A microgrid dispatch optimization method based on feature space and related equipment

Abstract

The present application relates to the field of microgrid technology, and provides a method for microgrid scheduling optimization based on feature space and related equipment, the method comprising: based on power supply load, average load power purchase unit price and average carbon emissions, using feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid; constructing a microgrid day-ahead scheduling optimization model according to the renewable energy utilization efficiency; constructing constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid; under the constraint of the constraint conditions, using an improved genetic algorithm to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid; and scheduling the target microgrid using the optimal scheduling strategy. The method of the present application can improve the reliability and accuracy of microgrid scheduling optimization.

Description

Technical Field

[0001] The present application relates to the field of microgrid technology, and in particular to a microgrid scheduling optimization method based on feature space and related equipment. Background Art

[0002] Microgrid is a new energy system that integrates distributed power generation, energy storage devices and loads. It provides a way for distributed power supply access systems and also provides a beneficial supplement to large power grids.

[0003] Due to the intermittent and volatile output of renewable energy sources such as photovoltaic and wind power, in order to meet the operation of microgrids and the power supply quality requirements, when renewable energy generation is connected to microgrids, the utilization efficiency of renewable energy in microgrids needs to be constantly balanced and optimized. In addition, factors such as load fluctuations, carbon emissions, and exchange power with the main grid will have different effects on the state of the microgrid, thereby affecting the efficiency and safety of the microgrid.

[0004] The dispatch optimization of microgrids is a problem that considers multiple objectives. The existing technology for the research on intelligent optimization algorithms has gradually matured, but its practical application in the field of microgrid operation optimization dispatching needs to be improved. It faces complex calculation problems and it is difficult to obtain the optimal dispatching plan, resulting in low reliability and accuracy of microgrid dispatch optimization. Summary of the invention

[0005] The present application provides a microgrid scheduling optimization method and related equipment based on feature space, which can solve the problem of low reliability and accuracy of microgrid scheduling optimization.

[0006] In a first aspect, an embodiment of the present application provides a microgrid scheduling optimization method based on feature space, and the microgrid scheduling optimization method includes:

[0007] Obtain the power supply load, average power purchase price and average carbon emissions of the target microgrid, and use the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid based on the power supply load, average power purchase price and average carbon emissions;

[0008] A microgrid day-ahead dispatch optimization model is constructed based on the utilization efficiency of renewable energy. The microgrid day-ahead dispatch optimization model includes the objective function of renewable energy utilization efficiency, the load fluctuation objective function of the target microgrid, the main grid power exchange objective function and the discharge depth objective function.

[0009] According to the exchange power between the target microgrid and the main grid, the constraint conditions for scheduling optimization of the target microgrid are established;

[0010] Under the constraints of the constraints, the improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid; each individual of the parent population of the improved genetic algorithm includes the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the total power exchanged between the target microgrid and the main grid within a scheduling cycle;

[0011] The target microgrid is dispatched using the optimal dispatching strategy.

[0012] In a second aspect, an embodiment of the present application provides a microgrid scheduling optimization device based on feature space, comprising:

[0013] An acquisition module is used to obtain the power supply load, the average power purchase price of the load, and the average carbon emissions of the target microgrid, and based on the power supply load, the average power purchase price of the load, and the average carbon emissions, the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid are calculated using the feature space;

[0014] A construction module is used to construct a microgrid day-ahead dispatch optimization model based on the utilization efficiency of renewable energy. The microgrid day-ahead dispatch optimization model includes a new energy utilization efficiency objective function, a load fluctuation objective function of the target microgrid, a main grid power exchange objective function, and a discharge depth objective function.

[0015] A constraint condition construction module is used to construct the constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid;

[0016] The solution module uses an improved genetic algorithm to solve the microgrid day-ahead scheduling optimization model under the constraints of the constraints to obtain the optimal scheduling strategy of the target microgrid; each individual of the parent population of the improved genetic algorithm includes the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the total power exchanged between the target microgrid and the main grid within a scheduling cycle;

[0017] The scheduling module uses the optimal scheduling strategy to schedule the target microgrid.

