A power grid early warning and dispatching method and system based on extreme disaster weather

Abstract

The present invention discloses a power grid early warning and dispatching method and system based on extreme disaster weather, which relates to the field of power grid dispatching and aims to solve the problem that the current power grid dispatching method cannot fully consider the impact factors of end disaster weather, resulting in one-sided dispatching. The method includes: determining the fault condition of the power grid equipment under extreme disaster weather according to the weather information of the distribution area and the status information of the power grid equipment; judging whether to issue a power grid early warning according to the fault condition of the power grid equipment and the use time of the power grid equipment, and predicting the load reduction; obtaining the location distribution information and energy information of the power grid energy storage equipment, and determining the executed power grid energy storage equipment scheme according to the first execution strategy; constructing an energy storage execution model according to the historically executed power grid energy storage equipment scheme, and judging whether the energy storage execution model is updated. The present invention performs targeted adjustment and dispatching of faulty power grid equipment to ensure the objectivity and comprehensiveness of the dispatching results.

Description

Technical Field

[0001] The present invention relates to the field of power grid dispatching, and in particular to a power grid early warning and dispatching method and system based on extreme disaster weather. Background Art

[0002] On the premise of ensuring the safe and stable operation of the power grid, considering the long-term stable operation background of the electricity spot market with the priority consumption of new energy, targeting the risks and challenges of safe and stable operation of new power systems under extreme disaster weather conditions, and considering the impact of extreme weather on the distribution network, it will help to allocate various resources in advance to reduce the impact of disasters.

[0003] At present, for some power grid dispatching methods under extreme disaster weather, all factors affecting extreme disaster weather are not considered, resulting in one-sided dispatching results; for example, the Chinese patent with publication number CN115640967A discloses a method for elastically allocating power grid resources based on extreme precipitation disaster prediction. The present invention includes the following steps: S1: dividing several control areas according to the geographical location of each level of equipment in the comprehensive power grid topology and the precipitation prediction according to the weather forecast; S2: for each control area, the precipitation disaster loss is predicted according to the precipitation prediction and historical disaster loss calculation, and the adjustable amount in each control area is determined in combination with the demand of each area; S3: according to the adjustable amount in each control area, the resource transfer point and the elastic allocation point are set in combination with the resource transfer path; S4: formulate an allocation plan between resource transfer points, and dispatch the elastic allocation point resources according to whether the allocation plan meets the actual precipitation disaster loss demand. However, it only considers the extreme precipitation situation to allocate power grid resources. Summary of the invention

[0004] The present invention solves the problem that the current power grid dispatching method cannot fully consider the impact factors of terminal disaster weather, resulting in one-sided dispatching. It proposes a power grid early warning and dispatching method and system based on extreme disaster weather, which comprehensively considers the weather information of the distribution area and uses the distribution of power grid energy storage equipment to carry out targeted adjustment and dispatch of faulty power grid equipment, thereby ensuring the objectivity and comprehensiveness of the dispatching results.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical scheme: a power grid early warning and dispatching method based on extreme disaster weather, comprising the following steps:

[0006] S1, determining the fault conditions of power grid equipment under extreme disaster weather conditions based on weather information in the power distribution area and status information of power grid equipment;

[0007] S2, judging whether to issue a power grid warning based on the fault condition of the power grid equipment and the usage time of the power grid equipment, and predicting the load reduction caused by the power grid equipment failure in the future period;

[0008] S3, obtaining the location distribution information and energy information of the power grid energy storage device, and determining the power grid energy storage device solution to be executed according to the first execution strategy;

[0009] S4, building an energy storage execution model based on the historically executed power grid energy storage equipment solutions, and determining whether the energy storage execution model is updated.

[0010] In this technical solution, weather information of the power distribution area and status information of the power grid equipment are first obtained, and the faulty power grid equipment and its fault condition are determined based on the two; then, whether to issue a power grid warning is determined based on the fault condition and the equipment usage time, and the load reduction caused by the fault is predicted; then, the final power grid energy storage equipment solution is determined according to the first execution strategy, and finally the power grid energy storage equipment solution is stored according to the created energy storage execution model.

[0011] The present invention is further configured as follows: Step S1 comprises:

[0012] S11, obtaining weather information of the power distribution area from the meteorological platform and obtaining status information of the power grid equipment from the power grid control center;

[0013] S12, establishing a three-dimensional map of the power grid equipment based on the location information of the power grid equipment, combined with the weather information and the status information;

[0014] S13, searching for corresponding power grid equipment status information according to the extreme disaster weather setting conditions, performing fault threshold judgment on the corresponding power grid equipment status information, and determining the fault condition of the power grid equipment.

