A campus microgrid variable monitoring method and system based on embedded system

Abstract

The present invention relates to the field of data processing technology, and discloses a method and system for monitoring variables of a park microgrid based on an embedded system. The method and system obtain electricity reservation information of a target park during a monitoring period, extract a standard power supply curve of each distributed power source, generate an initial distributed power dispatching strategy of the target park during the monitoring period, and drive a power dispatching device to execute a power dispatching action of each monitoring cycle during the monitoring period. At the same time, after each power dispatching action is executed, the variable information of the park microgrid is obtained, and the smoothness requirement of each power dispatching and the optimal number of overall dispatching times during the monitoring period are considered, and the distributed power dispatching strategy of each subsequent monitoring cycle in the monitoring period is optimized. By effectively monitoring the variables of the park microgrid, the execution difficulty of power users in switching between power reservation and distributed power dispatching is reduced, and the intelligence and rationality of distributed power dispatching in the park microgrid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for monitoring variables of a campus microgrid based on an embedded system. Background Art

[0002] A campus microgrid is a small power system that usually includes distributed power sources (such as solar photovoltaic power generation systems, wind power generation systems, micro gas turbines, etc.), energy storage systems (such as batteries, supercapacitors, etc.), loads (including industrial equipment, commercial power equipment, residential power equipment, etc.) and related power electronic converters and control devices. It can achieve self-control, protection and management to a certain extent, and can be connected to the external large power grid or run independently in island mode.

[0003] In practical applications, the application of campus microgrids is restricted and affected by specific scenarios. In some scenarios (such as isolated campus microgrids), the campus microgrid needs to ensure instantaneous power balance, that is, the electric energy generated by the power supply can just meet the immediate demand of the load. At this time, electricity users need to reserve electricity power in advance, and configure the access and scheduling switching of distributed power sources and electricity users in advance to avoid the impact of sudden startup of large equipment or simultaneous electricity consumption of a large number of electricity users on the load power and grid frequency of the microgrid. However, when electricity users make electricity power reservations and distributed power scheduling switching, it is impossible to accurately consider the errors and changes in electricity power (for example, temporary electricity, emergency electricity, and production equipment may have different electricity powers when performing different production tasks) and power supply power (for example, clean distributed power may have different power supplies in different environments) under different circumstances, which makes the execution of distributed power scheduling switching of the entire campus microgrid difficult and the execution efficiency is not ideal, affecting the power supply quality of the entire campus microgrid and the electricity experience of electricity users.

[0004] Therefore, how to effectively monitor the variables of the park microgrid, reduce the difficulty of power users in making power reservations and switching distributed power generation, and improve the intelligence and rationality of distributed power generation scheduling in the park microgrid is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a campus microgrid variable monitoring method and system based on an embedded system, aiming to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a campus microgrid variable monitoring method based on an embedded system, comprising:

[0007] Obtaining electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes electricity power demand data of several electricity users;

[0008] Calling a distributed power supply database of a park to extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database of the park;

[0009] Based on the standard power supply curve of each distributed power source and the power demand data of several power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to perform the power scheduling action of each monitoring cycle during the monitoring period;

[0010] After each power dispatching action is executed, the park microgrid variable information collected by several grid variable monitoring devices in the park embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the park microgrid variable information are extracted;

[0011] Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, update the standard power supply curve of each distributed power source and the power demand data of each power user, consider the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period, and optimize the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period;

[0012] The optimized distributed power generation scheduling strategy is sent to the park embedded power grid system, and the power generation scheduling action and significant difference determination action are repeatedly executed in each subsequent monitoring cycle during the monitoring period.

[0013] Optionally, the steps of obtaining the electricity reservation information of the target park during the monitoring period include:

[0014] Obtaining an electricity reservation request issued by each electricity user during a reservation period, and extracting the electricity user identifier, electricity reservation time, and electricity power demand data in the electricity reservation request;

[0015] Based on each electricity reservation request received, screening is performed according to the electricity user identification and the time period in which the electricity reservation time falls, and electricity reservation information of the target park including the electricity power of several electricity users during the monitoring period is generated.

[0016] Optionally, calling a distributed power supply database of a park to extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database of the park specifically includes:

[0017] Calling a park distributed power supply database; wherein the park distributed power supply database stores a power supply power curve of each distributed power supply in each historical monitoring period;

[0018] The power supply power curve of each distributed power source in each historical monitoring period in the distributed power supply database of the park is extracted, the average power supply power of each distributed power source at the corresponding sampling time point in all historical monitoring periods is calculated, and the standard power supply power curve of each distributed power source in the historical monitoring period is generated according to the average power supply power at each sampling time point.

