A dynamic flexible photovoltaic box transformer operation cost evaluation system and method

Abstract

The application discloses a kind of dynamic flexible photovoltaic box transformer operation cost evaluation system and method, including multiple function modules.First data acquisition module obtains the task data and regional setting data of the evaluated project, second data acquisition module obtains illumination data and power consumption data.Data processing module extracts task keywords by natural semantic algorithm word segmentation, matches related equipment parameters, operating conditions and operation and maintenance data from historical project database.Scheduling simulation module constructs first photovoltaic scheduling network according to regional setting and equipment parameter data, and determines expected power consumption scheduling quantity;History simulation module traverses historical project database to construct second photovoltaic scheduling network and actual power consumption scheduling quantity.Scheduling calculation module generates scheduling calculation data set by matching, and determines power consumption scheduling probability sequence.Cost evaluation module generates expected operating conditions and operation and maintenance data according to the sequence, obtains cost sequence, to evaluate the operation cost of dynamic flexible photovoltaic box transformer.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of cost evaluation model and big data technology, and particularly relates to a dynamic flexible photovoltaic box transformer operation cost evaluation system and method. BACKGROUND

[0002] With the increasing demand for clean energy worldwide, photovoltaic power generation, as an important renewable energy utilization method, is rapidly popularizing and developing in scale. Dynamic flexible photovoltaic box transformers play a key role in photovoltaic power stations. They not only need to adapt to the intermittent and volatile characteristics of photovoltaic power generation output, but also need to ensure the efficient and stable conversion and transmission of electric energy. However, accurate evaluation of its operation cost 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 device itself, complex and variable operating conditions, and diversified operation and maintenance activities. Traditional cost evaluation methods rely on simple empirical formulas and rough estimates, which are difficult to comprehensively consider these complex factors, resulting in poor accuracy of evaluation 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 evaluation results and the actual expenditure, which cannot provide reliable support for the planning, operation and investment decision-making of photovoltaic power stations.

[0004] Therefore, there is an urgent need for a dynamic flexible photovoltaic box transformer operation cost evaluation system and method. SUMMARY

[0005] To this end, the present application provides a dynamic flexible photovoltaic box transformer operation cost evaluation system and method.

[0006] In a first aspect of the present application, a dynamic flexible photovoltaic box transformer operation cost evaluation system is provided, comprising:

[0007] A first data acquisition module configured to acquire task data and regional setting data of the evaluated project;

[0008] A second data acquisition module configured to acquire illumination data and power consumption data of the evaluated project;

[0009] A data processing module configured to process and extract task keywords of the evaluated project by natural semantic algorithm word segmentation, match the task keywords from a historical project database, and obtain matched device parameter data, operating condition data and operation and maintenance data;

[0010] a scheduling simulation module configured to construct a plurality of first photovoltaic scheduling networks with photovoltaic box transformers and / or clusters of photovoltaic box transformers as simulation nodes according to the regional setting data and the equipment parameter data, and determine expected electricity consumption scheduling quantities from one of the simulation nodes to another according to the illumination data and the electricity consumption data;

[0011] a history simulation module configured to traverse the historical project database, construct second photovoltaic scheduling networks of respective historical projects, and determine actual electricity consumption scheduling quantities in the second photovoltaic scheduling networks;

[0012] a scheduling calculation module configured to perform matching between the actual electricity consumption scheduling quantities and the expected electricity consumption scheduling quantities in the second photovoltaic scheduling networks, generate a plurality of sets of scheduling calculation data sets, and determine electricity consumption scheduling probability sequences of respective nodes in the first photovoltaic scheduling networks through the scheduling calculation data sets;

[0013] a cost evaluation module configured to generate expected operation condition data and expected operation and maintenance data of respective first photovoltaic scheduling networks at current unit positions of the electricity consumption scheduling probability sequences according to the electricity consumption scheduling probability sequences, and obtain cost sequences with respect to the electricity consumption scheduling probability sequences.

[0014] As a preferred mode, the system further comprises a storage module configured to receive data when the first data acquisition module, the second data acquisition module, the data processing module, the scheduling simulation module, the history simulation module, the scheduling calculation module and the cost evaluation module execute respective configuration items, and generate corresponding log texts with time stamps.

[0015] As a preferred mode, the system further comprises an output module configured to receive data of respective unit positions of the cost sequences generated by the cost evaluation module and output to a carrier.

[0016] As a preferred mode, the carrier comprises an electronic carrier and a non-electronic carrier.

[0017] The electronic carrier is any one or more of a volatile storage carrier, a non-volatile storage medium or a display terminal.

[0018] The non-electronic carrier at least comprises a paper carrier.

