A smart management and operation method, product, equipment and medium based on energy station

Abstract

The present application relates to the field of energy management technology, and in particular to a smart management and operation method, product, device and medium based on an energy station. The method includes: obtaining the predicted energy demand and predicted environmental data within the next cycle, and predicting the predicted energy output of the first equipment set in the energy station based on the predicted environmental data, the first equipment set including equipment that utilizes environmental energy production capacity; judging whether the predicted energy output meets the predicted energy demand; if the predicted energy output meets the predicted energy demand, then obtaining the energy storage equipment information in the energy station, and formulating the first energy station management strategy for the next cycle based on the energy storage equipment information; if the predicted energy output does not meet the predicted energy demand, then obtaining the equipment information of the second equipment set in the energy station, and formulating the second energy station management strategy for the next cycle based on the equipment information, the second equipment set including equipment that utilizes non-environmental energy production capacity. The present application can improve the operational efficiency of energy stations and reduce management costs.

Description

Technical Field

[0001] The present application relates to the field of energy management technology, and in particular to an intelligent management and operation method, product, equipment and medium based on an energy station. Background Art

[0002] With the development of the global economy and the growth of population, energy demand continues to increase, while the limited energy resources make energy shortages increasingly prominent. In this case, energy conservation and improving energy efficiency have become an inevitable trend in social development, and are also tasks that all industries need to pay great attention to. As a key link in energy supply and management, the operating efficiency and energy utilization level of energy stations are of great significance to the stability and sustainability of the entire energy system.

[0003] However, the traditional energy station management model has many shortcomings. For example, most energy stations are still using a more traditional system management model, which consumes a lot of manpower and financial resources, not only directly causing energy waste and direct economic losses, but also has a relatively chaotic management model and poor security. In addition, the traditional energy station management model lacks overall analysis and energy efficiency evaluation, and cannot carry out scientific coordination and online management of energy stations. Summary of the invention

[0004] In order to solve the problem of high management cost of energy stations in the prior art, the present application provides an intelligent management and operation method, product, equipment and medium based on energy stations.

[0005] In the first aspect, the present application provides a smart management and operation method based on an energy station, which adopts the following technical solutions:

[0006] A smart management and operation method based on an energy station, comprising:

[0007] Obtaining predicted energy demand and predicted environmental data in a next cycle, and predicting predicted energy production of a first set of equipment in the energy station based on the predicted environmental data, wherein the first set of equipment includes equipment that utilizes environmental energy production capacity;

[0008] Determining whether the predicted energy output meets the predicted energy demand;

[0009] If the predicted energy output meets the predicted energy demand, then obtaining the energy storage device information in the energy station, and formulating the first energy station management strategy for the next cycle based on the energy storage device information;

[0010] If the predicted energy output does not meet the predicted energy demand, the device information of the second device set in the energy station is obtained, and the second energy station management strategy for the next cycle is formulated based on the device information, and the second device set includes devices that utilize non-environmental energy production capacity.

[0011] By adopting the above-mentioned technical scheme, the predicted energy demand and predicted environmental data for the next cycle are obtained, so that the energy station can plan in advance, avoid energy shortages or surpluses, and improve operational efficiency. Predicting the predicted energy output of the first equipment set based on the predicted environmental data helps to understand the potential production capacity of environmental energy, thereby optimizing the configuration and use of related equipment, and judging whether the predicted energy output meets the predicted energy demand, which can avoid operational interruptions caused by energy shortages. When the predicted energy output meets the predicted energy demand, energy storage equipment information is obtained and management strategies are formulated, which can optimize the use of energy storage equipment and improve energy storage efficiency. When the predicted energy output does not meet the predicted energy demand, consider using the second equipment set as a supplement, which increases the diversity of energy sources. By introducing equipment with non-environmental energy production capacity, the reliability of the energy station can be improved to ensure that energy demand can still be met when environmental energy is insufficient. This application achieves comprehensive optimization of energy station operations, improves operational efficiency, and reduces management costs.

[0012] In a preferred example, the present application may be further configured as follows: the equipment information includes production efficiency information, production cost information and equipment operation data, and the second energy station management strategy for the next cycle is formulated based on the equipment information, including:

[0013] Calculating the difference between the predicted energy demand and the predicted energy output as an energy difference;

[0014] Based on the production efficiency information, determining from the second equipment set a number of first equipment groups whose energy production in the next cycle can satisfy the energy difference;

[0015] Based on the production capacity cost information and the equipment operation data, a target first equipment group is determined from the plurality of first equipment groups, and an equipment operation strategy for the target first equipment group is generated, and the second energy station management strategy includes the equipment operation strategy.

