Energy optimization method and device, computer equipment, storage medium and program product

Abstract

The invention relates to an energy optimization method and device, computer equipment, a storage medium and a program product. The method comprises the steps of obtaining historical operation data of each piece of power equipment in a park within a preset duration, performing format conversion on each piece of historical operation data to obtain to-be-analyzed data, determining load prediction data and power prediction data of each piece of power equipment according to the to-be-analyzed data, and determining the load prediction data and the power prediction data of each piece of power equipment according to the to-be-analyzed data. And determining an energy regulation and control strategy based on the historical electricity price data within the preset duration and the energy storage data, the load prediction data and the power prediction data of each power device, so as to indicate each power device to perform energy optimization based on the energy regulation and control strategy, thereby performing energy optimization based on a whole set of complete park energy management and control system. And the data is converted into a uniform data format through format conversion, so that the phenomenon of'data islands' is avoided, and the accuracy and efficiency of energy optimization are improved.

Description

Technical Field

[0001] The present application relates to the technical field of energy optimization, and in particular to an energy optimization method, apparatus, computer equipment, storage medium and program product. Background Art

[0002] As the world responds to climate change, various parks (such as industrial parks, commercial parks, university campuses, etc.) are gradually transforming to "zero carbon". In order to achieve the zero carbon goal, energy needs to be optimized and managed through energy regulation and intelligent monitoring.

[0003] At present, energy is usually optimized and managed based on the park energy management and control system. However, the existing park energy management and control system is composed of multiple relatively independent subsystems, each of which performs different functions. Therefore, in the process of global energy optimization management, the phenomenon of "data islands" will appear, resulting in low accuracy of energy optimization. Summary of the invention

[0004] Based on this, it is necessary to provide an energy optimization method, device, computer equipment, storage medium and program product that can improve the accuracy of energy optimization in response to the above technical problems.

[0005] In a first aspect, the present application provides an energy optimization method. The method comprises:

[0006] Obtain the historical operation data of each power equipment in the park within a preset time period;

[0007] Convert the format of each historical operation data to obtain the data to be analyzed;

[0008] Determine the load prediction data and power prediction data of each of the electric power equipment according to the data to be analyzed;

[0009] An energy regulation strategy is determined based on the historical electricity price data within the preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy regulation strategy is used to instruct each power device to perform energy optimization based on the energy regulation strategy.

[0010] In one embodiment, the load prediction data and power prediction data of each of the power equipment are determined according to the data to be analyzed, including:

[0011] Obtain weather data of the park's environment;

[0012] For each power equipment, based on the forecast combination model, according to the weather data and the data to be analyzed, the load forecast data and power forecast data of the power equipment are determined; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

[0013] In one embodiment, the energy regulation strategy is determined based on the historical electricity price data within the preset time period, the energy storage data of each of the power equipment, the load forecast data, and the power forecast data, including:

[0014] Based on the target cost function and cost constraints, and based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data, the energy control strategy at each moment is determined.

[0015] In one embodiment, the method further comprises:

[0016] Determine fault data from the data to be analyzed according to the data to be analyzed and a preset threshold value;

[0017] The target electric equipment is determined according to the fault data, and an alarm message is displayed; the alarm message is used to indicate that a fault occurs in the target electric equipment.

[0018] In one embodiment, determining the target power equipment according to the fault data includes:

[0019] Build digital twin models of each power equipment;

[0020] The target power equipment is determined based on the fault data and the digital twin model.

[0021] In one embodiment, the method further comprises:

[0022] Get the modification instructions entered by the user;

[0023] The digital twin model is updated according to the modification instruction.

[0024] In a second aspect, the present application also provides an energy optimization device. The device comprises:

[0025] The first acquisition module is used to obtain the historical operation data of each power equipment in the park within a preset time period;

[0026] A format conversion module, used to convert the format of each historical operation data to obtain data to be analyzed;

[0027] A first determination module, used to determine the load prediction data and power prediction data of each of the electric power equipment according to the data to be analyzed;

[0028] The second determination module is used to determine the energy regulation strategy based on the historical electricity price data within the preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy regulation strategy is used to instruct each power device to perform energy optimization based on the energy regulation strategy.

