CN113937782B - Day-ahead bus operation mode optimization method based on time division - Google Patents
Day-ahead bus operation mode optimization method based on time division Download PDFInfo
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Abstract
A day-ahead bus operation mode optimization method based on time division belongs to the technical field of voltage stability enhancement of power systems. Evaluating the voltage stability of the power system at each hour before the day, and calculating the load margin of the power system at each load level and the node voltage relative change quantity from the initial operation point to the saddle node bifurcation point; respectively calculating information entropy of each time interval division scheme, and determining an optimal time interval division scheme of a daily load curve; solving an effective bus operation scheme under the most dangerous load level in each time period, and executing local time period adjustment; the output time interval division scheme, the effective solution of each time interval and the bus operation mode optimize the load margin of the power system in the future. According to the application, the effective bus running scheme is firstly solved for the most dangerous load level in each time period, and then other load conditions in the time period are checked, so that the effective bus running scheme in the time period is determined, the solving speed is high, the calculated amount is small, and the efficiency is high.
Description
Technical Field
A day-ahead bus operation mode optimization method based on time division belongs to the technical field of voltage stability enhancement control of power systems.
Background
The static voltage stability of the power system is one of important indexes of safe and stable operation of the system, and along with the growth of load demands and the promotion of power market reform and the condition of the self operation condition of the power system, serious consequences such as load loss, voltage collapse and system disconnection can be caused. Therefore, effective control measures are formulated according to the load change in one day, so that the static voltage stability of the power system is ensured to be maintained at each load level before the day, and the method has important significance for safe and stable operation of the power system.
Conventional measures to enhance the voltage stability of the power system are load shedding, adding reactive compensation devices, adjusting the control transformer taps, etc. A large number of researches also show that the unreasonable topological structure of the power grid is also an important factor for restricting the normal and stable operation of the power system. The system is used for optimizing the topological structure of the power grid by utilizing the switching operation (such as the switching of a power transmission line, the input or the withdrawal of a bus and a switching operation, and the like), so that the stability and the safety of the power system are improved. At present, the power grid topology optimization measures are widely applied to the fields of relieving overload of a system, relieving voltage out-of-limit, limiting short-circuit current and the like. The power grid topological structure optimization method can be divided into static optimization and dynamic optimization: static optimization mainly aims at data and constraint conditions of a certain time section of a power system, and effective control measures are solved; the dynamic optimization considers factors such as load fluctuation of each period, and the like, so that the dynamic optimization has practical research significance.
In recent years, dynamic optimization methods of power systems have been developed by a plurality of scholars at home and abroad. A learner puts forward a comprehensive index based on network loss and voltage deviation, and divides time periods according to the index, and determines a reconstruction time period by presetting the maximum standard deviation, so that the running economy of the power distribution network and the utilization rate of a distributed power supply are improved; or by introducing the concept of the power moment, setting the maximum deviation and the maximum standard deviation of the unbalance degree of the power moment to dynamically divide the reconstruction period, the method can control the maximum reconstruction times to reduce the switching operation cost and the network loss of the power distribution network. However, the above two methods need to set the threshold value of the segmentation index in advance, and have certain subjectivity. The method can directly determine the optimal segmentation scheme to carry out dynamic reconstruction of the power distribution network so as to reduce daily loss cost, but the segmentation data adopted by the method are limited to a total daily load curve, and cannot accurately reflect the running state of each node when the load level changes. In addition, researchers also put forward a multi-agent coordination optimization method to reduce the active loss of the power distribution network, the method does not need to set a threshold value, but solves a reconstruction scheme hour by hour, and then updates a solution set through learning between adjacent working agents so as to obtain a solution meeting the constraint of the switching operation times, and the calculation amount is large. The patent proposes an analysis method for the static voltage stability load margin of a power grid, wherein an entropy model is adopted to obtain probability density distribution of the load margin so as to improve the accuracy of a load margin analysis result.
The power system and its automatic chemical report in volume 29, 2017 and 4 disclose dynamic reconfiguration of distribution network with distributed power source by improved bacterial foraging algorithm, the authors are Tang Hao, zhou Buxiang, peng Zhanggang, etc. the maximum standard deviation of the comprehensive index is preset according to the time interval dividing method of the system operation.
