Method for scheduling operation of generator

Abstract

The invention relates to a method for dispatching the operation of a generator, and the method builds an AC / DC hybrid microgrid model based on a grey wolf algorithm, and the model integrates a plurality of power supplies such as photovoltaic power generation, a fan, a diesel generator and the like, and adapts to two operation states of grid connection and island. By setting objective functions and corresponding processing strategies in different operation states, the existing particle swarm optimization and genetic algorithm are optimized, and the optimization algorithm is further applied to the multi-generator system. The maximum frequency deviation of the system under disturbance is obtained by deducing the standard form of the primary frequency response characteristic of the power system and converting the standard form to the time domain. Therefore, a robust scheduling model based on improved frequency response is established, and a clear optimization target and constraint conditions are set, so that robust interval power scheduling of the power system is realized, and the stability and the safety of the system are ensured to be effectively guaranteed when the micro-grid fluctuates.

Description

Technical Field

[0001] The present invention relates to the field of methods for scheduling the operation of generators, and in particular to methods for scheduling the operation of generators. Background Art

[0002] An AC / DC hybrid microgrid refers to a small power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, related loads, and monitoring and protection devices. It is an autonomous system that can achieve self-control, protection, and management. Depending on the different distributed power sources, it includes both DC bus and AC bus. The microgrid can ensure the stable operation of the microgrid through the coordinated control of the output power of the distributed power sources in the microgrid; the microgrid energy management system can effectively maintain the optimal distribution and balance of energy in the microgrid to ensure the economic operation of the microgrid. Microgrids generally have the advantages of high energy utilization, high energy supply reliability, low pollutant emissions, and good operating economy.

[0003] In the field of electricity, the development and application of AC / DC hybrid microgrids are becoming more and more extensive, integrating multiple power sources such as photovoltaic power generation, wind turbines, and diesel generators. However, as the complexity and diversity of microgrids continue to increase, how to effectively dispatch the operation of generators to ensure the stability and safety of the system has become an urgent problem to be solved.

[0004] Existing technical solutions, such as particle swarm optimization and genetic algorithm, can achieve the dispatch of generators to a certain extent, but their dispatch effect and optimization ability still need to be improved when facing complex situations such as multi-generator systems and microgrid fluctuations. Especially when the microgrid fluctuates, how to ensure that the fluctuation is always within the safety limit is a technical problem that the existing technical solutions need to focus on.

[0005] Based on this, the present invention is proposed. Summary of the invention

[0006] The specific technical solutions of the present invention are as follows:

[0007] A method for scheduling the operation of generators, which constructs an AC / DC hybrid microgrid model based on the Grey Wolf Algorithm, aims to solve the problems of insufficient scheduling effect and optimization capability of existing technologies when facing complex situations such as multi-generator systems and microgrid fluctuations in AC / DC hybrid microgrids, so as to improve the stability and security of the power system.

[0008] To achieve the above objectives, the present invention first constructs an AC / DC hybrid microgrid model that includes multiple power sources such as photovoltaic power generation, wind turbines, and diesel generators, and enables it to flexibly switch between two operating states: grid-connected and islanded. On this basis, the present invention sets the objective function under different operating states, takes into account multiple constraints, and provides corresponding processing strategies to optimize the existing particle swarm algorithm and genetic algorithm.

[0009] Furthermore, the present invention applies the optimized algorithm to a multi-generator system and derives a standard form of the primary frequency response characteristic of the power system. By converting the standard form to the time domain and deriving the deviation between the generator rotor speed and the synchronous speed, the present invention can accurately obtain the maximum frequency deviation of the multi-generator system under disturbance.

[0010] Based on the above maximum frequency deviation, the present invention further establishes a robust interval dispatching model based on the improved frequency response characteristics of the generator, and clarifies its optimization objectives and constraints. Finally, according to these optimization objectives and constraints, the present invention performs robust interval power dispatching on the power system, thereby ensuring that the stability and security of the system are effectively guaranteed when the microgrid fluctuates.

