CN112070395A - Energy internet reliability evaluation system, model establishing method and evaluation method - Google Patents
Energy internet reliability evaluation system, model establishing method and evaluation method Download PDFInfo
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Abstract
The invention provides an energy internet reliability evaluation system, a model building method and an evaluation method. The evaluation system comprises primary indexes: safe energy supply capacity, topological structure reliability, load stability and energy storage system reliability; each first-level index comprises a plurality of second-level indexes, and each second-level index comprises a plurality of third-level indexes. The evaluation model establishing method comprises the steps of collecting information of the energy Internet and classifying according to the evaluation system; calibrating the classified indexes according to the energy Internet industrial standard; and establishing an evaluation model by using a deep learning algorithm according to the index and the calibration result. The evaluation method comprises the step of carrying out evaluation by utilizing the established evaluation model. The beneficial effects of the invention can include: the method considers the problem of comprehensive system, can better reflect the whole level and the relevant relation through the simultaneous description of a plurality of indexes, and has more advantages on the comprehensiveness and the reliability of the evaluation.
Description
Technical Field
The invention relates to the technical field of energy Internet, in particular to an index system, an evaluation method and an evaluation model establishing method for evaluating the reliability of energy Internet.
Background
Reliability evaluation in the energy Internet is a basic task for guaranteeing safe and stable operation of the energy Internet, not only can accidents be prevented, but also certain regulation effects on the electricity generation, cooling, heating, gas supply adequacy and operation economy of the energy Internet can be achieved, and references can be provided for planning, designing and operating an energy Internet system.
Due to the complexity of the energy internet system, the reliability level of the energy internet system cannot be represented by a single index, and the situation evaluation faces a plurality of difficulties and challenges, and an index system needs to be established from a plurality of aspects, and the safety operation state of the energy internet system needs to be evaluated macroscopically. Meanwhile, the selection of the evaluation indexes should follow the principles of state evaluation scientificity, comprehensiveness and the like.
Disclosure of Invention
In view of the deficiencies in the prior art, it is an object of the present invention to address one or more of the problems in the prior art as set forth above. For example, one of the objects of the present invention is to provide an index system, an evaluation method and an evaluation model establishing method for energy internet reliability evaluation.
In order to achieve the above object, an aspect of the present invention provides a method for establishing an energy internet reliability assessment model. The establishing method can comprise the following steps: collecting and classifying information of the energy Internet to obtain: the energy storage system comprises a first type of index, a second type of index, a third type of index and a fourth type of index, wherein the first type of index can be used for evaluating the safe energy supply capacity of the energy Internet, the second type of index can be used for evaluating the reliability of the topological structure of the energy Internet, the third type of index can be used for evaluating the load stability of the energy Internet, and the fourth type of index can be used for evaluating the reliability of the energy storage system of the energy Internet; respectively calibrating the classified first, second, third and fourth indexes according to an energy Internet industrial standard, and obtaining a calibration result; and establishing an evaluation model by utilizing a deep learning algorithm according to the first, second, third and fourth indexes and the calibration result.
The invention further provides a method for evaluating the reliability of the energy Internet. The method comprises the following steps: the method comprises the steps of collecting information of the energy Internet and classifying the information to obtain a first class three-level index, a second class three-level index, a third class three-level index and a fourth class three-level index, wherein the first class index can be used for evaluating the safe energy supply capacity of the energy Internet, the second class index can be used for evaluating the reliability of a topological structure of the energy Internet, the third class index can be used for evaluating the load stability of the energy Internet, and the fourth class index can be used for evaluating the reliability of an energy storage system of the energy Internet; calibrating the classified first, second, third and fourth indexes according to an energy Internet industrial standard; and evaluating the reliability of the energy Internet according to the calibration result.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model of the present invention, the deep learning algorithm may include: a deep learning convolutional neural network algorithm.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model of the present invention, the step of establishing the assessment model according to the first, second, third and fourth types of indicators and the calibration result and using the deep learning algorithm may include: building a convolutional neural network model; and training the convolutional neural network model by using the first, second, third and fourth indexes and the calibration result, and obtaining an evaluation model after training.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model of the present invention, the establishing method further includes the steps of: and expanding the obtained index by using a window translation method.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model or the method for assessing the reliability of the energy internet, the step of calibrating the classified first, second, third and fourth types of indexes according to the energy internet industry standard and obtaining the calibration result may include: the classified first, second, third and fourth indexes are respectively calibrated according to an energy internet industrial standard to obtain a first calibration result which can be used for evaluating the safe energy supply capacity of the energy internet, a second calibration result which can be used for evaluating the reliability of the topological structure of the energy internet, a third calibration result which can be used for evaluating the load stability of the energy internet and a fourth calibration result which can be used for evaluating the reliability of an energy storage system of the energy internet; and obtaining a calibration result which can be used for the reliability of the energy Internet according to the first, second, third and fourth calibration results.