Optical storage system scheduling method and device based on trigger mechanism, equipment and medium

Abstract

The invention relates to the technical field of optical storage system scheduling, and provides an optical storage system scheduling method, device, equipment and medium based on a trigger mechanism, which can construct multi-dimensional parameters by taking photovoltaic power generation prediction, load prediction, weather prediction, electricity price and energy storage of an optical storage system as multiple dimensions, and construct a day-ahead scheduling model according to the multi-dimensional parameters. Therefore, a more reasonable day-ahead scheduling plan can be generated according to the multi-dimensional data; according to execution data of a day-ahead scheduling plan, a trigger condition is detected in real time to determine a trigger event, and scheduling optimization of the optical storage system is executed according to the trigger event, so that the trigger event can be dynamically detected, and targeted scheduling strategy adjustment and optimization are performed based on a multi-level trigger mechanism. The problem of plan deviation caused by photovoltaic and load uncertainty in the scheduling process is effectively improved, the adaptivity, the anti-interference capability and the operation efficiency of the optical storage system are improved, meanwhile, the calculation burden is reduced, and the real-time performance is improved.

Description

Technical Field

[0001] The present invention relates to the scheduling of a photovoltaic energy storage system based on a trigger mechanism, and particularly to a scheduling method, device, equipment and medium for a photovoltaic energy storage system based on a trigger mechanism. Background Art

[0002] With the rapid development of renewable energy, the applications of photovoltaic power generation (PV) and energy storage systems (ESS) in power systems are becoming increasingly widespread. The distributed PV-ESS system can effectively improve the energy utilization efficiency and enhance the grid stability by combining photovoltaic power generation with energy storage devices. However, due to the intermittency and volatility of photovoltaic power generation, the scheduling optimization of energy storage systems has become a key issue to ensure the efficient operation of the system.

[0003] The scheduling methods of traditional photovoltaic energy storage systems usually formulate the scheduling plan for the next day the day before to ensure overall optimization, and at the same time, combine real-time scheduling to update the scheduling plan formulated the day before at fixed time intervals. However, this method has certain limitations: 1. Slow response to emergencies: The existing systems are difficult to respond to external signals such as electricity price fluctuations, demand response, and photovoltaic mutations in real time; 2. Increased computing burden on the server: The timing tasks of scheduling are generally triggered at regular intervals, which will cause a sudden increase in the server computing load when triggered, thus easily causing the server to crash; 3. Inflexible scheduling: Most methods perform scheduling updates based on fixed time intervals (such as 15 minutes, 30 minutes), and cannot respond to emergencies dynamically, resulting in reduced user benefits.

[0004] Therefore, there is an urgent need for a real-time scheduling method for a photovoltaic energy storage system with stronger adaptability, higher execution efficiency, and lower computing burden. Summary of the Invention

[0005] In view of the above, it is necessary to provide a scheduling method, device, equipment and medium for a photovoltaic energy storage system based on a trigger mechanism, aiming to solve the problems of low adaptability, low execution efficiency, large computing burden, and inability to respond in real time in the scheduling of a photovoltaic energy storage system.

[0006] A scheduling method for a photovoltaic energy storage system based on a trigger mechanism, the scheduling method for a photovoltaic energy storage system based on a trigger mechanism includes: In response to a scheduling instruction for a target photovoltaic energy storage system, constructing multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price and energy storage of the target photovoltaic energy storage system as multiple dimensions; Constructing a day-ahead scheduling model for the target photovoltaic energy storage system according to the multi-dimensional parameters; Generate a target day-ahead scheduling plan according to the above-mentioned day-ahead scheduling model, and send the target day-ahead scheduling plan to the target photovoltaic and energy storage system to execute the target day-ahead scheduling plan; Detect the triggering condition in real time according to the execution data of the target day-ahead scheduling plan, and determine the triggering event according to the triggering condition; Execute the scheduling optimization of the target photovoltaic and energy storage system according to the triggering event.

[0007] According to a preferred embodiment of the present invention, the construction of the day-ahead scheduling model of the target photovoltaic and energy storage system according to the multi-dimensional parameters includes: Construct the objective function of the day-ahead scheduling model; Construct the constraint conditions of the day-ahead scheduling model; Construct the decision variables of the day-ahead scheduling model; Integrate the objective function, the constraint conditions and the decision variables to obtain the day-ahead scheduling model.

[0008] According to a preferred embodiment of the present invention, the construction of the objective function of the day-ahead scheduling model includes: Construct the day-ahead scheduling model by using the following formula: ; Wherein, represents the maximum profit of the target photovoltaic and energy storage system; represents the selling electricity price of the target photovoltaic and energy storage system to the power market at time t; represents the selling electric power of the target photovoltaic and energy storage system to the power market at time t; represents the purchasing electricity price of the target photovoltaic and energy storage system from the power market at time t; represents the purchasing electric power of the target photovoltaic and energy storage system from the power market at time t; T represents the maximum value of t; Wherein, at the same moment, the target photovoltaic and energy storage system is in a power purchasing state or a power selling state.

