CN112070395B - Energy Internet reliability assessment system, model establishment method and assessment method - Google Patents
Energy Internet reliability assessment system, model establishment method and assessment method Download PDFInfo
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Abstract
The invention provides an energy Internet reliability assessment system, a model building method and an assessment method. The evaluation system comprises a first-level index: safety energy supply capability, 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 method for establishing the evaluation model 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 evaluating by using the established evaluation model. The beneficial effects of the invention can include: the invention considers the problem system comprehensively, can better reflect the whole level and the correlation by simultaneously describing a plurality of indexes, and has more advantages in the aspects of the comprehensiveness and the reliability of 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 establishment method for energy Internet reliability evaluation.
Background
The reliability evaluation in the energy Internet is a basic task for guaranteeing the safe and stable operation of the energy Internet, not only can prevent accidents, but also can play a certain role in adjusting the power generation, cooling, heating and air supply adequacy and the operation economy of the energy Internet, and can provide references for planning, designing and operating the energy Internet system.
Because of the complexity of the energy internet system, the reliability level cannot be characterized by a single index, and the state evaluation faces a plurality of difficulties and challenges, so that an index system needs to be established from multiple aspects, and the safe operation state of the energy internet system needs to be evaluated in a whole macroscopic manner. Meanwhile, the selection of the evaluation index should comply with the principles of scientificity, comprehensiveness and the like of state evaluation.
Disclosure of Invention
The present invention is directed to solving one or more of the problems of the prior art, including the shortcomings of the prior art. For example, it is an object of the present invention to provide an index system, an evaluation method and an evaluation model establishment method for energy internet reliability evaluation.
In order to achieve the above purpose, the present invention provides a method for establishing an energy internet reliability assessment model. The establishment method may include the steps of: collecting and classifying information of the energy Internet to obtain: the 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 safety 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 an energy storage system of the energy internet; calibrating the classified first, second, third and fourth indexes according to the energy Internet industrial standard, and obtaining a calibration result; and establishing an evaluation model according to the first, second, third and fourth indexes and the calibration result by using a deep learning algorithm.
The invention further provides a method for evaluating the reliability of the energy Internet. The method comprises the following steps: collecting information of the energy Internet and classifying the information to obtain a first class of three-level index, a second class of three-level index, a third class of three-level index and a fourth class of three-level index, wherein the first class of index can be used for evaluating the safety energy supply capacity of the energy Internet, the second class of index can be used for evaluating the reliability of the topological structure of the energy Internet, the third class of index can be used for evaluating the load stability of the energy Internet, and the fourth class of 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 an energy internet reliability assessment model of the present invention, the deep learning algorithm may include: deep learning convolutional neural network algorithm.
According to one or more exemplary embodiments of the method for building an energy internet reliability assessment model of the present invention, the step of building the assessment model according to the first, second, third and fourth types of indexes and the calibration result and using a deep learning algorithm may include: building a convolutional neural network model; and training the convolutional neural network model according to 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 building an energy internet reliability assessment model of the present invention, the method further comprises the steps of: and expanding the obtained index by using a method of translating a window.
According to one or more exemplary embodiments of the method for establishing an energy internet reliability assessment model or one or more exemplary embodiments of the method for energy internet reliability assessment, the step of calibrating the categorized first, second, third and fourth types of indexes according to the energy internet industry standard, respectively, and obtaining a calibration result may include: the classified first, second, third and fourth indexes are calibrated according to the energy internet industry standard to obtain a first calibration result which can be used for evaluating the safety 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 an energy internet reliability assessment model of the present invention, or one or more exemplary embodiments of the method for energy internet reliability assessment, the first type of index may include 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 categorized first class index according to the energy internet industry standard and obtaining a calibration result may include: calibrating the power supply reliability index, the heat supply reliability index, the air supply reliability index and the cooling reliability index according to the energy Internet industrial standard respectively, and obtaining a power supply reliability calibration result, a heat supply reliability calibration result, an air supply reliability calibration result and a cooling reliability calibration result; and obtaining a calibration result which can be used for evaluating the safety energy supply capacity of the energy Internet according to the power supply reliability calibration result, the heat supply reliability calibration result, the air supply reliability calibration result and the cooling reliability calibration result.
