High-permeability photovoltaic grid-connected cooperative control method and related device thereof

Abstract

The invention belongs to the technical field of power systems, and discloses a high-permeability photovoltaic grid-connected cooperative control method and a related device thereof, and the method comprises the steps: respectively calculating a voltage deviation and a frequency deviation according to a grid-connected point voltage and a grid-connected point frequency, correcting the output power of the inverter according to the dynamic virtual impedance calculated according to the voltage deviation and the frequency deviation and the equivalent impedance of the power grid; dynamically distributing energy storage charging and discharging power according to the corrected output power of the inverter and the charge state of the energy storage system; constructing an optimization model with the purpose of minimizing the sum of the voltage deviation, the frequency deviation, the energy storage cost and the light abandoning amount, solving the optimization model according to the energy storage charging and discharging power and the meteorological data, obtaining the optimal light abandoning amount and the optimal energy storage cost, and further updating the equivalent impedance of the power grid and the maximum photovoltaic output. And correcting the output power of the inverter through the updated power grid equivalent impedance and the photovoltaic maximum output. According to the invention, the technical problems of insufficient photovoltaic volatility and power grid inertia are improved.

Description

Technical Field

[0001] This application relates to the technical field of power systems, and in particular, to a coordinated control method for high-penetration photovoltaic grid connection and related devices. Background Art

[0002] Voltage and frequency are important parameters of the power quality of the power grid. For the power grid to operate stably, the voltage and frequency deviations must be controlled within a certain level. With the grid connection of high-penetration distributed photovoltaics, the stability of the distribution network voltage and frequency faces challenges. As the new energy power system transforms towards a high proportion of renewable energy, distribution networks with a photovoltaic penetration rate exceeding 80% face two core contradictions: the strong volatility of photovoltaic power generation and the weakening of the grid inertia support ability. The interaction between the two poses a severe challenge to system security.

[0003] The volatility of photovoltaics stems from its physical characteristics and meteorological dependence. The power generation of photovoltaics is affected by factors such as irradiance, temperature, and cloud occlusion, presenting random fluctuation characteristics from minutes to hours. For example, the movement of clouds can cause the photovoltaic output to drop by more than 30% within seconds, while the day-night alternation causes the peak-to-valley difference of the power within a day to exceed 80%. The superposition of this volatility and the regular changes of traditional loads leads to a significant increase in the probability of power supply-demand imbalance in the distribution network. In addition, after the photovoltaic inverter is connected to the grid, it presents the characteristics of a "current source" and lacks the voltage source support ability of synchronous generator sets, resulting in a decrease in the short-circuit capacity of the system and further weakening the anti-disturbance ability of the power grid.

[0004] The insufficient grid inertia is directly related to the replacement effect of synchronous units. The rotational inertia of traditional thermal / hydroelectric generator sets buffers power disturbances through the kinetic energy of the rotor. After the photovoltaic grid connection replaces the synchronous units, the equivalent inertia time constant of the system drops from 6 - 10 seconds in the traditional power grid to less than 2 seconds. The sensitivity of the low-inertia system to power deficits increases exponentially, and a small power imbalance can cause the frequency to drop rapidly. For example, when the system inertia is reduced by 50%, the rate of change of frequency under the same power disturbance will double, increasing the risk of frequency exceeding the limit within 0.5 seconds by more than 3 times.

[0005] The coupling effect of the two causes multiple chain risks, including:

[0006] 1. Voltage instability: The sudden change in photovoltaic output causes the power flow direction of the distribution network to reverse frequently, and the voltage fluctuation amplitude at the end of the feeder can reach ±15% of the rated value, inducing misoperation of the protection device;

[0007] 2. Frequency collapse: The insufficient inertia causes the system to lose the buffering ability for short-term power deficits of photovoltaics. In extreme cases, the frequency deviation can exceed 0.5 Hz within 200 ms, triggering the under-frequency load shedding device;

[0008] 3. Resonant amplification: A large number of grid-connected inverters exhibit negative impedance characteristics in a weak grid environment, interacting with the line impedance to trigger broadband oscillations. Measured cases show that the resonant frequency covers the range of 200 Hz - 2 kHz, leading to equipment insulation breakdown.

[0009] 4. Economic degradation: To suppress fluctuations, traditional solutions need to reserve 20% - 30% of the spinning reserve capacity and force the curtailment rate of light to exceed 15%, significantly increasing the power supply cost. Summary of the Invention

[0010] This application provides a high-penetration photovoltaic grid-connected collaborative control method and its related device, which are used to improve the technical problems of strong photovoltaic volatility and insufficient grid inertia existing in the distribution network.