[0009] According to a preferred embodiment of the present invention, the construction of the constraint conditions of the day-ahead scheduling model includes: Construct the constraint conditions of the day-ahead scheduling model by using the following formula: ; Wherein, represents the first state variable, and the first state variable is used to characterize the interaction state between the target photovoltaic and energy storage system and the power grid; represents the predicted photovoltaic power generation of the target photovoltaic and energy storage system at time t; represents the predicted load power of the target photovoltaic and energy storage system at time t; Indicates the charge and discharge power of the energy storage battery of the target photovoltaic energy storage system; Indicates the charging power of the energy storage battery of the target photovoltaic energy storage system; Indicates the discharging power of the energy storage battery of the target photovoltaic energy storage system; Indicates a second state variable, which is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic energy storage system; Indicates the maximum discharging power of the energy storage battery of the target photovoltaic energy storage system; Indicates the maximum charging power of the energy storage battery of the target photovoltaic energy storage system; Indicates the state of charge of the energy storage battery of the target photovoltaic energy storage system at the (t + 1)th moment; Indicates the initial state of charge of the energy storage battery of the target photovoltaic energy storage system; Indicates the charging efficiency of the energy storage battery of the target photovoltaic energy storage system; Indicates the discharging efficiency of the energy storage battery of the target photovoltaic energy storage system; Indicates the capacity of the energy storage battery of the target photovoltaic energy storage system; Indicates the time interval; Indicates the minimum state of charge of the energy storage battery of the target photovoltaic energy storage system; Indicates the maximum state of charge of the energy storage battery of the target photovoltaic energy storage system; Indicates the rated photovoltaic power generation of the target photovoltaic energy storage system; Wherein, at the same moment, the energy storage battery of the target photovoltaic energy storage system is in a charging state or a discharging state.

[0010] According to a preferred embodiment of the present invention, the generating of the target day-ahead scheduling plan according to the day-ahead scheduling model includes: Obtaining each variable in the decision variables; wherein, the decision variables are ; Performing data acquisition according to the multi-dimensional parameters; Inputting the acquired data into the day-ahead scheduling model to obtain the values of each variable; Generating the target day-ahead scheduling plan according to the values of each variable.

[0011] According to a preferred embodiment of the present invention, the real-time detection of the trigger condition according to the execution data of the target day-ahead scheduling plan and the determination of the trigger event according to the trigger condition include: Calculating the power deviation between the actual photovoltaic power generation and the predicted photovoltaic power generation of the target photovoltaic energy storage system at the tth moment according to the execution data ; wherein, Indicates the actual photovoltaic power generation of the target photovoltaic energy storage system at the tth moment; Calculate the state of charge deviation of the energy storage battery of the target optical storage system at time t according to the execution data ; where represents the actual state of charge of the energy storage battery of the target optical storage system at time t, represents the state of charge of the energy storage battery of the target optical storage system in the target day-ahead scheduling plan at time t; Calculate the load power deviation of the target optical storage system at time t according to the execution data ; where represents the actual load power of the target optical storage system at time t; When it is detected that the trigger condition is a sudden extreme weather, and / or an emergency power outage, and / or the need to enable a backup power supply, determine that the trigger event is a first-level response event; or When it is detected that the power deviation is greater than a first threshold, and / or the state of charge deviation is greater than a second threshold, and / or the load power deviation is greater than a third threshold, determine that the trigger event is a second-level response event.

[0012] According to a preferred embodiment of the present invention, the scheduling optimization of the target optical storage system according to the trigger event includes: When the trigger event is the first-level response event, start the backup power supply for power supply based on a fast compensation algorithm; or When the trigger event is the second-level response event, obtain multiple time steps of the day-ahead scheduling model; obtain a continuous preset number of time steps from the current moment as each time step to be processed from the multiple time steps; generate a new day-ahead scheduling plan according to the scheduling model at each time step to be processed; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, do not adjust the target day-ahead scheduling plan of the corresponding time step to be processed, or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, use the new day-ahead scheduling plan to replace the target day-ahead scheduling plan of the corresponding time step to be processed.

[0013] An optical storage system scheduling device based on a trigger mechanism, the optical storage system scheduling device based on a trigger mechanism includes: A construction unit, configured to respond to a scheduling instruction for a target optical storage system, and construct multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target optical storage system as multiple dimensions; The construction unit is further configured to construct a day-ahead scheduling model of the target optical storage system according to the multi-dimensional parameters; A generating unit, configured to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target photovoltaic energy storage system to execute the target day-ahead scheduling plan; A detecting unit, configured to detect a triggering condition in real time according to the execution data of the target day-ahead scheduling plan, and determine a triggering event according to the triggering condition; An executing unit, configured to execute scheduling optimization for the target photovoltaic energy storage system according to the triggering event.