According to one or more exemplary embodiments of the method for establishing an energy internet reliability assessment model of the present invention, or one or more exemplary embodiments of the method for energy internet reliability assessment, the power supply reliability index may include: at least one of failure rate, repair rate after failure, time between failures, repair time after failure, and power quality of power supply equipment, the power supply equipment including at least one of power generation equipment and a transformer; the heat supply reliability index may include: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a heating quality of the heating apparatus; the air supply reliability index may include: at least one of a failure rate of the air supply device, a post-failure repair rate, a failure interval time, a post-failure repair time, and an air supply quality; the cooling reliability index may include: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a cooling quality of the cooling apparatus.
According to one or more exemplary embodiments of the method for establishing an energy internet reliability assessment model or one or more exemplary embodiments of the method for energy internet reliability assessment, the step of calibrating power supply reliability indexes according to energy internet industry standards and obtaining a calibration result of 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 calibration result of the power supply reliability according to the quantity which accords with the energy Internet industrial standard in the calibration results of the indexes, wherein the calibration result of the power supply reliability is considered to be reliable under the condition that the quantity is above a preset value, and otherwise, the calibration result of the power supply reliability is not reliable.
According to one or more exemplary embodiments of the method for establishing an energy internet reliability assessment model of the present invention, or one or more exemplary embodiments of the method for energy internet reliability assessment, the second type of index may include: electrical network reliability, natural gas network reliability, heating network reliability, cooling network reliability, traffic network reliability, information network reliability and conversion element reliability. The third class of indicators may include: electrical load stability, thermal load stability, natural gas load stability, and traffic load stability. The fourth class of indicators may include: the discharging mode reliability, the charging mode reliability and the electric vehicle are used as the energy storage reliability.
The step of calibrating the categorized second, third or fourth indexes according to the energy internet industry standard and obtaining the calibration result may be similar to the step of calibrating the first index and obtaining the calibration result, and the differences include different specific indexes, different energy internet industry standards corresponding to the indexes, and the like.
In yet another aspect, the invention provides an index system for energy internet reliability assessment.
The index system may include a first level index: safety energy supply capability, topological structure reliability, load stability and energy storage system reliability, wherein the safety energy supply capability comprises a secondary index: at least one of power supply reliability, heat supply reliability, air supply reliability and cooling reliability; the topology reliability includes a secondary index: at least one of electrical network reliability, natural gas network reliability, heating network reliability, cooling network reliability, traffic network reliability, information network reliability, and conversion element reliability; load stability includes secondary indicators: at least one of electrical load stability, thermal load stability, natural gas load stability, and traffic load stability; the energy storage system reliability includes secondary indicators: at least one of discharge mode reliability, charge mode reliability, and energy storage reliability for an electric vehicle.
According to an exemplary embodiment of the index system for energy internet reliability assessment 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, an inter-failure time, a post-failure repair time, and a power quality of the power supply, the power supply may include at least one of a power generation device and a transformer; the heating reliability may include three levels of indicators: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a heating quality of the heating apparatus; the air supply reliability may include three levels of indicators: at least one of a failure rate of the air supply device, a post-failure repair rate, a failure interval time, a post-failure repair time, and an air supply quality; the cooling reliability may include three levels of indicators: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a cooling quality of the cooling apparatus.
According to an exemplary embodiment of the index system for energy internet reliability assessment of the present invention, the electrical network reliability may comprise three levels of indexes: at least one of node vulnerability, line vulnerability, maximum power supply area, probability index, frequency index and 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 breaks down, the probability index comprises the probability of the equipment or the system realizing the specified function, the frequency index is the average number of faults in the unit time of the system, and the time index comprises the average duration of the faults of the system. The natural gas network reliability may include three levels of indicators: at least one of cumulative flow, anoxic amount, gas usage satisfaction, duration of insufficient gas supply, and number of times of insufficient gas supply. The heating network reliability may include three levels of indicators: the system is characterized by comprising at least one of system annual heat supply deficiency, 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 value, pipe length limit value and segment valve spacing limit value. The traffic network reliability may include three levels of indicators: the charging pile is provided with at least one of rationality, cooperative reliability of the electric vehicle and a public network and cooperative dispatching reliability of the electric vehicle and renewable energy. The information network reliability includes three levels of indicators: at least one of failure rate of the information network and reliability of the information network. The conversion element reliability includes three levels of indicators: the conversion element comprises at least one of electric conversion equipment, a gas generating set, a combined cycle generating set, cogeneration equipment and an electric car charging pile.