[0011] In view of this, the first aspect of this application provides a high-penetration photovoltaic grid-connected collaborative control method, including:

[0012] Calculating the voltage deviation and frequency deviation according to the grid connection point voltage and grid connection point frequency respectively, and correcting the inverter output power according to the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation and the grid equivalent impedance;

[0013] Dynamically allocating the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system;

[0014] Constructing an optimization model with the goal of minimizing the sum of the voltage deviation, frequency deviation, energy storage cost, and curtailment amount of light, and solving the optimization model according to the energy storage charge and discharge power and meteorological data to obtain the optimal curtailment amount of light and the optimal energy storage cost;

[0015] Updating the grid equivalent impedance and the maximum photovoltaic output according to the optimal curtailment amount of light, the optimal energy storage cost, the grid connection point voltage, the grid connection point current, and meteorological data, and correcting the inverter output power through the updated grid equivalent impedance and the updated maximum photovoltaic output.

[0016] Optionally, the inverter output power includes the active power output of the inverter and the reactive power output of the inverter, and the correction formula for the inverter output power is:

[0017]

[0018] In the formula, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power point tracking output of the photovoltaic at time t, is the dynamic virtual impedance at time t, is the grid equivalent impedance at time t, is the grid connection point voltage at time t, is the reference voltage. is the reactive power regulation coefficient.

[0019] Optionally, the dynamic allocation of the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system includes:

[0020] Calculate the difference between the active power of the load and the corrected active output of the inverter to obtain the power deviation;

[0021] Calculate the difference between the reference value of the state of charge of the energy storage system and the state of charge of the energy storage system to obtain the load deviation;

[0022] Perform a weighted sum of the power deviation and the load deviation to obtain the allocated energy storage charge and discharge power.

[0023] Optionally, the correction of the inverter output power by the updated grid equivalent impedance and the updated maximum photovoltaic output includes:

[0024] Update the maximum power tracking output of the photovoltaic by the updated maximum photovoltaic output, and update the active output of the inverter by the updated maximum power tracking output of the photovoltaic and the updated grid equivalent impedance;

[0025] Update the reactive output of the inverter by the updated grid equivalent impedance.

[0026] Optionally, the objective function of the optimization model is:

[0027]

[0028] The constraint conditions of the optimization model include:

[0029]

[0030] In the formula, is the voltage deviation at time t, is the frequency deviation at time t, is the energy storage cost at time t, is the amount of abandoned light at time t, and T is the adjustment period; , , , are the weight coefficients of voltage deviation, frequency deviation, energy storage cost, and amount of abandoned light respectively; is the photovoltaic output at time t, is the energy storage charge and discharge power at time t, is the reactive output of the inverter at time t, is the active power of the load at time t, is the static var compensation power at time t; is the reactive power of the load at time t; is the state of charge of the energy storage system at time t, is the lower limit of the state of charge of the energy storage system, is the upper limit of the state of charge of the energy storage system.

[0031] Optionally, the calculation process of the curtailment of light includes:

[0032] Predict the photovoltaic output according to the predicted irradiance;

[0033] Judge whether the photovoltaic output is greater than the sum of the maximum charge-discharge power of the energy storage and the active power of the load;

[0034] If so, determine the curtailment of light according to the difference between the photovoltaic output, the maximum charge-discharge power of the energy storage, and the active power of the load;

[0035] If not, set the curtailment of light to 0.

[0036] The second aspect of the present application provides a high-penetration photovoltaic grid-connected collaborative control system, including:

[0037] A real-time control layer for calculating the voltage deviation and frequency deviation according to the grid connection point voltage and grid connection point frequency respectively, and correcting the inverter output power according to the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation and the grid equivalent impedance;

[0038] A short-term coordination layer for dynamically distributing the charge-discharge power of the energy storage according to the corrected inverter output power and the state of charge of the energy storage system;

[0039] A long-term optimization layer for constructing an optimization model with the goal of minimizing the sum of the voltage deviation, frequency deviation, energy storage cost, and curtailment of light, solving the optimization model according to the charge-discharge power of the energy storage and meteorological data, and obtaining the optimal curtailment of light and the optimal energy storage cost;

[0040] Update the grid equivalent impedance and the maximum photovoltaic output according to the optimal curtailment of light, the optimal energy storage cost, the grid connection point voltage, grid connection point current, and meteorological data uploaded by the real-time control layer, and correct the inverter output power through the updated grid equivalent impedance and the updated maximum photovoltaic output.

[0041] Optionally, the inverter output power includes the active power output of the inverter and the reactive power output of the inverter, and the correction formula for the inverter output power is:

[0042]

[0043] Wherein, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power tracking output of the photovoltaic at time t, is the dynamic virtual impedance at time t, is the equivalent impedance of the power grid at time t, is the grid connection point voltage at time t, is the reference voltage, is the reactive power regulation coefficient.