[0019] Optionally, based on the standard power supply curve of each distributed power source and the power demand data of a number of power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to execute the power scheduling action steps of each monitoring cycle during the monitoring period, specifically including:

[0020] The monitoring period is divided into several monitoring cycles according to the minimum unit monitoring time. According to each divided monitoring cycle, based on the matching relationship between the standard power supply power curve of each distributed power source and the power demand data of several power users in each monitoring cycle, the power supply and power consumption allocation between several distributed power sources and several power users in each monitoring cycle is executed on the principle of maximizing the cumulative sum of the matching degrees of the standard power supply power curve and the power demand data in each monitoring cycle of the monitoring period, and the distributed power generation scheduling strategy of the target park in the monitoring period is generated;

[0021] The distributed power supply scheduling strategy is sent to the campus embedded power grid system, driving the power scheduling device in the campus embedded power grid system to execute the power scheduling action of each monitoring cycle during the monitoring period; wherein, the power scheduling action is configured to connect the power supply lines of several distributed power sources having a power supply and power consumption distribution relationship with the power lines of several power users.

[0022] Optionally, after each power dispatching action is executed, the campus microgrid variable information collected by a plurality of grid variable monitoring devices in the campus embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information are extracted, specifically including:

[0023] After each power scheduling action is executed, the campus microgrid variable information collected by several grid variable monitoring devices in the campus embedded power grid system is obtained; wherein the campus microgrid variable information includes the actual power supply power value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point collected by the grid variable monitoring device between the current power scheduling action and the next power scheduling action;

[0024] According to the actual power supply value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point, an actual power supply power curve of each distributed power source and actual power consumption data of each power user are generated.

[0025] Optionally, the step of judging whether the actual power consumption data of each power user and the actual power supply power curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition includes:

[0026] Extract the actual power consumption data of each power user and the actual power supply power value at each sampling time point from the actual power supply power curve of each distributed power source;

[0027] Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition;

[0028] Among them, the significant difference judgment condition is configured as the proportion of sampling time points at which the difference between the actual power consumption value and the actual power supply power value at the corresponding moment in the actual power consumption data of several distributed power sources and several power users with a power supply and power consumption distribution relationship under the distributed power supply scheduling strategy exceeds the error value.

[0029] Optionally, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, and the smoothness requirement of each power supply scheduling and the optimal number of overall scheduling in the monitoring period are considered, and the distributed power supply scheduling strategy steps for each subsequent monitoring cycle in the monitoring period are optimized, specifically including:

[0030] According to the change ratio between the actual power consumption data and the power consumption demand data of each power user, the power consumption demand data of each power user in each subsequent monitoring cycle is updated;

[0031] Obtaining the influencing parameters of the distributed power sources in the park, inputting the influencing parameters of the distributed power sources into the power supply prediction model of each distributed power source, and predicting and obtaining the standard power supply power curve of each distributed power source in each subsequent monitoring cycle;

[0032] The constraint condition is that the difference between the change value of the power demand of each power user before and after each power scheduling and the change value of the standard power supply power of the corresponding distributed power source does not exceed the preset power supply smooth change value. The optimization goal is to minimize the sum of the distributed power scheduling switching times corresponding to the power users in the power scheduling of all subsequent monitoring cycles during the monitoring period. The distributed power scheduling strategy for each subsequent monitoring cycle during the monitoring period is optimized.

[0033] In addition, in order to achieve the above purpose, the present invention also provides a campus microgrid variable monitoring system based on an embedded system, comprising:

[0034] An acquisition module is used to acquire the electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes the electricity power demand data of several electricity users;

[0035] A calling module is used to call a distributed power supply database in a park, and extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database in the park;

[0036] A generation module is used to generate a distributed power supply scheduling strategy for the target park during the monitoring period based on the standard power supply power curve of each distributed power supply and the power demand data of a number of power users, and send the distributed power supply scheduling strategy to the park embedded power grid system to drive the power supply scheduling device in the park embedded power grid system to perform the power supply scheduling action of each monitoring cycle during the monitoring period;

[0037] An extraction module is used to obtain the campus microgrid variable information collected by a number of grid variable monitoring devices in the campus embedded grid system after each power scheduling action is executed, and extract the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information;

[0038] The optimization module is used to determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, considering the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period, and optimizing the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period;

[0039] The repetition module is used to send the optimized distributed power dispatching strategy to the park embedded power grid system, and repeatedly execute the power dispatching action and significant difference determination action of each subsequent monitoring cycle in the monitoring period.