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model, or one or more exemplary embodiments of the method for assessing the energy internet reliability, the first type of index may include a power supply reliability index, a heat supply reliability index, a gas supply reliability index, and a cold supply reliability index; the step of calibrating the classified first type of index according to the energy internet industrial standard and obtaining a calibration result may include: calibrating a power supply reliability index, a heat supply reliability index, an air supply reliability index and a cold supply reliability index according to an energy Internet industrial standard, and obtaining a power supply reliability calibration result, a heat supply reliability calibration result, an air supply reliability calibration result and a cold supply reliability calibration result; and obtaining a calibration result which can be used for evaluating the safe energy supply capacity of the energy Internet according to the calibration result of the power supply reliability, the calibration result of the heat supply reliability, the calibration result of the air supply reliability and the calibration result of the cooling reliability.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model or the method for assessing the energy internet reliability of the present invention, the power supply reliability index may include: at least one of a fault rate, a post-fault repair rate, a fault interval time, a post-fault repair time, and a power supply power quality of a power supply device, the power supply device including at least one of a power generation device and a transformer; the heating reliability indicators may include: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a heat supply quality of the heat supply equipment; the gas supply reliability indicator may include: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a gas supply quality of the gas supply apparatus; the cooling reliability indicators may include: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a cooling quality of the cooling equipment.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model or the method for assessing the energy internet reliability of the present invention, the steps of calibrating the power supply reliability indexes according to the energy internet industry standard and obtaining the calibration result of the power supply reliability may include: calibrating each index included in the power supply reliability index by using an energy internet industrial standard to obtain a calibration result of each index; and obtaining a power supply reliability calibration result according to the quantity which accords with the energy Internet industrial standard in the calibration results of the indexes, wherein the power supply reliability calibration result is considered to be reliable under the condition that the quantity is above a preset value, and otherwise, the power supply reliability calibration result is unreliable.
According to one or more exemplary embodiments of the method for establishing the energy internet reliability assessment model or the method for assessing the energy internet reliability of the present invention, the second type of index may include: electrical network reliability, natural gas network reliability, heat supply network reliability, cold supply network reliability, traffic network reliability, information network reliability, and conversion element reliability. The third type of indicator may include: electrical load stability, thermal load stability, natural gas load stability, and traffic load stability. The fourth type of indicator may include: discharge mode reliability, charge mode reliability, and electric vehicle use as energy storage reliability.
The step of calibrating the classified second, third or fourth type of index according to the energy internet industrial standard and obtaining the calibration result may be similar to the step of calibrating the first type of index and obtaining the calibration result, and the differences include differences of specific indexes, differences of energy internet industrial standards corresponding to the indexes, and the like.
In still another aspect, the invention provides an index system for energy internet reliability assessment.
The index system may include a primary index: safe energy supply ability, topological structure reliability, load stability and energy storage system reliability, wherein, safe energy supply ability includes the second grade index: at least one of power supply reliability, heat supply reliability, gas supply reliability, and cooling reliability; the reliability of the topological structure comprises two-level indexes: at least one of electrical network reliability, natural gas network reliability, heat supply network reliability, cold supply network reliability, traffic network reliability, information network reliability, and conversion element reliability; the load stability comprises two-level indexes: at least one of electrical load stability, thermal load stability, natural gas load stability, and traffic load stability; the reliability of the energy storage system comprises two-level indexes: at least one of a discharge mode reliability, a charge mode reliability, and an electric vehicle functioning as an energy storage reliability.
According to an exemplary embodiment of the index system for energy internet reliability evaluation of the present invention, the power supply reliability may include three levels of indexes: at least one of a failure rate, a post-failure repair rate, a time between failures, a post-failure repair time, and a supply power quality of a power supply device, the power supply device may include at least one of a power generation device and a transformer; the heating reliability can comprise three levels of indexes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a heat supply quality of the heat supply equipment; the reliability of the gas supply can comprise three levels of indexes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a gas supply quality of the gas supply apparatus; the cooling reliability may include three levels of indicators: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a cooling quality of the cooling equipment.
