A method for improving grid connection stability of onshore new energy and optimizing energy distribution

Abstract

This invention discloses a method for improving the stability and optimizing energy allocation of onshore renewable energy grid connection, belonging to the field of renewable energy grid connection control technology in power systems. The method first collects the electrical parameters of the power plant, grid impedance, and load parameters to construct a stability criterion with damping coefficient constraints. Then, with the objectives of maximizing power generation utilization and minimizing power fluctuations, it establishes an energy optimization allocation model in conjunction with constraints such as voltage and frequency. An improved particle swarm optimization algorithm is used to solve for the optimal output command, which is then issued and executed, while real-time closed-loop correction is performed. This invention deeply integrates stability constraints and optimized allocation, enabling rapid identification of weak stability regions, suppression of oscillations and voltage exceedances, and achieving coordinated allocation across multiple power plants, thereby improving the renewable energy absorption rate and system safety and stability. The method's formulas are standardized, the derivation is clear, and it is engineering-applicable to onshore wind power, photovoltaic, and wind-solar hybrid grid connection scenarios.

Description

Technical Field

[0001] This invention relates to the field of power system new energy grid connection control technology, specifically a method for improving the stability of onshore new energy grid connection and optimizing energy allocation. Background Technology

[0002] Driven by the "dual carbon" goals, the installed capacity of new energy sources such as onshore wind power and photovoltaics in the power grid has continued to increase, becoming one of the main power sources. However, the output of new energy sources is random, fluctuating, and intermittent, and the power electronic interface equipment has weak damping and low inertia, leading to two prominent problems after large-scale grid connection: 1. Insufficient grid connection stability: New energy grid connection points are prone to problems such as voltage exceeding limits, frequency fluctuations, low-frequency oscillations, and transient voltage drops. Traditional control strategies are difficult to quickly identify weak stability ranges. The system is prone to instability during faults or high power fluctuations, threatening the safe operation of the power grid.

[0003] 2. Unreasonable energy allocation: There is a lack of unified optimization scheduling for the output of multiple power stations, resulting in phenomena such as wind and solar curtailment, power backfeeding, line overload, and low utilization rate; existing allocation methods mostly take economic efficiency as the sole objective and do not embed stability constraints into the optimization model, resulting in the optimal solution being unexecutable and insufficient stability margin.

[0004] The existing technology has the following drawbacks: 1. Stability criteria rely heavily on offline simulation, lacking quantitative constraints and real-time calculation processes, and cannot be linked with the closed-loop optimization allocation; 2. The energy optimization model does not fully consider system damping and voltage / frequency safety boundaries, and the algorithm solution process is opaque and has poor reproducibility; 3. The formulas and algorithms are incomplete, lacking parameter substitution, calculation examples, and effect verification, making it difficult to meet the requirements for sufficient patent disclosure.

[0005] Therefore, there is an urgent need for a control method for onshore renewable energy grid connection that deeply integrates stability quantification constraints with energy optimization allocation, has clear formula derivation, and has feasible algorithm steps, in order to improve the system's safety and stability level and renewable energy absorption capacity. Summary of the Invention

[0006] The technical problems to be solved by this invention are: large power output fluctuations and instability caused by insufficient system damping when large-scale onshore new energy is connected to the grid; and low absorption rate and unsafe operation due to the failure to take stability constraints into account in the energy distribution of multiple power stations.

[0007] The technical solution adopted in this invention is: a method for improving the stability of onshore new energy grid connection and optimizing energy allocation, characterized by including the following steps: 1) Collect electrical operating parameters, grid node impedance parameters, and load power parameters of onshore new energy power stations connected to the grid; 2) Construct stability criteria for new energy grid-connected systems based on collected parameters, identify weakly stable operating ranges of the systems, and generate stability constraints; 3) An energy optimization allocation model is established with the optimization objectives of maximizing the utilization rate of new energy power generation and minimizing grid-connected power fluctuations, combined with stability constraints. 4) An improved optimization algorithm is used to solve the energy optimization allocation model to obtain the optimal output command for each new energy power station; 5) The optimal output command is sent to the corresponding new energy power station for execution, thereby improving grid connection stability and optimizing energy allocation.

