Optimization approaching method and system for multi-target scheduling and control of micro balance area in non-cooperative environment, storage medium and electronic equipment

Abstract

The invention discloses an optimization approaching method and system for multi-target scheduling and control of a micro balance area in a non-cooperative environment, a storage medium and electronic equipment, and relates to the technical field of distributed energy optimization scheduling in an intelligent micro-grid system. Comprising the following steps: configuring a collaborative optimization framework of energy multi-target scheduling and control of the micro balance area; establishing a scheduling optimization problem model of the miniature balance area in a non-cooperative environment; constructing a robust scheduling optimization method based on quantum inspired crayfish optimization; and constructing a robust control optimization method based on Kupman dynamic identification. According to the optimization approaching method and system for multi-target scheduling and control of the micro balance area in the non-cooperative environment, two technical means of quantum enlightenment of crayfish optimization and Kupman dynamic recognition are combined, and optimization of energy scheduling and control can be achieved in the complex and uncertain environment.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed energy optimization scheduling in a smart microgrid system, and in particular to an optimization method, system, storage medium and electronic device for multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment. Background Art

[0002] The optimized application of distributed dispatching and control technology is not only related to the improvement of energy management efficiency, but also directly related to the guarantee of system stability and reliability. Especially today when the level of intelligence and automation is increasing, distributed dispatching and control technology has become an important cornerstone to support energy market transactions and the innovative development of energy Internet.

[0003] In recent years, domestic and foreign scholars have conducted extensive and in-depth research on the multi-objective scheduling and control problems of micro-balancing areas. In the existing technology, various advanced scheduling methods and control technologies are tried to achieve the optimization of energy management in a complex and changing environment. However, most of these studies are based on an idealized friendly environment. In actual operation, the output equipment in the micro-balancing area often faces the challenges of internal and external uncertainties, which greatly reduces the effectiveness of the originally designed multi-objective scheduling and control methods in practical applications. The reason can be summarized as being caused by a non-cooperative environment. The non-cooperative environment includes two parts: the external environment and the internal environment. The external environment mainly refers to the uncertainty of natural factors such as weather and wind, while the internal environment involves parameter drift and failure caused by fatigue, service life and other factors of the output equipment. The existence of these non-cooperative factors makes it difficult for the scheduling model and control model to maintain accuracy, thereby affecting the energy management efficiency and system stability of the micro-balancing area. Summary of the invention

[0004] The technical problem to be solved by the present invention is: to overcome the shortcomings of the existing technology and provide a method, system, storage medium and electronic device for optimizing multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment, which combines the two technical means of quantum-inspired crayfish optimization and Koopman dynamic identification, and can achieve optimization of energy scheduling and control in a complex and uncertain environment.

[0005] The technical solution adopted by the present invention to solve its technical problem is: the optimization method of multi-objective scheduling and control of the micro-balance area under the non-cooperative environment includes: according to the composition of distributed energy in the micro-balance area, the internal non-cooperative environment of the distributed energy and the external non-cooperative environment of the distributed energy, constructing a robust energy scheduling and control system based on quantum-inspired crayfish optimization and Koopman dynamic identification; Based on the composition of distributed energy, a dispatch optimization problem model of micro-balancing areas in a non-cooperative environment is established; Based on the internal non-cooperative environment of distributed energy resources, a robust scheduling optimization method based on quantum-inspired crayfish optimization is constructed; Based on the external non-cooperative environment of distributed energy, a robust control optimization method based on Koopman dynamic identification is constructed.

[0006] Furthermore, based on the composition of distributed energy, a dispatch optimization problem model of micro-balancing areas in a non-cooperative environment is established, including: Based on the composition of distributed energy, a model is established, including: photovoltaic power generation output Model; Wind power output Model; determine the relationship between the model, output constraints and costs, including: Micro gas turbine power generation output Model, output constraints and cost relationship; micro diesel generator output Model, output constraints and cost relationship; relationship between energy storage system output and constraints; Based on the established model, the constraints are determined, including: the production and marketing conservation constraints of the overall output; the production and marketing conservation constraints of the overall output; According to the determined model, output constraint and cost relationship and constraint conditions, the dispatch optimization problem of micro-balancing area in non-cooperative environment is established.

[0007] Furthermore, photovoltaic power generation The model is: ; in, , , , , , , Respectively represent the power output of photovoltaic cells under standard conditions, the actual irradiance at the current time, the solar irradiance under specified standard conditions, the temperature of photovoltaic cells under current conditions, the temperature of photovoltaic cells under standard test conditions, the temperature coefficient of photovoltaic cells, and the battery derating coefficient; Wind power output The model is: ; in, , , , , They represent the wind turbine rated power, cut-in wind speed, rated wind speed, cut-out wind speed, and wind speed parameters respectively.