According to an exemplary embodiment of the index system for energy internet reliability evaluation of the present invention, the electrical network reliability may include three levels of indexes: the fault diagnosis method comprises the following steps of at least one of node vulnerability, line vulnerability, a maximum power supply area, a probability index, a frequency index and a time index, wherein the maximum power supply area is the ratio of the power supply load in the maximum power supply area after the power grid is cracked due to faults to the total load of the power grid, the probability index comprises the probability that equipment or a system achieves a specified function, the frequency index is the average number of times of faults occurring in unit time of the system, and the time index comprises the average duration time of faults occurring in the system. The natural gas network reliability may include three levels of indicators: at least one of accumulated flow, oxygen deficiency amount, gas use satisfaction, gas supply shortage duration and gas supply shortage times. The heating network reliability may include three levels of indicators: the system comprises at least one of system annual heat supply insufficiency, system heat supply rate, user connectivity, hydraulic characteristics and long-distance pipeline indexes, wherein the long-distance pipeline indexes comprise at least one of pipe diameter limit values, pipe length limit values and segmentation valve spacing limit values. The traffic network reliability may include three levels of metrics: charging pile sets up at least one of rationality, electric automobile and public network cooperation reliability, and electric automobile and renewable energy source coordination scheduling reliability. The reliability of the information network comprises three levels of indexes: at least one of information network failure rate and information network reliability. The reliability of the conversion element comprises three indexes: the conversion element comprises at least one of electric-to-gas equipment, a gas generator set, a combined cycle generator set, cogeneration equipment and a charging pile of an automobile.
According to an exemplary embodiment of the index system for energy internet reliability evaluation of the present invention, the electrical load stability may include three levels of indexes: at least one of the reliability of the electric equipment, the load adjustment frequency of the electric equipment, the load adjustment duration of the electric equipment and the load adjustment electric quantity of the electric equipment. The thermal load stability may include three levels of indicators: heating at least one of building thermal inertia, thermal utility reliability, thermal utility load adjustment frequency, and thermal utility load adjustment duration. The natural gas load stability can comprise three indexes: at least one of a gas-using equipment reliability, a gas-using equipment load adjustment frequency, and a gas-using equipment load adjustment duration. The traffic load stability may include three levels of metrics: at least one of safety reliability of the charging pile, reliability of the electric automobile, impact stability of the electric automobile on a power grid during charging and service life of a battery of the electric automobile.
According to an exemplary embodiment of the index system for energy internet reliability evaluation of the present invention, the discharge pattern reliability may include three levels of indexes: at least one of a capacity, a number of discharges, and an average depth of discharge of the energy storage system; the charge mode reliability may include three levels of metrics: at least one of a capacity, a number of charges, and an average depth of charge of the energy storage system; the reliability of the electric vehicle used as the energy storage can comprise three indexes: at least one of an operation rule, a battery characteristic, an automobile scale and an electric energy supply manner of the electric automobile.
Compared with the prior art, the beneficial effects of the invention can include: the method considers the problem of comprehensive system, can better reflect the whole level and the relevant relation through the simultaneous description of a plurality of indexes, and has more advantages on the comprehensiveness and the reliability of the evaluation.
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The above and other objects and features of the present invention will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:
fig. 1 shows a schematic diagram of the energy internet reliability evaluation process of the present invention.
Detailed Description
Hereinafter, an index system for energy internet reliability evaluation (may also be referred to as an energy internet reliability evaluation system or an energy internet reliability evaluation index system), an evaluation method, and a method for establishing an evaluation model according to the present invention will be described in detail with reference to the accompanying drawings and exemplary embodiments.
The invention provides an index system for evaluating the reliability of an energy Internet.
In one exemplary embodiment of the index system for evaluating the reliability of the energy Internet, the invention establishes a three-level index system of the reliability of the energy Internet from four aspects of source-network-load-storage in the energy Internet. The index system can comprise 4 specific indexes of energy internet safety energy supply capacity, energy internet topological structure reliability, energy internet load stability and energy internet energy storage system reliability. Besides the reliability of conventional power supply, cold supply, heat supply and gas supply systems, the reliability of a traffic network, an electric automobile and an energy storage system and the coupling reliability of each network are also considered for evaluating the comprehensiveness of the system.
Table 1 shows the index system for energy internet reliability assessment of the present invention.
TABLE 1 energy Internet reliability assessment index System
1. Energy internet safe energy supply capability and energy supply quality (energy internet safe energy supply capability, which can be referred to as safe energy supply capability for short)
The energy internet takes electric energy as a core, and integrates the production, transmission, conversion, storage and interaction of various forms of energy including various renewable energy sources, such as chemical energy, heat energy, wind energy, solar energy and the like. The safe energy supply capability can also be regarded as the reliability of the energy internet capacity side element, namely the equipment. The evaluation of the energy supply side should include whether the quality of the energy supplied to the customer is qualified or not, in addition to ensuring the safe operation of the system, reducing the occurrence of faults, reducing the downtime of the system and the like.
The energy internet safety energy supply capacity can comprise the following indexes:
(1) the power supply reliability index is as follows: refers to the reliability of the elements generating electric energy, such as coal-fired power plants, gas-fired power plants, combined cycle power plants, cogeneration plants, hydroelectric power plants, nuclear power plants, wind power plants, centralized solar power bases, distributed photovoltaic and other power generation equipment, the failure rate, the repair rate, the mean time between failures, the mean time between repairs, etc. of transformers, the quality of the supplied electric energy, etc.
