A method and device for optimizing inertia support of a gravity energy storage system based on VSG control

Abstract

The present application belongs to the technical field of power system energy storage, and discloses a gravity energy storage system inertia support optimization method and device based on VSG control, comprising the following steps: obtaining real-time power grid state parameters, the power grid state parameters including power grid frequency, active power and reactive power, dynamically calculating the virtual inertia support power required by the gravity energy storage system based on the VSG control algorithm; dynamically adjusting the control parameters of the VSG based on the power grid frequency and the virtual inertia support power; using the model predictive control method, taking the virtual inertia support power as the control target, rolling optimization of the power distribution strategy, and recording the state of charge change data of the gravity energy storage system in the optimization process; effectively solving the frequency stability problem caused by insufficient system inertia when a high proportion of new energy is connected to the power system in the prior art.

Description

Technical Field

[0001] This invention belongs to the field of power system energy storage technology, specifically relating to an optimization method and device for inertia support of a gravity energy storage system based on VSG control. Background Technology

[0002] With the large-scale grid connection of new energy sources such as wind power and photovoltaics into the power system, the proportion of traditional synchronous generator units has continued to decline, resulting in a significant reduction in the system's equivalent rotational inertia and severely weakening the grid's frequency stability capability. Against this backdrop, existing gravity energy storage systems exhibit significant technical deficiencies in addressing the frequency stability issues arising from the integration of high-proportion new energy sources (referring to a significant increase in the proportion of new energy generation in total power generation): their passive, mass-dependent inertial response mechanism struggles to achieve rapid and precise frequency regulation; their control strategies lack coordinated optimization for second-level inertia support and minute-level energy dispatch, failing to balance short-term frequency stability with long-term operational needs; and their fixed-parameter capacity configuration is mismatched with dynamically changing grid demands, affecting both support effectiveness and resource waste. These technical shortcomings make existing gravity energy storage systems unable to meet the urgent needs of new power systems for proactive, rapid, and economical inertia support; therefore, existing technologies suffer from frequency stability problems due to insufficient system inertia when high-proportion new energy sources are integrated into the power system. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide a method and device for optimizing the inertia support of gravity energy storage systems based on VSG control, thereby solving the frequency stability problem caused by insufficient system inertia when a high proportion of new energy sources are connected to the power system.

[0004] The objective of this invention can be achieved through the following technical solutions:

[0005] An optimization method for inertia support in a gravity energy storage system based on VSG control includes the following steps:

[0006] Obtain the real-time grid frequency and dynamically calculate the virtual inertia support power required by the gravity energy storage system based on the VSG control algorithm;

[0007] Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted.

[0008] A model predictive control method is adopted, with virtual inertia-supported power as the control target, and the power allocation strategy is continuously optimized. At the same time, the change data of the state of charge of the gravity energy storage system during the optimization process are recorded.

[0009] Based on grid frequency change data, VSG control parameter adjustment data, and state of charge change data, a cost model including initial investment cost and full life cycle frequency deviation penalty is established to solve and determine the optimal core capacity parameters of the gravity energy storage system.

[0010] The optimal core capacity parameter optimization results are fed back to the dynamic adjustment of control parameters, forming a closed-loop collaborative optimization between control parameters and core capacity parameters.

[0011] The VSG control algorithm employs dual closed-loop control, which includes an outer power tracking loop and an inner torque control loop.

[0012] The outer power tracking loop uses a second-order generalized integrator to track the grid frequency, calculates the grid angular frequency deviation and the rate of change of the grid angular frequency, and calculates the virtual inertia support power ΔP. vsg And generate feedforward compensation components;

[0013] The inner torque control loop will use virtual inertia to support the power ΔP. vsg The torque command is converted and synthesized with the feedforward compensation component, and then output to the drive motor of the gravity energy storage system after model reference adaptive control compensation.