[0015] In the present technical solution, weather information of the distribution area and status information of relevant power grid equipment in the distribution area are first obtained, and then a three-dimensional map of the power grid equipment is constructed. According to the three-dimensional map of the power grid equipment, the status information of the power grid equipment under the setting conditions of extreme disaster weather can be more conveniently queried, and then compared with the pre-set fault threshold, the final fault condition of the power grid equipment is determined.

[0016] The present invention is further configured as follows: Step S2 comprises:

[0017] S21, determining the usage time of the power grid equipment according to the time when the power grid equipment was first put into use, assigning corresponding weights to the fault condition of the power grid equipment and the usage time of the power grid equipment, and calculating the power grid early warning coefficient;

[0018] S22, judging whether the power grid warning coefficient is within the power grid warning safety range, if so, continuously monitoring; if not, issuing a power grid warning;

[0019] S23, predicting the load reduction caused by power grid equipment failure by constructing a load change model.

[0020] In the present technical solution, each power grid equipment will have corresponding records when it is put into use. The record information is used to determine the usage time of the power grid equipment. Afterwards, the weights of the failure conditions of the power grid equipment and the usage time of the power grid equipment are determined based on the failure conditions of the power grid equipment and the historical power grid warning conditions of the usage time of the power grid equipment, and combined with the accuracy of the historical power grid warnings. The failure degree and the usage time ratio are multiplied by the corresponding weights to obtain the power grid warning coefficient, and the power grid warning coefficient is used to determine whether to issue a power grid warning; finally, the load reduction amount is determined based on the established load change model.

[0021] The present invention is further configured as follows: Step S3 comprises:

[0022] S31, obtaining location distribution information and energy information of the grid energy storage device through the grid control center;

[0023] S32, filling the location distribution information and energy information of the power grid energy storage device into the three-dimensional map of the power grid equipment to form a three-dimensional map of the power grid equipment-energy storage;

[0024] S33, determining the number of energy storage devices and recommended lines based on the three-dimensional diagram of power grid equipment and energy storage combined with the fault conditions of power grid equipment, and determining the power grid energy storage equipment solution based on the lowest cost as the primary factor and the lowest loss as the secondary factor.

[0025] In the present technical solution, the location distribution information and energy information of the power grid energy storage device are first obtained, specifically through the power grid control center; after the acquisition is completed, the location distribution information and energy information of the power grid energy storage device obtained above are filled on the basis of the three-dimensional map of the power grid equipment, and a three-dimensional map of the power grid equipment-energy storage is obtained. Through the three-dimensional map of the power grid equipment-energy storage, the recommended energy storage device can be quickly determined according to the fault situation of the power grid equipment, and finally the final power grid energy storage device solution is obtained according to the first execution strategy.

[0026] The present invention is further configured as follows: Step S4 comprises:

[0027] S41, storing all the grid energy storage equipment solutions generated in step S3, and training and generating an energy storage execution model in combination with the fault conditions of the grid equipment;

[0028] S42, input the current fault condition of the power grid equipment into the energy storage execution model, output the power grid energy storage equipment plan of the energy storage execution model, verify the power grid energy storage equipment plan generated in step S3 with the power grid energy storage equipment plan of the energy storage execution model, and determine whether to update the energy storage execution model.

[0029] In the technical solution, a corresponding energy storage execution model is generated through training, through which a power grid energy storage device solution related to the model can be generated, and finally a verification step is used to determine whether to update the energy storage execution model.

[0030] The present invention is further configured as follows: Step S13 includes:

[0031] S131, determine the extreme disaster weather setting conditions;

[0032] S132, traversing to find out the status information of the power grid equipment under the extreme disaster weather equipment condition;

[0033] S133, judging the fault threshold of the state information of the power grid equipment under the extreme disaster weather equipment condition respectively with the state information of the corresponding power grid equipment; if the state information of the power grid equipment exceeds the fault threshold, the power grid equipment is a faulty power grid equipment.

[0034] In this technical solution, first, the extreme disaster weather setting conditions are clarified, and the conditions are determined as needed according to the local meteorological platform and the distribution of power grid equipment. The status information of the power grid equipment under the extreme disaster weather equipment conditions is found out by corresponding means. Based on the above-obtained status information of the power grid equipment under the extreme disaster weather equipment conditions, the fault threshold of the status information of the corresponding power grid equipment is judged respectively. If the status information of any one or more power grid equipment exceeds its corresponding fault threshold, then this power grid equipment is a faulty power grid equipment, and the fault condition of the power grid equipment needs to be recorded.

