CN115347569A - Power allocation system based on intelligent prediction - Google Patents
Power allocation system based on intelligent prediction Download PDFInfo
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The embodiment of the specification provides an electric power allocation system based on intelligent prediction, which is characterized in that temperature fluctuation, expected orders and holiday states of holiday power utilization attributes of a power utilization area in a period to be scheduled are input into a demand prediction model to predict power utilization demand trends of power sources, a visual electric power transmission diagram is constructed by using the length with a line transmission rate as a side, the capacity range of a non-renewable energy power source is collected, the dynamic capacity of the renewable power source is predicted, the power source and a line are searched according to the priority of the power source and the corresponding line, a dynamic matching relation is obtained and sent to a distribution station, and the distribution station switches the power source and the line at regular time according to the dynamic matching relation. By predicting dynamic power demand, cleaning power generation source capacity and preferentially matching, accurate balance of supply and demand is achieved, the proportion of non-renewable energy sources is reduced, the power distribution station is switched in a software-defined mode at regular time, automatic scheduling can be achieved by deploying in advance, dependence on manual operation is reduced, and scheduling effect is timely and reliable.
Description
Technical Field
The application relates to the field of electric power, in particular to an electric power dispatching system based on intelligent prediction.
Background
The existing power allocation method is characterized in that a traditional thermal power station is still used as a main part, a renewable energy power station is only used as a supplement to allocate, most of the existing allocation methods are dispatches on site by a dispatcher and draw up an instruction ticket, the corresponding relation between a frequently switched power supply and a power generation source and the circuit feasibility of the power generation source are very low, and the power consumption requirement and the capacity of the renewable energy power station are frequently fluctuated and cannot be controlled like a thermal power station.
Therefore, there is a need to provide a new system to increase the renewable energy ratio, reduce the use of non-renewable energy, and improve the timeliness and reliability of the deployment process.
Disclosure of Invention
The embodiment of the specification provides an electric power allocation system based on intelligent prediction, which is used for improving the proportion of renewable energy sources, reducing the use of non-renewable energy sources and improving the timeliness and reliability of an allocation process.
An embodiment of the present specification provides an electric power dispatching system based on intelligent prediction, including:
a plurality of grid nodes connected by grid lines, comprising: a power supply, a power generation source and a power distribution station;
the data acquisition center is used for acquiring air temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled, acquiring holiday power utilization attributes and holiday states of the area to which each power supply belongs in the future period to be scheduled, acquiring expected order data in the period to be scheduled, which are reported by each manufacturer, and classifying the expected order data reported by each manufacturer into each power supply according to the area;
the forecasting center calls a pre-trained demand forecasting model, inputs the holiday power utilization attribute of the region to which each power utilization source belongs, the air temperature fluctuation data of each power utilization source in a period to be scheduled, expected order data and holiday states into the demand forecasting model, and forecasts the power utilization demand trend data of each power utilization source;
the view center is used for acquiring the position of each power grid node, calculating the transmission rate of a line, constructing a visual power transmission view by taking each power grid node as a node, taking a power grid line as an edge and taking the transmission rate of the line as the length of the edge, configuring priorities according to the energy types of the power generation sources, hierarchically displaying the power generation sources according to the priority order, and configuring edge data and the priority of the edge according to the transmission rate of each line;
the matching center is used for collecting the capacity range of the non-renewable energy power generation source in the power grid, predicting dynamic capacity data of the renewable energy power generation source in the power grid in the period to be scheduled, searching a plurality of power generation sources and corresponding lines according to the priority of the power generation sources and the priority of the corresponding lines, matching the power consumption demand trend data of each power consumption source according to the power generation amount of the searched power generation sources to obtain the dynamic matching relation between the power generation sources and the power consumption sources, covering the power consumption demand trend data of the power consumption sources by the power generation capacity of the dynamically matched power generation sources, generating a matching file and transmitting the matching file to a power distribution station;
and the power distribution station receives the matching file and switches the power generation source and the line to execute the power transmission task at regular time according to the dynamic matching relation in the matching file.