[0019] The second aspect of the application provides a method for evaluating operation costs of a dynamically flexible photovoltaic box transformer, which is suitable for implementing the system of the first aspect of the application, and comprises the following steps:

[0020] S1, obtaining task data and regional setting data of an evaluated project;

[0021] S2, obtaining illumination data and power consumption data of the region setting data corresponding to the evaluated project;

[0022] S3, constructing a historical project database to generate a plurality of text keywords of each historical project;

[0023] S4, processing the task keywords of the evaluated project by natural semantic algorithm word segmentation, and matching a plurality of historical projects with a preset similarity threshold in the historical project database according to the task keywords;

[0024] obtaining device parameter data, operating condition data and operation and maintenance data of a plurality of historical projects;

[0025] S5, constructing a first photovoltaic scheduling network, taking photovoltaic box transformer and / or photovoltaic box transformer cluster as simulation nodes, and setting energy load nodes between two adjacent simulation nodes;

[0026] setting the energy supply data of the simulation nodes according to the illumination data;

[0027] setting the energy consumption data of the energy load nodes according to the power consumption data;

[0028] determining the expected power consumption scheduling quantity according to the difference between the energy supply data and the energy consumption data of two adjacent simulation nodes;

[0029] S6, constructing a second photovoltaic scheduling network according to the historical projects, which has the same setting as the first photovoltaic scheduling network, and writing the actual power consumption scheduling quantity recorded by the historical projects instead of the expected power consumption scheduling quantity;

[0030] S7, under the same energy supply data and energy consumption data, performing the matching of the power consumption scheduling quantity between the first photovoltaic scheduling network and the second photovoltaic scheduling network;

[0031] S8, generating the actual power consumption scheduling quantity of each simulation node and the power consumption scheduling probability sequence of each unit according to the actual power consumption scheduling quantity generated in S7;

[0032] S9, generating the expected operating condition data and the expected operation and maintenance data of each first photovoltaic scheduling network at the current unit position of the power consumption scheduling probability sequence according to the power consumption scheduling probability sequence;

[0033] S10, taking the probability in the power consumption scheduling probability sequence as a quantitative pricing parameter to calculate the expected operating condition cost and the expected operation and maintenance cost, and calculating the procurement cost according to the region setting data and the device parameter, to obtain the cost sequence of the power consumption scheduling probability sequence in a preset period.

[0034] As a preferred mode, in S3, the plurality of text keywords of each historical project includes the following steps:

[0035] Set the actual setting condition of the historical project as a semantic network with the photovoltaic box transformer and / or photovoltaic box transformer cluster as the semantic center;

[0036] Extract the associated vocabulary of each semantic center, including the energy supply data of the energy supply radiation range of each photovoltaic box transformer and / or photovoltaic box transformer cluster, the energy consumption data, and the illumination data corresponding to the energy supply radiation range;

[0037] 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;

[0038] Generate indexed text keywords including connection relationships.

[0039] As a preferred mode, the S7 specifically includes the following steps:

[0040] Perform matching on a single simulation node;

[0041] Obtain the energy supply data of the single simulation node and the total energy consumption data of each adjacent position thereof;

[0042] From the historical project, obtain one or more nodes corresponding to the energy supply data and the total energy consumption data within a preset interval;

[0043] Iteratively perform matching on each single simulation node to determine a first power consumption scheduling quantity of each single simulation node, the first power consumption scheduling quantity being the total power quantity subjected to exchange scheduling by the single simulation node;

[0044] Match the total quantity of each single simulation node in multiple historical projects performing scheduling to non-adjacent nodes to determine the scheduling probability thereof;

[0045] Increase the connection relationship of the single simulation node with its adjacent nodes, and perform the same steps as the matching of the single simulation node;

[0046] Match the probability of the single simulation node in multiple historical projects having scheduling relationship with its adjacent nodes under the connection relationship thereof;

[0047] Temporarily increase the total number of adjacent nodes connected by the single simulation node according to the sequence number until the total connection relationship of the single simulation node is determined, to obtain the probability of the single simulation node having scheduling relationship under the current connection relationship when each adjacent node is temporarily increased;

[0048] Obtain the probability of the single simulation node having scheduling relationship under the full-blind state, single-blind state, multi-blind state, and non-blind state;

[0049] Generate power consumption scheduling probability sequences under the full-blind state, single-blind state, multi-blind state, and non-blind state.

[0050] As a preferred mode, the S10, performing the running cost calculation specifically includes the following steps:

[0051] Obtaining the power consumption scheduling probability sequence of each simulation node in the non-blind state;

[0052] Determining the load duty cycle of each simulation node in the non-scheduling state, the load duty cycle being the ratio between full load and the load within the predetermined load ratio;

[0053] Determining the executed power consumption scheduling exchange and load condition of each simulation node under the current power consumption scheduling probability, and determining the load duty cycle at this time;

[0054] Calculating the expected running condition cost corresponding to the load duty cycle in the historical project database under the current load duty cycle;

[0055] Calculating the expected operation and maintenance cost corresponding to the load duty cycle in the historical project database under the current load duty cycle;

[0056] Generating the cost of the value of each unit in the power consumption scheduling probability sequence;

[0057] Obtaining the cost sequence in the preset period to complete the evaluation.

[0058] As a preferred mode, after the S10, S11 is further included to estimate the running cost under the fault disconnection, including the following steps:

[0059] Obtaining the fault rate of the node in the second photovoltaic scheduling network matched with the single simulation node in the historical project database,

[0060] Obtaining the power consumption scheduling probability sequence of the single-blind state and the multi-blind state corresponding to the node under the fault rate;

[0061] According to the power consumption scheduling probability sequence, performing the running cost calculation of S10.