[0029] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0031] In a fifth aspect, the present application also provides a computer program product, including a computer program, which implements the steps of any of the above methods when executed by a processor.

[0032] The above-mentioned energy optimization method, device, computer equipment, storage medium and program product obtain the historical operating data of each power equipment within a preset time period in the park, convert the format of each historical operating data to obtain the data to be analyzed, and determine the load forecast data and power forecast data of each power equipment based on the data to be analyzed. Based on the historical electricity price data within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data, an energy control strategy is determined to instruct each power equipment to perform energy optimization based on the energy control strategy, so that energy optimization can be performed based on a complete set of park energy management and control systems, and the data is converted into a unified data format through format conversion, thereby avoiding the emergence of the "data island" phenomenon and improving the accuracy and efficiency of energy optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is an internal structure diagram of a computer device provided in an embodiment of the present application;

[0034] Figure 2 It is a flow chart of an energy optimization method provided in an embodiment of the present application;

[0035] Figure 3 is a schematic diagram of energy optimization provided by an embodiment of the present application;

[0036] Figure 4 is a schematic diagram of data transmission provided by an embodiment of the present application;

[0037] Figure 5 It is a flowchart of a method for determining prediction data provided in an embodiment of the present application;

[0038] Figure 6 is a schematic diagram of load forecasting and power forecasting provided by an embodiment of the present application;

[0039] Figure 7 It is a flowchart of an alarm method provided in an embodiment of the present application;

[0040] Figure 8 It is a schematic diagram of a data analysis provided in an embodiment of the present application;

[0041] Fig. 9 It is a flowchart of a method for determining a target power device provided in an embodiment of the present application;

[0042] Fig.10 It is a schematic diagram of an integrated image and model provided in an embodiment of the present application;

[0043] Fig.11 It is a flowchart of a digital twin model updating method provided in an embodiment of the present application;

[0044] Fig.12 is a schematic diagram of an energy management and control system provided in an embodiment of the present application;

[0045] Fig.13 It is a structural block diagram of an energy optimization device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] As the world responds to climate change, various parks (such as industrial parks, commercial parks, university campuses, etc.) are gradually transforming to "zero carbon". In order to achieve the zero carbon goal, energy needs to be optimized and managed through energy regulation and intelligent monitoring.

[0048] At present, energy is usually optimized and managed based on the park energy management and control system. However, the existing park energy management and control system is composed of multiple relatively independent subsystems, each of which performs different functions. Therefore, in the process of global energy optimization management, the phenomenon of "data islands" will appear, resulting in low accuracy of energy optimization.

[0049] The energy optimization method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Figure 1 is an internal structure diagram of a computer device provided in an embodiment of the present application. The computer device may be a server, and its internal structure diagram may be as follows Figure 1As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, an energy optimization method is implemented.

[0050] Those skilled in the art will understand that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0051] In one embodiment, Figure 2 As shown, Figure 2 is a schematic diagram of a flow chart of an energy optimization method provided in an embodiment of the present application, which can be applied to Figure 1 In a computer device, the method comprises the following steps:

[0052] S201, obtaining historical operation data of each power equipment in the park within a preset time period.

[0053] Exemplarily, the historical operation data may include, for example, acquisition time, load history data, and power history data.

[0054] In one embodiment, each power device can report historical operation data to the message middleware via IEC 60870-5-104, MQTT (Message Queuing Telemetry Transport), Modbus, etc. Figure 1 The computer device shown can send a sending instruction to the message middleware, and then receive the historical operation data sent by the message middleware in response to the sending instruction.

[0055] Optionally, multiple monitoring moments may be set within a preset time period, and then historical operation data of each power device at each monitoring moment may be obtained.

