Optical storage adaptive virtual inertia cooperative control method and system based on risk perception

Abstract

The invention relates to a risk perception-based optical storage adaptive virtual inertia cooperative control method and system, and the method comprises the steps: collecting the operation data of an optical storage system in real time, the operation data comprising the power grid frequency and the change rate thereof, the photovoltaic array output power, and the real-time charge state of an energy storage battery; designing an adaptive risk coefficient model, and calculating an adaptive risk coefficient in real time based on the operation data; based on the adaptive risk coefficient, dynamically distributing the total active power increment demand from the traditional virtual inertia controller to a photovoltaic unit and an energy storage unit in the optical storage system; respectively issuing the distributed active power increment demand to inverter control modules corresponding to the photovoltaic unit and the energy storage unit for execution, and jointly completing virtual inertia support for the power grid; judging whether the power grid frequency reaches a stable state or not, and if not, re-executing the virtual inertia support control; and if the stable state is reached, switching to a standby state. According to the invention, risk adaptive optimization of the supporting capability of the optical storage system is realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of virtual inertia control, and particularly relates to a photovoltaic-thermal energy storage adaptive virtual inertia collaborative control method and system based on risk perception. BACKGROUND

[0002] In the field of virtual inertia control, there is a virtual inertia control method based on thermal energy storage SOC self-adaption in the prior art, and the adjustment of the support strength is only based on the long-term state of charge (SOC) of the thermal energy storage battery, without considering the operation risk brought by the output fluctuation of the photovoltaic power generation unit. This leads to the fact that when the light is stable and the photovoltaic output is reliable, the system can fully utilize the photovoltaic to provide strong inertia support, but due to the over-conservative control strategy, the system mainly depends on the thermal energy storage; and when the light fluctuates greatly, the system may be over-dependent on the photovoltaic, leading to unreliable support capacity. Such a control strategy based on a single SOC cannot achieve the optimal matching of the support strength and the real-time operation risk. At the same time, due to the significant randomness and fluctuation of the photovoltaic output, the traditional control method often regards it as a disturbance factor and adopts a conservative strategy, and the prior art lacks a quantitative means for the uncertainty of the photovoltaic output, and cannot incorporate the uncertainty into the control decision to realize intelligent strategy adjustment based on the reliability of the photovoltaic. In a photovoltaic-thermal energy storage hybrid system, an ideal support strategy should dynamically balance the economy (preferably using photovoltaic) and reliability (depending on thermal energy storage). The prior art lacks quantitative evaluation of the trade-off relationship between the two, leading to a control strategy that is either too conservative and increases thermal energy storage loss, or too aggressive and affects the support reliability. In actual operation, the system is simultaneously subjected to multiple dynamic processes such as grid frequency disturbance, photovoltaic output fluctuation, and thermal energy storage state change, and the traditional adaptive method of a single parameter cannot effectively coordinate these mutually coupled influencing factors, and lacks a risk evaluation system that integrates multiple variables, so that the control system can simultaneously respond to frequency change demand, photovoltaic output characteristics, and thermal energy storage state change, and it is difficult to maintain optimal support performance in various complex operating scenarios.

[0003] Chinese Patent Application CN120454212A discloses a virtual synchronous machine wind-solar-storage coordinated control method, which establishes a wind-solar-storage multi-source collaborative double-layer coupling mechanism multi-scenario optimization model in combination with the virtual synchronous control characteristics of the solar-thermal power station; but still has the following shortcomings: lack of risk perception and quantitative mechanism, the control strategy lacks dynamic reliability constraints; the power distribution strategy is open-loop controlled, lacking robustness; multiple devices are coordinated according to fixed parameters, the virtual inertia adjustment dimension is single, and in complex scenarios, parameter tuning mismatching may occur, leading to system overshoot.

