A microgrid AC / DC multi-channel energy intelligent dispatching system

Abstract

The present invention discloses a microgrid AC / DC multi-channel energy intelligent dispatching system, which relates to the field of energy intelligent dispatching technology, including an energy management module for managing energy and load equipment in a microgrid; a data acquisition module for real-time acquisition of state data of energy and load equipment in a microgrid; an optimization control module for processing and analyzing the acquired state data, using mixed integer linear programming for optimization calculation, and generating an optimal energy dispatching strategy; a system dispatching module for executing the optimal energy dispatching strategy generated by the optimization control module, and realizing multi-channel energy intelligent dispatching by changing the switch state and adjusting the output power. Through the energy management module and the optimization control module, the present invention can perform intelligent comprehensive management and dispatching of multi-channel energy in a microgrid, avoid the problem of a single dispatching strategy in a traditional microgrid system, and improve energy utilization and the overall efficiency of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of energy intelligent dispatching, and in particular to a microgrid AC / DC multi-channel energy intelligent dispatching system. Background Art

[0002] With the continuous growth of global energy demand and the enhancement of environmental awareness, microgrids, as a new type of power supply, have gradually attracted attention. Microgrids can integrate multiple energy forms, such as photovoltaics, wind energy, energy storage systems, etc., and flexibly respond to diverse power needs. However, existing microgrid systems have many problems in energy management and scheduling: Energy management lacks intelligent scheduling: The energy scheduling system of traditional microgrids usually relies on preset rules or simple control strategies, which is difficult to cope with complex and changeable energy supply and demand situations, resulting in low energy utilization efficiency; Insufficient data processing capabilities: The data acquisition and processing modules in the existing technology usually only perform simple processing on basic data, and fail to make full use of real-time data and historical data for in-depth analysis, which affects the scheduling accuracy and response speed of the system; System optimization is not comprehensive: The optimization control strategies of many microgrid systems only consider a single goal, such as minimizing costs or maximizing energy utilization, lack comprehensiveness and globality, and it is difficult to achieve the optimal scheduling of the entire system. Summary of the invention

[0003] Based on the above-mentioned shortcomings of the prior art, the purpose of the present invention is to provide a microgrid AC / DC multi-channel energy intelligent dispatching system to solve the above-mentioned technical problems.

[0004] To achieve the above object, the present invention provides the following technical solution: a microgrid AC / DC multi-channel energy intelligent dispatching system, comprising:

[0005] Energy management module, used to manage energy and load equipment in the microgrid, where energy includes AC energy, DC energy and energy storage energy, and energy management includes changing switch states, adjusting output power and AC / DC conversion;

[0006] Data acquisition module, used to collect status data of energy and load equipment in the microgrid in real time;

[0007] The optimization control module is used to process and analyze the collected status data, use mixed integer linear programming to perform optimization calculations, and generate the optimal energy scheduling strategy;

[0008] The system scheduling module is used to execute the optimal energy scheduling strategy generated by the optimization control module, and realize multi-channel energy intelligent scheduling by changing the switch state and adjusting the output power.

[0009] The present invention is further configured such that the status data includes switch status, output power, power loss, energy cost, energy conversion efficiency, energy transmission efficiency and load demand.

[0010] The present invention is further configured to process and analyze the collected state data, use mixed integer linear programming to perform optimization calculations, and generate an optimal energy scheduling strategy, including:

[0011] Normalize the state data of energy and load equipment in the collected microgrid;

[0012] Construct the global objective function f according to the normalized state data opt , the global objective function f opt Used for global optimization;

[0013] At each time step t, locally optimizing the global objective function through a mixed integer linear programming model;

[0014] At the end of each time step t, when the global objective function f opt Converge to within the set threshold and output the optimal scheduling strategy P for the current time step i,gen (t+1) and x i (t+1), where P i,gen (t+1) is the output power of the i-th energy source at the next time step t+1, x i (t+1) is the switching state of the i-th energy source at the next time step t+1.