[0035] The present invention is further configured as follows: the training process of the load variation model is:

[0036] The historical load data is obtained and the historical load changes are calculated. The historical load data and the historical load changes are preprocessed to generate a corresponding data set. The data set trains and tests the load change model in turn to finally obtain the load change model.

[0037] In this technical solution, the model obtained through the above training can predict the load reduction caused by power grid equipment failure.

[0038] The present invention is further configured as follows: the first execution strategy is specifically:

[0039] Under the consideration of the lowest cost, multiple intermediate power grid energy storage equipment solutions regarding the number of energy storage devices and recommended routes are obtained, and the loss values ​​of these intermediate power grid energy storage equipment solutions are calculated respectively, and the power grid energy storage equipment solution with the lowest loss is taken as the power grid energy storage equipment solution.

[0040] In the technical solution, under the condition of the lowest cost, there are also multiple intermediate power grid energy storage equipment solutions, and the costs of these intermediate power grid energy storage equipment solutions are all at the lowest level, that is, the solutions with the lowest cost or close to the lowest cost; then the loss values ​​of these intermediate power grid energy storage equipment solutions are respectively calculated, and the power grid energy storage equipment solution with the lowest loss is taken as the final power grid energy storage equipment solution. At the same time, several power grid energy storage equipment solutions close to the lowest loss are taken as alternative solutions. When temporary problems occur in the final power grid energy storage equipment solution, the alternative solutions can be selected for execution.

[0041] The present invention is further configured as follows: S42 specifically includes:

[0042] The various values ​​in the grid energy storage equipment solution generated in step S3 are compared with the various values ​​of the grid energy storage equipment solution output by the energy storage execution model. Whether to update the energy storage execution model is determined based on whether the comparison results are consistent. If the comparison results are consistent, the energy storage execution model does not need to be updated. If the comparison results are inconsistent, the energy storage execution model is updated.

[0043] In this technical solution, whether to update the energy storage execution model is determined based on the comparison results. If all numerical comparisons are consistent, the model is not updated. If any one or more numerical comparisons are inconsistent, the model needs to be updated.

[0044] A power grid early warning and dispatching system based on extreme disaster weather, using the above-mentioned power grid early warning and dispatching method based on extreme disaster weather, comprising:

[0045] A fault condition determination module determines the fault condition of the power grid equipment under extreme disaster weather conditions based on the status information of the power grid equipment;

[0046] Prediction module, predicting the load reduction caused by power grid equipment failure;

[0047] A strategy execution module, which determines a power grid energy storage device solution according to a first execution strategy;

[0048] The model verification module determines whether the energy storage execution model is updated.

[0049] In this technical solution, the fault situation determination module is connected to the prediction module, the prediction module is connected to the strategy execution module, and the strategy execution module is connected to the model verification module. The fault situation determination module, the prediction module, the strategy execution module and the model verification module are operated in sequence to ensure timely early warning and scheduling of the power grid and to ensure the execution of the optimal power grid energy storage equipment solution.

[0050] The present invention can bring the following beneficial effects:

[0051] 1. The present invention relates to a power grid early warning and dispatching method based on extreme disaster weather, which comprehensively considers the weather information of the distribution area and utilizes the distribution of power grid energy storage equipment to carry out targeted adjustment and dispatch of faulty power grid equipment, thereby ensuring the objectivity and comprehensiveness of the dispatching results;

[0052] 2. The present invention relates to a power grid early warning and dispatching system based on extreme disaster weather. The fault condition determination module, prediction module, strategy execution module and model verification module operate in sequence to ensure timely early warning and dispatching of the power grid and the execution of the optimal power grid energy storage equipment solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flow chart of a power grid early warning and dispatching method based on extreme disaster weather of the present invention.

[0054] Figure 2 It is a flow chart of step S2 of a power grid early warning and dispatching method based on extreme disaster weather of the present invention. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] This embodiment proposes a power grid early warning and dispatching method based on extreme disaster weather. Figure 1 , which includes the following steps.

[0058] Step S1, firstly obtain weather information based on the power distribution area and status information based on the power grid equipment, and determine the fault status of the power grid equipment under extreme disaster weather conditions based on the above weather information and status information.

[0059] Step S1 mainly includes the following sub-steps.

[0060] Step S11, collecting weather information of the power distribution area through the meteorological platform, and collecting status information about the power grid equipment through the power grid control center.

