
Figure BDA00041827264000001711
the constraint conditions of the correction model are mobile energy storage operation constraint, mobile soft switch operation constraint and power flow constraint of the power distribution network, and are similar to those of the upper-layer optimization model, so that the constraint conditions are not repeated here.
By the short-time rolling optimization, the movable energy storage output and SOP transmission power correction value vector delta P (k '+t' |k ') of the future k' +t 'can be obtained at the k' moment, and then T is calculated S The control sequence matrix composed of the correction value vectors of each time period is as follows:
[ΔP(k′+1|k′),ΔP(k′+2|k′),...,ΔP(k′+T s |k′)]
in order to prevent the control process from deviating from the ideal state, the short-time rolling optimization performed at time k 'only performs the first vector in the control sequence matrix, and obtains the mobile energy storage capacity at time k' +1 and the SOP transmission power:
P(k′+1|k′)＝P 0 (k′)+ΔP(k′+1|k′)
in consideration of the fact that the predicted value of the photovoltaic output is higher in prediction precision but still has certain uncertainty, the predicted value cannot be guaranteed to be identical to the actual output, and therefore optimal scheduling in the previous period cannot be guaranteed, the mobile energy storage is identical to the SOP actual output value and the scheduling value, and therefore a feedback correction link is required to be introduced. At time k '+1, taking the actually measured output values of the movable stored energy and the SOP as initial values of short-time rolling optimization at time k' +1 to form closed-loop control:
P 0 (k′+1)＝P real (k′+1)
wherein: p (P) 0 (k '+1) represents an initial value of the output of the mobile energy storage and SOP at the time k' +1; p (P) real And (k ' +1) represents the actual measured value of the k ' +1 time mobile energy storage and SOP output obtained through k ' time optimal scheduling.
For a two-stage robust optimization model, a column constraint generation (column and constraints generation, C & CG) algorithm may be employed for solution. Namely: the two-stage robust optimization problem is decomposed into a main problem and a sub-problem, wherein the main problem solves a first-stage decision variable, the sub-problem determines a compensation variable and an uncertainty variable, and a cut set is returned for the main problem to update the constraint. The C & CG algorithm converts the inner-layer min problem of the sub-problem maxmin double-layer structure into a max problem through a dual theory, and a single-layer max problem convenient to solve is formed. And then carrying out alternate iterative solution on the main problem and the sub problem until the upper and lower bounds tend to be consistent and the convergence condition based on the precision is met.
The short-time rolling optimization model based on model predictive control belongs to a second-order cone planning problem and can be solved by using a commercial solver GUROBI.
Example 2
As shown in fig. 2, an embodiment of the present invention provides a joint optimization scheduling system, which includes: the system comprises an acquisition module 1, a model construction module 2, a first solving module 3, a second solving module 4 and a determination module 5.
The acquisition module 1 is used for acquiring power grid data of a power distribution network of the power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource allocation information, the photovoltaic prediction information of the first time scale and the photovoltaic prediction information of the second time scale can be scheduled; the period of the first time scale is greater than the period of the second time scale; the photovoltaic prediction information includes: photovoltaic output and load requirements; the schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and current transformer configuration.
And the model construction module 2 is used for constructing an optimal scheduling model.
Wherein, the optimal scheduling model comprises: an optimization model and a correction model; an optimization model comprising: a first objective function and a first constraint; a correction model, comprising: a second objective function and a second constraint.
The first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information and schedulable resource configuration information of a first time scale; a first constraint comprising: resource allocation information constraints, power distribution network topology constraints, and power distribution network power flow constraints can be scheduled.
The second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information and the schedulable resource configuration information of the second time scale; the second constraint includes: resource allocation information constraints, power distribution network topology constraints, and power distribution network power flow constraints can be scheduled.
The first solving module 3 is configured to solve the first objective function according to a first constraint condition to obtain a first solution value; the first solution is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimal.
And the second solving module 4 is used for inputting the first solving value into the correction model, and solving the second objective function under the second constraint condition to obtain the first solving correction value.
The determining module 5 is used for adjusting the power grid data of the power system by adopting the first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
Example 3
The embodiment of the invention provides equipment, which comprises a memory and a processor, wherein the memory is used for storing a computer program, and the processor runs the computer program to enable the electronic equipment to execute the joint optimization scheduling method in the embodiment 1.
As an alternative embodiment, the electronic device may be a server.
In an embodiment, the present invention further provides a medium storing a computer program, where the computer program implements the joint optimization scheduling method in embodiment 1 when executed by a processor.
The invention has the advantages that:
1. dividing the post-disaster emergency dispatching process of the power distribution network into optimization and correction according to different time scales, wherein the optimization model establishes a two-stage robust optimization model aiming at the problems of vehicle dispatching and operation dispatching of emergency resources under a long time scale, so that the capability of the proposed dispatching method for coping with the uncertainty of the distributed power supply (DG) output is high, and meanwhile, the problem of low solving efficiency caused by the fact that the optimization model contains more integer variables is solved; in addition, the correction model establishes a rolling optimization model in a short time scale based on a Model Predictive Control (MPC) theory, and the problem of reduced accuracy of long-time-scale DG output prediction is better solved.
