CN115660326A - Power system standby management method, device, storage medium and system - Google Patents
Power system standby management method, device, storage medium and system Download PDFInfo
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Abstract
The invention discloses a power system standby management method, a device, a storage medium and a system. The method, the device, the storage medium and the system for standby management improve the real-time accuracy of the calculation of the standby capacity of the power system.
Description
Technical Field
The present invention relates to the field of power system standby management technologies, and in particular, to a power system standby management method, device, computer-readable storage medium, and system.
Background
With the continuous expansion of the scale of the power system, the power system faces a plurality of uncertain factors, the running risk of the system is increased, and the difficulty of the running control of the power grid is greatly increased. The interconnection of the current power grid systems enables long-distance and large-capacity power trading to be more and more frequent, and meanwhile, the power grid is closer to the operation limit of the power grid along with the increase of the transmission level. The standby calculation of the power system can not only provide accurate information of the current power grid use condition, but also reflect the safety and stability margin of the system operation, and becomes an important index concerned by operation scheduling personnel. And a plurality of uncertain factors bring serious challenges to the real-time standby evaluation of the power grid, such as sudden change of renewable energy power, failure of power transmission and transformation equipment, serious weakening of the power grid structure of a local area under an extreme weather condition and other uncertain factors. However, uncertain factors are complicated, and the uncertain factors with the largest risk need to be solved for intelligent selection as the standby influence evaluation object. Therefore, in order to realize the result of evaluating the power grid backup by uncertain factors under the real-time power system operation condition, a power system backup calculation method for time-varying risk evaluation needs to be introduced, and the optimal scheduling of mutual coordination of safety, economy, reliability, energy conservation and low carbon is realized.
In the prior art, the reserve capacity of the power system is generally calculated by empirical statistics, economic comparisons, and probabilistic theoretical calculations.
However, the prior art still has the following defects: uncertain factors such as sudden change of renewable energy power, power transmission and transformation equipment faults, severe weakening of a local area power grid structure under an extreme weather condition and the like are not considered, so that the real-time accuracy of the calculation of the reserve capacity of the power system in practical application is low.
Accordingly, there is a need for a power system backup management method, apparatus, computer readable storage medium, and system that overcome the above-mentioned deficiencies in the prior art.
Disclosure of Invention
The embodiment of the invention provides a power system standby management method, a device, a computer readable storage medium and a system, so that the real-time accuracy of the calculation of the standby capacity of a power system is improved.
An embodiment of the present invention provides a standby management method for a power system, where the standby management method includes: the method comprises the steps of acquiring a system operation data set, a meteorological data set and flow forecast information of an electric power system to be managed in real time, and acquiring actual power transmission quantity of the electric power system to be managed according to the system operation data set; acquiring the maximum power transmission amount of the electric power system to be managed under the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method and a preset dynamic power flow model; and estimating the system standby condition of the electric power system to be managed according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount, and managing the electric power system to be managed according to the system standby condition.
As an improvement of the above scheme, obtaining the maximum power transmission amount of the power system to be managed in the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method, and a preset dynamic power flow model specifically includes: acquiring a risk index of the power system to be managed according to a preset equipment outage probability model, a preset power grid topological model, the meteorological data set and the power flow forecast information; acquiring a risk scene corresponding to the power system to be managed according to a preset density clustering algorithm, the risk indexes and a preset historical risk index standard matrix; and calculating the maximum power transmission amount under the risk scene according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set.
As an improvement of the above scheme, obtaining a risk indicator of the power system to be managed according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set, and the power flow forecast information specifically includes: predicting a fault set of the power system to be managed and calculating the severity of each state in the fault set according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information; obtaining the fault rate of each state in the fault set according to the meteorological data, the tidal current forecast information and a preset fault rate determination method; and calculating the risk index of the power system to be managed according to the severity, the fault rate and a preset risk calculation formula.
As an improvement of the above scheme, calculating the maximum power transmission amount in the risk scenario according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set, specifically includes: constructing a first dynamic power flow objective function according to the risk scene and a preset dynamic power flow model; and solving the first dynamic power flow objective function according to a preset constraint condition group and the system operation data group to obtain the maximum power transmission amount corresponding to the risk scene.