Optionally, the prediction center is further configured to:
acquiring holiday and festival electricity attributes of an area to which each power supply belongs, and an air temperature fluctuation sequence, an expected order sequence, a holiday and festival state sequence and an electricity consumption sequence of each power supply in a historical period, building a neural network model, setting a training label according to the electricity consumption sequence, and training the neural network model by taking the holiday and festival electricity attributes of the area to which each power supply belongs, the air temperature fluctuation sequence, the expected order sequence and the holiday and festival state sequence of each power supply in the historical period as training samples to obtain a demand prediction model.
Optionally, the searching for multiple power generation sources and corresponding lines according to the priority of the power generation sources and the priority of the corresponding lines, and matching the power demand trend data of each power supply according to the searched power generation amount of the power generation source includes:
selecting a renewable energy power generation source as an initial object to be matched, searching for a power source in an area to which the renewable energy power generation source belongs, matching, calculating the power generation allowance of the renewable energy power generation source according to dynamic capacity data of the renewable energy power generation source and power demand trend data of the matched power source, if the power generation allowance is positive, continuing to search for matching by using the renewable energy power generation source as the object to be matched, searching for a power source in an adjacent area of the renewable energy power generation source, continuing to calculate the power generation allowance of the renewable energy power generation source, and preferentially calculating the power generation allowance according to the adjacent area with higher line transmission efficiency for a plurality of adjacent areas;
and if the power generation allowance is negative, the matched power supply is taken as an object to be matched, searching the non-renewable energy power generation source in the power supply area for matching, calculating and judging whether the power generation allowance exceeds the capacity range of the non-renewable energy power generation source, if so, continuously searching the non-renewable energy power generation source in the area adjacent to the power supply for matching, and if not, configuring the power generation task amount for the matched non-renewable energy power generation source according to the power generation allowance.
Optionally, the view center is further configured to:
and (4) placing the power generation source with negative power generation allowance after matching on the bottom layer or hiding the power generation source, and allowing a power distributor to adjust the dynamic matching relation between the power generation source and the power supply.
Optionally, the calculating a transmission rate of the line includes:
and learning the dynamic transmission rate of each line in the period to be scheduled from the temperature fluctuation data of the region to which each power supply belongs in the period to be scheduled and the length of each line by using a pre-trained dynamic transmission rate calculation rule.
Optionally, the performing a power transfer task comprises:
each power grid node receives a digital certificate issued by the block chain subsystem, and when each power grid node initiates or forwards a power utilization request, the private key in the digital certificate is used for signing the power utilization request, adding the identifier of the current power grid node and the identifier of the adjacent power grid node in the power transmission route, and uploading the current power utilization request and the identifier of the adjacent power grid node in the power transmission route to a block chain;
and the block chain subsystem acquires a corresponding public key according to the power utilization request and the identifier of the power grid node, carries out label release on the power utilization request, compares the power utilization request with the power utilization request which is not labeled, and authorizes each power grid node to execute a power transmission task corresponding to the power utilization request if the comparison is consistent.
Optionally, the authorizing, to each power grid node, execution of the power transmission task corresponding to the power consumption request includes:
and issuing a task switch with a validity period or an electric quantity threshold value to each power grid node, wherein the task switch is associated with the power consumption request, and each power grid node uses the task switch to start the power transmission task of the power consumption request and to close the power transmission task of the power consumption request when the validity period or the electric quantity threshold value is exceeded.
Optionally, the task switch has at least one of a time monitoring instruction and a power monitoring instruction and a judgment instruction.
Optionally, the data acquisition center is further configured to obtain a historical order sequence acquired by a third-party tax system, and calculate the reliability of the manufacturer according to the historical order sequence and an expected order sequence reported in a historical period of each manufacturer;
and adjusting the expected order data in the time period to be scheduled reported by each manufacturer according to the credibility.
Optionally, the method further comprises:
risks are identified and lines are switched according to the load state of equipment in the distribution substation.