[0008] Furthermore, the micro gas turbine power generation output The relationship between the model, output constraints and costs includes: Micro gas turbines generate electricity by consuming natural gas. It is expressed as: ; Micro gas turbines incur fuel costs during operation , operation and maintenance costs , and the cost of pollutant treatment , fuel costs , operation and maintenance costs , and the cost of pollutant treatment The expressions are: ; ); ); in, , , , , represents the maintenance cost coefficient, natural gas price, low calorific value of natural gas, the emission of type k pollutants during operation, and the cost coefficient for treating type k pollutants. The output constraint of the micro gas turbine is: ; in, , , It indicates the real-time output of the micro gas turbine, the output of the micro gas turbine at the previous moment, the output lower limit, the output upper limit, and the climbing limit in sequence; Furthermore, the output of micro diesel generator The relationship between the model, output constraints and costs includes: Micro diesel burns diesel to drive a generator to generate electricity, and also incurs fuel costs during operation , Operation and maintenance costs and pollutant treatment costs , fuel costs , Operation and maintenance costs and pollutant treatment costs The expressions are: ; ; ; in, Real-time power generated for micro diesel generators; is the maintenance factor; ( , , )=(0.00011,0.1801,6); The output constraints are: ; in, , , , They represent the lower limit, upper limit, output at the previous moment, and climbing limit of the micro diesel generator output respectively; Furthermore, the relationship between the energy storage system output and constraints includes: The remaining capacity of the energy storage system is expressed in terms of the state of charge It is expressed as: ; in, Indicates the charging power or discharging power of the energy storage device. and are the discharge and charge coefficients, is the charge state at the previous moment; The corresponding constraints are: ; in, , , , They represent the upper and lower limits of the energy storage system output and the upper and lower limits of the energy storage capacity, respectively. The conservation constraint of production and marketing of overall output is: ; in, is the power absorbed by the load.

[0009] Furthermore, the established multi-objective optimization problem for energy scheduling is: ; in, is the overall optimization goal, , is the weight, For running costs, For pollution control costs, is the interaction cost between the microgrid and the main grid, and its calculation basis is , , , and They are respectively for selling electricity and purchasing electricity; and To purchase real-time total price and unit price; and Selling real-time total price and unit price, , The total cost of the gas turbine and diesel generator during operation respectively; The maintenance cost of the energy storage system; , Operation and maintenance costs for photovoltaic and wind power generation; , and denote the costs of treating pollutants generated by the main grid, gas turbines, and diesel generators, respectively; is the amount of type k pollutants emitted when the main power grid is in operation; is the cost coefficient for treating type k pollutants, and n is the total number of pollutant categories.

[0010] Furthermore, based on the internal non-cooperative environment of distributed energy, a robust scheduling optimization method based on quantum-inspired crayfish optimization is constructed, including: Initialize the crayfish population as: ; in, represents the set of population positions, is a randomly generated set of initial positions, is the set of initial positions to be obtained through learning; The position of each crayfish is represented by a quantum bit to form a quantum superposition state, namely: ; in, represents the quantum state of the i-th crayfish, and is the probability density, and Represents quantum 0 and 1 states, corresponding to probability density, and also satisfies ; Formulate the behavior simulation rules for crayfish: define the ambient temperature T to determine the behavior of crayfish. When the temperature is high, it is considered as a high temperature state, and the crayfish will be triggered to avoid the heat; when When the temperature is above 100°C, it is considered as a suitable temperature, triggering the crayfish's foraging behavior; Design a fitness function fit( ), and determine its position on the Pareto frontier, update the crayfish position according to fitness and environmental factors, and select the optimal solution: ; in, For the updated location of the crayfish, is the current position of the crayfish, When the predetermined number of iterations is reached Or after meeting other termination conditions, output the Pareto optimal solution set.

[0011] Furthermore, when formulating the behavior simulation rules for crayfish: During the summer vacation period, the summer vacation sites are randomly selected to simulate the quantum tunneling effect: ; in, Indicates the location of the summer resort. represents the best position under the current iteration number, represents the optimal position of the previous population; In the competition stage, design a competition among crayfish for a summer retreat: , in, Indicates the new competitive position, and Respectively represent the current positions of the two crayfish; In the foraging phase, formulate the foraging behavior of crayfish according to the quantum state: ; in, Indicates the food intake of crayfish.

[0012] Furthermore, based on the external non-cooperative environment of distributed energy, a robust control optimization method based on Koopman dynamic identification is constructed, including: Conduct real-time data monitoring on each device in the micro-balancing area. The collected data should include but not be limited to power output, voltage, current, frequency, temperature, and status signals; The data is processed in sequence, including: cleaning to remove outliers and noise; standardizing or normalizing the data to make the data from different devices comparable; sampling the data to ensure the consistency of the time series data; determining first-order and second-order statistics of the power signal as candidates for the Koopman characteristic function; Construct a linear dynamic model of each device in the micro-balance area in high-dimensional space.