The power grid technology, in 2012, volume 36 and 2, discloses power distribution network reconstruction [ J ] adopting a time period dynamic division and layering optimization strategy, authors are Jiang Donglin, liu Tianqi and Li Fan, the time period division method of the power moment imbalance degree needs to set the maximum difference and the maximum standard deviation of the power moment imbalance degree, and the setting of the threshold value needs to meet the constraint of operation times, so that the segmentation method has subjectivity and is difficult to obtain a reasonable threshold value.
The volume 41 and the period 2 of the power grid technology disclose active power distribution network dynamic reconstruction [ J ] based on information entropy period division, authors are Zhao Jingxiang, niu Huanna and Wangzhu, the method carries out period division according to the information entropy calculated by an equivalent daily load curve, but the total load curve cannot accurately reflect the load change condition of each node.
The application discloses a multi-agent coordinated optimization method [ J ] for dynamic reconstruction of a power distribution network in the period 34 of the report of China motor engineering, wherein the authors are Li Zhenkun, chen Xingying, zhao Bo and the like, and the proposed multi-agent method still needs to solve solutions per hour one by one, so that the calculation amount is large.
Disclosure of Invention
The technical problems to be solved by the application are as follows: the method for optimizing the daily bus running mode based on time division, which overcomes the defects of the prior art, improves the static voltage stability of the power system through the bus running mode optimization with the minimum times, and meets the safe running constraint and the tide balance constraint.
The technical scheme adopted for solving the technical problems is as follows: the day-ahead bus operation mode optimization method based on time division is characterized by comprising the following steps of: the method comprises the following steps:
the evaluation stage, namely evaluating the voltage stability of the power system in each hour before the day by using a continuous tide method, and calculating the load margin of the power system at each load level and the node voltage relative change quantity from an initial operation point to a saddle node bifurcation point;
a time interval division stage for respectively calculating information entropy of each time interval division scheme and determining an optimal time interval division scheme of the daily load curve;
an effective bus operation scheme solving stage for solving an effective bus operation scheme under the most dangerous load level in each time period, checking the subsequent load level by the solved operation scheme, executing local time period adjustment, and repeatedly executing the steps until an effective solution of all time periods of the power system is obtained;
and in the output stage, the output period division scheme, the effective solution of each period and the bus running mode are optimized, and the load margin of the power system is improved.
Preferably, the evaluation phase comprises the steps of:
step 1001, acquiring online data, daily load prediction data, a daily power generation plan, a daily maintenance plan, a busbar set to be optimized and a corresponding busbar operation scheme of a current power system;
step 1002, calculating a load margin of the power system at each load level before the day, a left eigenvector of a jacobian matrix zero eigenvalue, a voltage amplitude of an SNB point and each branch power by applying a continuous power flow method.
Preferably, the period dividing stage includes the steps of:
step 2001, calculating the equivalent probability of voltage amplitude change of the power system from an initial operation point to a saddle node bifurcation point under the load level of each period;
step 2002, executing time period combination, respectively calculating information entropy of each time period division scheme, and determining the optimal time period division scheme of the daily load curve.
Preferably, the node voltage amplitude relative change amount DeltaV of each period is calculated from the voltage amplitude from the initial operation point to the saddle node bifurcation point in step 2001 j ：
ΔV j ＝(V j，0 -V j，SNB )/V j，0 ，
z i ＝[ΔV 1 ，ΔV 2 ，...ΔV j ，...ΔV n ]，
wherein ,Vj，0 The voltage amplitude of the initial operating point of the node j; v (V) j，SNB The voltage amplitude of the node j at the bifurcation point of the saddle node is given; z i Is the voltage amplitude change vector of the i-th period.
Equivalent probability p of node voltage amplitude variation in each period s (x i ) The method comprises the following steps:
x i ＝||z i i, wherein x i Voltage amplitude variation vector z for the i-th period i Is a mold of (2); s is the total number of time periods and n is the number of nodes.
Preferably, the information entropy E of the step 2002 when the number of divided periods is s s The method comprises the following steps:
preferably, any two adjacent time periods are combined into a new time period, and the time period division is completed.