The equipment failure rate refers to the percentage of the accident (failure) shutdown time to the time for which the equipment should be started, and is an index for assessing the technical state, failure strength, maintenance quality and efficiency of the equipment. The repair rate refers to the ratio of the total number of failures that the product has been repaired at any given repair level to the total time for repair at that level under given conditions and for a given period of time. The mean time between failures refers to the mean time that a product or a system works correctly in two adjacent failure intervals, and is also called mean time between failures. It is a quantity that marks how long a product or system can work on average. The average repair time is an average value describing the repair time when the product is converted from the failure state to the working state. The quality index of the power supply has various description modes, and the power quality problem refers to the deviation of voltage, current or frequency which causes the failure or abnormal operation of the electric equipment.
Compared with the traditional generator set, the new energy generator set depends on natural factors such as wind, light and heat more, has higher uncertainty and randomness, increases the complexity of modeling of the power system, provides new challenges for safe and reliable operation of the power system, and needs to reevaluate the operation reliability of the power system containing large-scale renewable energy access in a new environment.
(2) The gas supply reliability index is as follows: the method comprises the fault rate, the repair rate, the mean fault interval time, the mean repair time, the gas supply quality and the like of the centralized gas supply equipment, the pressure regulator and the like.
(3) Heat supply reliability index: the device fault rate, the repair rate, the average fault interval time, the average repair time, the heat supply quality and the like of the heat supply unit and the like.
(4) Index of cooling safety reliability: the equipment failure rate, the repair rate, the average failure interval time, the average repair time and the like of the central air conditioner and the like.
2. Robustness of energy internet topology (also called energy internet topology reliability, which may be referred to as topology reliability for short)
The network in the energy Internet is a bridge and a medium for realizing complex and multi-dimensional interconnection and fusion between energy production and conversion equipment, and ensures energy interaction and information interaction between the equipment. The network in the energy internet mainly comprises an electric network, a natural gas network, a heat supply network, a traffic network, an information network and the like. The index not only considers the network structures, but also considers the coupling among the networks. The energy internet realizes interconnection, intercommunication and fusion of multi-energy systems, risk evaluation of a single network no longer meets requirements, system coupling risk of the energy internet needs to be evaluated on the basis of key conversion elements, and network robustness of the multi-energy systems is analyzed on the basis of a complex network theory.
The energy internet topological structure reliability can comprise the following indexes:
(1) electrical network reliability index: the method comprises node vulnerability, line vulnerability, maximum power supply area, probability index, frequency index, time index and the like. The node vulnerability indexes and the line vulnerability indexes indicate the vulnerability of the nodes and the lines from the perspective of a network topological structure; the maximum power supply area index is defined as: and after the power grid breaks down, the ratio of the available power supply load in the maximum power supply area to the total load of the power grid. The probability index is the probability of realizing a specified function of the equipment or the system, and comprises the power shortage probability and the power shortage probability. The frequency index is the average frequency of faults occurring in the system unit time, and the common index is mainly the insufficient power frequency. The time index is the average duration time of the system failure, the expected value index is the expected days of the system failure within one year, and the common index is the expected value of the power-lack time of the system. Wherein, the device is realized by single function, and the system is realized by the function of the whole framework.
(2) The reliability index of the natural gas network is as follows: natural gas is also toxic gas with the characteristics of flammability, explosiveness and the like, and the natural gas is transported and used for a long time in China, pipelines and infrastructure for transporting and distributing the natural gas exist in various cities, so that the stability of the natural gas is also high in a gas transportation and distribution system in order to guarantee the life and property safety of people. The indexes mainly comprise accumulated flow, anoxic gas amount, gas utilization satisfaction, insufficient gas duration, insufficient gas supply frequency and the like.
(3) Reliability index of heat supply network: the criterion of the normal state of the heating network system is that all heat users can ensure the minimum heat supply, and the indexes can comprise the indexes of annual heat supply shortage of the system, the system heat supply rate, the user connectivity, the hydraulic characteristic, the pipe diameter limit value of the long-distance pipeline, the pipe length limit value, the spacing limit value of the segmented valves and the like.
(4) The reliability index of the traffic network is as follows: charging pile setting reasonableness, electric automobile and public network cooperative reliability, electric automobile and renewable energy source coordinated scheduling reliability and the like.
(5) Information network reliability index: the reliability of the information network is the capability of the information network system to complete the specified functions within the specified time and under the specified working environment conditions, and the measurement indexes can include network failure rate, network reliability and the like.