[0040] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a campus microgrid variable monitoring device based on an embedded system, and the campus microgrid variable monitoring device based on an embedded system includes: a memory, a processor, and a campus microgrid variable monitoring program based on an embedded system stored in the memory and executable on the processor. When the campus microgrid variable monitoring program based on the embedded system is executed by the processor, the steps of the campus microgrid variable monitoring method based on the embedded system as described above are implemented.

[0041] In addition, in order to achieve the above-mentioned purpose, the present invention also provides a storage medium, on which a campus microgrid variable monitoring program based on an embedded system is stored. When the campus microgrid variable monitoring program based on an embedded system is executed by a processor, the steps of the above-mentioned campus microgrid variable monitoring method based on an embedded system are implemented.

[0042] The beneficial effects of the present invention are as follows: a method and system for monitoring variables of a park microgrid based on an embedded system are proposed, by acquiring the power reservation information of the target park in the monitoring period, extracting the standard power supply power curve of each distributed power source in the historical monitoring period stored in the park distributed power database, generating the initial distributed power supply scheduling strategy of the target park in the monitoring period, and driving the power dispatching device of the embedded power grid system to perform the power dispatching action of each monitoring cycle in the monitoring period; at the same time, after each power dispatching action is executed, the variable information of the park microgrid is obtained, and when the actual power consumption data and the actual power supply power curve meet the significant difference judgment condition, the standard power supply power curve of each distributed power source and the power demand data of each power user are updated, considering the smoothness requirement of each power dispatch and the optimal overall dispatching number of the monitoring period, optimizing the distributed power dispatching strategy of each subsequent monitoring period in the monitoring period, driving the power dispatching device to always perform the global optimal distributed power dispatching in each subsequent monitoring period, and by effectively monitoring the variables of the park microgrid, reducing the execution difficulty of the power user to perform power reservation and distributed power dispatch switching, and improving the intelligence and rationality of distributed power dispatching in the park microgrid. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention;

[0044] Figure 2It is a flow chart of an embodiment of a method for monitoring variables of a campus microgrid based on an embedded system according to the present invention;

[0045] Figure 3 The present invention is a structural block diagram of a campus microgrid variable monitoring system based on an embedded system in an embodiment of the present invention.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention 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 invention and are not intended to limit the present invention.

[0048] like Figure 1 As shown, Figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the embodiment of the present invention.

[0049] like Figure 1 As shown, the device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or it may be a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art will understand that Figure 1 The structure of the device shown in the figure does not constitute a limitation of the device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0051] like Figure 1 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a campus microgrid variable monitoring program based on an embedded system.

[0052] exist Figure 1In the terminal shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the campus microgrid variable monitoring program based on the embedded system stored in the memory 1005, and perform the following operations:

[0053] Obtaining electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes electricity power demand data of several electricity users;

[0054] Calling a distributed power supply database of a park to extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database of the park;

[0055] Based on the standard power supply curve of each distributed power source and the power demand data of several power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to perform the power scheduling action of each monitoring cycle during the monitoring period;

[0056] After each power dispatching action is executed, the park microgrid variable information collected by several grid variable monitoring devices in the park embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the park microgrid variable information are extracted;

[0057] Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, update the standard power supply curve of each distributed power source and the power demand data of each power user, consider the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period, and optimize the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period;

[0058] The optimized distributed power generation scheduling strategy is sent to the park embedded power grid system, and the power generation scheduling action and significant difference determination action are repeatedly executed in each subsequent monitoring cycle during the monitoring period.

[0059] The specific embodiments of the present invention applied to the device are basically the same as the embodiments of the campus microgrid variable monitoring method based on the embedded system described below, and will not be described in detail here.

[0060] The embodiment of the present invention provides a method for monitoring variables of a campus microgrid based on an embedded system. Figure 2 , Figure 2 It is a flow chart of an embodiment of a method for monitoring variables of a campus microgrid based on an embedded system according to the present invention.

