A method and device for optimal configuration of hybrid DC and AC microgrid based on chaotic artificial bee colony algorithm

Abstract

This invention belongs to the field of hybrid DC and AC microgrid optimization configuration technology, specifically involving a method and device for hybrid DC and AC microgrid optimization configuration based on a chaotic artificial bee colony algorithm. It establishes a system model including photovoltaic, wind turbine, battery, and AC / DC loads, setting minimization as the objective. Multiple constraints are set. An artificial bee colony algorithm incorporating chaotic mapping is employed to iteratively search within the feasible solution space: diverse solutions are generated through chaotic initialization, high-quality solutions are retained through a roulette wheel selection mechanism, and stagnant solutions are replaced promptly through a scout bee mechanism to escape local optima. Finally, the configuration quantity and capacity that minimize the levelized cost of electricity (LCOE) are determined from all feasible schemes. The method enhances the algorithm's global search capability through a chaotic mechanism and ensures the feasibility and safety of all intermediate and final solutions based on constraint verification. The obtained optimal configuration scheme directly corresponds to the optimal long-term operating economy, providing a basis for design.

Description

Technical Field

[0001] This invention belongs to the field of hybrid DC and AC microgrid optimization configuration technology, specifically relating to a method and device for hybrid DC and AC microgrid optimization configuration based on chaotic artificial bee colony algorithm. Background Technology

[0002] A microgrid is a localized power system that can operate independently or be integrated into the main power grid. It consists of distributed power sources, including solar photovoltaic panels, wind turbines, batteries, and diesel generators. Microgrids can be classified according to their connection method with the main grid, energy type, and operation and control strategies.

[0003] In related technologies, when designing configuration schemes and setting the distribution of natural resources such as solar and wind energy, standardized configuration templates are used. This results in the mixing of DC and AC load types and power, which cannot meet the demand. Consequently, the configured power generation units are not compatible with local renewable energy conditions, making it impossible to fully utilize local resources and causing energy waste. Furthermore, the power supply capacity is insufficient to meet the load demand.

[0004] Related optimization algorithms are prone to getting stuck in local optima and have low search efficiency. This is mainly due to algorithms based on microgrid configuration optimization, which often suffer from uneven solution distribution or insufficient diversity in the initial solution generation stage. When the algorithm gets stuck in a local optimum, it struggles to escape the predetermined search range and fails to discover better configuration schemes. This results in a significant waste of computational resources on schemes with no optimization potential, leading to low optimization efficiency and an inability to quickly output high-quality configuration results. Summary of the Invention

[0005] This invention provides a method for optimizing the configuration of hybrid DC and AC microgrids based on a chaotic artificial bee colony algorithm. The combination of photovoltaic (PV) capacity, wind turbine capacity, and battery capacity determined by this method can minimize the unit energy cost while ensuring all-day load power supply and safe equipment operation. The algorithm maintains the engineering feasibility of the solution throughout the search process. The introduction of a chaotic mechanism enhances the uniformity of the initial solution distribution and the ability to escape local minima, reducing the risk of getting trapped in local minima. The linkage mechanism between scout bees and constraint verification ensures that the population is not contaminated by infeasible solutions during iteration, improving the reliability of the convergence results.

[0006] The methods include: S1: Determine the system composition of the hybrid DC and AC microgrid, the system including a power generation module, a battery energy storage device, a DC load module and an AC load module, wherein the power generation module includes a solar photovoltaic panel and a wind turbine. S2: Determine the energy cost of the hybrid DC and AC microgrid as the objective function for optimal configuration. The objective function is constructed based on the ratio of the microgrid's life cycle cost to the total energy demand in order to minimize the energy cost. S3: Determine the constraints for the optimal configuration of the hybrid DC and AC microgrid, including renewable energy constraints, microgrid power balance constraints, and battery energy storage constraints; S4: Initialize the nectar source locations for the chaotic artificial bee colony algorithm. Each nectar source location corresponds to a potential solution for the hybrid DC and AC microgrid configuration. The parameters of the potential solution include the number of solar photovoltaic panels, the number of wind turbines, and the maximum total battery capacity. S5: Generate a new nectar source location based on chaotic mapping. The new nectar source location is generated by worker bees according to the current nectar source location and the chaotic sequence, and the potential solution corresponding to the new nectar source location satisfies the constraint conditions determined in step S3. S6: Use a roulette wheel selection mechanism to select a high-quality potential solution from the nectar source locations that satisfy the constraints described in step S3; The selected potential solutions are verified by preset constraints. If a potential solution does not meet the preset constraints of the algorithm, the scout bee is activated to generate a new nectar source location, and the potential solution corresponding to the new nectar source location simultaneously meets the constraint conditions described in step S3. If a potential solution satisfies the algorithm's preset constraints, the nectar source location is retained and continues to participate in subsequent iterations; S7: Based on the optimal nectar source location that satisfies the constraints described in step S3 and corresponds to the minimum energy cost, determine the optimal configuration of the hybrid DC and AC microgrid. The optimal configuration includes the optimal number of solar photovoltaic panels, the optimal number of wind turbines, and the optimal total battery capacity.

[0007] According to another embodiment of this application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the hybrid DC and AC microgrid optimization configuration method based on the chaotic artificial bee colony algorithm.

[0008] As can be seen from the above technical solutions, the present invention has the following advantages: This invention provides a hybrid DC and AC microgrid optimization configuration method that clarifies the combination of photovoltaic and wind power, identifies the types and power ranges of DC and AC loads, and defines the system composition, functions, and connections of each component. It constructs the ratio of total lifecycle cost to total energy demand as the energy cost objective function, covering initial investment, operation and maintenance, component replacement, and other lifecycle costs, establishing a correlation between cost and energy supply and demand. By generating initial nectar source locations, it ensures that the initial solution covers a reasonable parameter range; it utilizes worker bees combined with chaotic mapping to explore new solutions in the neighborhood, enhancing search diversity; it uses a roulette wheel to select high-quality solutions and initiates scout bees to replace invalid solutions that do not meet preset constraints, maintaining the vitality and optimization direction of the solution set. By identifying three types of constraints—renewable energy output limits, AC / DC power balance, and the safe operating range of battery energy storage—it clarifies the specific operating rules for each constraint, ensuring that all optimization processes satisfy these constraints. By screening feasible solutions that meet the constraints, it focuses on the optimal solution with the lowest energy cost, extracting parameters such as the number of photovoltaic panels, wind turbines, and battery capacity that can be used for engineering implementation, ensuring that the results align with engineering implementation requirements.