[0044] The third aspect of this application provides an electronic device, which includes a processor and a memory;

[0045] The memory is used to store program code and transmit the program code to the processor;

[0046] The processor is used to execute any one of the high-penetration photovoltaic grid-connected collaborative control methods described in the first aspect according to the instructions in the program code.

[0047] The fourth aspect of this application provides a computer-readable storage medium, which is used to store program code, and when the program code is executed by a processor, it implements any one of the high-penetration photovoltaic grid-connected collaborative control methods described in the first aspect.

[0048] It can be seen from the above technical solutions that this application has the following advantages:

[0049] The high-penetration photovoltaic grid-connected collaborative control method provided by this application suppresses instantaneous fluctuations through the real-time control layer, compensates for the second-level power mutation of photovoltaic and adjusts the dynamic virtual impedance through the fast response of the inverter, dynamically adjusts the output impedance of the inverter by detecting the voltage deviation and frequency deviation of the grid connection point in real time, and cancels the voltage flicker caused by the sudden drop of photovoltaic output within 10 ms; suppresses the minute-level fluctuations through the short-term coordination layer, fills the minute-level power gap caused by the photovoltaic cloud occlusion by using the fast charge and discharge characteristics of energy storage, and dynamically allocates the charge and discharge tasks according to the state of charge of each energy storage system; reconstructs the power grid operation resilience through the long-term optimization layer, and solves the problem of systemic inertia loss caused by high-proportion photovoltaic through cluster coordination and optimization.

[0050] Furthermore, this application adopts a collaborative synergy mechanism. The millisecond-level disturbance information processed by the real-time control layer will be uploaded to the short-term coordination layer for correcting the energy storage action strategy. Through the architecture of hierarchical interception and collaborative defense, this application decomposes the impact of photovoltaic fluctuations on the power grid into different time scales for targeted processing, and at the same time reconstructs the equivalent grid inertia through space-time coordination, effectively reducing the voltage over-limit rate and reducing the amount of abandoned light.

[0051] Furthermore, this application decomposes the whole network control into three layers: the real-time control layer (node level), the short-term coordination layer (cluster level), and the long-term optimization layer (regional level). Each layer only needs to process the corresponding scale variables, which improves the calculation efficiency compared with the traditional centralized control method. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0053] Figure 1 It is a schematic flowchart of a high-penetration photovoltaic grid-connected collaborative control method provided by an embodiment of the present application;

[0054] Figure 2 It is a schematic structural diagram of a high-penetration photovoltaic grid-connected collaborative control device provided by an embodiment of the present application. Specific embodiments

[0055] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0056] For ease of understanding, please refer to Figure 1 , an embodiment of the present application provides a high-penetration photovoltaic grid-connected collaborative control method, including:

[0057] Step 110: Calculate the voltage deviation and frequency deviation according to the grid connection point voltage and grid connection point frequency respectively, and correct the inverter output power according to the dynamic virtual impedance calculated based on the voltage deviation and frequency deviation and the grid equivalent impedance;

[0058] The present application decomposes the whole network control into three layers: a real-time control layer, a short-term coordination layer, and a long-term optimization layer. The real-time control layer can obtain the high-penetration photovoltaic grid connection point voltage , grid connection point current in real time through a phasor measurement unit (PMU), and can also obtain the grid connection point frequency , meteorological data such as irradiance and temperature of the meteorological station in real time. The real-time control layer calculates the voltage deviation and frequency deviation respectively according to the grid connection point voltage and grid connection point frequency , and calculates the dynamic virtual impedance according to the voltage deviation and frequency deviation Calculate the dynamic virtual impedance and correct the inverter power output through the dynamic virtual impedance. Based on small-signal stability analysis, the real-time control layer constructs a dynamic virtual impedance model to calculate the dynamic virtual impedance, so as to correct the inverter output impedance in real time and suppress the resonance and voltage fluctuations caused by high-penetration photovoltaic power generation.

[0059] Among them, the dynamic virtual impedance model is:

[0060]

[0061] In the formula, is the dynamic virtual impedance at time t; is the equivalent reference impedance; is the voltage sensitivity coefficient; is the grid connection point voltage at time t, is the reference voltage, is the voltage deviation at time t; is the grid connection point frequency at time t, is the nominal frequency, is the frequency deviation at time t; is the frequency integral weight coefficient;

[0062] The correction formula for the inverter output power is:

[0063]

[0064] In the formula, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power tracking output of the photovoltaic, is the dynamic virtual impedance at time t, is the equivalent impedance of the power grid at time t, is the grid connection point voltage at time t, is the reference voltage, is the reactive power regulation coefficient.