[0061] In this embodiment, the campus microgrid variable monitoring method based on the embedded system includes the following steps:

[0062] S100: Obtaining electricity reservation information of a target park during a monitoring period; wherein the electricity reservation information includes electricity power demand data of a number of electricity users;

[0063] S200: calling a distributed power supply database in a park to extract a standard power supply curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database in the park;

[0064] S300: Based on the standard power supply curve of each distributed power source and the power demand data of a number of power users, a distributed power source scheduling strategy of the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, so as to drive the power scheduling device in the park embedded power grid system to perform the power scheduling action of each monitoring cycle during the monitoring period;

[0065] S400: After each power dispatching action is executed, the campus microgrid variable information collected by a plurality of grid variable monitoring devices in the campus embedded grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information are extracted;

[0066] S500: Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, update the standard power supply curve of each distributed power source and the power demand data of each power user, consider the smoothness requirements of each power source scheduling and the optimal overall scheduling times during the monitoring period, and optimize the distributed power source scheduling strategy for each subsequent monitoring cycle in the monitoring period;

[0067] S600: Send the optimized distributed power dispatching strategy to the park embedded power grid system, and repeat the power dispatching action and significant difference determination action in each subsequent monitoring cycle during the monitoring period.

[0068] It should be noted that in actual applications, the application of campus microgrids is subject to restrictions and influences in specific scenarios. In some scenarios (such as isolated campus microgrids), the campus microgrid needs to ensure instantaneous power balance, that is, the electric energy generated by the power supply can just meet the immediate demand of the load. At this time, electricity users need to reserve electricity power in advance, and configure the access and scheduling switching of distributed power sources and electricity users in advance to avoid the sudden start-up of large equipment or the simultaneous use of electricity by a large number of electricity users on the load power and grid frequency of the microgrid. However, when electricity users make electricity power reservations and distributed power scheduling switching, it is impossible to accurately consider the errors and changes in electricity power (for example, temporary electricity, emergency electricity, and production equipment may have different electricity powers when performing different production tasks) and power supply power (for example, clean distributed power may have different power supplies in different environments) under different circumstances, which makes the execution of distributed power scheduling switching of the entire campus microgrid difficult and the execution efficiency is not ideal, affecting the power supply quality of the entire campus microgrid and the electricity experience of electricity users.

[0069] In order to solve the above problems, this embodiment obtains the power reservation information of the target park during the monitoring period, extracts the standard power supply curve of each distributed power source, generates the initial distributed power scheduling strategy of the target park during the monitoring period, and drives the power scheduling equipment to execute the power scheduling action of each monitoring cycle during the monitoring period; at the same time, after each power scheduling action is executed, the variable information of the park microgrid is obtained, considering the smoothness requirements of each power scheduling and the optimal overall scheduling times during the monitoring period, and optimizes the distributed power scheduling strategy of each subsequent monitoring cycle in the monitoring period. By effectively monitoring the variables of the park microgrid, the difficulty of electricity users to perform power reservation and distributed power scheduling switching is reduced, and the intelligence and rationality of distributed power scheduling in the park microgrid are improved.

[0070] In a preferred embodiment, the step of obtaining the electricity reservation information of the target park during the monitoring period specifically includes:

[0071] S110: Obtaining an electricity reservation request issued by each electricity user during a reservation period, and extracting an electricity user identifier, electricity reservation time, and electricity power demand data from the electricity reservation request;

[0072] S120: Screening is performed according to each received electricity reservation request according to the electricity user identification and the time period in which the electricity reservation time falls, and electricity reservation information of the target park including the electricity power of several electricity users during the monitoring period is generated.

[0073] Considering that in some scenarios (such as the park microgrid operating in an isolated island), the park microgrid needs to ensure instantaneous power balance, that is, the electric energy generated by the power supply can just meet the immediate demand of the load, and the power users need to reserve the power in advance. In this case, the power reservation request of each power user is obtained, the power user identification, power reservation time and power demand data in the power reservation request are extracted, and then the power user identification and the power reservation time are filtered according to the time period, and finally the power reservation information of the target park including the power consumption of several power users during the monitoring period is generated. The power reservation information is used to provide data support for the subsequent park microgrid variable monitoring and distributed power scheduling, and improve the intelligence and rationality of distributed power scheduling in the park microgrid.