[0015] The present invention is further configured such that the global objective function f opt The construction logic is: Among them, T is the total number of time steps, N is the number of energy sources, P i,loss (t) is the power loss of the i-th energy source at time step t, P i,gen (t) is the output power of the i-th energy source at time step t, C i,cost (t) is the cost of the i-th energy at time step t, L j (t) is the required power of the jth load device at time step t, w j is the priority weight of the jth load device, M is the number of load devices, U j (t) is the satisfaction rate of the jth load device at time step t, η system (t) is the overall operating efficiency at time step t, λ1, λ2 and λ3 are weight coefficients used to adjust power loss, energy cost and load priority in the objective function f opt , α and β are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part and the load priority part respectively.

[0016] The present invention is further configured as follows: system The calculation logic of (t) is: Among them, η i,conv (t) is the energy conversion efficiency of the i-th energy source at time step t, γ j is the adjustment factor used to determine the weight of the load satisfaction of the jth load device, and η i,trans (t) is the energy transfer efficiency of the i-th energy source at time step t.

[0017] The present invention is further configured to locally optimize the global objective function through a mixed integer linear programming model, and the logic of the local optimization is: Among them, f MILP is the local optimization objective function, x i (t) is the switch state of the i-th energy at time step t, which takes the value of 0 or 1. i When (t) = 0, it means that the i-th energy source is in the off state at time step t. i When (t) = 1, it means that the i-th energy source is in the on state at time step t, ω 1,i ,ω 2,i and ω 3,j are weight coefficients, which are used to adjust the power loss of the i-th energy source, the energy cost of the i-th energy source, and the load priority of the j-th load device in the local optimization objective function f MILP The influence weight in i and β j are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part of the i-th energy source and the load priority part of the j-th load device;

[0018] The present invention is further configured to locally optimize the objective function f MILP The solution logic includes:

[0019] Set the local optimization objective function f MILP The constraints include and C max (t) is the total cost of energy source i at time step t;

[0020] Set the initial conditions and generate the initial solution by random or heuristic methods, where the initial conditions include the output power P of the i-th energy source i,gen (1) and the switch state x i (1);

[0021] Perform iterative optimization to determine whether the current solution converges to the global optimal solution or reaches the maximum number of iterations. If it converges or reaches the maximum number of iterations, output the optimal solution x i (t+1) and P i,gen (t+1), otherwise continue iterating.

[0022] The present invention is further configured that the system further includes a genetic algorithm optimization module, and the genetic algorithm optimization module is used to perform secondary optimization using a genetic algorithm on the result of solving the mixed integer linear programming to further improve the global optimality of the scheduling strategy, including:

[0023] The result of solving the mixed integer linear programming is set as the initial population of the genetic algorithm;

[0024] According to the preset fitness function, a selection operation is adopted to select a preset number of individuals from the initial population to enter the next generation population;

[0025] Generate new offspring individuals through single-point crossover, recalculate the fitness of the new offspring individuals, update the population according to the preset number of individuals selected in descending order of fitness value, and select the individual with the highest fitness value as the optimal solution through multiple iterations until the fitness function converges or reaches the preset number of iterations. and

[0026] The present invention is further configured such that the initial population is expressed in the form of: Chromosome k is the representation of the initial population of the kth scheduling scheme, is the output power of the i-th energy source in the k-th scheduling strategy at time step t+1, is the switching state of the i-th energy source in the k-th scheduling strategy at time step t+1;

[0027] The fitness function is: in, To meet the k-th scheduling strategy after iterative optimization conditions, is the fitness function;

[0028] The selection logic of the select operation is: in, is the selection probability of the kth scheduling strategy, K is the number of scheduling strategies;

[0029] The crossover logic of single-point crossover is: Offspring new =Parent1[1:n]+Parent2[n+1:N], where Offspring newis the new offspring individual, Parent1 and Parent2 are the first parent individual and the second parent individual, N is the number of energy sources, n is the random crossover position, and the value range of n is [1, N-1].