[0016] By adopting the above technical solution, the difference between the predicted energy demand and the predicted energy output is calculated to obtain the energy difference, which provides direction for subsequent equipment selection and strategy formulation. Based on the capacity efficiency information, several first equipment groups that meet the energy difference within the next week are concentratedly screened from the second equipment, avoiding unnecessary equipment investment and energy waste. The target first equipment group is determined based on the capacity cost information and equipment operation data, which helps to minimize costs. Generating an equipment operation strategy for the target first equipment group can ensure that the equipment operates in the best condition and improve the equipment's capacity efficiency and energy station operation efficiency.

[0017] In a preferred example, the present application may be further configured as follows: the second energy station management strategy further includes an energy selling strategy, and the method further includes:

[0018] Acquire energy ordering requirements and energy loss information, and determine a number of second equipment groups that can meet the energy ordering requirements based on the energy ordering requirements, the energy loss information, and the production efficiency information;

[0019] Calculate the total capacity cost of each second equipment group based on the capacity cost information;

[0020] A target second equipment group is determined from the plurality of second equipment groups based on the total capacity cost, and an energy selling strategy for the target second equipment group is generated.

[0021] By adopting the above technical solution, energy ordering needs are obtained, and combined with energy loss information and production capacity efficiency information, several second equipment groups that can meet these needs can be accurately determined, ensuring that the energy station can produce according to customer needs and improving customer satisfaction. By calculating the total production capacity cost of each second equipment group, the energy station can clearly understand the cost differences under different equipment combinations, which helps to select the equipment group with the best cost for production, and helps to formulate a more reasonable energy sales strategy to maximize profits. Generating corresponding energy sales strategies can ensure that the energy station meets customer needs at the lowest cost while maximizing profits.

[0022] In a preferred example, the present application may be further configured as follows: the energy ordering requirement includes a total ordering price, and determining a target second device group from the plurality of second device groups based on the total capacity cost includes:

[0023] Determine whether there is a second equipment group among the plurality of second equipment groups whose total capacity cost is less than the total order price;

[0024] If so, a second equipment group with the smallest total capacity cost is determined as the target second equipment group.

[0025] By adopting the above technical solution, it is determined whether there are options among several second equipment groups whose total production capacity cost is less than the total order price, and the one with the lowest cost is selected as the target second equipment group. This strategy ensures that the energy station minimizes costs while meeting customer needs, thereby improving profitability and enhancing market competitiveness.

[0026] In a preferred example, the present application may be further configured as follows: determining a target first device group from the plurality of first device groups based on the capacity cost information and the device operation data includes:

[0027] Calculating the sum of the capacity costs corresponding to each of the plurality of first equipment groups based on the capacity cost information;

[0028] Arrange the plurality of first equipment groups from small to large according to the sum of the production capacity costs to obtain a first list;

[0029] Calculating the equipment utilization rate of each device in the plurality of first equipment groups based on the equipment operation data, and calculating the sum of the equipment utilization rates corresponding to the plurality of first equipment groups;

[0030] Arrange the plurality of first equipment groups from small to large according to the sum of the equipment utilization rates to obtain a second list;

[0031] A target first device group is determined from the plurality of first device groups based on a position of each first device group in the first list and the second list respectively.

[0032] By adopting the above technical solution, the sum of the production capacity cost and the sum of the equipment utilization rate are comprehensively considered, and several first equipment groups are sorted according to these two indicators respectively. Then, the target first equipment group is determined based on the position of each equipment group in the two lists. This method not only optimizes the cost, but also improves the equipment utilization efficiency, thereby achieving a dual improvement in the economy and efficiency of energy station operations.

[0033] In a preferred example, the present application may be further configured as follows: the device information includes capacity cost information, and obtaining the capacity cost information of the second device set in the energy station includes:

[0034] Obtaining a device type of a target device, wherein the target device is any device in the second device set;

[0035] Obtaining historical cost data corresponding to the equipment type, the historical cost data including equipment purchase cost, failure repair cost, capacity consumption cost and full-cycle output;

[0036] Calculating the sum of the equipment purchase cost, the fault repair cost and the capacity consumption cost as the basic cost of the target equipment;

[0037] The ratio of the basic cost to the full-cycle output is calculated to obtain the capacity cost of the target equipment, and the capacity cost of each equipment in the second equipment set constitutes the capacity cost information of the second equipment set.