[0062] As a preferred mode, after each time the evaluated project is executed, the power consumption scheduling probability sequences in the full-blind state, the single-blind state, the multi-blind state, and the non-blind state are written into the historical project database;

[0063] And in the preset time period of the power consumption scheduling probability sequence, the actual scheduling relationship condition of the sampled evaluated project in the actual execution of each node is calculated, and the difference between the two is taken as the correction value;

[0064] After obtaining a plurality of groups of evaluated projects, the average correction value is obtained by averaging all the differences.

[0065] The above technical solutions of the present application have the following advantages compared with the prior art:

[0066] The present application can comprehensively obtain various key data of the evaluated project, including task data, regional setting data, illumination data and power consumption data, etc., through the first data acquisition module and the second data acquisition module. The data processing module can accurately extract task keywords and match related data from the historical project database by using natural semantic algorithm word segmentation processing, thereby providing comprehensive and accurate data support for subsequent evaluation. For example, when facing different types of photovoltaic project tasks, the related parameters of similar historical projects can be quickly and accurately found, thereby improving the evaluation efficiency and accuracy.

[0067] The present application can construct a plurality of first photovoltaic scheduling networks according to the regional setting data and the equipment parameter data through the scheduling simulation module, and determine the expected power consumption scheduling quantity in combination with the illumination and power consumption data, while the historical simulation module constructs a second photovoltaic scheduling network of each historical project to determine the actual power consumption scheduling quantity. Through the matching of the scheduling calculation module, the power consumption scheduling probability sequence can be generated, and the power consumption scheduling situation of the photovoltaic box transformer in the actual operation can be accurately simulated. This helps to more accurately grasp the energy flow within the photovoltaic system and provides a basis for optimizing the scheduling strategy.

[0068] The present application can also generate expected operation condition data and expected operation and maintenance data according to the power consumption scheduling probability sequence through the cost evaluation module, and then obtain the cost sequence. This evaluation method fully considers the relationship between the power consumption scheduling probability and the operation condition and operation and maintenance data, and can accurately calculate the operation cost. For example, when calculating the cost, the load duty ratio of the simulation node under different power consumption scheduling probabilities is determined, and the expected operation 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 the actual situation, and a reliable cost reference is provided for the investment decision and operation management of the project. In addition, after each execution of the evaluated project, the power consumption scheduling probability sequence is written into the historical project database, and the difference between the actual scheduling relationship and the calculated value is taken as a correction value, and the average correction value is obtained after a plurality of groups of evaluated projects. This method can continuously optimize the evaluation model, so that it is more in line with the actual operation situation, and the accuracy and reliability of the evaluation are improved.

[0069] The present application also receives data in the execution process of each module through the storage module and generates a log text with a time stamp, 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 sequence data to various carriers, including electronic carriers (such as volatile storage carriers, non-volatile storage media and display terminals) and non-electronic carriers (such as paper carriers), thereby meeting the needs of different users for data acquisition and use, and improving the availability and dissemination of the evaluation results. BRIEF DESCRIPTION OF DRAWINGS

[0070] Figure 1 is a schematic diagram of module connection of the system provided by the embodiment one of the present application. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0072] In a first aspect of the embodiments of the present disclosure, a system for evaluating operation cost of a dynamic flexible photovoltaic box transformer is provided, as shown in the figure, comprising 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. Figure 1

[0073] In the embodiments of the present disclosure, the first acquisition module is configured to acquire task data and regional setting data of the evaluated project, specifically, to acquire the power supply task and regional setting situation of the evaluated project in the region where it is set as the task data and the regional setting data from the contract and planning book of the project implementation, wherein the power supply task data is the data of the power supply executed by the photovoltaic box transformer and the expected ratio of the power supply amount to the non-photovoltaic power supply in the region; the second acquisition module is configured to acquire illumination data and power consumption data of the evaluated project, the illumination data is the data of the illumination time and the available light intensity of the photovoltaic power supply in a preset time period, and the power consumption data is the total power consumption data of the region where the evaluated project is located. It should be noted that in the embodiments of the present disclosure, the first data acquisition module and the second data acquisition module are only configured items to distinguish the two, and the two can be the same electronic device, terminal or module to execute the configured items of the first data acquisition module and the second data acquisition module.

[0074] In the embodiments of the present disclosure, the data processing module is configured to process and extract the task keywords of the evaluated project by natural semantic algorithm word segmentation, match the task keywords from the historical project database, and obtain the matched equipment parameter data, operation condition data and operation and maintenance data. It should be noted that in the field and the natural semantic algorithm field, extracting specific words, meanings and category words by natural semantic algorithm is a conventional technical means, which will not be described here.