[0013] Furthermore, a linear dynamic model of each device in the micro-balance area in a high-dimensional space is constructed, which specifically includes the following steps: Define the observation function ,in, Used to describe the controlled object The state at the moment; calculate the time evolution relationship of the observation function: ,in is the forward prediction quantity, is the forward moving time, is the Koopman operator, which is usually chosen to be a polynomial function; The state evolution equation is established according to the Koopman operator: ; in, for The Koopman dynamic identification operator matrix; Establish an objective function that includes tracking error and control input energy As the minimization optimization objective: ; in, for , which is the lifting function of the observation function; is the control input to be solved; is the expected trajectory, N represents the prediction time domain, Q and R are weight matrices; Establish the Koopman dynamic identification predictive control optimization problem: ; in, is the control input matrix; Make a prediction based on the current state z(t) and use the solution to the optimization problem to update the control input: ; in, is the feedback gain, and represents the feedforward control input; The update strategy of the Koopman dynamic identification control algorithm is established: ; in, is the learning rate, is the gradient of the objective function with respect to the control gain.

[0014] An optimization system, comprising: The system configuration module builds a robust energy dispatching and control system based on quantum-inspired crayfish optimization and Koopman dynamic identification according to the composition of distributed energy in the micro-balancing area, the internal non-cooperative environment of distributed energy, and the external non-cooperative environment of distributed energy; A model building module, whose input end is connected to the output end of the system configuration module, is used to build a dispatch optimization problem model of a micro-balancing area in a non-cooperative environment based on the composition of distributed energy; A first optimization method construction module, whose input end is connected to the output end of the system configuration module, is used to construct a robust scheduling optimization method based on quantum-inspired crayfish optimization based on an internal non-cooperative environment of distributed energy resources; The second optimization method building module has an input end connected to the output end of the system configuration module and is used to construct a robust control optimization method based on Koopman dynamic identification.

[0015] Furthermore, the model building module includes: A model building unit, used for building a model based on the composition of distributed energy; Cost relationship confirmation unit, used to determine the power generation output of micro gas turbines Model, output constraints and cost relationship; micro diesel generator output Model, output constraints and cost relationship; relationship between energy storage system output and constraints; The constraint condition establishment unit is used to determine the production and marketing conservation constraints of the overall output; the production and marketing conservation constraints of the overall output; and a scheduling optimization problem establishing unit, which is used to establish the scheduling optimization problem of the micro-balancing area in a non-cooperative environment according to the determined model, output constraint and cost relationship and constraint conditions.

[0016] Furthermore, the first optimization method constructs a module, including: An initialization unit, used to initialize the crayfish population; A quantum superposition state forming unit, used for representing the position of each crayfish by a quantum bit to form a quantum superposition state; A behavior rule formulation unit, used to formulate behavior simulation rules for crayfish; The optimal basis determination unit is used to design a fitness function for evaluating the position of the crayfish and determine its position on the Pareto frontier, update the position of the crayfish according to the fitness and environmental factors, select the optimal solution, and output the Pareto optimal solution set after reaching a predetermined number of iterations or meeting other termination conditions.

[0017] Furthermore, the second optimization method constructs a module, including: A real-time data detection unit, used for real-time data monitoring of each device in the micro-balancing area; A data processing unit, used for processing data; a function determination unit, for determining first-order and second-order statistics of the power signal as candidates for the Koopman characteristic function; The linear dynamic model determination unit is used to construct the linear dynamic model of each device in the micro-balance area in the high-dimensional space.

[0018] A storage medium stores computer instructions, which are used to enable a processor to implement an optimization method for multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment when executing the computer instructions.

[0019] An electronic device includes a processor and a storage medium connected to the processor; wherein the storage medium stores instructions that can be executed by the processor, and the instructions are executed by the processor so that the processor can execute an optimization method for multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment.

[0020] Compared with the prior art, the present invention has the following beneficial effects: The optimization method and system for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment proposed in this application combines the two technical means of quantum-inspired crayfish optimization and Koopman dynamic identification, and can achieve the optimization of energy scheduling and control in a complex and uncertain environment. It not only has broad application prospects, but will also have a profound impact on the development of smart microgrid systems, and promote the continuous improvement of the level of intelligence and automation in the energy field.

[0021] The optimization method and system for multi-objective scheduling and control of micro-balance areas in a non-cooperative environment proposed in this application integrate two technical means: robust scheduling based on quantum-inspired crayfish optimization and robust control based on Koopman dynamic identification. First, in terms of robust scheduling, a quantum-inspired crayfish optimization method is constructed by drawing on the parallelism and uncertainty principles in quantum computing and the adaptive survival strategy of crayfish in complex environments. This method can better cope with the uncertainty of the external environment. It can not only quickly find the optimal scheduling solution in a complex and changeable environment, but also adjust the scheduling strategy in time when the external environment changes to ensure the stable operation of the system. Secondly, in terms of robust control, the present invention constructs a predictive control method based on Koopman dynamic identification. The method optimizes the control model by real-time monitoring and identification of the system state, and predicting the operating trend of the output equipment. When the output equipment has problems such as parameter drift or failure, the method can respond quickly and adjust the control strategy to ensure the stable operation of the system and the continuity of energy supply.

[0022] The present invention provides substantial theoretical support for the design process of methods such as the collaborative optimization framework of multi-objective scheduling and control in the micro-balancing area, quantum-inspired crayfish optimization robust scheduling optimization, and Koopman dynamic identification robust control optimization, which in principle ensure the dynamic balance relationship between energy demand and supply in the micro-balancing area in an uncertain non-cooperative environment.