As an improvement of the above scheme, calculating the maximum power transmission amount in the risk scenario according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set, specifically includes: judging whether a first risk scene similar to the risk scene exists in a preset historical dynamic power flow optimization database or not; if the current power flow optimization result exists, mapping the dynamic power flow optimization result corresponding to the first risk scene to the risk scene, and obtaining the maximum power transmission amount in the risk scene according to the dynamic power flow optimization result and a preset mapping calculation formula.
As an improvement of the above scheme, the dynamic power flow model is:
wherein f (t) is expressed as all independent variables of the clustered system, including all power transmission line power and other state quantities of the system and control quantities of a generator set and the like, alpha is expressed as a power increase step length, namely a load increase parameterized scalar quantity which is artificially established, b Pi Expressed as the transmission power at that moment.
As an improvement of the above scheme, the mapping calculation formula is:
wherein x is p 、x m Respectively identical random variables in sub-scene x i And C m The value of the sample is taken,
is an input random variable x p Dynamic optimal power flow sensitivity.
The invention correspondingly provides a standby management device of an electric power system, which comprises an analysis acquisition unit, a model calculation unit and a standby estimation unit, wherein the analysis acquisition unit is used for acquiring a system operation data set, a meteorological data set and power flow forecast information of the electric power system to be managed in real time and acquiring the actual power transmission quantity of the electric power system to be managed according to the system operation data set; the model calculation unit is used for acquiring the maximum power transmission amount of the power system to be managed under the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method and a preset dynamic power flow model; the standby estimation unit is used for estimating the system standby condition of the electric power system to be managed according to a preset standby estimation formula, the maximum transmission amount and the actual transmission amount, and managing the electric power system to be managed according to the system standby condition.
As an improvement of the above, the model calculation unit is further configured to: acquiring a risk index of the power system to be managed according to a preset equipment outage probability model, a preset power grid topological model, the meteorological data set and the power flow forecast information; acquiring a risk scene corresponding to the power system to be managed according to a preset density clustering algorithm, the risk indexes and a preset historical risk index standard matrix; and calculating the maximum power transmission amount under the risk scene according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set.
As an improvement of the above, the model calculation unit is further configured to: predicting a fault set of the power system to be managed and calculating the severity of each state in the fault set according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information; obtaining the fault rate of each state in the fault set according to the meteorological data, the tidal current forecast information and a preset fault rate determination method; and calculating the risk index of the power system to be managed according to the severity, the fault rate and a preset risk calculation formula.
As an improvement of the above, the model calculation unit is further configured to: constructing a first dynamic power flow objective function according to the risk scene and a preset dynamic power flow model; and solving the first dynamic power flow objective function according to a preset constraint condition group and the system operation data group to obtain the maximum power transmission amount corresponding to the risk scene.
As an improvement of the above, the model calculation unit is further configured to: judging whether a first risk scene similar to the risk scene exists in a preset historical dynamic power flow optimization database or not; if the current power flow optimization result exists, mapping the dynamic power flow optimization result corresponding to the first risk scene to the risk scene, and obtaining the maximum power transmission amount in the risk scene according to the dynamic power flow optimization result and a preset mapping calculation formula.
Another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, where the computer program, when executed, controls a device in which the computer-readable storage medium is located to execute the power system backup management method as described above.
Another embodiment of the present invention provides a power system backup management system, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the power system backup management method as described above when executing the computer program.
Compared with the prior art, the technical scheme has the following beneficial effects:
the invention provides a power system standby management method, a device, a computer readable storage medium and a system, which are used for determining actual power transmission amount and a corresponding risk scene by acquiring various data of a power system to be managed in real time, performing model building and solving on the risk scene corresponding to the power system to be managed according to a preset dynamic power flow model to acquire the maximum power transmission amount under the risk scene, and estimating standby conditions according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount for subsequent management.