[0018] In a third aspect, an embodiment of the present application provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned feature space-based microgrid scheduling optimization method when executing the above-mentioned computer program.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned feature space-based microgrid scheduling optimization method.

[0020] The above solution of the present application has the following beneficial effects:

[0021] In an embodiment of the present application, the power supply load, the average unit price of electricity purchase and the average carbon emissions of the target microgrid are obtained, and based on the power supply load, the average unit price of electricity purchase and the average carbon emissions, the feature space is used to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid. Then, a microgrid day-ahead scheduling optimization model is constructed according to the renewable energy utilization efficiency, and then constraints for scheduling optimization of the target microgrid are constructed according to the exchange power between the target microgrid and the main grid. Then, under the constraints of the constraints, the improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid, and finally, the target microgrid is scheduled using the optimal scheduling strategy. Among them, the feature space is used to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, which can analyze the relationship between the power supply load, the average unit price of electricity purchase of the load, the average carbon emissions and the renewable energy utilization efficiency, and the exchange power between the target microgrid and the main grid in the feature space, improve the accuracy of the renewable energy utilization efficiency and the exchange power between the target microgrid and the main grid, and construct a microgrid day-ahead scheduling optimization model including multiple objective functions, which can provide a comprehensive and rich description of the target microgrid. The improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model, which can improve the reliability of the optimal scheduling strategy, and the target microgrid is scheduled based on the optimal scheduling strategy, which effectively improves the reliability and accuracy of the target microgrid scheduling optimization.

[0022] Other beneficial effects of the present application will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 A flowchart of a microgrid scheduling optimization method based on feature space provided in an embodiment of the present application;

[0025] Figure 2A schematic diagram of the structure of a microgrid scheduling optimization device based on feature space provided in one embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of a terminal device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0028] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0029] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0031] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0032] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0033] In response to the problems of low dispatching reliability and accuracy of existing microgrids, an embodiment of the present application provides a microgrid dispatching optimization method based on feature space. The microgrid dispatching optimization method obtains the power supply load, average load power purchase unit price and average carbon emissions of the target microgrid, and based on the power supply load, average load power purchase unit price and average carbon emissions, uses the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, and then constructs a microgrid day-ahead dispatching optimization model based on the renewable energy utilization efficiency, and then constructs constraint conditions for dispatching optimization of the target microgrid based on the exchange power between the target microgrid and the main grid, and then, under the constraints of the constraints, uses an improved genetic algorithm to solve the microgrid day-ahead dispatching optimization model to obtain the optimal dispatching strategy of the target microgrid, and finally uses the optimal dispatching strategy to dispatch the target microgrid. Among them, the feature space is used to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, which can analyze the relationship between the power supply load, the average unit price of electricity purchase of the load, the average carbon emissions and the renewable energy utilization efficiency, and the exchange power between the target microgrid and the main grid in the feature space, improve the accuracy of the renewable energy utilization efficiency and the exchange power between the target microgrid and the main grid, and construct a microgrid day-ahead scheduling optimization model including multiple objective functions, which can provide a comprehensive and rich description of the target microgrid. The improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model, which can improve the reliability of the optimal scheduling strategy, and the target microgrid is scheduled based on the optimal scheduling strategy, which effectively improves the reliability and accuracy of the target microgrid scheduling optimization.

[0034] Next, the microgrid scheduling optimization method based on feature space provided in this application is exemplified.

[0035] like Figure 1 As shown, the microgrid scheduling optimization method based on feature space provided in this application includes the following steps:

[0036] Step 11, obtain the power supply load, average power purchase price and average carbon emissions of the target microgrid, and based on the power supply load, average power purchase price and average carbon emissions, use the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid.