[0023] The robust optimization effect of multi-objective scheduling of distributed energy in micro-balance areas has been improved. By fully drawing on the parallelism and uncertainty principles of quantum computing and combining the adaptive survival strategy of crayfish in complex environments, the method can quickly find the optimal scheduling solution under conditions of uncertainty and dynamic changes. At the same time, the method also has good adaptability and can automatically adjust the scheduling strategy according to real-time changes in the external environment to ensure the stable operation of the micro-balance area.

[0024] The accuracy and reliability of energy equipment output are improved. By real-time monitoring and identifying the operating status of equipment in the micro-balancing area and predicting its possible future operating trends, the control model can be optimized and adjusted in real time. When abnormal conditions occur in the equipment, the method can respond quickly and ensure the stable operation of the system and the continuity of energy supply by adjusting the control strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the optimization method for multi-objective scheduling and control of micro-balancing areas in non-cooperative environments; Figure 2 It is a schematic diagram of the collaborative optimization framework for multi-objective scheduling and control of energy in micro-balancing areas; Figure 3 Schematic diagram of the robust scheduling scheme for energy in micro-balancing areas; Figure 4 Optimize algorithm flow chart for quantum-inspired crayfish; Figure 5 Schematic diagram of uncertain external non-cooperative factors of photovoltaic power generation; Figure 6 Schematic diagram of the external non-cooperative factors of uncertain wind power generation; Figures 7 to 12 Schematic diagram of the impact of non-cooperative factors on internal faults in the micro-balance area equipment; Fig.13 Schematic diagram of the optimization results of energy scheduling and control in the micro-balance zone under a non-cooperative environment. DETAILED DESCRIPTION

[0026] Figures 1 to 13 The best embodiment of the present invention is shown below in conjunction with the attached Figures 1 to 13 The present invention is further described.

[0027] like Figure 1 As shown in the figure, the optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment includes the following steps: Step S1, configuring a collaborative optimization framework for multi-objective scheduling and control of energy in micro-balancing areas; Considering the internal and external non-cooperative environment of distributed energy in the micro-balancing area, a robust energy scheduling and control structure with quantum-inspired crayfish optimization and Koopman dynamic identification as the core is constructed to address the uncertain effects brought by energy scheduling strategies and output control schemes.

[0028] like Figure 2As shown in the collaborative optimization framework of multi-objective scheduling and control of energy in micro-balancing areas, the micro-balancing area considered is a typical new type of integrated energy system with a high proportion of wind and solar access. The energy includes photovoltaic power generation, wind power generation, small-power diesel engine power generation, small-power gas turbine power generation, energy storage system storage, and interactive purchase and sale of electricity with the main grid.

[0029] Clarify the external non-cooperative environment and formulate a robust scheduling strategy. The external non-cooperative environment mainly refers to the uncertainty of natural factors such as weather and wind, which reduces the effectiveness of the scheduling strategy formulated for a high proportion of wind and solar energy, especially in situations where the natural environment changes greatly. The prediction accuracy of the day-ahead energy scheduling plan will be greatly reduced. This invention intends to draw on the parallelism and uncertainty principles of quantum computing, and combine the adaptive survival strategy of crayfish in complex environments to formulate a quantum-inspired crayfish optimization method to solve the optimal energy robust scheduling optimization problem in the micro-balancing area under the conditions of uncertainty and dynamic changes.

[0030] Clarify the internal non-cooperative environment and formulate a robust control strategy. Whether the execution of a robust scheduling scheme can achieve optimal energy allocation depends on whether the output equipment can normally complete the assigned tasks. It is worth noting that due to factors such as fatigue and increased service life, the output equipment will inevitably have potential threats such as parameter drift and failure, which can easily cause a dynamic mismatch between the model used for control and the actual output equipment. The inaccuracy of the control model will ultimately affect the energy management efficiency and system stability of the micro-balancing area. The present invention intends to construct a robust control method based on Koopman dynamic identification, which realizes real-time optimization and adjustment of the control model by real-time monitoring and identification of the operating status of the equipment in the micro-balancing area, and prediction of its possible future operating trends. When an abnormal situation occurs in the equipment, the method can respond quickly and ensure the stable operation of the system and the continuity of energy supply by adjusting the control strategy.

[0031] From the above, it can be seen that the collaborative optimization framework of multi-objective scheduling and control of micro-balancing area energy not only helps to improve the efficiency of energy management, but also helps to enhance the stability and reliability of the system, providing strong technical support for the development of smart microgrid systems.

[0032] Step S2, establishing a scheduling optimization problem model of a micro-balancing area in a non-cooperative environment; The steps include: Step S2-1, establish photovoltaic power generation output Model: ; in, , , , , , , They respectively represent the power output of photovoltaic cells under standard conditions, the actual irradiance at the current time, the solar irradiance under specified standard conditions, the temperature of photovoltaic cells under current conditions, the temperature of photovoltaic cells under standard test conditions, the temperature coefficient of photovoltaic cells, and the battery derating coefficient.