[0075] ​The dispatch simulation module is configured to construct a plurality of first photovoltaic networks with photovoltaic box transformers and / or photovoltaic box transformer clusters as simulation nodes according to the regional settings and the equipment parameters, and determine expected power consumption dispatch amounts from one simulation node to another simulation node according to the illumination data and the power consumption data. In the embodiments of the present disclosure, the photovoltaic box transformer refers to an independent dynamic flexible photovoltaic box transformer box with power supply function, and the photovoltaic box transformer cluster refers to a cluster power supply device composed of a plurality of dynamic flexible photovoltaic box transformer boxes. In an implementation, the photovoltaic box transformer or the photovoltaic box transformer cluster can be selectively set according to the areas of different regions, the power supply radiation ranges and the power consumption demands (energy consumption data) in the regional setting data. Therefore, in the embodiments of the present disclosure, the photovoltaic box transformers arranged in a cluster for the same power supply region are also set as simulation nodes. In addition, it still needs to be noted that, in the prior art, there are multiple power supply radiation ranges of photovoltaic box transformers or photovoltaic box transformer clusters in the preset regional setting data, and there are overlapping regions in the power supply radiation ranges. In the embodiments of the present disclosure, the multiple simulation nodes in the overlapping regions in the power supply radiation ranges which do not have the cluster arrangement characteristics are not regarded as the same photovoltaic box transformer cluster. In the prior art and the embodiments of the present disclosure, the multiple simulation nodes in the overlapping regions in the power supply radiation ranges are distributed according to the area ratios of the overlapping regions in the power supply radiation ranges or the ratios of the power supply data (the electric energy transmitted from the photovoltaic power generation device to the dynamic flexible photovoltaic box transformer) between two simulation nodes.

[0076] The historical simulation module in the embodiments of the present disclosure is configured to traverse the historical project database, construct a second photovoltaic dispatch network of the historical projects, and determine actual power consumption dispatch amounts in the second photovoltaic dispatch network. In the embodiments of the present disclosure, the historical project database stores a plurality of historical projects of the dynamic flexible photovoltaic box transformer construction which have been implemented, and stores the power supply data, the energy consumption data, the regional setting data and the illumination data of the historical projects.

[0077] The dispatch estimation module is configured to perform matching between the actual power consumption dispatch amounts in the second photovoltaic dispatch network and the expected power consumption dispatch amounts in the first photovoltaic dispatch network, generate a plurality of sets of dispatch estimation data sets, and determine power consumption dispatch probability sequences of the nodes in the first photovoltaic dispatch network through the dispatch estimation data sets.

[0078] The cost evaluation module in the embodiments of the present disclosure generates expected operation working condition data and expected operation and maintenance data of each first photovoltaic dispatch network at a current unit position of the power consumption dispatch probability sequence according to the power consumption dispatch probability sequence, obtains a cost sequence about the power consumption dispatch probability sequence, and further completes a plurality of cost evaluation results of the evaluated projects.

[0079] As a preferred mode of the embodiment of the present disclosure, a storage module is further included, 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 calculation module and the cost evaluation module execute respective configuration items, and generate corresponding log texts with time stamps.

[0080] As a preferred mode of the embodiment of the present disclosure, an output module is further included, which is configured to receive data of each unit position of the cost sequence generated by the cost evaluation module and output to a carrier, specifically, the carrier includes electronic carriers and non-electronic carriers; the electronic carriers are any one or more of volatile storage carriers, non-volatile storage media or display terminals; the non-electronic carriers at least include paper carriers.

[0081] In a second aspect of the embodiment of the present disclosure, a dynamic flexible photovoltaic box transformer operation cost evaluation method is provided, which is suitable for implementing the system of the first aspect of the embodiment of the present disclosure. First of all, it needs to be explained that the method of the embodiment of the present disclosure is executed in a preset time period and includes the following steps:

[0082] S1, acquiring task data and region setting data of the configured project.

[0083] S2, acquiring illumination data and power consumption data of the region setting data corresponding to the evaluated project.

[0084] S3, constructing a historical project database and generating a plurality of text keywords of each historical project;

[0085] As a preferred mode of S3, the text keywords of each historical project include the following steps:

[0086] 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;

[0087] The semantic center includes semantic description texts of device parameter data, operation condition data and operation and maintenance data of the photovoltaic box transformer;

[0088] The associated vocabulary of each semantic center is extracted, including energy supply data, energy consumption data of the energy supply radiation range of each photovoltaic box transformer and / or photovoltaic box transformer cluster, and illumination data corresponding to the energy supply radiation area, and the semantic description of the energy supply data, the energy consumption data and the illumination data is taken as a semantic connection relationship text;

[0089] Text keywords of the semantic description text and text keywords of the semantic connection relationship text are generated, and a plurality of text keywords are executed in the semantic connection of the region setting data defined in the historical project according to whether they exist energy supply scheduling relationship with the adjacent photovoltaic box transformer and / or photovoltaic box transformer cluster;

[0090] Generate the indexed text keywords including semantic connections.

[0091] S4, the task keywords of the evaluated project are processed by the natural semantic algorithm, and a plurality of historical projects with a preset similarity threshold are matched in the historical project database according to the task keywords;

[0092] Get the device parameter data, operation condition data and operation and maintenance data of the plurality of historical projects.

