CN117200271A - Power grid energy management method and system based on hybrid energy storage - Google Patents
Power grid energy management method and system based on hybrid energy storage Download PDFInfo
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Abstract
The invention provides a power grid energy management method and system based on hybrid energy storage, which relate to the technical field of data processing, collect basic information of hybrid energy storage, perform feature analysis on various energy storage types to construct a multi-type energy storage feature library so as to determine an energy cooperative relationship; the method comprises the steps of determining energy supply main line information based on power utilization characteristics of an output end user, constructing an optimizing space, carrying out iterative optimization to determine a power grid energy management strategy, solving the technical problems that in the prior art, aiming at the technical support degree of a multi-energy storage mode, the cooperative energy storage type with highest adaptation degree cannot be accurately screened, meanwhile, the determined management strategy is insufficient in adaptation degree with energy supply requirements, the energy allocation management effect is affected, strategy configuration and multi-level optimization are carried out on the basis by carrying out cooperative relation analysis among combined energy storage types, determining an optimal cooperative energy storage combination and an optimal energy management strategy, adapting energy supply targets to carry out targeted adjustment and optimization management, and guaranteeing the maximum operation benefit of a power grid.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a power grid energy management method and system based on hybrid energy storage.
Background
The power grid energy supply is performed on the basis of clean energy to be one of the current main energy supply modes, but the instability of the output power of the power grid energy supply influences the operation of the power grid, and the power fluctuation is balanced on the basis of the hybrid energy storage mode to cooperatively perform the optimized energy supply management of the power grid. At present, the power grid energy supply is mainly carried out aiming at a single energy storage mode, the technical support degree aiming at a plurality of energy storage modes is insufficient, the cooperative energy storage type with the highest adaptation degree cannot be accurately screened, and meanwhile, the adaptation degree of a determined management strategy and energy supply requirements is insufficient, so that the energy allocation management effect is affected.
Disclosure of Invention
The application provides a power grid energy management method and system based on hybrid energy storage, which are used for solving the technical problems that the technical support for multiple energy storage modes is insufficient, the cooperative energy storage type with highest adaptation degree cannot be accurately screened, and meanwhile, the adaptation degree of a determined management strategy and energy supply requirements is insufficient, so that the energy allocation management effect is affected in the prior art.
In view of the above problems, the application provides a hybrid energy storage-based power grid energy management method and system.
In a first aspect, the present application provides a hybrid energy storage-based power grid energy management method, the method comprising:
Acquiring hybrid energy storage basic information, wherein the hybrid energy storage basic information is used for describing the state of hybrid energy storage of a power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
respectively carrying out characteristic analysis on each energy storage type based on energy processing equipment, energy storage capacity and energy output parameters to construct a multi-type energy storage characteristic library;
performing energy collaborative analysis according to the multi-type energy storage feature library, and determining an energy collaborative relationship;
collecting the electricity utilization characteristics of an output end user, and analyzing the characteristic relevance based on the electricity utilization characteristics of the output end user and a multi-type energy storage characteristic library to determine the information of an energy supply main line;
and constructing an optimizing space based on the energy supply main line information, the power utilization characteristics of the output end users and the energy cooperative relationship, and determining an energy management strategy of the power grid by performing iterative optimization through the optimizing space.
In a second aspect, the present application provides a hybrid energy storage based power grid energy management system, the system comprising:
the information acquisition module is used for acquiring hybrid energy storage basic information, and the hybrid energy storage basic information is used for describing the state of hybrid energy storage of the power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
The characteristic analysis module is used for respectively carrying out characteristic analysis on each energy storage type based on the energy processing equipment, the energy storage capacity and the energy output parameters to construct a multi-type energy storage characteristic library;
the relation determining module is used for carrying out energy collaborative analysis according to the multi-type energy storage feature library to determine an energy collaborative relation;
the relevance analysis module is used for collecting the electricity utilization characteristics of the output end user, carrying out characteristic relevance analysis on the basis of the electricity utilization characteristics of the output end user and the multi-type energy storage characteristic library, and determining the information of the energy supply main line;
the strategy optimizing module is used for constructing an optimizing space based on the energy supply main line information, the power utilization characteristics of the output end user and the energy cooperative relationship, and determining the power grid energy management strategy by performing iterative optimizing through the optimizing space.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
the embodiment of the application provides a power grid energy management method based on hybrid energy storage, which is characterized in that hybrid energy storage basic information comprising energy storage types, energy processing equipment, energy storage capacity and energy output parameters is collected, and based on the information, characteristic analysis is carried out on each energy storage type to construct a multi-type energy storage characteristic library; performing energy collaborative analysis according to the multi-type energy storage feature library, and determining an energy collaborative relationship; collecting power utilization characteristics of an output end user, and carrying out characteristic association analysis by combining a multi-type energy storage characteristic library to determine energy supply main line information; and constructing an optimizing space based on the energy supply main line information, the electricity utilization characteristics of the output end users and the energy cooperative relation, performing iterative optimization to determine a power grid energy management strategy, solving the technical problems that the technical support degree aiming at a multi-energy storage mode is insufficient, the cooperative energy storage type with the highest adaptation degree cannot be accurately screened, meanwhile, the determined management strategy is insufficient in adaptation degree with energy supply requirements, the allocation management effect of energy is influenced, and the maximum operation benefit of a power grid is ensured by performing cooperative relation analysis among combined energy storage types, performing strategy configuration and multi-level optimizing on the basis, determining the optimal cooperative energy storage combination and the optimal energy management strategy, adapting the energy supply target to perform targeted optimization management.