[0009] The configuration scheme of this invention can match the resource conditions and load requirements of the application area. The power generation unit is compatible with renewable energy sources, and the AC / DC load power supply adaptability is good, effectively improving energy utilization efficiency and power supply security. The diverse initial solutions and global exploration mechanism of the chaotic artificial bee colony algorithm effectively avoid local optima problems. The invalid solution replacement mechanism of the scout bees reduces redundant calculations, making the optimization process more efficient and the output configuration scheme of higher quality. Attached Figure Description

[0010] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 Flowchart of a method for optimizing the configuration of hybrid DC and AC microgrids; Figure 2 Timing diagram for the optimal configuration method of hybrid DC and AC microgrids; Figure 3 This is a schematic diagram of an electronic device. Detailed Implementation

[0012] This invention relates to a method based on a hybrid DC and AC microgrid, proposing an optimized configuration method based on energy cost optimization. Leveraging the advantages of the artificial bee colony algorithm (ASA) such as ease of implementation and few control parameters, this invention determines the optimal configuration scheme for the hybrid DC and AC microgrid. To improve the performance of the ASA, an improved chaotic ASA algorithm is further proposed. Unlike traditional ASA, genetic algorithms, and particle swarm optimization algorithms, the chaotic ASA algorithm utilizes the ergodicity and sensitivity characteristics of chaotic sequences to effectively enhance population diversity, accelerate the convergence process, and avoid premature stagnation. During configuration, this invention must simultaneously satisfy DC and AC power balance, renewable energy volatility, and battery operation constraints.

[0013] The following describes in detail the hybrid DC and AC microgrid optimization configuration method based on the chaotic artificial bee colony algorithm involved in this application. Specific details, such as particular system structures and technologies, are presented for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application can also be implemented in other embodiments without these specific details.

[0014] It should be understood that, when used in this specification, the term "comprising" indicates the presence of the described feature, integral, step, operation, element, and / or component, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0015] The terms "one embodiment" or "some embodiments" used in this application mean that one or more embodiments of this application include the specific features, structures, or characteristics described in that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this application do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Please see Figure 1 and Figure 2The diagram shows a flowchart of a hybrid DC and AC microgrid optimization configuration method based on a chaotic artificial bee colony algorithm in a specific embodiment. The method includes: S1: Determine the system composition of the hybrid DC and AC microgrid, the system including a power generation module, a battery energy storage device, a DC load module and an AC load module, wherein the power generation module includes a solar photovoltaic panel and a wind turbine.

[0018] In some embodiments, the system is a microgrid system, primarily applied in various scenarios such as industrial parks and commercial buildings. The types of loads connected include DC loads such as LED lighting and DC motors, and AC loads such as household appliances and AC water pumps, clearly defining the power range of each type of load. The power generation end is then determined to use a combination of solar photovoltaic panels and wind turbines. Based on the power supply stability requirements, battery energy storage devices are included, ultimately resulting in the system's equipment configuration and clarifying the functional positioning and connection relationships of each component.

[0019] As one implementation of step S1 of the present invention, it specifically includes the following steps: S11: Obtain the installation area, unit efficiency and solar radiation data of the solar photovoltaic panel, and calculate the output power of the solar photovoltaic panel at different time points t according to the following formula (1); (1) in, This indicates the output power of the solar photovoltaic panel; The number of solar photovoltaic panels; The efficiency of the solar photovoltaic panel is assumed to be a constant value. The area of ​​the solar photovoltaic panel; This is solar radiation.

[0020] In some embodiments, the number of solar photovoltaic panels is determined by their individual area and the overall installation space. The area of ​​each photovoltaic panel is obtained through measurement or design drawings. Based on the on-site solar radiation G, the photovoltaic output power at any time t is calculated using formula (1). This process does not depend on the real-time control strategy and is only used to construct the basic parameters of the system energy input.

[0021] S12: Obtain data on the swept area, air density, wind speed and power coefficient of the wind turbine, and calculate the output power of the wind turbine at different time points t according to formula (2); (2) in, This indicates the output power of the wind turbine. The number of wind turbines; Indicates air density; The swept area of ​​the wind turbine rotor; Wind speed; The power coefficient is assumed to be constant. This refers to the efficiency of the wind turbine.

[0022] In some embodiments, the output power of a wind turbine depends on changes in ambient wind speed. This is achieved by collecting long-term wind speed observation data of the target area to obtain the wind speed v at a given time t, and then combining this data with known air density. Wind turbine sweep area and constant power coefficient Substituting into formula (2), we obtain the wind power generation at that moment.

[0023] It should be noted that this step utilizes the cubic law characteristic of wind speed to accurately estimate the wind power generation capacity using formula (2). Changes in wind speed directly affect the output capacity of wind turbines, especially exhibiting nonlinear responses in low and high wind speed areas. By introducing... As a variable, the system can adjust the scale of wind turbines under different wind resource conditions. The power estimation method in this step can effectively capture the randomness and volatility of wind power, providing reliable data support for multi-source collaborative configuration and enhancing the microgrid's adaptability to uncertainty.

[0024] S13: Set the maximum total capacity, charge and discharge efficiency and upper and lower limits of state of charge (SOC) of the battery energy storage system, and establish a dynamic model of the battery's state of charge at each time according to formula (3) and formula (4). (3) (4) in, and These represent the battery's charge and discharge efficiencies, respectively. and These represent the charging and discharging power at time t; Indicates the maximum total capacity of the battery; This is the power directly supplied to the DC load; This refers to the power supplied to the AC load after conversion.

[0025] In some embodiments, the battery energy storage system acts as an energy buffer unit, and its operating characteristics are determined by the State of Charge (SOC). This is based on a preset maximum total battery capacity. The minimum state of charge (SOCmin) and maximum state of charge (SOCmax) are set to ensure safe operation. Within each time step Δt, the battery's charging power... (t) and discharge power (t) is driven by DC load demand and AC load demand respectively, and the SOC value at the next moment is updated by formula (3). At the same time, formula (4) is used to allocate the power provided by the battery to the DC side and the AC side. This modeling method ignores complex factors such as thermal effects and self-discharge, simplifies the calculation process but retains the key physical relationships.

[0026] S2: Determine the energy cost of the hybrid DC and AC microgrid as the objective function for optimal configuration. The objective function is constructed based on the ratio of the microgrid's life cycle cost to the total energy demand in order to minimize the energy cost.