[0093] Specifically, when matching in S4 of the embodiment of the present disclosure, the matching of the task keywords to the indexed text keywords is performed, and the power supply task and the regional setting of the evaluated project in the region set by the evaluated project are obtained as the task data and the regional setting data in the contract and the planning book of the project implementation in the present disclosure and the field, wherein the power supply task data is the data of the power supply performed by the photovoltaic box transformer and the expected power supply amount ratio in the region with non-photovoltaic power generation, and then the semantic connection relationship text in the historical project can be matched, and then it is determined whether there is a matching condition in the regional setting data under the current semantic connection relationship text, and the adjacent photovoltaic box transformer and / or photovoltaic box transformer cluster (semantic center) is determined by the semantic connection of the indexed text keywords, and then the setting mode of the evaluated project of the plurality of reference historical project setting modes is obtained.

[0094] In addition, the similarity threshold is set when the matching is performed, which includes text similarity and numerical range similarity, and the text range similarity in the embodiment of the present disclosure is calculated by the cosine similarity of the text or other types of semantic similarity calculation methods in the natural semantic algorithm, and the technical means for calculating the similarity after normalizing the text in the field is a common technical means in the field, which will not be described in detail here.

[0095] In addition, the similarity threshold includes the numerical range similarity of the regional setting data, the energy supply data numerical range similarity, the energy consumption data numerical range similarity and the illumination data numerical range similarity in addition to the text similarity, and specifically, the numerical range similarity in the embodiment of the present disclosure is set according to the power supply redundancy in the field.

[0096] S5, construct a first photovoltaic scheduling network, take the photovoltaic box transformer and / or photovoltaic box transformer cluster as the simulation node, and set the energy load node between the two adjacent simulation nodes;

[0097] Set the energy supply data of the simulation node according to the illumination data;

[0098] Set the energy consumption data of the energy load node according to the electricity consumption data;

[0099] determining an expected electricity scheduling quantity according to a difference between the energy supply data and the energy consumption data between two adjacent simulation nodes.

[0100] S6, constructing a second photovoltaic scheduling network according to the historical projects, which has the same settings as the first photovoltaic scheduling network, and writing the actual electricity scheduling quantity recorded in the historical projects instead of the expected electricity scheduling quantity.

[0101] S7, performing matching of the electricity scheduling quantity between the first photovoltaic scheduling network and the second photovoltaic scheduling network under the same energy supply data and energy consumption data, specifically including the following steps:

[0102] performing matching of a single simulation node;

[0103] obtaining the energy supply data of the single simulation node and the total energy consumption data of each adjacent position thereof;

[0104] from the historical projects, obtaining one or more nodes corresponding to the energy supply data and the total energy consumption data within a preset interval;

[0105] performing matching of each single simulation node to determine a first electricity scheduling quantity of each single simulation node, the first electricity scheduling quantity being the total electricity quantity exchanged by the single simulation node;

[0106] matching the total quantity of each single simulation node in multiple historical projects to determine a scheduling probability thereof;

[0107] increasing the connection relationship of the single simulation node with its adjacent nodes and performing the same steps as the matching of the single simulation node;

[0108] matching the probability of the single simulation node in multiple historical projects to determine the scheduling relationship thereof with its adjacent nodes under the connection relationship thereof;

[0109] temporarily increasing the total number of adjacent nodes connected to the single simulation node according to the sequence number until the total connection relationship of the single simulation node is determined, to obtain the probability of the single simulation node having a scheduling relationship under the current connection relationship when each adjacent node is temporarily increased;

[0110] obtaining the probability of the single simulation node having a scheduling relationship under the full-blind state, the single-blind state, the multiple-blind state, and the non-blind state;

[0111] generating a sequence of electricity scheduling probabilities of each simulation node under the full-blind state, the single-blind state, the multiple-blind state, and the non-blind state.

[0112] S8, generating the actual electricity scheduling quantity of each simulation node and the sequence of electricity scheduling probabilities of each simulation node according to the generation of the sequence of electricity scheduling probabilities of each simulation node in S7.

[0113] S9, generating expected operating condition data and expected operation and maintenance data of each first photovoltaic scheduling network at a unit position of the power consumption scheduling probability sequence.

[0114] S10, calculating expected operating cost and expected operation and maintenance cost by taking the probability in the power consumption scheduling probability sequence as a quantitative pricing parameter, and calculating the procurement cost according to the regional setting data and the equipment parameters, to obtain a cost sequence of the power consumption scheduling probability sequence within a preset period, specifically including the following steps:

[0115] obtaining the power consumption scheduling probability sequence of each simulation node in the non-blind state;

[0116] determining the load duty cycle of each simulation node in the non-scheduling state, the load duty cycle being the ratio between full load and load within a predetermined load ratio;

[0117] determining the executed power consumption scheduling exchange and load condition of each simulation node under the current power consumption scheduling probability, and determining the load duty cycle at this time;

[0118] calculating the expected operating cost of the current load duty cycle corresponding to the load duty cycle in the historical project database;

[0119] calculating the expected operation and maintenance cost of the current load duty cycle corresponding to the load duty cycle in the historical project database;

[0120] generating the cost of the value of each unit in the power consumption scheduling probability sequence;

[0121] obtaining the cost sequence within the preset period to complete the evaluation.