[0014] A computer device, the computer device includes: A memory, storing at least one instruction; and A processor, executing the instruction stored in the memory to implement the scheduling method for a photovoltaic energy storage system based on a triggering mechanism.

[0015] A computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in a computer device to implement the scheduling method for a photovoltaic energy storage system based on a triggering mechanism.

[0016] It can be seen from the above technical solutions that the present invention can construct multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price and energy storage of the target photovoltaic energy storage system as multiple dimensions, and construct a day-ahead scheduling model according to the multi-dimensional parameters, so as to generate a more reasonable target day-ahead scheduling plan according to multi-dimensional data; detect the triggering condition in real time according to the execution data of the target day-ahead scheduling plan, determine the triggering event according to the triggering condition, and execute the scheduling optimization for the photovoltaic energy storage system according to the triggering event, so as to dynamically detect the triggering event, and perform targeted scheduling strategy adjustment and optimization based on a multi-level triggering mechanism, effectively improving the problem of plan deviation caused by the uncertainty of photovoltaic and load during the scheduling process, improving the self-adaptability, anti-interference ability and operation efficiency of the photovoltaic energy storage system, reducing the calculation burden at the same time, and improving the real-time performance. Description of the Drawings

[0017] Figure 1 is a flowchart of a preferred embodiment of the scheduling method for a photovoltaic energy storage system based on a triggering mechanism of the present invention; Figure 2 is a functional module diagram of a preferred embodiment of the scheduling device for a photovoltaic energy storage system based on a triggering mechanism of the present invention; Figure 3 is a structural schematic diagram of a computer device of a preferred embodiment for implementing the scheduling method for a photovoltaic energy storage system based on a triggering mechanism of the present invention. Detailed Embodiments

[0018] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail below with reference to the drawings and specific embodiments.

[0019] As shown Figure 1 in the figure, it is a flowchart of a preferred embodiment of the scheduling method of the optical storage system based on the trigger mechanism of the present invention. According to different requirements, the order of the steps in this flowchart can be changed, and some steps can be omitted.

[0020] The scheduling method of the optical storage system based on the trigger mechanism is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0021] The computer device can be any electronic product that can interact with users, such as personal computers, tablet computers, smart phones, personal digital assistants (PDAs), game consoles, Internet Protocol Televisions (IPTVs), smart wearable devices, etc.

[0022] The computer device can also include network devices and / or user devices. Among them, the network devices include but are not limited to a single network server, a server group composed of multiple network servers, or a cloud composed of a large number of hosts or network servers based on cloud computing.

[0023] The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0024] Among them, artificial intelligence (AI) is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0025] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0026] The network where the computer device is located includes but is not limited to the Internet, wide area network, metropolitan area network, local area network, virtual private network (VPN), etc.

[0027] S10. In response to a scheduling instruction for a target photovoltaic energy storage system, construct multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic energy storage system as multiple dimensions.

[0028] In this embodiment, the target photovoltaic energy storage system is a power generation system composed of photovoltaic devices and energy storage devices. The target photovoltaic energy storage system directly converts solar radiation energy into electrical energy using the photovoltaic effect and stores the excess electrical energy through the energy storage device.

[0029] In this embodiment, the scheduling instruction can be automatically triggered when the target photovoltaic energy storage system is started to achieve comprehensive monitoring of the target photovoltaic energy storage system.

[0030] S11. Construct a day-ahead scheduling model for the target photovoltaic energy storage system according to the multi-dimensional parameters.

[0031] In this embodiment, constructing the day-ahead scheduling model for the target photovoltaic energy storage system according to the multi-dimensional parameters includes: Construct the objective function of the day-ahead scheduling model; Construct the constraint conditions of the day-ahead scheduling model; Construct the decision variables of the day-ahead scheduling model; Integrate the objective function, the constraint conditions, and the decision variables to obtain the day-ahead scheduling model.

[0032] Among them, the objective function of constructing the day-ahead scheduling model includes: Construct the day-ahead scheduling model using the following formula: ; Among them, represents the maximum profit of the target photovoltaic energy storage system; represents the electricity selling price of the target photovoltaic energy storage system to the power market at time t; represents the electricity selling power of the target photovoltaic energy storage system to the power market at time t; Denote the electricity purchase price of the target photovoltaic and energy storage system from the electricity market at time t; Denote the electricity purchase power of the target photovoltaic and energy storage system from the electricity market at time t; T represents the maximum value of t; Wherein, at the same moment, the target photovoltaic and energy storage system is in the electricity purchase state or the electricity sale state.