[0030] The present invention provides a microgrid AC / DC multi-channel energy intelligent dispatching system, comprising an energy management module for managing energy and load equipment in the microgrid, wherein the energy includes AC energy, DC energy and energy storage energy, and managing energy includes changing switch states, adjusting output power and AC / DC conversion; a data acquisition module for real-time acquisition of state data of energy and load equipment in the microgrid; an optimization control module for processing and analyzing the acquired state data, using mixed integer linear programming for optimization calculation, and generating an optimal energy dispatching strategy; a system dispatching module for executing the optimal energy dispatching strategy generated by the optimization control module, and realizing multi-channel energy intelligent dispatching by changing switch states and adjusting output power, and the beneficial effects generated include:

[0031] 1. Intelligent energy management: Through the energy management module and the optimization control module, the present invention can perform intelligent comprehensive management and dispatch of multiple energy sources in the microgrid, avoiding the problem of single dispatch strategy in the traditional microgrid system and improving the energy utilization rate and the overall efficiency of the system;

[0032] 2. Real-time data processing and optimization: Through the data acquisition module, the energy status and operating status of the load equipment in the microgrid can be monitored in real time. Combined with the optimization control module, it can quickly analyze and process the data collected in real time, dynamically adjust the energy scheduling strategy, achieve optimal energy distribution and scheduling, and effectively respond to complex and changeable energy supply and demand;

[0033] 3. Comprehensive performance improvement: By establishing a multi-objective optimization model, the present invention can optimize scheduling in multiple dimensions, including power loss, energy cost, and load demand, which not only minimizes operating costs, but also improves operating efficiency and reliability. At the same time, by considering the operating status and efficiency of the load equipment, the overall performance of the system is further improved, ensuring efficient and stable operation under different load conditions.

[0034] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0036] Figure 1 A flowchart of a microgrid AC / DC multi-channel energy intelligent dispatching system is shown as an exemplary embodiment of the present invention;

[0037] Figure 2 A logic flow chart of a mixed integer linear programming in an optimization control module is shown for an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for illustrating the present invention, not for limiting the scope of protection of the present invention.

[0039] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0040] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0041] A microgrid AC / DC multi-channel energy intelligent dispatching system, such as Figure 1 As shown, including:

[0042] Energy management module, used to manage energy and load equipment in the microgrid, where energy includes AC energy, DC energy and energy storage energy, and energy management includes changing switch states, adjusting output power and AC / DC conversion;

[0043] Data acquisition module, used to collect status data of energy and load equipment in the microgrid in real time;

[0044] The optimization control module is used to process and analyze the collected status data, use mixed integer linear programming to perform optimization calculations, and generate the optimal energy scheduling strategy;

[0045] The system scheduling module is used to execute the optimal energy scheduling strategy generated by the optimization control module, and realize multi-channel energy intelligent scheduling by changing the switch state and adjusting the output power.

[0046] Specifically, in a microgrid, multiple energy forms are integrated, such as photovoltaics, wind energy, energy storage systems, etc., and AC and DC multi-channel energy is provided. In practical applications, it is necessary to manage and dispatch AC and DC multi-channel energy. Energy management includes changing the switch state, adjusting the output power and AC / DC conversion. Changing the switch state is used to control the switch state of the energy and determine which energy is connected to the system; adjusting the output power is used to dynamically adjust the output power of each energy according to demand; AC / DC conversion is used to manage the conversion process between AC and DC to ensure that different types of load equipment can obtain the required form of electrical energy.

[0047] The data acquisition module is responsible for real-time acquisition of the status data of all energy and load devices in the microgrid to ensure accurate monitoring and feedback of the current operating status. The status data includes switch status, output power, power loss, energy cost, energy conversion efficiency, energy transmission efficiency and load demand. Specifically, the switch status of the i-th energy is represented by x i The switch state of the i-th energy at time step t is represented by x i (t); similarly, the output power of the i-th energy source at time step t is represented by P i,gen (t), the cost of the i-th energy at time step t is represented by C i,cost (t), the energy conversion efficiency of the i-th energy at time step t is represented by η i,conv (t), the energy transfer efficiency of the i-th energy at time step t is represented by η i,trans (t) is used to represent the required power of the jth load device at time step t, and L j (t) is indicated.