[0056] For example, assuming that the preset duration is 24 hours, a monitoring moment is set every 15 minutes within 24 hours, that is, a total of 96 monitoring moments are set, then 96 historical operation data can be obtained for each power equipment.

[0057] In a possible implementation, collection points can be set according to actual needs, and then historical operation data of each collection point at each monitoring time can be obtained. Among them, one power device can be set as a collection point, and a system composed of multiple power devices can also be set as a collection point.

[0058] For example, if one power device is set as a collection point, the historical operation data of the power device at each monitoring time can be obtained. Alternatively, if a system composed of multiple power devices is set as a collection point, the historical operation data of the system at each monitoring time can be obtained.

[0059] Optionally, the historical operation data may include a unique identifier of a collection point.

[0060] S202: Convert the format of each historical operation data to obtain data to be analyzed.

[0061] In the embodiment of the present application, protocol conversion can be performed on each historical operation data, and each historical operation data can be converted into a unified JSON data format to obtain data to be analyzed.

[0062] In an embodiment of the present application, the historical operation data may be checked. If the historical operation data lacks a collection time field, the current system time may be used as the collection time of the historical operation data.

[0063] S203: Determine the load prediction data and power prediction data of each power device according to the data to be analyzed.

[0064] In one embodiment, the data to be analyzed may include the data to be analyzed of each power device at each monitoring moment. Then, for each power device, the data to be analyzed and the weather data of the power device at each monitoring moment can be input into the prediction combination model to obtain the load prediction data and power prediction data of the power device at each monitoring moment.

[0065] Optionally, ultra-short-term (taking 3 hours as an example) prediction and short-term (taking 3 days as an example) prediction can be performed based on the data to be analyzed of the power equipment and the weather data. Therefore, the load prediction data of the power equipment can include ultra-short-term load prediction data and short-term load prediction data, and the power prediction data can include ultra-short-term power prediction data and short-term power prediction data.

[0066] S204, determining an energy control strategy based on historical electricity price data within a preset time period, energy storage data of each power device, load forecast data, and power forecast data.

[0067] The energy control strategy is used to instruct each power device to perform energy optimization based on the energy control strategy.

[0068] In one embodiment, a target cost function and cost constraints can be set, and then the historical electricity price data within a preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data are substituted into the target cost function and the cost constraints. With the minimum or maximum cost as the goal, under the constraints of the cost constraints, the optimal solution of the coefficients to be solved in the target cost function is determined, and then the energy regulation strategy is determined based on the optimal solution.

[0069] Optionally, the coefficients to be solved may include, for example, energy storage power, load, power generation power, etc.

[0070] For example, refer to Figure 3 , Figure 3 Schematic diagram of energy optimization provided by the embodiment of the present application. Figure 3 As shown, based on the historical electricity price data within a preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data, the energy regulation strategy is determined. After the energy regulation strategy is determined, a control instruction can be generated based on the energy regulation strategy, and then the control instruction is sent to the target power device through the message middleware, so as to regulate the target power device through the control instruction, thereby achieving energy optimization. At the same time, the energy regulation strategy can also be sent to the WEB front-end page for user operation and query.

[0071] It should be noted that one control instruction can be generated according to the energy regulation strategy to regulate a target power device alone, or multiple control instructions can be generated according to the energy regulation strategy to regulate multiple power devices at the same time, which is not limited here.

[0072] For example, refer to Figure 4 , Figure 4 Schematic diagram of a data transmission provided by an embodiment of the present application. Figure 4 As shown in the figure, each power device can report historical operation data to the message middleware through IEC 60870-5-104, MQTT (Message Queuing Telemetry Transport), Modbus, etc. and receive the following information through the message middleware: Figure 1 Control instructions issued by the computer device shown.