[0037] The above-mentioned target microgrid is a microgrid that needs to be dispatched and optimized. The microgrid refers to a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, power supply loads, monitoring and protection devices, etc. The main grid refers to the main network for power supply, which is a network connecting the power plant and the microgrid. The power supply load refers to the total amount of electric power taken from the microgrid by the power users' electrical equipment at a certain moment. The average unit price of load purchase electricity refers to the cost generated by the electricity purchased from the main grid per unit load in a day. The average carbon emissions refer to the average carbon emissions emitted by the microgrid during operation over a period of time. The utilization efficiency of renewable energy refers to the utilization efficiency of wind turbines and the utilization efficiency of photovoltaic power sources. The utilization efficiency of wind turbines refers to the power generation efficiency of wind turbines, and the utilization efficiency of photovoltaic power sources refers to the power generation efficiency of photovoltaic power sources. The exchange power is the amount of electric power exchanged between the microgrid and the main grid. The above-mentioned power supply load, average unit price of load purchase electricity and average carbon emissions are all data within the dispatch period of the target microgrid before the current moment. For example, if the current moment is 8 o'clock, then the dispatch period is from 7 o'clock to 8 o'clock.

[0038] In some embodiments of the present application, the power supply load, the average power purchase price of the load, and the average carbon emissions of the target microgrid can be obtained by accessing the control system and other devices of the target microgrid. The above steps of calculating the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid using the feature space based on the power supply load, the average power purchase price of the load, and the average carbon emissions are specifically as follows:

[0039] By formula:

[0040]

[0041] Calculating the efficiency of renewable energy utilization And the exchange power between the target microgrid and the main grid .

[0042] in, Indicates the power supply load, Indicates the average unit price of electricity purchased by load, represents the average carbon emissions, represents the feature space diagonal matrix, Indicates the duration of electricity consumption in stages. Indicates the battery discharge cost.

[0043] It should be noted that the above-mentioned power supply load is a microgrid safety indicator, the average unit price of electricity purchase is a microgrid economic indicator, and the average carbon emissions of the load is a microgrid low-carbon indicator.

[0044] For example, the above formula can be run using computer software such as MATLAB and Python to obtain the renewable energy utilization efficiency , the exchange power between the target microgrid and the main grid .

[0045] It is worth mentioning that by using the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, it is possible to analyze the relationship between the power supply load, the average unit price of electricity purchase of the load, the average carbon emissions and the renewable energy utilization efficiency, and the exchange power between the target microgrid and the main grid in the feature space, thereby improving the accuracy of the renewable energy utilization efficiency and the exchange power between the target microgrid and the main grid.

[0046] Step 12: construct a microgrid day-ahead dispatch optimization model based on renewable energy utilization efficiency.

[0047] The above-mentioned microgrid day-ahead dispatch optimization model includes the objective function of new energy utilization efficiency, the load fluctuation objective function of the target microgrid, the main grid power exchange objective function and the discharge depth objective function.

[0048] Specifically, the objective function of new energy utilization efficiency is:

[0049]

[0050]

[0051]

[0052] in, represents the utilization efficiency of wind turbines, Represents the photovoltaic power utilization efficiency, , represents the efficiency of renewable energy utilization, Indicates The actual maximum power that the wind turbine can generate in a certain time period is: Indicates The power generated by the wind turbine is used in each time period. Indicates The actual maximum power that can be generated by the photovoltaic power source in a time period is Indicates The power generated by photovoltaic power source is used in each time period. , Indicates the total number of time periods in a scheduling cycle.

[0053] The load fluctuation objective function is:

[0054]

[0055] in, represents the variance of the daily load curve, Indicates The load of a time period, represents the average value of load in a scheduling cycle, Indicates the total number of time periods in a scheduling cycle.

[0056] The main grid power exchange objective function is:

[0057]

[0058] in, It represents the total power exchanged between the target microgrid and the main grid in a scheduling cycle, Indicates The state of the target microgrid purchasing electricity from the main grid in each time period. Indicates The power purchased by the target microgrid from the main grid in each time period, Indicates The state of the target microgrid selling electricity to the main grid in each time period. Indicates The power fed back by the target microgrid to the main grid in a time period.