Drawings
FIG. 1 is a schematic flow diagram of a hybrid energy storage-based power grid energy management method;
FIG. 2 is a schematic diagram of a multi-type energy storage feature library construction flow in a hybrid energy storage-based power grid energy management method;
FIG. 3 is a schematic diagram of a power grid energy management strategy acquisition flow in a power grid energy management method based on hybrid energy storage;
fig. 4 is a schematic structural diagram of a hybrid energy storage-based power grid energy management system according to the present application.
Reference numerals illustrate: the system comprises an information acquisition module 11, a characteristic analysis module 12, a relation determination module 13, a relevance analysis module 14 and a strategy optimizing module 15.
Detailed Description
The application provides a power grid energy management method and system based on hybrid energy storage, which are used for collecting hybrid energy storage basic information, carrying out feature analysis on each energy storage type to construct a multi-type energy storage feature library, and carrying out energy collaborative analysis to determine an energy collaborative relationship; the method comprises the steps of collecting power utilization characteristics of an output end user, analyzing characteristic relevance, determining energy supply main line information, constructing an optimizing space, performing iterative optimization, and determining a power grid energy management strategy, wherein the method is used for solving the technical problems that in the prior art, the technical support degree for multiple energy storage modes is insufficient, the cooperative energy storage type with highest adaptation degree cannot be accurately screened, meanwhile, the determined management strategy is insufficient in adaptation degree with energy supply requirements, and the energy allocation management effect is affected.
Example 1
As shown in fig. 1, the application provides a hybrid energy storage-based power grid energy management method, which comprises the following steps:
step S100: acquiring hybrid energy storage basic information, wherein the hybrid energy storage basic information is used for describing the state of hybrid energy storage of a power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
specifically, the power grid energy supply is performed based on clean energy sources to be one of the current main energy supply modes, but the instability of output power of the clean energy sources affects the operation of the power grid, and the power fluctuation is balanced based on a hybrid energy storage mode to cooperatively perform the optimized energy supply management of the power grid. According to the power grid energy management method based on hybrid energy storage, collaborative analysis and energy supply distribution of various energy storage modes are performed according to the power utilization characteristics of users, and optimal decision management is performed based on the maximum operation benefit of the power grid.
Specifically, a distributed source for carrying out mixed energy storage of a power grid is analyzed, and the energy storage type, such as the type taking wind energy, solar energy and the like as energy supply resources, is obtained; determining equipment, such as a wind turbine generator, a solar battery and the like, which is applied to different energy storage types and is used for carrying out energy conversion processing and storage, as energy processing equipment; metering the energy storage capacity of the energy storage equipment, namely taking the maximum storage capacity as the energy storage capacity; and collecting energy supply parameters of the distributed equipment, such as unit energy, power and the like, as the energy output parameters, wherein the energy storage type, the energy processing equipment and the energy storage capacity are mapped and correspond to the energy output parameters, and the energy output parameters are integrated and summarized to generate the hybrid energy storage basic information which is used for describing the hybrid energy storage state of the power grid.
Step S200: respectively carrying out characteristic analysis on each energy storage type based on energy processing equipment, energy storage capacity and energy output parameters to construct a multi-type energy storage characteristic library;
further, as shown in fig. 2, the step S200 of the present application further includes:
step S210: based on each energy storage type, collecting an abnormal accident case set of each energy storage type;
step S220: respectively setting a clustering center by the energy processing equipment, the energy storage capacity and the energy output parameters, clustering the abnormal accident case set, and determining an abnormal accident case cluster;
step S230: carrying out abnormal characteristic analysis on energy processing equipment, energy storage capacity and energy output parameters of each abnormal accident case cluster to determine the characteristics of each abnormal parameter;
step S240: and correlating abnormal parameter characteristics of each energy storage type to construct the multi-type energy storage characteristic library.
Specifically, the energy supply characteristics of the energy storage types are different, and the reasonable allocation of the hybrid energy storage is carried out by taking the energy storage characteristics as a reference so as to ensure the energy supply stability of the power grid. Abnormal characteristic analysis and statistics are carried out on each energy storage type, for example, photovoltaic power generation and wind power generation are not stable enough, and multi-energy complementary cooperative energy supply is needed according to the large-scale power consumption requirement of a user side. Specifically, for each energy storage type, abnormal accident case retrieval, such as energy supply interruption caused by insufficient energy storage, is performed respectively, and is integrated as the abnormal accident case set, and analysis is performed based on the abnormal accident cases which occur, so as to ensure the actual fitness of the subsequent analysis results.
Further, a clustering center is set based on the energy processing device, the energy storage capacity and the energy output parameters, for example, different unit devices, different capacity batteries and the like, the abnormal accident case set is clustered by taking the latest target as a dividing standard, and a plurality of clustering results with the same number as the clustering center are determined to be used as the abnormal accident case cluster.