[0048] like Figure 2 As shown, the present invention is further configured to process and analyze the collected state data, use mixed integer linear programming to perform optimization calculations, and generate an optimal energy scheduling strategy, including:

[0049] Normalize the state data of energy and load devices in the collected microgrid; specifically, the normalization process includes minimum-maximum normalization and Z-Score normalization, which are not limited here;

[0050] Construct the global objective function f according to the normalized state data opt , the global objective function f opt For global optimization; the present invention is further configured as follows: opt The construction logic is:

[0051] Among them, T is the total number of time steps, N is the number of energy sources, P i,loss (t) is the power loss of the i-th energy source at time step t, P i,gen (t) is the output power of the i-th energy source at time step t, C i,cost (t) is the cost of the i-th energy at time step t, L j (t) is the required power of the jth load device at time step t, w j is the priority weight of the jth load device, M is the number of load devices, U j (t) is the satisfaction rate of the jth load device at time step t, η system (t) is the overall operating efficiency at time step t, λ1, λ2 and λ3 are weight coefficients used to adjust power loss, energy cost and load priority in the objective function f opt The impact weights in α and β are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part and the load priority part respectively; specifically, the global objective function f opt Comprehensively consider energy loss, operating costs, load demand satisfaction and overall operating efficiency; is the energy loss term, which considers the ratio of the total energy loss of multiple energy sources to the total power generation in each time step t, reflecting the energy efficiency. By minimizing the ratio, energy waste can be reduced and energy utilization efficiency can be improved. The coefficient λ1 is used to adjust the importance of energy loss in the objective function; is the economic cost term, which reflects the ratio of the total operating cost to the load demand at each time step t. By minimizing the ratio, the economic benefits of the system can be optimized to ensure that the operating cost is within a reasonable range. The coefficient α is used to adjust the nonlinearity of the economic cost, and λ2 is used to adjust the weight of the economic cost in the objective function; The load satisfaction term measures the relationship between load demand satisfaction and the overall operating efficiency of the system. j (t) and system operation efficiency η system(t), which can ensure that the system can effectively meet the needs of all loads while maintaining an efficient operating state. The coefficient β is used to adjust the nonlinear impact of load satisfaction, while λ3 determines the importance of this item in the objective function. By constructing the global objective function f opt , to achieve comprehensive optimization of the microgrid system, which not only takes into account energy efficiency and economic benefits, but also takes into account load demand and the overall operating efficiency of the system. It can provide powerful optimization capabilities in complex practical applications and ensure that the system achieves the best operating state under the constraints of multiple objectives.

[0052] The present invention is further configured as follows: system The calculation logic of (t) is: Among them, η i,conv (t) is the energy conversion efficiency of the i-th energy source at time step t, γ j is the adjustment factor used to determine the weight of the load satisfaction of the jth load device, and η i,trans (t) is the energy transfer efficiency of the i-th energy source at time step t; specifically, η i,conv (t)·P i,gen (t) is the output power of each energy source multiplied by the conversion efficiency, reflecting the actual effective output of the energy source at time step t. Specifically, when no conversion is required, the conversion efficiency is 1; ln(1+γ j ·U j (t)) reflects the nonlinear effect of load satisfaction on the overall efficiency of the system; the denominator is the total required power L of all load devices at time step t. j (t) and the output power of each energy source multiplied by (1-η i,trans (t)), which represents the reduction in total output power due to transmission loss; it combines multiple factors such as the system's energy conversion efficiency, transmission efficiency, and load demand satisfaction to reflect the overall operating efficiency of the system at each time step.