[0011] Through the above technical scheme, the present invention realizes efficient scheduling of generator operation in AC / DC hybrid microgrid. Compared with the prior art, the present invention shows a more superior scheduling effect and optimization capability when facing complex situations such as multi-generator systems and microgrid fluctuations. Especially when the microgrid fluctuates, the present invention can ensure that the fluctuation is always within the safety limit, thereby significantly improving the stability and safety of the power system. In addition, the implementation of the present invention also has good universality and scalability, and can be widely used in fields such as power dispatching, smart grid or renewable energy. DETAILED DESCRIPTION

[0012] The present invention will be further explained below in conjunction with specific embodiments.

[0013] In the present invention, the gray wolf algorithm is an optimization algorithm that simulates the hunting behavior of gray wolf groups and is used to solve complex optimization problems; the grid-connected state refers to the state in which the microgrid is connected to the main grid and runs synchronously; the island state refers to the state in which the microgrid is disconnected from the main grid and runs independently; the particle swarm algorithm and the genetic algorithm are two common intelligent optimization algorithms used to solve various optimization problems; the robust interval refers to the parameter range in which the system can maintain stable operation.

[0014] The present invention provides a method for scheduling the operation of a generator, which is mainly used in an AC / DC hybrid microgrid. The method is based on the Gray Wolf Algorithm, and optimizes the existing particle swarm algorithm and genetic algorithm by establishing objective functions and constraints under different operating states, and extends them to a multi-generator system. By deriving the standard form of the primary frequency response characteristics of the power system and converting it to the time domain, the maximum frequency deviation of the multi-generator system under disturbance is obtained. Furthermore, a scheduling model based on the robust interval of the improved frequency response characteristics of the generator is established, the optimization objectives and constraints are determined, and robust interval power scheduling is performed on the power system. When the microgrid fluctuates, the method can ensure that the fluctuation is always within the safety limit, thereby improving the stability and safety of the power system. In specific implementation, the algorithm parameters and optimization objectives can be adjusted according to actual needs to adapt to the power scheduling needs in different scenarios.

[0015] Example 1

[0016] The method for scheduling the operation of a generator comprises the following steps:

[0017] Step 101. In the embodiment of the present invention, the objective functions under different operating states are first set. The AC / DC hybrid microgrid has two operating states: grid-connected and islanded. For these two states, corresponding objective functions are constructed respectively. In the grid-connected state, the objective function mainly considers factors such as the interaction power between the microgrid and the main grid, the power balance within the microgrid, and the operating cost of the generator. In the islanded state, the objective function focuses more on factors such as the power balance within the microgrid, voltage stability, and the operating efficiency of the generator.

[0018] Step 102: In the embodiment of the present invention, the constraints are taken into account and corresponding processing strategies are given. The constraints include the output constraints of the generator, the power balance constraints of the microgrid, the stability constraints of the voltage and frequency, etc. In view of these constraints, the embodiment of the present invention adopts the penalty function method to process and convert the constraints into a part of the objective function, thereby converting the constrained optimization problem into an unconstrained optimization problem for solution.

[0019] Step 103, in an embodiment of the present invention, the existing particle swarm algorithm and genetic algorithm are optimized. Particle swarm algorithm and genetic algorithm are two typical intelligent optimization algorithms, which have good global search capabilities and convergence speed when solving complex optimization problems. However, these two algorithms also have certain limitations when dealing with specific problems. Therefore, the embodiment of the present invention improves and optimizes these two algorithms to improve their solution efficiency and accuracy. Specifically, for the particle swarm algorithm, the embodiment of the present invention introduces strategies such as dynamically adjusting inertia weight and acceleration coefficient to balance the global search capability and local search capability of the algorithm. For the genetic algorithm, the embodiment of the present invention adopts strategies such as adaptively adjusting the crossover rate and mutation rate to improve the convergence speed and solution accuracy of the algorithm.

[0020] Step 104: In an embodiment of the present invention, the optimized algorithm is extended to a multi-generator system. The multi-generator system is a complex nonlinear system whose dynamic characteristics are affected by many factors. In order to obtain the maximum frequency deviation of the multi-generator system under disturbance, the embodiment of the present invention applies the optimized algorithm to the system, and obtains key information such as the deviation between the generator rotor speed and the synchronous speed and its derivative by solving the corresponding optimization problem. This information is of great significance for evaluating the stability of the system and formulating corresponding control strategies.