Further, the abnormal case clusters are subjected to abnormal parameter feature analysis and extraction based on abnormal case in the class, such as abnormal power grid operation caused by power fluctuation, abnormal critical amplitude interval and the like, power supply interruption caused by insufficient energy storage and the like, and the representative abnormal parameter features corresponding to the abnormal case clusters are determined by carrying out abnormal positioning and extraction and regular statistics on the abnormal case clusters. Further, mapping and associating each energy storage type with each abnormal parameter feature, determining a plurality of feature groups corresponding to each energy storage type, and integrating the feature groups as the multi-type energy storage feature library, wherein the multi-type energy storage feature library is a basic basis for carrying out multi-type energy collaborative analysis.
Step S300: performing energy collaborative analysis according to the multi-type energy storage feature library, and determining an energy collaborative relationship;
Further, according to the multi-type energy storage feature library, energy synergy analysis is performed to determine an energy synergy relationship, and step S300 of the present application further includes:
step S310: fitting characteristic curves of various abnormal parameters according to the multi-type energy storage characteristic library;
step S320: positioning notch trend based on characteristic curves of various abnormal parameters, and determining abnormal curve sections;
step S330: determining a collaborative fitting starting point and a collaborative fitting finishing point according to the abnormal curve section;
step S340: aligning the characteristic curves of other abnormal parameters based on the collaborative fitting starting point and the collaborative fitting ending point, wherein the characteristic curves of the other abnormal parameters are characteristic curves of abnormal parameters of other energy storage types except the energy storage type corresponding to the abnormal curve section;
step S350: and determining the energy cooperative relationship according to the notch fitting relationship of the aligned characteristic curves.
Further, the determining the energy cooperative relationship according to the notch fitting relationship of the aligned characteristic curves in step S350 of the present application further includes:
step S351: constructing a coordinate system, wherein the horizontal axis of the coordinate system is time, and the vertical axis of the coordinate system is an abnormal parameter change value;
Step S352: constructing characteristic curves and marked abnormal curve sections of the abnormal parameters in the coordinate system;
step S353: adding the characteristic curves of other abnormal parameters into the coordinate system, and aligning based on a collaborative fitting starting point and a collaborative fitting ending point;
step S354: performing superposition fitting on the abnormal curve section and characteristic curves of other abnormal parameters to determine a superposition fitting curve;
step S355: and setting a standard fitting line, carrying out fitting degree calculation on the superimposed fitting curve and the standard fitting line, and determining the energy cooperative relationship according to the fitting degree.
Specifically, according to the multi-type energy storage feature library, extracting abnormal parameter features corresponding to each energy storage type, wherein each abnormal parameter feature is provided with a time sequence identifier. And constructing an abscissa axis and an ordinate axis based on the abnormal change values of the time and the parameters, and combining the abscissa axis and the ordinate axis into the coordinate system. And carrying out positioning analysis on coordinate points in the coordinate system on the abnormal parameter characteristics used by each energy storage type pair, and determining a characteristic curve of the parameter. By means of curve trend identification, the abnormal fluctuation trend is intercepted and marked, such as amplitude overrun, curve notch and the like, and the abnormal curve section is a section to be subjected to multi-energy storage type collaborative allocation. And determining a starting point and an ending point of each abnormal curve section as the collaborative fitting starting point and the collaborative fitting ending point to be subjected to collaborative compensation respectively based on the abnormal curve section, wherein the abnormal curve section is at least one section, such as a plurality of intermittent abnormal sections with different time limit sections.
Furthermore, the construction of the characteristic curve of the abnormal parameters is carried out on other energy storage types except the energy storage type corresponding to the abnormal curve section, and the characteristic curve is added into the coordinate system, so that the multi-characteristic curve distribution under the same coordinate system is realized, the visual display of the multi-energy storage type characteristic curve is carried out, and the collaborative analysis processing is facilitated. And aligning endpoints of a plurality of characteristic curves in the coordinate system based on the collaborative fitting starting point and the collaborative fitting ending point, and determining a characteristic curve section to be fitted.
And further performing time sequence correspondence superposition fitting on the characteristic curves of the abnormal curve section and other abnormal parameters, and taking the characteristic curves as superposition fitting curves to characterize a cooperative complementary state. Specifically, the abnormal curve section and the characteristic curve of each abnormal parameter are fitted respectively, so that analysis of the energy synergistic relationship of the abnormal curve section and the characteristic curve is performed. And further setting the standard fitting curve, namely, a standard trend curve in a normal energy supply state of the power grid, correcting the superimposed fitting curve and the standard fitting curve, determining the fitting degree based on the difference of the superimposed fitting curve and the standard fitting curve, wherein the smaller the difference is, the higher the fitting degree is, the more the fitting degree is approaching to the standard fitting curve, and the energy synergistic relationship between the two energy storage types is better. The energy cooperative relationship is the basis for making a hybrid energy storage allocation decision.