[0027] In some embodiments, the cost structure of the microgrid throughout its entire lifecycle is first analyzed, covering initial construction costs, maintenance expenses during operation, and component replacement costs over long-term use. Then, the total energy demand that the microgrid needs to meet throughout its lifecycle is calculated. Combining the cost structure and total energy demand, a ratio between the two is constructed as a quantitative expression of energy costs. The entire process must ensure the comprehensiveness of cost statistics and the accuracy of energy demand calculations, so that the objective function truly reflects the economic differences between different configuration schemes.

[0028] As one implementation of step S2 of the present invention, it specifically includes the following steps: S21: Determine the total energy demand of the hybrid DC and AC microgrid. The total energy demand is calculated according to formula (7). (7) Where T is the microgrid lifetime, which can be taken as 20 years. Δt is the step size. (t) represents the AC power demand at time t. (t) represents the DC power demand at time t. The inverter efficiency is set to 95% here.

[0029] In some embodiments, when a microgrid is operating, AC loads directly consume the corresponding power, while DC loads incur losses when their power is converted to AC by the inverter. Therefore, the DC power demand must be converted into equivalent energy consumption. By adding up the AC and DC energy consumption at each time step throughout the entire lifespan, the total energy required to be supplied by the microgrid over its entire lifespan can be obtained. This clarifies the total energy scale that the microgrid needs to provide throughout its entire lifespan, allowing cost calculations to correspond with the actual total energy to be supplied.

[0030] S22: Determine the lifecycle cost of the hybrid DC and AC microgrid according to formula (6). , (6) in, The investment cost of the microgrid (RMB / year); The operating and maintenance cost of the microgrid (RMB / year); Replacement cost of microgrid components (RMB / year); In some embodiments, the lifecycle cost of a microgrid covers all expenditures from construction to operation and component replacement. Investment cost is the initial investment in equipment, operation and maintenance cost is the expense of routine maintenance, and replacement cost is the cost of replacing consumable components such as batteries. Adding these three expenditures together gives the total cost over the entire lifecycle.

[0031] S23: Construct the objective function for the optimal configuration of the hybrid DC and AC microgrids. The objective function is calculated according to formula (5). (5) in, This represents the total energy demand of the microgrid (kWh / year).

[0032] In some embodiments, energy cost is the total lifecycle cost per unit of energy. Dividing the total expenditure over the entire lifecycle by the total energy demand over the entire lifecycle yields the cost per unit of energy, which serves as the core reference for optimal configuration. By combining the total lifecycle cost with the total energy demand and quantifying the cost per unit of energy, the optimization direction for microgrid configuration becomes clearer, directly moving towards reducing unit energy expenditure.

[0033] In this embodiment, step S2 determines the energy cost of the hybrid DC and AC microgrid as the objective function for optimal configuration in the following ways: S221: Calculate the investment cost of hybrid DC and AC microgrids according to formula (8). , (8) in, , and The figures represent the capital costs (in yuan) for solar photovoltaic panels, wind turbines, and batteries, respectively. (9) (10) (11) in, This refers to the maximum total capacity of the battery (kWh). , and These represent the capital cost (in yuan) per unit of solar photovoltaic panel, wind turbine, and battery.

[0034] In some embodiments, the investment cost of a microgrid is the sum of the initial purchase expenditures of each power generation and energy storage device. The purchase cost of each device is determined by the product of its deployment quantity (or capacity) and unit purchase cost. The initial investment amount of the entire system is obtained by calculating the purchase cost of each individual device separately and then summing them up. This allows for the breakdown of the initial investment of different devices, making the composition of the investment cost clearly traceable.

[0035] S222: Calculate the operation and maintenance costs of a hybrid DC and AC microgrid using formula (12). , (12) (13) (14) (15) in, , and The figures represent the operating and maintenance costs (in yuan) of the solar photovoltaic panels, wind turbines, and batteries, respectively. , and These represent the operating and maintenance costs (in yuan) per unit of solar photovoltaic panel, wind turbine, and battery.

[0036] In some embodiments, the operation and maintenance cost is the total expenditure on routine inspection and maintenance of each device throughout its entire life cycle. The operation and maintenance cost of each device is determined by multiplying its quantity (or capacity) by the unit cycle operation and maintenance cost. The total operation and maintenance cost is obtained by accumulating the operation and maintenance expenditures of all devices. This covers the routine maintenance expenditures throughout the entire operation cycle of the microgrid.

[0037] S223: Calculate the component replacement cost of a hybrid DC and AC microgrid using formula (16). , (16) (17) in, Number of replacements; Battery life (years).

[0038] In some embodiments, batteries are consumable components with a limited number of charge-discharge cycles, and their lifespan is shorter than the overall lifespan of the microgrid. The number of replacements throughout the entire lifespan is calculated, and then multiplied by the purchase cost of a single battery replacement to obtain the total battery replacement cost for the entire lifespan. This approach considers the replacement cost of consumable components, compensating for the shortcomings of only calculating the initial investment, and making the cost accounting of the microgrid throughout its lifespan more closely reflect actual operational needs.

[0039] S3: Determine the constraints for the optimal configuration of the hybrid DC and AC microgrid, including renewable energy constraints, microgrid power balance constraints, and battery energy storage constraints.

[0040] In some embodiments, constraints are hard rules for the safe and stable operation of the microgrid. Any configuration scheme can only be optimized within the scope of these rules. The function of constraints is to filter out invalid data that does not meet the physical operation requirements.

[0041] As one implementation of step S3 of the present invention, it specifically includes the following steps: S31: Set renewable energy constraints for the hybrid DC and AC microgrid, limiting the output power of solar photovoltaic panels according to formula (18) and the output power of wind turbines according to formula (19). (18) (19) in, This represents the maximum power output of a single photovoltaic panel. Let be the photovoltaic output power at time t. This represents the maximum power of a single fan. Let t be the output power of the fan at time t.

[0042] In some embodiments, the output capacity of solar photovoltaic panels and wind turbines is limited by the rated power of a single device. The number of devices deployed determines the maximum total output capacity of the device cluster. Formulas (18) and (19) limit the output power of the cluster to this capacity range, which can prevent photovoltaic panels and wind turbines from failing due to long-term overload operation and ensure that the power generation equipment always operates within a safe power range.

[0043] S32: Set the power balance constraints for the hybrid DC and AC microgrid. The DC side power balance is limited by formulas (20) and (21). (20) (twenty one) The AC power balance is limited by formulas (22) and (23). (twenty two) , (twenty three) in, This refers to the output power of the solar photovoltaic panel. To convert the output power of a wind turbine into DC power; Total battery power; This refers to the output power of the wind turbine. To convert the output power of solar photovoltaic panels into AC power; Convert battery power into AC power; To improve rectifier efficiency, optionally, =93%. For inverter efficiency; To meet the load requirements of AC communication.