[0073] In an embodiment of the present application, by acquiring the historical operating data of each power equipment within a preset time period in the park, the format of each historical operating data is converted to obtain the data to be analyzed, and the load forecast data and power forecast data of each power equipment are determined according to the data to be analyzed. Based on the historical electricity price data within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data, an energy control strategy is determined to instruct each power equipment to perform energy optimization based on the energy control strategy, so that energy optimization can be performed based on a complete set of park energy management and control systems, and the data is converted into a unified data format through format conversion, thereby avoiding the emergence of the "data island" phenomenon and improving the accuracy and efficiency of energy optimization.

[0074] Reference Figure 5 , Figure 5 1 is a flow chart of a method for determining prediction data provided by an embodiment of the present application. This embodiment involves a possible implementation method of how to determine the load prediction data and power prediction data of each power device based on the data to be analyzed. Based on the above embodiment, the above S203 includes the following steps:

[0075] S501, obtaining weather data of the environment in which the park is located.

[0076] In one embodiment, weather data of the environment in which the park is located can be obtained according to the set monitoring time within a preset time period.

[0077] S502, for each power device, based on the forecast combination model, according to the weather data and the data to be analyzed, determine the load forecast data and power forecast data of the power device.

[0078] Among them, the forecasting combination model includes time series model, machine learning model and deep learning model.

[0079] For example, refer to Figure 6 , Figure 6 Schematic diagram of load forecasting and power forecasting provided by the embodiment of the present application. Figure 6 As shown, the data to be analyzed may include the data to be analyzed of each power device at each monitoring time. Then, for each power device, the data to be analyzed and the weather data of the power device at each monitoring time may be input into the forecast combination model to obtain the load forecast data and power forecast data of the power device at each monitoring time. The data to be analyzed may include load history data and power history data.

[0080] Optionally, ultra-short-term (taking 3 hours as an example) prediction and short-term (taking 3 days as an example) prediction can be performed based on the data to be analyzed of the power equipment and the weather data. Therefore, the load prediction data of the power equipment can include ultra-short-term load prediction data and short-term load prediction data, and the power prediction data can include ultra-short-term power prediction data and short-term power prediction data.

[0081] In one embodiment, the prediction accuracy and qualification rate can be calculated based on the load prediction data and the power prediction data to evaluate the prediction combination model, and the parameters of the prediction combination model can be fine-tuned according to the evaluation results, so as to continuously optimize the prediction combination model.

[0082] Optionally, since the forecasting combination model is obtained by combining the time series model, the machine learning model and the deep learning model, when performing load forecasting and power forecasting, different models are selected for forecasting according to the characteristics of load forecasting and power forecasting, so as to improve the flexibility and accuracy of load forecasting and power forecasting.

[0083] In the embodiment of the present application, the weather data of the environment in which the park is located is obtained, and for each power equipment, based on the forecast combination model, the load forecast data and power forecast data of the power equipment are determined according to the weather data and the data to be analyzed. Since the forecast combination model in the embodiment of the present application includes a time series model, a machine learning model and a deep learning model, when performing load forecasting and power forecasting, different models can be selected for forecasting according to the characteristics of load forecasting and power forecasting, thereby improving the flexibility and accuracy of load forecasting and power forecasting.

[0084] Based on the above embodiment, the above S204 can be implemented by the following steps:

[0085] Based on the target cost function and cost constraints, the energy control strategy at each moment is determined based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data.

[0086] In one embodiment, a target cost function and cost constraints can be set, and then the historical electricity price data within a preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data are substituted into the target cost function and the cost constraints. With the minimum cost as the goal, under the constraints of the cost constraints, the optimal solution of the coefficients to be solved in the target cost function is determined, and then the energy storage control curve plan is determined based on the optimal solution, and the energy storage control curve plan is used as the energy control strategy.

[0087] Optionally, the coefficients to be solved may include, for example, energy storage power, load, power generation power, etc.