[0059] The discharge depth objective function is:

[0060]

[0061] in, Indicates the actual number of times the battery is charged in a scheduling cycle. Indicates the actual number of times the battery is discharged in a scheduling cycle. Indicates The battery charging status of each time period, Indicates The battery charging status of each time period, Indicates The battery discharge status of a time period, Indicates The battery discharge status during a period of time.

[0062] It should be noted that in terms of the efficiency of new energy utilization, it is hoped that the abandonment of wind and photovoltaic power will be reduced as much as possible, so as to improve the efficiency of new energy utilization. At the same time, in order to ensure the balance of new energy utilization and avoid excessive consumption of wind power or excessive abandonment of photovoltaic power, it is necessary to reduce the gap between the utilization efficiency of wind power generation and photovoltaic power generation as much as possible. Therefore, the objective function of new energy utilization efficiency is constructed. For the interest subject of the power grid, the impact of load fluctuation on it and its load autonomous absorption capacity are mainly considered. In terms of load fluctuation, it is hoped that the fluctuation of load in a dispatching cycle is as small as possible. The variance of load in different periods of a dispatching cycle is used to represent the flatness of the load curve. Therefore, the load fluctuation objective function is constructed. In terms of the autonomous absorption capacity of the load of the microgrid, it is hoped that the balance of power supply and consumption can be achieved within the microgrid as much as possible, and the power exchange with the main grid can be reduced as much as possible. Therefore, the main grid power exchange objective function is constructed. For the interest subject of the battery, in order to ensure the service life of the battery, its state of charge, number of charge and discharge, and discharge depth should be considered. Therefore, the discharge depth objective function is constructed.

[0063] It is worth mentioning that constructing a microgrid day-ahead dispatch optimization model including multiple objective functions can provide a comprehensive and rich description of the target microgrid.

[0064] Step 13: construct constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid.

[0065] Specifically, the constraints include power constraints, battery constraints, exchange power constraints and new energy constraints.

[0066] The power constraints are:

[0067]

[0068]

[0069]

[0070] in, Indicates The power consumed by the load in a time period, Indicates The power generated by the wind turbine is used in each time period. Indicates The power generated by photovoltaic power source is used in each time period. Indicates The power exchanged between the microgrid and the main grid in each time period, Indicates The charging and discharging power of the battery in a certain period of time, Indicates The battery charging status in a certain period of time. At that time, The battery is charged during this period of time. At that time, The battery is not charged for a period of time. Indicates The charging power of the battery in a certain period of time, Indicates The battery discharge status in a certain period of time. Indicates The discharge power of the battery in a certain period of time, Indicates The exchange power between the target microgrid and the main grid in each time period, Indicates The state of the target microgrid purchasing electricity from the main grid in each time period. Indicates The power purchased by the target microgrid from the main grid in each time period, Indicates The state of the target microgrid selling electricity to the main grid in each time period. Indicates The power fed back by the target microgrid to the main grid in a time period.

[0071] The battery constraints are:

[0072]

[0073]

[0074]

[0075]

[0076] in, Indicates the lower limit of the battery's state of charge. Indicates The battery state of charge for a period of time, Indicates the upper limit of the battery's state of charge. Indicates the rated capacity of the battery. Indicates The battery charging status of each time period, Indicates The battery charging status of each time period, Indicates The battery discharge status of a time period, Indicates The battery discharge status of a time period, Indicates the total number of time periods in a scheduling cycle. Indicates the battery charging times limit value. Indicates the battery discharge times limit.

[0077] The switching power constraint is:

[0078]

[0079] ;

[0080] in, Indicates The exchange power threshold between the target microgrid and the main grid in a time period.

[0081] The new energy constraints are:

[0082]

[0083]

[0084] in, Indicates The actual maximum power that the photovoltaic power source can generate in a certain period of time, Indicates The actual maximum power that the wind turbine can generate in a certain time period is: Represents the efficiency of renewable energy utilization.