[0004] Chinese patent application CN115940257A discloses a kind of light storage generation virtual inertia control method, compare frequency variation with frequency variation threshold;When frequency variation exceeds threshold, energy is released by the capacitor at the end of boost converter;Increase energy storage device, additional droop control and virtual inertia control are added to energy storage device, cooperate photovoltaic power generation system operation.But there are still the following deficiencies: no risk perception and quantification mechanism, control strategy lacks reliability constraints;Power distribution is fixed proportion distribution mode, without dynamic cooperation and deviation compensation;Inertia support resource utilization is low, unable to distinguish multiple scenarios, increase energy storage cycle number and operation and maintenance cost.

[0005] In summary, there is an urgent need for a light storage collaborative virtual inertia control scheme with risk perception, dynamic reliable constraint and flexible and robust power distribution. SUMMARY

[0006] To solve the above problems, to solve the problem that the virtual inertia support strength and the instantaneous available energy of the system in the prior art do not match, the energy storage dependence is too high, which increases the operation cost, and the photovoltaic output uncertainty limits its participation in power grid support, etc.This application builds a multivariate fusion risk assessment model, calculates the adaptive risk coefficient in real time, and dynamically optimizes the power distribution strategy of photovoltaic and energy storage based on the coefficient, specifically including a kind of photovoltaic and energy storage adaptive virtual inertia collaborative control method and system based on risk perception.

[0007] The application adopts the following technical solutions.

[0008] In a first aspect, the application discloses a kind of photovoltaic and energy storage adaptive virtual inertia collaborative control method based on risk perception, the method includes the following steps: Step 1: real-time acquisition of photovoltaic- energy storage system operation data, the operation data includes grid frequency and its change rate, photovoltaic array output power, energy storage battery real-time state of charge; Step 2: design adaptive risk coefficient model, and calculate adaptive risk coefficient in real time based on the operation data; Step 3: based on the adaptive risk coefficient, the total active power increment demand from traditional virtual inertia controller is dynamically distributed to photovoltaic unit and energy storage unit in photovoltaic- energy storage system; Step 4: the active power increment demand after distribution is respectively issued to the corresponding inverter control module of photovoltaic unit and energy storage unit to execute, to complete the virtual inertia support to power grid together; Step 5: based on grid frequency and its change rate, combined with preset steady state criterion, judge whether grid frequency reaches steady state, if not, re-execute step 1 to step 4;If it reaches steady state, switch to standby state.

[0009] Further preferably, Step 2, the adaptive risk coefficient is determined in particular as follows:

[0010] wherein, is an adaptive risk coefficient, and satisfies ; , and are respectively a photovoltaic fluctuation risk component, an energy storage state risk component and a power grid disturbance risk component at the moment t; t , , and are respectively a weight coefficient corresponding to the photovoltaic fluctuation risk component, the energy storage state risk component and the power grid disturbance risk component, and satisfy .

[0011] Further preferably, the photovoltaic fluctuation risk component is determined in particular as follows:

[0012] wherein, is the photovoltaic power at the current t moment t, is the power at the previous moment t-1, is a calculation time window, is the photovoltaic rated power; is a preset fluctuation rate threshold value for defining a risk threshold value of photovoltaic power fluctuation, when the power fluctuation rate in a unit time reaches or exceeds of the photovoltaic rated power, the photovoltaic fluctuation risk component takes the maximum value 1; the energy storage state risk component is determined in particular as follows:

[0013] wherein, is the state of charge of the energy storage battery at the moment t; t is a high-risk threshold value of the state of charge of the energy storage battery, b is a no-risk threshold value of the state of charge of the energy storage battery, and satisfies c ; the power grid disturbance risk component is determined in particular as follows: wherein,

[0014] is the frequency deviation of the power grid at the moment t, i.e. the difference between the actual frequency of the power grid at the moment t and the rated frequency; t t t ​​​​the rate of change of the grid frequency at the moment; d , e are the reference values of the grid frequency deviation and the rate of change of the grid frequency, respectively.