Step S400: collecting the electricity utilization characteristics of an output end user, and analyzing the characteristic relevance based on the electricity utilization characteristics of the output end user and a multi-type energy storage characteristic library to determine the information of an energy supply main line;
further, based on the feature correlation analysis of the power consumption feature of the output end user and the multi-type energy storage feature library, the energy supply main line information is determined, and the step S400 of the present application further includes:
step S410: inputting the electricity utilization characteristics of the output end user into a weighting channel, carrying out weight distribution on all the electricity utilization characteristics, and determining electricity utilization characteristic weights;
step S420: calculating the support degree according to the electricity utilization characteristics of the output end user and each parameter in the multi-type energy storage characteristic library, and determining the mapping relation between the electricity utilization characteristics and the energy storage parameters based on the support degree;
step S430: when the power utilization characteristic-energy storage parameter mapping relation has a multi-characteristic mapping relation, fusing the multi-characteristics based on the power utilization characteristic weight, determining a fused power utilization characteristic weight, and marking the power utilization characteristics participating in fusion;
step S440: sequencing and determining a matching sequence by utilizing the fused electricity utilization characteristic weight and the electricity utilization characteristic weight of the unlabeled electricity utilization characteristic;
Step S450: and carrying out feature matching with the multi-type energy storage feature library sequentially based on the matching sequence, taking the energy storage type with the highest matching weight as an energy supply main line, wherein the energy supply main line information comprises basic information of the energy storage type and the energy storage feature.
Specifically, the power supply load of the power grid is subjected to power utilization characteristic extraction, such as power load, power utilization time, power utilization mode and the like, and the power utilization characteristic is used as the power utilization characteristic of the output end user. And inputting the electricity utilization characteristics of the output end user into the weighting channel, carrying out weight distribution on each electricity utilization characteristic, wherein the higher the degree of association with energy supply is, the higher the corresponding characteristic weight is, and acquiring the electricity utilization characteristic weight, and the sum of the electricity utilization characteristic weights is 1. The weighting channel can be generated by training based on sample data, and the weighting configuration of input data is carried out.
Further, the power consumption characteristics of the output end user and parameters in the multi-type energy storage characteristic library are calculated to obtain a support degree, and the support degree can be determined based on the power supply duty ratio of each energy storage type, for example, the support degree of the characteristic parameters of photovoltaic energy storage in illumination time is higher, and the support degree is obtained. And carrying out mapping association of electricity utilization characteristics and energy storage parameters based on the support degree, for example, associating the electricity utilization characteristics of the output end user with the energy storage characteristic parameters aiming at the support degree meeting the threshold standard, wherein the threshold standard is the minimum support degree for measuring the energy supply state, and acquiring the electricity utilization characteristics-energy storage parameter mapping relation. When the electricity utilization characteristic-energy storage parameter mapping relation has a multi-characteristic mapping relation, namely one electricity utilization characteristic corresponds to a plurality of energy storage parameters or a plurality of electricity utilization characteristics corresponds to one energy storage parameter, multi-characteristic fusion is carried out, addition of application electricity characteristic weights is carried out, the fusion electricity utilization characteristic weights are used as fusion electricity utilization characteristic weights, and the same marker identification is carried out on the fusion electricity utilization characteristics.
Further, the positive serialization ordering integration of the weight is carried out on the fusion power consumption characteristic weight and the power consumption characteristic weight of the unlabeled power consumption characteristic, so that the matching sequence is generated. And matching the power utilization characteristics corresponding to each matching sequence with the multi-type energy storage characteristic library to determine a plurality of matching energy storage types, taking the energy storage type corresponding to the sequence item with the highest weight as the energy supply main line, extracting basic information and the energy storage characteristics of the energy storage type as the energy supply main line information, carrying out main energy supply based on the energy supply main line, and carrying out energy supply abnormality compensation based on other energy supply modes.
Step S500: and constructing an optimizing space based on the energy supply main line information, the power utilization characteristics of the output end users and the energy cooperative relationship, and determining an energy management strategy of the power grid by performing iterative optimization through the optimizing space.
Further, as shown in fig. 3, an optimizing space is constructed based on the information of the main energy supply line, the electricity utilization characteristics of the users at the output end and the energy cooperative relationship, and iterative optimization is performed through the optimizing space to determine an energy management strategy of the power grid, and the step S500 of the present application further includes:
Step S510: performing energy supply limit values according to the energy supply main line information, wherein the energy supply limit values comprise an output maximum value, a storage maximum/minimum value and an equipment operation limit value;
step S520: constructing a collaborative evaluation loss function based on the energy supply main line information, the power utilization characteristics of the output end user and the energy collaborative relation;
step S530: constructing an optimizing space based on the collaborative evaluation loss function, and adding the energy supply limit value into the optimizing space as a constraint condition;
step S540: and optimizing the power grid energy supply strategy by utilizing the optimizing space, calculating the loss amount of the power grid energy management strategy by utilizing the collaborative evaluation loss function, taking the power grid energy management strategy with the minimum loss amount as an optimizing scheme, continuously iterating until reaching an optimizing target or the iteration times, and outputting the optimal power grid energy management strategy.