[0038] By adopting the above technical solution, the equipment type of the target equipment and its corresponding historical cost data (including equipment purchase cost, fault repair cost, capacity consumption cost and full-cycle output) are obtained, the basic cost of the target equipment is calculated, and the capacity cost is further obtained by the ratio of the basic cost to the full-cycle output. This method provides accurate data support for the capacity cost information of the second equipment set, which helps energy stations make more economical and reasonable decisions when formulating management strategies and achieve effective cost control.

[0039] In a preferred example, the present application may be further configured as follows: formulating the first energy station management strategy for the next cycle based on the energy storage device information includes:

[0040] Calculating the difference between the predicted energy output and the predicted energy demand as the energy surplus, and determining the storable surplus of the energy storage device in the energy station based on the energy storage device information;

[0041] The energy surplus is compared with the storable surplus. If the energy surplus exceeds the storable surplus, the difference between the energy surplus and the storable surplus is calculated as the sold surplus, and an energy selling strategy for the sold surplus is generated. The energy selling strategy for the sold surplus constitutes the first energy station management strategy.

[0042] By adopting the above technical solution, the difference between the predicted energy output and the predicted energy demand is calculated to determine the energy surplus, and the storable surplus is determined based on the energy storage device information. When the energy surplus exceeds the storable range, the energy selling strategy for selling the surplus is calculated and generated. This strategy not only effectively utilizes the production capacity of the energy station, but also maximizes the economic benefits by selling excess energy, while avoiding energy waste and overload of energy storage equipment, and improving the overall operating efficiency of the energy station.

[0043] In a second aspect, the present application provides a computer program product, which adopts the following technical solution:

[0044] A computer program product includes a computer program. When the computer program is executed by a processor, it implements the smart management and operation method based on the energy station as described in any one of the first aspects.

[0045] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0046] one or more processors;

[0047] Memory;

[0048] At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the energy station-based smart management and operation method as described in any one of the first aspects.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0050] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, enables the computer to execute the energy station-based intelligent management and operation method as described in any one of the first aspects.

[0051] In summary, this application includes the following beneficial technical effects:

[0052] This application obtains the predicted energy demand and predicted environmental data for the next cycle, so that the energy station can plan in advance, avoid energy shortages or surpluses, and improve operational efficiency. Predicting the predicted energy output of the first equipment set based on the predicted environmental data helps to understand the potential production capacity of environmental energy, thereby optimizing the configuration and use of related equipment, and judging whether the predicted energy output meets the predicted energy demand, which can avoid operational interruptions caused by energy shortages. When the predicted energy output meets the predicted energy demand, energy storage equipment information is obtained and management strategies are formulated, which can optimize the use of energy storage equipment and improve energy storage efficiency. When the predicted energy output does not meet the predicted energy demand, consider using the second equipment set as a supplement, thereby increasing the diversity of energy sources. By introducing equipment with non-environmental energy production capacity, the reliability of the energy station can be improved to ensure that energy demand can still be met when environmental energy is insufficient. This application achieves comprehensive optimization of energy station operations, improves operational efficiency, and reduces management costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a flowchart of a smart management and operation method based on an energy station provided in an embodiment of the present application;

[0054] Figure 2 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The following is combined with Figure 1 -Attached Figure 2 This application is described in further detail.

[0056] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, those skilled in the art may make modifications to the present embodiment without any creative contribution as needed, but such modifications are protected by the patent law as long as they are within the scope of the claims of the present application.

[0057] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0059] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0060] The present application embodiment provides a smart management and operation method based on an energy station, such as Figure 1 As shown, the method provided in the embodiment of the present application is executed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein:

[0061] S101. Obtain predicted energy demand and predicted environmental data for the next cycle, and predict the predicted energy output of a first equipment set in the energy station based on the predicted environmental data, where the first equipment set includes equipment that utilizes clean energy production capacity.

[0062] Specifically, management strategies are formulated for energy stations in cycles. The specific cycle duration can be flexibly set by the energy station manager, and this embodiment does not limit it. Optionally, each week can be divided into a cycle, and the management strategy of the energy station within the cycle is formulated before the start of each cycle.

[0063] According to the different functions of the equipment in the energy station, the equipment in the energy station is divided into three categories, namely: the first equipment set (equipment that uses natural energy such as wind energy and solar energy to generate energy), energy storage equipment (equipment that stores excess energy produced), and the second equipment set (equipment that uses non-environmental energy such as electricity and natural gas combustion, such as gas turbines, heat pumps, and spot refrigerants). The energy generation method and the type of energy produced in the energy station are determined by the construction properties of the energy station, and this embodiment does not limit it. The energy station can generate energy by wind power and / or solar energy, and the type of energy produced can be electricity and / or thermal energy.