[0033] Step S2-2 Wind power output The model is: ; in, , , , , They represent the wind turbine rated power, cut-in wind speed, rated wind speed, cut-out wind speed, and wind speed parameters respectively.

[0034] Step S2-3, determine Micro gas turbine power generation output The relationship between the model, output constraints and costs includes: Micro gas turbines generate electricity by consuming natural gas. It is expressed as: ; Micro gas turbines incur fuel costs during operation , operation and maintenance costs , and the cost of pollutant treatment , fuel costs , operation and maintenance costs , and the cost of pollutant treatment The expressions are: ; ); );in, , , , , represents the maintenance cost coefficient, natural gas price, low calorific value of natural gas, the emission of type k pollutants during operation, and the cost coefficient for treating type k pollutants. The output constraint of the micro gas turbine is: ; in, , , It represents the real-time output of the micro gas turbine, the output of the micro gas turbine at the previous moment, the output lower limit, the output upper limit, and the climbing limit in sequence.

[0035] Step S2-4, Micro diesel generator output The relationship between the model, output constraints and costs includes: Micro diesel burns diesel to drive a generator to generate electricity, and also incurs fuel costs during operation , Operation and maintenance costs and pollutant treatment costs , fuel costs , Operation and maintenance costs and pollutant treatment costs The expressions are: ; ; ; in, Real-time power generated for micro diesel generators; is the maintenance factor; ( , , )=(0.00011,0.1801,6); The output constraints are: ; in, , , , They respectively represent the lower output limit, upper output limit, output at the previous moment, and climbing limit of the micro diesel generator.

[0036] Step S2-5, determining the relationship between the energy storage system output and constraints; The remaining capacity of the energy storage system is expressed in terms of the state of charge It is expressed as: ; in, Indicates the charging power or discharging power of the energy storage device. and are the discharge and charge coefficients, is the charge state at the previous moment; The corresponding constraints are: ; in, , , , They represent the upper and lower limits of the energy storage system output, and the upper and lower limits of the energy storage capacity respectively.

[0037] Step S2-6, determine The conservation constraint of production and marketing of overall output is: ; in, is the power absorbed by the load.

[0038] Furthermore, the established multi-objective optimization problem for energy scheduling is: ; in, is the overall optimization goal, , is the weight, For running costs, For pollution control costs, is the interaction cost between the microgrid and the main grid, and its calculation basis is , , , and They are respectively for selling electricity and purchasing electricity; and To purchase real-time total price and unit price; and Selling real-time total price and unit price, , The total cost of the gas turbine and diesel generator during operation respectively; The maintenance cost of the energy storage system; , Operation and maintenance costs for photovoltaic and wind power generation; , and denote the costs of treating pollutants generated by the main grid, gas turbines, and diesel generators, respectively; is the amount of type k pollutants emitted when the main power grid is in operation; is the cost coefficient for treating type k pollutants, and n is the total number of pollutant categories.

[0039] Step 3, construct a robust scheduling optimization method based on quantum-inspired crayfish optimization; like Figure 4 As shown in the figure, a quantum-inspired crayfish optimization method is developed to solve the optimal energy robust scheduling optimization problem of micro-balancing areas under uncertain and dynamically changing conditions. The specific steps include: Step S3-1, initializing the population; first, Initialize the crayfish population as: ; in, represents the set of population positions, is a randomly generated set of initial positions, is the set of initial positions to be obtained through learning.

[0040] Step S3-2, quantum bit representation: The position of each crayfish is represented by a quantum bit to form a quantum superposition state, namely: ; in, represents the quantum state of the i-th crayfish, and is the probability density, and Represents quantum 0 and 1 states, corresponding to probability density, and also satisfies .

[0041] Step S3-3, behavior simulation; Define the ambient temperature T to determine the behavior of crayfish. When the temperature is high, it is considered as a high temperature state, and the crayfish will be triggered to avoid the heat; when When the temperature rises, it is considered to be suitable, triggering the foraging behavior of crayfish. and Both threshold parameters were determined based on real-life observations of crayfish's emergency behavior to temperature.

[0042] Formulate behavioral simulation rules for crayfish. During the summer vacation period, randomly select a summer vacation location. To simulate the quantum tunneling effect: ; in, Indicates the location of the summer resort. represents the best position under the current iteration number, represents the optimal position of the previous population.

[0043] In the foraging phase, formulate the foraging behavior of crayfish according to the quantum state: ; in, Indicates the food intake of crayfish, which is affected by temperature.

[0044] Step S3-4, evaluating fitness; Design a fitness function fit( ) and determine its position on the Pareto front. Update the crayfish position based on fitness and environmental factors and select the optimal solution: ; in, For the updated location of the crayfish, is the current position of the crayfish.

[0045] Step S3-5, determining whether the termination condition is met; When the predetermined number of iterations is reached Or when other termination conditions are met, the Pareto optimal solution set is output, otherwise, the process returns to step S3-1 and executes steps S3-1 to S3-4 in a loop.

[0046] When updating the position of the crayfish mentioned above, the core of the quantum optimization used is quantum superposition and quantum revolving gate. The former allows the crayfish to explore multiple positions at the same time, while the latter is used to update the probability amplitude and enhance the exploration of the global optimal solution.