[0061] Step S12, obtaining the location information of the power grid equipment, and combining the above-mentioned weather information and status information to construct a three-dimensional map of the power grid equipment.

[0062] Step S13, setting corresponding extreme disaster weather setting conditions. After setting the extreme disaster weather setting conditions, the status information of the power grid equipment under the conditions is found through the above-mentioned three-dimensional graph of the power grid equipment, and then the fault threshold judgment is performed on the found status information of the power grid equipment. After the judgment, it is determined that there is corresponding faulty equipment and the fault condition of the equipment.

[0063] For step S131, in more detail, it includes the following process.

[0064] Step S131, determining extreme disaster weather setting conditions. The conditions include all kinds of extreme weather conditions, including high temperature, heavy precipitation and low temperature.

[0065] Step S132, finding out the state information of the power grid equipment under the conditions according to the three-dimensional diagram of the power grid equipment.

[0066] Step S133, finally, based on the status information found above, the fault threshold of the status information of the corresponding power grid equipment is judged respectively; if the status information of the power grid equipment exceeds the range specified by the fault threshold, then the above-mentioned power grid equipment is a faulty power grid equipment; if the status information of the power grid does not exceed the range specified by the fault threshold, then the power grid equipment is a power grid equipment in a normal state.

[0067] In this technical solution, first, the extreme disaster weather setting conditions are clarified, and the conditions are determined as needed according to the local meteorological platform and the distribution of power grid equipment. The status information of the power grid equipment under the extreme disaster weather equipment conditions is found out by corresponding means. Based on the above-obtained status information of the power grid equipment under the extreme disaster weather equipment conditions, the fault threshold of the status information of the corresponding power grid equipment is judged respectively. If the status information of any one or more power grid equipment exceeds its corresponding fault threshold, then this power grid equipment is a faulty power grid equipment, and the fault condition of the power grid equipment needs to be recorded.

[0068] In the present technical solution, weather information of the distribution area and status information of relevant power grid equipment in the distribution area are first obtained, and then a three-dimensional map of the power grid equipment is constructed. According to the three-dimensional map of the power grid equipment, the status information of the power grid equipment under the setting conditions of extreme disaster weather can be more conveniently queried, and then compared with the pre-set fault threshold, the final fault condition of the power grid equipment is determined.

[0069] In order to more accurately judge the fault conditions of power grid equipment, weather information of the distribution area needs to be directly obtained from the meteorological platform; more detailed distribution area location information can also be provided to the meteorological platform, allowing the meteorological platform to specifically provide corresponding weather information.

[0070] As for the status information of the power grid equipment, since the power grid control center can directly or indirectly obtain the status data of each power grid equipment, the data is directly obtained from the power grid control center.

[0071] For some power grid equipment in the distribution area, part of the power grid equipment is directly connected to the power grid control center, and the specific connection method is not limited here; for some power grid equipment in the distribution area, another part of the power grid equipment is indirectly connected to the power grid control center. For example, a certain power grid equipment is connected to another power grid equipment, and the other power grid equipment is connected to the power grid control center. In this case, a certain power grid equipment is indirectly connected to the power grid control center.

[0072] The location information of power grid equipment can be obtained through special navigation equipment, and the location can be confirmed through Beidou navigation satellites; the location information of each power grid equipment is obtained through the navigation equipment and converted into the form of three-dimensional coordinates, that is, each power grid equipment has corresponding three-dimensional coordinates.

[0073] Based on the three-dimensional coordinates corresponding to each power grid device, weather information and status information of the power grid device are added to each three-dimensional coordinate point, and finally a three-dimensional map of the power grid device is formed.

[0074] After the three-dimensional diagram of the power grid equipment is established, the overall preview and traversal of the power grid equipment information can be more convenient and intuitive.

[0075] After establishing the three-dimensional diagram of the power grid equipment, it is necessary to find out the fault conditions of the power grid equipment.

[0076] First, determine the extreme disaster weather setting conditions, which are determined on demand based on the local meteorological platform and the distribution of power grid equipment.

[0077] Traverse to find out the status information of power grid equipment under extreme disaster weather equipment conditions.

[0078] Generally, the power information is based on the status information of the power grid equipment or the information collected by the sensors attached thereto.

[0079] Based on the above-mentioned acquired status information of the power grid equipment under extreme disaster weather equipment conditions, judgment is made with the fault threshold of the status information of the corresponding power grid equipment. If the status information of any type of power grid equipment exceeds its corresponding fault threshold, the power grid equipment is a faulty power grid equipment, and the fault condition of the power grid equipment is recorded.