[0014] Calculate the virtual inertia support power ΔP vsg Specifically, it includes the following steps:

[0015] Real-time acquisition of power grid frequency f;

[0016] Based on the VSG control algorithm, the virtual inertia support power is dynamically calculated using the swing equation. The specific calculation formula is as follows:

[0017]

[0018] In the formula, ΔP vsg Indicates the virtual inertia support power;

[0019] J vsg Represents the virtual moment of inertia;

[0020] ω represents the angular frequency of the power grid, ω = 2πf;

[0021] dω / dt represents the rate of change of the grid's angular frequency;

[0022] D p Indicates the damping coefficient;

[0023] Δω represents the angular frequency deviation of the power grid, Δω=ω-ω ref , where ω ref This indicates the rated angular frequency of the power grid.

[0024] The control parameters of VSG include virtual moment of inertia J. vsgInertial time constant H, damping coefficient D p and droop coefficient K p ;

[0025] Core capacity parameters include the mass of the object (m). opt .

[0026] Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted, specifically including the following steps:

[0027] Based on the grid frequency and the grid rated frequency, the frequency deviation Δf is calculated in real time.

[0028] The adjustment logic expression for the inertial time constant H is as follows:

[0029] H = H0 + k1 · |Δf|

[0030] In the formula, H0 represents the basic value of the time inertia constant; k1 is the adjustment coefficient of the inertia time constant, and k1 is a positive value;

[0031] Damping coefficient D p The adjustment logic expression is as follows:

[0032]

[0033] In the formula, D po k1 represents the base value of the damping coefficient; k2 is the adjustment coefficient of the damping coefficient, and k2 is a positive value.

[0034] Sag coefficient K p The adjustment logic expression is as follows:

[0035] K p =K po -k3|Δf|

[0036] In the formula, K p0 This represents the base value of the droop coefficient; k3 is the adjustment coefficient of the droop coefficient, and k3 is a positive value;

[0037] Virtual moment of inertia J vsg It is proportional to the inertial time constant H, and the specific expression is as follows:

[0038]

[0039] In the formula, S base This represents the reference power of the gravity energy storage system.

[0040] The model predictive control method is adopted, with virtual inertia-supported power as the control objective, and the power allocation strategy is continuously optimized. The specific steps include:

[0041] In the prediction time domain, an objective function is constructed that includes power deviation, frequency deviation, and state of charge. The expression of the objective function is as follows:

[0042]

[0043] In the formula, k represents the current time; T represents the prediction time domain; ΔPt represents the virtual inertia support power ΔP at time t. vsg The power deviation from the actual active power output of the gravity energy storage system; Δf t Represents the frequency deviation at time t; SOC t State of charge (SOC) represents the state of charge of the gravity energy storage system at time t. ref Indicates the target state of charge;

[0044] When optimizing the power allocation strategy, the physical operating constraints of the gravity energy storage system must also be met. These physical operating constraints include the height limit of the heavy object, the lifting speed limit, and the motor torque and power limit.

[0045] Establish a cost model that includes initial investment costs and frequency deviation penalties throughout the entire life cycle, and solve for the optimal core capacity parameters of the gravity energy storage system. This process includes the following steps:

[0046] The expression for the cost model Q(m) is as follows:

[0047]

[0048] In the formula, m represents the mass of the object; Represents the expectation operator; C inv (m) represents the initial investment cost associated with the mass m of the object; T life L represents the entire lifecycle of the system; L(·) represents the frequency deviation penalty function, the larger the frequency deviation, the higher the penalty value; f(t) represents the sequence of grid frequency changes over time; P g (t) represents the sequence of active power output by the gravity energy storage system over time;

[0049] The optimal mass of the object is found using stochastic dynamic programming, and the specific expression is as follows:

[0050]

[0051] In the formula, m opt This represents the optimal mass of the object.