[0033] Wherein, the constraint conditions for constructing the day-ahead scheduling model include: Use the following formula to construct the constraint conditions of the day-ahead scheduling model: ; Wherein, Denote the first state variable, and the first state variable is used to characterize the interaction state between the target photovoltaic and energy storage system and the power grid; Denote the predicted photovoltaic power generation of the target photovoltaic and energy storage system at time t; Denote the predicted load power of the target photovoltaic and energy storage system at time t; Denote the charge and discharge power of the energy storage battery of the target photovoltaic and energy storage system; Denote the charging power of the energy storage battery of the target photovoltaic and energy storage system; Denote the discharge power of the energy storage battery of the target photovoltaic and energy storage system; Denote the second state variable, and the second state variable is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic and energy storage system; Denote the maximum discharge power of the energy storage battery of the target photovoltaic and energy storage system; Denote the maximum charging power of the energy storage battery of the target photovoltaic and energy storage system; Denote the state of charge of the energy storage battery of the target photovoltaic and energy storage system at (t + 1) moment; Denote the initial state of charge of the energy storage battery of the target photovoltaic and energy storage system; Denote the charging efficiency of the energy storage battery of the target photovoltaic and energy storage system; Denote the discharge efficiency of the energy storage battery of the target photovoltaic and energy storage system; Denote the capacity of the energy storage battery of the target photovoltaic and energy storage system; Denote the time interval; Denote the minimum state of charge of the energy storage battery of the target photovoltaic and energy storage system; Denote the maximum state of charge of the energy storage battery of the target photovoltaic and energy storage system; Denote the rated photovoltaic power generation of the target photovoltaic and energy storage system; Wherein, at the same moment, the energy storage battery of the target photovoltaic and energy storage system is in the charging state or the discharging state.

[0034] Specifically, by deploying a multi-source data fusion engine, event features such as real-time capture of photovoltaic output gradient change (dP / dt), energy storage SOC (State of Charge) overlimit, SOC deviation, and load mutation can be used as the multi-dimensional parameters.

[0035] Through the above embodiments, a day-ahead scheduling model can be constructed based on multi-dimensional parameters, thereby assisting in generating a more reasonable day-ahead scheduling plan.

[0036] S12. Generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target photovoltaic-energy storage system to execute the target day-ahead scheduling plan.

[0037] In this embodiment, the generating a target day-ahead scheduling plan according to the day-ahead scheduling model includes: Obtain each variable in the decision variables; wherein, the decision variables are ; Collect data according to the multi-dimensional parameters; Input the collected data into the day-ahead scheduling model to obtain the value of each variable; Generate the target day-ahead scheduling plan according to the value of each variable.

[0038] Among them, a dynamic programming solver can be used for solving, such as Cplex, Gurobi, etc.

[0039] Among them, the state of charge of the energy storage battery of the target photovoltaic-energy storage system at time t in the target day-ahead scheduling plan can also be calculated according to the solved decision variables.

[0040] In the above embodiments, since there are errors in both photovoltaic and load predictions, and some unexpected situations will occur during the actual execution process, an intra-day or real-time scheduling model based on event and time trigger mechanisms is introduced in this embodiment, thereby improving the rationality and accuracy of the generated day-ahead scheduling plan.

[0041] S13. Detect trigger conditions in real time according to the execution data of the target day-ahead scheduling plan, and determine trigger events according to the trigger conditions.

[0042] In this embodiment, after the target day-ahead scheduling plan is sent to the target photovoltaic-energy storage system to execute the target day-ahead scheduling plan, a real-time condition judgment mechanism can be triggered.

[0043] In this embodiment, the detecting trigger conditions in real time according to the execution data of the target day-ahead scheduling plan, and determining trigger events according to the trigger conditions includes: Calculate the power deviation between the actual photovoltaic power generation and the predicted photovoltaic power generation of the target photovoltaic energy storage system at time t according to the execution data ; where represents the actual photovoltaic power generation of the target photovoltaic energy storage system at time t; Calculate the state of charge deviation of the energy storage battery of the target photovoltaic energy storage system at time t according to the execution data ; where represents the actual state of charge of the energy storage battery of the target photovoltaic energy storage system at time t, represents the state of charge of the energy storage battery of the target photovoltaic energy storage system at time t in the target day-ahead scheduling plan; Calculate the load power deviation of the target photovoltaic energy storage system at time t according to the execution data ; where represents the actual load power of the target photovoltaic energy storage system at time t; When it is detected that the trigger condition is sudden extreme weather, and / or emergency power outage, and / or the need to enable the backup power supply, determine that the trigger event is a first-level response event; or When it is detected that the power deviation is greater than the first threshold, and / or the state of charge deviation is greater than the second threshold, and / or the load power deviation is greater than the third threshold, determine that the trigger event is a second-level response event.

[0044] Among them, the first threshold, the second threshold, and the third threshold can be custom-configured. For example: the first threshold can be configured as 15%, the second threshold can be configured as 15%, and the third threshold can be configured as 0.2.