According to an exemplary embodiment of the index system for energy internet reliability assessment of the present invention, the electrical load stability may include three levels of indexes: at least one of consumer reliability, consumer load adjustment frequency, consumer load adjustment duration, and consumer load adjustment power. The thermal load stability may include three levels of indicators: at least one of heating building thermal inertia, thermal plant reliability, thermal plant load adjustment frequency, and thermal plant load adjustment duration. The natural gas load stability may include three levels of indicators: at least one of gas usage reliability, gas usage load adjustment frequency, and gas usage load adjustment duration. Traffic load stability may include three levels of indicators: the charging pile is at least one of safety reliability of the charging pile, reliability of the electric automobile, impact stability of the electric automobile to 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 assessment of the present invention, the discharge pattern reliability may include three levels of indexes: at least one of capacity, number of discharges, and average depth of discharge of the energy storage system; the charge mode reliability may include three levels of indicators: at least one of capacity, number of charges, and average depth of charge of the energy storage system; the energy storage reliability for the electric automobile can comprise three levels of indexes: at least one of an operation rule, battery characteristics, an automobile scale and an electric energy supply mode of the electric automobile.
Compared with the prior art, the invention has the beneficial effects that: the invention considers the problem system comprehensively, can better reflect the whole level and the correlation by simultaneously describing a plurality of indexes, and has more advantages in the aspects of the comprehensiveness and the reliability of evaluation.
Drawings
The foregoing and other objects and features of the 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 flow of the present invention.
Detailed Description
Hereinafter, an index system for energy internet reliability evaluation (which may also be referred to as an energy internet reliability evaluation system or an energy internet reliability evaluation index system), an evaluation method, and an evaluation model establishing method of the present invention will be described in detail with reference to the drawings and exemplary embodiments.
The invention provides an index system for evaluating the reliability of the 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 for 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 the conventional power supply, cooling, heating and air supply systems, the invention is the comprehensiveness of the evaluation system, and also considers the reliability of the traffic network, the electric automobile and the energy storage system and the coupling reliability of each network.
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 safety energy supply capability and energy supply quality (namely energy internet safety energy supply capability, which can be simply called as safety energy supply capability)
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 secure power capability can also be seen as the reliability of the energy internet power-generating side element, i.e. the device. The evaluation of the energy supply side should include whether the quality of the energy supplied to the customer is acceptable, in addition to ensuring safe operation of the system, reducing the occurrence of faults, reducing system downtime, etc.
The energy internet security capability may include the following indicators:
(1) Power supply reliability index: the reliability of the electric energy generating element such as a coal-fired power plant, a gas power plant, a combined cycle power plant, a cogeneration power plant, a hydroelectric power plant, a nuclear power plant, a wind power plant, a centralized solar power generation base, distributed photovoltaic power generation equipment, a transformer failure rate, a repair rate, an average failure interval time, an average repair time and the like, and power supply power quality and the like.
The equipment failure rate refers to the percentage of accident (failure) downtime and equipment start-up time, and is an index for checking the technical state, failure strength, maintenance quality and efficiency of equipment. The repair rate refers to the ratio of the total number of failures that a product is repaired at any given repair level to the total time of repair at that level under specified conditions and for a specified period of time. The average failure interval time refers to the average time that a product or system is operating properly during two adjacent failure intervals, also referred to as average failure-free operating time. It is an amount that marks how long a product or system can operate on average. The average repair time is an average value describing repair time when the product is changed from a fault state to an operating state. The power quality index of the power supply has various description modes, and the power quality problem refers to deviation of voltage, current or frequency which causes the fault or can not work normally of the electric equipment.
Compared with the traditional generator set, the new energy set depends on natural factors such as wind, light, heat and the like more, has higher uncertainty and randomness, increases the modeling complexity of the power system, presents 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) Gas supply reliability index: the method comprises the steps of fault rate, repair rate, average fault interval time, average repair time, air supply quality and the like of centralized air supply equipment, voltage regulators and the like.