[0088] In an embodiment of the present application, based on the target cost function and cost constraints, based on the historical electricity price data at each moment within a preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data, the energy control strategy at each moment is determined, so that the target cost function can be used as the energy control strategy with the minimum cost as the goal, thereby reducing costs and improving the accuracy and reliability of energy control.

[0089] Reference Figure 7 , Figure 7 : is a flowchart of an alarm method provided by an embodiment of the present application. Based on the above embodiment, the method further includes the following steps:

[0090] S701 : determining fault data from the data to be analyzed according to the data to be analyzed and a preset threshold.

[0091] In one embodiment, a preset threshold may be set according to actual needs, and then the data to be analyzed is compared with the preset threshold. If the data to be analyzed exceeds the preset threshold, it indicates that the data to be analyzed is abnormal, and fault data may be determined from the data to be analyzed.

[0092] S702, determine the target power equipment according to the fault data, and display alarm information; the alarm information is used to indicate that the target power equipment has a fault.

[0093] In one embodiment, a digital twin model can be constructed based on each power device, and then the target power device can be located based on the fault data and the digital twin model, and alarm information can be generated, and the alarm information can be transmitted to a WEB front-end page to display the alarm information through the WEB front-end page.

[0094] For example, refer to Figure 8 , Figure 8 Schematic diagram of a data analysis provided by an embodiment of the present application. Figure 8 As shown, the historical operation data of each collection point at each monitoring moment reported by the message middleware can be obtained, and the format of each historical operation data can be converted to obtain the data to be analyzed. Then the calculation point is determined according to the collection point to perform local point calculation. Under normal circumstances, one collection point can correspond to one calculation point. Statistical analysis is performed on the data to be analyzed corresponding to each calculation point to determine the maximum value, minimum value, average value and other statistical analysis data of the data to be analyzed corresponding to each calculation point within the preset time. Then, based on the data to be analyzed corresponding to each calculation point and the preset threshold, alarm analysis is performed to obtain alarm data such as fault data, target power equipment, and alarm information.

[0095] Optionally, statistical analysis data and alarm data can be stored in a relational database, so that complex data query and analysis can be performed using the SQL language while ensuring data consistency and integrity. The data to be analyzed corresponding to each calculation point can also be stored in a time series database and a memory database. The time series database is used to query historical data and perform statistical analysis based on historical data, and the memory database is used to query real-time data.

[0096] In an embodiment of the present application, fault data is determined from the data to be analyzed based on the data to be analyzed and a preset threshold value, target power equipment is determined based on the fault data, and alarm information is displayed to indicate that a fault has occurred in the target power equipment. This enables real-time monitoring of the working status of each power equipment and timely sending of alarm information so that measures can be taken in time for the target power equipment that has failed, thereby improving the safety and stability of park operations.

[0097] Reference Fig. 9 , Fig. 9 1 is a flow chart of a method for determining a target power device provided by an embodiment of the present application. This embodiment involves a possible implementation method of how to determine a target power device based on fault data. Based on the above embodiment, the above S702 includes the following steps:

[0098] S901, build digital twin models of each power equipment.

[0099] In one embodiment, the operating parameters, environmental data, performance indicators and other data of each actual power equipment in the park can be obtained, and then a digital twin model of the power equipment can be established based on the operating parameters, environmental data, performance indicators and other data of each actual power equipment to reflect the structure and behavior of the actual power equipment, including geometric modeling, physical modeling and performance modeling.

[0100] S902: Determine the target power equipment based on the fault data and the digital twin model.

[0101] Optionally, the digital twin model of each power device can be diagnosed based on the fault data to locate the target power device where the fault occurs.

[0102] For example, refer to Fig.10 , Fig.10 Schematic diagram of an integrated image and model provided in the embodiment of the present application. Fig.10 As shown in the figure, a digital twin model of each power device can be constructed, and the graph model management and graph data binding can be realized. Then, the fault data corresponding to the data to be analyzed is obtained from the memory database, and then the graph model data of the digital twin model is queried based on the fault data to locate the target power device with fault.