[0045] This embodiment can perform reasoning in combination with time-driven and event-driven to evaluate the coupling influence degree of composite events (such as sudden drop in photovoltaic power + sudden increase in load).

[0046] S14, perform scheduling optimization on the target photovoltaic energy storage system according to the trigger event.

[0047] In this embodiment, the performing scheduling optimization on the target photovoltaic energy storage system according to the trigger event includes: When the trigger event is the first-level response event, start the backup power supply for power supply based on the fast compensation algorithm; or When the trigger event is the secondary response event, obtain multiple time steps of the day-ahead scheduling model; obtain a continuous preset number of time steps starting from the current moment from the multiple time steps as each time step to be processed; generate a new day-ahead scheduling plan according to the scheduling model at each time step to be processed; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, do not adjust the target day-ahead scheduling plan corresponding to the time step to be processed, or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, use the new day-ahead scheduling plan to replace the target day-ahead scheduling plan corresponding to the time step to be processed.

[0048] Among them, the day-ahead scheduling model may include 24 time steps in a day. If the current is at the first time step and the preset number is 5, then data is re-collected at the second to sixth time steps later to generate a new day-ahead scheduling plan. Assume that the day-ahead scheduling plan regenerated at one of the time steps is the same as the original plan, then no other operations are performed; assume that the day-ahead scheduling plan regenerated at one of the time steps is different from the original plan, then use the newly generated day-ahead scheduling plan to overwrite the original plan to optimize the day-ahead scheduling plan based on the event trigger mechanism.

[0049] Among them, if the primary response event or the secondary response event does not occur, the original day-ahead scheduling plan is maintained.

[0050] Among them, a hierarchical optimization method is adopted. The upper layer uses a long-term economic scheduling model (such as a 24-hour scale), and the lower layer embeds an event-triggered real-time correction model (which can reach the second scale), and a "rolling time domain + event replanning" dual-mode optimization algorithm is proposed. The predictive scheduling is executed under normal conditions, and local re-optimization is started when a trigger event occurs, which can optimize the scheduling plan of the photovoltaic energy storage system more accurately, flexibly and reasonably.

[0051] Through the above embodiments, changes in photovoltaic power generation, energy storage status, load demand, electricity price signals, etc. are detected through a multi-level trigger mechanism, and the scheduling optimization strategy is dynamically executed, which greatly improves the plan deviation caused by the uncertainty of photovoltaic and load, improves the system's self-adaptability, anti-interference ability and operation efficiency, and at the same time can reduce the calculation burden and improve the real-time performance.

[0052] This embodiment can be applied to fields such as intelligent microgrids.

[0053] As can be seen from the above technical solutions, the present invention can construct multi-dimensional parameters in multiple dimensions such as photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic energy storage system, and construct a day-ahead scheduling model according to the multi-dimensional parameters, so as to be able to generate a more reasonable target day-ahead scheduling plan according to the multi-dimensional data; detect the trigger condition in real time according to the execution data of the target day-ahead scheduling plan, determine the trigger event according to the trigger condition, and execute the scheduling optimization of the photovoltaic energy storage system according to the trigger event, so as to be able to dynamically detect the trigger event and perform targeted scheduling strategy adjustment and optimization based on the multi-level trigger mechanism, effectively improving the problem of plan deviation caused by the uncertainty of photovoltaic and load during the scheduling process, improving the self-adaptability, anti-interference ability and operation efficiency of the photovoltaic energy storage system, reducing the calculation burden at the same time, and improving the real-time performance.

[0054] As Figure 2 shown, it is a functional block diagram of a preferred embodiment of the photovoltaic energy storage system scheduling device based on the trigger mechanism of the present invention. The photovoltaic energy storage system scheduling device 11 based on the trigger mechanism includes a construction unit 110, a generation unit 111, a detection unit 112, and an execution unit 113. The module / unit referred to in the present invention means a series of computer program segments that can be executed by a processor and can complete fixed functions, and are stored in a memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0055] Among them, the construction unit is used to respond to the scheduling instruction for the target photovoltaic energy storage system, and construct multi-dimensional parameters in multiple dimensions such as photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic energy storage system; The construction unit is further used to construct a day-ahead scheduling model of the target photovoltaic energy storage system according to the multi-dimensional parameters; The generation unit is used to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target photovoltaic energy storage system to execute the target day-ahead scheduling plan; The detection unit is used to detect the trigger condition in real time according to the execution data of the target day-ahead scheduling plan, and determine the trigger event according to the trigger condition; The execution unit is used to execute the scheduling optimization of the target photovoltaic energy storage system according to the trigger event.