[0053] At each time step t, locally optimizing the global objective function through a mixed integer linear programming model;

[0054] The present invention is further configured to locally optimize the global objective function through a mixed integer linear programming model, and the logic of the local optimization is: Among them, f MILP is the local optimization objective function, x i (t) is the switch state of the i-th energy at time step t, which takes the value of 0 or 1. i When (t) = 0, it means that the i-th energy source is in the off state at time step t. iWhen (t) = 1, it means that the i-th energy source is in the on state at time step t, ω 1,i ,ω 2,i and ω 3,j are weight coefficients, which are used to adjust the power loss of the i-th energy source, the energy cost of the i-th energy source, and the load priority of the j-th load device in the local optimization objective function f MILP The influence weight in i and β j are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part of the i-th energy source and the load priority part of the j-th load device. Specifically, the local optimization objective function f MILP The aim is to use a mixed integer linear programming model to solve the global objective function f opt Therefore, the meaning of each item is similar, the difference is: in the local optimization objective function f MILP In the i,gen (t) and x i (t) Make specific settings to achieve local optimization and thus achieve global optimization.

[0055] The present invention is further configured to locally optimize the objective function f MILP The solution logic includes:

[0056] Set the local optimization objective function f MILP The constraints include and C max (t) is the total cost of the i energy sources at time step t; the constraints ensure that during the optimization process, the output power of all energy sources and the total cost of the system meet the actual needs of the system and the budget constraints.

[0057] Set the initial conditions and generate the initial solution by random or heuristic methods, where the initial conditions include the output power P of the i-th energy source i,gen (1) and the switch state x i (1) The setting of initial conditions can provide a feasible starting point for the system, ensure that the optimization algorithm can start smoothly, and explore the possibility of the optimal solution. The random method can randomly assign initial parameters, and the heuristic method can preliminarily set parameters based on prior knowledge or experience. There is no restriction on this here.

[0058] Perform iterative optimization to determine whether the current solution converges to the global optimal solution or reaches the maximum number of iterations. If it converges or reaches the maximum number of iterations, output the optimal solution x i (t+1) and P i,gen(t+1), otherwise continue to iterate; specifically, the iterative optimization process can gradually approach the global optimal solution, ensure that the system can operate stably in a complex environment, and achieve optimal energy utilization and economic benefits; through multiple iterations, the system can find the best scheduling strategy in continuous adjustment, thereby improving the operating efficiency and stability of the system.

[0059] At the end of each time step t, when the global objective function f opt Converge to within the set threshold and output the optimal scheduling strategy P for the current time step i,gen (t+1) and x i (t+1), where P i,gen (t+1) is the output power of the i-th energy source at the next time step t+1, x i (t+1) is the switching state of the i-th energy source at the next time step t+1. Specifically, P i,gen (t+1) is the optimal power generation calculated by the optimization algorithm based on factors such as current load demand, energy cost, and energy conversion efficiency. It directly determines how much power each energy source needs to output in the next time step to meet the overall needs of the system; x i (t+1) is the optimization algorithm that decides whether to enable the energy source in the next time step by comprehensively considering factors such as the current state of the energy source, load demand, and cost.

[0060] The present invention is further configured that the system further includes a genetic algorithm optimization module, and the genetic algorithm optimization module is used to perform secondary optimization using a genetic algorithm on the result of solving the mixed integer linear programming to further improve the global optimality of the scheduling strategy, including:

[0061] The result of solving the mixed integer linear programming is set as the initial population of the genetic algorithm; the present invention is further set to that the initial population is expressed in the form of: Chromosome k is the representation of the initial population of the kth scheduling scheme, is the output power of the i-th energy source in the k-th scheduling strategy at time step t+1, is the switching state of the ith energy source in the kth scheduling strategy at time step t+1; specifically, by taking the solution of the mixed integer linear programming as the initial population, it can be ensured that the initial solution already has a high optimization level, reducing the convergence time of the genetic algorithm, which not only improves the computational efficiency, but also enhances the effect of subsequent global optimization.

[0062] According to the preset fitness function, a selection operation is adopted to select a preset number of individuals from the initial population to enter the next generation population;

[0063] The present invention is further configured such that the fitness function is: in, To meet the k-th scheduling strategy after iterative optimization conditions, is the fitness function; specifically, the larger the fitness value, the better the scheduling scheme corresponding to the individual is in terms of energy loss, cost-effectiveness, and load satisfaction. By setting the fitness function, superior scheduling strategies can be effectively screened out and ensure that these strategies have more advantages in the subsequent selection, crossover, and mutation operations of the genetic algorithm. This improves the efficiency of global optimization and the quality of the final scheduling strategy.