[0052] When multiple gravity energy storage units operate in parallel to form a gravity energy storage system, a distributed coordination strategy based on a consensus algorithm is adopted, which specifically includes the following steps:

[0053] Each gravity energy storage unit exchanges output power information through a communication network between neighboring units;

[0054] The virtual inertia support power allocation weight of each gravity energy storage unit is dynamically adjusted based on the consensus algorithm.

[0055] The reactive circulating current between parallel units is suppressed by the circulating current suppression control strategy.

[0056] An inertia support optimization device for a gravity energy storage system based on VSG control, comprising:

[0057] The mechanical energy storage module, comprising a weight, a lifting mechanism, and a speed control device, is used to convert mechanical energy into electrical energy through the lifting and lowering of the weight.

[0058] The power conversion module, connected to the mechanical energy storage module, includes a bidirectional converter and a filter for bidirectional power exchange between electrical energy and the power grid.

[0059] The VSG controller module is used to execute the inertia support optimization method, dynamically adjust the virtual inertia parameters and optimize power allocation. When multiple devices are connected in parallel, it executes a distributed coordination strategy to achieve power balance and circulating current suppression.

[0060] The status monitoring module collects real-time data on grid frequency, output power, and state of charge, providing feedback to the control module.

[0061] The beneficial effects of this invention are:

[0062] This invention proposes an optimization method and device for inertia support in gravity energy storage systems based on VSG control. Addressing the problem of insufficient system inertia caused by a high proportion of renewable energy integration, it achieves multiple technological breakthroughs through innovative control strategies. The method employs a dual-closed-loop adaptive VSG control algorithm. The outer loop uses an improved second-order generalized integrator to achieve rapid and accurate tracking of the grid frequency, while the inner loop combines model reference adaptive control technology for dynamic torque compensation, significantly improving the system's response to frequency fluctuations. Regarding power coordination, a rolling optimization framework is constructed based on model predictive control, optimizing power allocation strategies while meeting inertia support requirements, effectively avoiding excessive consumption of the energy storage system. By establishing an optimization model that integrates initial investment costs and full lifecycle frequency deviation penalties, optimal configuration of the core parameters of the gravity energy storage system is achieved. For multi-unit parallel operation scenarios, a distributed coordination strategy based on a consensus algorithm is designed. Through information interaction between neighboring units, balanced power distribution and circulating current suppression are ensured, providing a reliable and efficient inertia support solution for high-proportion renewable energy power systems. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0065] Figure 2 This is a schematic diagram of the closed-loop collaborative optimization of the present invention;

[0066] Figure 3 This is a simulation result diagram comparing the performance of the method of this invention with that of the traditional method. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] like Figures 1 to 3 As shown, an optimization method for inertia support in a gravity energy storage system based on VSG control includes the following steps:

[0069] The system acquires real-time grid state parameters, including grid frequency, active power, and reactive power, and dynamically calculates the virtual inertia support power required by the gravity energy storage system based on the VSG control algorithm.

[0070] Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted.

[0071] A model predictive control method is adopted, with virtual inertia-supported power as the control target, and the power allocation strategy is continuously optimized. At the same time, the change data of the state of charge of the gravity energy storage system during the optimization process are recorded.

[0072] Based on grid frequency change data, VSG control parameter adjustment data, and state of charge change data, a cost model including initial investment cost and full life cycle frequency deviation penalty is established to solve and determine the optimal core capacity parameters of the gravity energy storage system.

[0073] The optimal core capacity parameter optimization results are fed back to the dynamic adjustment of control parameters, forming a closed-loop collaborative optimization between control parameters and core capacity parameters.

[0074] The VSG control algorithm employs dual closed-loop control, which includes an outer power tracking loop and an inner torque control loop.

[0075] The outer power tracking loop uses a second-order generalized integrator to track the grid frequency, calculates the grid angular frequency deviation and the rate of change of the grid angular frequency, and calculates the virtual inertia support power ΔP. vsg And generate feedforward compensation components;

[0076] The inner torque control loop will use virtual inertia to support the power ΔP. vsg The torque command is converted and synthesized with the feedforward compensation component, and then output to the drive motor of the gravity energy storage system after model reference adaptive control compensation.