[0056] As can be seen from the above technical solutions, the present invention can construct multi-dimensional parameters in multiple dimensions such as photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic energy storage system, and construct a day-ahead scheduling model according to the multi-dimensional parameters, so as to be able to generate a more reasonable target day-ahead scheduling plan according to the multi-dimensional data; detect the trigger conditions in real time according to the execution data of the target day-ahead scheduling plan, determine the trigger events according to the trigger conditions, and execute the scheduling optimization of the photovoltaic energy storage system according to the trigger events, so as to be able to dynamically detect the trigger events and perform targeted scheduling strategy adjustment and optimization based on the multi-level trigger mechanism, effectively improving the problem of plan deviation caused by the uncertainty of photovoltaic and load during the scheduling process, improving the self-adaptability, anti-interference ability and operation efficiency of the photovoltaic energy storage system, reducing the calculation burden at the same time, and improving the real-time performance.

[0057] As Figure 3 shown, it is a schematic structural diagram of a computer device of a preferred embodiment of the method for scheduling a photovoltaic energy storage system based on a trigger mechanism according to the present invention.

[0058] The computer device 1 may include a memory 12, a processor 13 and a bus (the arrow in the figure is the bus), and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a scheduling program for a photovoltaic energy storage system based on a trigger mechanism.

[0059] Those skilled in the art can understand that the schematic diagram is only an example of the computer device 1 and does not constitute a limitation on the computer device 1. The computer device 1 can be either a bus structure or a star structure. The computer device 1 may also include more or fewer other hardware or software than shown in the figure, or different component arrangements. For example, the computer device 1 may also include input / output devices, network access devices, etc.

[0060] It should be noted that the computer device 1 is only an example, and other existing or future possible electronic products that can be adapted to the present invention should also be included within the protection scope of the present invention and are included herein by reference.

[0061] Among them, the memory 12 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (such as SD or DX memory, etc.), magnetic memory, magnetic disk, optical disc, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as the mobile hard disk of the computer device 1. In some other embodiments, the memory 12 can also be an external storage device of the computer device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the computer device 1. Further, the memory 12 can also include both the internal storage unit and the external storage device of the computer device 1. The memory 12 can not only be used to store application software installed on the computer device 1 and various types of data, such as the code of the optical storage system scheduler based on the trigger mechanism, etc., but also be used to temporarily store the data that has been output or will be output.

[0062] In some embodiments, the processor 13 can be composed of integrated circuits. For example, it can be composed of a single packaged integrated circuit, or can be composed of multiple integrated circuits with the same or different functions packaged together, including the combination of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips, etc. The processor 13 is the control core (Control Unit) of the computer device 1, connecting all components of the entire computer device 1 through various interfaces and lines, and by running or executing programs or modules stored in the memory 12 (such as executing the optical storage system scheduler based on the trigger mechanism, etc.), and calling the data stored in the memory 12, to execute various functions of the computer device 1 and process data.

[0063] The processor 13 executes the operating system of the computer device 1 and various installed application programs. The processor 13 executes the application programs to implement the steps in the above embodiments of various optical storage system scheduling methods based on the trigger mechanism, such as Figure 1 the steps shown.

[0064] Exemplarily, the computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a construction unit 110, a generation unit 111, a detection unit 112, and an execution unit 113.

[0065] The integrated units implemented in the form of software function modules as described above may be stored in a computer-readable storage medium. The above software function modules stored in a storage medium include several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a processor to execute a part of the method for scheduling a photovoltaic energy storage system based on a trigger mechanism according to each embodiment of the present invention.

[0066] If the modules / units integrated in the computer device 1 are implemented in the form of software function units and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-mentioned method embodiments of the present invention, it may also be completed by a computer program instructing relevant hardware devices. The computer program may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned method embodiments may be implemented.

[0067] Among them, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only Memory), a random access memory, etc.

[0068] Further, the computer-readable storage medium mainly includes a storage program area and a storage data area. Among them, the storage program area may store an operating system, application programs required for at least one function, etc.; the storage data area may store data created according to the use of the blockchain node.

[0069] The blockchain referred to in the present invention is a new application mode of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanism, and encryption algorithm. Blockchain, in essence, is a decentralized database, a series of data blocks generated by using cryptographic methods. Each data block contains information about a batch of network transactions, which is used to verify the validity of the information (anti-counterfeiting) and generate the next block. The blockchain can include the blockchain underlying platform, the platform product service layer, and the application service layer, etc.

[0070] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience in representation, in Figure 3 it is only represented by a single straight line, but it does not mean that there is only one bus or one type of bus. The bus is arranged to realize the connection and communication between the memory 12 and at least one processor 13, etc.

[0071] Although not shown, the computer device 1 may further include a power supply (such as a battery) for powering each component. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, so as to realize functions such as charge management, discharge management, and power consumption management through the power management device. The power supply may further include any components such as one or more DC or AC power supplies, a recharge device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The computer device 1 may further include a variety of sensors, a Bluetooth module, a Wi-Fi module, etc., which will not be elaborated here.

[0072] Furthermore, the computer device 1 may further include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used to establish a communication connection between the computer device 1 and other computer devices.