[0085] It should be noted that the power provided by the three power supply entities, the main grid, batteries and renewable energy, in each period should just meet the power demand of the load, so the power constraint condition is constructed. Considering the role of the battery, in order to prevent the battery from being overcharged or over-discharged, its state of charge should meet the upper and lower limit constraints. From the perspective of battery life, in order to prevent the improper charging rate from damaging the battery life, it is necessary to limit the charging and discharging power per unit time. The number of battery charge and discharge times will also affect the battery life, and the battery cannot be charged and discharged at the same time, so the battery constraint condition is constructed. The exchange power between the main grid and the microgrid does not exceed the threshold. In any scheduling cycle, the exchange power between the main grid and the microgrid is the sum of the input power and the output power, so the exchange power constraint condition is constructed. In order to ensure the full utilization of renewable energy, new energy constraints are constructed.

[0086] Step 14, under the constraints of the constraints, using the improved genetic algorithm to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid.

[0087] Each individual of the parent population of the improved genetic algorithm includes the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the total power exchanged between the target microgrid and the main grid in one scheduling cycle. The flatness of the load curve describes the load variation of the target microgrid. The flatter the load curve, the smaller the load variation.

[0088] In some embodiments of the present application, under the constraints of the constraints, the steps of solving the microgrid day-ahead dispatch optimization model using the improved genetic algorithm are specifically as follows:

[0089] In the first step, under the constraints, multiple individuals of the parent population are generated, crossover mutation is performed on all individuals in the parent population to generate a child population including multiple individuals, and the parent population and the child population are merged to obtain an intermediate population including multiple individuals.

[0090] It should be noted that crossover mutation is a mating process in which the parameter characteristics of individuals in the offspring population differ from those in the parent population, and the number of individuals generated by crossover mutation is the same as the number of individuals in the parent population. When generating multiple individuals of the parent population, for each individual, under the constraints of the constraints, the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the total power exchanged between the target microgrid and the main grid in a scheduling cycle are randomly generated to obtain individuals including 5 values.

[0091] In the second step, the individual fitness of each individual in the intermediate population is calculated based on the objective function of new energy utilization efficiency, the load fluctuation objective function of the target microgrid, the main grid power exchange objective function and the discharge depth objective function.

[0092] Specifically, for each individual, the average load power supply cost, the average load electricity purchase unit price, the inverse of the new energy utilization efficiency, the difference in utilization efficiency between wind power generation and photovoltaic power generation, the flatness of the load curve, and the sum of the total power exchanged between the target microgrid and the main grid within a scheduling cycle are substituted into the new energy utilization efficiency objective function, the load fluctuation objective function of the target microgrid, the main grid power exchange objective function and the discharge depth objective function, and the values ​​of the four objective functions are calculated and used as the individual fitness of the individual.

[0093] The third step is to use the individual fitness of all individuals to perform non-dominated sorting on all individuals in the intermediate population, and take multiple individuals in the first non-dominated layer in the non-dominated sorting result as the first winning individuals.

[0094] Specifically, the first non-dominated layer is a layer composed of multiple individuals whose sum of four objective function values ​​is smaller than that of other individuals.

[0095] The fourth step is to perform chaotic mapping on all individuals that are not the first winning individuals in the non-dominated sorting results to obtain multiple new individuals, and select multiple second winning individuals from all the new individuals.

[0096] Specifically, all individuals other than the first winning individual refer to all individuals except the first winning individual in the non-dominated sorting result, and the condition for selecting multiple second winning individuals from all new individuals is the individual with the smallest sum of the four objective function values ​​excluding the first non-dominated layer.

[0097] The fifth step is to merge all the first-winning individuals and all the second-winning individuals to obtain the true offspring population, perform non-dominated sorting on all individuals in the true offspring population, and take each individual in the first non-dominated layer in the non-dominated sorting result as an optimal solution.

[0098] The sixth step is to determine whether the solution converges. If so, each optimal solution is used as an optimal scheduling strategy. Otherwise, the true offspring population is used as the parent population, and the crossover mutation is performed between all individuals in the parent population to generate an offspring population including multiple individuals, and the parent population and the offspring population are merged to obtain an intermediate population including multiple individuals.