[0044] In some embodiments, the power balance on the DC side is processed first. The output power Pwt(t) of the wind turbine is obtained at a certain time t, and then the efficiency parameter ηrec of the rectifier is taken and substituted into formula (21) to calculate the DC power that the wind turbine can provide after conversion. Obtain the photovoltaic output power at the same time t. The current total power of the battery And the power demand of the DC load at this time. Substitute these values ​​into formula (20) to ensure that the total power supply on the left side of the equation is equal to the load demand on the right side. Then, process the power balance on the AC side, obtain the photovoltaic output power at the same time t, and take the inverter efficiency parameter η. inv Substitute into formula (23) to calculate the AC power that can be provided after photovoltaic conversion. Obtain the current output power of the fan. The AC power that the battery can provide after conversion and the power requirements of AC loads Substituting into formula (22), we can ensure that the power supply and load requirements on both sides of the equation are consistent.

[0045] In this embodiment, the DC loads in the microgrid require the combined power supplied by the photovoltaic system, the rectified DC power from the wind turbine, and the power released by the battery. The AC loads require the combined power supplied by the photovoltaic system after inversion, the AC power supplied by the wind turbine, and the AC power supplied by the battery after inversion. Formulas (20), (21), (22), and (23) link the power supply and load demand on each side to ensure supply and demand parity.

[0046] S33: Set the battery energy storage constraints for the hybrid DC and AC microgrid. The battery charging and discharging power is limited according to formulas (25) and (26). (25) (26) The state of charge of the battery is limited by formula (27). (27) in, The battery charging power at time t. Let be the battery discharge power at time t. Maximum charging power for the battery. This is the battery's maximum discharge power. The state of charge of the battery at time t. This is the battery's minimum state of charge. This represents the battery's maximum state of charge.

[0047] In some embodiments, excessive charging and discharging power of the battery can damage the internal cells. A state of charge below the minimum or above the maximum limit will significantly shorten the battery's lifespan. Formulas (25), (26), and (27) limit the battery's operating parameters within a safe range, ensuring the battery operates under reasonable conditions. This reduces abnormal battery wear, extends the actual usable time of the battery, and lowers the cost of subsequent battery replacements in the microgrid.

[0048] S4: Initialize the nectar source locations for the chaotic artificial bee colony algorithm. Each nectar source location corresponds to a potential solution for a hybrid DC and AC microgrid configuration. The parameters of the potential solution include the number of solar photovoltaic panels, the number of wind turbines, and the maximum total battery capacity.

[0049] In some embodiments, the core configuration parameters that the algorithm needs to optimize are clearly defined. Based on these parameters and the actual construction limitations of the microgrid, a reasonable range of values ​​for each parameter is defined. Then, based on this range, multiple different parameter combinations are generated, each combination corresponding to a nectar source location, and each location representing a potential microgrid configuration scheme.

[0050] As one implementation of step S4 of the present invention, it specifically includes the following steps: S41: Determine the variables for the potential solution of the hybrid DC and AC microgrid configuration, including the number of solar photovoltaic panels Npv, the number of wind turbines Nwt, and the maximum total battery capacity Ebat, with each variable corresponding to a parameter of the microgrid configuration.

[0051] In some embodiments, the power supply and energy storage capacity of a microgrid are determined by three parameters: the number of photovoltaic panels, the number of wind turbines, and the battery capacity. These three parameters are set as variables, and different combinations of variables correspond to different configuration schemes. These schemes are the potential solutions that the algorithm searches for.

[0052] The number of solar photovoltaic panels deployed directly affects the total power supply capacity on the photovoltaic side, defined as the variable Npv; the number of wind turbines deployed determines the total power supply capacity on the wind turbine side, defined as the variable Nwt; and the total battery capacity limits the charging and discharging scale of the energy storage system, defined as the variable Ebat. Different combinations of these three variables correspond to different hardware configuration schemes of the microgrid, which are the potential solutions to be searched in the chaotic artificial bee colony algorithm.

[0053] S42: Set the value boundaries of the variables, and determine the value range of Npv [N pv,min N pv,max The range of values ​​for Nwt is [N wt,min N wt,max The value range of Ebat [E bat,min E bat,max ]; where N pv,min N wt,min E represents the minimum number of devices to be deployed. bat,min For the minimum battery capacity, N pv,max N wt,max E represents the maximum number of devices that can be deployed. bat,max This is the maximum capacity of the battery.

[0054] In some embodiments, each configuration parameter is limited by the actual scenario. Setting the value range can filter out solutions that are beyond the actual feasible range, keep the potential solutions within a reasonable range, narrow the search range of the algorithm, and reduce the time spent on invalid calculations.

[0055] S43: Generate chaotic sequences based on logarithmic mapping, and calculate the initial chaotic values ​​of variables according to the chaotic sequence generation rules of formula (33); (33) in, ; . and For chaotic sequences based on logarithmic mappings, . , The variable value boundaries are set for step S42.

[0056] In some embodiments, logarithmic mapping is a type of chaotic mapping, and this embodiment uses logarithmic mapping to generate chaotic sequences. The chaotic sequence generation method based on logarithmic mapping in formula (33) is selected, and an initial value between 0 and 1 is set for the chaotic sequence corresponding to each variable, then substituted into... Where α=4, multiple sets of results are obtained through iterative calculation. The values ​​form a chaotic sequence.

[0057] Taking Npv as an example, let its value boundary N pv,min ,Right now N pv,max ,Right now Substitute into formula (33) to calculate the initial chaotic value corresponding to Npv; repeat this operation to obtain the initial chaotic values ​​corresponding to Nwt and Ebat.

[0058] S44: Assign the chaotic initial values ​​to Npv, Nwt, and Ebat respectively to obtain N random nectar source locations. Each location corresponds to a combination of Npv, Nwt, and Ebat, which is a potential solution for the configuration of a hybrid DC and AC microgrid.

[0059] In some embodiments, the initial number N of nectar sources required by the chaotic artificial bee colony algorithm is first determined. Then, the initial chaotic value of Npv obtained in S43 is sequentially assigned to the Npv parameter of the N nectar source locations. Next, the initial chaotic values ​​of Nwt and Ebat are assigned to the Nwt and Ebat parameters of each nectar source location, respectively. The combination of Npv, Nwt, and Ebat values ​​corresponding to each nectar source location constitutes a complete microgrid configuration scheme, i.e., a potential solution of the algorithm.

[0060] S5: Generate a new nectar source location based on chaotic mapping. The new nectar source location is generated by worker bees according to the current nectar source location and the chaotic sequence, and the potential solution corresponding to the new nectar source location satisfies the constraint conditions determined in step S3.