[0074] In a preferred embodiment, calling a park distributed power supply database and extracting a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the park distributed power supply database specifically includes:

[0075] S210: calling a park distributed power supply database; wherein the park distributed power supply database stores a power supply power curve of each distributed power supply in each historical monitoring period;

[0076] S220: Extract the power supply power curve of each distributed power source in each historical monitoring period in the distributed power source database of the park, calculate the average power supply power of each distributed power source at the corresponding sampling time point in all historical monitoring periods, and generate the standard power supply power curve of each distributed power source in the historical monitoring period according to the average power supply power at each sampling time point.

[0077] In this embodiment, after obtaining the power reservation information (i.e., power demand), it is also necessary to determine the standard power supply curve of each distributed power source, which is generated by obtaining the power supply curve of each distributed power source in the historical monitoring period and calculating the average power supply value at each sampling time point. After that, the standard power supply curve and the power demand are used to perform the initial scheduling of the distributed power sources of the target park.

[0078] In a preferred embodiment, based on the standard power supply curve of each distributed power source and the power demand data of several power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to perform the power scheduling action steps of each monitoring cycle during the monitoring period, specifically including:

[0079] S310: Divide the monitoring period into several monitoring cycles according to the minimum unit monitoring time, and according to each divided monitoring cycle, based on the matching relationship between the standard power supply power curve of each distributed power source and the power demand data of several power users in each monitoring cycle, and on the principle of maximizing the cumulative sum of the matching degrees of the standard power supply power curve and the power demand data in each monitoring cycle of the monitoring period, execute the power supply and power consumption allocation between several distributed power sources and several power users in each monitoring cycle, and generate the distributed power supply scheduling strategy of the target park in the monitoring period;

[0080] S320: Send the distributed power supply scheduling strategy to the campus embedded power grid system, driving the power scheduling device in the campus embedded power grid system to execute the power scheduling action of each monitoring cycle during the monitoring period; wherein, the power scheduling action is configured to connect the power supply lines of several distributed power sources having a power supply and power consumption distribution relationship with the power lines of several power users.

[0081] In this embodiment, after obtaining the electricity reservation information and the power supply power curve, the monitoring period is first divided into several monitoring cycles according to the minimum unit monitoring time, and then the matching relationship between the standard power supply power curve of each distributed power source and the power demand data of several power users in each monitoring cycle is considered. Based on the principle of maximizing the cumulative sum of the matching degree of the standard power supply power curve and the power demand data in each monitoring cycle of the monitoring period, the power supply and power consumption allocation of several distributed power sources and several power users in each monitoring cycle is executed to obtain the distributed power supply scheduling strategy of the target park in the monitoring period. It should be noted that the distributed power supply scheduling strategy records the access strategy of the power supply lines of several distributed power sources and the power lines of each power user in each monitoring cycle of the monitoring period, and through this access strategy, the power consumption and power supply power of the park are balanced as much as possible in each monitoring cycle.

[0082] In a preferred embodiment, after each power scheduling action is executed, the campus microgrid variable information collected by a plurality of grid variable monitoring devices in the campus embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information are extracted, specifically including:

[0083] S410: After each power scheduling action is executed, obtain the campus microgrid variable information collected by several grid variable monitoring devices in the campus embedded power grid system; wherein the campus microgrid variable information includes the actual power supply power value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point collected by the grid variable monitoring device between the current power scheduling action and the next power scheduling action;

[0084] S420: Generate an actual power supply curve of each distributed power source and actual power consumption data of each power user according to the actual power supply value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point.

[0085] Considering that when electricity users make power reservations and switch between distributed power scheduling, it is impossible to accurately consider the errors and changes in power consumption (for example, temporary power consumption, emergency power consumption, and production equipment may have different power consumption when performing different production tasks) and power supply (for example, clean distributed power may have different power supply in different environments) under different circumstances. In this embodiment, after each power scheduling action is executed, the park microgrid variable information collected by several grid variable monitoring devices in the embedded power grid system is used. The park microgrid variable information includes the actual power supply power value of each distributed power source and the actual power value of each electricity user. By calculating the difference between the two, it can be determined whether the current power consumption and power supply of the park microgrid are affected by the environment and actual conditions, and then by updating the distributed power scheduling strategy, the power users and distributed power sources can be intelligently adjusted to adapt to changes in the environment and actual conditions.