[0080] The fault condition of the power grid equipment includes not only the state information of the specific fault, but also the above-mentioned location information and time information.

[0081] After completing the judgment of the fault conditions of all power grid equipment, the number and overall conditions of the faulty power grid equipment are counted, and the fault conditions of all power grid equipment are recorded.

[0082] Regarding the fault conditions of the power grid equipment, this embodiment only targets the faulty power grid equipment, but can also include faulty power grid equipment and non-faulty power grid equipment. When recording the fault conditions of non-faulty power grid equipment, blank data is used to represent it.

[0083] Step S2, combining the grid equipment failure status and grid equipment usage time obtained in step S1 to determine whether a grid early warning is needed, and then predicting the load reduction caused by the grid equipment failure based on the grid equipment failure status and grid equipment usage time. The load reduction caused by the grid equipment failure is the load reduction in the future time period.

[0084] For step S2, refer to Figure 2 , which specifically includes the following sub-steps.

[0085] Step S21, after obtaining the power grid equipment failure status and the power grid equipment usage time obtained in step S1, the corresponding weights are respectively assigned to the above two according to historical experience data, and finally the corresponding power grid early warning coefficient is calculated.

[0086] Step S22, after the corresponding power grid warning coefficient is generated, it is judged whether the value of the power grid warning coefficient is within the power grid warning safety range. If the value of the power grid warning coefficient is within the above-mentioned power grid warning safety range, then there is no need for power grid warning, and continuous monitoring is performed; if the value of the power grid warning coefficient is outside the above-mentioned power grid warning safety range, then power grid warning is required.

[0087] After completing step S21 and step S22, the process of step S23 is performed, that is, a corresponding load change model is constructed, and the load reduction caused by the power grid equipment failure is output by inputting corresponding variables.

[0088] The training of the above-mentioned load variation model includes the following steps.

[0089] First, historical load data is obtained, and historical load changes are analyzed and calculated based on the historical load data. Subsequently, the obtained historical load changes and historical load data are preprocessed. The specific method of preprocessing mainly includes the removal of obvious outliers and the filling of corresponding removal positions.

[0090] After preprocessing, a data set can be generated, and the load change model is trained according to the data set. After the training is completed, the model is tested to finally obtain a complete load change model. In this embodiment, the load change model is created based on the LTSM model.

[0091] In this technical solution, the model obtained through the above training can predict the load reduction caused by power grid equipment failure.

[0092] In the present technical solution, each power grid equipment will have corresponding records when it is put into use. The record information is used to determine the usage time of the power grid equipment. Afterwards, the weights of the failure conditions of the power grid equipment and the usage time of the power grid equipment are determined based on the failure conditions of the power grid equipment and the historical power grid warning conditions of the usage time of the power grid equipment, and combined with the accuracy of the historical power grid warnings. The failure degree and the usage time ratio are multiplied by the corresponding weights to obtain the power grid warning coefficient, and the power grid warning coefficient is used to determine whether to issue a power grid warning; finally, the load reduction amount is determined based on the established load change model.

[0093] The fault degree is the value corresponding to the status information of the fault state divided by the value corresponding to the status information of the normal state. Generally, the status information of the normal state corresponds to a status information value interval, and the value corresponding to the status information of the normal state is replaced by the average value of this status information value interval.

[0094] For example, when the status information is voltage information, the fault degree is the value corresponding to the voltage information in the fault state is in the value corresponding to the voltage information in the normal state; since the voltage information in the normal state corresponds to a status information value interval, the value corresponding to the voltage information in the normal state is replaced by the average value of this voltage information value interval.

[0095] The duration ratio is calculated by dividing the usage duration of the grid equipment by the total planned usage duration.

[0096] After obtaining the corresponding fault degree and duration ratio, multiply the fault degree by the weight corresponding to the fault condition of the network equipment, and multiply the duration ratio by the weight corresponding to the usage time of the power grid equipment. Then add the results of the two products to finally obtain the power grid warning coefficient.

[0097] Generally, the power grid warning coefficient is between 0.001 and 0.1.

[0098] After calculating the grid warning coefficient, compare the obtained grid warning coefficient with the grid warning safety range. If the grid warning coefficient is within the grid warning safety range, there is no need to issue a grid warning and continuous monitoring is performed; if the grid warning coefficient is outside the grid warning safety range, a grid warning is issued.