[0064] The selection logic of the select operation is: in, is the selection probability of the kth scheduling strategy, and K is the number of scheduling strategies. The selection operation can effectively screen out the optimal scheduling strategy through the fitness value and eliminate the strategies with poor performance, thus ensuring the quality of individuals in the next generation population and improving the convergence speed and optimization effect of the genetic algorithm.

[0065] Generate new offspring individuals through single-point crossover, recalculate the fitness of the new offspring individuals, update the population according to the preset number of individuals selected in descending order of fitness value, and select the individual with the highest fitness value as the optimal solution through multiple iterations until the fitness function converges or reaches the preset number of iterations. and Specifically, the crossover logic of single-point crossover is: Offspring new =Parent1[1:n]+Parent2[n+1:N], where Offspring new is the new offspring individual, Parent1 and Parent2 are the first and second parent individuals, N is the number of energy sources, n is the random crossover position, and the value range of n is [1, N-1]. Specifically, two parent individuals with higher fitness are selected from the population for crossover operation. The crossover operation will be based on the gene sequence of the parent individuals, rather than directly on the fitness value. The gene sequence represents the various decision variables in the scheduling scheme, including the output power P i,gen (t+1) and switch state x i(t+1), randomly select a crossover point position n, cut the two parent chromosomes at the crossover point n, and exchange the gene fragments after the cut point to generate new offspring individuals. Recalculate the fitness value of the newly generated offspring individuals and evaluate their performance in the objective function; the crossover operation combines the excellent genes of the two parent individuals to generate offspring individuals with new characteristics, which helps to explore a better solution space and avoid falling into the local optimum. In addition, by randomly selecting crossover points, the diversity of the population can be increased, and the ability of global optimization can be further improved; by calculating the fitness of the new generation population, the best individuals can be screened out and unsuitable individuals can be eliminated, thereby gradually optimizing the quality of the population. The iterative process can continuously improve the scheduling strategy and improve the global optimality of the system; finally, the individual with the highest fitness value is selected as the optimal solution, which can ensure that the output scheduling strategy is optimal under given conditions, and can achieve maximum energy efficiency, minimum cost, and optimal satisfaction of load demand.

[0066] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0067] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0068] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0069] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0070] 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.

[0071] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0072] In the several embodiments provided in the present application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0073] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0074] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0075] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0076] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A microgrid AC / DC multi-channel energy intelligent dispatching system, characterized in that: include: Energy management module, used to manage energy and load equipment in the microgrid, where energy includes AC energy, DC energy and energy storage energy, and energy management includes changing switch states, adjusting output power and AC / DC conversion; Data acquisition module, used to collect status data of energy and load equipment in the microgrid in real time; The optimization control module is used to process and analyze the collected status data, use mixed integer linear programming to perform optimization calculations, and generate the optimal energy scheduling strategy; The system scheduling module is used to execute the optimal energy scheduling strategy generated by the optimization control module, and realize multi-channel energy intelligent scheduling by changing the switch state and adjusting the output power; The collected status data is processed and analyzed, and mixed integer linear programming is used for optimization calculation to generate the optimal energy scheduling strategy, including: Normalize the state data of energy and load equipment in the collected microgrid; Construct the global objective function f according to the normalized state data opt , the global objective function f opt Used for global optimization; At each time step t, locally optimizing the global objective function through a mixed integer linear programming model; At the end of each time step t, when the global objective function f opt Converge to within the set threshold and output the optimal scheduling strategy P for the current time step i，gen (t+1) and x i (t+1), where P i，gen (t+1) is the output power of the i-th energy source at the next time step t+1, x i (t+1) is the switching state of the i-th energy source at the next time step t+1: The global objective function f opt The construction logic is: Among them, T is the total number of time steps, N is the number of energy sources, P i，loss (t) is the power loss of the i-th energy source at time step t, P i，gen (t) is the output power of the i-th energy source at time step t, C i，cost (t) is the cost of the i-th energy at time step t, L j (t) is the required power of the jth load device at time step t, w j is the priority weight of the jth load device, M is the number of load devices, U j (t) is the satisfaction rate of the jth load device at time step t, η system (t) is the overall operating efficiency at time step t, λ1, λ2 and λ3 are weight coefficients used to adjust power loss, energy cost and load priority in the objective function f opt , α and β are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part and the load priority part respectively.