[0077] A second-order generalized integrator (SOGI) is used to track the grid angular frequency in real time to obtain the grid angular frequency ω at a certain moment. g Then the feedforward compensation component T ff The expression is as follows:

[0078]

[0079] In the formula, k ff Indicates the feedforward gain coefficient;

[0080] The feedforward compensation component T ff With virtual inertia support power ΔP vsg Perform conversion and synthesis to T ref The expression is as follows:

[0081]

[0082] In the formula, ω m This indicates the actual angular velocity of the drive motor;

[0083] Model reference adaptive control is used to dynamically generate a compensation signal ΔT to correct nonlinear errors;

[0084] T ref The final driving torque is obtained by adding the compensation signal ΔT to the torque signal, and then input to the drive motor of the gravity energy storage system.

[0085] Calculate the virtual inertia support power ΔP vsg Specifically, it includes the following steps:

[0086] Real-time acquisition of power grid frequency f;

[0087] Based on the VSG control algorithm, the virtual inertia support power is dynamically calculated using the swing equation. The specific calculation formula is as follows:

[0088]

[0089] In the formula, ΔPvsg Indicates the virtual inertia support power;

[0090] J vsg Represents the virtual moment of inertia;

[0091] ω represents the angular frequency of the power grid, ω = 2πf;

[0092] dω / dt represents the rate of change of the grid's angular frequency;

[0093] D p Indicates the damping coefficient;

[0094] Δω represents the angular frequency deviation of the power grid, Δω=ω-ω ref , where ω ref This indicates the rated angular frequency of the power grid.

[0095] The control parameters of VSG include virtual moment of inertia J. vsg Inertial time constant H, damping coefficient D p and droop coefficient K p ;

[0096] Core capacity parameters include the mass of the object (m). opt .

[0097] Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted, specifically including the following steps:

[0098] Based on the grid frequency and the grid rated frequency, the frequency deviation Δf is calculated in real time.

[0099] The adjustment logic expression for the inertial time constant H is as follows:

[0100] H = H0 + k1 · |Δf|

[0101] In the formula, H0 represents the basic value of the time inertia constant; k1 is the adjustment coefficient of the inertia time constant, and k1 is a positive value;

[0102] Damping coefficient D p The adjustment logic expression is as follows:

[0103]

[0104] In the formula, D po k1 represents the base value of the damping coefficient; k2 is the adjustment coefficient of the damping coefficient, and k2 is a positive value.

[0105] Sag coefficient K p The adjustment logic expression is as follows:

[0106] K p =K po -k3|Δf|

[0107] In the formula, K p0 This represents the base value of the droop coefficient; k3 is the adjustment coefficient of the droop coefficient, and k3 is a positive value;

[0108] Virtual moment of inertia J vsg It is proportional to the inertial time constant H, and the specific expression is as follows:

[0109]

[0110] In the formula, S base This represents the reference power of the gravity energy storage system;

[0111] The model predictive control method is adopted, with virtual inertia-supported power as the control objective, and the power allocation strategy is continuously optimized. The specific steps include:

[0112] In the prediction time domain, an objective function is constructed that includes power deviation, frequency deviation, and state of charge. The expression of the objective function is as follows:

[0113]

[0114] In the formula, k represents the current time; T represents the prediction time domain; ΔPt represents the virtual inertia support power ΔP at time t. vsg The power deviation from the actual active power output of the gravity energy storage system; Δf t Represents the frequency deviation at time t; SOC t State of charge (SOC) represents the state of charge of the gravity energy storage system at time t. ref The target state of charge or reference state of charge is an ideal or desired state of charge that is preset in the gravity energy storage system. The target state of charge is usually set in the middle range of the energy storage system (such as 50% or 60%).