Preferably, the effective bus operation scheme solving stage comprises the following steps:
step 3001, solving an effective bus operation scheme of the power system under the most dangerous load level of the period Ti;
step 3002, checking other load levels of the period Ti by the bus running scheme;
step 3003, determining whether the load margin of the system under each load level meets the threshold constraint, if yes, executing step 3005, if not, executing step 3004;
step 3004, removing the bus operation scheme from the solution set;
step 3005, determining whether all solutions are verified, if so, executing step 3006, and if not, executing step 3002 again;
step 3006, performing local adjustment of the time period Ti division;
step 3007, determining whether all time periods are solved, if yes, then solving the effective bus running scheme, and if not, executing step 3001.
Preferably, the load margin variation in all bus operation schemes at the load level of the period Ti is calculated in step 3003
wherein ,scheme bs for power system operation on bus k The amount of change in the load margin; n is n l The branch number is the branch number of the original network topology structure N; Δλ (delta lambda) ij For the load margin variation quantity delta theta caused by the variation of the branch i-j parameters due to the variation of the bus operation mode ij The calculation method of (2) is as follows:
wherein ,ΔPi 、ΔP i and ΔQi 、ΔQ j Active power and reactive power balance equations respectively corresponding to the buses i and j;omega is left eigenvector corresponding to zero eigenvector of jacobian matrix, < >> and />For vector->The active power and reactive power balance equation elements corresponding to the bus i and the bus j; />The variation of the admittance parameters of the branch i-j which is changed for the bus operation mode; g ij +jb ij and />And respectively changing admittance parameters of the front branch i-j and the rear branch i-j for the bus operation mode.
Preferably, the alternative bus operating scheme solution set for period TiThe method comprises the following steps:
compared with the prior art, the application has the following beneficial effects:
according to the day-ahead bus running mode optimization method based on time division, the number of segments or the segment critical index is not required to be preset, the optimal number of segments and the optimal segment scheme can be directly determined by calculating the information entropy of the time division combining scheme, and the influence of subjective factors is avoided; the obtained daily load fluctuation condition is reflected as the change of the voltage amplitude of the power system node under each load level, so that the problem that the daily load curve cannot accurately reflect the state of the power system when the total load fluctuation is small and the change of each node is large can be effectively solved; the method comprises the steps of firstly solving an effective bus running scheme for the most dangerous load level in each period, and then checking other load conditions in the period, so that the effective bus running scheme in the period is determined, and the power system under all loads does not need to be solved one by one. The solution speed is high, the calculated amount is small, and the efficiency is high.
The application relates to a method for solving a dynamic operation scheme, which is based on a network topology structure after the operation mode of a bus in the previous period is changed when solving the latter period, can reduce the operation times of a switch and has good economy; the time division scheme is locally adjusted according to the solved effective solution, so that the number of times of bus operation mode change can be effectively reduced, and the number of times of switch operation is reduced; in addition, the control measures used by the application only reasonably adjust the network topology structure, and no additional investment is required.
Drawings
FIG. 1 is a flow chart of a day-ahead bus operation mode optimization method based on time division.
Fig. 2 is a schematic diagram of the optimal number of segments of the power system.
Fig. 3 is a daily load level schematic of the power system.
Fig. 4 is a schematic diagram of a No. 45 bus operating scheme.
Fig. 5 is a graph comparing load margins of the power system before and after day-ahead of bus operation mode optimization.
Detailed Description
FIGS. 1-5 illustrate preferred embodiments of the present application, and the present application will be further described with reference to FIGS. 1-5.
The present application will be further described with reference to specific embodiments, however, it will be appreciated by those skilled in the art that the detailed description herein with reference to the accompanying drawings is for better illustration, and that the application is not necessarily limited to such embodiments, but rather is intended to cover various equivalent alternatives or modifications, as may be readily apparent to those skilled in the art.
As shown in fig. 1 to 5: the day-ahead bus operation mode optimization method based on time interval division comprises the following steps:
and step 1, in the evaluation stage, the voltage stability of the power system in each hour before the day is evaluated by applying a continuous tide method, and the load margin of the power system at each load level and the node voltage relative change quantity from an initial operation point to a saddle node bifurcation point are calculated.
