Dynamic flexible photovoltaic box transformer substation operation cost evaluation system and method

Abstract

The invention discloses an operation cost evaluation system and method for a dynamic flexible photovoltaic box transformer substation. The operation cost evaluation system comprises a plurality of functional modules. The first data acquisition module acquires task data and area setting data of an evaluated project, and the second data acquisition module acquires illumination data and power consumption data. And the data processing module extracts task keywords through word segmentation of a natural semantic algorithm, and matches related equipment parameters, operation conditions and operation and maintenance data from a historical project database. The scheduling simulation module constructs a first photovoltaic scheduling network according to area setting and equipment parameter data, and determines an expected power consumption scheduling amount; and the historical simulation module traverses the historical project database to construct a second photovoltaic scheduling network and an actual power consumption scheduling amount. And the scheduling calculation module generates a scheduling calculation data set through matching, and determines a power consumption scheduling probability sequence. And the cost evaluation module generates an expected operation condition and operation and maintenance data according to the sequence to obtain a cost sequence so as to evaluate the operation cost of the dynamic flexible photovoltaic box transformer substation.

Description

Technical Field

[0001] The present invention belongs to the field of cost assessment models and big data technology, and specifically relates to an operating cost assessment system and method for a dynamic flexible photovoltaic box transformer. Background Art

[0002] As the global demand for clean energy grows day by day, photovoltaic power generation, as an important way to utilize renewable energy, is rapidly becoming popular and developing on a large scale. Dynamic flexible photovoltaic box transformers play a key role in photovoltaic power stations. They not only need to adapt to the intermittent and fluctuating characteristics of photovoltaic power generation output, but also need to ensure efficient and stable power conversion and transmission. However, accurately evaluating its operating costs faces many challenges.

[0003] On the one hand, the operation of photovoltaic box transformers involves many complex factors, including task allocation, multiple parameters of the equipment itself, complex and changeable operating conditions, and diverse operation and maintenance activities. Traditional cost assessment methods mostly rely on simple empirical formulas and rough estimates, which are difficult to comprehensively consider these complex factors, resulting in poor accuracy of assessment results. On the other hand, the massive historical operation data and cost records have not been effectively utilized, resulting in a large deviation between the cost assessment results and the actual expenditure, which cannot provide reliable support for the planning, operation and investment decisions of photovoltaic power stations.

[0004] Therefore, there is an urgent need for a dynamic flexible photovoltaic box-type transformer operation cost evaluation system and method. Summary of the invention

[0005] To this end, the present invention provides an operating cost evaluation system and method for a dynamic flexible photovoltaic box transformer.

[0006] A first aspect of the present invention provides an operating cost evaluation system for a dynamic flexible photovoltaic box transformer, comprising: A first data acquisition module is configured to acquire task data and regional setting data of the project to be evaluated; A second data acquisition module is configured to acquire lighting data and electricity consumption data of the project being evaluated; A data processing module is configured to process and extract the task keywords of the project to be evaluated by using a natural semantic algorithm, match the task keywords from a historical project database, and obtain matched equipment parameter data, operating condition data, and operation and maintenance data; A scheduling simulation module is configured to construct a plurality of first photovoltaic scheduling networks with photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes according to the regional setting data and the equipment parameter data, and determine an expected power scheduling amount of one of the simulation nodes to another simulation node according to the illumination data and the power consumption data; A historical simulation module is configured to traverse the historical project database, construct a second photovoltaic dispatching network for each historical project, and determine an actual power dispatching amount in each of the second photovoltaic regulation networks; a scheduling and calculation module configured to match the actual power dispatch amount in each of the second photovoltaic scheduling networks with the expected power dispatch amount, generate multiple groups of scheduling and calculation data sets, and determine the power dispatch probability sequence of each node in the first photovoltaic scheduling network through the scheduling and calculation data sets; The cost evaluation module generates, according to the power dispatch probability sequence, the expected operating condition data and the expected operation and maintenance data of each of the first photovoltaic dispatch networks at the unit position where the power dispatch probability sequence is currently located, and obtains a cost sequence for the power dispatch probability sequence.

[0007] As a preferred embodiment, it also includes a storage module, which is configured to receive data when the first data acquisition module, the second data acquisition module, the data processing module, the scheduling simulation module, the historical simulation module, the scheduling measurement module and the cost evaluation module execute their respective configuration items, and generate corresponding log text with a timestamp.

[0008] As a preferred embodiment, it further comprises an output module, wherein the output module is configured to receive the data of each unit position of the cost sequence generated by the cost evaluation module and output the data to the carrier.

[0009] As a preferred embodiment, the carrier includes an electronic carrier and a non-electronic carrier; The electronic carrier is any one or more of a volatile storage carrier, a non-volatile storage medium or a display terminal; The non-electronic carrier at least includes a paper carrier.