In the technical solutions provided in the embodiments of the present description, the temperature fluctuation, the expected order and the holiday power consumption state of the power consumption region in the period to be scheduled are input into a demand prediction model to predict the power consumption demand trend of each power consumption source, a visual power transmission diagram is constructed with the length of the line transmission rate as the edge, the capacity range of the non-renewable energy power generation source is collected, the dynamic capacity of the renewable power generation source is predicted, the power generation source and the line are searched according to the priority of the power generation source and the corresponding line, a dynamic matching relationship is obtained, and the power generation source and the line are switched at regular time by the power distribution station according to the dynamic matching relationship. By predicting dynamic power demand, cleaning power generation source capacity and preferentially matching, accurate balance of supply and demand is achieved, the proportion of non-renewable energy sources is reduced, the power distribution station is switched in a software-defined mode at regular time, automatic scheduling can be achieved by deploying in advance, dependence on manual operation is reduced, and scheduling effect is timely and reliable.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent prediction based power dispatching system according to an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment may not be excluded from being combined in a suitable manner in one or more other embodiments, while still complying with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The term "and/or" and/or "includes all combinations of any one or more of the associated listed items.
Fig. 1 is a schematic structural diagram of an intelligent prediction based power dispatching system provided in an embodiment of the present disclosure, where the system may include:
a plurality of grid nodes 101 connected by grid lines, comprising: power consumption source 1011, power consumption source 1012, power consumption source 1013, distribution station 1014, power generation source 1015, and power generation source 1016;
the data acquisition center 102 is used for acquiring air temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled, acquiring holiday power utilization attributes and holiday states of the area to which each power supply belongs in the future period to be scheduled, acquiring expected order data in the period to be scheduled, which are reported by each manufacturer, and classifying the expected order data reported by each manufacturer into each power supply according to the area;
the forecasting center 103 calls a pre-trained demand forecasting model, inputs the holiday power utilization attribute of the region to which each power utilization source belongs, the air temperature fluctuation data of each power utilization source in a period to be scheduled, expected order data and the holiday state into the demand forecasting model, and forecasts the power utilization demand trend data of each power utilization source;
the view center 104 is used for acquiring the positions of the grid nodes, calculating the transmission rates of the lines, constructing a visual power transmission view by taking the grid nodes as the nodes, the grid lines as the edges and the transmission rates of the lines as the lengths of the edges, configuring priorities according to the energy types of the power generation sources, hierarchically displaying the power generation sources according to the priority order, and configuring edge data and priorities according to the transmission rates of the lines;
the matching center 105 is used for collecting the capacity range of the non-renewable energy power generation source in the power grid, predicting dynamic capacity data of the renewable energy power generation source in the power grid in the period to be scheduled, searching a plurality of power generation sources and corresponding lines according to the priority of the power generation sources and the priority of the corresponding lines, matching the power consumption demand trend data of each power consumption source according to the power generation amount of the searched power generation sources to obtain the dynamic matching relation between the power generation sources and the power consumption sources, covering the power consumption demand trend data of the power consumption sources by the power generation capacity of the dynamically matched power generation sources, generating a matching file and sending the matching file to a power distribution station;
and the power distribution station 1014 receives the matching file and switches the power generation source and the line to execute the power transmission task at regular time according to the dynamic matching relation in the matching file.
The software defined network refers to a process function of a program is moved down, and the core technology of the software defined network is OpenFlow, wherein a control plane and a data plane of network equipment are separated, so that the flexible control of network flow is realized.
Therefore, in order to realize timely and automatic power dispatching, a software definition device can be configured in the power distribution station and used for uploading, downloading, forwarding and executing various data and instructions, dependence of a dispatching process on manual operation is eliminated, and timely and reliable power dispatching is realized.
When the electricity demand is predicted, multiple factors can be considered, for example, the air temperature can affect the electricity consumption of the air conditioner, and the future air temperature can be obtained through monitoring and prediction; the holiday also affects the electricity consumption, for example, the spring festival returns to the country to cause the rural electricity consumption to increase, which is the holiday electricity consumption attribute in the rural area, and the spring festival electricity consumption of the first-line city decreases, which is the holiday electricity consumption attribute of the first-line city.