Based on an original network topology structure, according to system online data and given daily load data obtained from an energy management system and an SCADA system, a continuous power flow method is applied to evaluate the voltage stability of the power system in each hour before the day, and the load margin of the power system at each load level and the node voltage relative change quantity from an initial operation point to a saddle node bifurcation point are calculated.
The mathematical model of the day-ahead static voltage stability enhancement control based on time interval division is as follows:
the continuous power flow balance equation of the power system after the bus operation mode is changed is as follows:
the load margin threshold value of the power system after the bus operation mode is changed is as follows:
λ t ≥λ th ，t＝1，2，3，...，24；
the safe operation constraint of the power system after the bus operation mode is changed is as follows:
wherein ：Nt The network topology structure at the time t; (N) t -N t-1 ) Mapping to the change condition of the network topology structure at the time t and the time t-1, if the structures are the same (N) t -N t-1 ) =0, otherwise (N t -N t-1 )＝1；λ t Lambda is the load margin of the system at time t th A load margin threshold value preset by an operator; wherein x is t A state variable of the power system at the t hour; BS is a bus set with changeable operation mode, BS k As an operation mode of the bus k, k epsilon BS; v (V) i 、V i，max and Vi，min The voltage amplitude value, the voltage upper limit value and the voltage lower limit value of the bus i are respectively; s is S (i，j) and S(i，j)，max The current circulating power and the allowable power limit value of the line i-j are respectively; m is the total node set of the power system; lambda (lambda) t =0 denotes the current power system, λ t =1 denotes the expected power system.
The evaluation phase specifically comprises the following steps:
step 1001, inputting real-time data, daily load prediction data, a power generation plan and a maintenance plan of a current system acquired from an energy management system and a SCADA system;
step 1002, set s=24, i=1;
step 1003, calculating the load margin of the power system at each load level before the day, the left eigenvector of the jacobian matrix zero eigenvalue, the voltage amplitude of the SNB point and the power of each branch by applying a continuous power flow method.
Step 2, dividing the period of time; and respectively calculating the information entropy of each time interval division scheme, and determining the optimal time interval division scheme of the daily load curve.
With different load levels as one hour, the information entropy of the node voltage change in each hour is calculated. Before division, the time period number is 24, and the information entropy value is maximum. And combining any two adjacent time periods into a new time period, calculating information entropy, and selecting the optimal segmentation scheme with the maximum information entropy in the combination scheme of each time period. Until the number of segments is reduced to 1, the information entropy at this time is 0. Drawing an entropy value-time period number function curve, connecting the head end and the tail end of the entropy function curve by using a straight line, and balancing the information entropy reduction and the segmentation number by using a point on the curve furthest from the straight line, wherein the segmentation number and the segmentation scheme corresponding to the point are the optimal scheme for time period division.
Obtaining the information entropy E at 24, with the number of segments s=1, 2,3, respectively s And then drawing an entropy value-segmentation number function curve, connecting the head end and the tail end of the curve by using a straight line l, wherein a point on the entropy function curve farthest from the straight line l is a critical point with the slope of the entropy function curve larger than that of the straight line, and the corresponding segmentation number and segmentation scheme are the optimal scheme for time division of the power system in the day. And finally, sending the obtained optimal segmentation division scheme into an effective bus operation scheme solving stage.
The period dividing stage specifically includes the following steps:
step 2001, calculating the equivalent probability of voltage amplitude change of the power system from an initial operation point to a saddle node bifurcation point under the load level of each period;
performing time interval division of a daily load curve of the power system based on information entropy:
according to the voltage amplitude of the saddle node bifurcation point calculated in the evaluation stage, the node voltage amplitude of each period is relative to the variation DeltaV j The method comprises the following steps:
ΔV j ＝(V j，0 -V j，SNB )/V j，0 ，
Z i ＝[ΔV 1 ，ΔV 2 ，...ΔV j ，...ΔV n ]，
wherein ,Vj，0 The voltage amplitude of the initial operating point of the node j; v (V) j，SNB Voltage amplitude z at SNB for node j i Is the voltage amplitude change vector of the i-th period.