Drawings
Fig. 1 is a schematic flowchart of a power system standby management method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a standby management device of a power system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Detailed description of the preferred embodiment
The embodiment of the invention first describes a power system standby management method. Fig. 1 is a flowchart illustrating a power system standby management method according to an embodiment of the present invention.
As shown in fig. 1, the standby management method includes:
the method comprises the steps of S1, obtaining a system operation data set, a meteorological data set and power flow forecast information of the electric power system to be managed in real time, and obtaining actual power transmission quantity of the electric power system to be managed according to the system operation data set.
In order to improve the real-time accuracy of subsequent estimation, before the subsequent estimation, the embodiment of the invention firstly obtains various data of the power system to be managed in real time for subsequent evaluation of risk indexes and determination of risk scenes. Since the backup estimation of the system needs to be performed according to the maximum power transmission value and the actual power transmission value of the system, the actual power transmission amount of the power system to be managed in the current state is determined in real time according to the system operation data set of the power system to be managed.
And S2, acquiring the maximum power transmission amount of the power system to be managed under the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method and a preset dynamic power flow model.
After the actual power transmission amount of the system in the current state is obtained through calculation, a risk scene corresponding to the current state of the system needs to be determined, and a model is built after the risk scene is determined to solve the maximum power transmission amount in the risk scene. In the embodiment of the invention, the risk scene is determined through a preset clustering algorithm, and a model for solving the maximum power transmission quantity is built by adopting a preset dynamic power flow model as a model frame. In one embodiment, the preset clustering algorithm comprises a dense clustering algorithm such as a DBSCAN algorithm.
When a risk scene is determined, firstly, according to the meteorological data set and the power flow forecast information of the electric power system to be managed, the risk indexes of the system are evaluated and calculated through a preset equipment outage probability model and a preset power grid topological model, and after the corresponding risk indexes are obtained, the risk indexes are clustered according to a preset historical risk index standard matrix and a preset clustering algorithm of the electric power system to be managed so as to determine the current corresponding risk scene of the system.
Specifically, when calculating the Risk index, according to a basic idea of Risk calculation, namely "Risk (Risk) = fault rate (Probability) × fault occurrence consequence (Consequences)", the Risk assessment takes meteorological information, a device outage Probability model including new energy devices, a power grid topology model and a power flow forecast as input, predicts a fault set of power grid operation and calculates the severity of each state, and then weights the fault severity and the Probability of the expected fault set, so that the Risk index of the power grid can be obtained. The failure rate is a time-varying coefficient, the self-adaptive impact weighting change is carried out according to equipment indexes under the influence of different environments, temperatures, humidity and other factors, and the real-time Risk (Risk) calculation result is changed in real time according to the factors such as equipment temperature, line icing and extreme weather in local areas. The fault rate is divided according to a fault classification system specified by a current power grid company, and classification is performed by taking real-time operation experience as reference, for example, when the oil temperature of the transformer exceeds 80 ℃, the fault probability reaches 80% according to the operation experience, and when the ice coating diameter of the line is 0.4% compared with the original diameter ratio of the line, the probability of line fault caused by breaking of the ice coating of the line is 100%. Thus, the failure probability of each system sound function device is confirmed.
As can be understood from the above, in an embodiment, obtaining the risk indicator of the electric power system to be managed according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set, and power flow forecast information specifically includes: predicting a fault set of the power system to be managed and calculating the severity of each state in the fault set according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information; obtaining the fault rate of each state in the fault set according to the meteorological data, the tidal current forecast information and a preset fault rate determination method; and calculating the risk index of the power system to be managed according to the severity, the fault rate and a preset risk calculation formula.
The embodiment of the invention extracts the state parameters of the real-time operation equipment, calculates the fault rate of the equipment in operation in a real-time rolling manner, ensures that the operation condition of the equipment with higher fault rate can be mastered in a key way in the dispatching operation, and makes corresponding technical measures. The real-time statistical characteristics of the fault consequences include: the method comprises the steps of calculating the maximum load multiple and the duration time of the equipment under the current working condition according to the time-varying relation of the equipment capacity, the real-time load, the equipment state and external environmental factors contained in an equipment model, and referring to and seeking the influence of historical faults, maintenance records and defect states on reducing the conventional load capacity. The fault consequences refer to the number of influencing users, mode adjustment strategies, loss load numbers and the like of various types of equipment after faults compiled by a power grid company and the actual influencing conditions of the equipment after the faults in a historical database.