[0064] The electronic device is provided with a database for storing historical data during the operation of the energy station, and retrieves historical energy demand data and historical environmental data from the database. The historical energy demand data includes the type of energy demand and the amount of energy demand, and the historical environmental data includes historical wind data and historical light data, etc. Use machine learning or deep learning algorithms (such as time series analysis, neural networks, and support vector machines, etc.) to establish energy demand prediction models and environmental data prediction models, and train the models so that they can predict the predicted energy demand in the next cycle based on the historical energy demand data in a period of time before the current moment, and predict the predicted environmental data in the next cycle based on the historical environmental data in a period of time before the current moment.

[0065] The database also stores the production efficiency of the first equipment set corresponding to different environmental data in the historical time period (the sum of the energy produced by the first equipment set per unit time), obtains the correspondence between the environmental data and the corresponding production efficiency of the first equipment set, and determines the production efficiency of the first equipment set at different times in the next cycle based on the correspondence and the predicted environmental data. An interpolation algorithm can be used to generate a production efficiency curve that changes with time in the next cycle, and the obtained curve is integrated to obtain the predicted energy output of the first equipment set in the next cycle.

[0066] S102: Determine whether the predicted energy output meets the predicted energy demand.

[0067] If the predicted energy output is not less than the predicted energy demand, it is determined that the predicted energy output meets the predicted energy demand; if the predicted energy output is less than the predicted energy demand, it is determined that the predicted energy output does not meet the predicted energy demand.

[0068] S103: If the predicted energy output meets the predicted energy demand, the energy storage device information in the energy station is obtained, and a first energy station management strategy for the next cycle is formulated based on the energy storage device information.

[0069] Specifically, when the predicted energy output is equal to the predicted energy demand, no surplus storage or energy selling operation is performed, and energy is only produced through the first device set to meet the predicted energy demand. When the predicted energy output exceeds the predicted energy demand, the difference between the predicted energy output and the predicted energy demand is calculated as the energy surplus, and the energy storage device information includes the storable surplus of the energy storage device, and the energy surplus and the storable surplus are compared. If the energy surplus is not greater than the storable surplus, the management strategy of the first energy station is determined as: storing the energy surplus in the energy storage device. If the energy surplus is greater than the storable surplus, the management strategy of the first energy station is determined as: selling the portion of the energy surplus that exceeds the storable surplus.

[0070] S104. If the predicted energy output does not meet the predicted energy demand, obtain the equipment information of the second equipment set in the energy station, and formulate the second energy station management strategy for the next cycle based on the equipment information. The second equipment set includes equipment that utilizes non-environmental energy production capacity.

[0071] Specifically, the equipment information includes capacity efficiency information, capacity cost information, and equipment operation data. Different types of equipment have different capacity efficiencies. The capacity efficiency of each type of equipment is determined based on the historical capacity data of the equipment, the equipment type of each equipment in the second equipment set is determined, and the capacity efficiency of each equipment in the second equipment set is determined according to the equipment type. The capacity efficiency of each equipment in the second equipment set constitutes the capacity efficiency information.

[0072] The second energy station management strategy includes the energy operation strategy and energy sales strategy of the second equipment group. For the energy operation strategy, the target first equipment group is selected from the second equipment group, and the target first equipment group is a combination of equipment whose sum of the output energy and the predicted energy output of the first equipment set can meet the predicted energy demand and has the lowest production cost. The energy operation strategy includes: operating the equipment in the target first equipment group for production capacity in the next cycle. For the energy sales strategy, the target second equipment group is selected from the second equipment group, and the target second equipment group is a combination of equipment that can meet the energy ordering demand (ordering energy from the energy station externally) and has the lowest actual production capacity cost and is less than the total ordering price of the energy ordering demand. The energy sales strategy includes: operating the equipment in the target second equipment group for production capacity in the next cycle, and selling the energy produced by the target second equipment group.

[0073] This embodiment obtains the predicted energy demand and predicted environmental data for the next cycle, so that the energy station can plan in advance, avoid energy shortages or surpluses, and improve operational efficiency. Predicting the predicted energy output of the first equipment set based on the predicted environmental data helps to understand the potential production capacity of environmental energy, thereby optimizing the configuration and use of related equipment, and judging whether the predicted energy output meets the predicted energy demand, which can avoid operational interruptions caused by energy shortages. When the predicted energy output meets the predicted energy demand, energy storage equipment information is obtained and management strategies are formulated, which can optimize the use of energy storage equipment and improve energy storage efficiency. When the predicted energy output does not meet the predicted energy demand, consider using the second equipment set as a supplement, thereby increasing the diversity of energy sources. By introducing equipment with non-environmental energy production capacity, the reliability of the energy station can be improved to ensure that energy demand can still be met when environmental energy is insufficient. This application achieves comprehensive optimization of energy station operations, improves operational efficiency, and reduces management costs.