(6) Conversion element/system coupling reliability (also referred to as conversion element reliability): the key elements for realizing the multi-energy conversion and transmission functions exist in the multi-energy system, the reliability analysis is the key point of the whole energy system, the conversion element refers to the key element for realizing the energy conversion in the energy internet and is a coupling node between different energy systems, wherein the conversion elements such as the electricity-to-gas equipment, the gas generator set, the combined cycle generator set and the like realize the interconnection and coupling of an electric system and a natural gas system, the heat pump realizes the mutual coupling of an electricity network and a heat network, the cogeneration power plant realizes the mutual coupling of the electricity network, the heat network and the gas network, and the electric vehicle charging pile realizes the coupling of the electric system and a traffic system. The operation state of the charging pile can affect the traditional power distribution network bearing capacity indexes such as voltage level qualification rate, reactive power configuration, single-circuit line safe operation state, short-time load rate, network and equipment loss, reactive power compensation consumption and the like. The reliability indexes are the failure rate, the repair rate, the mean failure interval time, the mean repair time and the like of the equipment.
3. Energy internet energy stability (i.e. energy internet load stability, load stability for short)
The energy utilization element is an energy consumption unit of an energy internet, is a terminal for energy transmission and utilization, has a wide meaning, and can generally refer to energy utilization equipment in all industries and residential users, such as heat pumps, gas boilers, electric automobiles and the like. Among them, the electric vehicle is a clean and low-carbon novel vehicle, and is also a key development direction of future electrified traffic. For the energy internet, an electric vehicle is a more specific element: on the one hand, electric vehicles are important loads of power systems as consumers of electric energy; on the other hand, the electric energy stored in the electric vehicle can flow back to the power system through the charging pile, and is an energy storage device of the power system. Meanwhile, the electric automobile charging pile is also a key coupling element of a power system and a traffic system.
The energy use stability of the energy Internet can comprise the following indexes:
(1) electrical load stability index: the load adjustment frequency reflects the frequency at which the user has load adjustments occurring over the study period. The load adjustment duration is the duration of time that the user's power usage is in a reduced or increased state during the study period. The load adjustment electric quantity refers to the total quantity of the electricity consumption change of a user in a research period.
(2) Thermal load stability index: heating building thermal inertia, thermal equipment reliability, load adjustment frequency and load adjustment duration.
(3) Natural gas load stability index: reliability of gas-using equipment, load adjustment frequency and load adjustment duration.
(4) The traffic load stability index is as follows: the charging pile has the advantages of self safety and reliability, electric automobile reliability, impact stability to a power grid (impact stability to the power grid when the electric automobile is charged), battery life (namely, an electric automobile battery) and the like. Wherein, the electric automobile evaluation index includes the reliability: the probability of fulfilling a given function is met under given conditions and within a given test range. Failure rate: the failure rate curve has three types, namely the failure rate is continuously reduced along with the increase of mileage (early failure period), the failure is kept unchanged (accidental failure period), and the failure rate is continuously reduced along with the increase of mileage (loss failure period). Failure probability density: density of probability of a car failing within a unit mileage.
4. Energy internet energy storage system reliability (may be referred to as energy storage system reliability for short)
The energy storage element realizes long-time and large-scale storage of energy, and comprises a pumped storage hydropower station, a battery energy storage, a hydrogen energy storage, a compressed natural gas/liquefied natural gas storage and the like.
The reliability of the energy internet energy storage system can comprise the following indexes:
(1) discharge mode reliability index: the method comprises the steps of discharging times and average discharging depth, wherein the discharging times index reflects the discharging times of the energy storage device in a research period. The average depth of discharge indicator refers to the amount of charge that the energy storage device releases on average per discharge over a study period.
(2) Reliability index of charging mode: including the number of charges and the average depth of charge.
(3) The electric automobile is used as an energy storage system reliability index: the method comprises the operation rule, the battery characteristic, the automobile scale and the electric energy supply mode of the electric automobile.
The invention further provides a method for establishing the energy internet reliability evaluation model.
In an exemplary embodiment of the method for establishing the energy internet reliability assessment model of the present invention, the method may include the steps of:
s10: and collecting information of the energy Internet and classifying to obtain the index system for evaluating the reliability of the energy Internet. And obtaining the first-level index, the second-level index and the third-level index. The classification can be performed according to the above sources, networks, loads and stores, for example, the classification of the first-level index can be obtained as follows: the method can be used for evaluating the safe energy supply capacity of the energy Internet, evaluating the reliability of the topological structure of the energy Internet, evaluating the load stability of the energy Internet and evaluating the first type index, the second type index, the third type index and the fourth type index of the reliability of the energy storage system of the energy Internet.
S20: and calibrating each index in an index system for evaluating the reliability of the energy Internet according to the industrial standard of the energy Internet. The energy internet industry standard and calibration method can be the standard and method which are conventional in the field.
And establishing an evaluation model by utilizing a deep learning algorithm according to each index and the calibration result of each index. The deep learning algorithm may include a deep learning algorithm known in the art.
In another exemplary embodiment of the method for establishing the energy internet reliability evaluation model of the present invention, the establishing method may include the steps of:
step 1: and establishing a model input sample matrix.
And selecting data from an energy Internet actual monitoring or database to form an index input sample matrix.