When clustering is carried out, each column forms the implementation state of the input random variable of the power system based on the obtained S multiplied by N matrix, and because the same area is affected by the same climatic condition or load similarly or the factory characteristic parameters of the products in the same batch are similar, the generated real-time risks have correlation. Massive single influence factor risk probability index clustering can be realized through multi-scene target clustering. The DBSCAN algorithm is the most representative one in density clustering. The density clustering algorithm considers that the clusters can be regarded as a high-density area which is divided by a low-density area, so that the influence of noise and isolated point data can be ignored, and the clusters with any shapes can be found. The DBSCAN algorithm is a density clustering algorithm for dividing clusters by using high-density connection areas. In this algorithm, clusters are characterized by a maximum set of densely connected points and high-density regions. Unlike the partitioning and hierarchical clustering method, which defines clusters as the largest set of densely connected points, it is possible to partition areas with a sufficiently high density into clusters and find clusters of arbitrary shape in a noisy spatial database. The previously acquired S N matrix is first normalized to unify the various factor risk objectives.
The algorithm comprises the following steps: setting a clustering radius Eps and a density threshold MinPts; (2) Inputting a data set D, wherein the aim is to output clustering results and noise data; (3) Randomly extracting an unprocessed object p from the data set D and designating the object p as a kernel object when its Eps-neighbors meet a density threshold requirement; (4) Traversing the whole data set, finding all the reachable objects with the density p of the slave objects, and forming a new cluster; (5) producing a final cluster result by density linking; (6) The fourth and fifth steps are repeated until all objects in the data set are in a "processed" state.
After the cluster obtains the current corresponding risk scene of the system, the cluster can be built and solved according to the risk scene and a preset dynamic power flow model to obtain the maximum transmission capacity (ATC). ATC is the maximum power that can be delivered between a given area or node pair under conditions that ensure safe and reliable operation of the system. The ATC size is mainly determined by the current operating state, the constraint condition and the uncertainty factor. In the probabilistic analysis method, the operation state is determined by a sampling scenario. The sampling scene comprises: the method comprises the following steps of obtaining a large number of sample scenes by a system operation mode, an economic dispatching strategy, flexible alternating current transmission, automatic power generation control, static reactive compensation and other modes, accurately calculating the ATC of each sample scene, and finally counting probability distribution.
Based on a single scene ATC calculation model of the optimal power flow (only considering static safety constraints such as generator capacity, node voltage, transmission line thermal limit and the like), the algorithm comprises the following steps:
first, for risk scenario C m Establishing a dynamic power flow model, and solving the model to obtain an optimal operating point and an ATC (automatic traffic control) with maximum transmission capability m Linearizing ATC calculation model at the optimal point, solving objective function pair input random variable x p Dynamic optimal power flow sensitivity of
In one embodiment, the dynamic power flow model is:
wherein f (t) is expressed as all independent variables of the clustered system, including all power transmission line power and other state quantities of the system and control quantities of a generator set and the like, alpha is expressed as a power increase step length, namely a load increase parameterized scalar quantity which is artificially established, b Pi Expressed as the transmission power at that moment.
The constraints are as follows:
h(t)+αD(t)＝0；
wherein D (t) = [ b = P b Q ] T As the direction of vector growth, S L For the aggregation of load nodes on the transmission and receiving sides, h (t) is a power flow equation constraint condition, g (t) is a static safety constraint condition, and the upper limit and the lower limit of the constraint condition are respectivelygAnd
based on the objective function and the constraint conditions, the maximum transmission capacity of system operation, the operation power of each node and the generating plan of the unit under the scene can be obtained.