[0061] In some embodiments, worker bees focus on existing nectar source locations. For each location's corresponding configuration scheme, they explore new parameter combinations in the surrounding area of ​​the current scheme, combining the diverse search directions brought about by chaotic mapping, to form new nectar source locations. After a new location is generated, it is checked against the constraints determined in step S3 to determine whether the corresponding configuration scheme conforms to the operating rules. Only new locations that conform to the constraints are retained; those that do not are discarded.

[0062] Here, worker bees explore the current solution domain to gradually optimize the quality of existing solutions. Chaotic mapping can enhance the diversity of the search, and constraint checks ensure the feasibility of new solutions.

[0063] As one implementation of step S5 of the present invention, it specifically includes the following steps: S51: Generate the chaotic sequence required for worker bees based on the logarithmic mapping, calculated according to the chaotic sequence generation rule of formula (32). (32) in These are the parameters of the current solution. Other solution parameters are randomly selected. These are the parameters for the new solution.

[0064] The chaotic sequence in formula (32) satisfies Where α=4, initial value The chaotic sequence is used for parameter adjustment in the generation of new solutions by worker bees.

[0065] In some embodiments, based on the objective function The optimal configuration of a hybrid DC and AC microgrid is determined using a chaotic artificial bee colony algorithm. The specific steps are as follows. The artificial bee colony algorithm simulates the foraging behavior of bees in nature, involving three types of bees: worker bees, observer bees, and scout bees. In the initial stage, N random nectar source locations are generated, each representing a potential solution. Worker bees generate new solutions by exploring their current neighborhood, thereby optimizing the search process for the optimal configuration. The new solution is given by the following formula: (28) in, and This represents the new solution between the i-th current value and the j-th variable. and ; The solution is randomly selected. ; The number of worker bees corresponds to the number of solutions; The number of variables; The random numbers are uniformly distributed. .

[0066] The roulette wheel selection mechanism is used to select the location of the nectar source, which has a higher fitness function probability. This probability is given by the following formula: (29) in, The probability is the fitness function. Let be the fitness value of the i-th solution.

[0067] If a solution cannot exceed the preset limit value If so, then abandon that nectar source. Let's assume the abandoned nectar source is... Scout bees will discover new nectar sources to replace [other sources] in the following ways. : (30) in, and To estimate the boundary conditions of the parameters; The random numbers are uniformly distributed. .

[0068] This embodiment also demonstrates that the chaotic sequence generated by the logarithmic mapping using a fixed iterative formula exhibits a uniform numerical distribution characteristic, providing irregular and wide-ranging adjustment parameters, and offering diverse search directions for worker bees to explore the neighborhood space of the current solution. The chaotic sequence generated in step S51 avoids the problem of uneven distribution of traditional random numbers.

[0069] S52: Obtain the potential solution parameters (Npv, Nwt, Ebat) corresponding to the current nectar source location in the chaotic artificial bee colony algorithm, randomly select the solution parameters of another different nectar source location, and combine them with the chaotic sequence generated in step S51 to calculate the new solution parameters according to formula (32); In some embodiments, taking the Npv parameter as an example, the corresponding chaotic sequence value generated in step S51 is retrieved. ,Will , and Substitute into formula (32) to calculate the new Npv parameter value; repeat this operation to calculate the new Nwt and Ebat parameter values ​​in turn, forming a complete set of new solution parameters.

[0070] Here, worker bees use the current nectar source as a basis and adjust the difference between the current solution and other randomly selected solutions through chaotic sequences to generate new parameter combinations. This allows them to explore the neighborhood space of the current solution and gradually approach a better solution. This ensures the continuity of the search while introducing sufficient randomness through chaotic characteristics.

[0071] S53: Substitute the new solution parameters obtained in step S52 into the constraint formula determined in step S3, and verify in turn whether the renewable energy constraint, microgrid power balance constraint and battery energy storage constraint are satisfied.

[0072] In some embodiments, renewable energy constraints are verified by substituting Npv and Nwt from the new solution into formulas (18) and (19), and combining them with the maximum power parameters of the corresponding equipment to determine whether the output power of the photovoltaic panel and wind turbine under the new solution is within the limit range. Then, power balance constraints are verified by substituting the power generation and energy storage power corresponding to the parameters of the new solution into formulas (20), (21), (22), and (23) to determine whether the power supply on the AC and DC sides is balanced with the load demand. Finally, battery energy storage constraints are verified by substituting the charge and discharge power and state of charge corresponding to Ebat from the new solution into formulas (25), (26), and (27) to determine whether the battery operating parameters are within the safe range.

[0073] It should be noted that the constraints determined in step S3 are a prerequisite for the feasibility of the microgrid configuration scheme. Substituting the new solution parameters into the corresponding constraint formula for verification is essentially to determine whether the new configuration scheme can meet the requirements of safe and stable power supply in actual operation.

[0074] S54: If the new solution parameters satisfy all the constraints of step S3, then the position corresponding to the new solution parameters is determined as the effective new nectar source position; If the conditions are not met, the new solution parameters are discarded, and the corresponding nectar source location is not generated.

[0075] In some embodiments, the constraint verification results of step S53 are summarized. If a set of new solution parameters meets the requirements in the verification of renewable energy constraints, power balance constraints, and battery energy storage constraints, that is, the equality or inequality relationship of all constraint formulas is valid, then the position corresponding to the set of new solution parameters is determined as a valid new source of nectar and included in the solution set of the algorithm. If the set of new solution parameters does not meet the requirements in any constraint verification, that is, there is an equality or inequality relationship of a certain constraint formula that is not valid, then the set of new solution parameters is directly discarded and its corresponding position is not considered a new source of nectar.

[0076] It can be seen that the new solution that passes the constraint verification is the feasible solution that meets the actual operation requirements of the microgrid. The feasible solution is retained and the invalid solution is discarded to ensure that the subsequent selection and optimization process of the algorithm revolves only around the effective solution.

[0077] S6: Use a roulette wheel selection mechanism to select a high-quality potential solution from the nectar source locations that satisfy the constraints described in step S3; The selected potential solutions are verified by preset constraints. If a potential solution does not meet the preset constraints of the algorithm, the scout bee is activated to generate a new nectar source location, and the potential solution corresponding to the new nectar source location simultaneously meets the constraint conditions described in step S3. If a potential solution satisfies the algorithm's preset constraints, the nectar source location is retained and continues to participate in subsequent iterations.