[0015] Further preferably, In step 3, the dynamic allocation of the total active power increment demand from the conventional virtual inertia controller to the photovoltaic unit and the energy storage unit in the photovoltaic- energy storage system includes two allocation stages, namely a feedforward pre-allocation stage and a feedback compensation stage; the feedforward pre-allocation stage determines the pre-allocated photovoltaic support instruction and the pre-allocated energy storage basic support instruction based on an adaptive risk coefficient; the feedback compensation stage calculates the power compensation amount of the energy storage unit based on the deviation between the actual output of the photovoltaic unit and the pre-allocated photovoltaic support instruction. Further preferably, In the feedforward pre-allocation stage, the pre-allocated photovoltaic support instruction and the pre-allocated energy storage basic support instruction are calculated in the following manner:

[0016]

[0017] wherein, is the total active power increment demand, is the pre-allocated photovoltaic support instruction, is the pre-allocated energy storage basic support instruction.

[0018] Further preferably, In the feedback compensation stage, the power compensation amount of the energy storage unit is calculated in the following manner:

[0019] wherein, is the power compensation amount of the energy storage unit; is the actual active power output of the photovoltaic unit; is a limiting function, which limits the absolute value of to be within the maximum adjustable power range of the energy storage unit, so as to avoid the energy storage unit exceeding the safe operation boundary due to excessive compensation amplitude.

[0020] Further preferably, In step 3, the pre-allocated photovoltaic support instruction is taken as the photovoltaic final instruction; the sum of the pre-allocated energy storage basic support instruction and the power compensation amount of the energy storage unit is taken as the energy storage final instruction; the photovoltaic final instruction and the energy storage final instruction are respectively allocated to the photovoltaic unit and the energy storage unit in the photovoltaic- energy storage system.

[0021] In a second aspect, the application discloses a photovoltaic- energy storage adaptive virtual inertia collaborative control system based on the foregoing method, comprising a system operation data acquisition module, an adaptive risk coefficient calculation module, a total active power increment demand dynamic distribution module, a power grid virtual inertia support execution module, and a power grid frequency steady state judgment and control process switching module. The system operation data acquisition module acquires photovoltaic- energy storage operation data in real time, wherein the operation data comprises power grid frequency and its change rate, photovoltaic array output power, and energy storage battery real-time state of charge. The adaptive risk coefficient calculation module designs an adaptive risk coefficient model and calculates the adaptive risk coefficient in real time based on the operation data. The total active power increment demand dynamic distribution module dynamically distributes total active power increment demand from a traditional virtual inertia controller to photovoltaic units and energy storage units in photovoltaic- energy storage based on the adaptive risk coefficient. The power grid virtual inertia support execution module sends the distributed active power increment demand to photovoltaic unit and energy storage unit corresponding inverter control modules for execution, thereby jointly completing virtual inertia support for the power grid.

[0022] The power grid frequency steady state judgment and control process switching module judges whether the power grid frequency reaches a steady state based on the power grid frequency and its change rate in combination with a preset steady state criterion, and if not, sequentially re-executes the system operation data acquisition module, the adaptive risk coefficient calculation module, the total active power increment demand dynamic distribution module, and the power grid virtual inertia support execution module; if so, switches the photovoltaic- energy storage adaptive virtual inertia collaborative control system to a standby state.

[0023] In a third aspect, the application provides a terminal comprising a processor and a storage medium. The storage medium is used for storing instructions. The processor is used for operating according to the instructions to perform the steps of the method in any one of the first aspect of the application.

[0024] In a fourth aspect, the application provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the method in any one of the first aspect of the application.

[0025] Compared with the prior art, the application has the beneficial effects that 1. The risk adaptive optimization of the support capability of the optical storage system is realized, by introducing the risk coefficient Krisk of multivariate fusion, the system can evaluate the reliability of the support behavior in real time according to the fluctuation of photovoltaic output, the change trend of energy storage state and the severity of grid frequency deviation. This makes the system fully utilize the strong inertia support provided by photovoltaic when the light is stable, significantly reduces the energy storage loss; automatically switch to the conservative support mode dominated by energy storage when the light fluctuates sharply, effectively avoids the risk of support interruption caused by photovoltaic power drop, and realizes the optimal balance of support economy and reliability.