Further, the optimizing space is utilized to optimize the power grid energy supply strategy, and step S540 of the present application further includes:
step S541: based on the electricity utilization characteristics of the output end users, according to the energy supply main line information and the energy cooperative relationship, selecting an energy supply combination with the maximum energy cooperative relationship as a first power grid energy management strategy;
Step S542: calculating the loss amount of the first power grid energy management strategy through the cooperative evaluation loss function to obtain a first loss value;
step S543: selecting a second power grid energy management strategy based on the energy supply main line information and the energy cooperative relationship, wherein the cooperative relationship of the second power grid energy management strategy is smaller than that of the first power grid energy management strategy;
step S544: calculating the loss amount of the first power grid energy management strategy through the collaborative evaluation loss function to obtain a second loss value;
step S545: comparing the first loss value with the second loss value, and determining that the power grid energy management strategy with small loss value is the most current optimal strategy;
step S546: and then analogically, after optimizing all the cooperative relation schemes, selecting a preset number of preferred strategies from the optimized strategies;
step S547: taking the preferred strategies as reproduction parents, carrying out parameter derivation based on each preferred strategy, and determining derived individuals and relationships;
step S548: evaluating the loss amount of each derivative individual, analyzing the propagation loss amount trend based on the relationship, selecting the derivative individual with the best trend to continuously propagate, and eliminating the derivative group with the poor trend;
Step S549: and (3) continuously evaluating the loss value of the derivative individual through the optimizing space, taking the current strategy with the minimum loss as the current optimum, and obtaining the optimum power grid energy management strategy for output when the derivative stopping condition is continuously reached.
Specifically, based on the energy supply main line information, identification determination of an upper limit and a lower limit of energy supply is performed, for example, storage maximum/minimum value or the like is determined based on the energy storage capacity; determining the equipment operation limit value based on the specification and the like of the energy processing equipment; and determining the output maximum value based on the energy output parameter, and taking the output maximum value, the storage maximum/minimum value and the equipment operation limit value as the energy supply limit value, wherein the energy supply trend fluctuates in the energy supply limit value due to the energy supply fluctuation in the actual energy supply process. Furthermore, the energy supply main line information and the electricity utilization characteristics of the output end users are taken as quantification, and the energy cooperative relation is taken as a variable, so that a cooperative evaluation loss function is constructed. The collaborative evaluation loss function is used as a reference for carrying out energy collaborative allocation analysis decision, the optimizing space is constructed based on the collaborative evaluation loss function, the energy supply limit value is used as a constraint condition for optimizing the power grid supply strategy and is added into the optimizing space, so that the accuracy of optimizing is ensured, and the power grid energy supply strategy is optimized in the optimizing space.
Specifically, based on the electricity utilization characteristics of the output end users, the energy supply main line information is taken as a basis of collaborative analysis, an energy storage type with the largest collaborative relation with the energy supply main line is determined according to the energy collaborative relation, and the energy storage type and the energy supply main line information are combined to generate the first power grid energy management strategy. Inputting the energy supply combination of the first power grid energy management strategy into the collaborative evaluation loss function, and calculating the first loss value, namely, the deviation based on the standard energy supply state; and similarly, determining an energy storage type which is in a second position with the energy supply main line cooperative relationship according to the energy cooperative relationship based on the energy supply main line information as a basis of cooperative analysis, inputting the energy storage type as the second power grid energy management strategy into the cooperative evaluation loss function, and calculating to obtain the second loss value. And checking the first loss value and the second loss value, and taking the power grid energy management strategy with smaller loss value as the current optimal strategy. And determining an energy storage type with a cooperative relationship with the energy supply main line based on the energy cooperative relationship, determining a third power grid energy management strategy, calculating a loss value, acquiring a third loss value, performing an angle with the loss value of the current optimal strategy, iterating the electric energy management strategy with a small loss value into the current optimal strategy, repeating the steps until all cooperative relationship schemes are optimized, and selecting a preset number of preferred strategies from the optimal strategies, wherein the preset number can be set in a self-defining mode.
Further, the preferred strategy is used as a reproduction parent, and the derived quantity is determined based on the degree of preference of each strategy, namely, the higher the preference is, the more the corresponding parameter derived quantity is. And carrying out parameter derivatization on each preferred strategy based on the corresponding derivatization quantity, determining a plurality of derivatization strategies as the derivatization individuals, and associating the derivatization individuals with the corresponding reproduction parents to be used as the relationship. Further, based on the collaborative evaluation loss function, respectively calculating loss values of the derivative individuals, and based on the genetic relationship, carrying out trend analysis of propagation loss amounts, if the loss amounts of the derivative individuals are larger than the corresponding propagation parent, indicating that the derivation trend is not good, carrying out elimination of the corresponding derivative population; if the loss quantity of the derivative individuals is smaller than the corresponding derivative parent, indicating that the derivative individuals are in an optimal verification situation, determining the optimal preset quantity of the trend, continuing to carry out reproduction and loss quantity analysis based on the steps, carrying out iterative process of the current optimal strategy by checking the loss value of the strategy until the derivative stopping condition is met, for example, the preset reproduction times are reached, outputting the current optimal strategy determined by iterative process as the optimal power grid energy management strategy, and executing power grid energy management based on the optimal power grid energy management strategy, so that the implementation and energy allocation management effect of the strategy can be effectively ensured.