[0074] In a possible implementation of the embodiment of the present application, the equipment information includes production efficiency information, production cost information and equipment operation data, and a second energy station management strategy for the next cycle is formulated based on the equipment information, including:

[0075] Calculating the difference between the predicted energy demand and the predicted energy production as the energy difference;

[0076] Based on the production efficiency information, determining from the second equipment set a number of first equipment groups whose energy production in the next cycle can meet the energy difference;

[0077] Based on the capacity cost information and the equipment operation data, a target first equipment group is determined from a plurality of first equipment groups, and an equipment operation strategy for the target first equipment group is generated. The second energy station management strategy includes the equipment operation strategy.

[0078] In this embodiment, the energy difference is the amount of energy that the second equipment set needs to produce when the predicted energy output of the first equipment set is insufficient to meet the predicted energy demand, so that the sum of the energy output of the first equipment set and the second equipment set meets the predicted energy demand.

[0079] The capacity efficiency information includes the capacity efficiency of each device in the second device set (the amount of energy produced per unit time). For any device in the second device set, the capacity efficiency of the device and the cycle length of the next cycle are calculated, and the result is used as the amount of energy that the device can produce in the next cycle. Among them, the device types of different devices may be different, and the capacity efficiency corresponding to the device types is different. The capacity efficiency of each device can be determined by the amount of energy produced per unit time in the historical capacity data and stored in the database in advance.

[0080] Traverse the second device set, combine the energy that each device in the second device set can produce in the next cycle, find all combinations whose sum of energy produced in the next cycle is not less than the energy difference, and obtain a number of first device groups. Determine a target first device group from the first device groups, and the operation strategy of the target first device group is: operate the devices of the target first device group for production capacity in the next cycle, and the energy produced meets the predicted energy demand.

[0081] This embodiment obtains the energy difference by calculating the difference between the predicted energy demand and the predicted energy output, which provides direction for subsequent equipment selection and strategy formulation. Based on the capacity efficiency information, several first equipment groups that meet the energy difference within the next week are screened from the second equipment, avoiding unnecessary equipment investment and energy waste. The target first equipment group is determined based on the capacity cost information and equipment operation data, which helps to minimize costs. The equipment operation strategy for the target first equipment group is generated to ensure that the equipment operates in the best state and improve the equipment's capacity efficiency and the energy station operation efficiency.

[0082] A possible implementation of the embodiment of the present application is to determine a target first device group from a plurality of first device groups based on production capacity cost information and device operation data, including:

[0083] Calculate the sum of the capacity costs corresponding to the first equipment groups based on the capacity cost information;

[0084] Arrange the first equipment groups in ascending order according to the sum of their production capacity costs to obtain a first list;

[0085] Calculating the equipment utilization rate of each device in the plurality of first equipment groups based on the equipment operation data, and calculating the sum of the equipment utilization rates corresponding to the plurality of first equipment groups;

[0086] Arrange the first equipment groups in ascending order according to the sum of equipment utilization rates to obtain a second list;

[0087] A target first device group is determined from the plurality of first device groups based on a position of each first device group in the first list and the second list, respectively.

[0088] In this embodiment, the capacity cost information includes the capacity cost of each device in the second device set. For any first device group, the sum of the capacity costs of each device in the first device group is calculated. The equipment operation data includes the total operation time and equipment service life of each device in the second device set. The total operation time represents the sum of the operation time of the device from the start of operation to the current moment. The equipment service life can be obtained from the factory configuration of the device, which represents the time from the start of use of the device to the maximum effective use time. For each device in several device groups, the ratio of the total operation time of the device to the equipment service life is calculated as the equipment utilization rate, and the sum of the equipment utilization rates of each device in each first device group is calculated.

[0089] For any first device group among the plurality of first device groups, the position of the first device group in the first list, i.e., the number of its ranking, is represented as a first value, and the position of the first device group in the second list is represented as a second value, and the first value and the second value of the first device group are weighted and summed to obtain a target value of the first device group. The one with the smallest target value is determined from the plurality of first device groups as the target first device group. The sum of the weights of the first value and the second value is 1, and the weights of the first value and the second value are set by the manager of the energy station based on actual experience, and are not limited in this embodiment.