Considering that the order quantity is large, the factory power consumption is large, and therefore, the future industrial power demand can be predicted through the order quantity.
Therefore, a pre-trained demand prediction model can be called, and the power consumption attribute of the holiday of the region to which each power consumption belongs, the air temperature fluctuation data of each power consumption in the period to be scheduled, the expected order data and the holiday state are input into the demand prediction model, so that the power consumption trend data of each power consumption can be predicted.
Before that, the power demand trend data can be trained in a supervised learning manner.
In an embodiment of the present specification, the prediction center is further configured to:
acquiring holiday and festival electricity attributes of an area to which each power supply belongs, and an air temperature fluctuation sequence, an expected order sequence, a holiday and festival state sequence and an electricity consumption sequence of each power supply in a historical period, building a neural network model, setting a training label according to the electricity consumption sequence, and training the neural network model by taking the holiday and festival electricity attributes of the area to which each power supply belongs, the air temperature fluctuation sequence, the expected order sequence and the holiday and festival state sequence of each power supply in the historical period as training samples to obtain a demand prediction model.
In order to fully excavate the power generation capacity of the renewable energy power generation source, thereby reducing the capacity of the traditional thermal power station and reducing the emission, different power generation sources need to be prioritized, and the priority of the renewable energy power generation source is higher than that of the non-renewable energy power generation source, so that during matching, matching can be performed according to the priority, and the power generation capacity of the renewable energy power generation source is preferentially used.
At present, the transmission efficiency of the line is calculated by parameters of 25 degrees centigrade, however, the parameters can change with temperature, such as dielectric constant, therefore, for two transmission lines with close transmission rate, the priority can be reversed due to the change of temperature, and due to different temperatures in different regions in the country, if the transmission rate is calculated and the line is selected according to the parameters of 25 degrees centigrade, unnecessary loss of electric energy can be caused, and therefore, the transmission rate of each line can be predicted or calculated by combining the predicted temperature.
The transmission rate is the product of the transmission efficiency and the line length, the longer the distance, the greater the line resistance, and the lower the transmission rate.
By reasonably predicting the transmission rate, the influence of temperature difference on the transmission rate is considered, so that the rationality and the accuracy are improved.
The matching can be carried out in an automatic searching and calculating mode, the object to be matched can be a power generation source and a power utilization source, and the positive and negative are judged by calculating the power generation margin so as to determine whether the object to be matched is switched in the next step or the searching is continued.
The transmission rate can be directly set as a parameter at normal temperature, and can also be a dynamic parameter changing along with temperature, so that the dynamic transmission rate is more accurate.
In an embodiment of the present specification, the calculating a transmission rate of the line includes:
and learning the dynamic transmission rate of each line in the period to be scheduled from the temperature fluctuation data of the region to which each power supply belongs in the period to be scheduled and the length of each line by using a pre-trained dynamic transmission rate calculation rule.
Wherein, the transmission rate represents 1-percentage of power loss, the transmission rate is smaller as the line is longer, and the specific value and the calculation mode are not limited herein.
Since the requirement is a dynamic fluctuation curve, the matching is performed at each time point, and thus a dynamic matching relationship is obtained.
Specifically, in the embodiment of the present specification, the searching for a plurality of power generation sources and corresponding lines according to the priority of the power generation source and the priority of the corresponding line, and matching the power demand trend data of each power supply according to the searched power generation amount of the power generation source includes:
selecting a renewable energy power generation source as an initial object to be matched, searching for a power supply in an area to which the renewable energy power generation source belongs, matching, calculating the power generation allowance of the renewable energy power generation source according to dynamic capacity data of the renewable energy power generation source and power demand trend data of the matched power supply, if the power generation allowance is positive, continuing to search for matching by using the renewable energy power generation source as the object to be matched, searching for a power supply in an adjacent area of the renewable energy power generation source, continuing to calculate the power generation allowance of the renewable energy power generation source, and preferentially calculating the power generation allowance according to an adjacent area with higher line transmission efficiency for a plurality of adjacent areas;
and if the power generation allowance is negative, taking the matched power supply as an object to be matched, searching the non-renewable energy power generation source in the power supply area, matching, calculating and judging whether the power generation allowance exceeds the capacity range of the non-renewable energy power generation source, if so, continuing searching the non-renewable energy power generation source in the area adjacent to the power supply for matching, and if not, configuring the power generation task amount for the matched non-renewable energy power generation source according to the power generation allowance.