[0010] A second aspect of the present invention provides an operating cost evaluation method for a dynamic flexible photovoltaic box transformer, which is suitable for implementing the system of the first aspect of the present invention, and comprises the following steps: S1. Obtaining task data and regional setting data of the project to be evaluated; S2, obtaining the lighting data and electricity consumption data of the project under evaluation corresponding to the regional setting data; S3, building a historical project database and generating multiple text keywords for each historical project; S4, processing the task keywords of the evaluated project by natural semantic algorithm segmentation, and matching multiple historical projects with a preset similarity threshold in the historical project database according to the task keywords; Obtain equipment parameter data, operating condition data, and operation and maintenance data for multiple historical projects; S5, constructing a first photovoltaic dispatching network, using photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes, and setting energy load nodes between two adjacent simulation nodes; Setting energy supply data of the simulation node according to the illumination data; Set energy consumption data of energy load nodes according to electricity consumption data; Determine the expected power dispatch amount according to the difference between the energy supply data and the energy consumption data between two adjacent simulation nodes; S6. Construct a second photovoltaic dispatching network based on historical projects, which has the same settings as the first photovoltaic dispatching network, and writes the actual power dispatching amount recorded in the historical projects instead of the expected power dispatching amount; S7, under the same energy supply data and power consumption data, performing matching of power dispatching amounts between the first photovoltaic dispatching network and the second photovoltaic dispatching network; S8, generating the actual power dispatch amount of each simulation node and the power dispatch probability sequence with the probability as a unit according to S7; S9. Generate expected operating condition data and expected operation and maintenance data of each first photovoltaic scheduling network at a unit location currently located in the power scheduling probability sequence according to the power scheduling probability sequence; S10. The probability in the power dispatch probability sequence is used as a quantitative pricing parameter to calculate the expected operating condition cost and the expected operation and maintenance cost, and the procurement cost is calculated according to the regional setting data and equipment parameters to obtain the cost sequence of the power dispatch probability sequence within a preset period.

[0011] As a preferred method, in S3, generating multiple text keywords for each historical item includes the following steps: The actual setting situation of the historical project is set as a semantic network with the photovoltaic box transformer and / or photovoltaic box transformer cluster as the semantic center; Extracting the associated words of each semantic center, including the energy supply data, energy consumption data and illumination data corresponding to the energy supply radiation range of each photovoltaic box transformer and / or photovoltaic box transformer cluster; Generate text keywords for each semantic center, and perform semantic connection on multiple text keywords according to whether they have energy supply scheduling relationship with adjacent photovoltaic box transformers and / or photovoltaic box transformer clusters; Generate indexed text keywords including connection relationships.

[0012] As a preferred embodiment, the step S7 specifically includes the following steps: Perform matching on a single simulation node; Obtaining energy supply data of the single simulation node and the sum of energy consumption data of each of its adjacent locations; From the historical projects, obtain one or more nodes corresponding to the sum of the energy supply data and the energy consumption data within a preset interval; Traversing and performing matching of each of the single simulation nodes, determining a first power scheduling amount of each of the single simulation nodes, wherein the first power scheduling amount is a total amount of electricity that is exchange-scheduled by the single simulation node; Match the total amount of scheduling performed by each single simulation node to non-adjacent nodes in multiple historical projects, and determine its scheduling probability; Add a connection relationship between a single simulation node and its adjacent nodes, and perform the same steps as matching a single simulation node; Matching the probability of a scheduling relationship between the single simulation node and its adjacent nodes in multiple historical projects; Temporarily increase the total number of adjacent nodes connected to the single simulation node according to the sequence number until all connection relationships of the single simulation node are determined, and obtain the probability that the single simulation node has a scheduling relationship under the current connection relationship when each adjacent node is temporarily added; Obtain the probability of a scheduling relationship for a single simulation node in full-blind state, single-blind state, multi-blind state and non-blind state; Generate various power dispatch probability sequences under full-blind state, single-blind state, multi-blind state and non-blind state.

[0013] As a preferred embodiment, the step S10 of calculating the operating cost specifically includes the following steps: Obtaining a power consumption scheduling probability sequence of each simulation node in a non-blind state; Determine a load duty cycle of each simulation node when not scheduled, wherein the load duty cycle is a ratio between a full load and a load within a predetermined load ratio; Determine the power scheduling exchange amount and load condition executed by each simulation node under the current power scheduling probability, and determine the load duty cycle at this time; Calculate the expected operating condition cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; Calculate the expected operation and maintenance cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; generating a cost of the value of each unit in the power dispatch probability sequence; Obtain the cost sequence within the preset period to complete the evaluation.

[0014] As a preferred embodiment, after S10, the process further includes S11, estimating the operating cost under fault disconnection, including the following steps: Obtaining the failure rate of nodes in the second photovoltaic scheduling network that matches the single simulation node in the historical project database, Under the failure rate, a power dispatch probability sequence corresponding to the single-blind state and the multi-blind state of the node is obtained; The operation cost calculation of S10 is performed according to the power dispatch probability sequence.