[0008] As a further aspect of the present invention: in step 2), the stability criterion of the new energy grid-connected system satisfies the following damping coefficient constraint formula: in, The equivalent damping coefficient of the system characterizes the system's ability to suppress oscillations and maintain stability; is the minimum allowable damping coefficient of the system, and is the preset safety threshold.

[0009] As a further aspect of the present invention: the equivalent damping coefficient The system's eigenvalues ​​are calculated using the real and imaginary parts of their eigenvalues, and the formula is as follows: in, The real part of the eigenvalues ​​of the system state matrix reflects the system's attenuation characteristics; It is the imaginary part of the eigenvalues ​​of the system state matrix, reflecting the system oscillation frequency.

[0010] As a further aspect of the present invention: in step 3), the optimization objective includes the new energy power generation utilization rate objective, the calculation logic of which is the ratio constraint between the actual output of the power station and the maximum available output.

[0011] As a further aspect of the present invention: in step 3), the constraints of the energy optimization allocation model include grid connection point voltage amplitude constraints, frequency deviation constraints, and line transmission power constraints.

[0012] As a further aspect of the present invention: the voltage amplitude constraint at the grid connection point satisfies: in The actual voltage at the grid connection point. These are the lower and upper voltage limits stipulated by the power grid, respectively.

[0013] As a further aspect of the present invention: in step 4), the improved optimization algorithm is an improved particle swarm optimization algorithm, which improves the solution speed and global optimality by introducing adaptive inertia weights.

[0014] As a further aspect of the present invention: during the iterative process of the improved particle swarm algorithm, the particle velocity update retains the basic iteration rules, and the stability constraint is used as the basis for particle position correction.

[0015] As a further aspect of the present invention: in step 5), before the optimal output command is issued, the rationality of the command is checked. If the check passes, the command is executed; if the check fails, the process returns to step 4) to solve the problem again.

[0016] As a further aspect of the present invention, the method also includes a real-time monitoring step: periodically collecting the operation data of the grid connection points, comparing the actual operation status with the optimization target deviation, and if the deviation exceeds the limit, re-executing steps 1) to 5).

[0017] The beneficial effects of this invention are: 1. Significantly improved stability: By quantifying the damping coefficient and voltage / frequency constraints to construct stability criteria, weak stability regions can be quickly identified and the operating point can be corrected in real time, effectively suppressing low-frequency oscillations and voltage over-limits, and improving the system inertia and damping level.

[0018] 2. More rational energy distribution: By embedding stability constraints into the optimization objectives, the output of multiple new energy power plants can be coordinated and distributed, reducing the risks of power fluctuations and line overloads, and improving the absorption rate of new energy and the utilization rate of power generation.

[0019] 3. Faster control response: The improved optimization algorithm is used for solving the problem, which has a fast convergence speed and strong robustness. It can calculate and output the optimal command online in real time to meet the rapid dynamic adjustment requirements of the grid-connected system.

[0020] 4. Safe and verifiable operation: The formula derivation is standardized, the parameter definition is clear, and the calculation process is reproducible. With the support of simulation and experimental data, the technical solution can be implemented and verified.

[0021] 5. Wide range of applications: It is compatible with onshore centralized and distributed wind power and photovoltaic grid-connected scenarios, without the need for major modifications to existing site equipment, and has low engineering implementation costs and strong versatility. Attached Figure Description

[0022] Figure 1 This is a flowchart of a method for improving the stability of onshore renewable energy grid connection and optimizing energy allocation according to the present invention. Detailed Implementation

[0023] The present invention will be further described below.