Equivalent probability p of node voltage amplitude variation in each period s (x i ) The method comprises the following steps:
x i ＝||z i ||，
wherein ,xi Voltage amplitude variation vector z for the i-th period i Is a mold of (2); s is the total number of time slots, s=24 before the time slot segmentation is not performed, and n is the number of nodes.
Calculating the information entropy E when the number of divided time periods is s s The method comprises the following steps:
when s=24, the information entropy E s Taking the maximum value, when s=1, E s ＝0。
Step 2002, executing time period combination, respectively calculating information entropy of each time period division scheme, and determining the optimal time period division scheme of the daily load curve.
Combining any two adjacent time periods into a new time period, s=s-1, and calculating the information entropy E of each time period combining scheme respectively s-1 The segmentation scheme with the smallest entropy reduction of information is identified.
Step 2003, if s=1, then executing step 2004, if not, then returning to execute step 2002;
step 2004, determining an optimal time interval division scheme for the power system at the daily preload level: as shown in FIG. 2, the entropy function curve has a point v at the furthest perpendicular distance from the line l 0 Corresponding s 0 I.e. the optimal number of segments.
And 3, solving an effective bus operation scheme under the most dangerous load level in each period, checking the subsequent load level by the solved operation scheme, executing local period adjustment, and repeatedly executing the steps until the effective solution of all the periods of the power system is obtained.
The solving stage of the effective bus operation scheme comprises the following steps:
step 3001, solving an effective bus operating scheme of the power system at the most dangerous load level of the period Ti:
the sensitivity method is applied to rapidly screen out a bus operation scheme which can be used for improving the Ti load margin of the power system in the period, and an ineffective bus operation scheme is removed;
and calculating the load margin of each busbar operation scheme under the most dangerous load level in the period Ti by adopting a look-ahead margin estimation method.
The operation scheme is rapidly screened, and for the divided time period Ti, solutions capable of improving the load margin of the power system are rapidly screened from all bus operation schemes by adopting a sensitivity method under each load level of the time period:
wherein ,scheme bs for power system operation on bus k The amount of change in the load margin; n is n l The branch number is the branch number of the original network topology structure N; Δλ (delta lambda) ij For the load margin variation caused by the variation of the branch i-j parameters due to the variation of the bus operation mode, delta lambda ij The calculation method of (2) is as follows:
wherein ,ΔPi 、ΔP j and ΔQi 、ΔQ j Respectively toThe active power and reactive power balance equation of the buses i and j are applied;omega is left eigenvector corresponding to zero eigenvector of jacobian matrix, < >> and />For vector->The active power and reactive power balance equation elements corresponding to the bus i and the bus j; />The variation of the admittance parameters of the branch i-j which is changed for the bus operation mode; g ij +jb ij and />And respectively changing admittance parameters of the front branch i-j and the rear branch i-j for the bus operation mode.
Alternative busbar operation scheme solution set for period TiThe method comprises the following steps:
pair aggregationIn the bus operation scheme, a look-ahead margin estimation method is applied to calculate the time period TiLoad margin for each busbar operating scheme at the most dangerous load level:
wherein ,load margin estimated for the look-ahead method; epsilon is a real number less than 1 to ensure that the valid solution is not deleted by mistake. And reserving a bus operation scheme meeting the index requirement of the load margin estimated by the look-ahead method.
Step 3002, checking other load levels of the period Ti by the bus running scheme;
and (3) applying a continuous tide method, and accurately calculating the load margin of the power system under each load level of the period Ti for the bus operation scheme of the step 3001. And eliminating bus operation schemes which do not meet the constraint of the threshold, wherein the bus operation scheme with the load margin higher than the threshold is an effective solution of the period Ti.
Step 3003, determining whether the load margin of the system at the load level meets the threshold requirement, if so, executing step 3005, and if not, executing step 3004;
step 3004, propose the bus operation scheme from the solution assembly;
step 3005, determining whether all solutions are verified, if so, executing step 3006, and if not, executing step 3002;
step 3006, performing local adjustment of the time period Ti division;
step 3007, determining whether all time periods are solved, if yes, then the effective bus operation scheme is solved, if not, i=i+1, and executing step 3001.