[0065] The real-time control layer (which can achieve millisecond-level control) is used to suppress instantaneous fluctuations, compensate for the second-level power mutation of the photovoltaic through the fast response of the inverter and adjust the dynamic virtual impedance: it detects the voltage deviation and frequency deviation of the grid connection point in real time, and dynamically adjusts the inverter output impedance, which can offset the voltage flicker caused by the sudden drop of the photovoltaic output within 10 ms. For example, when a voltage dip is detected, the virtual impedance of the inverter is instantly increased to limit the current output to prevent equipment overcurrent.

[0066] Step 120: Dynamically allocate the charge and discharge power of the energy storage according to the corrected inverter output power and the state of charge of the energy storage system;

[0067] The short - term coordination layer calculates the difference between the active power of the load and the corrected active power output of the inverter to obtain the power deviation; calculates the difference between the reference value of the state of charge of the energy storage system and the state of charge of the energy storage system to obtain the load deviation; and performs a weighted sum of the power deviation and the load deviation to obtain the allocated charge - discharge power of the energy storage. The short - term coordination layer suppresses the PV fluctuations and achieves power balance by dynamically allocating the charge - discharge power of the energy storage.

[0068] Among them, the energy storage power distribution formula is:

[0069]

[0070] In the formula, is the charge - discharge power of the energy storage at time t, is the power distribution coefficient, is the state - of - charge adjustment coefficient, is the active power of the load at time t, is the reference value of the state of charge of the energy storage system, is the state of charge of the energy storage system at time t.

[0071] The short - term coordination layer (which can achieve second - level control) is used to suppress the minute - level fluctuations, utilize the fast charge - discharge characteristics of the energy storage to fill the minute - level power gap caused by the PV cloud shading, and dynamically allocate the charge - discharge tasks according to the state of charge (SOC) of each energy storage system. For medium - and short - term changes such as the gradual change of PV output and the periodic fluctuation of the load, the control period is synchronized with the meteorological prediction update frequency (such as the satellite cloud map refresh). When dynamically allocating the energy storage power, based on the ultra - short - term PV output prediction (such as the next 30 seconds) and the load trend, the charge - discharge priority of the energy storage is dynamically adjusted. For example, if it is predicted that the cloud will cover the PV station in 10 seconds, the energy storage is commanded in advance to discharge at the maximum power to fill the power gap. It can coordinate devices such as capacitor banks to achieve refined reactive power compensation on the second - level time scale.

[0072] Step 130: Construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment of light. Solve the optimization model according to the charge - discharge power of the energy storage and meteorological data to obtain the optimal curtailment of light and the optimal energy storage cost;

[0073] The long - term optimization layer constructs an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost, and curtailment of light to achieve multi - objective collaborative optimization of voltage - frequency - economy. Solve the optimization model according to the charge - discharge power of the energy storage and meteorological data to obtain the compromised optimal model values after multi - objective collaborative optimization, including the optimal curtailment of light, the optimal energy storage cost, the optimal grid - connection point voltage, the optimal grid - connection point frequency, etc. It can perform voltage and frequency control on the PV grid - connection according to the optimal grid - connection point voltage and the optimal grid - connection point frequency.

[0074] Among them, the objective function of the optimization model is:

[0075]

[0076] The constraint conditions of the optimization model include:

[0077]

[0078] In the formula, is the voltage deviation at time t, is the frequency deviation at time t, is the energy storage cost at time t, is the amount of curtailed light at time t; T is the adjustment period; , , , are the weight coefficients of voltage deviation, frequency deviation, energy storage cost, and curtailed light respectively; is the photovoltaic output at time t, is the charge and discharge power of the energy storage at time t, is the reactive power output of the inverter at time t, is the active power of the load at time t, is the static reactive power compensation power at time t; is the reactive power of the load at time t; is the state of charge of the energy storage system at time t, is the lower limit of the state of charge of the energy storage system, is the upper limit of the state of charge of the energy storage system.

[0079] The energy storage SOC is updated through the short-term layer. SOC is used as a constraint condition ( ) to limit the feasible region of the energy storage charge and discharge power, prevent overcharging / overdischarging of the energy storage, and optimize its economy at the same time.

[0080] The impact of SOC change on battery life is reflected through the energy storage cost item in the objective function, that is:

[0081]

[0082] In the formula, is the energy storage attenuation coefficient. The charge and discharge cost is higher when the SOC(t) is lower.

[0083] Meteorological data can be used to adjust the energy storage and curtailed light strategies in advance to cope with photovoltaic fluctuations. The predicted maximum photovoltaic output at time t = , as the input of the power balance constraint:

[0084]

[0085] In the formula, is the photovoltaic output at time t, is the predicted irradiance at time t, and the predicted irradiance can be obtained through meteorological data, is the photovoltaic conversion efficiency, is the effective light-receiving area of the photovoltaic array; is the maximum charge-discharge power of the energy storage at time t.