[0021] Step 105. In an embodiment of the present invention, the optimization objectives and constraints of the dispatch model of the robust interval based on the improved frequency response characteristics of the generator are determined. Robust interval power dispatch is a dispatch method that takes system uncertainty into consideration, and its goal is to improve the economy and reliability of the system as much as possible while ensuring the safe and stable operation of the system. To achieve this goal, the embodiment of the present invention constructs a corresponding robust interval dispatch model based on the improved frequency response characteristics of the generator, and determines its optimization objectives and constraints.

[0022] Step 106. In an embodiment of the present invention, the power system is robustly dispatched in intervals according to the optimization objectives and constraints. Specifically, the embodiment of the present invention uses an optimized intelligent algorithm to solve the robust interval dispatch model to obtain the optimal output plan of each generator. The plan not only meets the constraints such as power balance and stable operation of the system, but also fully considers factors such as uncertainty and economy of the system. Therefore, in actual operation, the plan can effectively deal with uncertain factors such as microgrid fluctuations and ensure that fluctuations are always within safety limits.

[0023] Through the implementation of the above steps, the method provided by the embodiment of the present invention can effectively optimize the dispatching operation of generators in AC / DC hybrid microgrids. Specifically, the method achieves the improvement and optimization of the existing typical intelligent optimization algorithm by setting the objective function and taking into account the constraints under different operating states and giving the corresponding processing strategy; further, the optimized algorithm is extended to a multi-generator system and key information is obtained, which provides strong support for evaluating system stability and formulating control strategies; finally, the optimal output plan of each generator is obtained by determining the optimization objectives and constraints of the dispatching model based on the robust interval of the improved frequency response characteristics of the generator and solving them. In actual operation, the plan can effectively deal with uncertain factors and ensure the safe and stable operation of the system, thereby improving the economy and reliability of the entire microgrid system.

[0024] Example 2

[0025] A method for optimizing the frequency response of a power system, the method may include the following steps:

[0026] Step 201, in the embodiment of the present invention, firstly, an AC / DC hybrid microgrid model based on the Grey Wolf Algorithm is established. The model includes multiple power generation units such as photovoltaic power generation, wind turbines, and diesel generators, and considers the characteristics of the microgrid in two operating states: grid-connected and islanded. For these two states, objective functions are established respectively, and various constraints are taken into account, such as the output limit of the generator, the voltage and frequency stability range of the power grid, etc. At the same time, corresponding processing strategies are given to deal with various possible situations.

[0027] Step 202: Optimize and improve the existing typical intelligent optimization algorithms, such as particle swarm optimization and genetic algorithm. These algorithms often have the disadvantages of being easy to fall into local optimum and slow convergence speed when solving complex optimization problems. Therefore, the embodiment of the present invention uses the gray wolf algorithm to optimize them to improve the global search ability and convergence speed of the algorithm. The gray wolf algorithm is an optimization algorithm that simulates the hunting behavior of gray wolves in nature. It has good global optimization ability and robustness.

[0028] Step 203, apply the optimized algorithm to the multi-generator system. In this step, the mutual influence and coordination between multiple generators need to be considered. By scheduling the output of each generator through the optimization algorithm, the operation of the entire power system can be made more economical, safe and stable. At the same time, based on the dynamic characteristics of the power system, the standard form of the primary frequency response characteristics of the power system is derived. This standard form can reflect the law and characteristics of the frequency change of the power system after being disturbed.

[0029] Step 204: convert and process the derived standard form. Converting the standard form from the frequency domain to the time domain makes it easier to analyze and process practical problems. At the same time, the deviation between the generator rotor speed and the synchronous speed is derived to obtain the maximum frequency deviation of the multi-generator system under disturbance. This maximum frequency deviation is one of the important indicators for evaluating the stability of the power system.

[0030] Step 205: Determine the optimization target and constraint conditions of the dispatch model based on the robust interval of the generator's improved frequency response characteristics according to the obtained maximum frequency deviation and other relevant information. This dispatch model is intended to ensure that the power system can maintain stable operation when subjected to various disturbances. By optimizing the dispatch strategy, the frequency fluctuation of the power system can always be within the safety limit.