[0090] This embodiment comprehensively considers the sum of the production capacity costs and the sum of the equipment utilization rates, sorts a number of first equipment groups according to these two indicators, and then determines the target first equipment group based on the position of each equipment group in the two lists. This method not only optimizes the cost, but also improves the equipment utilization efficiency, thereby achieving a dual improvement in the economy and efficiency of the energy station operation.

[0091] In a possible implementation of the embodiment of the present application, the second energy station management strategy further includes an energy selling strategy, and the method further includes:

[0092] Obtaining energy ordering requirements and energy loss information, and determining a number of second equipment groups that can meet the energy ordering requirements based on the energy ordering requirements, the energy loss information, and the production efficiency information;

[0093] calculating a total capacity cost of each second equipment group based on the capacity cost information;

[0094] A target second equipment group is determined from a plurality of second equipment groups based on the total capacity cost, and an energy selling strategy for the target second equipment group is generated.

[0095] In this embodiment, the energy ordering demand includes the amount of energy ordered in the next cycle, that is, the effective amount of energy transported from the energy station to the energy demand location. The energy ordering demand can be determined based on the reservation order, and can also be predicted based on historical ordering data, which is not limited in this embodiment. The energy loss information includes the energy loss rate of each device, including the energy loss rate in the process of energy output and transportation from the device to the energy demand location. The energy loss rate of different types of equipment is different. The energy loss rate of each type of equipment can be determined based on historical loss data and stored in the database. For example, if the energy loss rate of a device is 10%, it means that 90% of the energy produced by the device can be transmitted to the energy demand location, and 10% will be lost during the transmission process.

[0096] The target first equipment group in the second equipment set is used to produce capacity to meet the predicted energy demand. For each device in the second equipment set except the target first equipment group, the device type of the device is determined and then the energy loss rate of the device is determined. The capacity efficiency information includes the capacity efficiency of each device, that is, the capacity per unit time. For each device in the second equipment set except the target first equipment group, the product of the equipment's capacity efficiency and the next cycle length is calculated, and the product of the obtained product and (1-energy loss rate) is used as the effective capacity of the device in the next cycle.

[0097] Traverse each device in the second device set except the target first device group, combine each device and calculate the sum of effective capacity, and take the combination whose sum of effective capacity of each device in the combination can meet the energy ordering demand as several second device groups. The capacity cost information includes the capacity cost of each device, and the sum of the capacity cost of each device in each second device group is calculated as the total capacity cost of the corresponding second device group.

[0098] Furthermore, a target second device group is determined from a number of second device groups, and the energy selling strategy of the target second device group includes: operating the target second device group in the next cycle, and selling the energy produced by the target second device group; if the energy produced during the operation of the target second device group in the next cycle can meet the energy ordering demand, the target second device group can be shut down to save costs, and the storable balance of the energy storage device can be obtained. If the storable balance of the energy storage device is not zero, the energy produced by the target second device group is stored.

[0099] This embodiment obtains energy ordering needs and combines energy loss information and production capacity efficiency information to accurately determine a number of second equipment groups that can meet these needs, thereby ensuring that the energy station can produce according to customer needs and improving customer satisfaction. By calculating the total production capacity cost of each second equipment group, the energy station can clearly understand the cost differences under different equipment combinations, which helps to select the equipment group with the best cost for production, and helps to formulate a more reasonable energy sales strategy to maximize profits. Generating corresponding energy sales strategies can ensure that the energy station meets customer needs at the lowest cost while maximizing profits.

[0100] In a possible implementation of the embodiment of the present application, the energy ordering requirement includes a total ordering price, and a target second device group is determined from a plurality of second device groups based on a total capacity cost, including:

[0101] Determine whether there is a second equipment group whose total production capacity cost is less than the total order price among the plurality of second equipment groups;

[0102] If so, a second equipment group with the smallest total capacity cost is determined as the target second equipment group.

[0103] In this embodiment, the operation of the equipment in the second equipment set requires costs. Assuming that the price of the energy produced by the equipment is not enough to cover the energy production cost, the strategy of selling energy will cause the energy station to lose money. If there is no second equipment group whose total production capacity cost is less than the total order price among the plurality of second equipment groups, the step of determining the target second equipment group from the plurality of second equipment groups is not performed, that is, the energy selling strategy of the target second equipment group is not performed.

[0104] This embodiment determines whether there is an option with a total production capacity cost less than the total order price among several second equipment groups, and selects the one with the lowest cost as the target second equipment group. This strategy ensures that the energy station minimizes costs while meeting customer needs, thereby improving profitability and enhancing market competitiveness.