(3) Heat supply reliability index: equipment failure rate, repair rate, average failure interval time, average repair time, heat supply quality and the like of a heat supply unit and the like.
(4) Cold supply safety reliability index: the equipment failure rate, repair rate, average failure interval time, average repair time and the like of the central air conditioner and the like.
2. Robustness of energy internet topology (also referred to as energy internet topology reliability, which may be referred to simply as topology reliability)
The network in the energy internet is a bridge and a medium for realizing complex and multidimensional 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 considers not only the respective network structure but also the coupling between the respective network structures. The energy Internet realizes interconnection, intercommunication and fusion of the multi-energy systems, the risk assessment of a single network is no longer satisfactory, the system coupling risk of the energy Internet needs to be assessed on the basis of a critical conversion element, and the network robustness of the multi-energy systems is analyzed on the basis of a complex network theory.
The reliability of the energy internet topology may include the following indicators:
(1) Electric network reliability index: including node vulnerability, line vulnerability and maximum power supply area, probability index, frequency index, time index, etc. The node vulnerability index and the line vulnerability index point out 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: after the power grid breaks down, the ratio of the power-available load in the maximum power supply area to the total load of the power grid. The probability index is the probability of the equipment or the system realizing the specified function, and comprises the probability of insufficient power and the probability of insufficient electric quantity. The frequency index is the average number of faults in the unit time of the system, and the common index is mainly the frequency of insufficient power. The time index is the average duration of the failure of the system, the expected value index is the expected number of days of failure in one year of the system, and the common index is the expected value of the system power failure time. Wherein, the device means to realize a single function, and the system means to realize the function of the whole framework.
(2) Natural gas network reliability index: natural gas is also a toxic gas with the characteristics of inflammability, explosiveness and the like, and the natural gas is transported and used for a long period of time in China, and pipelines and infrastructures for transporting and distributing the natural gas exist in various cities, so that the gas transportation and distribution system has high requirements on the stability of the gas transportation and distribution system in order to ensure the life and property safety of people. The indexes mainly comprise accumulated flow, anoxic gas quantity, gas utilization satisfaction, insufficient gas supply duration, insufficient gas supply times and the like.
(3) Heat supply network reliability index: the criterion of the normal state of the heating network system is that all heat users can guarantee the minimum heat supply quantity, and the indexes can comprise indexes such as annual heat supply shortage of the system, heat supply rate of the system, connectivity of users, hydraulic characteristics, pipe diameter limit value of long-distance pipeline, pipe length limit value, segment valve spacing limit value and the like.
(4) Traffic network reliability index: the charging pile is reasonable in arrangement, the cooperative reliability of the electric vehicle and the public network, the coordinated scheduling reliability of the electric vehicle and renewable energy sources 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 comprise network failure rate, network reliability and the like.
(6) Conversion element/system coupling reliability (also referred to as conversion element reliability): the multi-energy system is characterized in that key elements for realizing multi-energy conversion and transmission functions are necessarily present in the multi-energy system, reliability analysis is the key point of the whole energy system, conversion elements refer to key elements for realizing energy conversion in the energy internet and are coupling nodes among different energy systems, wherein conversion elements such as electric conversion equipment, a gas generator set and a combined cycle generator set realize interconnection and coupling of an electric system and a natural gas system, a heat pump realizes mutual coupling of an electric network and a heat network, a cogeneration power plant realizes mutual coupling of an electric network, a heat network and a gas network, and an electric vehicle charging pile realizes coupling of an electric system and a traffic system. The charging pile running state can influence the traditional power distribution network bearing capacity indexes such as voltage level qualification rate, reactive power configuration, single-circuit line safe running state, short-time load rate, network and equipment loss, reactive power compensation consumption and the like. The reliability indexes are the equipment failure rate, the repair rate, the average failure interval time, the average repair time and the like.