[0015] As a preferred method, after each execution of the evaluated project, the power dispatch probability sequence in the full-blind state, the single-blind state, the multi-blind state and the non-blind state is written into the historical project database; The actual scheduling relationship of the sampled evaluated project at each node in the preset time period of the power scheduling probability sequence is used to calculate the difference between the two as the correction value; After obtaining multiple groups of evaluated items, all differences are averaged to obtain the average correction value.

[0016] The above technical solution of the present invention has the following advantages compared with the prior art: The present invention can comprehensively acquire various key data of the project being evaluated, including task data, regional setting data, lighting data, and electricity consumption data, etc., through the first data acquisition module and the second data acquisition module. The data processing module uses natural semantic algorithm word segmentation processing to accurately extract task keywords and match relevant data from the historical project database, providing comprehensive and accurate data support for subsequent evaluation. For example, when faced with different types of photovoltaic project tasks, it can quickly and accurately find relevant parameters of similar historical projects, thereby improving evaluation efficiency and accuracy.

[0017] The present invention constructs multiple first photovoltaic dispatching networks according to regional setting data and equipment parameter data through the dispatching simulation module, and determines the expected power dispatching amount in combination with the light and power consumption data. At the same time, the historical simulation module constructs the second photovoltaic dispatching network of each historical project to determine the actual power dispatching amount. Matching is performed through the dispatching and calculation module to generate a power dispatching probability sequence, which can accurately simulate the power dispatching situation of the photovoltaic box transformer in actual operation. This helps to more accurately grasp the energy flow within the photovoltaic system and provide a basis for optimizing the dispatching strategy.

[0018] The present invention also generates expected operating condition data and expected operation and maintenance data according to the power scheduling probability sequence through a cost evaluation module, thereby obtaining a cost sequence. This evaluation method fully considers the relationship between the power scheduling probability and the operating condition and operation and maintenance data, and can accurately calculate the operating cost. For example, when calculating the cost, by determining the load duty cycle of the simulation node under different power scheduling probabilities, the expected operating condition cost and the expected operation and maintenance cost are calculated in combination with the historical project database, so that the cost evaluation result is more in line with reality, and a reliable cost reference is provided for the investment decision and operation management of the project. After each execution of the evaluated project, the power scheduling probability sequence is written into the historical project database, and the difference with the actual scheduling relationship is calculated as a correction value, and the average correction value is obtained after obtaining multiple groups of evaluated projects. This method can continuously optimize the evaluation model to make it more in line with the actual operating conditions and improve the accuracy and reliability of the evaluation.

[0019] The present invention also receives data from the execution of each module through the storage module and generates a log text with a timestamp, which facilitates the tracing and data analysis of the evaluation process. This helps the project team to review the evaluation process, find problems in time and optimize them, and also provides rich historical data resources for the evaluation of subsequent similar projects; the output module can output the cost series data to a variety of carriers, including electronic carriers (such as volatile storage carriers, non-volatile storage media, display terminals) and non-electronic carriers (such as paper carriers), meeting the needs of different users for data acquisition and use, and improving the availability and dissemination of evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of module connection of the system provided in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] In a first aspect of the embodiments of the present disclosure, a system for evaluating the operating cost of a dynamic flexible photovoltaic box transformer is provided. Figure 1 As shown, it includes a first data acquisition module, a second data acquisition module, a data processing module, a scheduling simulation module, a historical simulation module, a scheduling calculation module and a cost evaluation module.

[0023] In the embodiment of the present disclosure, the first acquisition module is configured to obtain the task data and regional setting data of the project to be evaluated. Specifically, the power supply task and sub-regional setting of the project to be evaluated in its set area are obtained from the contract and plan for the project implementation as the task data and regional setting data, wherein the power supply task data is the data of power supply executed by the photovoltaic box transformer and the expected ratio of power supply to non-photovoltaic power generation in the area; the second acquisition module is configured to obtain the illumination data and power consumption data of the project to be evaluated, the illumination data is the illumination time within a preset time period and the light intensity data available to the photovoltaic power station, and the power consumption data is the total power consumption data of the area where the project to be evaluated is located. It should be noted that in the embodiment of the present disclosure, the first data acquisition module and the second data acquisition module are only the configuration items to distinguish the two, and the two can be the configuration items of the first data acquisition module and the second data acquisition module executed by the same electronic device, terminal or module.

[0024] In the disclosed embodiment, the data processing module is configured to process and extract the task keywords of the project being evaluated by using a natural semantic algorithm, match the task keywords from the historical project database, and obtain the matched equipment parameter data, operating condition data, and operation and maintenance data. It should be noted that in this field and the field of natural semantic algorithms, extracting specific parts of speech, meanings, and categories of vocabulary by using natural semantic algorithms is a conventional technical means, which will not be elaborated here.