To sum up, in an embodiment, the obtaining the maximum power transmission amount of the to-be-managed power system in the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, the preset scene clustering method, and the preset dynamic power flow model specifically includes: acquiring a risk index of the power system to be managed according to a preset equipment outage probability model, a preset power grid topological model, the meteorological data set and the power flow forecast information; acquiring a risk scene corresponding to the power system to be managed according to a preset density clustering algorithm, the risk indexes and a preset historical risk index standard matrix; and calculating the maximum power transmission amount under the risk scene according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set.
2. Since the backup estimation is performed in real time, that is, the process is performed multiple times within a period of time (to achieve real-time accuracy), when the maximum power transmission is calculated, in addition to the direct calculation as described above, in order to improve the calculation efficiency, the maximum power transmission may be determined according to a preset mapping calculation formula under the condition of similar scenes by using the principle of similar mapping. Specifically, when scene C m And scene C m Subset x i When the scenes are similar, according to the result of the cluster analysis, this C m And scene C m Subset x i There is also a high degree of similarity. For scene C m Mapping the established dynamic power flow optimization result to any scene subset under the m types, wherein the scene C is known m The ATC value of the calculation result of (2), and rapidly calculating the scene C by the following formula m Subset x i Any maximum transmission capacity.
When the calculation is directly performed, in an embodiment, the calculating the maximum power transmission amount in the risk scenario according to the system operation data set, a preset dynamic power flow model, and a preset constraint condition set specifically includes: constructing a first dynamic power flow objective function according to the risk scene and a preset dynamic power flow model; and solving the first dynamic power flow objective function according to a preset constraint condition group and the system operation data group to obtain the maximum power transmission amount corresponding to the risk scene.
When similar scenes exist and similar mapping can be performed, in an embodiment, the calculating the maximum power transmission amount in the risk scene according to the system operation data group, a preset dynamic power flow model and a preset constraint condition group specifically includes: judging whether a first risk scene similar to the risk scene exists in a preset historical dynamic power flow optimization database or not; if the current power flow optimization result exists, mapping the dynamic power flow optimization result corresponding to the first risk scene to the risk scene, and obtaining the maximum power transmission amount in the risk scene according to the dynamic power flow optimization result and a preset mapping calculation formula.
In one embodiment, the mapping calculation formula is:
wherein x is p 、x m Respectively identical random variables in sub-scene x i And C m The value of the sample is taken,
is an input random variable x p Dynamic optimal power flow sensitivity. C obtained according to the dynamic power flow model m The optimal system operation maximum transmission capacity, each node operation power and the unit power generation plan under the scene reversely work out the ATCM to the input variable x p Is/are as follows
X is to be p ，x m And
the scene C can be obtained by substituting the mapping calculation formula m Subset x i Any maximum transmission capacity.
And S3, estimating the system standby condition of the electric power system to be managed according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount, and managing the electric power system to be managed according to the system standby condition.
Clustering through time-varying riskObtaining a scene C by calculating the key characteristic scene m The maximum transmission capacity ATCM is obtained through retrieval and scene C m Subset x i The high similarity scene of the scene can obtain various subsets x concerned in operation i And (4) ATC power transmission capacity in a scene. Based on the obtained various subsets x i The system standby condition under a certain fault condition can be obtained through the ATC power transmission capacity and the current actual power transmission state quantity under the scene, and the system standby condition under the fault condition is listed in sequence according to the fault probability risk.
In one embodiment, the back-up estimation formula is:
Δx pi ＝ATC i -Real pi ；
wherein, Δ x pi As a scene x i Maximum reserve of lower, real pi Indicated as the current actual transmission power.
In addition to the above, the embodiment of the present invention may further extend a single-scene ATC calculation model based on the optimal power flow, and include an economic dispatching strategy, flexible ac transmission, automatic power generation control, static reactive power compensation, and other various manners besides the conventional power plant and the power transmission line power flow constraint, and the actual standby condition under the failure condition.
The real-time standby clearing result of the system and the early warning of the system when the serious fault of the system is not considered when the current spot market clears in real time, and the embodiment of the invention can also be used for supplementing the standby calculation and the early warning of risks of the spot market under the conditions.