[0073] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display the information processed in the computer device 1 and to display a visual user interface.

[0074] It should be understood that the above embodiments are only for illustration purposes and are not limited by this structure in the scope of the patent application.

[0075] Those skilled in the art can understand that Figure 3 the structure shown does not constitute a limitation on the computer device 1, and it may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0076] In combination with Figure 1 , the memory 12 in the computer device 1 stores multiple instructions to implement a scheduling method for a photovoltaic energy storage system based on a trigger mechanism, and the processor 13 can execute the multiple instructions to implement: In response to a scheduling instruction for a target photovoltaic energy storage system, construct multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic energy storage system as multiple dimensions; Construct a day-ahead scheduling model for the target photovoltaic energy storage system according to the multi-dimensional parameters; Generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target photovoltaic energy storage system to execute the target day-ahead scheduling plan; Real-time detect trigger conditions according to the execution data of the target day-ahead scheduling plan, and determine trigger events according to the trigger conditions; Execute scheduling optimization for the target photovoltaic energy storage system according to the trigger event.

[0077] Specifically, the specific implementation method of the above instructions by the processor 13 can refer to Figure 1 the description of the relevant steps in the corresponding embodiments, which will not be elaborated here.

[0078] It should be noted that all the data involved in this case are legally obtained. The non-company software tools or components appearing in the embodiments of this application are only for illustrative introduction and do not represent actual use.

[0079] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation.

[0080] The present invention can be used in many general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present invention can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0081] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] In addition, in each embodiment of the present invention, the functional modules can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a hardware plus software functional module.

[0083] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0084] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be encompassed by the present invention. Any associated drawing marks in the claims should not be regarded as limiting the claimed rights.

[0085] In addition, it is obvious that the term "including" does not exclude other units or steps, and the singular form does not exclude the plural form. The multiple units or devices described in the present invention can also be implemented by one unit or device through software or hardware. Terms such as "first" and "second" are used to denote names and do not denote any particular order.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A scheduling method for a photovoltaic energy storage system based on a triggering mechanism, characterized in that, The dispatching method for the photovoltaic-storage system based on the triggering mechanism includes: In response to the dispatching instruction for the target photovoltaic-storage system, multi-dimensional parameters are constructed with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target photovoltaic-storage system as multiple dimensions; Construct a day-ahead dispatching model for the target photovoltaic-storage system according to the multi-dimensional parameters; Generate a target day-ahead dispatching plan according to the day-ahead dispatching model, and send the target day-ahead dispatching plan to the target photovoltaic-storage system to execute the target day-ahead dispatching plan; Detect the triggering condition in real time according to the execution data of the target day-ahead dispatching plan, and determine the triggering event according to the triggering condition; Execute the dispatching optimization of the target photovoltaic-storage system according to the triggering event.

2. The scheduling method of the optical storage system based on the trigger mechanism according to claim 1, wherein, The constructing of the day-ahead dispatching model for the target photovoltaic-storage system according to the multi-dimensional parameters includes: Construct the objective function of the day-ahead dispatching model; Construct the constraint conditions of the day-ahead dispatching model; Construct the decision variables of the day-ahead dispatching model; Integrate the objective function, the constraint conditions, and the decision variables to obtain the day-ahead dispatching model.

3. The scheduling method of the optical storage system based on the trigger mechanism according to claim 2, wherein The constructing of the objective function of the day-ahead dispatching model includes: Construct the day-ahead dispatching model using the following formula: ； Among them, represents the maximum benefit of the target photovoltaic and energy storage system; represents the electricity selling price of the target photovoltaic and energy storage system to the power market at time t; represents the electricity selling power of the target photovoltaic and energy storage system to the power market at time t; represents the electricity purchase price of the target photovoltaic and energy storage system from the power market at time t; represents the electricity purchase power of the target photovoltaic and energy storage system from the power market at time t; T represents the maximum value of t. Wherein, at the same moment, the target photovoltaic-storage system is in the power purchase state or the power selling state.