[0025] The scheduling simulation module is configured to construct multiple first photovoltaic networks with photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes according to the regional settings and equipment parameters, and determine the expected power scheduling amount of one simulation node to another simulation node based on the lighting data and power consumption data. In the embodiments of the present disclosure, a photovoltaic box transformer refers to an independent dynamic flexible photovoltaic box transformer box with energy supply function, and a photovoltaic box transformer cluster is a cluster power supply device composed of multiple dynamic flexible photovoltaic box transformer boxes. In one embodiment, a photovoltaic box transformer or a photovoltaic box transformer cluster can be selectively set according to the area, power supply radiation range and power demand (energy consumption data) of different areas in the regional setting data. Therefore, in the embodiments of the present disclosure, photovoltaic box transformers for the same energy supply area and set in a cluster are also set as simulation nodes. In addition, it is still necessary to explain that in this field, there are multiple photovoltaic box transformers or photovoltaic box transformer clusters in the preset regional setting data, and there are overlapping areas in the power supply radiation range. In the embodiments of the present disclosure, these energy supply radiation ranges that have partial area overlaps but do not have the characteristics of cluster setting are not regarded as the same photovoltaic box transformer cluster. In this field and the embodiments of the present disclosure, these multiple simulation nodes with overlapping energy supply radiation ranges are allocated according to the proportion of the overlapping coverage area to the energy supply radiation range or the ratio of the energy supply data (electric energy transmitted from the photovoltaic power generation equipment to the dynamic flexible photovoltaic box transformer) between two simulation nodes.

[0026] The historical simulation module in the disclosed embodiment is configured to traverse the historical project database, construct a second photovoltaic scheduling network for historical projects, and determine the actual power scheduling amount in the second photovoltaic scheduling network. In the disclosed embodiment, the historical project database stores multiple historical projects of dynamic flexible photovoltaic box transformer construction that have been implemented, and stores the energy supply data, energy consumption data, area setting data and lighting data of these historical projects.

[0027] The scheduling and calculation module is configured to perform a match between the actual power scheduling amount in the second photovoltaic scheduling network and the expected power scheduling amount in the first photovoltaic scheduling network, generate multiple groups of scheduling and calculation data sets, and determine the power scheduling probability sequence of each node in the first photovoltaic scheduling network through the scheduling and calculation data sets.

[0028] The cost evaluation module in the disclosed embodiment generates the expected operating condition data and expected operation and maintenance data of each first photovoltaic scheduling network at the current unit position of the power scheduling probability sequence according to the power scheduling probability sequence, obtains the cost sequence about the power scheduling probability sequence, and then completes multiple cost evaluation results for the evaluated project.

[0029] As a preferred embodiment of the present disclosure, it also includes a storage module, which is configured to receive data when the first data acquisition module, the second data acquisition module, the data processing module, the scheduling simulation module, the historical simulation module, the scheduling measurement module and the cost evaluation module execute their respective configuration items, and generate corresponding log text with a timestamp.

[0030] As a preferred embodiment of the present disclosure, it also includes an output module, which is configured to receive data of each unit position of the cost sequence generated by the cost evaluation module and output it to a carrier. Specifically, the carrier includes an electronic carrier and a non-electronic carrier; the electronic carrier is any one or more of a volatile storage carrier, a non-volatile storage medium or a display terminal; the non-electronic carrier includes at least a paper carrier.

[0031] The second aspect of the embodiment of the present disclosure provides an operating cost evaluation method for a dynamic flexible photovoltaic box transformer, which is suitable for implementing the system of the first aspect of the embodiment of the present disclosure. First, it should be explained that the method of the embodiment of the present disclosure is executed in a preset time period and includes the following steps: S1. Obtain task data and regional setting data of the configured project.

[0032] S2. Obtaining the lighting data and electricity consumption data of the project under evaluation corresponding to the regional setting data.

[0033] S3, building a historical project database and generating multiple text keywords for each historical project; As a preferred method of S3, generating text keywords for each historical item includes the following steps: The actual setting situation of the historical project is set as a semantic network with the photovoltaic box transformer and / or photovoltaic box transformer cluster as the semantic center; The semantic center includes the semantic description text of the equipment parameter data, operating condition data and operation and maintenance data of the photovoltaic box transformer; Extract the associated words of each semantic center, including the energy supply data, energy consumption data and illumination data corresponding to the energy supply radiation range of each photovoltaic box transformer and / or photovoltaic box transformer cluster, and use the semantic description of the energy supply data, energy consumption data and illumination data as the semantic connection relationship text; Generate text keywords of the semantic description text and text keywords of the semantic connection relationship text, and perform semantic connection of multiple text keywords within the area setting data defined by the historical project according to whether they have an energy supply scheduling relationship with adjacent photovoltaic box transformers and / or photovoltaic box transformer clusters; Generates indexed text keywords including semantic connections.

[0034] S4, processing the task keywords of the evaluated project by natural semantic algorithm, and matching multiple historical projects with a preset similarity threshold in the historical project database according to the task keywords; Obtain equipment parameter data, operating condition data, and operation and maintenance data for multiple historical projects.