[0031] Step 206: Robust interval power dispatching is performed on the power system according to the optimization objectives and constraints. In this step, various factors need to be considered comprehensively, such as the output limit of the generator, the transmission capacity of the power grid, the needs of users, etc. Through a reasonable dispatching strategy, the power system can meet the needs of users while maintaining high stability and economy.

[0032] In this embodiment, the frequency response characteristics of the power system can be effectively optimized, and the stability and economy of the power system can be improved. Specifically, the method makes the operation of the power system more intelligent and efficient by establishing an AC / DC hybrid microgrid model based on the Gray Wolf algorithm and optimizing the existing intelligent optimization algorithm. At the same time, by deriving the standard form of the primary frequency response characteristics of the power system and converting and processing it, the frequency changes of the power system after being disturbed can be more accurately analyzed and predicted. Finally, by determining the optimization objectives and constraints of the scheduling model of the robust interval based on the improved frequency response characteristics of the generator and performing robust interval power scheduling, the operation of the power system can be made safer and more reliable.

[0033] Example 3

[0034] In this embodiment, the method for scheduling the operation of the generator specifically involves the steps of converting the standard form into the time domain and deriving the deviation between the generator rotor speed and the synchronous speed to obtain the maximum frequency deviation of the multi-generator system under disturbance. The method mainly includes the following steps:

[0035] Step 301: Establish an AC / DC hybrid microgrid model based on the Grey Wolf algorithm

[0036] First, it is necessary to establish an AC / DC hybrid microgrid model based on the Grey Wolf Algorithm. The model includes a variety of power generation equipment such as photovoltaic power generation, wind turbines, and diesel generators. These devices have two operating states in the microgrid: grid-connected and islanded. For these two states, objective functions are established respectively, and various constraints are considered, and corresponding processing strategies are given.

[0037] Step 302: Optimize the smart algorithm and expand to a multi-generator system

[0038] Next, the existing typical intelligent optimization algorithms, such as particle swarm optimization and genetic algorithm, were optimized. The optimized algorithms were successfully extended to multi-generator systems, thereby obtaining the standard form of the primary frequency response characteristics of the power system.

[0039] Step 303: Convert the standard form to the time domain and take the derivative

[0040] After obtaining the standard form of the primary frequency response characteristics of the power system, it is converted to the time domain. Then, the deviation between the generator rotor speed and the synchronous speed is derived. This step is the key to obtaining the maximum frequency deviation of the multi-generator system under disturbance.

[0041] By taking the derivative of the deviation, we can more accurately understand the dynamic relationship between the generator rotor speed and the synchronous speed under disturbance conditions. This helps predict and control the frequency response of the system, thereby ensuring the stable operation of the power system.

[0042] Step 304: Determine the dispatch model of the robust interval and perform power dispatch

[0043] After obtaining the maximum frequency deviation, the dispatch model of the robust interval based on the improved frequency response characteristics of the generator can be determined. By setting the optimization objectives and constraints, the power system can be dispatched in a robust interval. This helps to ensure that the fluctuation is always within the safety limit when the microgrid fluctuates.

[0044] By accurately analyzing the frequency response characteristics of the multi-generator system, power dispatch can be carried out more effectively to ensure the stable operation of the power system. At the same time, the present invention can also improve the anti-interference ability of the power system and reduce power loss and equipment damage caused by disturbances.

[0045] In general, the present invention provides a comprehensive and effective generator scheduling method. By establishing an AC / DC hybrid microgrid model based on the Grey Wolf algorithm, optimizing the intelligent algorithm, converting the standard form to the time domain and taking derivatives, and determining the scheduling model of the robust interval, the power system can be better controlled and managed to ensure its safe, stable and efficient operation.

[0046] In addition, this embodiment can also bring the following additional effects:

[0047] 1. Improve the economic benefits of the power system: Through accurate power dispatching, unnecessary energy waste can be reduced, thereby improving the economic benefits of the power system.

[0048] 2. Enhance the sustainability of the power system: The method of the present invention helps to reduce the carbon emissions of the power system, thereby enhancing the sustainability of the power system.