[0047] Step S4, constructing a robust control optimization method based on Koopman dynamic identification; By real-time monitoring and identifying the operating status of the equipment in the micro-balancing area and predicting its possible future operating trends, the control model can be optimized and adjusted in real time. The specific steps are as follows: Step S4-1, real-time data monitoring of each device in the micro-balance area (wind turbine, photovoltaic panel, energy storage battery, diesel generator, gas turbine, etc.), the collected data should include but not be limited to power output, voltage, current, frequency, temperature, state signal (on / off, charge / discharge, etc.). After that, the data is processed in sequence, including: cleaning to remove outliers and noise; standardizing or normalizing data to make data from different devices comparable; sampling data to ensure the consistency of time series data.

[0048] Step S4-2, determining the first-order and second-order statistics (mean, variance, skewness, kurtosis, etc.) of the power signal as candidates for the Koopman characteristic function.

[0049] Step S4-3, constructing a linear dynamic model of each device in the micro-balance area in a high-dimensional space.

[0050] Taking photovoltaic power generation equipment as an example, its main design steps include: Step S4-3-1, define the observation function , in, Used to describe the controlled object The state at the moment; calculate the time evolution relationship of the observation function: ,in is the forward prediction quantity, is the forward moving time, is the Koopman operator, which is generally chosen to be a polynomial function.

[0051] Step S4-3-2, establish the state evolution equation according to the Koopman operator: ; in, for The Koopman dynamic identification operator matrix.

[0052] Step S4-3-3, establish an objective function including tracking error and control input energy As the minimization optimization objective: ; in, for , which is the lifting function of the observation function; is the control input to be solved; is the expected trajectory, N represents the prediction time domain, and Q and R are weight matrices.

[0053] Step S4-3-4, establish the Koopman dynamic identification predictive control optimization problem: ; in, is the control input matrix.

[0054] Step S4-3-5, make a prediction based on the current state z(t) and use the solution to the optimization problem to update the control input: ; in, is the feedback gain, and represents the feedforward control input.

[0055] In step S4-3-6, considering the influence of internal non-cooperative factors such as equipment parameter drift and actuator failure on the control effect, the update strategy of the Koopman dynamic identification control algorithm is further established: ; in, is the learning rate, is the gradient of the objective function with respect to the control gain.

[0056] Step S5, verifying the established collaborative optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment; In the simulation, the relevant parameters of each unit equipment in the micro-balance area are set as shown in Tables 1 to 3.

[0057] Table 1 Unit parameters of equipment in the micro-balance area

[0058] Table 2 Pollutant treatment costs of equipment in the micro-balance area

[0059] Table 3 Pollutant emission coefficients of equipment in the micro-balance area

[0060] Consider the impact of external non-cooperative factors caused by uncertain wind and solar power generation, as well as internal non-cooperative factors caused by equipment parameter drift, on the optimal scheduling of micro-balancing areas. Figure 5 , Figure 6 , Figures 7 to 12 Describes the fluctuation range of the two non-cooperative factors respectively. Figure 5 and Figure 6 It can be seen that photovoltaic and wind power generation are affected by the weather, and there are large fluctuations in output, which has caused considerable trouble for the reliable operation of scheduling optimization. Figures 7 to 12 The comparison describes the impact of the micro-balance zone equipment on the output power when it is operating normally and when it is faulty. The black solid line represents the output of the equipment when it is normal, and the dotted line represents the output of the equipment when it is faulty. The fault is assumed to occur at 12 hours. It can be seen that it is difficult for the equipment in the faulty state to output normally according to the task assigned by the scheduling optimization result, which will inevitably cause the instability of the power supply on the load side.

[0061] at last, Fig.13 The simulation results are shown by using the quantum-inspired crayfish optimization-Koopman dynamic identification robust control optimization method proposed in the present invention to solve the problem of reliable optimal control in internal and external non-cooperative environments. It can be seen that each power supply device in the micro-balancing area can produce stable and adaptive energy to meet the energy demand of the load side. In summary, the method of the present invention can achieve the optimization of energy scheduling and control in a complex and uncertain environment.

[0062] A system for realizing the optimization method of multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment, comprising: The system configuration module constructs a robust energy dispatching and control system based on quantum-inspired crayfish optimization and Koopman dynamic identification according to the composition of distributed energy in the micro-balancing area, the internal non-cooperative environment of distributed energy and the external non-cooperative environment of distributed energy.

[0063] The model building module, whose input end is connected to the output end of the system configuration module, is used to establish a scheduling optimization problem model of a micro-balancing area in a non-cooperative environment based on the composition of distributed energy.

[0064] Model building modules, including: Model building unit, used to build a model based on the composition of distributed energy. Cost relationship confirmation unit, used to determine the power generation output of micro gas turbines Model, output constraints and cost relationship; micro diesel generator output Model, output constraint and cost relationship; storage system output and constraint relationship. Constraint condition establishment unit, used to determine the production and marketing conservation constraint of the overall output; production and marketing conservation constraint of the overall output. And the scheduling optimization problem establishment unit, used to establish the scheduling optimization problem of the micro-balancing area in a non-cooperative environment based on the determined model, output constraint and cost relationship and constraint conditions.