[0122] It should be noted that in the non-blind state, how the simulation nodes are scheduled, to which node the simulation nodes perform scheduling, and the proportion of the scheduled power have been determined by the process of calculating the power scheduling probability of the simulation nodes and their adjacent nodes in S7 in the embodiments of the present disclosure. The implementation principle is that the simulation nodes and their adjacent simulation nodes are taken as a whole in the temporary calculation process, the power supply data and power consumption data are limited in the referenceable historical project database, and the illumination data is limited to obtain the probability of the whole in the expected historical project running process compared with the historical project running. The probability of each simulation node existing scheduling is verified by the temporary calculation method. Specifically, in the embodiments of the present disclosure, if one simulation node performs matching, according to the setting of the embodiments of the present disclosure, whether the simulation node has a scheduling probability and a scheduling amount is determined, but the specific scheduling direction is not considered. Then, the connection of the simulation node with one of the adjacent nodes is determined by the temporary calculation method, and whether the simulation node has a scheduling probability and a value of the scheduling probability with the adjacent node are calculated. Finally, when only one node is left, the step of returning to the single simulation node is performed. However, the connection relationship of the nodes in the temporary group is determined at this time, so that the verification is realized in the process.

[0123] Through the steps performed by S7, the total probability of a single simulation node in all the historical projects that can be matched by the single simulation node is obtained. Then, the fitting probability or the Poisson distribution probability is obtained by the regular fitting or Poisson calculation. It should be noted that the processing of discrete data is a common technical means in the art, and is not limited to regular fitting or Poisson calculation. Any conventional calculation method that can obtain the expected and fitting probability and can realize the generation of the power scheduling probability sequence in the embodiments of the present disclosure should be regarded as the implementation of the embodiments of the present disclosure. Each unit in the power scheduling probability sequence in the embodiments of the present disclosure is the probability when the unit is matched to different historical projects in step S4. Therefore, the unit includes a plurality of unit bits.

[0124] In addition, in the embodiments of the present disclosure, the executed power scheduling exchange amount and the load condition of each simulation node at the current power scheduling probability are determined, and the load duty cycle at this time is determined. When the unit bit in the selected power scheduling probability sequence is determined, since the power supply data, the power consumption data, the illumination data, and the scheduling probability are all determined, the running state of each node when scheduling is determined, and the node to which the node performs scheduling, the node path of the scheduling, and the amount of the scheduling are also determined. Therefore, the load duty cycle at the specified time can be calculated.

[0125] In addition, for the generation of the full-blind, single-blind, and multi-blind states, the non-blind steps of the embodiments of the present disclosure are the same, and the difference lies in that the specified number of the connection relationship of the single simulation node is shielded.

[0126] It should be noted that the power consumption scheduling probability sequence of each single simulation node is generated.

[0127] After step S10, S11 is further included, estimating the operation cost under fault disconnection, including the following steps:

[0128] Obtaining the fault rate of the node in the second photovoltaic scheduling network matched with the single simulation node in the historical project database,

[0129] Obtaining the power consumption scheduling probability sequence of the single-blind state and the multi-blind state corresponding to the node under the fault rate;

[0130] According to the power consumption scheduling probability sequence, the operation cost calculation of S10 is performed, and the quantitative pricing parameter in S10 is the execution step of S7 in the embodiment of the present disclosure.

[0131] As a further preferred mode of the embodiment of the present disclosure, after each execution of the evaluated project, the power consumption scheduling probability sequence under the full-blind state, the single-blind state, the multi-blind state and the non-blind state is written into the historical project database;

[0132] And in the preset time period of the power consumption scheduling probability sequence, the actual scheduling relationship of the sampled evaluated project in the actual execution of each node is calculated as a correction value;

[0133] After obtaining a plurality of evaluated projects, the average correction value is obtained by averaging all the differences, and the average correction value is used as the correction value of the probability in the power consumption scheduling probability sequence when evaluating the cost of the next evaluated project.

[0134] The embodiment of the present disclosure can comprehensively obtain various key data of the evaluated project through the first data acquisition module and the second data acquisition module, including task data, region setting data, illumination data and power consumption data. The data processing module can accurately extract task keywords and match related data from the historical project database by using natural semantic algorithm word segmentation processing, to provide comprehensive and accurate data support for subsequent evaluation. For example, when facing different types of photovoltaic project tasks, the related parameters of similar historical projects can be quickly and accurately found, to improve the evaluation efficiency and accuracy.

[0135] The embodiment of the present disclosure can construct a plurality of first photovoltaic scheduling networks according to the region setting data and the equipment parameter data through the scheduling simulation module, and determine the expected power consumption scheduling quantity in combination with the illumination and power consumption data, while the historical simulation module constructs the second photovoltaic scheduling network of each historical project to determine the actual power consumption scheduling quantity. Through matching by the scheduling calculation module, the power consumption scheduling probability sequence is generated, which can accurately simulate the power consumption scheduling situation of the photovoltaic box transformer in actual operation. This helps to more accurately grasp the energy flow inside the photovoltaic system, to provide a basis for optimizing the scheduling strategy.