[0103] Optionally, a connection can be established with a WEB front-end page via websocket, and after the target power equipment where the fault occurs is determined, an alarm message can be generated and pushed to the WEB front-end page so that the alarm message can be displayed via the WEB front-end page.

[0104] In an embodiment of the present application, a digital twin model of each power equipment is constructed, and the target power equipment is determined based on the fault data and the digital twin model, so that the faulty target power equipment can be located based on the digital twin model, so that treatment measures can be taken in time for the faulty target power equipment, further improving the safety and stability of the park operations.

[0105] Reference Fig.11 , Fig.11 : is a flowchart of a digital twin model update method provided in an embodiment of the present application. Based on the above embodiment, the method further includes the following steps:

[0106] S1101, obtaining a modification instruction input by a user.

[0107] For example, Fig.10 As shown, a modification instruction input by a user through a WEB front-end page can be received. The modification instruction can be, for example, a modification instruction for a parameter of a digital twin model or a new instruction for a digital twin model.

[0108] S1102, update the digital twin model according to the modification instruction.

[0109] For example, if the operating parameters of the actual power equipment A in the park change, the modification instructions entered by the user through the WEB front-end page can be obtained, and then the operating parameters of the digital twin model corresponding to the power equipment A can be updated according to the modification instructions.

[0110] Alternatively, if an actual power device B is added to the park, the modification instruction input by the user through the WEB front-end page can be obtained, and then the digital twin model corresponding to the power device B is added according to the modification instruction. The modification instruction includes the operating parameters, environmental data, performance indicators and other data of the power device B.

[0111] In an embodiment of the present application, modification instructions input by the user are obtained, and the digital twin model is updated according to the modification instructions. Thus, when the actual power equipment in the park changes, the digital twin model can be updated in time according to the modification instructions, thereby improving the accuracy of positioning the target power equipment and further improving the safety and stability of the park operations.

[0112] In order to introduce the embodiments of the present application more clearly, Fig.12 An exemplary description is given. Fig.12Schematic diagram of an energy management and control system provided in an embodiment of the present application. The above energy optimization method can be applied to Fig.12 A complete energy management and control system shown in FIG. 1 can convert data formats based on the message middleware, obtain historical operation data reported by each power device, and issue energy control strategies to each power device. After obtaining the data to be analyzed, statistical analysis and alarm analysis can be performed according to the calculation point. Then, based on the data to be analyzed and the weather data of the power device, ultra-short-term load forecast data, short-term load forecast data, ultra-short-term power forecast data, and short-term power forecast data are performed. After that, based on the target cost function and cost constraints, the energy control strategy at each moment can be determined based on the historical electricity price data at each moment within the preset time, the energy storage data of each power device, the load forecast data, and the power forecast data. In addition, the digital twin model of each power device can be constructed, and the graph model management and graph data binding can be realized. Based on the digital twin model, the target power device with a fault is located, and an alarm message is generated. Then, the alarm message can be pushed to the WEB front-end page to display the alarm message through the WEB front-end page, and receive the modification instructions for the digital twin model input by the user through the WEB front-end page.

[0113] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0114] Based on the same inventive concept, the embodiment of the present application also provides an energy optimization device for implementing the energy optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more energy optimization device embodiments provided below can refer to the limitations of the energy optimization method above, and will not be repeated here.

[0115] In one embodiment, Fig.13 As shown, Fig.13 1300 is a structural block diagram of an energy optimization device provided in an embodiment of the present application. The device 1300 includes:

[0116] The first acquisition module 1301 is used to acquire the historical operation data of each power equipment in the park within a preset time period.

[0117] The format conversion module 1302 is used to convert the format of each historical operation data to obtain data to be analyzed.

[0118] The first determination module 1303 is used to determine the load prediction data and power prediction data of each power device according to the data to be analyzed.