[0035] Specifically, when performing matching in S4 of the embodiment of the present disclosure, matching of task keywords with indexed text keywords is performed, and in the embodiment of the present disclosure and this field, the task data of the evaluated project is the contract and plan for project implementation, and the power supply task and sub-regional setting of the evaluated project in its set area are obtained as task data and regional setting data, wherein the power supply task data is the data of power supply executed by photovoltaic box transformer and the expected ratio of power supply to non-photovoltaic power generation in the area, and then the semantic connection relationship text in the historical project can be matched, and then it is obtained whether there is a match in the regional setting data under the current semantic connection relationship text. In this matching situation, the semantic connection of the indexed text keywords is used to determine the adjacent photovoltaic box transformer and / or photovoltaic box transformer cluster (semantic center), and then obtain the setting method of the evaluated project with reference to multiple historical project setting methods.

[0036] In addition, S4 needs to be further explained that when performing matching, a similarity threshold is set, which includes text similarity and numerical range similarity. The text range similarity in the embodiment of the present disclosure is processed in the embodiment of the present disclosure by the cosine similarity of the text or other types of semantic similarity calculation methods in the natural semantic algorithm. The technical means of calculating the similarity after normalizing the text is a common technical means in the field and will not be described in detail here.

[0037] In addition to text similarity, the similarity threshold also includes the numerical range similarity of regional setting data, the numerical range similarity of energy supply data, the numerical range similarity of energy consumption data and the numerical range similarity of lighting data. Specifically, the numerical range similarity in the embodiment of the present disclosure is set as appropriate according to the power supply redundancy in this field.

[0038] S5, constructing a first photovoltaic dispatching network, using photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes, and setting energy load nodes between two adjacent simulation nodes; Setting energy supply data of the simulation node according to the illumination data; Set energy consumption data of energy load nodes according to electricity consumption data; The expected electricity scheduling amount is determined according to the difference between the energy supply data and the energy consumption data between two adjacent simulation nodes.

[0039] S6. Construct a second photovoltaic dispatching network based on historical projects, which has the same settings as the first photovoltaic dispatching network, and write the actual power dispatching amount recorded in the historical projects instead of the expected power dispatching amount.

[0040] S7: Under the same energy supply data and power consumption data, matching of power dispatching amounts between the first photovoltaic dispatching network and the second photovoltaic dispatching network is performed, specifically comprising the following steps: Perform matching on a single simulation node; Obtaining energy supply data of the single simulation node and the sum of energy consumption data of each of its adjacent locations; From the historical projects, obtain one or more nodes corresponding to the sum of the energy supply data and the energy consumption data within a preset interval; Traversing and performing matching of each of the single simulation nodes, determining a first power scheduling amount of each of the single simulation nodes, wherein the first power scheduling amount is a total amount of electricity that is exchange-scheduled by the single simulation node; Match the total amount of scheduling performed by each single simulation node to non-adjacent nodes in multiple historical projects, and determine its scheduling probability; Add a connection relationship between a single simulation node and its adjacent nodes, and perform the same steps as matching a single simulation node; Matching the probability of a scheduling relationship between the single simulation node and its adjacent nodes in multiple historical projects; Temporarily increase the total number of adjacent nodes connected to the single simulation node according to the sequence number until all connection relationships of the single simulation node are determined, and obtain the probability that the single simulation node has a scheduling relationship under the current connection relationship when each adjacent node is temporarily added; Obtain the probability of a scheduling relationship for a single simulation node in full-blind state, single-blind state, multi-blind state and non-blind state; Generate various power dispatch probability sequences under full-blind state, single-blind state, multi-blind state and non-blind state.

[0041] S8. Generate the actual power dispatch amount of each simulation node and the power dispatch probability sequence with the probability as a unit according to S7.

[0042] S9. Generate expected operating condition data and expected operation and maintenance data of each of the first photovoltaic scheduling networks at the current unit position of the power scheduling probability sequence according to the power scheduling probability sequence.

[0043] S10, using the probability in the power dispatch probability sequence as a quantitative pricing parameter to calculate the expected operating condition cost and the expected operation and maintenance cost, and calculating its procurement cost according to the regional setting data and equipment parameters, to obtain the cost sequence of the power dispatch probability sequence within a preset period, specifically including the following steps: Obtaining a power consumption scheduling probability sequence of each simulation node in a non-blind state; Determine a load duty cycle of each simulation node when not scheduled, wherein the load duty cycle is a ratio between a full load and a load within a predetermined load ratio; Determine the power scheduling exchange amount and load condition executed by each simulation node under the current power scheduling probability, and determine the load duty cycle at this time; Calculate the expected operating condition cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; Calculate the expected operation and maintenance cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; generating a cost of the value of each unit in the power dispatch probability sequence; Obtain the cost sequence within the preset period to complete the evaluation.