[0026] 2. The intelligent adaptive ability of the system to complex operation scenes is improved, and the cooperative allocation strategy based on risk perception makes the system can respond to multiple dynamic processes such as frequency disturbance, photovoltaic fluctuation and energy storage state change at the same time. Compared with the traditional single parameter adaptive method, this method can automatically select the most suitable source and storage matching strategy in complex scenes such as sharp change of grid frequency, unstable photovoltaic output and poor energy storage state, and shows stronger environmental adaptability and operation robustness.

[0027] 3. A reliable technical path for photovoltaic participation in active support of power grid is established, by quantifying the uncertainty of photovoltaic output as a controllable risk index, this method successfully changes the photovoltaic power generation system from a passive energy provider to a reliable power grid stability participant. On the premise of ensuring the reliability of support, the adjustment potential of photovoltaic unit is maximized, providing a low-cost and high-reliability inertia support solution for high-proportion new energy power system.

[0028] 4. The operation state of the energy storage is optimized and the service life of the system is prolonged, and the intelligent allocation mechanism based on risk coefficient makes the energy storage system change from "always output" to "output on demand", avoiding unnecessary deep discharge and frequent action. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The flow chart of the risk perception based photovoltaic storage adaptive virtual inertia cooperative control method of the application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical scheme and advantages of the present application clearer, the technical scheme of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. The embodiments described in the present application are only a part of the embodiments of the present application, not all the embodiments. Based on the spirit of the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0031] As shown in Figure 1 The present application discloses a risk perception based photovoltaic storage adaptive virtual inertia cooperative control method, which comprises the following steps: Step 1: Collect real-time operating data of the photovoltaic-energy storage system, including grid frequency and its rate of change, photovoltaic array output power, and real-time state of charge of the energy storage battery; Step 2: Design an adaptive risk coefficient model and calculate the adaptive risk coefficient in real time based on the operational data; The adaptive risk coefficient is determined in the following manner:

[0032] in, It is an adaptive risk coefficient, and satisfies ; , as well as They are respectively t The risk components of photovoltaic fluctuations, energy storage status, and grid disturbances at any given time; , as well as These are the weighting coefficients corresponding to the photovoltaic fluctuation risk component, the energy storage state risk component, and the grid disturbance risk component, respectively, and satisfy the following conditions: .

[0033] The photovoltaic volatility risk component is determined as follows:

[0034] in, For the present t Photovoltaic power at all times The power at the previous moment, To calculate the time window, Rated power of photovoltaic power; A preset volatility threshold is used to define the critical risk value for photovoltaic power fluctuations. When the power fluctuation rate per unit time reaches or exceeds a certain threshold of the photovoltaic rated power... At that time, the photovoltaic volatility risk component takes the maximum value of 1; The energy storage state risk component is determined as follows:

[0035] in, for t The state of charge of the energy storage battery at all times; b This represents a high-risk threshold for the state of charge of energy storage batteries. c The risk-free threshold for the state of charge of the energy storage battery, and satisfying the following conditions. ; The power grid disturbance risk component is determined as follows:

[0036] wherein, is t the grid frequency deviation at the moment, i.e. t the difference between the actual grid frequency and the rated grid frequency at the moment; is t the grid frequency change rate at the moment; d , e are the reference values of the grid frequency deviation and the grid frequency change rate, respectively.