The embodiment of the invention describes a power system standby management method, which comprises the steps of obtaining various data of a power system to be managed in real time to determine actual power transmission amount and a corresponding risk scene, carrying out model building and solving on the risk scene corresponding to the power system to be managed according to a preset dynamic power flow model to obtain the maximum power transmission amount under the risk scene, and estimating a standby condition according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount for subsequent management.
Detailed description of the preferred embodiment
Besides, the embodiment of the invention also discloses a power system standby management device. Fig. 2 is a schematic structural diagram of a standby management device of a power system according to an embodiment of the present invention.
As shown in fig. 2, the backup management apparatus includes an analysis acquisition unit 11, a model calculation unit 12, and a backup estimation unit 13.
The analysis and acquisition unit 11 is configured to acquire a system operation data set, a meteorological data set, and power flow forecast information of the electric power system to be managed in real time, and acquire an actual power transmission amount of the electric power system to be managed according to the system operation data set.
The model calculation unit 12 is configured to obtain the maximum power transmission amount in the risk scene corresponding to the electric power system to be managed according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method, and a preset dynamic power flow model.
In one embodiment, the model calculation unit 12 is further configured to: acquiring a risk index of the power system to be managed according to a preset equipment outage probability model, a preset power grid topological model, the meteorological data set and the power flow forecast information; acquiring a risk scene corresponding to the power system to be managed according to a preset density clustering algorithm, the risk indexes and a preset historical risk index standard matrix; and calculating the maximum power transmission amount under the risk scene according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set.
In one embodiment, the model calculation unit 12 is further configured to: predicting a fault set of the power system to be managed and calculating the severity of each state in the fault set according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information; obtaining the fault rate of each state in the fault set according to the meteorological data, the tidal current forecast information and a preset fault rate determination method; and calculating the risk index of the power system to be managed according to the severity, the fault rate and a preset risk calculation formula.
In one embodiment, the model calculation unit 12 is further configured to: constructing a first dynamic power flow objective function according to the risk scene and a preset dynamic power flow model; and solving the first dynamic power flow objective function according to a preset constraint condition group and the system operation data group to obtain the maximum power transmission amount corresponding to the risk scene.
In one embodiment, the model calculation unit 12 is further configured to: judging whether a first risk scene similar to the risk scene exists in a preset historical dynamic power flow optimization database or not; if the current power flow optimization result exists, mapping the dynamic power flow optimization result corresponding to the first risk scene to the risk scene, and obtaining the maximum power transmission amount in the risk scene according to the dynamic power flow optimization result and a preset mapping calculation formula.
The backup estimation unit 13 is configured to estimate a system backup condition of the electric power system to be managed according to a preset backup estimation formula, the maximum transmission amount, and the actual transmission amount, and manage the electric power system to be managed according to the system backup condition.
The unit integrated by the standby management device can be stored in a computer readable storage medium if the unit is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow in the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium and used by a processor to implement the steps of the above embodiments of the method. That is, another embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, a device where the computer-readable storage medium is located is controlled to execute the power system backup management method as described above.
The computer program comprises computer program code which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relationship between the units indicates that the units have communication connection therebetween, and the connection relationship can be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
The embodiment of the invention discloses a power system standby management device and a computer readable storage medium, which are used for determining actual power transmission amount and a corresponding risk scene by acquiring various data of a power system to be managed in real time, performing model building and solving on the risk scene corresponding to the power system to be managed according to a preset dynamic power flow model to acquire the maximum power transmission amount under the risk scene, and estimating the standby condition according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount for subsequent management.
Detailed description of the preferred embodiment
In addition to the above method and apparatus, an embodiment of the present invention further describes a power system standby management system.
The backup management system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the power system backup management method as previously described when executing the computer program.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center for the device and that connects the various parts of the overall device using various interfaces and lines.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the apparatus by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
The embodiment of the invention describes a power system standby management system, which determines actual power transmission amount and a corresponding risk scene by acquiring various data of a power system to be managed in real time, performs model building and solving on the risk scene corresponding to the power system to be managed according to a preset dynamic power flow model to acquire the maximum power transmission amount under the risk scene, and estimates a standby condition according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount for subsequent management.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. A power system backup management method, the backup management method comprising:
the method comprises the steps of acquiring a system operation data set, a meteorological data set and flow forecast information of an electric power system to be managed in real time, and acquiring actual power transmission quantity of the electric power system to be managed according to the system operation data set;
acquiring the maximum power transmission amount of the electric power system to be managed under the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method and a preset dynamic power flow model;
and estimating the system standby condition of the electric power system to be managed according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount, and managing the electric power system to be managed according to the system standby condition.