[0105] In a possible implementation manner of the embodiment of the present application, the device information includes capacity cost information, and obtaining the capacity cost information of the second device set in the energy station includes:

[0106] Obtaining a device type of a target device, where the target device is any device in the second device set;

[0107] Obtain the historical cost data corresponding to the equipment type, including equipment purchase cost, fault repair cost, capacity consumption cost and full-cycle output;

[0108] Calculate the sum of equipment purchase cost, fault repair cost and capacity consumption cost as the basic cost of the target equipment;

[0109] The ratio of the basic cost to the full-cycle output is calculated to obtain the capacity cost of the target equipment, and the capacity cost of each equipment in the second equipment set constitutes the capacity cost information of the second equipment set.

[0110] In this embodiment, the historical cost data corresponding to the device type of the target device includes the historical cost data of the historical devices of the device type. The historical devices are devices that have been in normal operation until they are eliminated. For the historical cost data, the equipment purchase cost is the cost of the historical equipment when it was purchased, the fault repair cost is the sum of the repair costs spent from the start of the historical equipment to its elimination, and the full-cycle output is the amount of energy produced from the start of the historical equipment to its elimination. The ratio of the basic cost and the full-cycle output is calculated to obtain the capacity cost of the target device, that is, the cost required for the equipment to produce unit energy.

[0111] This embodiment calculates the basic cost of the target equipment by obtaining the equipment type of the target equipment and its corresponding historical cost data (including equipment purchase cost, fault repair cost, capacity consumption cost and full-cycle output), and further obtains the capacity cost by the ratio of the basic cost to the full-cycle output. This method provides accurate data support for the capacity cost information of the second equipment set, which helps energy stations make more economical and reasonable decisions when formulating management strategies and achieve effective cost control.

[0112] A possible implementation of the embodiment of the present application is to formulate a first energy station management strategy for the next cycle based on energy storage device information, including:

[0113] Calculate the difference between the predicted energy output and the predicted energy demand as the energy surplus, and determine the storable surplus of the energy storage device in the energy station based on the energy storage device information;

[0114] The energy surplus and the storable surplus are compared. If the energy surplus exceeds the storable surplus, the difference between the energy surplus and the storable surplus is calculated as the sold surplus, and an energy selling strategy for the sold surplus is generated. The energy selling strategy for the sold surplus constitutes the first energy station management strategy.

[0115] In this embodiment, if the energy surplus does not exceed the storable surplus, the produced energy surplus is directly stored in the energy storage device. If the energy surplus exceeds the storable surplus, it means that the energy storage device cannot store all the energy surplus, and the sold surplus can be calculated. The energy selling strategy for the sold surplus includes: running the first device set in the next cycle, and storing the energy surplus in the energy storage device after the predicted energy output produced by the first device set meets the predicted energy demand. In the case that the energy storage device cannot store all the energy surplus, the sold surplus is sold to the energy buyer (such as the upper power grid).

[0116] This embodiment determines the energy surplus by calculating the difference between the predicted energy output and the predicted energy demand, and determines the storable surplus based on the energy storage device information. When the energy surplus exceeds the storable range, an energy selling strategy for selling the surplus is calculated and generated. This strategy not only effectively utilizes the production capacity of the energy station, but also maximizes the economic benefits by selling excess energy, while avoiding energy waste and overload of energy storage equipment, thereby improving the overall operating efficiency of the energy station.

[0117] An embodiment of the present application provides a computer program product, including a computer program. When the computer program is executed by a processor, the contents shown in the aforementioned embodiment of the smart management and operation method based on the energy station are implemented.

[0118] An electronic device is provided in an embodiment of the present application, such as Figure 2 As shown, Figure 2 The electronic device 200 shown includes: a processor 201 and a memory 203. The processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 may also include a transceiver 204. It should be noted that in actual applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation on the embodiments of the present application.

[0119] The processor 201 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 201 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0120] The bus 202 may include a path to transmit information between the above components. The bus 202 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 202 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0121] The memory 203 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0122] The memory 203 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 201. The processor 201 is used to execute the application code stored in the memory 203 to implement the contents shown in the embodiment of the smart management and operation method based on the energy station.

[0123] Figure 2 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0124] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the contents shown in the aforementioned embodiment of the smart management and operation method based on the energy station.