[0086] In a preferred embodiment, the step of judging whether the actual power consumption data of each power user and the actual power supply power curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition includes:

[0087] S510: extracting actual power consumption data of each power user and actual power supply power value at each sampling time point from actual power supply power curve of each distributed power source;

[0088] S520: Determine whether the actual power consumption data of each power user and the actual power supply power curve of each distributed power source under the distributed power source scheduling strategy meet a significant difference determination condition;

[0089] Among them, the significant difference judgment condition is configured as the proportion of sampling time points at which the difference between the actual power consumption value and the actual power supply power value at the corresponding moment in the actual power consumption data of several distributed power sources and several power users with a power supply and power consumption distribution relationship under the distributed power supply scheduling strategy exceeds the error value.

[0090] Furthermore, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, and the smoothness requirements of each power supply scheduling and the optimal number of overall scheduling during the monitoring period are considered, and the distributed power supply scheduling strategy steps for each subsequent monitoring cycle in the monitoring period are optimized, specifically including:

[0091] S530: updating the power demand data of each power user in each subsequent monitoring cycle according to the change ratio between the actual power consumption data and the power demand data of each power user;

[0092] S540: Obtaining the influencing parameters of the distributed power sources in the park, inputting the influencing parameters of the distributed power sources into the power supply prediction model of each distributed power source, and predicting and obtaining the standard power supply curve of each distributed power source in each subsequent monitoring cycle;

[0093] S550: The difference between the change value of the power demand of each power user before and after each power scheduling and the change value of the standard power supply power of the corresponding distributed power source shall not exceed the preset power supply smooth change value as a constraint condition, and the sum of the distributed power supply scheduling switching times corresponding to the power user in the power scheduling of all subsequent monitoring cycles during the monitoring period is minimized as the optimization goal, and the distributed power supply scheduling strategy for each subsequent monitoring cycle during the monitoring period is optimized.

[0094] In this embodiment, by obtaining the variable information of the park microgrid after each power scheduling action is executed, and when the actual power consumption data and the actual power supply power curve meet the significant difference judgment condition, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, the smoothness requirement of each power scheduling and the optimal overall scheduling number during the monitoring period are considered, the distributed power scheduling strategy of each subsequent monitoring cycle in the monitoring period is optimized, and the power scheduling equipment is driven to always execute the globally optimal distributed power scheduling in each subsequent monitoring cycle. By effectively monitoring the variables of the park microgrid, the difficulty of power users in making power reservations and switching between distributed power scheduling is reduced, and the intelligence and rationality of distributed power scheduling in the park microgrid are improved.

[0095] Reference Figure 3 , Figure 3 It is a structural block diagram of an embodiment of a campus microgrid variable monitoring system based on an embedded system of the present invention.

[0096] like Figure 3 As shown, the campus microgrid variable monitoring system based on the embedded system proposed in the embodiment of the present invention includes:

[0097] The acquisition module 10 is used to acquire the electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes the electricity power demand data of several electricity users;

[0098] The calling module 20 is used to call the park distributed power supply database and extract the standard power supply power curve of each distributed power supply in the historical monitoring period stored in the park distributed power supply database;

[0099] A generating module 30 is used to generate a distributed power supply scheduling strategy for the target park during the monitoring period based on the standard power supply power curve of each distributed power supply and the power demand data of a number of power users, and send the distributed power supply scheduling strategy to the park embedded power grid system to drive the power supply scheduling device in the park embedded power grid system to perform the power supply scheduling action of each monitoring cycle during the monitoring period;

[0100] The extraction module 40 is used to obtain the campus microgrid variable information collected by a plurality of grid variable monitoring devices in the campus embedded power grid system after each power scheduling action is executed, and extract the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information;

[0101] The optimization module 50 is used to determine whether the actual power consumption data of each power user and the actual power supply power curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply power curve of each distributed power source and the power demand data of each power user are updated, and the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period are considered to optimize the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period;

[0102] The repetition module 60 is used to send the optimized distributed power generation scheduling strategy to the park embedded power grid system, and repeatedly execute the power generation scheduling action and the significant difference determination action in each subsequent monitoring cycle in the monitoring period.

[0103] Other embodiments or specific implementation methods of the campus microgrid variable monitoring system based on the embedded system of the present invention can refer to the above-mentioned method embodiments and will not be repeated here.

[0104] In addition, the present invention also proposes a campus microgrid variable monitoring device based on an embedded system, and the campus microgrid variable monitoring device based on an embedded system includes: a memory, a processor, and a campus microgrid variable monitoring program based on an embedded system stored in the memory and executable on the processor. When the campus microgrid variable monitoring program based on an embedded system is executed by the processor, the steps of the campus microgrid variable monitoring method based on an embedded system as described above are implemented.