[0049] 3. Improve the reliability of the power system: By optimizing the power dispatch strategy, the failure rate of the power system can be reduced and its reliability can be improved.

[0050] Example 4

[0051] A method for establishing a dispatching model of a robust interval based on improved frequency response characteristics of a generator, the method may include the following steps:

[0052] Step 401, in the embodiment of the present invention, first, the frequency response characteristics of the generator need to be improved. This involves optimizing the existing typical intelligent optimization algorithms such as particle swarm optimization and genetic algorithm to better adapt to the characteristics of the multi-generator system. The optimized algorithm can more accurately obtain the standard form of the primary frequency response characteristics of the power system.

[0053] Step 402: Next, the obtained standard form is converted to the time domain, so that the deviation between the generator rotor speed and the synchronous speed can be derived. The purpose of this step is to obtain the maximum frequency deviation of the multi-generator system under disturbance, which is a key parameter for establishing a robust interval scheduling model.

[0054] Step 403: After obtaining the maximum frequency deviation, the optimization objective and constraint conditions of the dispatch model based on the robust interval of the improved frequency response characteristics of the generator can be determined. The optimization objective is mainly to ensure that the fluctuation is always within the safety limit when the microgrid fluctuates. The constraint conditions may include the output limit of the generator, the power balance of the system, etc.

[0055] Step 404: Finally, according to the determined optimization objectives and constraints, the power system is subjected to robust interval power dispatching. This step is actually to apply the previously established model and the obtained parameters to the actual power dispatching to achieve the stable operation of the power system.

[0056] Through the above steps, a dispatch model based on the robust interval of the improved frequency response characteristics of the generator can be established, and its optimization objectives and constraints can be determined. This model can not only improve the stability of the power system, but also improve the economic benefits of the system while ensuring system safety.

[0057] The method provided in the embodiment of the present invention can also be flexibly adjusted according to actual conditions. For example, the optimization objectives and constraints can be adjusted according to different microgrid structures and operation requirements to achieve more refined power dispatching. At the same time, the method provided in the embodiment of the present invention can also be combined with other intelligent optimization algorithms to further improve the operating efficiency and stability of the power system.

[0058] In general, the method for establishing a dispatching model based on the robust interval of the improved frequency response characteristics of the generator provided by the embodiment of the present invention has the advantages of strong flexibility, good adaptability, high stability, etc. It can effectively improve the operating efficiency and stability of the power system and provide a strong guarantee for the safe and economical operation of the power system.

[0059] By implementing the method provided in the embodiment of the present invention, a dispatch model based on the robust interval of the improved frequency response characteristics of the generator can be established, and its optimization objectives and constraints can be determined. This model can more accurately reflect the frequency response characteristics of the multi-generator system under disturbance conditions, thereby providing strong support for achieving stable operation of the power system.

[0060] Specifically, the method provided by the embodiment of the present invention can bring the following technical effects:

[0061] 1. Improve the stability of the power system: By optimizing the frequency response characteristics of the generator and establishing a robust interval scheduling model, the power system can recover to a stable state more quickly when facing disturbances, thereby improving the stability of the power system.

[0062] 2. Ensure safe operation of the system: The method provided by the embodiment of the present invention fully considers the safety factors of the system when determining the optimization objectives and constraints. Therefore, by implementing the method provided by the embodiment of the present invention, it can be ensured that the power system is always within the safety limit during operation to avoid safety accidents.

[0063] 3. Improve the economic benefits of the system: Under the premise of ensuring system safety, the method provided by the embodiment of the present invention can also optimize the operation mode of the power system, reduce the operation cost of the system, and thus improve the economic benefits of the system.

[0064] 4. Provide new optimization scheduling ideas and methods: The method provided by the embodiment of the present invention provides new ideas and methods for the optimization scheduling of the power system. By combining with other intelligent optimization algorithms, the application scope of the embodiment of the present invention can be further expanded, providing more options and possibilities for the optimization scheduling of the power system.

[0065] Example 5

[0066] A method for robust interval power dispatching may include the following steps to ensure that the stability and security of the system are effectively guaranteed when a microgrid fluctuates.