[0037] Step 3: based on the adaptive risk coefficient, dynamically allocating the total active power increment demand from the traditional virtual inertia controller to the photovoltaic unit and the energy storage unit in the photovoltaic-energy storage system; The dynamic allocation of the total active power increment demand from the traditional virtual inertia controller to the photovoltaic unit and the energy storage unit in the photovoltaic-energy storage system includes two allocation stages, namely the feedforward pre-allocation stage and the feedback compensation stage. The feedforward pre-allocation stage determines the pre-allocated photovoltaic support instruction and the pre-allocated energy storage basic support instruction based on the adaptive risk coefficient. The feedback compensation stage calculates the power compensation amount of the energy storage unit based on the deviation between the actual output of the photovoltaic unit and the pre-allocated photovoltaic support instruction. In the feedforward pre-allocation stage, the pre-allocated photovoltaic support instruction and the pre-allocated energy storage basic support instruction are calculated in the following manner:

[0038]

[0039] wherein, is the total active power increment demand, is the pre-allocated photovoltaic support instruction, is the pre-allocated energy storage basic support instruction.

[0040] In the feedback compensation stage, the power compensation amount of the energy storage unit is calculated in the following manner:

[0041] wherein, is the power compensation amount of the energy storage unit; is the actual active power output of the photovoltaic unit; is a limiting function, which limits the absolute value of to be within the maximum adjustable power range of the energy storage unit, so as to avoid the energy storage unit exceeding the safe operation boundary due to excessive compensation amplitude.

[0042] The pre-allocated photovoltaic support instruction is taken as a photovoltaic final instruction; a sum of the pre-allocated energy storage basic support instruction and a power compensation amount of the energy storage unit is taken as an energy storage final instruction; and the photovoltaic final instruction and the energy storage final instruction are respectively allocated to photovoltaic units and energy storage units in the photovoltaic-energy storage system.

[0043] Step 4: The allocated active power increment demand is respectively fed into the corresponding inverter control modules of the photovoltaic units and the energy storage units to execute, and virtual inertia support for the power grid is completed. Step 5: Based on the power grid frequency and the change rate thereof, in combination with a preset steady state criterion, it is judged whether the power grid frequency reaches a steady state, if not, steps 1 to 4 are re-executed, and if yes, a standby state is switched to.

[0044] The application further discloses a photovoltaic-energy storage adaptive virtual inertia collaborative control system based on the foregoing method, which comprises a system operation data acquisition module, an adaptive risk coefficient calculation module, a total active power increment demand dynamic allocation module, a power grid virtual inertia support execution module and a power grid frequency steady state judgment and control process switching module. The system operation data acquisition module acquires photovoltaic-energy storage operation data in real time, and the operation data comprises a power grid frequency and a change rate thereof, photovoltaic array output power and energy storage battery real-time state of charge. The adaptive risk coefficient calculation module designs an adaptive risk coefficient model and calculates an adaptive risk coefficient in real time based on the operation data. The total active power increment demand dynamic allocation module dynamically allocates total active power increment demand from a traditional virtual inertia controller to photovoltaic units and energy storage units in the photovoltaic-energy storage based on the adaptive risk coefficient. The power grid virtual inertia support execution module feeds the allocated active power increment demand into corresponding inverter control modules of the photovoltaic units and the energy storage units to execute, and jointly completes virtual inertia support for the power grid.

[0045] The power grid frequency steady state judgment and control process switching module judges whether the power grid frequency reaches a steady state based on the power grid frequency and the change rate thereof in combination with a preset steady state criterion, if not, the system operation data acquisition module, the adaptive risk coefficient calculation module, the total active power increment demand dynamic allocation module and the power grid virtual inertia support execution module are re-executed in sequence, and if yes, the photovoltaic-energy storage adaptive virtual inertia collaborative control system is switched to a standby state.

[0046] Embodiment one: As Figure 1As shown, this invention provides a risk-aware adaptive virtual inertia cooperative control method for photovoltaic and energy storage systems, applied to a typical photovoltaic-energy storage system comprising a photovoltaic array, an energy storage battery, and a grid-connected inverter. The method includes the following steps: Step 1: Collect real-time operating data of the photovoltaic-energy storage system, including grid frequency and its rate of change, photovoltaic array output power, and real-time state of charge of the energy storage battery; Step 2: Design an adaptive risk coefficient model and calculate the adaptive risk coefficient in real time based on the operational data; The adaptive risk coefficient model comprehensively analyzes multi-dimensional information from the power grid, photovoltaic system, and energy storage system, and outputs an adaptive risk coefficient that comprehensively characterizes the current reliability of the system. The adaptive risk coefficient satisfies .