[0099] Exemplarily, the condition for determining whether the solution converges may be whether the number of times the optimal solution is obtained reaches a preset number, etc.

[0100] It should be noted that after this step, if the number of optimal scheduling strategies is greater than 1, an optimal scheduling strategy is selected from all optimal scheduling strategies manually as the final optimal scheduling strategy to proceed to the next step.

[0101] It is worth mentioning that the use of improved genetic algorithm to solve the microgrid day-ahead dispatch optimization model can improve the reliability of the optimal dispatch strategy.

[0102] Step 15: Use the optimal dispatching strategy to dispatch the target microgrid.

[0103] Specifically, the optimal scheduling strategy includes the utilization efficiency of new energy, the difference in utilization efficiency between wind power generation and photovoltaic power generation, the flatness of the load curve, and the sum of the total power exchanged between the target microgrid and the main grid within a scheduling cycle. The utilization efficiency of new energy of the target microgrid, the difference in utilization efficiency between wind power generation and photovoltaic power generation, the flatness of the load curve, and the sum of the total power exchanged between the target microgrid and the main grid within a scheduling cycle are adjusted to the corresponding values ​​in the optimal scheduling strategy.

[0104] It is worth mentioning that scheduling the target microgrid based on the optimal scheduling strategy can effectively improve the reliability and accuracy of the target microgrid scheduling optimization.

[0105] The following is an exemplary description of the microgrid scheduling optimization device based on feature space provided in this application.

[0106] like Figure 2 As shown, the embodiment of the present application provides a microgrid scheduling optimization device based on feature space, and the microgrid scheduling optimization device 200 based on feature space includes:

[0107] The acquisition module 201 acquires the power supply load, the average power purchase price of the load, and the average carbon emissions of the target microgrid, and calculates the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid based on the power supply load, the average power purchase price of the load, and the average carbon emissions by using the feature space;

[0108] Building module 202, building a microgrid day-ahead dispatch optimization model according to the utilization efficiency of renewable energy; the microgrid day-ahead dispatch optimization model includes a new energy utilization efficiency objective function, a load fluctuation objective function of the target microgrid, a main grid power exchange objective function and a discharge depth objective function;

[0109] A constraint condition building module 203 builds constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid;

[0110] The solving module 204 solves the microgrid day-ahead dispatch optimization model using the improved genetic algorithm under the constraints of the constraints to obtain the optimal dispatch strategy of the target microgrid; the individuals include the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the value of the total power exchanged between the target microgrid and the main grid within a dispatch cycle;

[0111] The dispatching module 205 dispatches the target microgrid using the optimal dispatching strategy.

[0112] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0113] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0114] like Figure 3 As shown, an embodiment of the present application provides a terminal device. The terminal device D10 of this embodiment includes: at least one processor D100 ( Figure 3 Only one processor is shown in the figure), a memory D101, and a computer program D102 stored in the memory D101 and executable on the at least one processor D100, wherein the processor D100 implements the steps of any of the above-mentioned method embodiments when executing the computer program D102.

[0115] Specifically, when the processor D100 executes the computer program D102, it obtains the power supply load, the average unit price of electricity purchase and the average carbon emissions of the target microgrid, and based on the power supply load, the average unit price of electricity purchase and the average carbon emissions, uses the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, and then constructs a microgrid day-ahead scheduling optimization model according to the renewable energy utilization efficiency, and then constructs constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid, and then, under the constraints of the constraints, uses the improved genetic algorithm to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid, and finally uses the optimal scheduling strategy to schedule the target microgrid. Among them, the feature space is used to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid, which can analyze the relationship between the power supply load, the average unit price of electricity purchase of the load, the average carbon emissions and the renewable energy utilization efficiency, and the exchange power between the target microgrid and the main grid in the feature space, improve the accuracy of the renewable energy utilization efficiency and the exchange power between the target microgrid and the main grid, and construct a microgrid day-ahead scheduling optimization model including multiple objective functions, which can provide a comprehensive and rich description of the target microgrid. The improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model, which can improve the reliability of the optimal scheduling strategy, and the target microgrid is scheduled based on the optimal scheduling strategy, which effectively improves the reliability and accuracy of the target microgrid scheduling optimization.