[0078] In some embodiments, a higher-quality potential solution is first selected from all nectar source locations that meet the constraints of step S3 using a roulette wheel selection mechanism, based on the economic performance of the solution. The selected high-quality solutions are then tracked and evaluated to verify whether they meet the algorithm's preset constraints and to determine if the solution has further optimization potential. If a solution does not meet the preset constraints, it indicates that it has no room for optimization. In this case, a scout bee is activated to explore new nectar source locations as alternatives. After the new location is generated, it is still necessary to check whether it meets the S3 constraints. If the solution meets the preset constraints, the location is retained and allowed to continue participating in subsequent iterative optimization. High-quality solutions with optimization potential are retained, while ineffective solutions with no value are promptly eliminated. Simultaneously, new solutions are added through scout bees to maintain the diversity of the solution set and ensure the continuous progress of the optimization process.

[0079] As an implementation of step S6 of the present invention, which verifies the selected potential solutions under preset constraints, if a potential solution does not meet the algorithm's preset constraints, then the scout bee is activated to generate a new alternative nectar source location, and the potential solution corresponding to the new alternative nectar source location simultaneously satisfies the constraint conditions described in step S3, the specific implementation includes the following steps: S61: Determine the preset constraint criteria for the chaotic artificial bee colony algorithm. The preset constraint is executed according to the following formula, which predefines the constraint value lim. The judgment index is the fitness iteration improvement of the potential solution. (31) In some embodiments, the core criterion for determining the preset limitations of the algorithm is first defined as the fitness iteration improvement of the potential solution, which directly reflects the trend of the optimization potential of the potential solution. The predefined limit value lim of formula (31) is retrieved and set as the threshold for determining the fitness improvement. At the same time, the statistical standard for the number of consecutive iterations is determined. That is, when the fitness improvement of the potential solution is lower than the threshold for M consecutive iterations, the potential solution is determined to not meet the preset limitations. The fitness value here is converted from the objective function value. The objective function is the energy cost determined in step S2. The fitness value and the energy cost are negatively correlated.

[0080] S62: Substitute the high-quality potential solutions obtained by the roulette wheel selection into the preset constraint judgment criteria, compare the fitness improvement of its continuous iteration with the predefined constraint value lim of formula (31), and complete the preset constraint verification.

[0081] In some embodiments, all high-quality potential solutions retained after the roulette wheel selection are first extracted. These potential solutions all satisfy all the constraints in step S3. For each potential solution, its fitness data for M consecutive iterations are retrieved, and the fitness improvement in each iteration is calculated.

[0082] The calculated improvement magnitude is compared with the predefined limit value lim of formula (31) one by one. If there is any improvement magnitude higher than lim, the potential solution is determined to meet the preset limit; if the improvement magnitude is lower than lim for M consecutive times, the potential solution is determined not to meet the preset limit.

[0083] S63: If the fitness improvement of the potential solution does not meet the predefined limit value lim of formula (31), then start the scout bee and generate the potential solution parameters corresponding to the new nectar source location according to the chaotic sequence generation rule of formula (33).

[0084] In some embodiments, for potential solutions that are determined not to meet preset constraints, their corresponding nectar source locations are marked and discarded.

[0085] The new solution generation process of the scout bee is initiated. First, the iterative formula of the logarithmic mapping is determined according to the chaotic sequence generation rule of formula (33). Where α=4, initial value The potential solution parameters are obtained through iterative calculation. These parameters involve the chaotic sequence values ​​corresponding to the number of solar photovoltaic panels (Npv), the number of wind turbines (Nwt), and the maximum total battery capacity (Ebat). These parameters are then substituted into formula (33) to generate new potential solution parameters. , The parameter value boundaries are set for step S4.

[0086] It can be seen that the scout bee generates new solutions based on the chaotic sequence of the logarithmic mapping. The irregular distribution of the chaotic sequence allows the new solutions to escape the range of local optima and generate diverse potential solutions within the parameter value boundary, thus realizing the global exploration of the solution space.

[0087] S64: Substitute the parameters of the new potential solution generated by the scout bee into the constraints of step S3, and verify in turn whether they satisfy the renewable energy constraints, microgrid power balance constraints and battery energy storage constraints.

[0088] In some embodiments, the new potential solution parameters generated by the scout bee are substituted into formulas (18) and (19) of the renewable energy constraint to verify whether the output power of the solar photovoltaic panel and wind turbine is within the limit under the new parameters. Then, the new parameters are substituted into formulas (20), (21), (22), and (23) of the power balance constraint to verify whether the power supply on the AC and DC sides matches the load demand. Finally, the new parameters are substituted into formulas (25), (26), and (27) of the battery energy storage constraint to verify whether the battery's charging and discharging power and state of charge are within the safe range. Only when all the equality or inequality relationships of the constraint formulas are true can the subsequent retention process be entered.

[0089] It can be seen that the constraints in step S3 are a prerequisite for the microgrid configuration scheme to have practical operational feasibility. Substituting the new solution generated by the reconnaissance bee into the constraints for verification is essentially to determine whether the new configuration scheme conforms to the physical operation rules of the microgrid.

[0090] If the potential solution does not meet the algorithm's preset constraints in step S6, the subsequent steps include: S611: Discard potential solutions that do not meet the algorithm's preset constraints and their corresponding nectar source locations, and extract the parameter value boundaries (N) corresponding to the invalid solutions. pv,min N pv,max N wt,min N wt,max E bat,min E bat,max This serves as the basis for the parameter range used by the scout bee to generate new solutions.

[0091] In some embodiments, First, locate the nectar source position corresponding to the potential solution that does not meet the preset constraints. Mark this position and the corresponding Npv, Nwt, and Ebat parameter combinations as invalid and remove them from the solution set. Then, retrieve the parameter value boundaries used to generate this invalid solution, i.e., the number of solar photovoltaic panels [N]. pv,min N pv,max The number of wind turbines [N] wt,min N wt,max [E], the maximum total capacity of the battery bat,min E bat,maxThese boundary parameters will serve as the benchmark for the range of values ​​for new solutions generated by the scout bee, ensuring that the new solutions do not exceed the actually feasible configuration range.

[0092] S612: Start the reconnaissance bee, generate a chaotic sequence based on the logarithmic mapping, and generate alternative new potential solution parameters according to formula (33) by combining the parameter value boundary extracted in step S611. (33) In some embodiments, the chaotic sequence generated by the logarithmic mapping has the characteristic of uniform distribution and can provide diverse values ​​within the parameter boundary. By converting the chaotic sequence into specific configuration parameters through formula (33), the new solution generated by the scout bee can jump out of the local optimum range of the original invalid solution and realize the global exploration of the solution space. The new solution generated here has good diversity and is not limited to the neighborhood range of the original invalid solution, effectively breaking through the limitation of local optima.