Taking the power supply reliability as an example: selecting voltage U, frequency f, active power P and reactive power Q data values of each key node of the energy internet in a characteristic time period T to form a model input sample matrix as shown in the following formula:
and the lower corner marks (i, j) of the voltage U, the frequency f, the active power P and the reactive power Q are used, wherein the first lower corner mark i represents the ith sample, and the second lower corner mark j represents the jth time acquisition point.
Step 2: and (4) input data stability judgment and marking.
According to the energy Internet industrial standard, the reliability of input sample data is calibrated, and each index has different judgment modes. For example, calibrating the stability of the input voltage data may include: and judging whether the voltage U value at a specific moment is stable, if the node voltage U value can be recovered to be more than 0.8 time of the standard value, marking the node voltage U value as stable, and otherwise, if the node voltage U value can not be recovered to be more than 0.8 time of the standard value, marking the node voltage U value as unstable, and marking the node voltage U value as 0.
And step 3: and (5) data expansion.
Considering that the positive and negative samples under reliable and unreliable conditions are unbalanced in actual conditions, the translation window method is used for expanding input sample data, and bias is avoided in the training process.
And 4, step 4: convolutional neural network model construction
And constructing a convolutional neural network which is constructed by an input layer, a convolutional layer, a pooling layer, an output layer and the like. The convolution layer of the convolution neural network performs convolution operation on the input layer and the convolution kernel weight matrix so as to extract the characteristics of the input layer, and the pooling layer performs downsampling on the characteristic mapping and plays a role in extracting the characteristics for the second time.
The convolutional layer of the convolutional neural network performs convolution operation on the input layer and the convolutional kernel weight matrix so as to extract the characteristics of the input layer, the convolutional layer of the front layer extracts low-level characteristics, and the convolutional layer of the rear layer extracts higher-level characteristics. The convolution layer calculation can be expressed by the following formula:
In the convolutional neural network, the same input characteristic surface and the same output characteristic surface are subjected to weight sharing, so that the quantity of parameters is controlled, the complexity of a neural network model is reduced, and the difficulty of training the model is reduced.
The build-up layer is usually followed by a pooling layer. The pooling layer is used for down-sampling the feature mapping and has the function of secondary feature extraction. By moving the windows and performing operations such as maximum value, minimum value, average value or random value on the local value in each window, the input dimensionality of the convolution layer in the next layer can be reduced, so that the operation complexity is reduced, the most significant features in the feature map can be extracted, more abstract features in high-level expression are obtained, and the influence of local tiny change or phase shift on feature extraction is reduced. The pooling layer may be represented by the following formula:
In the convolutional neural network, after a plurality of convolutional layers and pooling layers are combined, the convolutional neural network is finally ended by a full connection layer and connected with an output layer. Each neuron in the full-connection layer is fully connected with all neurons in the previous layer, so that local characteristic information extracted by the convolutional layer and the pooling layer in the previous layer is obtained and integrated and output. The output value of the last full connection layer is generally classified by soft-max logistic regression through a soft max layer and is transmitted to the output layer.
In the training process of the convolutional neural network, firstly, calculating the difference 'residual error' between an output value and an expected value through forward propagation, then, applying a gradient descent method and reversely propagating the residual error, updating the convolutional kernel weight matrix and the offset vector of each layer in the convolutional neural network layer by layer, and finally achieving the aim of minimizing a loss function.
And 5: and (5) off-line training.
And performing off-line training by using the historical data to obtain an evaluation model. As an example, the model output result is regarded as reliable by taking 0.5 as a boundary, and a value greater than or equal to 0.5 is regarded as reliable, and a value less than 0.5 is regarded as unreliable by being regarded as 0.
The trained evaluation model may then be applied to an online real-time reliability state evaluation process.
In still another aspect, the invention provides a method for evaluating reliability of an energy internet.
In one exemplary embodiment of the method of energy internet reliability evaluation of the present invention, the evaluation method includes the steps of:
steps S10 and S20, and S30, which are the same as in the exemplary embodiment of the method of establishing the energy internet reliability evaluation model described above: and evaluating the reliability of the energy Internet according to the calibration result.
In another exemplary embodiment of the method of energy internet reliability evaluation of the present invention, the method of reliability evaluation may include: establishing an evaluation model by adopting the establishing method of the energy Internet reliability evaluation model; and evaluating the reliability of the energy Internet by using the evaluation model.
In still another exemplary embodiment of the method for energy internet reliability evaluation of the present invention, as shown in fig. 1, the method for energy internet reliability evaluation may include: firstly, classifying and sorting various kinds of state information of source network load storage collected by the existing state monitoring system of the system, wherein the information comprises on-line monitoring and historical data in a database; the reliability evaluation process is carried out on the required information collected from the monitoring system, and the reliability evaluation process mainly comprises two parts: and establishing an evaluation system and applying an evaluation algorithm. The evaluation system scientifically classifies the components of the energy internet according to four aspects, establishes evaluation indexes and finally forms a clear evaluation system, and for example, the evaluation system can be the index system for evaluating the reliability of the energy internet. The evaluation algorithm is to establish an evaluation model by applying the proposed deep learning convolutional neural network algorithm and further generate an energy internet reliability evaluation result by using the evaluation model.