[0115] When optimizing the power allocation strategy, the physical operating constraints of the gravity energy storage system must also be met. These physical operating constraints include the height limit of the heavy object, the lifting speed limit, and the motor torque and power limit.

[0116] Establish a cost model that includes initial investment costs and frequency deviation penalties throughout the entire life cycle, and solve for the optimal core capacity parameters of the gravity energy storage system. This process includes the following steps:

[0117] The expression for the cost model Q(m) is as follows:

[0118]

[0119] In the formula, m represents the mass of the object; Represents the expectation operator; C inv (m) represents the initial investment cost associated with the mass m of the object; T lifeL represents the entire lifecycle of the system; L(·) represents the frequency deviation penalty function, the larger the frequency deviation, the higher the penalty value; f(t) represents the sequence of grid frequency changes over time; P g (t) represents the sequence of active power output by the gravity energy storage system over time;

[0120] The optimal mass of the object is found using stochastic dynamic programming, and the specific expression is as follows:

[0121]

[0122] In the formula, m opt This represents the optimal mass of the object.

[0123] When multiple gravity energy storage units operate in parallel to form a gravity energy storage system, a distributed coordination strategy based on a consensus algorithm is adopted, which specifically includes the following steps:

[0124] Each gravity energy storage unit exchanges output power information through a communication network between neighboring units;

[0125] The virtual inertia support power allocation weight of each gravity energy storage unit is dynamically adjusted based on the consensus algorithm to achieve a balanced distribution of output power.

[0126] By employing a circulating current suppression control strategy, reactive circulating current between parallel units is suppressed to ensure stable system operation.

[0127] Its control law can be simplified to:

[0128]

[0129] In the formula, u i Additional control signals for the i-th gravity energy storage unit;

[0130] f i P i Let represent the local frequency and power of the i-th gravity energy storage unit, respectively;

[0131] N i Let represent the set of adjacent units of the i-th gravity energy storage unit;

[0132] a ij Indicates communication weight.

[0133] An inertia support optimization device for a gravity energy storage system based on VSG control, comprising:

[0134] The mechanical energy storage module, comprising a weight, a lifting mechanism, and a speed control device, is used to convert mechanical energy into electrical energy through the lifting and lowering of the weight.

[0135] The power conversion module, connected to the mechanical energy storage module, includes a bidirectional converter and a filter for bidirectional power exchange between electrical energy and the power grid.

[0136] The VSG controller module is used to execute the inertia support optimization method, dynamically adjust the virtual inertia parameters and optimize power allocation. When multiple devices are connected in parallel, it executes a distributed coordination strategy to achieve power balance and circulating current suppression.

[0137] The status monitoring module collects real-time data on grid frequency, output power, and state of charge, providing feedback to the control module.

[0138] Preferably, the lifting mechanism includes either a winch or a guide rail, which is driven by an electric motor to lift and lower the load, thereby storing and releasing energy.

[0139] Preferably, the power conversion module is typically a power electronic converter, which connects the motor to the power grid to achieve bidirectional conversion between electromechanical energy and electrical energy.

[0140] This invention proposes an optimization method and device for inertia support in gravity energy storage systems based on VSG control. Addressing the problem of insufficient system inertia caused by a high proportion of renewable energy integration, it achieves multiple technological breakthroughs through innovative control strategies. The method employs a dual-closed-loop adaptive VSG control algorithm. The outer loop uses an improved second-order generalized integrator to achieve rapid and accurate tracking of the grid frequency, while the inner loop combines model reference adaptive control technology for dynamic torque compensation, significantly improving the system's response to frequency fluctuations. Regarding power coordination, a rolling optimization framework is constructed based on model predictive control, optimizing power allocation strategies while meeting inertia support requirements, effectively avoiding excessive consumption of the energy storage system. By establishing an optimization model that integrates initial investment costs and full lifecycle frequency deviation penalties, optimal configuration of the core parameters of the gravity energy storage system is achieved. For multi-unit parallel operation scenarios, a distributed coordination strategy based on a consensus algorithm is designed. Through information interaction between neighboring units, balanced power distribution and circulating current suppression are ensured, providing a reliable and efficient inertia support solution for high-proportion renewable energy power systems.