[0047] The adaptive risk coefficient is calculated in the following manner: The adaptive risk coefficient proposed in this invention is calculated using a multivariate weighted model, as shown in the following formula:

[0048] in, , as well as They are respectively t The risk components of photovoltaic fluctuations, energy storage status, and grid disturbances at any given time; , as well as These are the weighting coefficients corresponding to the photovoltaic fluctuation risk component, the energy storage state risk component, and the grid disturbance risk component, respectively, and satisfy the following conditions: .

[0049] The photovoltaic volatility risk component The specific calculation method is as follows:

[0050] in, For the present t Photovoltaic power at all times The power at the previous moment, To calculate the time window, Rated power of photovoltaic power; A preset volatility threshold is used to define the critical risk value for photovoltaic power fluctuations. When the power fluctuation rate per unit time reaches or exceeds a certain threshold of the photovoltaic rated power... At that time, the photovoltaic volatility risk component is set to the maximum value of 1 to reflect the high-risk characteristics in this scenario.

[0051] The energy storage state risk component By a reverse proportional piecewise function calculation, specifically:

[0052] Wherein, is the energy storage battery state of charge at the moment, t is the high-risk threshold of the energy storage battery state of charge, b is the no-risk threshold of the energy storage battery state of charge, and satisfies c That is, the energy storage battery state of charge is less than or equal to The risk is the highest, and higher than b It means no risk. c The grid disturbance risk component

[0053] The calculation formula is:

[0054] Wherein, is the grid frequency deviation at the moment, that is, t The difference between the actual grid frequency at the moment and the rated frequency; t is the grid frequency change rate at the moment; , t The grid frequency deviation and the grid frequency change rate are respectively the reference values. d e Step 3: Based on the adaptive risk coefficient, the total active power increment demand from the traditional virtual inertia controller is dynamically allocated to the photovoltaic unit and the energy storage unit in the photovoltaic-energy storage system; The total active power increment demand from the traditional virtual inertia controller is dynamically allocated to the photovoltaic unit and the energy storage unit in the photovoltaic-energy storage system, and the allocation method includes two allocation stages, namely the feedforward pre-allocation stage and the feedback compensation stage; Specifically, it includes:

[0055] Step 301: Perform the feedforward pre-allocation stage, preliminarily determine the basic support power of the photovoltaic unit and the energy storage unit according to the system operation risk, realize the dynamic matching of "the higher the risk, the greater the energy storage support ratio; the lower the risk, the greater the photovoltaic support ratio"; The calculation method is specifically:

[0056]

[0057] Wherein, is the total active power increment demand, is the pre-allocated photovoltaic support instruction, is the pre-allocated energy storage basic support instruction. ​​​

[0058] Step 302: Perform feedback compensation stage. Since photovoltaic output has randomness and volatility, its actual output may deviate from the pre-allocated instruction, so feedback compensation of the energy storage unit is needed to make up for the power gap and ensure that the total active power increment demand of the system is met. The calculation method is as follows: Power compensation amount calculated by the following formula:

[0059] wherein, is the actual active output of the photovoltaic unit, is the power compensation amount of the energy storage unit; is a limiting function, which limits the absolute value of to not exceed the maximum adjustable power range of the energy storage unit, so as to avoid the energy storage unit exceeding the safe operation boundary due to excessive compensation amplitude.

[0060] Integrate the feedforward pre-allocated instruction and the feedback compensation amount to generate the final instruction of the photovoltaic unit and the energy storage unit, which is specifically: Photovoltaic final instruction: ; Energy storage final instruction: .

[0061] Step 4: The allocated active power increment demand is respectively sent to the corresponding inverter control module of the photovoltaic unit and the energy storage unit for execution, and the virtual inertia support for the power grid is completed together.