Therefore, the matching relation of the whole power grid can be obtained through recursion and matching until all power supplies are matched.
The power generation allowance of the renewable energy power generation source is calculated according to the dynamic capacity data of the renewable energy power generation source and the matched power demand trend data of the power supply, and the following conditions may be possible:
at any moment, the dynamic capacity is greater than the demand trend, and at the moment, the power generation margin is always positive;
at any moment, the dynamic capacity is smaller than the demand trend, and at the moment, the power generation margin is always negative;
there is also a complication where some time periods are positive and some time periods are negative. In this case, the processing may be performed in time periods, the search range is expanded for a time period in which the power generation margin is positive, the power supply is continuously searched, and the power generation margin is calculated until the power generation margin at any time in the time period is 0 or negative, which indicates that the power generation capacity of the power generation source is sufficiently mined, and at this time, the object to be matched may be switched to the power supply to match the adjacent power supply.
Therefore, after matching, different matching relations may exist at different time points, that is, the matching relation between the power generation source and the power consumption source may be switched as time progresses, and this can be predicted by the above matching method, so that a matching file can be generated in advance according to the matching relation and sent to the distribution station, so that the distribution station automatically switches the subsequent matching relation according to time and transmits power according to the matching relation.
In an embodiment of the present specification, the view center is further configured to:
and (4) placing the power generation source with negative power generation allowance after matching on the bottom layer or hiding the power generation source, and allowing a power distributor to adjust the dynamic matching relation between the power generation source and the power supply.
In this way, hiding facilitates intuitive handling by the distributor.
In the process of power transmission, the power transmission passes through a plurality of power grid nodes, a transmission link can be generated according to the plurality of power grid nodes passing through the process of power transmission, and verification can be performed by means of a block chain technology in order to prevent power stealing.
Specifically, a block chain subsystem may be used to issue a digital certificate to each grid node, before power transmission is performed, a power supply needs to be used to initiate a power consumption request, and the block chain subsystem performs identity verification on an initiator of the power consumption request, and allows power transmission only after verification is passed.
Thus, in an embodiment of the present specification, the performing a power transfer task includes:
each power grid node receives a digital certificate issued by the block chain subsystem, and when each power grid node initiates or forwards a power utilization request, the private key in the digital certificate is used for signing the power utilization request, adding the identifier of the current power grid node and the identifier of the adjacent power grid node in the power transmission route, and uploading the current power utilization request and the identifier of the adjacent power grid node in the power transmission route to a block chain;
and the block chain subsystem acquires a corresponding public key according to the power consumption request and the identifier of the power grid node, carries out label release on the power consumption request, compares the power consumption request with the power consumption request without label release, and authorizes each power grid node to execute the power transmission task corresponding to the power consumption request if the comparison is consistent.
The power consumption source can add the label request, add the identifier of the current power grid node and the identifier of the adjacent power grid node in the power transmission route, upload the current power consumption request and the non-labeled power consumption request to the block chain, send the labeled power consumption request to the next power grid node in the transmission link, and the next power grid node continues to label the labeled power consumption request by using the private key of the next power grid node.
Therefore, the transmission link can be determined according to the same power utilization request, so that the power grid nodes in the transmission link are verified during transmission, and the power is prevented from being transmitted by mistake.
In order to transmit the electric quantity in the correct transmission link, each power grid node in the transmission link can be authorized, and only the authorized power grid nodes can transmit the electric power, so that mistransmission can be avoided.
Specifically, in this embodiment of the present specification, the authorizing, to each grid node, to execute a power transmission task corresponding to the power consumption request includes:
and issuing a task switch with a validity period or an electric quantity threshold value to each power grid node, wherein the task switch and the power consumption request are used by each power grid node to start a power transmission task of the power consumption request, and the power transmission task of the power consumption request is closed when the validity period or the electric quantity threshold value is exceeded.