[0119] The second determination module 1304 is used to determine the energy regulation strategy based on the historical electricity price data within a preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy regulation strategy is used to instruct each power device to perform energy optimization based on the energy regulation strategy.

[0120] In one embodiment, the first determining module 1303 includes:

[0121] The acquisition unit is used to acquire weather data of the environment in which the park is located.

[0122] The first determination unit is used to determine the load forecast data and power forecast data of the power equipment for each power equipment based on the forecast combination model, according to the weather data and the data to be analyzed; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

[0123] In one embodiment, the second determining module 1304 includes:

[0124] The second determination unit is used to determine the energy regulation strategy at each moment based on the target cost function and cost constraints, based on the historical electricity price data at each moment within a preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data.

[0125] In one embodiment, the apparatus 1300 further includes:

[0126] The third determination module is used to determine fault data from the data to be analyzed according to the data to be analyzed and a preset threshold.

[0127] The display module is used to determine the target power equipment according to the fault data and display the alarm information; the alarm information is used to indicate that the target power equipment has a fault.

[0128] In one embodiment, the display module includes:

[0129] The model building unit is used to build the digital twin model of each power equipment.

[0130] The third determination unit is used to determine the target power equipment according to the fault data and the digital twin model.

[0131] In one embodiment, the apparatus 1300 further includes:

[0132] The second acquisition module is used to acquire the modification instruction input by the user.

[0133] The update module is used to update the digital twin model according to the modification instructions.

[0134] Each module in the above energy optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0135] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0136] Obtain the historical operation data of each power equipment in the park within a preset time period;

[0137] Convert the format of each historical operation data to obtain the data to be analyzed;

[0138] Determine the load forecast data and power forecast data of each power equipment according to the data to be analyzed;

[0139] The energy control strategy is determined based on the historical electricity price data within a preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy control strategy is used to instruct each power device to perform energy optimization based on the energy control strategy.

[0140] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0141] Obtain weather data of the park's environment;

[0142] For each power equipment, based on the forecast combination model, the load forecast data and power forecast data of the power equipment are determined according to the weather data and the data to be analyzed; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

[0143] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0144] Based on the target cost function and cost constraints, the energy control strategy at each moment is determined based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data.

[0145] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0146] determining fault data from the data to be analyzed according to the data to be analyzed and a preset threshold value;

[0147] The target power equipment is determined according to the fault data, and an alarm message is displayed; the alarm message is used to indicate that a fault has occurred in the target power equipment.

[0148] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0149] Build digital twin models of various power equipment;

[0150] Determine the target power equipment based on fault data and digital twin models.

[0151] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0152] Get the modification instructions entered by the user;

[0153] Update the digital twin model according to the modification instructions.

[0154] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0155] Obtain the historical operation data of each power equipment in the park within a preset time period;

[0156] Convert the format of each historical operation data to obtain the data to be analyzed;

[0157] Determine the load forecast data and power forecast data of each power equipment according to the data to be analyzed;

[0158] The energy control strategy is determined based on the historical electricity price data within a preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy control strategy is used to instruct each power device to perform energy optimization based on the energy control strategy.

[0159] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0160] Obtain weather data of the park's environment;

[0161] For each power equipment, based on the forecast combination model, the load forecast data and power forecast data of the power equipment are determined according to the weather data and the data to be analyzed; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

[0162] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0163] Based on the target cost function and cost constraints, the energy control strategy at each moment is determined based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data.

[0164] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0165] determining fault data from the data to be analyzed according to the data to be analyzed and a preset threshold value;

[0166] The target power equipment is determined according to the fault data, and an alarm message is displayed; the alarm message is used to indicate that a fault has occurred in the target power equipment.

[0167] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0168] Build digital twin models of various power equipment;

[0169] Determine the target power equipment based on fault data and digital twin models.

[0170] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0171] Get the modification instructions entered by the user;

[0172] Update the digital twin model according to the modification instructions.