[0105] The specific implementation of the campus microgrid variable monitoring device based on an embedded system in the present application is basically the same as the various embodiments of the campus microgrid variable monitoring method based on an embedded system described above, and will not be repeated here.

[0106] In addition, the present invention also proposes a readable storage medium, which includes a computer-readable storage medium on which a campus microgrid variable monitoring program based on an embedded system is stored. The readable storage medium may be Figure 1 The memory 1005 in the terminal may also be at least one of a ROM (Read-Only Memory) / RAM (Random Access Memory), a magnetic disk, and an optical disk. The readable storage medium includes a number of instructions for enabling a campus microgrid variable monitoring device based on an embedded system with a processor to execute the campus microgrid variable monitoring method based on an embedded system described in each embodiment of the present invention.

[0107] The specific implementation methods in the readable storage medium of the present application are basically the same as the above-mentioned embodiments of the campus microgrid variable monitoring method based on the embedded system, and will not be repeated here.

[0108] It is understood that, in the description of this specification, the description with reference to the terms "one embodiment", "another embodiment", "other embodiments", or "first to Nth embodiments" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0109] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0110] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0111] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0112] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for monitoring variables of a campus microgrid based on an embedded system, characterized in that: include: Obtaining electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes electricity power demand data of several electricity users; Calling a distributed power supply database of a park to extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database of the park; Based on the standard power supply curve of each distributed power source and the power demand data of several power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to perform the power scheduling action of each monitoring cycle during the monitoring period; After each power dispatching action is executed, the park microgrid variable information collected by several grid variable monitoring devices in the park embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the park microgrid variable information are extracted; Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, update the standard power supply curve of each distributed power source and the power demand data of each power user, consider the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period, and optimize the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period; The optimized distributed power generation scheduling strategy is sent to the park embedded power grid system, and the power generation scheduling action and significant difference determination action are repeatedly executed in each subsequent monitoring cycle during the monitoring period.

2. The campus microgrid variable monitoring method based on embedded system as claimed in claim 1, characterized in that: The steps to obtain the target park's electricity reservation information during the monitoring period include: Obtaining an electricity reservation request issued by each electricity user during a reservation period, and extracting the electricity user identifier, electricity reservation time, and electricity power demand data in the electricity reservation request; Based on each electricity reservation request received, screening is performed according to the electricity user identification and the time period in which the electricity reservation time falls, and electricity reservation information of the target park including the electricity power of several electricity users during the monitoring period is generated.

3. The campus microgrid variable monitoring method based on embedded system as claimed in claim 1, characterized in that: The steps of calling a distributed power supply database of a park and extracting a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database of the park specifically include: Calling a park distributed power supply database; wherein the park distributed power supply database stores a power supply power curve of each distributed power supply in each historical monitoring period; The power supply power curve of each distributed power source in each historical monitoring period in the distributed power supply database of the park is extracted, the average power supply power of each distributed power source at the corresponding sampling time point in all historical monitoring periods is calculated, and the standard power supply power curve of each distributed power source in the historical monitoring period is generated according to the average power supply power at each sampling time point.

4. The campus microgrid variable monitoring method based on embedded system as claimed in claim 1, characterized in that: Based on the standard power supply curve of each distributed power source and the power demand data of several power users, a distributed power source scheduling strategy for the target park during the monitoring period is generated, and the distributed power source scheduling strategy is sent to the park embedded power grid system, driving the power scheduling device in the park embedded power grid system to execute the power scheduling action steps of each monitoring cycle during the monitoring period, specifically including: The monitoring period is divided into several monitoring cycles according to the minimum unit monitoring time. According to each divided monitoring cycle, based on the matching relationship between the standard power supply power curve of each distributed power source and the power demand data of several power users in each monitoring cycle, the power supply and power consumption allocation between several distributed power sources and several power users in each monitoring cycle is executed on the principle of maximizing the cumulative sum of the matching degrees of the standard power supply power curve and the power demand data in each monitoring cycle of the monitoring period, and the distributed power generation scheduling strategy of the target park in the monitoring period is generated; The distributed power supply scheduling strategy is sent to the campus embedded power grid system, driving the power scheduling device in the campus embedded power grid system to execute the power scheduling action of each monitoring cycle during the monitoring period; wherein, the power scheduling action is configured to connect the power supply lines of several distributed power sources having a power supply and power consumption distribution relationship with the power lines of several power users.