[0125] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0126] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A smart management and operation method based on an energy station, characterized in that: include: Obtaining predicted energy demand and predicted environmental data in a next cycle, and predicting predicted energy production of a first set of equipment in the energy station based on the predicted environmental data, wherein the first set of equipment includes equipment that utilizes environmental energy production capacity; Determining whether the predicted energy output meets the predicted energy demand; If the predicted energy output meets the predicted energy demand, then obtaining the energy storage device information in the energy station, and formulating the first energy station management strategy for the next cycle based on the energy storage device information; If the predicted energy output does not meet the predicted energy demand, obtaining device information of a second device set in the energy station, and formulating a second energy station management strategy for the next cycle based on the device information, wherein the second device set includes devices that utilize non-environmental energy production capacity; The equipment information includes production efficiency information, production cost information and equipment operation data, and the second energy station management strategy for the next cycle is formulated based on the equipment information, including: Calculating the difference between the predicted energy demand and the predicted energy output as an energy difference; Based on the production efficiency information, determining from the second equipment set a number of first equipment groups whose energy production in the next cycle can satisfy the energy difference; Based on the capacity cost information and the equipment operation data, determining a target first equipment group from the plurality of first equipment groups, generating an equipment operation strategy for the target first equipment group, wherein the second energy station management strategy includes the equipment operation strategy; The determining a target first device group from the plurality of first device groups based on the capacity cost information and the device operation data includes: Calculating the sum of the capacity costs corresponding to each of the plurality of first equipment groups based on the capacity cost information; Arrange the plurality of first equipment groups from small to large according to the sum of the production capacity costs to obtain a first list; Calculating the equipment utilization rate of each device in the plurality of first equipment groups based on the equipment operation data, and calculating the sum of the equipment utilization rates corresponding to the plurality of first equipment groups; Arrange the plurality of first equipment groups in ascending order according to the sum of the equipment utilization rates to obtain a second list; Determine a target first device group from the plurality of first device groups based on a position of each first device group in the first list and the second list respectively; The equipment operation data includes the total operation time and equipment service life of each equipment in the second equipment set; For any device in the second device set, calculating the device utilization of the device, including: calculating a ratio of the total operating time of the device to the device service life as the device utilization of the device; The determining a target first device group from the plurality of first device groups based on the position of each first device group in the first list and the second list respectively includes: For any first device group among the plurality of first device groups, determining a first order of the first device group in the first list and a second order of the first device group in the second list, and performing a weighted summation of the first order and the second order based on a preset weight to obtain a target value of the first device group; Determine, from the plurality of first device groups, one with the smallest target value as a target first device group; The device information includes capacity cost information, and obtaining the capacity cost information of the second device set in the energy station includes: Obtaining a device type of a target device, wherein the target device is any device in the second device set; Obtaining historical cost data corresponding to the equipment type, the historical cost data including equipment purchase cost, failure repair cost, capacity consumption cost and full-cycle output; Calculating the sum of the equipment purchase cost, the fault repair cost and the capacity consumption cost as the basic cost of the target equipment; The ratio of the basic cost to the full-cycle output is calculated to obtain the capacity cost of the target equipment, and the capacity cost of each equipment in the second equipment set constitutes the capacity cost information of the second equipment set.

2. The intelligent management and operation method based on the energy station according to claim 1 is characterized in that: The second energy station management strategy also includes an energy selling strategy, and the method further includes: Acquire energy ordering requirements and energy loss information, and determine a number of second equipment groups that can meet the energy ordering requirements based on the energy ordering requirements, the energy loss information, and the production efficiency information; Calculate the total capacity cost of each second equipment group based on the capacity cost information; A target second equipment group is determined from the plurality of second equipment groups based on the total capacity cost, and an energy selling strategy for the target second equipment group is generated.

3. The intelligent management and operation method based on energy station according to claim 2 is characterized in that: The energy ordering requirement includes a total ordering price, and determining a target second device group from the plurality of second device groups based on the total capacity cost includes: Determine whether there is a second equipment group among the plurality of second equipment groups whose total capacity cost is less than the total order price; If so, a second equipment group with the smallest total capacity cost is determined as the target second equipment group.

4. The intelligent management and operation method based on energy station according to claim 1 is characterized in that: The formulating the first energy station management strategy for the next cycle based on the energy storage device information includes: Calculating the difference between the predicted energy output and the predicted energy demand as the energy surplus, and determining the storable surplus of the energy storage device in the energy station based on the energy storage device information; The energy surplus is compared with the storable surplus. If the energy surplus exceeds the storable surplus, the difference between the energy surplus and the storable surplus is calculated as the sold surplus, and an energy selling strategy for the sold surplus is generated. The energy selling strategy for the sold surplus constitutes the first energy station management strategy.

5. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, implements the steps of the energy station-based intelligent management and operation method described in any one of claims 1 to 4.

6. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the energy station-based smart management and operation method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the energy station-based intelligent management and operation method as described in any one of claims 1-4.

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