3. Energy stability for energy Internet (i.e. energy Internet load stability, which may be simply referred to as load stability)
The energy consumption element refers to an energy consumption unit of the energy internet, is a terminal for energy transmission and utilization, has a wider meaning, and can generally refer to energy consumption equipment in all industries and residential users, such as heat pumps, gas boilers, electric automobiles and the like. The electric automobile is a novel clean and low-carbonization vehicle and is also an important development direction of electrified traffic in the future. For the energy internet, electric automobiles are a special class of elements: on one hand, an electric automobile is an important load of an electric power system as a consumer of electric energy; on the other hand, the electric energy stored in the electric automobile can flow back to the electric power system through the charging pile, and is an energy storage device of the electric power system. Meanwhile, the electric automobile charging pile is also a key coupling element of a power system and a traffic system.
The energy consumption 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 adjustment occurred during the study period. The load adjustment duration refers to the duration during which the user power is in a reduced or increased state during the study period. The load adjustment electric quantity refers to the total quantity of the electric quantity change of a user in a research period.
(2) Heat load stability index: heating building thermal inertia, thermal equipment reliability, load adjustment frequency, load adjustment duration.
(3) Natural gas load stability index: gas usage reliability, load regulation frequency, load regulation duration.
(4) Traffic load stability index: the charging pile is safe and reliable, the reliability of the electric automobile, the stability of impact to the power grid (the stability of impact to the power grid when the electric automobile is charged), the service life of the battery (namely the battery of the electric automobile) and the like. Wherein, electric automobile evaluation index includes the reliability: the probability of completing a specified function is satisfactory under specified conditions and within specified test mileage. Failure rate: the probability of the automobile to fail in unit mileage is 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). Probability density of failure: the density of the probability of failure of the vehicle per unit mileage.
4. Energy internet energy storage system reliability (which may be simply referred to as energy storage system reliability)
The energy storage element realizes long-time and large-scale storage of energy, including pumped storage hydropower stations, battery energy storage, hydrogen energy storage, 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 pattern 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 index refers to the average amount of electricity released by the energy storage device per discharge over a research period.
(2) Charging mode reliability index: including the number of charges and the average depth of charge.
(3) The reliability index of the energy storage system is used for the electric automobile: including the operating laws of electric vehicles, battery characteristics, vehicle scale and electric energy supply modes.
The invention further provides a method for establishing the energy Internet reliability assessment model.
In an exemplary embodiment of the method for establishing an energy internet reliability assessment model of the present invention, the method may comprise the steps of:
s10: and collecting and classifying information of the energy Internet to obtain the index system for evaluating the reliability of the energy Internet. The primary index, the secondary index and the tertiary index are obtained. The method can be classified according to the source, the network, the load and the storage, for example, the first-level index can be obtained after being classified: the method can be used for evaluating the safety 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 reliability of an 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 energy Internet industrial standard. The energy internet industry standard and calibration method can be standard and method conventional in the art.
And establishing an evaluation model according to each index and the calibration result of each index by using a deep learning algorithm. The deep learning algorithm may include a deep learning algorithm existing in the art, among others.
In another exemplary embodiment of the method for establishing an energy internet reliability evaluation model of the present invention, the method may include the steps of:
step 1: and establishing a model input sample matrix.
And selecting numbers from the actual monitoring or database of the energy Internet seeds to form an index input sample matrix.
Taking power supply reliability as an example: selecting the data values of the voltage U, the frequency f, the active power P and the reactive power Q of each key node of the energy Internet in a characteristic time period T, and forming a model input sample matrix as shown in the following formula:
the voltage U, the frequency f, the active power P and the reactive power Q are represented by subscripts (i, j), wherein the first subscript i represents the ith sample, and the second subscript j represents the jth time acquisition point.
Step 2: and (5) inputting data stability judgment and marking.
According to the energy Internet industry standard, the reliability of input sample data is calibrated, and each index has different judging modes. For example, calibrating the stability of the input voltage data may include: the voltage U value at a specific time is used to determine whether the voltage is stable, if the voltage U value at the node can be recovered to more than 0.8 times of the standard value, the voltage is regarded as stable, and if the voltage U value at the node cannot be recovered to 0.8 times of the standard value, the voltage is regarded as unstable, and the voltage is marked as 0.
Step 3: and (5) data expansion.
In consideration of imbalance of positive and negative samples in reliable and unreliable conditions in actual conditions, a translation window method is used for expanding input sample data, and bias is avoided in the training process.
Step 4: convolutional neural network model building
The convolutional neural network is built and 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 extracts the characteristics of the input layer by carrying out convolution operation on the input layer and the convolution kernel weight matrix, and the pooling layer performs downsampling on the characteristic mapping to play a role in secondarily extracting the characteristics.