[0086] The photovoltaic output affects the calculation of the curtailed light amount. Specifically, the photovoltaic output is predicted based on the predicted irradiance; it is determined whether the photovoltaic output is greater than the sum of the maximum charge-discharge power of the energy storage and the active power of the load; if so, the curtailed light amount is determined according to the difference between the photovoltaic output and the maximum charge-discharge power of the energy storage and the active power of the load; if not, the curtailed light amount is set to 0, that is .

[0087] By solving the above optimization model, the optimal curtailed light amount, the optimal energy storage cost, the optimal grid connection point voltage, the optimal grid connection point frequency, etc. are finally obtained.

[0088] The long-term optimization layer (implementing minute-level control) is used to optimize the system resilience. By means of cluster-based coordinated optimization, the problem of the lack of systematic inertia caused by a high proportion of photovoltaics is solved.

[0089] Step 140: Update the equivalent impedance of the power grid and the maximum photovoltaic output according to the optimal curtailed light amount, the optimal energy storage cost, the grid connection point voltage, the grid connection point current and the meteorological data uploaded by the real-time control layer, and correct the output power of the inverter through the updated equivalent impedance of the power grid and the updated maximum photovoltaic output.

[0090] The long-term optimization layer updates the equivalent impedance of the power grid and the maximum photovoltaic output by using the recursive least squares method according to the optimal curtailed light amount, the optimal energy storage cost, the grid connection point voltage, the grid connection point current and the meteorological data uploaded by the real-time control layer. Specifically, the long-term optimization layer takes the equivalent impedance of the power grid and the maximum photovoltaic output as the parameters to be identified, and takes the optimal curtailed light amount , the optimal energy storage cost , the grid connection point voltage , the grid connection point current and the irradiance to generate an input feature vector, and constructs a parameter update model to be identified:

[0091]

[0092] Among them, is the parameter to be identified at time t, is the input feature vector at time t, , , are the grid connection point voltage and grid connection point current at time t, respectively, is the normalized irradiance at time t, is the curtailment rate at time t, is the proportion of energy storage cost at time t, is the maximum energy storage cost, is the Kalman gain matrix, represents the actual measured output value of the system at time t, which is used to compare with the model prediction value to drive parameter update. Generally includes the following two types of data:

[0093] (1) For : , Re( ) represents taking the real part of a complex number;

[0094] (2) For : .

[0095] In the model for updating the parameters to be identified, the and the predicted value The error function is:

[0096]

[0097] The error directly participates in parameter correction. is the parameter to be identified at time t - 1.

[0098] When performing parameter update, the parameter update is achieved through the Kalman gain matrix , determines the weight of the new measurement data on parameter estimation:

[0099]

[0100] Among them, is the covariance matrix at time t - 1, , is the forgetting factor (used to weaken the influence of old data). The parameter update process is as follows:

[0101] First, calculate the error: Generate an error signal through the deviation between and the model predicted value ;

[0102] Then, adjust the gain: Dynamically adjust the Kalman gain matrix according to the current input feature and the covariance matrix ;

[0103] Finally, correct the parameters: Utilize the current Kalman gain matrix and the error signal to iteratively update the parameter vector, that is .

[0104] Furthermore, when the optimal energy storage cost exceeds the threshold , limit the update amplitude of :

[0105]

[0106] In the formula, is the cost sensitivity coefficient, usually taking 0.1 - 0.3. The embodiments of this application constrain the economic boundary of parameter update through the energy storage cost.

[0107] After obtaining the updated grid equivalent impedance and the updated maximum PV output, correct the inverter output power through the updated grid equivalent impedance and the maximum PV output. Specifically, first, update the PV maximum power tracking output The reference value of the PV maximum power tracking output is dynamically updated by the maximum PV output :

[0108]

[0109] In the formula, is the PV rated capacity. Dynamically correct the reference value of the maximum power point tracking according to the current irradiance, temperature and other meteorological data to avoid overload or light abandonment caused by environmental mutations.

[0110] Furthermore, update the active power output of the inverter through the updated grid equivalent impedance and the updated PV maximum power tracking output, and update the reactive power output of the inverter through the updated grid equivalent impedance.

[0111] Secondly, adjust the energy storage power distribution through the updated maximum PV output and the updated grid equivalent impedance . Since the real-time value of the inverter active power output is affected by the maximum PV output and the grid equivalent impedance , after updating the inverter active power output , further adjust the energy storage power distribution . When the PV output suddenly changes (such as being blocked by clouds), quickly update the energy storage charge and discharge demand through the maximum PV output to maintain power balance.