[0099] Data is processed by obtaining historical load data and historical load changes and used as training data and test data for the load change model. Specifically, the historical load data and historical load changes are first preprocessed to obtain a data set, which can be used to train and test the load change model. After the training and testing are completed, a load change model is finally constructed, through which the load reduction caused by power grid equipment failure can be predicted.

[0100] Then, step S3 is performed. Specifically, first, based on the location distribution information and energy information of the power grid energy storage device and the load reduction caused by the above-mentioned power grid device failure, and according to the first execution strategy, the power grid energy storage device solution that needs to be finally executed is determined.

[0101] Step S3 mainly includes the following sub-steps.

[0102] Step S31, obtaining the location distribution information and energy information of the power grid energy storage equipment, which is specifically collected and obtained by the power grid control center.

[0103] Step S32, then, fill the collected and acquired information into the three-dimensional map of the power grid equipment in step S1, and finally obtain a three-dimensional map of power grid equipment-energy storage.

[0104] Step S33, after completing step S31 and step S32, comprehensively determine the number of energy storage devices and recommended lines based on the grid equipment-energy storage three-dimensional diagram obtained by step S32, the fault conditions of the grid equipment, and the load reduction caused by the grid equipment failure, and then consider the cost and loss factors to determine the final grid energy storage equipment plan according to the first execution strategy.

[0105] In the present technical solution, the location distribution information and energy information of the power grid energy storage device are first obtained, specifically through the power grid control center; after the acquisition is completed, the location distribution information and energy information of the power grid energy storage device obtained above are filled on the basis of the three-dimensional map of the power grid equipment, and a three-dimensional map of the power grid equipment-energy storage is obtained. Through the three-dimensional map of the power grid equipment-energy storage, the recommended energy storage device can be quickly determined according to the fault situation of the power grid equipment, and finally the final power grid energy storage device solution is obtained according to the first execution strategy.

[0106] The location distribution information of the power grid energy storage device mainly includes the installation location of the power grid equipment, and the installation location is mainly obtained by combining the location information reported during the first installation and the special navigation device. When the location information reported during the first installation is the same as the location information displayed by the special navigation device, the location information is used as the installation location of the power grid equipment; when the location information reported during the first installation is different from the location displayed by the special navigation device, first determine whether the installation location of the power grid energy storage device has been changed. If it has been changed, the location information displayed by the special navigation device is used as the installation location. If it has not been changed, the location information reported during the first installation is used as the installation location.

[0107] After the corresponding installation location is determined, the installation location is converted into a three-dimensional coordinate form, and finally the location distribution information of the power grid energy storage device is obtained.

[0108] The energy information of the power grid energy storage device mainly includes the capacity information, type information and specification information of the power grid energy storage device. The capacity information is the maximum electric energy capacity of the power grid energy storage device; the type information is the energy storage principle type of the power grid energy storage device; the specification information is the size of the power grid energy storage device.

[0109] When the grid energy storage equipment is installed, it will report its own energy information to the grid control center and accept dispatch from the grid control center.

[0110] After obtaining the location distribution information and energy information of the power grid energy storage equipment, in order to facilitate data viewing and analysis, the location distribution information and energy information are filled into the original three-dimensional map of the power grid equipment. The corresponding location distribution information and energy information will also be displayed in the map, that is, the final power grid equipment-energy storage three-dimensional map can be obtained.

[0111] In step S33, the energy storage device and its number are comprehensively determined based on the three-dimensional diagram of power grid equipment and energy storage, the fault conditions of power grid equipment, and the load reduction caused by the fault of power grid equipment, and then the recommended route is determined based on the three-dimensional diagram of power grid equipment and energy storage and the fault conditions of power grid equipment.

[0112] For the first execution strategy, the execution process is specifically as follows.

[0113] Firstly, considering the principle of minimum cost, several intermediate grid energy storage equipment solutions are obtained, and the costs of these intermediate grid energy storage equipment solutions are not the lowest or close to the lowest.

[0114] Subsequently, the grid equipment operation loss values ​​of these intermediate grid energy storage equipment solutions are calculated, and several grid energy storage equipment solutions with the lowest losses are obtained as the grid energy storage equipment solutions. At the same time, several grid energy storage equipment solutions with losses close to the lowest losses are obtained as alternative solutions.

[0115] In the technical solution, under the condition of the lowest cost, there are also multiple intermediate power grid energy storage equipment solutions, and the costs of these intermediate power grid energy storage equipment solutions are all at the lowest level, that is, the solutions with the lowest cost or close to the lowest cost; then the loss values ​​of these intermediate power grid energy storage equipment solutions are respectively calculated, and the power grid energy storage equipment solution with the lowest loss is taken as the final power grid energy storage equipment solution. At the same time, several power grid energy storage equipment solutions close to the lowest loss are taken as alternative solutions. When temporary problems occur in the final power grid energy storage equipment solution, the alternative solutions can be selected for execution.