The convolution layer of the convolution neural network extracts the characteristics of the input layer by carrying out convolution operation on the input layer and the convolution kernel weight matrix, the front layer of the convolution layer extracts low-level characteristics, and the rear layer of the convolution layer extracts higher-level characteristics. The calculation of the convolutional layer can be expressed by the following formula:
wherein W is i Weight vector representing convolution kernel of the i-th layer, operator ">"means that the i-th layer convolution kernel performs convolution operation with the i-1-th layer feature, b i And f (x) is an excitation function, wherein the offset vector is an i-th layer.
And weight sharing is carried out on the same input characteristic surface and the same output characteristic surface in the convolutional neural network, so that the number of parameters is controlled, the complexity of a neural network model is reduced, and the difficulty of training the model is reduced.
The convolutional layer is typically followed by a pooling layer. The pooling layer is used for downsampling the feature map and has the function of secondarily extracting features. By moving the windows and taking the maximum value, the minimum value, the average value or the random value of the local values in each window, the input dimension of the next layer of convolution layer can be reduced, so that the operation complexity is reduced, meanwhile, the most obvious feature in the feature mapping can be extracted, the more abstract feature in the high-level expression is obtained, and the influence of local tiny change or phase translation on feature extraction is reduced. The pooling layer may be represented by the following formula:
Wherein (1)>Representing the output value of the q-th neuron in the n-th input feature plane of the pooling layer, f sub (x) For averaging functions, random number functions or maximum value functions, etc.
In the convolutional neural network, after a plurality of convolutional layers and pooling layers are combined, the convolutional neural network ends with a full connection layer and is connected with an output layer. Each neuron in the full-connection layer is fully connected with all neurons of the previous layer, so that local characteristic information extracted by the convolution layer and the pooling layer of each previous layer is obtained, integrated and output. The output value of the last fully-connected layer is typically passed through a softmax layer, classified using soft-max logistic regression, and passed to the output layer.
In the training process of the convolutional neural network, firstly, calculating a difference residual error between an output value and an expected value through forward propagation, then, applying a gradient descent method to reversely propagate the residual error, and updating a convolutional kernel weight matrix and a bias vector of each layer in the convolutional neural network layer by layer to finally achieve the aim of minimizing a loss function.
Step 5: and (5) offline training.
And performing offline training by using the historical data to obtain an evaluation model. In this example, the model output result is classified as reliable by classifying a value equal to or greater than 0.5 into 1 and classifying a value less than 0.5 into 0, respectively, with 0.5 as a boundary.
The trained assessment model may then be applied to an online real-time reliability state assessment process.
In yet 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 assessment of the present invention, the assessment method comprises the steps of:
steps S10 and S20, and S30, which are the same as those in the exemplary embodiment of the above-described method for establishing an energy internet reliability evaluation model: and evaluating the reliability of the energy Internet according to the calibration result.
In another exemplary embodiment of the method for evaluating reliability of energy internet of the present invention, the method for evaluating reliability may include: the method for establishing the energy internet reliability assessment model is adopted to establish the assessment model; and carrying out energy Internet reliability assessment by using the assessment model.
In still another exemplary embodiment of the method for energy internet reliability assessment of the present invention, as shown in fig. 1, the method for energy internet reliability assessment may include: firstly classifying and sorting various state information of source network charge storage collected by the existing state monitoring system of the system, wherein the information comprises on-line monitoring and historical data in a database; and carrying out a reliability evaluation process on the required information acquired from the monitoring system, wherein the evaluation process mainly comprises two parts: the evaluation system establishes an application of an evaluation algorithm. The evaluation system is to scientifically classify the composition of the energy internet according to four aspects, establish evaluation indexes and finally form a clear-level evaluation system, for example, the index system for evaluating the reliability of the energy internet can be used. The evaluation algorithm is to build an evaluation model by using the proposed deep learning convolutional neural network algorithm, and then to generate an energy internet reliability evaluation result by using the evaluation model.
In summary, the index system, the evaluation method and the evaluation model establishing method for evaluating the reliability of the energy internet according to the present invention may have the following advantages:
(1) The index system of the invention has more scientificalness in evaluating the reliability of the energy Internet.