[0173] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the following steps:

[0174] Obtain the historical operation data of each power equipment in the park within a preset time period;

[0175] Convert the format of each historical operation data to obtain the data to be analyzed;

[0176] Determine the load forecast data and power forecast data of each power equipment according to the data to be analyzed;

[0177] The energy control strategy is determined based on the historical electricity price data within a preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data; the energy control strategy is used to instruct each power device to perform energy optimization based on the energy control strategy.

[0178] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0179] Obtain weather data of the park's environment;

[0180] For each power equipment, based on the forecast combination model, the load forecast data and power forecast data of the power equipment are determined according to the weather data and the data to be analyzed; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

[0181] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0182] Based on the target cost function and cost constraints, the energy control strategy at each moment is determined based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power equipment, the load forecast data, and the power forecast data.

[0183] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0184] determining fault data from the data to be analyzed according to the data to be analyzed and a preset threshold value;

[0185] The target power equipment is determined according to the fault data, and an alarm message is displayed; the alarm message is used to indicate that a fault has occurred in the target power equipment.

[0186] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0187] Build digital twin models of various power equipment;

[0188] Determine the target power equipment based on fault data and digital twin models.

[0189] In one embodiment, when the computer program is executed by a processor, the following steps are also implemented:

[0190] Get the modification instructions entered by the user;

[0191] Update the digital twin model according to the modification instructions.

[0192] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0193] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0194] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An energy optimization method, characterized in that: The method comprises: Obtain the historical operation data of each power equipment in the park within a preset time period; Convert the format of each of the historical operation data to obtain data to be analyzed; Determining load prediction data and power prediction data of each of the electric power equipment according to the data to be analyzed; An energy regulation strategy is determined based on the historical electricity price data within the preset time period, the energy storage data of each of the power devices, the load forecast data, and the power forecast data; the energy regulation strategy is used to instruct each of the power devices to perform energy optimization based on the energy regulation strategy.

2. The method according to claim 1, characterized in that Determining the load prediction data and power prediction data of each of the electric power equipment according to the data to be analyzed includes: Obtaining weather data of the environment in which the park is located; For each of the above-mentioned power equipment, based on the forecast combination model, the load forecast data and power forecast data of the power equipment are determined according to the weather data and the data to be analyzed; the forecast combination model includes a time series model, a machine learning model and a deep learning model.

3. The method according to claim 1, characterized in that The energy control strategy is determined based on the historical electricity price data within the preset time period, the energy storage data of each of the power equipment, the load forecast data, and the power forecast data, including: Based on the target cost function and cost constraints, and based on the historical electricity price data at each moment within the preset time period, the energy storage data of each power device, the load forecast data, and the power forecast data, the energy control strategy for each moment is determined.

4. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: determining fault data from the data to be analyzed according to the data to be analyzed and a preset threshold; The target power equipment is determined according to the fault data, and an alarm message is displayed; the alarm message is used to indicate that a fault occurs in the target power equipment.

5. The method according to claim 4, characterized in that The step of determining the target power equipment according to the fault data comprises: Constructing a digital twin model of each of the power equipment; The target power equipment is determined according to the fault data and the digital twin model.

6. The method according to claim 5, characterized in that The method further comprises: Get the modification instructions entered by the user; The digital twin model is updated according to the modification instruction.

7. An energy optimization device, characterized in that: The device comprises: The first acquisition module is used to obtain the historical operation data of each power equipment in the park within a preset time period; A format conversion module, used for converting the format of each of the historical operation data to obtain data to be analyzed; A first determination module, used to determine the load prediction data and power prediction data of each of the electric power equipment according to the data to be analyzed; The second determination module is used to determine the energy regulation strategy based on the historical electricity price data within the preset time period, the energy storage data of each of the power devices, the load forecast data, and the power forecast data; the energy regulation strategy is used to instruct each of the power devices to perform energy optimization based on the energy regulation strategy.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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