[0136] The embodiments of the present disclosure further generate expected operation condition data and expected operation and maintenance data according to the power consumption scheduling probability sequence through the cost evaluation module, and then obtain the cost sequence. This evaluation method fully considers the relationship between the power consumption scheduling probability and the operation condition and operation and maintenance data, and can accurately calculate the operation cost. For example, when calculating the cost, the load duty cycle of the simulation node under different power consumption scheduling probabilities is determined, and the expected operation 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 the actual situation, and a reliable cost reference is provided for the investment decision and operation management of the project. After each time the evaluated project is executed, the power consumption scheduling probability sequence is written into the historical project database, and the difference between the actual scheduling relationship and the calculated actual scheduling relationship is calculated as a correction value, and the average correction value is obtained after a plurality of evaluated projects are obtained. This method can continuously optimize the evaluation model, so that it is more in line with the actual operation condition, and the accuracy and reliability of the evaluation are improved.

[0137] The embodiments of the present disclosure further receive data in the execution process of each module through the storage module and generate log text with time stamp, which is convenient for tracing and data analysis of the evaluation process. This helps the project team to review the evaluation process, find problems in time and optimize, and also provides rich historical data resources for the evaluation of subsequent similar projects; the output module can output the cost sequence data to various carriers, including electronic carriers (such as volatile storage carriers, non-volatile storage media, display terminals) and non-electronic carriers (such as paper carriers), to meet the needs of different users for data acquisition and use, and improve the availability and dissemination of the evaluation results.

[0138] The above description and drawings are illustrative of embodiments of the present disclosure and are not intended to be limiting. Other embodiments can include structural, logical, electrical, process, and other changes. Embodiments are merely representative of possible variations. Individual components and functions are optional unless explicitly required, and the order of operations can be varied. Portions and features of some embodiments can be included in, or substituted for, those of other embodiments. Also, words used in this document and claims are words of description, not limitation. As used in the description and claims herein, the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Similarly, the term "and / or" as used herein refers to any one or any combination of the associated listed items. Additionally, as used in this document, the term "comprises" and variations of the term, such as "comprising", "comprises" and / or "comprising", etc., mean that the stated features, integers, steps, operations, elements, and / or components are present but not excluding the presence of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Unless otherwise required by context, recitation of an element in the singular is not intended to exclude the plural. Where particular examples are described in the embodiments, the same or similar examples can be used in other embodiments unless specifically stated otherwise. For example, methods, products, etc. disclosed in embodiments can be used in other embodiments corresponding to the method part of the embodiments.

[0139] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods for each specific application to realize the described functions, but such implementation should not be considered beyond the scope of the embodiments of the present disclosure. The skilled person can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described devices, apparatuses and units can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0140] The diagrams of the flow and block diagrams show the architecture, functionality, and operation of possible implementations of apparatuses, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the actions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or the actions of a block can be performed in the reverse order, depending upon the functionality involved. These diagrams of the flow and block diagrams are also intended to include any connected data storage and data processing artifacts and structures that can affect the operation of the subject matter described. If warranted, specific data storage artifacts can be shown in a block diagram and / or a flow diagram and referred to in the accompanying text. Conversely, no indication of such data storage artifacts should not be construed to imply that such data storage is not a possible implementation. In some alternative implementations, the actions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or the actions of a block can be performed in the reverse order, depending upon the functionality involved. The description of a flow or block diagram of a process, method, or computer program product should not be construed to mean that all of the actions or steps are required to be performed in the order presented, nor that they are performed at all.

Claims

1. A system for assessing the operating cost of a dynamic flexible photovoltaic transformer substation, characterized in that, include: The first data acquisition module is configured to acquire task data and regional setting data of the project being evaluated. The second data acquisition module is configured to acquire the illumination data and electricity consumption data of the project being evaluated. The data processing module is configured to use natural language processing algorithms to segment words and extract task keywords of the evaluated project, match the task keywords from the historical project database, and obtain matched equipment parameter data, operating condition data, and maintenance data. The scheduling simulation module is configured to construct multiple first photovoltaic scheduling networks with photovoltaic transformer substations and / or photovoltaic transformer substation clusters as simulation nodes based on the regional setting data and equipment parameter data, and to determine the expected power dispatch amount from one of the simulation nodes to another simulation node based on the illumination data and the power consumption data. The historical simulation module is configured to traverse the historical project database, construct a second photovoltaic dispatch network for each historical project, and determine the actual electricity dispatch volume in each second photovoltaic regulation network. The scheduling calculation module is configured to perform matching between the actual power consumption scheduling amount and the expected power consumption scheduling amount in each of the second photovoltaic scheduling networks, generate multiple sets of scheduling calculation datasets, exhaustively obtain all probabilities of a single simulated node in all historical projects it can match, and then obtain the fitted probability or Poisson distribution probability by regular fitting or Poisson calculation of these probabilities. The power consumption scheduling probability sequence of each node in the first photovoltaic scheduling network is determined by the scheduling calculation dataset. Each unit in the power consumption probability scheduling sequence is the probability of matching to different historical projects, which includes multiple unit bits. The cost assessment module generates expected operating condition data and expected maintenance data for each of the first photovoltaic dispatch networks at the current unit location in the power dispatch probability sequence, based on the power dispatch probability sequence, and obtains a cost sequence for the power dispatch probability sequence.