(2) The invention has more comprehensive evaluation, and besides the reliability of the conventional power supply, cooling, heating and air supply systems, the reliability of the traffic network, the electric automobile and the energy storage system and the coupling reliability of each network are also considered.
Although the present invention has been described above by way of the combination of the exemplary embodiments, it should be apparent to those skilled in the art that various modifications and changes can be made to the exemplary embodiments of the present invention without departing from the spirit and scope defined in the appended claims.
Claims (7)
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 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 safety 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 an energy storage system of the energy internet;
Calibrating the classified first, second, third and fourth indexes according to the energy Internet industrial standard, and obtaining a calibration result;
establishing an evaluation model according to the first, second, third and fourth indexes and the calibration result by using a deep learning algorithm;
the first class of indicators includes: a power supply reliability index, a heat supply reliability index, a gas supply reliability index and a cooling reliability index; the second category of indicators includes: electrical network reliability, natural gas network reliability, heating network reliability, cooling network reliability, traffic network reliability, information network reliability, and conversion element reliability; the third class of indicators includes: electrical load stability, thermal load stability, natural gas load stability, and traffic load stability; the fourth class of indicators includes: the reliability of the discharging mode, the reliability of the charging mode and the reliability of energy storage for the electric automobile;
the steps of calibrating the classified first, second, third and fourth indexes according to the energy internet industrial standard and obtaining a calibration result comprise the following steps: the classified first, second, third and fourth indexes are calibrated according to the energy Internet industrial standard to obtain a first calibration result which can be used for evaluating the safety 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; according to the first, second, third and fourth calibration results, obtaining a calibration result which can be used for the reliability of the energy Internet;
The establishing method further comprises the steps of: expanding the obtained index by using a method of translating a window;
the method further comprises the steps of: and performing offline training on the evaluation model by using the historical data.
2. The method for building an energy internet reliability assessment model according to claim 1, wherein the deep learning algorithm comprises: deep learning convolutional neural network algorithm.
3. The method for building an energy internet reliability evaluation model according to claim 1, wherein the step of calibrating the categorized first class index according to an energy internet industry standard and obtaining a calibration result comprises:
calibrating the power supply reliability index, the heat supply reliability index, the air supply reliability index and the cooling reliability index according to the energy Internet industrial standard respectively, and obtaining a power supply reliability calibration result, a heat supply reliability calibration result, an air supply reliability calibration result and a cooling reliability calibration result;
and obtaining a calibration result which can be used for evaluating the safety energy supply capacity of the energy Internet according to the power supply reliability calibration result, the heat supply reliability calibration result, the air supply reliability calibration result and the cooling reliability calibration result.
4. The method for building an energy internet reliability assessment model according to claim 3, wherein the power supply reliability index comprises: at least one of failure rate, repair rate after failure, time between failures, repair time after failure, and power quality of power supply equipment, the power supply equipment including at least one of power generation equipment and a transformer;
the heat supply reliability index includes: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a heating quality of the heating apparatus;
the air supply reliability index includes: at least one of a failure rate of the air supply device, a post-failure repair rate, a failure interval time, a post-failure repair time, and an air supply quality;
the cooling reliability index comprises: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a cooling quality of the cooling apparatus.
5. The method for building an energy internet reliability assessment model according to claim 4, wherein the step of calibrating the power supply reliability index 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 calibration result of the power supply reliability according to the quantity which accords with the energy Internet industrial standard in the calibration results of the indexes, wherein the calibration result of the power supply reliability is considered to be reliable under the condition that the quantity is above a preset value, and otherwise, the calibration result of the power supply reliability is not reliable.