[0141] like Figure 3 As shown in the figure, this diagram compares the performance of the proposed method with that of the traditional fixed-parameter VSG method under the same grid frequency disturbance. The simulation results comparing the proposed method (solid line) and the traditional fixed-parameter VSG control method (dashed line) under the same grid frequency disturbance are as follows:

[0142] Traditional method (dashed line): The maximum frequency deviation reaches -0.65Hz, and the system takes about 12 seconds to recover stability;

[0143] The method of this invention (solid line): the maximum frequency deviation is only -0.45Hz, the deviation amplitude is reduced by about 30%, and the system recovers to stability within 8 seconds, with the recovery speed improved by about 33%;

[0144] Simulation results show that the adaptive optimization control method of the present invention is significantly better than the traditional method in suppressing frequency drop amplitude and accelerating system recovery speed.

[0145] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0146] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for optimizing the inertia support of a gravity energy storage system based on VSG control, characterized in that, Includes the following steps: Obtain the real-time grid frequency and dynamically calculate the virtual inertia support power required by the gravity energy storage system based on the VSG control algorithm; Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted. A model predictive control method is adopted, with virtual inertia-supported power as the control target, and the power allocation strategy is continuously optimized. At the same time, the change data of the state of charge of the gravity energy storage system during the optimization process are recorded. Based on grid frequency change data, VSG control parameter adjustment data, and state of charge change data, a cost model including initial investment cost and full life cycle frequency deviation penalty is established to solve and determine the optimal core capacity parameters of the gravity energy storage system. The optimal core capacity parameter optimization results are fed back to the dynamic adjustment of control parameters, forming a closed-loop collaborative optimization between control parameters and core capacity parameters. The VSG control algorithm employs dual closed-loop control, which includes an outer power tracking loop and an inner torque control loop. The outer power tracking loop uses a second-order generalized integrator to track the grid frequency, calculates the grid angular frequency deviation and the rate of change of the grid angular frequency, and calculates the virtual inertia-supported power Δ. P vsg And generate feedforward compensation components; The inner loop torque control loop will use virtual inertia to support power Δ P vsg The torque command is converted and synthesized with the feedforward compensation component, and then output to the drive motor of the gravity energy storage system after model reference adaptive control compensation.

2. The inertia support optimization method for gravity energy storage systems according to claim 1, characterized in that, Calculate the virtual inertia support power Δ P vsg Specifically, it includes the following steps: Real-time acquisition of power grid frequency f ; Based on the VSG control algorithm, the virtual inertia support power is dynamically calculated using the swing equation. The specific calculation formula is as follows: In the formula, Δ P vsg Indicates the virtual inertia support power; J vsg Represents the virtual moment of inertia; ω Indicates the angular frequency of the power grid. ; dω / dt Indicates the rate of change of the grid angular frequency; D p Indicates the damping coefficient; Δ ω Indicates the angular frequency deviation of the power grid. ,in ω ref This indicates the rated angular frequency of the power grid.

3. The inertia support optimization method for gravity energy storage systems according to claim 1, characterized in that, VSG control parameters include virtual moment of inertia. J vsg Inertial time constant H Damping coefficient D p and droop coefficient K p ; Core capacity parameters include the mass of the heavy object. m opt .