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

3. The operating cost assessment system for dynamic flexible photovoltaic transformer substations according to claim 2, characterized in that, It also includes an output module, which is configured to receive data of each unit position of the cost sequence generated by the cost assessment module and output it to the carrier.

4. The operating cost assessment system for dynamic flexible photovoltaic transformer substations according to claim 3, characterized in that, The carrier includes 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 includes at least a paper carrier.

5. A method for assessing the operating cost of a dynamic flexible photovoltaic transformer substation, suitable for implementing the system described in any one of claims 1-4. Its features are, Includes the following steps: S1. Obtain task data and regional setting data for the project being evaluated; S2. Obtain the illumination data and electricity consumption data of the area corresponding to the project being evaluated; S3. Construct a historical project database and generate multiple text keywords for each historical project; S4. The task keywords of the evaluated project are processed by natural language processing algorithm for word segmentation, and the results are matched with the task keywords. Multiple historical items in the historical project database with a preset similarity threshold; Obtain equipment parameter data, operating condition data, and maintenance data from multiple historical projects; S5. Construct the first photovoltaic dispatch network, using photovoltaic transformer substations and / or photovoltaic transformer substation clusters as simulation nodes, and setting energy load nodes between two adjacent simulation nodes; The power supply data of the simulated nodes are set according to the illumination data; Set the energy consumption data of the energy load nodes based on the electricity consumption data; The expected power dispatch amount is determined based on the difference between the power supply data and the power consumption data between two adjacent simulated nodes; S6. Construct a second photovoltaic dispatch network based on historical projects. It has the same settings as the first photovoltaic dispatch network and writes the actual electricity dispatch amount recorded in the historical projects instead of the expected electricity dispatch amount. S7. Under the same energy supply data and power consumption data, perform matching of power dispatch quantities between the first photovoltaic dispatch network and the second photovoltaic dispatch network; S8. Based on the actual power consumption scheduling amount of each simulated node and its probability unit power consumption scheduling probability sequence generated by S7. S9. Based on the power consumption dispatch probability sequence, generate the expected operating condition data and expected operation and maintenance data of each of the first photovoltaic dispatch networks at the current unit location in the power consumption dispatch probability sequence; S10. Calculate the expected operating condition cost and expected maintenance cost using the probability in the power dispatch probability sequence as a quantitative pricing parameter, and calculate its procurement cost based on the regional setting data and equipment parameters to obtain the cost sequence of the power dispatch probability sequence within the preset period.

6. The method for assessing the operating cost of dynamic flexible photovoltaic transformer substations according to claim 5, characterized in that, In step S3, generating multiple text keywords for each historical item includes the following steps: Set the actual settings of historical projects as a semantic network with photovoltaic transformer substations and / or photovoltaic transformer substation clusters as the semantic center; Extract the related words of each semantic center, including the energy supply data, energy consumption data and the corresponding light data of the energy supply radiation range of each photovoltaic transformer and / or photovoltaic transformer cluster; Generate text keywords for each semantic center, and perform semantic connections between multiple text keywords based on whether they have a power supply scheduling relationship with their adjacent photovoltaic transformer substations and / or photovoltaic transformer substation clusters; Generate indexed text keywords that include connection relationships.

7. The method for assessing the operating cost of dynamic flexible photovoltaic transformer substations according to claim 6, characterized in that, S10, the calculation of operating costs, specifically includes the following steps: Obtain the power consumption scheduling probability sequence of each of the simulated nodes in a non-blind state; Determine the load duty cycle of each simulated node when it is not scheduled, wherein the load duty cycle is the ratio between full load and load within a predetermined load ratio; Determine the power dispatching exchange volume and load status of each simulated node under the current power dispatching probability, and determine the load duty cycle at this time; Calculate the expected operating cost under the current load duty cycle in the historical project database at the corresponding load duty cycle; Calculate the expected maintenance cost under the current load duty cycle in the historical project database; The cost of generating the value of each unit in the power dispatch probability sequence; The cost sequence within the preset period is obtained to complete the evaluation.

8. The method for assessing the operating cost of a dynamic flexible photovoltaic transformer according to claim 7, characterized in that, Following S10, S11 is also included, which estimates the operating cost under fault disconnection, including the following steps: Obtain the failure rate of nodes in the second photovoltaic scheduling network that match the single simulated node from the historical project database. Under this failure rate, obtain the power dispatch probability sequence for the single-blind state and multi-blind state corresponding to the node; The operating cost calculation for S10 is performed based on the power dispatch probability sequence.

9. The method for assessing the operating cost of a dynamic flexible photovoltaic transformer according to claim 8, characterized in that, After each evaluation project is executed, the power dispatch probability sequence under fully blind, single-blind, multi-blind, and non-blind states is written into the historical project database. The difference between the actual scheduling relationship of the evaluated item and the actual execution of each node is calculated as a correction value within the preset time period of the power dispatch probability sequence. After obtaining multiple sets of evaluated items, the average difference of all the items is averaged to obtain the average correction value.

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