[0044] It should be noted that, in a non-blind state, how each simulation node performs scheduling, to which node the scheduling is performed, and the proportion of the scheduled power have been determined by the process of temporarily calculating the power scheduling probability of the simulation node and its adjacent nodes in S7 in the embodiment of the present disclosure. The implementation principle is that in the temporary calculation process, the simulation node and its adjacent simulation nodes are taken as a whole, and in the reference historical project database, the energy supply data and energy consumption data are limited, and the illumination data is limited at the same time, and the probability of the whole being compared with the historical project operation during the expected historical project operation is obtained. Through the temporary calculation method, the probability of scheduling of each simulation node is verified multiple times. Specifically, by example, in the embodiment of the present disclosure, if matching is performed with a simulation node, according to the setting of the embodiment of the present disclosure, it is determined whether it has a scheduling probability and a scheduling amount, but the specific scheduling direction is not considered, and then through the temporary calculation method, it is determined to obtain its connection with one of the adjacent nodes, and then calculate whether there is scheduling with the adjacent node and the value of the scheduling probability. Finally, when only one node is left, the step of returning to the single simulation node is returned, but at this time, the connection relationship of each node in the temporary composition is determined, so as to realize verification in the process.

[0045] Through the steps executed in S7, all probabilities of a single simulation node in all historical items that it can match can be exhaustively obtained, and then these probabilities are used to obtain fitting probabilities or Poisson distribution probabilities by regular fitting or Poisson calculation. It should be noted that the processing of discrete data is a common technical means in this field, and is not limited to regular fitting or Poisson calculation. Any conventional calculation method that can obtain expected and fitting probabilities and can realize the generation of the electricity scheduling probability sequence in the embodiment of the present disclosure should be regarded as the implementation of the embodiment of the present disclosure. Each unit in the electricity scheduling probability sequence in the embodiment of the present disclosure is the probability when matched to different historical items as in step S4, so it includes multiple unit bits.

[0046] In addition, in the embodiment of the present disclosure, the power scheduling exchange volume and load conditions executed by each simulated node under the current power scheduling probability are determined, and when the load duty cycle at this time is determined, when the unit bit in the selected power scheduling probability sequence is determined, since the energy supply data, power consumption data, lighting data and scheduling probability have all been determined, it can be obtained that the operating state of each node during scheduling is determined, and the node to which the node executes scheduling, the node path through which the scheduling passes, and the scheduling amount are also determined, so the load duty cycle at a specified time can be calculated.

[0047] In addition, the generation of full-blind, single-blind and multi-blind states is the same as the non-blind steps of the embodiment of the present disclosure, the difference being that a specified number of connection relationships in a single simulation node is shielded.

[0048] It should be noted that a power dispatch probability sequence is generated for each single simulation node.

[0049] After step S10, the method further includes step S11, estimating the operating cost under fault disconnection, including the following steps: Obtaining the failure rate of nodes in the second photovoltaic scheduling network that matches the single simulation node in the historical project database, Under the failure rate, a power dispatch probability sequence corresponding to the single-blind state and the multi-blind state of the node is obtained; The operation cost calculation of S10 is performed according to the electricity scheduling probability sequence, wherein the quantitative pricing parameters in S10 are the execution steps of S7 in the embodiment of the present disclosure.

[0050] As a further preferred embodiment of the present disclosure, after each execution of the evaluated project, the power dispatch probability sequence in the full-blind state, the single-blind state, the multi-blind state and the non-blind state is written into the historical project database; The actual scheduling relationship of the sampled evaluated project at each node in the preset time period of the power scheduling probability sequence is used to calculate the difference between the two as the correction value; After obtaining multiple groups of evaluated projects, all differences are averaged to obtain an average correction value, which is used as the correction value of the probability in the power dispatch probability sequence when the cost evaluation of the evaluated project is performed next time.

[0051] The disclosed embodiment can comprehensively acquire various key data of the project being evaluated, including task data, regional setting data, lighting data, and electricity consumption data, etc., through the first data acquisition module and the second data acquisition module. The data processing module uses natural semantic algorithm word segmentation processing to accurately extract task keywords and match relevant data from the historical project database, providing comprehensive and accurate data support for subsequent evaluation. For example, when faced with different types of photovoltaic project tasks, it can quickly and accurately find relevant parameters of similar historical projects, thereby improving evaluation efficiency and accuracy.

[0052] The disclosed embodiment constructs multiple first photovoltaic dispatching networks according to regional setting data and equipment parameter data through the dispatching simulation module, and determines the expected power dispatching amount in combination with the light and power consumption data. At the same time, the historical simulation module constructs the second photovoltaic dispatching network of each historical project to determine the actual power dispatching amount. The dispatching and calculation module is matched to generate a power dispatching probability sequence, which can accurately simulate the power dispatching situation of the photovoltaic box transformer in actual operation. This helps to more accurately grasp the energy flow within the photovoltaic system and provide a basis for optimizing the dispatching strategy.