Example two
Based on the same inventive concept as the hybrid energy storage-based power grid energy management method in the foregoing embodiment, as shown in fig. 4, the present application provides a hybrid energy storage-based power grid energy management system, which includes:
the information acquisition module 11 is used for acquiring hybrid energy storage basic information, wherein the hybrid energy storage basic information is used for describing the state of hybrid energy storage of a power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
the characteristic analysis module 12 is used for respectively carrying out characteristic analysis on each energy storage type based on energy processing equipment, energy storage capacity and energy output parameters, and constructing a multi-type energy storage characteristic library;
the relation determining module 13 is used for carrying out energy collaborative analysis according to the multi-type energy storage characteristic library to determine an energy collaborative relation;
the relevance analysis module 14 is used for collecting the electricity utilization characteristics of the output end user, carrying out characteristic relevance analysis on the basis of the electricity utilization characteristics of the output end user and a multi-type energy storage characteristic library, and determining the energy supply main line information;
The policy optimizing module 15 is configured to construct an optimizing space based on the information of the main energy supply line, the electricity utilization characteristics of the output end user and the energy cooperative relationship, and perform iterative optimization through the optimizing space to determine an energy management policy of the power grid.
Further, the feature analysis module further includes:
the abnormal accident case collection module is used for collecting an abnormal accident case set of each energy storage type based on each energy storage type;
the abnormal event case clustering module is used for setting a clustering center according to the energy processing equipment, the energy storage capacity and the energy output parameters, clustering the abnormal event case set and determining an abnormal event case cluster;
the characteristic determining module is used for carrying out abnormal characteristic analysis on the energy processing equipment, the energy storage capacity and the energy output parameters of each abnormal accident case cluster and determining the characteristics of each abnormal parameter;
the characteristic library construction module is used for associating abnormal parameter characteristics of all energy storage types and constructing the multi-type energy storage characteristic library.
Further, the relationship determination module further includes:
the characteristic curve fitting module is used for fitting characteristic curves of various abnormal parameters according to the multi-type energy storage characteristic library;
the abnormal curve section positioning module is used for positioning notch trend based on characteristic curves of various abnormal parameters and determining an abnormal curve section;
the fitting point determining module is used for determining a collaborative fitting starting point and a collaborative fitting end point according to the abnormal curve section;
the curve alignment module is used for aligning the characteristic curves of other abnormal parameters based on the collaborative fitting starting point and the collaborative fitting end point, wherein the characteristic curves of the other abnormal parameters are characteristic curves of abnormal parameters of other energy storage types except the energy storage type corresponding to the abnormal curve section;
and the energy cooperative relation determining module is used for determining the energy cooperative relation according to the notch fitting relation of the aligned characteristic curves.
Further, the energy cooperative relationship determining module further includes:
The coordinate system construction module is used for constructing a coordinate system, wherein the horizontal axis of the coordinate system is time, and the vertical axis of the coordinate system is an abnormal parameter change value;
the curve construction module is used for constructing characteristic curves of the abnormal parameters and marking abnormal curve sections in the coordinate system;
the fitting point alignment module is used for adding the characteristic curves of other abnormal parameters into the coordinate system and aligning based on the collaborative fitting starting point and the collaborative fitting ending point;
the curve fitting module is used for performing superposition fitting on the abnormal curve section and characteristic curves of other abnormal parameters to determine a superposition fitting curve;
and the fitness calculation module is used for setting a standard fitting line, carrying out fitness calculation on the superimposed fitting curve and the standard fitting line, and determining the energy cooperative relationship according to the fitness.
Further, the relevance analysis module further includes:
the weight configuration module is used for inputting the electricity utilization characteristics of the output end user into a weighting channel, carrying out weight distribution on the electricity utilization characteristics and determining electricity utilization characteristic weights;
The mapping relation determining module is used for calculating the support degree according to the electricity utilization characteristics of the output end user and each parameter in the multi-type energy storage characteristic library, and determining the mapping relation between the electricity utilization characteristics and the energy storage parameters based on the support degree;
the feature fusion module is used for fusing the multiple features based on the power utilization feature weight when the power utilization feature-energy storage parameter mapping relation has the multiple feature mapping relation, determining the power utilization feature weight for fusion and marking the power utilization features participating in fusion;
the feature ordering module is used for ordering and determining a matching sequence by utilizing the fused electricity utilization feature weight and the electricity utilization feature weight of the unlabeled electricity utilization feature;
the energy supply main line determining module is used for sequentially carrying out feature matching with the multi-type energy storage feature library based on the matching sequence, taking the energy storage type with the highest matching weight as an energy supply main line, and the energy supply main line information comprises basic information of the energy storage type and the energy storage feature.