6. A method for evaluating the reliability of an energy internet, the method comprising the steps of:
collecting information of the energy Internet and classifying the information to obtain a first type index, a second type index, a third type index and a fourth type index, wherein the first type index can be used for evaluating the safety energy supply capacity of the energy Internet, the second type index can be used for evaluating the reliability of the topological structure of the energy Internet, the third type index can be used for evaluating the load stability of the energy Internet, and the fourth type 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;
According to the calibration result, evaluating the reliability of the energy Internet;
the first class of indicators includes: a power supply reliability index, a heat supply reliability index, a gas supply reliability index and a cooling reliability index; the second category of indicators includes: electrical network reliability, natural gas network reliability, heating network reliability, cooling network reliability, traffic network reliability, information network reliability, and conversion element reliability; the third class of indicators includes: electrical load stability, thermal load stability, natural gas load stability, and traffic load stability; the fourth class of indicators includes: the reliability of the discharging mode, the reliability of the charging mode and the reliability of energy storage for the electric automobile;
the step of calibrating the classified first, second, third and fourth indexes according to the energy internet industry standard comprises the following steps: the classified first, second, third and fourth indexes are calibrated according to the energy Internet industrial standard to obtain a first calibration result which can be used for evaluating the safety 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.
7. An index system for evaluating the reliability of the energy internet, which is characterized by comprising a first-level index: safety power capability, topology reliability, load stability, and energy storage system reliability, wherein,
the safety capability includes secondary indicators: at least one of power supply reliability, heat supply reliability, air supply reliability and cooling reliability;
the topology reliability includes a secondary index: at least one of electrical network reliability, natural gas network reliability, heating network reliability, cooling network reliability, traffic network reliability, information network reliability, and conversion element reliability;
load stability includes secondary indicators: at least one of electrical load stability, thermal load stability, natural gas load stability, and traffic load stability;
the energy storage system reliability includes secondary indicators: at least one of discharge mode reliability, charge mode reliability, and energy storage reliability for an electric vehicle;
the power supply reliability comprises three levels of indexes: at least one of failure rate, repair rate after failure, time between failures, repair time after failure, and power quality of power supply equipment, the power supply equipment including at least one of power generation equipment and a transformer;
The heat supply reliability comprises three levels of indexes: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a heating quality of the heating apparatus;
the air supply reliability comprises three levels of indexes: at least one of a failure rate of the air supply device, a post-failure repair rate, a failure interval time, a post-failure repair time, and an air supply quality;
the cooling reliability comprises three levels of indexes: at least one of a failure rate, a post-failure repair rate, an inter-failure time, a post-failure repair time, and a cooling quality of the cooling apparatus;
the electrical network reliability includes three levels of indicators: at least one of node vulnerability, line vulnerability, maximum power supply area, probability index, frequency index and 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 breaks down, the probability index comprises the probability of the equipment or the system realizing the specified function, the frequency index is the average number of faults in the unit time of the system, and the time index comprises the average duration of faults of the system;
the natural gas network reliability comprises three levels of indexes: at least one of cumulative flow, anoxic amount, gas usage satisfaction, insufficient supply duration, and number of insufficient supply times;
The reliability of the heating network comprises three levels of indexes: the system is characterized by comprising at least one of system annual heat supply deficiency, 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 value, pipe length limit value and segment valve spacing limit value;
the traffic network reliability comprises three levels of indexes: at least one of rationality of charging pile setting, cooperative reliability of electric vehicles and a public network, and cooperative scheduling reliability of electric vehicles and renewable energy sources;
the information network reliability includes three levels of indicators: at least one of failure rate of the information network and reliability of the information network;
the conversion element reliability includes three levels of indicators: the conversion element comprises at least one of electric conversion equipment, a gas generator set, a combined cycle generator set, cogeneration equipment and an electric car charging pile;
the electrical load stability includes three levels of indicators: at least one of consumer reliability, consumer load adjustment frequency, consumer load adjustment duration, and consumer load adjustment power;
The thermal load stability comprises three levels of indicators: at least one of heating building thermal inertia, thermal plant reliability, thermal plant load adjustment frequency, and thermal plant load adjustment duration;
the natural gas load stability comprises three levels of indexes: at least one of gas usage reliability, gas usage load adjustment frequency, and gas usage load adjustment duration;
the traffic load stability comprises three levels of indicators: the charging pile is 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;
the discharge mode reliability includes three levels of indicators: at least one of capacity, number of discharges, and average depth of discharge of the energy storage system;
the charging mode reliability includes three levels of indicators: at least one of capacity, number of charges, and average depth of charge of the energy storage system;
the energy storage reliability for the electric automobile comprises three levels of indexes: at least one of an operation rule, battery characteristics, an automobile scale and an electric energy supply mode of the electric automobile.
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