4. The inertia support optimization method for gravity energy storage systems according to claim 3, characterized in that, Based on the grid frequency and virtual inertia-supported power, the control parameters of the VSG are dynamically adjusted, specifically including the following steps: Based on the grid frequency and the grid rated frequency, the frequency deviation Δ is calculated in real time. f ; Inertial time constant H The adjustment logic expression is as follows: In the formula, H 0 Represents the fundamental value of the time inertia constant; k 1 This is the adjustment coefficient for the inertial time constant. k 1 It is a positive value; Damping coefficient D p The adjustment logic expression is as follows: In the formula, D po This represents the basic value of the damping coefficient; k 2 This is the adjustment coefficient for the damping coefficient. k 2 It is a positive value; Sag coefficient K p The adjustment logic expression is as follows: In the formula, K p0 This represents the base value of the droop coefficient; k 3 This is the adjustment coefficient for the droop coefficient. k 3 It is a positive value; Virtual moment of inertia J vsg With inertial time constant H It is directly proportional, and the specific expression is as follows: In the formula, S base This represents the reference power of the gravity energy storage system.

5. The inertia support optimization method for gravity energy storage systems according to claim 4, characterized in that, The model predictive control method is adopted, with virtual inertia-supported power as the control objective, and the power allocation strategy is continuously optimized. The specific steps include: In the prediction time domain, an objective function is constructed that includes power deviation, frequency deviation, and state of charge. The expression of the objective function is as follows: In the formula, k Indicates the current time; T Indicates the prediction time domain; Δ Pt express t Moment-time virtual inertia support power Δ P vsg The power deviation from the actual active power output of the gravity energy storage system; Δ f t express t Frequency deviation at any given moment; SOC t express t The state of charge of the gravity energy storage system at any given time; SOC ref Indicates the target state of charge; When optimizing the power allocation strategy, the physical operating constraints of the gravity energy storage system must also be met. These physical operating constraints include the height limit of the heavy object, the lifting speed limit, and the motor torque and power limit.

6. The inertia support optimization method for gravity energy storage systems according to claim 5, characterized in that, Establish a cost model that includes initial investment costs and frequency deviation penalties throughout the entire life cycle, and solve for the optimal core capacity parameters of the gravity energy storage system. This process includes the following steps: Build a cost model Q ( m The expression is as follows: In the formula, m Indicates the mass of the object; [⋅] denotes the expectation operator; C inv ( m () indicates the mass of the heavy object m Related initial investment costs; T life Represents the entire lifecycle of the system; L (⋅) represents the frequency deviation penalty function; the larger the frequency deviation, the higher the penalty value. f ( t This represents a sequence of changes in the power grid frequency over time. P g ( t This represents the sequence of active power output from a gravity energy storage system over time. The optimal mass of the object is found using stochastic dynamic programming, and the specific expression is as follows: In the formula, m opt This represents the optimal mass of the object.

7. The inertia support optimization method for gravity energy storage systems according to claim 6, characterized in that, When multiple gravity energy storage units operate in parallel to form a gravity energy storage system, a distributed coordination strategy based on a consensus algorithm is adopted, which specifically includes the following steps: Each gravity energy storage unit exchanges output power information through a communication network between neighboring units; The virtual inertia support power allocation weight of each gravity energy storage unit is dynamically adjusted based on the consensus algorithm. The reactive circulating current between parallel units is suppressed by the circulating current suppression control strategy.

8. An inertia support optimization device for a gravity energy storage system based on VSG control, characterized in that, include: The mechanical energy storage module, comprising a weight, a lifting mechanism, and a speed control device, is used to convert mechanical energy into electrical energy through the lifting and lowering of the weight. The power conversion module, connected to the mechanical energy storage module, includes a bidirectional converter and a filter for bidirectional power exchange between electrical energy and the power grid. The VSG controller module is used to execute the inertia support optimization method according to any one of claims 1-7, dynamically adjust the virtual inertia parameters and optimize power allocation, and execute a distributed coordination strategy when multiple devices are connected in parallel to achieve power balance and circulating current suppression. The status monitoring module collects real-time data on grid frequency, output power, and state of charge, providing feedback to the control module.

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