Further, the policy optimizing module further includes:
The energy supply limit module is used for carrying out energy supply limit according to the energy supply main line information, and the energy supply limit comprises an output maximum value, a storage maximum/minimum value and an equipment operation limit;
the loss function construction module is used for constructing a collaborative evaluation loss function based on the energy supply main line information, the power utilization characteristics of the output end user and the energy collaborative relation;
the optimizing space construction module is used for constructing an optimizing space based on the collaborative evaluation loss function and adding the energy supply limit value into the optimizing space as a constraint condition;
and the strategy output module is used for optimizing the power grid energy supply strategy by utilizing the optimizing space, calculating the loss amount of the power grid energy management strategy by the collaborative evaluation loss function, taking the power grid energy management strategy with the minimum loss amount as an optimizing scheme, and continuously iterating until reaching an optimizing target or iteration times, and outputting the optimal power grid energy management strategy.
Further, the policy output module further includes:
The first power grid energy management strategy determining module is used for selecting an energy supply combination with the largest energy cooperative relationship as a first power grid energy management strategy according to the energy supply main line information and the energy cooperative relationship based on the power utilization characteristics of the output end users;
the first loss value calculation module is used for calculating the loss amount of the first power grid energy management strategy through the collaborative evaluation loss function to obtain a first loss value;
the second power grid energy management strategy determining module is used for selecting a second power grid energy management strategy based on the energy supply main line information and the energy cooperative relationship, and the cooperative relationship of the second power grid energy management strategy is smaller than that of the first power grid energy management strategy;
the second loss value calculation module is used for calculating the loss amount of the first power grid energy management strategy through the collaborative evaluation loss function to obtain a second loss value;
the current optimal strategy determining module is used for comparing the first loss value and the second loss value and determining that the power grid energy management strategy with a small loss value is the most current optimal strategy;
The optimization strategy acquisition module is used for analogizing in sequence, and selecting a preset number of optimization strategies from all the optimization schemes after optimizing the cooperative relation schemes;
the parameter deriving module is used for taking the preferred strategies as reproduction parents, carrying out parameter derivation based on each preferred strategy and determining derived individuals and relationships;
the trend analysis module is used for evaluating the loss amount of each derivative individual, carrying out propagation loss amount trend analysis based on the relationship, selecting the derivative individual with the best trend to continuously propagate, and eliminating the derivative group with the poor trend;
the power grid energy management strategy acquisition module is used for evaluating the continuous loss value of the derivative individuals through the optimizing space, taking the current strategy with the minimum loss as the current optimum, and obtaining the optimum power grid energy management strategy for output when the deriving stopping condition is continuously reached.
In the foregoing description, it may be clearly known to those skilled in the art that a method and a system for managing power grid energy based on hybrid energy storage in this embodiment, and for a device disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The utility model provides a power grid energy management method based on hybrid energy storage, which is characterized by comprising the following steps:
acquiring hybrid energy storage basic information, wherein the hybrid energy storage basic information is used for describing the state of hybrid energy storage of a power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
respectively carrying out characteristic analysis on each energy storage type based on energy processing equipment, energy storage capacity and energy output parameters to construct a multi-type energy storage characteristic library;
performing energy collaborative analysis according to the multi-type energy storage feature library, and determining an energy collaborative relationship;
collecting the electricity utilization characteristics of an output end user, and analyzing the characteristic relevance based on the electricity utilization characteristics of the output end user and a multi-type energy storage characteristic library to determine the information of an energy supply main line;
And constructing an optimizing space based on the energy supply main line information, the power utilization characteristics of the output end users and the energy cooperative relationship, and determining an energy management strategy of the power grid by performing iterative optimization through the optimizing space.
2. The method of claim 1, wherein the constructing a multi-type energy storage feature library based on the energy processing device, the energy storage capacity, and the energy output parameters, respectively, by performing feature analysis on each energy storage type, comprises:
based on each energy storage type, collecting an abnormal accident case set of each energy storage type;
respectively setting a clustering center by the energy processing equipment, the energy storage capacity and the energy output parameters, clustering the abnormal accident case set, and determining an abnormal accident case cluster;
carrying out abnormal characteristic analysis on energy processing equipment, energy storage capacity and energy output parameters of each abnormal accident case cluster to determine the characteristics of each abnormal parameter;
and correlating abnormal parameter characteristics of each energy storage type to construct the multi-type energy storage characteristic library.
3. The method of claim 2, wherein performing an energy synergy analysis from the multi-type energy storage feature library to determine an energy synergy relationship comprises:
Fitting characteristic curves of various abnormal parameters according to the multi-type energy storage characteristic library;
positioning notch trend based on characteristic curves of various abnormal parameters, and determining abnormal curve sections;
determining a collaborative fitting starting point and a collaborative fitting finishing point according to the abnormal curve section;
aligning the characteristic curves of other abnormal parameters based on the collaborative fitting starting point and the collaborative fitting ending point, wherein the characteristic curves of the other abnormal parameters are characteristic curves of abnormal parameters of other energy storage types except the energy storage type corresponding to the abnormal curve section;
and determining the energy cooperative relationship according to the notch fitting relationship of the aligned characteristic curves.
4. The method of claim 3, wherein said determining said energy synergy from a notch fit relationship of the aligned characteristic curves comprises:
constructing a coordinate system, wherein the horizontal axis of the coordinate system is time, and the vertical axis of the coordinate system is an abnormal parameter change value;
constructing characteristic curves and marked abnormal curve sections of the abnormal parameters in the coordinate system;
adding the characteristic curves of other abnormal parameters into the coordinate system, and aligning based on a collaborative fitting starting point and a collaborative fitting ending point;
Performing superposition fitting on the abnormal curve section and characteristic curves of other abnormal parameters to determine a superposition fitting curve;
and setting a standard fitting line, carrying out fitting degree calculation on the superimposed fitting curve and the standard fitting line, and determining the energy cooperative relationship according to the fitting degree.