[0067] Step 501: Establishing a Gray Wolf Algorithm Model

[0068] In the embodiment of the present invention, it is first necessary to establish a gray wolf algorithm model. The gray wolf algorithm is an optimization algorithm that simulates the hunting behavior of gray wolves. By simulating the hunting process of gray wolves, the optimal solution is found. In this model, the gray wolf population is divided into four levels: α, β, δ, and ω, which represent the optimal solution, the suboptimal solution, the third optimal solution, and the remaining solutions, respectively. By continuously updating the position of the gray wolf, the optimal solution is gradually approached.

[0069] Step 502: Setting optimization goals and constraints

[0070] In the embodiment of the present invention, it is necessary to set the optimization objectives and constraints of power dispatching. The optimization objectives may include minimizing the system operating cost, maximizing the system stability, etc. The constraints may include the output limit of the generator, the transmission capacity limit of the line, the power balance of the system, etc. These objectives and conditions will serve as the basis for the gray wolf algorithm to find the best solution.

[0071] Step 503: Initialize the gray wolf group

[0072] In the embodiment of the present invention, the positions of the gray wolf group need to be initialized. These positions represent possible power dispatching schemes. During initialization, a certain number of gray wolf individuals can be randomly generated, and each individual represents a power dispatching scheme.

[0073] Step 504: Calculate fitness value

[0074] In the embodiment of the present invention, it is necessary to calculate the fitness value of each individual gray wolf. The fitness value is used to evaluate the pros and cons of each power dispatching scheme. The operating cost, stability and other indicators of each scheme can be calculated according to the set optimization objectives and constraints, so as to obtain the fitness value.

[0075] Step 505: Update the location of the gray wolf group

[0076] In the embodiment of the present invention, it is necessary to update the position of the gray wolf group according to the principle of the gray wolf algorithm. Specifically, the position of the ω wolf can be updated by calculating the guiding effect of the α, β and δ wolves on the ω wolf. At the same time, random disturbances can also be introduced to increase the diversity of the search. Through continuous iterative updates, the gray wolf group gradually approaches the optimal solution.

[0077] Step 506: Determine the termination condition

[0078] In the embodiment of the present invention, a termination condition needs to be set to determine whether the algorithm converges or reaches the maximum number of iterations. If the termination condition is met, the iteration is stopped and the optimal solution is output; otherwise, the process returns to step 204 to continue the iteration.

[0079] Step 507: Implement robust interval power dispatching

[0080] In the embodiment of the present invention, robust interval power dispatching is implemented according to the optimal solution obtained by the gray wolf algorithm. Specifically, the operating state and output size of each generator can be adjusted according to the output plan of the generator in the optimal solution. At the same time, considering the volatility and uncertainty factors of the microgrid, a certain safety margin needs to be set to ensure the stability and safety of the system.

[0081] Through the above steps, the embodiment of the present invention can realize the robust interval power dispatch in the AC / DC hybrid microgrid based on the gray wolf algorithm. The method can fully consider the volatility and uncertainty factors of the microgrid to ensure that the stability and security of the system are effectively guaranteed. At the same time, through the optimization process of the optimization algorithm, a better power dispatching scheme can be found to reduce the system operation cost and improve the economic benefit.

[0082] Furthermore, in the embodiment of the present invention, the gray wolf algorithm has a strong global search capability and a fast convergence speed. By simulating the hunting behavior of the gray wolf, the optimal solution can be quickly found in a complex solution space. In addition, the method also has certain adaptability and robustness, and can adapt to different operating environments and system parameter changes.

[0083] In summary, the robust interval power dispatching method in the AC / DC hybrid microgrid based on the gray wolf algorithm provided by the embodiment of the present invention has significant advantages and practical value. By optimizing the power dispatching scheme, the system operation cost can be reduced, the system stability and safety can be improved, and the economic and social benefits of the system can be enhanced. At the same time, the method can also provide useful reference and reference for other similar optimization problems.

[0084] Example 6

[0085] This embodiment also provides a second module for monitoring and displaying the operating status of the microgrid and the dispatching results of the generator. The second module can be integrated in the control system of the microgrid and used to monitor the operating status of the microgrid in real time, including the grid-connected status, the island status, and the operating status and dispatching results of each generator. Through the second module, the operating status of the microgrid can be intuitively understood, and possible problems can be discovered and handled in a timely manner to ensure the stable operation of the microgrid.