[0062] Step 5: Based on the grid frequency and its change rate, combined with the preset steady state criterion, it is judged whether the grid frequency reaches the steady state. If not, steps 1 to 4 are re-executed. If it reaches the steady state, it switches to standby state.

[0063] The preset steady state criterion is specifically: The following two conditions are met simultaneously and the system runs stably for a preset time T (T value range is 2-5 seconds, which can be adjusted adaptively according to the operation requirements of the power grid), then it is determined that the grid frequency reaches the steady state: (1) the absolute value of the grid frequency deviation is ≤ , wherein is the frequency deviation stability threshold, which is 0.05 Hz (which can be adjusted according to the actual power grid level and operation scene); (2) the absolute value of the grid frequency change rate is ≤ , wherein is the frequency change rate stability threshold, which is ±0.1 Hz / s (which can be adjusted according to the actual power grid level and operation scene).

[0064] Example two: The embodiment of the present application provides a terminal, comprising a processor and a storage medium; The storage medium is used for storing instructions. The processor is used for operating according to the instructions to perform steps of the method according to any one of the embodiment one.

[0065] Embodiment three: The embodiment of the present application provides a computer readable storage medium, which has a computer program stored thereon, and the program is executed by a processor to implement steps of the method according to any one of the embodiment one.

[0066] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.

[0067] The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or punched tape, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0068] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0069] Computer readable program instructions for carrying out operations of the present disclosure can be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0070] Finally, it should be noted that the above-mentioned embodiments are merely used to illustrate the technical solutions of the present application, but not to limit it. Although the present application has been described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced, and any modification or replacement without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

Claims

1. A risk-aware adaptive virtual inertia cooperative control method based on optical storage, characterized in that, The method includes the following steps: Step 1: Collect real-time operating data of the photovoltaic-energy storage system, including grid frequency and its rate of change, photovoltaic array output power, and real-time state of charge of the energy storage battery; Step 2: Design an adaptive risk coefficient model and calculate the adaptive risk coefficient in real time based on the operational data; Step 3: Based on the adaptive risk coefficient, dynamically allocate the total active power increment demand from the traditional virtual inertia controller to the photovoltaic units and energy storage units in the photovoltaic-energy storage system; Step 4: Distribute the allocated incremental active power demand to the inverter control modules corresponding to the photovoltaic unit and the energy storage unit for execution, thereby completing the virtual inertia support for the power grid; Step 5: Based on the power grid frequency and its rate of change, and combined with the preset steady-state criterion, determine whether the power grid frequency has reached a steady state. If it has not reached a steady state, repeat steps 1 to 4. If it has reached a steady state, switch to standby mode.

2. The risk-aware optical-storage adaptive virtual inertia cooperative control method according to claim 1, characterized in that: Step 2, the adaptive risk coefficient is determined in the following manner: in, It is an adaptive risk coefficient, and satisfies ; , as well as They are respectively t The risk components of photovoltaic fluctuations, energy storage status, and grid disturbances at any given time; , as well as These are the weighting coefficients corresponding to the photovoltaic fluctuation risk component, the energy storage state risk component, and the grid disturbance risk component, respectively, and satisfy the following conditions: .

3. The risk-aware, light-storage adaptive virtual inertia cooperative control method according to claim 2, characterized in that: The photovoltaic volatility risk component is determined as follows: in, For the present t Photovoltaic power at all times The power at the previous moment, To calculate the time window, Rated power of photovoltaic power; A preset volatility threshold is used to define the critical risk value for photovoltaic power fluctuations. When the power fluctuation rate per unit time reaches or exceeds a certain threshold of the photovoltaic rated power... At that time, the photovoltaic volatility risk component takes the maximum value of 1; The energy storage state risk component is determined as follows: in, for t The state of charge of the energy storage battery at all times; b This represents a high-risk threshold for the state of charge of energy storage batteries. c The risk-free threshold for the state of charge of the energy storage battery, and satisfying the following conditions. ; The power grid disturbance risk component is determined as follows: in, for t The power grid frequency deviation at time t, i.e. t The difference between the actual frequency and the rated frequency of the power grid at any given time; for t The rate of change of the power grid frequency at any given time; d , e These are the reference values ​​for grid frequency deviation and grid frequency change rate, respectively.