5. The method of claim 2, wherein determining energy supply mainline information based on feature correlation analysis of the output user electricity usage features with a multi-type energy storage feature library comprises:
inputting the electricity utilization characteristics of the output end user into a weighting channel, carrying out weight distribution on all the electricity utilization characteristics, and determining electricity utilization characteristic weights;
calculating the support degree according to the electricity utilization characteristics of the output end user and each parameter in the multi-type energy storage characteristic library, and determining the mapping relation between the electricity utilization characteristics and the energy storage parameters based on the support degree;
when the power utilization characteristic-energy storage parameter mapping relation has a multi-characteristic mapping relation, fusing the multi-characteristics based on the power utilization characteristic weight, determining a fused power utilization characteristic weight, and marking the power utilization characteristics participating in fusion;
sequencing and determining a matching sequence by utilizing the fused electricity utilization characteristic weight and the electricity utilization characteristic weight of the unlabeled electricity utilization characteristic;
And carrying out feature matching with the multi-type energy storage feature library sequentially based on the matching sequence, taking the energy storage type with the highest matching weight as an energy supply main line, wherein the energy supply main line information comprises basic information of the energy storage type and the energy storage feature.
6. The method of claim 1, wherein constructing a optimizing space based on the energy supply main line information, the output end user electricity characteristics, and the energy cooperative relationship, performing iterative optimization through the optimizing space, and determining a power grid energy management strategy, comprises:
performing energy supply limit values according to the energy supply main line information, wherein the energy supply limit values comprise an output maximum value, a storage maximum/minimum value and an equipment operation limit value;
constructing a collaborative evaluation loss function based on the energy supply main line information, the power utilization characteristics of the output end user and the energy collaborative relation;
constructing an optimizing space based on the collaborative evaluation loss function, and adding the energy supply limit value into the optimizing space as a constraint condition;
and optimizing the power grid energy supply strategy by utilizing the optimizing space, calculating the loss amount of the power grid energy management strategy by utilizing the collaborative evaluation loss function, taking the power grid energy management strategy with the minimum loss amount as an optimizing scheme, continuously iterating until reaching an optimizing target or the iteration times, and outputting the optimal power grid energy management strategy.
7. The method of claim 6, wherein optimizing the grid energy supply strategy using the optimizing space comprises:
based on the electricity utilization characteristics of the output end users, according to the energy supply main line information and the energy cooperative relationship, selecting an energy supply combination with the maximum energy cooperative relationship as a first power grid energy management strategy;
calculating the loss amount of the first power grid energy management strategy through the cooperative evaluation loss function to obtain a first loss value;
selecting a second power grid energy management strategy based on the energy supply main line information and the energy cooperative relationship, wherein the cooperative relationship of the second power grid energy management strategy is smaller than that of the first power grid energy management strategy;
calculating the loss amount of the first power grid energy management strategy through the collaborative evaluation loss function to obtain a second loss value;
comparing the first loss value with the second loss value, and determining that the power grid energy management strategy with small loss value is the most current optimal strategy;
and then analogically, after optimizing all the cooperative relation schemes, selecting a preset number of preferred strategies from the optimized strategies;
taking the preferred strategies as reproduction parents, carrying out parameter derivation based on each preferred strategy, and determining derived individuals and relationships;
Evaluating the loss amount of each derivative individual, analyzing the propagation loss amount trend based on the relationship, selecting the derivative individual with the best trend to continuously propagate, and eliminating the derivative group with the poor trend;
and (3) continuously evaluating the loss value of the derivative individual through the optimizing space, taking the current strategy with the minimum loss as the current optimum, and obtaining the optimum power grid energy management strategy for output when the derivative stopping condition is continuously reached.
8. A hybrid energy storage-based power grid energy management system, comprising:
the information acquisition module is used for acquiring hybrid energy storage basic information, and the hybrid energy storage basic information is used for describing the state of hybrid energy storage of the power grid and comprises an energy storage type, energy processing equipment, energy storage capacity and energy output parameters;
the characteristic analysis module is used for respectively carrying out characteristic analysis on each energy storage type based on the energy processing equipment, the energy storage capacity and the energy output parameters to construct a multi-type energy storage characteristic library;
the relation determining module is used for carrying out energy collaborative analysis according to the multi-type energy storage feature library to determine an energy collaborative relation;
The relevance analysis module is used for collecting the electricity utilization characteristics of the output end user, carrying out characteristic relevance analysis on the basis of the electricity utilization characteristics of the output end user and the multi-type energy storage characteristic library, and determining the information of the energy supply main line;
the strategy optimizing module is used for constructing an optimizing space based on the energy supply main line information, the power utilization characteristics of the output end user and the energy cooperative relationship, and determining the power grid energy management strategy by performing iterative optimizing through the optimizing space.
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