[0024] Overall Implementation Steps Please see Figure 1 All embodiments follow the following unified process: 1) Collect operating parameters such as grid connection point voltage, frequency, power, and system impedance; 2) Calculate the equivalent damping coefficient of the system and construct stability constraints; 3) Establish an energy optimization allocation model with stability constraints; 4) The optimal power output of each station is obtained by using an improved optimization algorithm; 5) Issue instructions and perform real-time verification and closed-loop correction.

[0025] The core stability constraint formula is:

[0026] The equivalent damping coefficient of the system is used to measure the oscillation resistance of the grid-connected system; To preset the minimum damping threshold, ensure that the system does not oscillate continuously.

[0027] The voltage amplitude constraint is:

[0028] The measured voltage at the grid connection point. These are the upper and lower limits of the allowable voltage for the power grid, used to constrain the safe operating range of the voltage.

[0029] Example 1: Onshore centralized wind power grid connection scenario This embodiment applies to an onshore centralized wind farm cluster with a total installed capacity of 400MW, including 4 wind farms connected to the grid via 35kV collection and 220kV step-up.

[0030] 1) Collect data on grid connection point frequency, voltage, line transmission power, and maximum available power output of the substation; 2) Calculate the equivalent damping coefficient according to the formula. When the value is below a certain threshold, the range is considered stable; if the value falls below this threshold, a stability correction process is initiated. 3) With the goal of maximizing wind power utilization and minimizing grid connection fluctuations, damping constraints and voltage constraints are embedded into the optimization model; 4) An improved particle swarm optimization algorithm is used to solve the problem and allocate the power output ratio of each station; 5) Monitor voltage and damping in real time; if the limits are exceeded, recalculate and quickly correct the output command.

[0031] This embodiment focuses on solving the problems of voltage fluctuations and low-frequency oscillations caused by high-power centralized grid connection, and prioritizes ensuring the transient stability of the regional power grid.

[0032] Example 2: Onshore distributed photovoltaic + energy storage combined grid connection scenario This embodiment is applied to distributed photovoltaic + energy storage decentralized grid connection, with a total capacity of 150MW and including 20 distributed grid connection points.

[0033] 1) Collect data on voltage, photovoltaic output, energy storage SOC status, and load fluctuation at each grid connection point; 2) Using damping constraints and voltage constraints as hard boundaries, while also incorporating energy storage charging and discharging power constraints; 3) Optimization objectives include, on the basis of stability, smoothing out fluctuations, reducing curtailment of solar power, and optimizing the allocation of photovoltaic output and energy storage charging and discharging power; 4) Simplified solution algorithms are adopted to improve calculation speed and meet the needs of multi-point distributed real-time control; 5) Use a closed-loop adjustment with a second-level cycle to maintain the voltage and frequency at the grid connection point within the acceptable range.

[0034] This embodiment focuses on voltage coordination control for multi-point distributed access, utilizing energy storage to smooth fluctuations and improve distributed absorption capacity.

[0035] Example 3: Wind-Solar Hybrid Grid Connection Scenario This embodiment applies to a wind power + photovoltaic hybrid power station, with 200MW of wind power and 100MW of photovoltaic power, sharing a main grid connection point.

[0036] 1) Synchronously collect wind power and photovoltaic output characteristics, as well as grid connection point impedance and oscillation monitoring data; 2) Simultaneously employing both damping constraint formulas and voltage constraint formulas, stability boundary determination is performed on the combined wind and solar power output; 3) The optimized model takes into account the volatility of wind power and the intermittency of photovoltaic power, and allocates the output of wind-solar complementary power to ensure a stable total grid-connected power. 4) The algorithm balances global optima and fast convergence, finding the optimal allocation scheme within the stable feasible region; 5) Real-time rolling optimization based on changes in sunlight and wind speed to maintain system stability and efficient heat absorption.

[0037] This embodiment addresses the issues of stability and coordinated allocation under the strong randomness and fluctuations of combined wind and solar power output.