[0112] Finally, the curtailment amount is updated with the updated maximum PV output : . In the long-term optimization layer, based on the predicted maximum PV output the curtailment strategy is adjusted to balance system security and economy.

[0113] The online identification results will be fed back into the parameter update model to be identified in step 140 to form a closed-loop update, ensuring that the model parameters are continuously optimized with the change of the environment, so as to improve the system adaptability. Through multi-level parameter transfer and closed-loop feedback, multi-objective coordinated optimization of voltage-frequency-economy of the PV high-penetration distribution network is realized. In the embodiment of the present application, the long-term layer can generate a scheduling benchmark every 5 minutes (which can be set according to the actual situation), the short-term layer fine-tunes the action strategy per second, and the real-time layer executes closed-loop control every 10 milliseconds, forming a nested structure of "coarse adjustment - fine adjustment - fine tuning". Through multi-level coordination in the present application, the maximum PV output becomes the core parameter connecting the physical model and data-driven, realizing precise control of PV output and improving system stability.

[0114] For the high-penetration PV grid-connected coordinated control method provided by the present application, the instantaneous fluctuations are suppressed through the real-time control layer. The rapid response of the inverter compensates for the second-level power mutation of the PV and adjusts the dynamic virtual impedance. By real-time detecting the voltage deviation and frequency deviation at the grid connection point, the output impedance of the inverter is dynamically adjusted to cancel the voltage flicker caused by the sudden drop of the PV output within 10 ms; the minute-level fluctuations are suppressed through the short-term coordination layer. The fast charge and discharge characteristics of the energy storage are used to fill the minute-level power gap caused by the cloud occlusion of the PV. According to the state of charge of each energy storage system, the charge and discharge tasks are dynamically allocated; the grid operation resilience is reconstructed through the long-term optimization layer. Through cluster coordinated optimization, the problem of systematic inertia loss caused by high proportion of PV is solved.

[0115] Furthermore, the present application adopts a synergistic mechanism. The millisecond-level disturbance information processed by the real-time control layer will be uploaded to the short-term coordination layer for correcting the energy storage action strategy. Through the architecture of hierarchical interception and collaborative defense, the present application decomposes the impact of PV fluctuations on the grid into different time scales for targeted processing. At the same time, the equivalent grid inertia is reconstructed through space-time coordination, effectively reducing the voltage over-limit rate and reducing the curtailment amount.

[0116] Most of the existing technologies adopt centralized control of voltage and frequency. The centralized control method requires unified modeling of all node parameters (such as voltage, power, etc.) across the entire network, resulting in an exponential increase in the number of optimization variables with the number of nodes. For example, the dimension of the power flow equation of a power grid with N nodes is 2N×2N. When N>100, the computational complexity exceeds the processing capacity of a conventional dispatching server. Millisecond-level fast control requires frequent calls to the state estimation of the entire network. However, the time consumption of a single state estimation in a centralized architecture can reach hundreds of milliseconds, which cannot meet the high-frequency regulation requirements. All measurement data needs to be uploaded to the central controller for processing, and the communication delay may exceed 100ms in complex topologies. Especially after the deployment of PMUs in rural distribution networks, the transmission congestion problem caused by the sharp increase in data volume is significant.

[0117] This application decomposes the whole-network control into three layers: a real-time control layer (node level), a short-term coordination layer (cluster level), and a long-term optimization layer (area level). Each layer only needs to process variables corresponding to its scale, which improves the computational efficiency compared with the traditional centralized control method.

[0118] Existing technologies usually adopt centralized control methods. The response delay of centralized frequency modulation exceeds the second level and cannot track the millisecond-level fluctuations of photovoltaic power. The fixed virtual impedance model is prone to overshoot under operating conditions. Offline parameter tuning results in poor adaptability of the controller. This application forms a full-chain solution that runs through "physical response - data perception - optimization decision" by suppressing fluctuations in real time through multi-time scale collaborative control, reshaping the system damping characteristics with dynamic virtual impedance, improving the robustness of the model through data-driven parameter identification, and achieving a safe, economic, and balanced multi-objective optimization. Its technical advantages are accurately mapped to the causes of problems, providing a new way to break the deadlock for high-penetration photovoltaic grid connection.

[0119] The above is an embodiment of a collaborative control method for high-penetration photovoltaic grid connection provided by this application. The following is an embodiment of a collaborative control system for high-penetration photovoltaic grid connection provided by this application.