5. The campus microgrid variable monitoring method based on embedded system as claimed in claim 1, characterized in that: After each power dispatching action is executed, the park microgrid variable information collected by several grid variable monitoring devices in the park embedded power grid system is obtained, and the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the park microgrid variable information are extracted, specifically including: After each power scheduling action is executed, the campus microgrid variable information collected by several grid variable monitoring devices in the campus embedded power grid system is obtained; wherein the campus microgrid variable information includes the actual power supply power value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point collected by the grid variable monitoring device between the current power scheduling action and the next power scheduling action; According to the actual power supply value of each distributed power source at each sampling time point and the actual power consumption value of each power user at each sampling time point, an actual power supply power curve of each distributed power source and actual power consumption data of each power user are generated.

6. The method for monitoring variables of a campus microgrid based on an embedded system according to claim 1, characterized in that: The steps of judging whether the actual power consumption data of each power user and the actual power supply power curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition include: Extract the actual power consumption data of each power user and the actual power supply power value at each sampling time point from the actual power supply power curve of each distributed power source; Determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power source scheduling strategy meet the significant difference judgment condition; Among them, the significant difference judgment condition is configured as the proportion of sampling time points at which the difference between the actual power consumption value and the actual power supply power value at the corresponding moment in the actual power consumption data of several distributed power sources and several power users with a power supply and power consumption distribution relationship under the distributed power supply scheduling strategy exceeds the error value.

7. The method for monitoring variables of a campus microgrid based on an embedded system as claimed in claim 6, characterized in that: Based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, and the smoothness requirements of each power supply scheduling and the optimal number of overall scheduling during the monitoring period are considered, and the distributed power supply scheduling strategy steps for each subsequent monitoring cycle in the monitoring period are optimized, specifically including: According to the change ratio between the actual power consumption data and the power consumption demand data of each power user, the power consumption demand data of each power user in each subsequent monitoring cycle is updated; Obtaining the influencing parameters of the distributed power sources in the park, inputting the influencing parameters of the distributed power sources into the power supply prediction model of each distributed power source, and predicting and obtaining the standard power supply power curve of each distributed power source in each subsequent monitoring cycle; The constraint condition is that the difference between the change value of the power demand of each power user before and after each power scheduling and the change value of the standard power supply power of the corresponding distributed power source does not exceed the preset power supply smooth change value. The optimization goal is to minimize the sum of the distributed power scheduling switching times corresponding to the power users in the power scheduling of all subsequent monitoring cycles during the monitoring period. The distributed power scheduling strategy for each subsequent monitoring cycle during the monitoring period is optimized.

8. A campus microgrid variable monitoring system based on an embedded system, characterized in that: include: An acquisition module is used to acquire the electricity reservation information of the target park during the monitoring period; wherein the electricity reservation information includes the electricity power demand data of several electricity users; A calling module is used to call a distributed power supply database in a park, and extract a standard power supply power curve of each distributed power supply in a historical monitoring period stored in the distributed power supply database in the park; A generation module is used to generate a distributed power supply scheduling strategy for the target park during the monitoring period based on the standard power supply power curve of each distributed power supply and the power demand data of a number of power users, and send the distributed power supply scheduling strategy to the park embedded power grid system to drive the power supply scheduling device in the park embedded power grid system to perform the power supply scheduling action of each monitoring cycle during the monitoring period; An extraction module is used to obtain the campus microgrid variable information collected by a number of grid variable monitoring devices in the campus embedded grid system after each power scheduling action is executed, and extract the actual power supply power curve of each distributed power source and the actual power consumption data of each power user in the campus microgrid variable information; The optimization module is used to determine whether the actual power consumption data of each power user and the actual power supply curve of each distributed power source under the distributed power supply scheduling strategy meet the significant difference judgment condition. If so, based on the actual power consumption data of each power user and the influencing parameters of the distributed power sources in the park, the standard power supply curve of each distributed power source and the power demand data of each power user are updated, considering the smoothness requirements of each power supply scheduling and the optimal overall scheduling times during the monitoring period, and optimizing the distributed power supply scheduling strategy for each subsequent monitoring cycle in the monitoring period; The repetition module is used to send the optimized distributed power dispatching strategy to the park embedded power grid system, and repeatedly execute the power dispatching action and significant difference determination action of each subsequent monitoring cycle in the monitoring period.

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