[0116] The processor D100 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0117] In some embodiments, the memory D101 may be an internal storage unit of the terminal device D10, such as a hard disk or memory of the terminal device D10. In other embodiments, the memory D101 may also be an external storage device of the terminal device D10, such as a plug-in hard disk, a smart memory card (SMC, SmartMedia Card), a secure digital (SD, Secure Digital) card, a flash card (Flash Card), etc. equipped on the terminal device D10. Further, the memory D101 may also include both an internal storage unit of the terminal device D10 and an external storage device. The memory D101 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program, etc. The memory D101 may also be used to temporarily store data that has been output or is to be output.

[0118] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0119] An embodiment of the present application provides a computer program product. When the computer program product is run on a terminal device, the terminal device can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the microgrid scheduling optimization method device / terminal device based on feature space, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a disk or an optical disk.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0123] The above is a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A microgrid dispatch optimization method based on feature space, characterized in that: include: Obtain the power supply load, average power purchase price and average carbon emissions of the target microgrid, and based on the power supply load, average power purchase price and average carbon emissions, use the feature space to calculate the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid; A microgrid day-ahead dispatch optimization model is constructed according to the renewable energy utilization efficiency; the microgrid day-ahead dispatch optimization model includes a new energy utilization efficiency objective function, a load fluctuation objective function of the target microgrid, a main grid power exchange objective function and a discharge depth objective function; Establishing constraint conditions for scheduling optimization of the target microgrid according to the exchange power between the target microgrid and the main grid; Under the constraints of the constraints, the improved genetic algorithm is used to solve the microgrid day-ahead scheduling optimization model to obtain the optimal scheduling strategy of the target microgrid; each individual of the parent population of the improved genetic algorithm includes the reciprocal of the wind turbine utilization efficiency, the reciprocal of the photovoltaic power utilization efficiency, the difference between the wind turbine utilization efficiency and the photovoltaic power utilization efficiency, the flatness of the load curve, and the total power exchanged between the target microgrid and the main grid within a scheduling cycle; Using the optimal scheduling strategy to schedule the target microgrid; The method of calculating the renewable energy utilization efficiency of the target microgrid and the exchange power between the target microgrid and the main grid by using the feature space based on the power supply load, the average power purchase unit price of the load and the average carbon emissions includes: By formula: Calculating the efficiency of renewable energy utilization And the exchange power between the target microgrid and the main grid ; in, Indicates the power supply load, Indicates the average unit price of electricity purchased by load, represents the average carbon emissions, represents the feature space diagonal matrix, Indicates the duration of electricity consumption in stages. Indicates the battery discharge cost; The new energy utilization efficiency objective function is: in, represents the utilization efficiency of wind turbines, Represents the photovoltaic power utilization efficiency, , Indicates The actual maximum power that the wind turbine can generate in a certain time period is: Indicates The power generated by the wind turbine is used in each time period. Indicates The actual maximum power that the photovoltaic power source can generate in a certain period of time, Indicates The power generated by photovoltaic power source is used in each time period. , Indicates the total number of time periods in a scheduling cycle.

2. The microgrid dispatch optimization method according to claim 1, characterized in that: The load fluctuation objective function is: in, represents the variance of the daily load curve, Indicates The load of a time period, represents the average value of load in a scheduling cycle, Indicates the total number of time periods in a scheduling cycle.

3. The microgrid dispatch optimization method according to claim 2, characterized in that: The main network power exchange objective function is: in, It represents the total power exchanged between the target microgrid and the main grid in a scheduling cycle, Indicates The state of the target microgrid purchasing electricity from the main grid in a time period, Indicates The power purchased by the target microgrid from the main grid in a time period, Indicates The state of the target microgrid selling electricity to the main grid in a time period, Indicates The power fed back by the target microgrid to the main grid in a time period.