2. The power system backup management method according to claim 1, wherein the obtaining of the maximum power transmission amount in the risk scenario corresponding to the power system to be managed according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scenario clustering method, and a preset dynamic power flow model specifically comprises:
acquiring a risk index of the power system to be managed according to a preset equipment outage probability model, a preset power grid topological model, the meteorological data set and the power flow forecast information;
acquiring a risk scene corresponding to the power system to be managed according to a preset density clustering algorithm, the risk indexes and a preset historical risk index standard matrix;
and calculating the maximum power transmission amount under the risk scene according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set.
3. The power system backup management method according to claim 2, wherein obtaining the risk indicator of the power system to be managed according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information specifically comprises:
predicting a fault set of the power system to be managed and calculating the severity of each state in the fault set according to a preset equipment outage probability model, a preset power grid topology model, the meteorological data set and the power flow forecast information;
obtaining the fault rate of each state in the fault set according to the meteorological data, the tidal current forecast information and a preset fault rate determination method;
and calculating the risk index of the power system to be managed according to the severity, the fault rate and a preset risk calculation formula.
4. The power system backup management method according to claim 3, wherein calculating the maximum power transmission amount in the risk scenario according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set specifically comprises:
constructing a first dynamic power flow objective function according to the risk scene and a preset dynamic power flow model;
and solving the first dynamic power flow objective function according to a preset constraint condition group and the system operation data group to obtain the maximum power transmission amount corresponding to the risk scene.
5. The power system backup management method according to claim 3, wherein calculating the maximum power transmission amount in the risk scenario according to the system operation data set, a preset dynamic power flow model and a preset constraint condition set specifically comprises:
judging whether a first risk scene similar to the risk scene exists in a preset historical dynamic power flow optimization database or not;
if the current power flow optimization result exists, mapping the dynamic power flow optimization result corresponding to the first risk scene to the risk scene, and obtaining the maximum power transmission amount in the risk scene according to the dynamic power flow optimization result and a preset mapping calculation formula.
6. The power system backup management method according to claim 5, wherein the dynamic power flow model is:
wherein f (t) is expressed as all independent variables of the clustered system, including all power transmission line power and other state quantities of the system and control quantities of a generator set and the like, alpha is expressed as a power increase step length, namely a load increase parameterized scalar quantity which is artificially established, b Pi Expressed as the transmission power at that moment.
7. The power system backup management method according to claim 6, wherein the mapping calculation formula is:
wherein x is p 、x m Respectively identical random variables in sub-scene x i And C m The value of the sample is taken,
is an input random variable x p Dynamic optimal power flow sensitivity.
8. A power system backup management apparatus, characterized in that the backup management apparatus includes an analysis acquisition unit, a model calculation unit, and a backup estimation unit, wherein,
the analysis acquisition unit is used for acquiring a system operation data set, a meteorological data set and power flow forecast information of the electric power system to be managed in real time and acquiring the actual power transmission quantity of the electric power system to be managed according to the system operation data set;
the model calculation unit is used for acquiring the maximum power transmission amount of the power system to be managed under the corresponding risk scene according to the system operation data set, the meteorological data set, the power flow forecast information, a preset scene clustering method and a preset dynamic power flow model;
the standby estimation unit is used for estimating the system standby condition of the electric power system to be managed according to a preset standby estimation formula, the maximum power transmission amount and the actual power transmission amount, and managing the electric power system to be managed according to the system standby condition.
9. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the power system backup management method according to any one of claims 1 to 7.
10. A power system backup management system, characterized in that the backup management system comprises a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the power system backup management method according to any of claims 1 to 7 when executing the computer program.
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