4. The scheduling method of the optical storage system based on the trigger mechanism according to claim 3, wherein The constructing of the constraint conditions of the day-ahead dispatching model includes: Construct the constraint conditions of the day-ahead dispatching model using the following formula: ； wherein, represents a first state quantity, and the first state quantity is used to characterize the interaction state between the target photovoltaic energy storage system and the power grid; represents the predicted photovoltaic power generation of the target photovoltaic energy storage system at time t; represents the predicted load power of the target photovoltaic energy storage system at time t; represents the charge and discharge power of the energy storage battery of the target photovoltaic energy storage system; represents the charging power of the energy storage battery of the target photovoltaic energy storage system; represents the discharge power of the energy storage battery of the target photovoltaic energy storage system; represents a second state quantity, and the second state quantity is used to characterize the charge and discharge state of the energy storage battery of the target photovoltaic energy storage system; represents the maximum discharge power of the energy storage battery of the target photovoltaic energy storage system; represents the maximum charging power of the energy storage battery of the target photovoltaic energy storage system; represents the state of charge of the energy storage battery of the target photovoltaic energy storage system at (t + 1) moment; represents the initial state of charge of the energy storage battery of the target photovoltaic energy storage system; represents the charging efficiency of the energy storage battery of the target photovoltaic energy storage system; represents the discharge efficiency of the energy storage battery of the target photovoltaic energy storage system; represents the capacity of the energy storage battery of the target photovoltaic energy storage system; represents a time interval; represents the minimum state of charge of the energy storage battery of the target photovoltaic energy storage system; represents the maximum state of charge of the energy storage battery of the target photovoltaic energy storage system; represents the rated photovoltaic power generation of the target photovoltaic energy storage system; Wherein, at the same moment, the energy storage battery of the target photovoltaic-storage system is in the charging state or the discharging state.

5. The scheduling method of the optical storage system based on the trigger mechanism according to claim 4, characterized in that The generating of the target day-ahead dispatching plan according to the day-ahead dispatching model includes: Obtain each of the decision variables; wherein, the decision variable is ; Collect data according to the multi-dimensional parameters; Input the collected data into the day-ahead dispatching model to obtain the value of each variable; Generate the target day-ahead dispatching plan according to the value of each variable.

6. The scheduling method of the optical storage system based on the triggering mechanism according to claim 4, wherein, The detecting of the triggering condition in real time according to the execution data of the target day-ahead dispatching plan, and the determining of the triggering event according to the triggering condition includes: Calculate the power deviation between the actual photovoltaic power generation and the predicted photovoltaic power generation of the target photovoltaic and energy storage system at time t according to the execution data ; where represents the actual photovoltaic power generation of the target photovoltaic and energy storage system at time t; Calculate the state of charge deviation of the energy storage battery of the target photovoltaic and energy storage system at time t according to the execution data ; where represents the actual state of charge of the energy storage battery of the target photovoltaic and energy storage system at time t, represents the state of charge of the energy storage battery of the target photovoltaic and energy storage system in the target day-ahead scheduling plan at time t; Calculate the load power deviation of the target optical storage system at time t according to the execution data ; where represents the actual load power of the target optical storage system at time t; When it is detected that the triggering condition is sudden extreme weather, and / or emergency power outage, and / or the need to enable the standby power supply, determine that the triggering event is a first-level response event; or When it is detected that the power deviation is greater than the first threshold, and / or the state of charge deviation is greater than the second threshold, and / or the load power deviation is greater than the third threshold, determine that the triggering event is a second-level response event.

7. The scheduling method for the optical storage system based on the trigger mechanism according to claim 6, characterized in that, The executing of the dispatching optimization of the target photovoltaic-storage system according to the triggering event includes: When the triggering event is the first-level response event, start the standby power supply for power supply based on the fast compensation algorithm; or When the trigger event is the secondary response event, obtain multiple time steps of the day-ahead scheduling model; obtain a continuous preset number of time steps from the current moment as each time step to be processed from the multiple time steps; generate a new day-ahead scheduling plan according to the scheduling model at each time step to be processed; for each time step to be processed, when the new day-ahead scheduling plan is the same as the target day-ahead scheduling plan, do not adjust the target day-ahead scheduling plan corresponding to the time step to be processed, or when the new day-ahead scheduling plan is different from the target day-ahead scheduling plan, use the new day-ahead scheduling plan to replace the target day-ahead scheduling plan corresponding to the time step to be processed.

8. A dispatching device for a photovoltaic and energy storage system based on a triggering mechanism, characterized in that, The trigger mechanism-based optical storage system scheduling device includes: A construction unit, configured to respond to a scheduling instruction for a target optical storage system, and construct multi-dimensional parameters with the photovoltaic power generation prediction, load prediction, weather prediction, electricity price, and energy storage of the target optical storage system as multiple dimensions; The construction unit is further configured to construct a day-ahead scheduling model of the target optical storage system according to the multi-dimensional parameters; A generation unit, configured to generate a target day-ahead scheduling plan according to the day-ahead scheduling model, and send the target day-ahead scheduling plan to the target optical storage system to execute the target day-ahead scheduling plan; A detection unit, configured to detect a trigger condition in real time according to the execution data of the target day-ahead scheduling plan, and determine a trigger event according to the trigger condition; An execution unit, configured to execute scheduling optimization of the target optical storage system according to the trigger event.

9. A computer device, characterized in that, The computer device includes: A memory that stores at least one instruction; and A processor that executes the instructions stored in the memory to implement the trigger mechanism-based optical storage system scheduling method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: At least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in a computer device to implement the trigger mechanism-based optical storage system scheduling method according to any one of claims 1 to 7.

Images