[0120] Please refer to Figure 2 , a collaborative control system for high-penetration photovoltaic grid connection provided by an embodiment of this application, includes:

[0121] A real-time control layer 210, configured to calculate a voltage deviation and a frequency deviation respectively according to the grid connection point voltage and the grid connection point frequency, and correct the inverter output power according to the dynamic virtual impedance calculated based on the voltage deviation and the frequency deviation and the grid equivalent impedance;

[0122] A short-term coordination layer 220, configured to dynamically allocate the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system;

[0123] The long-term optimization layer 230 is used to build an optimization model aiming at minimizing the sum of voltage deviation, frequency deviation, energy storage cost and curtailment of PV power. The optimization model is solved according to the charge and discharge power of the energy storage and meteorological data to obtain the optimal curtailment of PV power and the optimal energy storage cost;

[0124] The equivalent impedance of the power grid and the maximum PV output are updated according to the optimal curtailment of PV power, the optimal energy storage cost, the grid-connected point voltage, grid-connected point current and meteorological data uploaded by the real-time control layer. The output power of the inverter is corrected by the updated equivalent impedance of the power grid and the updated maximum PV output.

[0125] As a further improvement, the output power of the inverter includes the active power output and reactive power output of the inverter. The correction formula for the output power of the inverter is:

[0126]

[0127] In the formula, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power tracking output of the PV at time t, is the dynamic virtual impedance at time t, is the equivalent impedance of the power grid at time t, is the grid-connected point voltage at time t, is the reference voltage, is the reactive power regulation coefficient.

[0128] The high-penetration PV grid-connected collaborative control method provided by this application suppresses instantaneous fluctuations through the real-time control layer, compensates for the second-level power mutation of PV and adjusts the dynamic virtual impedance through the fast response of the inverter. By real-time detecting the voltage deviation and frequency deviation of the grid-connected point, the output impedance of the inverter is dynamically adjusted to cancel the voltage flicker caused by the sudden drop of PV output within 10 ms; the minute-level fluctuations are suppressed through the short-term coordination layer, and the fast charge and discharge characteristics of the energy storage are used to fill the minute-level power gap caused by the cloud occlusion of PV. According to the state of charge of each energy storage system, the charge and discharge tasks are dynamically allocated; the long-term optimization layer reconstructs the operation resilience of the power grid, and solves the problem of systemic inertia loss caused by high proportion of PV through cluster coordination optimization.

[0129] Furthermore, this application adopts a collaborative synergy mechanism. The millisecond-level disturbance information processed by the real-time control layer will be uploaded to the short-term coordination layer for correcting the energy storage action strategy. Through the architecture of hierarchical interception and collaborative defense, this application decomposes the impact of PV fluctuations on the power grid into different time scales for targeted processing. At the same time, the equivalent grid inertia is reconstructed through space-time coordination, effectively reducing the voltage over-limit rate and reducing the curtailment of PV power.

[0130] This application decomposes the whole network control into three layers: the real-time control layer (node level), the short-term coordination layer (cluster level), and the long-term optimization layer (regional level). Each layer only needs to process variables at the corresponding scale, which improves the computing efficiency compared with the traditional centralized control method.

[0131] An embodiment of this application also provides an electronic device, which includes a processor and a memory;

[0132] The memory is used to store program codes and transmit the program codes to the processor;

[0133] The processor is used to execute the high-penetration photovoltaic grid-connected collaborative control method in the foregoing method embodiment according to the instructions in the program code.

[0134] An embodiment of this application also provides a computer-readable storage medium, which is used to store program codes. When the program codes are executed by a processor, the high-penetration photovoltaic grid-connected collaborative control method in the foregoing method embodiment is implemented.

[0135] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the system described above can refer to the corresponding process in the foregoing method embodiment, and will not be elaborated here.

[0136] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of this application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0137] It should be understood that in this application, "at least one (item)" means one or more, and "multiple" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression refers to any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0138] In several embodiments provided in the present application, it should be understood that the disclosed 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 units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated. The components displayed as units 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 units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, in each embodiment of the present application, the functional units 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-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0141] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. And the aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (English full name: Read-Only Memory, English abbreviation: ROM), random access memories (English full name: Random Access Memory, English abbreviation: RAM), magnetic disks or optical discs and other various media that can store program codes.

[0142] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present application.

Claims

1. A high-permeability photovoltaic grid-connected collaborative control method, characterized in that, Including: Calculating voltage deviation and frequency deviation respectively according to the grid connection point voltage and grid connection point frequency, and correcting the inverter output power according to the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation and the grid equivalent impedance; Dynamically allocating the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system; Constructing an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost and light curtailment amount, solving the optimization model according to the energy storage charge and discharge power and meteorological data, and obtaining the optimal light curtailment amount and the optimal energy storage cost; Updating the grid equivalent impedance and the maximum PV output according to the optimal light curtailment amount, the optimal energy storage cost, the grid connection point voltage, the grid connection point current and meteorological data, and correcting the inverter output power through the updated grid equivalent impedance and the updated maximum PV output.