In summary, the advantages of the index system, the evaluation method and the evaluation model establishing method for the reliability evaluation of the energy internet according to the present invention may include:
(1) the index system of the invention is more scientific in evaluating the reliability of the energy Internet.
(2) The evaluation of the invention is more comprehensive, and besides the reliability of conventional power supply, cold supply, heat supply and gas supply systems, the reliability of traffic networks, electric automobiles and energy storage systems and the coupling reliability of each network are also considered.
Although the present invention has been described above in connection with exemplary embodiments, it will be apparent to those skilled in the art that various modifications and changes may be made to the exemplary embodiments of the present invention without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The method for establishing the energy Internet reliability evaluation model is characterized by comprising the following steps of:
collecting and classifying information of the energy Internet to obtain: the energy storage system comprises a first type of index, a second type of index, a third type of index and a fourth type of index, wherein the first type of index can be used for evaluating the safe energy supply capacity of the energy Internet, the second type of index can be used for evaluating the reliability of the topological structure of the energy Internet, the third type of index can be used for evaluating the load stability of the energy Internet, and the fourth type of index can be used for evaluating the reliability of the energy storage system of the energy Internet;
respectively calibrating the classified first, second, third and fourth indexes according to an energy Internet industrial standard, and obtaining a calibration result;
and establishing an evaluation model by utilizing a deep learning algorithm according to the first, second, third and fourth indexes and the calibration result.
2. The method for establishing the energy internet reliability evaluation model according to claim 1, wherein the deep learning algorithm comprises: a deep learning convolutional neural network algorithm.
3. The method for establishing the energy internet reliability evaluation model according to claim 1, wherein the step of calibrating the classified first, second, third and fourth types of indexes according to the energy internet industry standard and obtaining the calibration result comprises:
the classified first, second, third and fourth indexes are respectively calibrated according to an energy internet industrial standard to obtain a first calibration result which can be used for evaluating the safe energy supply capacity of the energy internet, a second calibration result which can be used for evaluating the reliability of the topological structure of the energy internet, a third calibration result which can be used for evaluating the load stability of the energy internet and a fourth calibration result which can be used for evaluating the reliability of an energy storage system of the energy internet;
and obtaining a calibration result which can be used for the reliability of the energy Internet according to the first, second, third and fourth calibration results.
4. The method for establishing the energy internet reliability assessment model according to claim 3, wherein the first-class index comprises: a power supply reliability index, a heat supply reliability index, a gas supply reliability index, and a cooling reliability index;
the step of calibrating the classified first type of indexes according to the energy Internet industrial standard and obtaining the calibration result comprises the following steps:
calibrating a power supply reliability index, a heat supply reliability index, an air supply reliability index and a cold supply reliability index according to an energy Internet industrial standard, and obtaining a power supply reliability calibration result, a heat supply reliability calibration result, an air supply reliability calibration result and a cold supply reliability calibration result;
and obtaining a calibration result which can be used for evaluating the safe energy supply capacity of the energy Internet according to the calibration result of the power supply reliability, the calibration result of the heat supply reliability, the calibration result of the air supply reliability and the calibration result of the cooling reliability.
5. The method for establishing the energy internet reliability assessment model according to claim 4, wherein the power supply reliability index comprises: at least one of a fault rate, a post-fault repair rate, a fault interval time, a post-fault repair time, and a power supply power quality of a power supply device, the power supply device including at least one of a power generation device and a transformer;
the heat supply reliability indexes include: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a heat supply quality of the heat supply equipment;
the gas supply reliability index includes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a gas supply quality of the gas supply apparatus;
the cooling reliability indicators include: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a cooling quality of the cooling equipment.
6. The method for establishing the energy internet reliability evaluation model according to claim 5, wherein the step of calibrating the power supply reliability indexes according to the energy internet industry standard and obtaining the calibration result of the power supply reliability comprises:
calibrating each index included in the power supply reliability index by using an energy internet industrial standard to obtain a calibration result of each index;
and obtaining a power supply reliability calibration result according to the quantity which accords with the energy Internet industrial standard in the calibration results of the indexes, wherein the power supply reliability calibration result is considered to be reliable under the condition that the quantity is above a preset value, and otherwise, the power supply reliability calibration result is unreliable.
7. The method for building the energy internet reliability evaluation model according to claim 1, wherein the building method further comprises the steps of: and expanding the obtained index by using a window translation method.