4. The risk-aware optical-storage adaptive virtual inertia cooperative control method according to claim 3, characterized in that: In step 3, the dynamic allocation of the total active power increment demand from the traditional virtual inertia controller to the photovoltaic units and energy storage units in the photovoltaic-energy storage system includes two allocation stages: a feedforward pre-allocation stage and a feedback compensation stage. The feedforward pre-allocation stage determines the pre-allocated photovoltaic support instructions and the pre-allocated energy storage basic support instructions based on an adaptive risk coefficient. The feedback compensation stage calculates the power compensation amount of the energy storage unit based on the deviation between the actual active power output of the photovoltaic unit and the pre-allocated photovoltaic support command.

5. The risk-aware optical-storage adaptive virtual inertia cooperative control method according to claim 4, characterized in that: In the feedforward pre-allocation phase, the pre-allocated photovoltaic support instructions and the pre-allocated energy storage infrastructure support instructions are calculated in the following manner: in, For the incremental demand of total active power, For pre-allocated photovoltaic support directives, Instructions for pre-allocated energy storage infrastructure support.

6. The risk-aware optical-storage adaptive virtual inertia cooperative control method according to claim 5, characterized in that: During the feedback compensation phase, the power compensation amount of the energy storage unit is calculated as follows: in, This is the power compensation amount for the energy storage unit; The actual active power output of the photovoltaic unit; This is a limiting function, its function is to limit... The absolute value should not exceed the maximum adjustable power range of the energy storage unit to avoid the energy storage unit exceeding the safe operating boundary due to excessive compensation.

7. The risk-aware optical-storage adaptive virtual inertia cooperative control method according to claim 6, characterized in that: In step 3, the pre-allocated photovoltaic support instructions are used as the photovoltaic final instructions; the sum of the pre-allocated energy storage basic support instructions and the power compensation amount of the energy storage unit is used as the energy storage final instructions; the photovoltaic final instructions and the energy storage final instructions are respectively allocated to the photovoltaic units and energy storage units in the photovoltaic-energy storage system.

8. A risk-aware, adaptive virtual inertia-based photovoltaic-storage cooperative control system based on the method of any one of claims 1-7, comprising a system operation data acquisition module, an adaptive risk coefficient calculation module, a total active power incremental demand dynamic allocation module, a grid virtual inertia support execution module, and a grid frequency steady-state determination and control process switching module, characterized in that: The system operates a data acquisition module to collect real-time photovoltaic-energy storage operation data, including grid frequency and its rate of change, photovoltaic array output power, and real-time state of charge of energy storage batteries. An adaptive risk coefficient calculation module designs an adaptive risk coefficient model and calculates the adaptive risk coefficient in real time based on the operating data. The total active power incremental demand dynamic allocation module, based on the adaptive risk coefficient, dynamically allocates the total active power incremental demand from the traditional virtual inertia controller to the photovoltaic units and energy storage units in the photovoltaic-energy storage system. The grid virtual inertia support execution module distributes the allocated active power increment demand to the inverter control modules corresponding to the photovoltaic unit and the energy storage unit for execution, jointly completing the virtual inertia support for the grid.

9. The grid frequency steady-state determination and control process switching module, based on the grid frequency and its rate of change, combined with the preset steady-state criteria, determines whether the grid frequency has reached a steady state. If it has not reached a steady state, the system operation data acquisition module, the adaptive risk coefficient calculation module, the total active power incremental demand dynamic allocation module, and the grid virtual inertia support execution module are executed in sequence. If a steady state is reached, the photovoltaic-storage adaptive virtual inertia collaborative control system is switched to standby mode.

10. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-7.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-7.

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