4. The microgrid dispatch optimization method according to claim 3, characterized in that: The discharge depth objective function is: in, Indicates the actual number of times the battery is charged in a scheduling cycle. Indicates the actual number of times the battery is discharged in a scheduling cycle. Indicates Charging status mark for each time period, Indicates Charging status mark for each time period, Indicates The discharge status mark of each time period, Indicates The discharge status mark of each time period.

5. The microgrid dispatch optimization method according to claim 1, characterized in that: The constraints include power constraints, battery constraints, exchange power constraints and new energy constraints; The power constraint condition is: in, Indicates The power consumed by the load in a time period, Indicates The power generated by the wind turbine is used in each time period. Indicates The power generated by photovoltaic power source is used in each time period. Indicates The power exchanged between the microgrid and the main grid in each time period, Indicates The charging and discharging power of the battery in a certain period of time, Indicates The battery charging status in a certain period of time. At that time, The battery is charged during this period of time. At that time, The battery is not charged for a period of time. Indicates The charging power of the battery in a certain period of time, Indicates The battery discharge status in a certain period of time, Indicates The discharge power of the battery in a certain period of time, Indicates The exchange power between the target microgrid and the main grid in each time period, Indicates The state of the target microgrid purchasing electricity from the main grid in a time period, Indicates The power purchased by the target microgrid from the main grid in a time period, Indicates The state of the target microgrid selling electricity to the main grid in a time period, Indicates The power fed back by the target microgrid to the main grid in a time period; The battery constraints are: in, Indicates the lower limit of the battery's state of charge. Indicates The battery state of charge for a period of time, Indicates the upper limit of the battery's state of charge. Indicates the rated capacity of the battery. Indicates The battery charging status of each time period, Indicates The battery charging status of each time period, Indicates The battery discharge status of a time period, Indicates The battery discharge status of a time period, Indicates the total number of time periods in a scheduling cycle. Indicates the battery charging times limit value. Indicates the battery discharge times limit value; The switching power constraint condition is: ； in, Indicates The exchange power threshold between the target microgrid and the main grid in each time period; The new energy constraints are: in, Indicates The actual maximum power that the photovoltaic power source can generate in a certain period of time, Indicates The actual maximum power that the wind turbine can generate in a certain time period is: Represents the efficiency of renewable energy utilization.

6. The microgrid dispatch optimization method according to claim 1, characterized in that: The improved genetic algorithm is used to solve the microgrid day-ahead dispatch optimization model to obtain the optimal dispatch strategy of the target microgrid, including: Under the constraints, multiple individuals of a parent population are generated, crossover mutation is performed on all individuals in the parent population to generate a child population including multiple individuals, and the parent population and the child population are merged to obtain an intermediate population including multiple individuals; Calculating the individual fitness of each individual in the intermediate population according to the new energy utilization efficiency objective function, the load fluctuation objective function of the target microgrid, the main grid power exchange objective function and the discharge depth objective function; Using the individual fitness of all individuals to perform non-dominated sorting on all individuals in the intermediate population, and taking multiple individuals in the first non-dominated layer in the non-dominated sorting result as first winning individuals; Performing chaotic mapping on all individuals that are not the first winning individuals in the non-dominated sorting results to obtain multiple new individuals, and selecting multiple second winning individuals from all the new individuals; Merge all first-winning individuals and all second-winning individuals to obtain a true offspring population, perform non-dominated sorting on all individuals in the true offspring population, and take each individual in the first non-dominated layer in the non-dominated sorting result as an optimal solution; Determine whether the solution converges. If so, take each of the optimal solutions as an optimal scheduling strategy. Otherwise, take the true offspring population as the parent population, return to the step of performing crossover mutation on all individuals in the parent population to generate an offspring population including multiple individuals, and merge the parent population and the offspring population to obtain an intermediate population including multiple individuals.

7. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the microgrid scheduling optimization method based on feature space as described in any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the microgrid scheduling optimization method based on feature space as described in any one of claims 1 to 6 is implemented.

Images