In an embodiment of the present specification, the task switch has a judgment instruction and at least one of a time monitoring instruction and a power amount monitoring instruction.
The topological structure among the power grid nodes can be tree-shaped, and the current power grid node can be communicated with a plurality of power grid nodes of the next level, so that the sum of the electric quantity transmitted to the current node can be calculated according to the electric quantity of the plurality of power grid nodes communicated with the current node, and the electric power can be transmitted to the current power grid node according to the sum of the electric quantity.
In an embodiment of the present specification, the data acquisition center is further configured to acquire a historical order sequence acquired by a third-party tax system, and calculate the credibility of the manufacturer according to the historical order sequence and an expected order sequence reported in a historical period of each manufacturer;
and adjusting the expected order data in the time period to be scheduled reported by each manufacturer according to the credibility.
In the embodiment of this specification, still include:
risks are identified and lines are switched according to the load states of equipment in the distribution substation.
In an embodiment of the present specification, the data collection center is further configured to: constructing a waveform recording queue in a network access node, monitoring and recording waveform data of an electric signal passing through the network access node, monitoring the running states of the plurality of electric devices, and configuring and uploading a fault label to the current waveform data in the waveform recording queue when a fault state is monitored;
the prediction center is also used for building a neural network model, performing machine learning by taking the waveform data as a training sample and taking the fault label as a training label of the training sample, and training a fault pre-recognition model;
and deploying a fault pre-recognition model in a matching center, acquiring waveform data of electric signals of each network access node in real time, inputting the waveform data into the fault pre-recognition model, predicting and recognizing a fault event, and if the fault event is recognized, performing fault isolation on the network access nodes in a power transmission task.
In this embodiment, the acquiring temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled includes:
and acquiring air temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled from a third-party system.
In embodiments of the present description, the grid nodes and lines in the power transmission diagram are configured with operational components by which an electrical distributor selects grid nodes and lines.
The system predicts the power demand trend of each power supply by inputting the temperature fluctuation, expected orders and the holiday state of the power consumption attribute of the power supply area in the period to be scheduled into a demand prediction model, constructs a visual power transmission diagram by taking the line transmission rate as the side length, collects the capacity range of the non-renewable energy power generation source, predicts the dynamic capacity of the renewable power generation source, searches the power generation source and the line according to the priority of the power generation source and the corresponding line, obtains the dynamic matching relationship and sends the dynamic matching relationship to a distribution station, and the distribution station switches the power generation source and the line at regular time according to the dynamic matching relationship. The method has the advantages that accurate balance of supply and demand is realized by predicting dynamic electricity demand and cleaning power generation source capacity and preferentially matching, the proportion of non-renewable energy sources is reduced, the power distribution station is regularly switched by using a software-defined mode, automatic scheduling can be realized by deploying in advance, dependence on manual operation is reduced, and the scheduling effect is timely and reliable.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. An intelligent prediction based power dispatching system, comprising:
a plurality of grid nodes connected by grid lines, comprising: a power supply, a power generation source and a power distribution station;
the data acquisition center is used for acquiring air temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled, acquiring holiday power utilization attributes and holiday states of the area to which each power supply belongs in the future period to be scheduled, acquiring expected order data in the period to be scheduled, which are reported by each manufacturer, and classifying the expected order data reported by each manufacturer into each power supply according to the area;
the forecasting center calls a pre-trained demand forecasting model, inputs the holiday power utilization attribute of the region to which each power utilization source belongs, the air temperature fluctuation data of each power utilization source in a period to be scheduled, expected order data and holiday states into the demand forecasting model, and forecasts the power utilization demand trend data of each power utilization source;
the view center is used for acquiring the position of each power grid node, calculating the transmission rate of a line, constructing a visual power transmission view by taking each power grid node as a node, taking a power grid line as an edge and taking the transmission rate of the line as the length of the edge, configuring priorities according to the energy types of the power generation sources, hierarchically displaying the power generation sources according to the priority order, and configuring edge data and priorities according to the transmission rate of each line;