2. An intelligent soft Switch (SOP) is added in an emergency dispatching model after the disaster of the power distribution network to dispatch resource allocation information as a dispatching object, and the capability of the SOP for quickly adjusting power and providing voltage reactive power support in the power supply recovery process of the power distribution network is fully utilized, so that the dispatching method can further improve the power supply recovery effect and reduce the loss caused by large-scale power failure accidents of the power distribution network.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (7)

1. A joint optimization scheduling method, the method comprising:
acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource allocation information, the photovoltaic prediction information of the first time scale and the photovoltaic prediction information of the second time scale can be scheduled; the period of the first time scale is greater than the period of the second time scale; the photovoltaic prediction information includes: photovoltaic output and load requirements; the schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and the configuration of the converter;
constructing an optimized scheduling model;
wherein the optimized scheduling model comprises: an optimization model and a correction model; the optimization model comprises: a first objective function and a first constraint; the correction model includes: a second objective function and a second constraint;
the first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information of a first time scale and the schedulable resource configuration information; the first constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
The second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information of the second time scale and the schedulable resource configuration information; the second constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
solving the first objective function according to the first constraint condition to obtain a first solution value; the first solution value is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimum;
inputting the first solving value into the correction model, and solving the second objective function by using the second constraint condition to obtain a first solving correction value;
adjusting the power grid data of the power system by adopting a first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
2. The joint optimization scheduling method of claim 1, wherein the schedulable resource configuration information constraint comprises: mobile energy storage scheduling constraint, mobile energy storage operation constraint, mobile energy storage energy constraint, mobile energy storage charge state upper and lower limit constraint, mobile energy storage charge and discharge constraint, emergency repair personnel scheduling constraint, fault repair condition constraint and converter configuration operation constraint.
3. The joint optimization scheduling method of claim 1, wherein the expression of the first objective function is:
Figure FDA0004182726390000021
z is an integer decision variable output by the optimization model;
Figure FDA0004182726390000022
the photovoltaic output of the load node i at the moment t; omega shape T Is a set of time periods; i is a node set of the load; omega i,c The importance coefficient of the load node i; alpha i,t To represent the load shedding state at load node i at time t; />
Figure FDA0004182726390000023
Representing the active cut power of the load node i at the moment t; b is a branch set of the power distribution network; epsilon is the operation cost coefficient of the tie switch; alpha i-j,t The open or close state of the line (i-j) between the load node i and the adjacent coincidence node j at the moment t; w is a decision variable output by the optimization model.
4. The joint optimization scheduling method of claim 1, wherein the expression of the second objective function is:
Figure FDA0004182726390000024
wherein Y is a decision variable output by the correction model; t (T) S The number of time periods in one period; k' is a time sequence number in the scheduling process; t is t ′ Is a time within the cycle; i is a load node; i is a node set of the load; omega i,c The importance coefficient of the node i; theta is the power grid dataA collection; l represents the number of the power grid data; kappa represents a mobile energy storage and converter configuration optimization adjustment penalty coefficient;
Figure FDA0004182726390000025
Represents k ′ +t ′ Active power reduction of the moment load node i; />
Figure FDA0004182726390000026
Representing k of the ith grid data in the optimization model ′ +t ′ A decision value of time; ΔP l (k ′ +t ′ ) Representing the ith grid data at k ′ +t ′ A time output correction amount; p (P) 0,l (k ′ +t ′ -1) represents the first grid data at k ′ +t ′ -initial value of the output at time 1.
5. A joint optimization scheduling system, the system comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring power grid data of a power distribution network of a power system after disaster and road fault information in a traffic network corresponding to the power distribution network; the grid data includes: the resource allocation information, the photovoltaic prediction information of the first time scale and the photovoltaic prediction information of the second time scale can be scheduled; the period of the first time scale is greater than the period of the second time scale; the photovoltaic prediction information includes: photovoltaic output and load requirements; the schedulable resource configuration information includes: mobile energy storage, the number of rush repair personnel and the configuration of the converter;
the model construction module is used for constructing an optimal scheduling model;
wherein the optimized scheduling model comprises: an optimization model and a correction model; the optimization model comprises: a first objective function and a first constraint; the correction model includes: a second objective function and a second constraint;
The first objective function is constructed by taking the minimum sum of the costs of the power system as a target according to photovoltaic prediction information of a first time scale and the schedulable resource configuration information; the first constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
the second objective function is constructed by taking the minimum power failure loss, the minimum correction of the movable energy storage and the minimum correction of the converter configuration as targets according to the photovoltaic prediction information of the second time scale and the schedulable resource configuration information; the second constraint includes: scheduling resource allocation information constraint, distribution network topology constraint and distribution network tide constraint;
the first solving module is used for solving the first objective function according to the first constraint condition to obtain a first solving value; the first solution value is the transmission power of the corresponding mobile energy storage output and converter configuration when the sum of the costs of the power system is minimum;
the second solving module is used for inputting the first solving value into the correction model, and solving the second objective function by the second constraint condition to obtain a first solving correction value;
The determining module is used for adjusting the power grid data of the power system by adopting the first solving correction value to obtain a scheduling scheme; the scheduling scheme is used for distributing photovoltaic output and schedulable resource configuration information so as to recover power supply after disaster.
6. An apparatus comprising a memory for storing a computer program and a processor that runs the computer program to cause the electronic apparatus to perform the joint optimization scheduling method of any one of claims 1 to 4.
7. A medium, characterized in that it stores a computer program which, when executed by a processor, implements the joint optimization scheduling method according to any one of claims 1 to 4.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117277392A (en) * 2023-11-22 2023-12-22 国网山西省电力公司经济技术研究院 Emergency resource optimal configuration method for elastic lifting of power distribution system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117277392A (en) * 2023-11-22 2023-12-22 国网山西省电力公司经济技术研究院 Emergency resource optimal configuration method for elastic lifting of power distribution system
CN117277392B (en) * 2023-11-22 2024-04-09 国网山西省电力公司经济技术研究院 Emergency resource optimal configuration method for elastic lifting of power distribution system

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