[0053] The disclosed embodiment also generates expected operating condition data and expected operation and maintenance data according to the power dispatch probability sequence through a cost evaluation module, thereby obtaining a cost sequence. This evaluation method fully considers the relationship between the power dispatch probability and the operating condition and operation and maintenance data, and can accurately calculate the operating cost. For example, when calculating the cost, by determining the load duty cycle of the simulation node under different power dispatch probabilities, the expected operating condition cost and the expected operation and maintenance cost are calculated in combination with the historical project database, so that the cost evaluation result is more in line with reality, and a reliable cost reference is provided for the investment decision and operation management of the project. After each execution of the evaluated project, the power dispatch probability sequence is written into the historical project database, and the difference with the actual dispatch relationship is calculated as a correction value, and the average correction value is obtained after obtaining multiple groups of evaluated projects. This method can continuously optimize the evaluation model to make it more in line with the actual operating conditions and improve the accuracy and reliability of the evaluation.

[0054] The disclosed embodiment also receives data from the execution of each module through the storage module and generates a log text with a timestamp, which facilitates the tracing and data analysis of the evaluation process. This helps the project team to review the evaluation process, find problems in time and optimize them, and also provides rich historical data resources for the evaluation of subsequent similar projects; the output module can output the cost series data to a variety of carriers, including electronic carriers (such as volatile storage carriers, non-volatile storage media, display terminals) and non-electronic carriers (such as paper carriers), meeting the needs of different users for data acquisition and use, and improving the availability and dissemination of evaluation results.

[0055] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the presence of other identical elements in the process, method or device including the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0056] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these effects are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described effects, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, devices and units may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0057] The flowchart and block diagram in the accompanying drawings show the possible architecture, role and operation of the device, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical role. In some alternative implementations, the role marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed in parallel, and they can sometimes be executed in the opposite order, which can depend on the role involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed in parallel, and they can sometimes be executed in the opposite order, which can depend on the role involved. Each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based device that performs the specified role or action, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A dynamic flexible photovoltaic box transformer operation cost evaluation system, characterized in that: include: A first data acquisition module is configured to acquire task data and regional setting data of the project to be evaluated; A second data acquisition module is configured to acquire lighting data and electricity consumption data of the evaluated project; A data processing module is configured to process and extract the task keywords of the project to be evaluated by using a natural semantic algorithm, match the task keywords from a historical project database, and obtain matched equipment parameter data, operating condition data, and operation and maintenance data; A scheduling simulation module is configured to construct a plurality of first photovoltaic scheduling networks with photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes according to the regional setting data and the equipment parameter data, and determine an expected power scheduling amount of one of the simulation nodes to another simulation node according to the illumination data and the power consumption data; A historical simulation module is configured to traverse the historical project database, construct a second photovoltaic dispatching network for each historical project, and determine an actual power dispatching amount in each of the second photovoltaic regulation networks; a scheduling and calculation module configured to match the actual power dispatch amount in each of the second photovoltaic scheduling networks with the expected power dispatch amount, generate multiple groups of scheduling and calculation data sets, and determine the power dispatch probability sequence of each node in the first photovoltaic scheduling network through the scheduling and calculation data sets; The cost evaluation module generates, according to the power dispatch probability sequence, the expected operating condition data and the expected operation and maintenance data of each of the first photovoltaic dispatch networks at the unit position where the power dispatch probability sequence is currently located, and obtains a cost sequence for the power dispatch probability sequence.

2. The operating cost evaluation system of the dynamic flexible photovoltaic box transformer according to claim 1 is characterized in that: It also includes a storage module, which is configured to receive data when the first data acquisition module, the second data acquisition module, the data processing module, the scheduling simulation module, the historical simulation module, the scheduling measurement module and the cost evaluation module execute their respective configuration items, and generate corresponding log text with a timestamp.

3. The operating cost evaluation system of the dynamic flexible photovoltaic box transformer according to claim 2 is characterized in that: The system further comprises an output module, wherein the output module is configured to receive the data of each unit position of the cost sequence generated by the cost evaluation module and output the data to the carrier.

4. The operating cost evaluation system of the dynamic flexible photovoltaic box transformer according to claim 3 is characterized in that: The carriers include electronic carriers and non-electronic carriers; The electronic carrier is any one or more of a volatile storage carrier, a non-volatile storage medium or a display terminal; The non-electronic carrier at least includes a paper carrier.