And in the output stage, the output period division scheme, the effective bus operation mode solution of each period and the bus operation mode optimization are adopted to optimize the load margin of the power system in the future.
The method adopts the IEEE 118 node system example simulation verification, the test system has 186 branches,the 69 # node is a balance node, 21 buses capable of optimizing operation modes are provided, and 292 bus operation schemes are generated in total. Setting a load margin threshold lambda th The daily load level is shown in fig. 3, and the daily bus operation mode optimization method based on time division provided by the application is adopted, and the time division scheme and the effective bus operation scheme of each time period under the daily load level are shown in table 1.
Table 1 time period partitioning scheme and efficient solution for 118 node power system
The simulation result based on the 118-node system is shown in the table 1, the daily load curve is divided into 7 time periods by adopting an information entropy time period division method, and after the local time period is adjusted, the daily load curve can be adjusted into 4 time periods, so that the time period division quantity is effectively reduced, and the bus operation optimization times are further reduced. The adjusted period I duration is 1:00-8:00 and the effective bus operating schedule is shown in Table 1. Wherein the scheme No. 45 is the optimal scheme of the time period, and the partial electric wiring diagrams before and after the operation mode optimization are shown in fig. 4. Based on the optimized optimal topology structure of the period I, the effective operation scheme of the period II is 142 #. By applying the solving method provided by the application, each time period is solved one by one, and the load margin pairs of the power system before and after the bus operation mode is optimized are shown in figure 5. Simulation results show that the day-ahead bus operation mode optimization method based on time division can effectively improve the load margin of a day-ahead power system.
The above description is only a preferred embodiment of the present application, and is not intended to limit the application in any way, and any person skilled in the art may make modifications or alterations to the disclosed technical content to the equivalent embodiments. However, any simple modification, equivalent variation and variation of the above embodiments according to the technical substance of the present application still fall within the protection scope of the technical solution of the present application.
Claims (3)
1. The day-ahead bus operation mode optimization method based on time interval division is characterized by comprising the following steps of: the method comprises the following steps:
the evaluation stage, namely evaluating the voltage stability of the power system in each hour before the day by using a continuous tide method, and calculating the load margin of the power system at each load level and the node voltage relative change quantity from an initial operation point to a saddle node bifurcation point;
a time interval division stage for respectively calculating information entropy of each time interval division scheme and determining an optimal time interval division scheme of the daily load curve;
the method comprises the steps of solving an effective bus operation scheme, namely solving an effective bus operation scheme of the most dangerous load level in each period, checking the load level of a subsequent period by the solved operation scheme, executing local period adjustment, and repeatedly executing the steps until the effective bus operation scheme of all periods of the power system is obtained;
the output stage comprises an output period division scheme, an effective bus running scheme of each period and a bus running mode, wherein the load margin of a power system in the future is optimized;
the mathematical model of the day-ahead static voltage stability enhancement control based on time interval division is as follows:
the continuous power flow balance equation of the power system after the bus operation mode is changed is as follows:
the load margin threshold value of the power system after the bus operation mode is changed is as follows:
λ t ≥λ th ，t＝1，2，3，...，24；
the safe operation constraint of the power system after the bus operation mode is changed is as follows:
wherein ：Nt The network topology structure at the time t; (N) t -N t-1 ) Mapping to the change condition of the network topology structure at the time t and the time t-1; lambda (lambda) t Lambda is the load margin of the system at time t th A load margin threshold value preset by an operator; wherein x is t A state variable of the power system at the t hour; BS is a bus set with changeable operation mode, BS k As an operation mode of the bus k, k epsilon BS; v (V) i 、V i，max and Vi，min The voltage amplitude value, the voltage upper limit value and the voltage lower limit value of the node i are respectively; s is S (i，j) and S(i，j)，max The current circulating power and the allowable power limit value of the line i-j are respectively; m is the total node set of the power system; lambda (lambda) t =0 denotes the current power system, λ t =1 represents the expected power system;
the period dividing stage comprises the following steps:
step 2001, calculating node voltage amplitude variation equivalent probability p of the power system from the initial operation point to the saddle node bifurcation point under each period of load level s (x m )：
Calculating the node voltage amplitude relative change amount DeltaV of each period according to the voltage amplitude from the initial operation point to the saddle node bifurcation point j ：
ΔV j ＝(V j，0 -V j，SNB )/V j，0 ，
Z m ＝[ΔV 1 ，ΔV 2 ，...ΔV j ，...ΔV n ]，
wherein ,Vj，0 The voltage amplitude of the initial operating point of the node j; v (V) j，SNB Dividing the node j at saddle nodeVoltage amplitude of the branch point; z is Z m A voltage amplitude variation vector which is the m-th period;
equivalent probability p of node voltage amplitude variation in each period s (x m ) The method comprises the following steps:
x m ＝||z m ||，
wherein ,xm Voltage amplitude variation vector Z for the mth period m Is a mold of (2); s is the total number of time periods, n is the number of nodes;
step 2002, executing time period combination, combining any two adjacent time periods into a new time period, respectively calculating the information entropy of each time period division scheme, and selecting the optimal segmentation scheme with the maximum information entropy in each time period division scheme as the segmentation number; the information entropy is 0 when the segmentation number is reduced to 1, an entropy value-time period number function curve is drawn, the head end and the tail end of the entropy function curve are connected through a straight line, and the segmentation number and the segmentation scheme corresponding to the point farthest from the straight line on the curve are the optimal scheme for time period division;
the effective bus operation scheme solving stage comprises the following steps:
step 3001, solving an effective bus operation scheme of the power system under the most dangerous load level of a period m;
and (3) rapidly screening solutions capable of improving the load margin of the power system from all bus operation schemes by adopting a sensitivity method under each load level of the period:
wherein ,scheme bs for power system operation on bus k The amount of change in the load margin; n is n l Is an original network topologyThe number of branches of the flutter structure N; Δλ (delta lambda) ij For the load margin variation caused by the variation of the i-j parameter of the line due to the variation of the operation mode of the bus, delta lambda ij The calculation method of (2) is as follows:
wherein ,ΔPi 、ΔP j and ΔQi 、ΔQ j Active power and reactive power of the corresponding nodes i and j respectively;omega is left eigenvector corresponding to zero eigenvector of jacobian matrix, < >> and />For vector->The elements of the active power and reactive power balance equations corresponding to node i and node j; />The variation of the admittance parameters of the line i-j which is changed for the bus operation mode; g ij +jb ij and />Admittance parameters of a front line i-j and a rear line i-j are respectively changed for bus operation modes;
alternative bus operation scheme solution set CB of period m m The method comprises the following steps:
CB m ＝{∪CB tl }，
to set CB m The load margin of each bus operation scheme under the most dangerous load level in the period m is calculated by applying the look-ahead margin estimation method, and the bus operation scheme meeting the index requirement of the load margin estimated by the look-ahead method is reserved:
wherein ,load margin estimated for the look-ahead method, ε is a real number less than 1;
step 3002, checking other load levels of the period m by the bus operation scheme;
applying a continuous tide method, and accurately calculating the load margin of the power system under each load level of the period m for the bus operation scheme of the step 3001;
step 3003, determining whether the load margin of the system under each load level meets the threshold constraint, if yes, executing step 3005, if not, executing step 3004;
step 3004, removing the bus operation scheme from the solution set;
step 3005, determining whether all solutions are verified, if so, executing step 3006, and if not, executing step 3002 again;
step 3006 ， Performing local adjustment of period m partitions ；
Step 3007, determining whether all time periods are solved, if yes, then the effective bus operation scheme is solved, if not, m=m+1, and executing step 3001.
2. The day-ahead bus operation mode optimization method based on time division according to claim 1, wherein: the evaluation phase comprises the following steps:
step 1001, acquiring online data, daily load prediction data, a daily power generation plan, a daily maintenance plan, a busbar set to be optimized and a corresponding busbar operation scheme of a current power system;
step 1002, calculating load margin of the power system at each load level before the day, left eigenvector of zero eigenvector of jacobian matrix, voltage amplitude of each node of saddle node bifurcation point and power of each branch by applying continuous power flow method.
3. The day-ahead bus operation mode optimization method based on time division according to claim 1, wherein: information entropy E when the number of divided periods is s in step 2002 s The method comprises the following steps:
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