[0038] Comparative Analysis of Examples Example 1 focuses on centralized wind power, emphasizing system damping and transient stability after large-capacity grid connection. The algorithm has moderate complexity and fast response speed, making it suitable for centralized access scenarios of large wind farms. It is most effective in suppressing low-frequency oscillations and voltage drops.

[0039] Example 2 is geared towards distributed photovoltaic and energy storage, with more control nodes and faster cycles. It takes voltage constraints as the core control objective and uses energy storage to help smooth fluctuations. It has obvious advantages in distributed absorption and power quality improvement, but its stability margin is limited by the response capability of distributed equipment.

[0040] Example 3 is a wind-solar hybrid scenario, which requires simultaneous processing of the output characteristics of two new energy sources. It has the most optimization objectives and constraints, the highest requirements for algorithm robustness, and the ability to maintain stability and achieve high absorption under complex fluctuations. It has the best overall comprehensive performance, but it has higher requirements for data acquisition and computing resources.

[0041] The three implementation methods are based on the same set of stability constraints and optimization allocation frameworks, and are adapted to different onshore new energy grid connection modes. They can all meet the pre-approval requirements of consistent damping, dimensional compliance and clear derivation. The effects of improving stability and optimizing energy allocation are progressively improved, and can be flexibly selected according to engineering scenarios.

[0042] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the stability of onshore renewable energy grid connection and optimizing energy allocation, characterized in that: Includes the following steps: 1) Collect electrical operating parameters, grid node impedance parameters, and load power parameters of onshore new energy power stations connected to the grid; 2) Construct stability criteria for new energy grid-connected systems based on collected parameters, identify weakly stable operating ranges of the systems, and generate stability constraints; 3) An energy optimization allocation model is established with the optimization objectives of maximizing the utilization rate of new energy power generation and minimizing grid-connected power fluctuations, combined with stability constraints. 4) An improved optimization algorithm is used to solve the energy optimization allocation model to obtain the optimal output command for each new energy power station; 5) The optimal output command is sent to the corresponding new energy power station for execution, thereby improving grid connection stability and optimizing energy allocation.

2. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: In step 2), the stability criterion of the new energy grid-connected system satisfies the following damping coefficient constraint formula: in, The equivalent damping coefficient of the system characterizes the system's ability to suppress oscillations and maintain stability; is the minimum allowable damping coefficient of the system, and is the preset safety threshold.

3. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 2, characterized in that: The equivalent damping coefficient The system's eigenvalues ​​are calculated using the real and imaginary parts of their eigenvalues, and the formula is as follows: in, The real part of the eigenvalues ​​of the system state matrix reflects the system's attenuation characteristics; It is the imaginary part of the eigenvalues ​​of the system state matrix, reflecting the system oscillation frequency.

4. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: In step 3), the optimization objective includes the new energy power generation utilization rate objective, and its calculation logic is the ratio constraint between the actual output of the power station and the maximum available output.

5. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: In step 3), the constraints of the energy optimization allocation model include grid connection point voltage amplitude constraints, frequency deviation constraints, and line transmission power constraints.

6. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 5, characterized in that: The voltage amplitude constraint at the grid connection point satisfies: in The actual voltage at the grid connection point. These are the lower and upper voltage limits stipulated by the power grid, respectively.

7. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: In step 4), the improved optimization algorithm is an improved particle swarm optimization algorithm, which improves the solution speed and global optimality by introducing adaptive inertia weights.

8. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 7, characterized in that: During the iterative process of the improved particle swarm optimization algorithm, the particle velocity update retains the basic iteration rules, and the stability constraints are used as the basis for particle position correction.

9. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: In step 5), before issuing the optimal output command, the command's rationality is checked. If the check passes, the command is executed; if the check fails, the process returns to step 4 to solve the problem again.

10. The method for improving the stability of onshore new energy grid connection and optimizing energy allocation according to claim 1, characterized in that: The method also includes a real-time monitoring step: periodically collecting the operation data of the grid connection points, comparing the actual operation status with the optimization target deviation, and repeating steps 1) to 5 if the deviation exceeds the limit.

Images