[0116] After completing the process of step S3, proceed to step S4, i.e., establish a corresponding energy storage execution model. The model mainly stores the power grid energy storage equipment solution obtained in the above step S3. The model can timely retrieve the required power grid energy storage equipment solution and determine whether the energy storage execution model needs to be updated.

[0117] Step S4 mainly includes the following sub-steps.

[0118] In step S41, the generated grid energy storage device solution is stored and an energy storage execution model is constructed. When the grid energy storage device solution is specifically retrieved, it can be retrieved according to the fault condition of the grid equipment.

[0119] Step S42, after obtaining the current fault condition of the power grid equipment, input it into the energy storage execution model, and obtain the power grid energy storage equipment solution related to the model through the model output.

[0120] Then, the power grid energy storage device solution obtained in step S3 is compared with the power grid energy storage device solution related to the model, and it is determined whether to update the above-mentioned model according to the comparison result.

[0121] In this technical solution, a corresponding energy storage execution model is generated through training, through which a power grid energy storage device solution related to the model can be generated, and finally a verification step is used to determine whether to update the energy storage execution model.

[0122] First, according to step S3, corresponding electric energy storage equipment solutions are obtained, and the obtained electric energy storage equipment solutions are stored. According to the electric energy storage equipment solutions and the fault conditions of the power grid equipment, continuous training is performed to obtain an energy storage execution model.

[0123] By verifying the power grid energy storage device solution obtained in step S3, it is determined whether the model should be updated to ensure the timeliness of the energy storage execution model.

[0124] For step S42, in more detail, the various values ​​in the power grid energy storage device solution obtained in step S3 are compared with the various values ​​of the power grid energy storage device solution related to the model, and after the comparison, it is determined whether to update the model. If the comparison results are consistent, the model is not updated; if at least one of the comparison results is inconsistent, the model is updated.

[0125] In this technical solution, whether to update the energy storage execution model is determined based on the comparison results. If all numerical comparisons are consistent, the model is not updated. If any one or more numerical comparisons are inconsistent, the model needs to be updated.

[0126] In this embodiment, weather information of the power distribution area and status information about the power grid equipment are first obtained, and the faulty power grid equipment and its fault condition are determined based on the two; then, whether to issue a power grid early warning is determined based on the fault condition and the equipment usage time, and the load reduction caused by the fault is predicted; then, the final power grid energy storage device solution is determined according to the first execution strategy, and finally the power grid energy storage device solution is stored according to the created energy storage execution model.

[0127] Example 2

[0128] On the basis of Example 1, a power grid early warning and dispatching system based on extreme disaster weather is proposed, which mainly includes a fault situation determination module, a prediction module, a strategy execution module and a model verification module.

[0129] The fault condition determination module has the function of determining the fault condition of the power grid equipment under extreme disaster weather, which is specifically obtained based on the analysis of the status information of the power grid equipment; for details, please refer to step S1 of Example 1.

[0130] The prediction module can construct a load variation model and then predict the load reduction; for details, please refer to step S2 of Example 1.

[0131] The function performed by the strategy execution module is to generate a final grid energy storage device solution according to the first execution strategy. For details, please refer to step S3 of embodiment 1.

[0132] The function performed by the model verification module is to determine whether to update the energy storage execution model. For details, please refer to step S4 of Example 1.

[0133] In this technical solution, the fault situation determination module is connected to the prediction module, the prediction module is connected to the strategy execution module, and the strategy execution module is connected to the model verification module. The fault situation determination module, the prediction module, the strategy execution module and the model verification module are operated in sequence to ensure timely early warning and scheduling of the power grid and to ensure the execution of the optimal power grid energy storage equipment solution.

[0134] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc., which contain computer-usable program codes.