5. A method for evaluating the operating cost of a dynamic flexible photovoltaic box transformer, suitable for implementing the system as described in any one of claims 1 to 4, characterized in that: The steps include: S1. Obtaining task data and regional setting data of the project to be evaluated; S2, obtaining the lighting data and electricity consumption data of the project under evaluation corresponding to the regional setting data; S3, building a historical project database and generating multiple text keywords for each historical project; S4, processing the task keywords of the evaluated project by natural semantic algorithm segmentation, and matching multiple historical projects with a preset similarity threshold in the historical project database according to the task keywords; Obtain equipment parameter data, operating condition data, and operation and maintenance data for multiple historical projects; S5, constructing a first photovoltaic dispatching network, using photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes, and setting energy load nodes between two adjacent simulation nodes; Setting energy supply data of the simulation node according to the illumination data; Set energy consumption data of energy load nodes according to electricity consumption data; Determine the expected power dispatch amount according to the difference between the energy supply data and the energy consumption data between two adjacent simulation nodes; S6. Construct a second photovoltaic dispatching network based on historical projects, which has the same settings as the first photovoltaic dispatching network, and writes the actual power dispatching amount recorded in the historical projects instead of the expected power dispatching amount; S7, under the same energy supply data and power consumption data, performing matching of power dispatching amounts between the first photovoltaic dispatching network and the second photovoltaic dispatching network; S8, generating the actual power dispatch amount of each simulation node and the power dispatch probability sequence with the probability as a unit according to S7; S9. Generate expected operating condition data and expected operation and maintenance data of each of the first photovoltaic scheduling networks at the current unit position of the power scheduling probability sequence according to the power scheduling probability sequence; S10. The probability in the power dispatch probability sequence is used as a quantitative pricing parameter to calculate the expected operating condition cost and the expected operation and maintenance cost, and the procurement cost is calculated according to the regional setting data and equipment parameters to obtain the cost sequence of the power dispatch probability sequence within a preset period.

6. The operating cost evaluation method of the dynamic flexible photovoltaic box transformer according to claim 5 is characterized in that: In S3, generating multiple text keywords for each historical item includes the following steps: The actual setting situation of the historical project is set as a semantic network with the photovoltaic box transformer and / or photovoltaic box transformer cluster as the semantic center; Extracting the associated words of each semantic center, including the energy supply data, energy consumption data and illumination data corresponding to the energy supply radiation range of each photovoltaic box transformer and / or photovoltaic box transformer cluster; Generate text keywords for each semantic center, and perform semantic connection on multiple text keywords according to whether they have energy supply scheduling relationship with adjacent photovoltaic box transformers and / or photovoltaic box transformer clusters; Generate indexed text keywords including connection relationships.

7. The method for evaluating the operating cost of a dynamic flexible photovoltaic box-type transformer according to claim 5 is characterized in that: The S7 specifically includes the following steps: Perform matching on a single simulation node; Obtaining energy supply data of the single simulation node and the sum of energy consumption data of each of its adjacent locations; From the historical projects, obtain one or more nodes corresponding to the sum of the energy supply data and the energy consumption data within a preset interval; Traversing and performing matching of each of the single simulation nodes, determining a first power scheduling amount of each of the single simulation nodes, wherein the first power scheduling amount is a total amount of electricity that is exchange-scheduled by the single simulation node; Match the total amount of scheduling performed by each single simulation node to non-adjacent nodes in multiple historical projects, and determine its scheduling probability; Add a connection relationship between a single simulation node and its adjacent nodes, and perform the same steps as matching a single simulation node; Matching the probability of a scheduling relationship between the single simulation node and its adjacent nodes in multiple historical projects; Temporarily increase the total number of adjacent nodes connected to the single simulation node according to the sequence number until all connection relationships of the single simulation node are determined, and obtain the probability that the single simulation node has a scheduling relationship under the current connection relationship when each adjacent node is temporarily added; Obtain the probability of a scheduling relationship for a single simulation node in full-blind state, single-blind state, multi-blind state and non-blind state; Generate various power dispatch probability sequences under full-blind state, single-blind state, multi-blind state and non-blind state.

8. The method for evaluating the operating cost of a dynamic flexible photovoltaic box-type transformer according to claim 7 is characterized in that: The step S10 of executing the operation cost calculation specifically includes the following steps: Obtaining a power consumption scheduling probability sequence of each simulation node in a non-blind state; Determine a load duty cycle of each simulation node when not scheduled, wherein the load duty cycle is a ratio between a full load and a load within a predetermined load ratio; Determine the power scheduling exchange amount and load condition executed by each simulation node under the current power scheduling probability, and determine the load duty cycle at this time; Calculate the expected operating condition cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; Calculate the expected operation and maintenance cost under the load duty cycle in the historical project database corresponding to the current load duty cycle; generating a cost of the value of each unit in the power dispatch probability sequence; Obtain the cost sequence within the preset period to complete the evaluation.

9. The method for evaluating the operating cost of a dynamic flexible photovoltaic box-type transformer according to claim 8 is characterized in that: After S10, the process further includes S11, estimating the operation cost under fault disconnection, including the following steps: Obtaining the failure rate of nodes in the second photovoltaic scheduling network that matches the single simulation node in the historical project database, Under the failure rate, a power dispatch probability sequence corresponding to the single-blind state and the multi-blind state of the node is obtained; The operation cost calculation of S10 is performed according to the power dispatch probability sequence.

10. The method for evaluating the operating cost of a dynamic flexible photovoltaic box-type transformer according to claim 9 is characterized in that: After each execution of the evaluated project, the power dispatch probability sequence in the full-blind state, single-blind state, multi-blind state and non-blind state is written into the historical project database; The actual scheduling relationship of the sampled evaluated project at each node in the preset time period of the power scheduling probability sequence is used to calculate the difference between the two as the correction value; After obtaining multiple groups of evaluated items, all differences are averaged to obtain the average correction value.

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