[0065] The first optimization method building module, whose input end is connected to the output end of the system configuration module, is used to build a robust scheduling optimization method based on quantum-inspired crayfish optimization in an internal non-cooperative environment based on distributed energy.

[0066] The first optimization method construction module includes: an initialization unit for initializing the crayfish population; a quantum superposition state forming unit for representing the position of each crayfish by a quantum bit to form a quantum superposition state; a behavior rule formulation unit for formulating behavior simulation rules for crayfish; an optimal basis determination unit for designing a fitness function for evaluating the position of crayfish and determining its position on the Pareto frontier, updating the position of crayfish according to fitness and environmental factors, selecting the optimal solution, and outputting the Pareto optimal solution set after reaching a predetermined number of iterations or meeting other termination conditions.

[0067] The second optimization method construction module, whose input end is connected to the output end of the system configuration module, is used to construct a robust control optimization method based on Koopman dynamic identification. It includes: a real-time data detection unit, which is used to monitor the real-time data of each device in the micro-balancing area; a data processing unit, which is used to process the data; a function determination unit, which is used to determine the first-order and second-order statistics of the power signal as candidates for the Koopman characteristic function; and a linear dynamic model determination unit, which is used to construct a linear dynamic model of each device in the micro-balancing area in a high-dimensional space.

[0068] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0069] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0070] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0071] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0072] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment, characterized by: include: According to the composition of distributed energy in the micro-balancing area, the internal non-cooperative environment of distributed energy and the external non-cooperative environment of distributed energy, a robust energy dispatching and control system based on quantum-inspired crayfish optimization and Koopman dynamic identification is constructed; Based on the composition of distributed energy, a dispatch optimization problem model of micro-balancing areas in a non-cooperative environment is established; Based on the internal non-cooperative environment of distributed energy resources, a robust scheduling optimization method based on quantum-inspired crayfish optimization is constructed; Based on the external non-cooperative environment of distributed energy, a robust control optimization method based on Koopman dynamic identification is constructed.

2. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 1 is characterized by: Based on the composition of distributed energy, a dispatch optimization problem model of micro-balancing areas in a non-cooperative environment is established, including: Based on the composition of distributed energy, a model is established, including: photovoltaic power generation output Model; Wind power output Model; determine the relationship between the model, output constraints and costs, including: Micro gas turbine power generation output Model, output constraints and cost relationship; micro diesel generator output Model, output constraints and cost relationship; relationship between energy storage system output and constraints; Based on the established model, the constraints are determined, including: the production and marketing conservation constraints of the overall output; the production and marketing conservation constraints of the overall output; According to the determined model, the relationship between output constraints and costs, and the constraints, the dispatch optimization problem of the micro-balancing area in a non-cooperative environment is established.

3. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 2 is characterized by: The established multi-objective optimization problem for energy scheduling is: ； in, is the overall optimization goal, , is the weight, For operating costs, For pollution control costs, is the interaction cost between the microgrid and the main grid, and its calculation basis is , , , and They are respectively for selling electricity and purchasing electricity; and To purchase real-time total price and unit price; and Selling real-time total price and unit price, , The total cost of the gas turbine and diesel generator during operation respectively; The maintenance cost of the energy storage system; , Operation and maintenance costs for photovoltaic and wind power generation; , and denote the costs of treating pollutants generated by the main grid, gas turbines, and diesel generators, respectively; is the amount of type k pollutants emitted when the main power grid is in operation; is the cost coefficient for treating type k pollutants, and n is the total number of pollutant categories.

4. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 1 is characterized by: Based on the internal non-cooperative environment of distributed energy, a robust scheduling optimization method based on quantum-inspired crayfish optimization is constructed, including: Initialize the crayfish population as: ； in, represents the set of population positions, is a randomly generated set of initial positions, is the set of initial positions to be obtained through learning; The position of each crayfish is represented by a quantum bit to form a quantum superposition state, namely: ; in, represents the quantum state of the i-th crayfish, and is the probability density, and Represents quantum 0 and 1 states, corresponding to probability density, and also satisfies ; Formulate the behavior simulation rules for crayfish: define the ambient temperature T to determine the behavior of crayfish. When the temperature is high, it is considered as a high temperature state, and the crayfish will be triggered to avoid the heat; when When the temperature is above 100°C, it is considered as a suitable temperature, triggering the crayfish's foraging behavior; Design a fitness function fit( ), and determine its position on the Pareto frontier, update the crayfish position according to fitness and environmental factors, and select the optimal solution: ； in, For the updated location of the crayfish, is the current position of the crayfish, When the predetermined number of iterations is reached Or after meeting other termination conditions, output the Pareto optimal solution set.

5. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 4 is characterized by: When formulating the behavior simulation rules for crayfish: During the summer vacation period, randomly select the summer vacation location to simulate the quantum tunnel effect: ； in, Indicates the location of the summer resort. represents the best position under the current iteration number, represents the optimal position of the previous population; In the competition stage, design a competition among crayfish for a summer retreat: , in, Indicates the new competitive position, and Respectively represent the current positions of the two crayfish; In the foraging phase, formulate the foraging behavior of crayfish according to the quantum state: ； in, Indicates the food intake of crayfish.

6. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 1 is characterized by: Based on the external non-cooperative environment of distributed energy, a robust control optimization method based on Koopman dynamic identification is constructed, including: Conduct real-time data monitoring on each device in the micro-balancing area. The collected data should include but not be limited to power output, voltage, current, frequency, temperature, and status signals; The data is processed in sequence, including: cleaning to remove outliers and noise; standardizing or normalizing the data to make the data from different devices comparable; sampling the data to ensure the consistency of the time series data; determining first-order and second-order statistics of the power signal as candidates for the Koopman characteristic function; Construct a linear dynamic model of each device in the micro-balance area in high-dimensional space.

7. The optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment according to claim 6 is characterized by: Constructing a linear dynamic model of each device in the micro-balance area in a high-dimensional space includes the following steps: Define the observation function ,in, Used to describe the controlled object The state at the moment; calculate the time evolution relationship of the observation function: ,in is the forward prediction quantity, is the forward moving time, is the Koopman operator, which is usually chosen to be a polynomial function; The state evolution equation is established according to the Koopman operator: ； in, for The Koopman dynamic identification operator matrix; Establish an objective function that includes tracking error and control input energy As the minimization optimization objective: ； in, for , which is the lifting function of the observation function; is the control input to be solved; is the expected trajectory, N represents the prediction time domain, Q and R are weight matrices; Establish the Koopman dynamic identification predictive control optimization problem: ； in, is the control input matrix; Make a prediction based on the current state z(t) and use the solution to the optimization problem to update the control input: ； in, is the feedback gain, and represents the feedforward control input; The update strategy of the Koopman dynamic identification control algorithm is established: ； in, is the learning rate, is the gradient of the objective function with respect to the control gain.

8. An optimization system for realizing the optimization method for multi-objective scheduling and control of micro-balancing areas in a non-cooperative environment as claimed in any one of claims 1 to 7, characterized in that: include: The system configuration module builds a robust energy dispatching and control system based on quantum-inspired crayfish optimization and Koopman dynamic identification according to the composition of distributed energy in the micro-balancing area, the internal non-cooperative environment of distributed energy, and the external non-cooperative environment of distributed energy; A model building module, whose input end is connected to the output end of the system configuration module, is used to build a dispatch optimization problem model of a micro-balancing area in a non-cooperative environment based on the composition of distributed energy; A first optimization method construction module, whose input end is connected to the output end of the system configuration module, is used to construct a robust scheduling optimization method based on quantum-inspired crayfish optimization based on an internal non-cooperative environment of distributed energy resources; The second optimization method building module has an input end connected to the output end of the system configuration module and is used to construct a robust control optimization method based on Koopman dynamic identification.

9. The system according to claim 8, characterized in that: Model building modules, including: A model building unit, used for building a model based on the composition of distributed energy; Cost relationship confirmation unit, used to determine the power generation output of micro gas turbines Model, output constraints and cost relationship; micro diesel generator output Model, output constraints and cost relationship; relationship between energy storage system output and constraints; The constraint condition establishment unit is used to determine the production and marketing conservation constraints of the overall output; the production and marketing conservation constraints of the overall output; and a scheduling optimization problem establishing unit, which is used to establish the scheduling optimization problem of the micro-balancing area in a non-cooperative environment according to the determined model, output constraint and cost relationship and constraint conditions.

10. The system according to claim 8, characterized in that: The first optimization method building block includes: An initialization unit, used to initialize the crayfish population; A quantum superposition state forming unit, used for representing the position of each crayfish by a quantum bit to form a quantum superposition state; A behavior rule formulation unit, used to formulate behavior simulation rules for crayfish; The optimal basis determination unit is used to design a fitness function for evaluating the position of the crayfish and determine its position on the Pareto frontier, update the position of the crayfish according to the fitness and environmental factors, select the optimal solution, and output the Pareto optimal solution set after reaching a predetermined number of iterations or meeting other termination conditions.

11. The system according to claim 8, characterized in that: The second optimization method building block includes: A real-time data detection unit, used for real-time data monitoring of each device in the micro-balancing area; A data processing unit, used for processing data; a function determination unit, for determining first-order and second-order statistics of the power signal as candidates for the Koopman characteristic function; The linear dynamic model determination unit is used to construct the linear dynamic model of each device in the micro-balance area in the high-dimensional space.

12. A storage medium, characterized in that: The storage medium stores computer instructions, and the computer instructions are used to enable the processor to implement the optimization method for multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment as described in any one of claims 1 to 7 when executed.

13. An electronic device, characterized in that: It includes a processor and a storage medium connected to the processor; wherein the storage medium stores instructions that can be executed by the processor, and the instructions are executed by the processor so that the processor can execute the optimization method for multi-objective scheduling and control of a micro-balancing area in a non-cooperative environment as described in any one of claims 1 to 7.

Images