[0086] In a specific implementation, the second module may include a data acquisition unit, a data processing unit, and a data display unit. The data acquisition unit is responsible for real-time collection of various operating data of the microgrid, including photovoltaic power generation, wind turbine power generation, diesel generator power generation, voltage and current of the AC / DC hybrid microgrid, etc. The data processing unit processes and analyzes the collected data to obtain the operating status of the microgrid and the dispatching results of the generator. The data display unit displays the processed data in the form of graphics or tables for easy viewing and analysis by users.

[0087] In addition, the second module may also include an alarm unit for sending out an alarm signal when an abnormal situation occurs in the microgrid to remind the user to handle it in time.

[0088] Through the second module provided by the embodiment of the present invention, the operation status of the microgrid can be fully monitored and displayed in real time, thereby improving the management efficiency and operation safety of the microgrid. At the same time, the module can also provide strong data support for the dispatch of the power system, helping dispatchers make more reasonable dispatch decisions.

[0089] Furthermore, the second module can also be combined with the optimized gray wolf algorithm to achieve intelligent scheduling of the microgrid. Specifically, the optimized gray wolf algorithm can automatically adjust the operating parameters of the generator according to the operating status of the microgrid and the scheduling results of the generator, so that the microgrid always maintains the optimal operating state. The second module can display the optimized scheduling results in real time, allowing users to intuitively see the optimization effect.

[0090] This intelligent scheduling method can not only improve the operating efficiency of the microgrid, but also reduce operating costs and energy waste. At the same time, since the gray wolf algorithm has the advantages of strong global optimization ability and fast convergence speed, it can quickly find the optimal scheduling solution to meet the real-time scheduling needs of the microgrid.

[0091] In general, the second module for monitoring and displaying the operating status of the microgrid and the generator scheduling results provided in the embodiment of the present invention has multiple functions such as real-time monitoring, data display, abnormal alarm and intelligent scheduling, which can effectively improve the management efficiency and operation safety of the microgrid, reduce operating costs, and provide strong technical support for the sustainable development of the power system.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for scheduling the operation of a generator, characterized in that: An AC / DC hybrid microgrid model is constructed based on the Grey Wolf Algorithm. The AC / DC hybrid microgrid model includes photovoltaic power generation, wind turbines and diesel generators. The AC / DC hybrid microgrid model switches between two operating states: grid-connected and islanded.

2. The method for scheduling the operation of a generator according to claim 1, characterized in that: The objective function under different operating conditions is set, the constraints are taken into account and the corresponding processing strategies are given to optimize the existing particle swarm algorithm and genetic algorithm.

3. The method for scheduling the operation of a generator according to claim 2, characterized in that: The optimized particle swarm algorithm and genetic algorithm are applied to a multi-generator system, and the standard form of the primary frequency response characteristics of the power system is derived.

4. The method for scheduling the operation of a generator according to claim 3, characterized in that: The standard form is converted to the time domain and the deviation of the generator rotor speed from the synchronous speed is derived to obtain the maximum frequency deviation of the multi-generator system under disturbance.

5. The method for scheduling the operation of a generator according to claim 1, characterized in that: A robust interval dispatching model based on the improved frequency response characteristics of the generator is established, and its optimization objectives and constraints are determined.

6. The method for scheduling the operation of a generator according to claim 5, characterized in that: According to the optimization objectives and constraints, the power system is dispatched in a robust interval.

7. A system for generator scheduling using the Grey Wolf Algorithm in an AC / DC hybrid microgrid, characterized in that: The invention comprises a module for collecting and processing microgrid operation data, and a first module for executing the method described in any one of claims 1 to 6.

8. A system for applying the Grey Wolf Algorithm to perform generator scheduling in an AC / DC hybrid microgrid according to claim 7, characterized in that: It also includes a second module for monitoring and displaying the microgrid operating status and generator dispatch results.

9. Application of the method as claimed in any one of claims 1 to 6 in the field of power dispatching, smart grid or renewable energy.