2. The high-permeability photovoltaic grid-connected collaborative control method according to claim 1, wherein The inverter output power includes the active power output of the inverter and the reactive power output of the inverter, and the correction formula of the inverter output power is: Wherein, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power tracking output of the PV at time t, is the dynamic virtual impedance at time t, is the equivalent impedance of the power grid at time t, is the grid connection point voltage at time t, is the reference voltage, is the reactive power regulation coefficient.

3. The high-permeability photovoltaic grid-connected collaborative control method according to claim 2, characterized in that, The dynamically allocating the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system includes: Calculating the difference between the active power of the load and the corrected active power output of the inverter to obtain the power deviation; Calculating the difference between the reference value of the state of charge of the energy storage system and the state of charge of the energy storage system to obtain the load deviation; Performing weighted summation on the power deviation and the load deviation to obtain the allocated energy storage charge and discharge power.

4. The high-permeability photovoltaic grid-connected cooperative control method according to claim 2, wherein The correcting the inverter output power through the updated grid equivalent impedance and the updated maximum PV output includes: Updating the maximum PV power tracking output through the updated maximum PV output, and updating the active power output of the inverter through the updated maximum PV power tracking output and the updated grid equivalent impedance; Updating the reactive power output of the inverter through the updated grid equivalent impedance.

5. The high-permeability photovoltaic grid-connected collaborative control method according to claim 1, characterized in that The objective function of the optimization model is: The constraint conditions of the optimization model include: Wherein, is the voltage deviation at time t, is the frequency deviation at time t, is the energy storage cost at time t, is the amount of curtailed photovoltaic power at time t, and T is the adjustment period; , , , are the weight coefficients of voltage deviation, frequency deviation, energy storage cost, and curtailed photovoltaic power respectively; is the photovoltaic output at time t, is the charge and discharge power of the energy storage at time t, is the reactive power output of the inverter at time t, is the active power of the load at time t, is the static reactive power compensation power at time t; is the reactive power of the load at time t; is the state of charge of the energy storage system at time t, is the lower limit of the state of charge of the energy storage system, is the upper limit of the state of charge of the energy storage system.

6. The high-permeability photovoltaic grid-connected collaborative control method according to claim 1, characterized in that The calculation process of the light curtailment amount includes: Predicting the PV output according to the predicted irradiance; Judging whether the PV output is greater than the sum of the maximum energy storage charge and discharge power and the active power of the load; If so, determining the light curtailment amount according to the difference between the PV output and the maximum energy storage charge and discharge power and the active power of the load; If not, setting the light curtailment amount to 0.

7. A high-permeability photovoltaic grid-connected collaborative control system, characterized in that, Including: The real-time control layer is used to calculate the voltage deviation and the frequency deviation respectively according to the grid connection point voltage and the grid connection point frequency, and correct the inverter output power according to the dynamic virtual impedance calculated from the voltage deviation and the frequency deviation and the grid equivalent impedance; The short-term coordination layer is used to dynamically allocate the energy storage charge and discharge power according to the corrected inverter output power and the state of charge of the energy storage system; The long-term optimization layer is used to construct an optimization model with the goal of minimizing the sum of voltage deviation, frequency deviation, energy storage cost and light curtailment amount, solve the optimization model according to the energy storage charge and discharge power and meteorological data, and obtain the optimal light curtailment amount and the optimal energy storage cost; Update the grid equivalent impedance and the maximum PV output according to the optimal PV curtailment amount, the optimal energy storage cost, the grid connection point voltage, grid connection point current, and meteorological data uploaded by the real-time control layer, and correct the inverter output power through the updated grid equivalent impedance and the updated maximum PV output.

8. The high-permeability photovoltaic grid-connected collaborative control system according to claim 7, characterized in that, The inverter output power includes the active power output and the reactive power output of the inverter, and the correction formula for the inverter output power is: Wherein, is the active power output of the inverter at time t, is the reactive power output of the inverter at time t, is the maximum power point tracking output of the photovoltaic at time t, is the dynamic virtual impedance at time t, is the equivalent impedance of the power grid at time t, is the grid connection point voltage at time t, is the reference voltage, is the reactive power regulation coefficient.

9. An electronic device, characterized in that, The device includes a processor and a memory; The memory is used to store program codes and transmit the program codes to the processor; The processor is used to execute the high-penetration PV grid-connected collaborative control method according to any one of claims 1-6 based on the instructions in the program codes.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store program codes, and when the program codes are executed by a processor, the high-penetration PV grid-connected collaborative control method according to any one of claims 1-6 is implemented.

Images