2. According to claim 1, a microgrid AC / DC multi-channel energy intelligent dispatching system is characterized in that: The status data includes switch status, output power, power loss, energy cost, energy conversion efficiency, energy transmission efficiency and load demand.

3. According to claim 1, a microgrid AC / DC multi-channel energy intelligent dispatching system is characterized in that: η system The calculation logic of (t) is: Among them, η i，conv (t) is the energy conversion efficiency of the i-th energy source at time step t, γ j is the adjustment factor used to determine the weight of the load satisfaction of the jth load device, and η i，trans (t) is the energy transfer efficiency of the i-th energy source at time step t.

4. A microgrid AC / DC multi-channel energy intelligent dispatching system according to claim 3, characterized in that: The global objective function is locally optimized through a mixed integer linear programming model. The logic of local optimization is: Among them, f MILP is the local optimization objective function, x i (t) is the switch state of the i-th energy at time step t, which takes the value of 0 or 1. i When (t) = 0, it means that the i-th energy source is in the off state at time step t. i When (t) = 1, it means that the i-th energy source is in the on state at time step t, ω 1，i ,ω 2，i and ω 3，j are weight coefficients, which are used to adjust the power loss of the i-th energy source, the energy cost of the i-th energy source, and the load priority of the j-th load device in the local optimization objective function f MILP The influence weight in i and β j are adjustment factors, which are used to adjust the nonlinear relationship between the cost-effectiveness part of the i-th energy source and the load priority part of the j-th load device.

5. A microgrid AC / DC multi-channel energy intelligent dispatching system according to claim 4, characterized in that: Local optimization objective function f MILP The solution logic includes: Set the local optimization objective function f MILP The constraints include and C max (t) is the total cost of energy i at time step t: Set the initial conditions and generate the initial solution by random or heuristic methods, where the initial conditions include the output power P of the i-th energy source i，gen (1) and the switch state x i (1); Perform iterative optimization to determine whether the current solution converges to the global optimal solution or reaches the maximum number of iterations. If it converges or reaches the maximum number of iterations, output the optimal solution x i (t+1) and P i，gen (t+1), otherwise continue iterating.

6. A microgrid AC / DC multi-channel energy intelligent dispatching system according to claim 1, characterized in that: The system further includes a genetic algorithm optimization module, which is used to perform secondary optimization using a genetic algorithm on the result of solving the mixed integer linear programming to further improve the global optimality of the scheduling strategy, including: The result of solving the mixed integer linear programming is set as the initial population of the genetic algorithm; According to the preset fitness function, a selection operation is adopted to select a preset number of individuals from the initial population to enter the next generation population; Generate new offspring individuals through single-point crossover, recalculate the fitness of the new offspring individuals, update the population according to the preset number of individuals selected in descending order of fitness value, and select the individual with the highest fitness value as the optimal solution through multiple iterations until the fitness function converges or reaches the preset number of iterations. and 7. A microgrid AC / DC multi-channel energy intelligent dispatching system according to claim 6, characterized in that: The initial population is expressed as: Chromosome k is the representation of the initial population of the kth scheduling scheme, is the output power of the i-th energy source in the k-th scheduling strategy at time step t+1, is the switching state of the i-th energy source in the k-th scheduling strategy at time step t+1; The fitness function is: in, To meet the k-th scheduling strategy after iterative optimization conditions, is the fitness function; The selection logic of the select operation is: in, is the selection probability of the kth scheduling strategy, K is the number of scheduling strategies; The crossover logic of single-point crossover is: Offspring new =Parent1[1:n]+Parent2[n+1:N], where Offspring new is the new offspring individual, Parent1 and Parent2 are the first parent individual and the second parent individual, N is the number of energy sources, n is the random crossover position, and the value range of n is [1, N-1].

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