Claims

1. A power grid early warning and dispatching method based on extreme disaster weather, characterized in that: include: S1, determining the fault conditions of power grid equipment under extreme disaster weather conditions based on weather information in the power distribution area and status information of power grid equipment; S2, judging whether to issue a power grid warning based on the fault condition of the power grid equipment and the usage time of the power grid equipment, and predicting the load reduction caused by the power grid equipment failure in the future period; S3, obtaining location distribution information and energy information of the power grid energy storage device, and determining the power grid energy storage device solution to be executed according to the first execution strategy; The first execution strategy is: considering the lowest cost, obtain multiple intermediate grid energy storage equipment solutions regarding the number of energy storage devices and recommended routes, calculate the loss values ​​of the intermediate grid energy storage equipment solutions respectively, and take the grid energy storage equipment solution with the lowest loss as the grid energy storage equipment solution; S4, constructing an energy storage execution model based on the historically executed grid energy storage equipment solutions, and determining whether the energy storage execution model is updated; S41, the grid energy storage equipment solution generated by S3 is stored, and combined with the fault conditions of the grid equipment, an energy storage execution model is trained and generated; S42, the fault conditions of the current grid equipment are input into the energy storage execution model, and the grid energy storage equipment solution of the energy storage execution model is output. The grid energy storage equipment solution generated by S3 is verified with the grid energy storage equipment solution of the energy storage execution model to determine whether to update the energy storage execution model.

2. The power grid early warning and dispatching method based on extreme disaster weather according to claim 1 is characterized in that: The step S1 comprises: S11, obtaining weather information of the power distribution area from the meteorological platform and obtaining status information of the power grid equipment from the power grid control center; S12, establishing a three-dimensional map of the power grid equipment based on the location information of the power grid equipment, combined with the weather information and the status information; S13, searching for corresponding power grid equipment status information according to the extreme disaster weather setting conditions, performing fault threshold judgment on the corresponding power grid equipment status information, and determining the fault condition of the power grid equipment.

3. A power grid early warning and dispatching method based on extreme disaster weather according to claim 1 or 2, characterized in that: The step S2 comprises: S21, determining the usage time of the power grid equipment according to the time when the power grid equipment was first put into use, assigning corresponding weights to the fault condition of the power grid equipment and the usage time of the power grid equipment, and calculating the power grid early warning coefficient; S22, judging whether the power grid warning coefficient is within the power grid warning safety range, if so, continuously monitoring; if not, issuing a power grid warning; S23, predicting the load reduction caused by power grid equipment failure by constructing a load change model.

4. The power grid early warning and dispatching method based on extreme disaster weather according to claim 3 is characterized in that: The step S3 comprises: S31, obtaining location distribution information and energy information of the grid energy storage device through the grid control center; S32, filling the location distribution information and energy information of the power grid energy storage device into the three-dimensional map of the power grid equipment to form a three-dimensional map of the power grid equipment-energy storage; S33, determining the number of energy storage devices and recommended lines based on the three-dimensional diagram of power grid equipment and energy storage combined with the fault conditions of power grid equipment, and determining the power grid energy storage equipment solution based on the lowest cost as the primary factor and the lowest loss as the secondary factor.

5. The power grid early warning and dispatching method based on extreme disaster weather according to claim 2 is characterized in that: The step S13 comprises: S131, determine the extreme disaster weather setting conditions; S132, traversing to find out the status information of the power grid equipment under the extreme disaster weather equipment condition; S133, judging the fault threshold of the state information of the power grid equipment under the extreme disaster weather equipment condition respectively with the state information of the corresponding power grid equipment; if the state information of the power grid equipment exceeds the fault threshold, the power grid equipment is a faulty power grid equipment.

6. The power grid early warning and dispatching method based on extreme disaster weather according to claim 3 is characterized in that: The training process of the load variation model is as follows: The historical load data is obtained and the historical load changes are calculated. The historical load data and the historical load changes are preprocessed to generate a corresponding data set. The data set trains and tests the load change model in turn to finally obtain the load change model.

7. The power grid early warning and dispatching method based on extreme disaster weather according to claim 1 is characterized in that: The S42 specifically includes: The various values ​​in the grid energy storage equipment solution generated in step S3 are compared with the various values ​​of the grid energy storage equipment solution output by the energy storage execution model. Whether to update the energy storage execution model is determined based on whether the comparison results are consistent. If the comparison results are consistent, the energy storage execution model does not need to be updated. If the comparison results are inconsistent, the energy storage execution model is updated.

8. A power grid early warning and dispatching system based on extreme disaster weather, using a power grid early warning and dispatching method based on extreme disaster weather as described in any one of claims 1 to 7, characterized in that: include: A fault condition determination module determines the fault condition of the power grid equipment under extreme disaster weather conditions based on the status information of the power grid equipment; Prediction module, predicting the load reduction caused by power grid equipment failure; A strategy execution module, which determines a power grid energy storage device solution according to a first execution strategy; The model verification module determines whether the energy storage execution model is updated.

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