the matching center is used for collecting the capacity range of the non-renewable energy power generation source in the power grid, predicting dynamic capacity data of the renewable energy power generation source in the power grid in the period to be scheduled, searching a plurality of power generation sources and corresponding lines according to the priority of the power generation sources and the priority of the corresponding lines, matching the power consumption demand trend data of each power consumption source according to the power generation amount of the searched power generation sources to obtain the dynamic matching relation between the power generation sources and the power consumption sources, covering the power consumption demand trend data of the power consumption sources by the power generation capacity of the dynamically matched power generation sources, generating a matching file and transmitting the matching file to a power distribution station;
the power distribution station receives the matching file and switches the power generation source and the line to execute the power transmission task at regular time according to the dynamic matching relation in the matching file;
the method for searching a plurality of power generation sources and corresponding lines according to the priorities of the power generation sources and the priorities of the corresponding lines and matching power consumption demand trend data of all power sources according to the searched power generation amount of the power generation sources comprises the following steps:
selecting a renewable energy power generation source as an initial object to be matched, searching for a power source in an area to which the renewable energy power generation source belongs, matching, calculating the power generation allowance of the renewable energy power generation source according to dynamic capacity data of the renewable energy power generation source and power demand trend data of the matched power source, if the power generation allowance is positive, continuing to search for matching by using the renewable energy power generation source as the object to be matched, searching for a power source in an adjacent area of the renewable energy power generation source, continuing to calculate the power generation allowance of the renewable energy power generation source, and preferentially calculating the power generation allowance according to the adjacent area with higher line transmission efficiency for a plurality of adjacent areas;
and if the power generation allowance is negative, taking the matched power supply as an object to be matched, searching the non-renewable energy power generation source in the power supply area, matching, calculating and judging whether the power generation allowance exceeds the capacity range of the non-renewable energy power generation source, if so, continuing searching the non-renewable energy power generation source in the area adjacent to the power supply for matching, and if not, configuring the power generation task amount for the matched non-renewable energy power generation source according to the power generation allowance.
2. The system of claim 1, wherein the prediction center is further configured to:
acquiring holiday power utilization attributes of regions to which the power sources belong, and air temperature fluctuation sequences, expected order sequences, holiday state sequences and power consumption sequences of the power sources in a historical period, building a neural network model, setting training labels according to the power consumption sequences, and training the neural network model by taking the holiday power utilization attributes of the regions to which the power sources belong, the air temperature fluctuation sequences, the expected order sequences and the holiday state sequences of the power sources in the historical period as training samples to obtain a demand prediction model.
3. The system of claim 1, wherein the view center is further configured to:
and (4) placing the power generation source with negative power generation allowance after matching on the bottom layer or hiding the power generation source, and allowing a power distributor to adjust the dynamic matching relation between the power generation source and the power supply.
4. The system of claim 1, wherein said calculating the transmission rate of the line comprises:
and learning the dynamic transmission rate of each line in the period to be scheduled from the temperature fluctuation data of the region to which each power supply belongs in the period to be scheduled and the length of each line by using a pre-trained dynamic transmission rate calculation rule.
5. The system of claim 1, wherein the data collection center is further configured to obtain a historical order sequence collected by a third party tax system, and calculate a credibility of each manufacturer according to the historical order sequence and an expected order sequence reported by each manufacturer in a historical period;
and adjusting the expected order data in the time period to be scheduled reported by each manufacturer according to the credibility.
6. The system of claim 1, wherein the matching center is further configured to:
risks are identified and lines are switched according to the load state of equipment in the distribution substation.
7. The system according to claim 1, wherein the obtaining of the air temperature fluctuation data of the area to which each power consumption source belongs in the future period to be scheduled comprises:
and acquiring air temperature fluctuation data of the area to which each power supply belongs in a future period to be scheduled from a third-party system.
8. The system of claim 1, wherein grid nodes and lines in the power transmission graph are configured with operational components to which an electrical distributor selects grid nodes and lines.
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