8. A method for evaluating reliability of an energy Internet, which is characterized by comprising the following steps:
the method comprises the steps of collecting information of the energy Internet and classifying the information to obtain a first class three-level index, a second class three-level index, a third class three-level index and a fourth class three-level index, wherein the first class index can be used for evaluating the safe energy supply capacity of the energy Internet, the second class index can be used for evaluating the reliability of a topological structure of the energy Internet, the third class index can be used for evaluating the load stability of the energy Internet, and the fourth class index can be used for evaluating the reliability of an energy storage system of the energy Internet;
calibrating the classified first, second, third and fourth indexes according to an energy Internet industrial standard;
and evaluating the reliability of the energy Internet according to the calibration result.
9. An index system for evaluating reliability of an energy internet, which is characterized by comprising a first-level index: safe energy supply capability, topology reliability, load stability, and energy storage system reliability, wherein,
the safe energy supply capacity comprises two levels of indexes: at least one of power supply reliability, heat supply reliability, gas supply reliability, and cooling reliability;
the reliability of the topological structure comprises two-level indexes: at least one of electrical network reliability, natural gas network reliability, heat supply network reliability, cold supply network reliability, traffic network reliability, information network reliability, and conversion element reliability;
the load stability comprises two-level indexes: at least one of electrical load stability, thermal load stability, natural gas load stability, and traffic load stability;
the reliability of the energy storage system comprises two-level indexes: at least one of a discharge mode reliability, a charge mode reliability, and an electric vehicle functioning as an energy storage reliability.
10. The index system for energy internet reliability assessment according to claim 9, wherein the power supply reliability comprises three levels of indexes: at least one of a fault rate, a post-fault repair rate, a fault interval time, a post-fault repair time, and a power supply power quality of a power supply device, the power supply device including at least one of a power generation device and a transformer;
the heat supply reliability comprises three levels of indexes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a heat supply quality of the heat supply equipment;
the gas supply reliability comprises three levels of indexes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a gas supply quality of the gas supply apparatus;
the cooling reliability comprises three levels of indexes: at least one of a failure rate, a post-failure repair rate, a failure interval time, a post-failure repair time, and a cooling quality of the cooling equipment;
the electrical network reliability comprises three levels of indicators: the fault diagnosis method comprises the following steps that at least one of node vulnerability, line vulnerability, a maximum power supply area, a probability index, a frequency index and a time index is adopted, wherein the maximum power supply area is the ratio of the power supply load in the maximum power supply area after the power grid is cracked due to faults to the total load of the power grid, the probability index comprises the probability of realizing a specified function of equipment or a system, the frequency index is the average frequency of faults occurring in unit time of the system, and the time index comprises the average duration time of faults occurring in the system;
the natural gas network reliability comprises three levels of indexes: at least one of accumulated flow, anoxic gas amount, gas use satisfaction, insufficient gas supply duration and insufficient gas supply times;
the reliability of the heating network comprises three levels of indexes: at least one of annual heat supply insufficiency of the system, system heat supply rate, user connectivity, hydraulic characteristics and long-distance pipeline indexes, wherein the long-distance pipeline indexes comprise at least one of pipe diameter limit values, pipe length limit values and segmentation valve spacing limit values;
the traffic network reliability comprises three levels of indexes: at least one of charging pile setting reasonability, electric vehicle and public network cooperation reliability and electric vehicle and renewable energy source coordination scheduling reliability;
the reliability of the information network comprises three levels of indexes: at least one of information network failure rate and information network reliability;
the conversion element reliability comprises three levels of indicators: the conversion element comprises at least one of electric-to-gas equipment, a gas generator set, a combined cycle generator set, cogeneration equipment and a charging pile of an automobile;
the electrical load stability comprises three levels of indicators: at least one of the reliability of the electric equipment, the load adjustment frequency of the electric equipment, the load adjustment duration time of the electric equipment and the load adjustment electric quantity of the electric equipment;
the thermal load stability comprises three indexes: at least one of heating building thermal inertia, thermal utility reliability, thermal utility load adjustment frequency, and thermal utility load adjustment duration;
the natural gas load stability comprises three indexes: at least one of gas-using equipment reliability, gas-using equipment load adjustment frequency, and gas-using equipment load adjustment duration;
the traffic load stability comprises three indexes: at least one of safety reliability of a charging pile, reliability of an electric automobile, impact stability of the electric automobile on a power grid during charging and service life of a battery of the electric automobile;
the discharge mode reliability comprises three levels of indexes: at least one of a capacity, a number of discharges, and an average depth of discharge of the energy storage system;
the charging mode reliability comprises three levels of indexes: at least one of a capacity, a number of charges, and an average depth of charge of the energy storage system;
the reliability of the electric automobile used as the energy storage comprises three indexes: at least one of an operation rule, a battery characteristic, an automobile scale and an electric energy supply manner of the electric automobile.
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