[0093] S613: Substitute the parameters of the new potential solution generated in step S612 into the constraints of step S3, and verify in turn whether the renewable energy constraints (Equations 18 and 19), microgrid power balance constraints (Equations 20, 21, 22, and 23) and battery energy storage constraints (Equations 25, 26, and 27) are satisfied, and confirm the physical feasibility of the new solution.

[0094] In some embodiments, the constraints in step S3 are the hard physical rules governing the actual operation of the microgrid. Only when the new solution satisfies these constraints can it become a feasible configuration scheme. By substituting the constraint formulas for verification, it can be accurately determined whether the new solution meets the requirements for the safe and stable operation of the microgrid. S613 filters out invalid solutions generated by the reconnaissance bees that do not conform to the physical constraints, ensuring that the alternative solutions are always within the feasible solution space, so that the iterative optimization of the algorithm does not deviate from the actual application requirements and guarantees the practicality of the optimization results.

[0095] S7: Based on the optimal nectar source location that satisfies the constraints described in step S3 and corresponds to the minimum energy cost, determine the optimal configuration of the hybrid DC and AC microgrid. The optimal configuration includes the optimal number of solar photovoltaic panels, the optimal number of wind turbines, and the optimal total battery capacity.

[0096] In some embodiments, the ultimate goal of optimization is to find the feasible solution with the lowest energy cost. By screening the location of the nectar source with the lowest cost, the optimization results are transformed into specific configuration parameters, realizing the implementation from algorithmic solution to actual engineering solution.

[0097] As one implementation of step S7 of the present invention, it specifically includes the following steps: S71: Construct a set of nectar source locations that satisfy the constraints of step S3. Verify all remaining nectar source locations after iteration of the chaotic artificial bee colony algorithm one by one. Retain nectar source locations that meet the constraints of renewable energy (Equations 18 and 19), microgrid power balance (Equations 20, 21, 22, and 23), and battery energy storage (Equations 25, 26, and 27) to form a feasible solution set.

[0098] In some embodiments, the configuration scheme of the microgrid must meet the physical operation constraints in order to be implemented. By verifying whether the location of the nectar source conforms to the constraint formula corresponding to S3 one by one, the scheme with practical operation feasibility can be separated from the solution set after iteration, and all invalid solutions that violate the physical rules can be eliminated.

[0099] S72: Calculate the energy cost corresponding to each nectar source location in the feasible solution set. First, calculate the total energy demand Eload using formula (7), then calculate the life cycle cost LCC using formulas (8)-(17), and finally substitute it into formula (5) to obtain the energy cost value of each feasible solution.

[0100] In some embodiments, the cost of energy (COE) is a core quantitative indicator for measuring the economic efficiency of a configuration scheme. Its calculation relies on the ratio of life-cycle cost (LCC) to total energy demand (Eload). By substituting the cost breakdown formula and energy demand formula layer by layer, the "configuration parameters" of each feasible solution can be transformed into "economic indicators," enabling comparable quantification of different schemes.

[0101] S73: Sort all energy cost values ​​in the feasible solution set, locate the nectar source location corresponding to the energy cost with the smallest value, extract the number of solar photovoltaic panels Npv, the number of wind turbines Nwt, and the maximum total battery capacity Ebat corresponding to that location, and determine the optimal configuration parameters for the hybrid DC and AC microgrid.

[0102] In some embodiments, the goal of this optimization is to minimize energy costs. The configuration scheme with the lowest COE value after ascending sorting has the lowest total lifecycle expenditure per unit of energy, perfectly matching the optimization objective. Extracting the core parameters of this scheme yields the optimal configuration that meets economic requirements. Ensuring that the final microgrid configuration has the lowest unit energy cost while meeting safe operation constraints allows the optimization objective to be grounded in specific hardware configuration parameters.

[0103] It should be understood that the sequence number of each step in the above embodiments does not imply 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 invention.

[0104] like Figure 3As shown, this application also provides an electronic device, including a display module 103, a memory 102, a processor 101, a communication module 104, and a computer program stored in the memory and executable on the processor 101. When the processor 101 executes the program, it implements the steps of a hybrid DC and AC microgrid optimization configuration method based on a chaotic artificial bee colony algorithm.

[0105] In embodiments of the present invention, electronic devices include, but are not limited to, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0106] In this embodiment, processor 101 may be implemented using at least one of an application-specific integrated circuit, a programmable logic device, a field-programmable gate array, a processor, a controller, a microcontroller, a microprocessor, or an electronic unit designed to perform the functions described herein. In some cases, such an implementation may be implemented within a controller. For software implementation, implementations such as processes or functions may be implemented with separate software modules that allow the performance of at least one function or operation. Software code may be implemented by a software application (or program) written in any suitable programming language, and the software code may be stored in memory and executed by the controller.

[0107] The display module 103 is used to display information input by the user or information provided to the user. The display module 103 may include a display panel, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like.

[0108] The memory 102 can be used to store software programs and various data. The memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0109] The communication module 104 transmits radio signals to and / or receives radio signals from at least one of a base station, an external terminal, and a server. Such radio signals may include voice call signals, video call signals, or various types of data sent and / or received according to text and / or multimedia messages.

[0110] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm, characterized in that the method... include: S1: Determine the system composition of the hybrid DC and AC microgrid, the system including a power generation module, a battery energy storage device, a DC load module and an AC load module, wherein the power generation module includes a solar photovoltaic panel and a wind turbine. S2: Determine the energy cost of the hybrid DC and AC microgrid as the objective function for optimal configuration. The objective function is constructed based on the ratio of the microgrid's life cycle cost to the total energy demand in order to minimize the energy cost. S3: Determine the constraints for the optimal configuration of the hybrid DC and AC microgrid, including renewable energy constraints, microgrid power balance constraints, and battery energy storage constraints; S4: Initialize the nectar source locations for the chaotic artificial bee colony algorithm. Each nectar source location corresponds to a potential solution for the hybrid DC and AC microgrid configuration. The parameters of the potential solution include the number of solar photovoltaic panels, the number of wind turbines, and the maximum total battery capacity. S5: Generate a new nectar source location based on chaotic mapping. The new nectar source location is generated by worker bees according to the current nectar source location and the chaotic sequence, and the potential solution corresponding to the new nectar source location satisfies the constraint conditions determined in step S3. S6: Use a roulette wheel selection mechanism to select a high-quality potential solution from the nectar source locations that satisfy the constraints described in step S3; The selected potential solutions are verified by preset constraints. If a potential solution does not meet the preset constraints of the algorithm, the scout bee is activated to generate a new nectar source location, and the potential solution corresponding to the new nectar source location simultaneously meets the constraint conditions described in step S3. If a potential solution satisfies the algorithm's preset constraints, the nectar source location is retained and continues to participate in subsequent iterations; S7: Based on the optimal nectar source location that satisfies the constraints described in step S3 and corresponds to the minimum energy cost, determine the optimal configuration of the hybrid DC and AC microgrid. The optimal configuration includes the optimal number of solar photovoltaic panels, the optimal number of wind turbines, and the optimal total battery capacity.

2. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, S1 specifically includes the following steps: Obtain the installation area, unit efficiency and solar radiation data of the solar photovoltaic panel, and calculate the output power of the solar photovoltaic panel at different time points t according to the following formula (1); (1) in, This indicates the output power of the solar photovoltaic panel; The number of solar photovoltaic panels; The efficiency of the solar photovoltaic panel is assumed to be a constant value. The area of ​​the solar photovoltaic panel; Solar radiation; Obtain data on the swept area, air density, wind speed and power coefficient of the wind turbine, and calculate the output power of the wind turbine at different time points t according to formula (2); (2) in, This indicates the output power of the wind turbine. The number of wind turbines; Indicates air density; The swept area of ​​the wind turbine rotor; Wind speed; The power coefficient is assumed to be constant. The efficiency of the wind turbine; Set the maximum total capacity, charge and discharge efficiency and upper and lower limits of state of charge of the battery energy storage system, and establish a dynamic model of the state of charge of the battery at each time according to formula (3) and formula (4). (3) (4) in, and These represent the battery's charge and discharge efficiencies, respectively. and These represent the charging and discharging power at time t; Indicates the maximum total capacity of the battery; This is the power directly supplied to the DC load; This refers to the power supplied to the AC load after conversion.

3. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, S2 specifically includes the following steps: S21: Determine the total energy demand of the hybrid DC and AC microgrid. The total energy demand is calculated according to formula (7). (7) Where T is the microgrid lifetime and Δt is the step size. (t) represents the AC power demand at time t. (t) represents the DC power demand at time t. For inverter efficiency; S22: Determine the lifecycle cost of the hybrid DC and AC microgrid according to formula (6). , (6) in, The investment cost of a microgrid; For microgrid operation and maintenance costs; Cost of replacing microgrid components; S23: Construct the objective function for the optimal configuration of the hybrid DC and AC microgrids. The objective function is calculated according to formula (5). (5) in, This represents the total energy demand of the microgrid.

4. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, Step S2 determines the energy cost of the hybrid DC and AC microgrid as the objective function for optimal configuration, including the following methods: S221: Calculate the investment cost of hybrid DC and AC microgrids according to formula (8). , (8) in, , and These represent the capital costs of solar photovoltaic panels, wind turbines, and batteries, respectively. (9) (10) (11) in, This refers to the maximum total capacity of the battery. , and These represent the capital cost per unit of solar photovoltaic panel, wind turbine, and battery, respectively. S222: Calculate the operation and maintenance costs of a hybrid DC and AC microgrid using formula (12). , (12) (13) (14) (15) in, , and These represent the operating and maintenance costs of solar photovoltaic panels, wind turbines, and batteries, respectively. , and These represent the operating and maintenance costs per unit of solar photovoltaic panel, wind turbine, and battery, respectively. S223: Calculate the component replacement cost of a hybrid DC and AC microgrid using formula (16). , (16) (17) in, Number of replacements; For battery life.

5. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, S3 specifically includes the following steps: S31: Set renewable energy constraints for the hybrid DC and AC microgrid, limiting the output power of solar photovoltaic panels according to formula (18) and the output power of wind turbines according to formula (19). (18) (19) in, This represents the maximum power output of a single photovoltaic panel. Let be the photovoltaic output power at time t. This represents the maximum power of a single fan. The fan output power at time t; S32: Set the power balance constraints for the hybrid DC and AC microgrid. The DC side power balance is limited by formulas (20) and (21). (20) (21) The AC power balance is limited by formulas (22) and (23). (22) 、 (23) in, This refers to the output power of the solar photovoltaic panel. To convert the output power of a wind turbine into DC power; Total battery power; This refers to the output power of the wind turbine. To convert the output power of solar photovoltaic panels into AC power; Convert battery power into AC power; For the rectifier efficiency, For inverter efficiency; For AC load requirements; S33: Set the battery energy storage constraints for the hybrid DC and AC microgrid. The battery charging and discharging power is limited according to formulas (25) and (26). (25) (26) The state of charge of the battery is limited by formula (27). (27) in, The battery charging power at time t. Let be the battery discharge power at time t. Maximum charging power for the battery. This is the battery's maximum discharge power. The state of charge of the battery at time t. This is the battery's minimum state of charge. This represents the battery's maximum state of charge.

6. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, S4 specifically includes the following steps: S41: Determine the variables for the potential solution of the hybrid DC and AC microgrid configuration, including the number of solar photovoltaic panels Npv, the number of wind turbines Nwt, and the maximum total battery capacity Ebat, with each variable corresponding to a parameter of the microgrid configuration; S42: Set the value boundaries of the variables, and determine the value range of Npv [N pv,min N pv,max The range of values ​​for Nwt is [N wt,min N wt,max The value range of Ebat [E bat,min E bat,max ]; where N pv,min N wt,min E represents the minimum number of devices to be deployed. bat,min For the minimum battery capacity, N pv,max N wt,max E represents the maximum number of devices that can be deployed. bat,max This is the maximum battery capacity; S43: Generate chaotic sequences based on logarithmic mapping, and calculate the initial chaotic values ​​of variables according to the chaotic sequence generation rules of formula (33); (33) in, ; , and For chaotic sequences based on logarithmic mappings, , The variable value boundaries set for step S42; S44: Assign the chaotic initial values ​​to Npv, Nwt, and Ebat respectively to obtain N random nectar source locations. Each location corresponds to a combination of Npv, Nwt, and Ebat, which is a potential solution for the configuration of a hybrid DC and AC microgrid.

7. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, S5 specifically includes the following steps: S51: Generate the chaotic sequence required for worker bees based on the logarithmic mapping, calculated according to the chaotic sequence generation rule of formula (32). (32) in These are the parameters of the current solution. Other solution parameters are randomly selected. For the new solution parameters; The chaotic sequence in formula (32) satisfies initial value Chaotic sequences are used to adjust parameters for worker bees to generate new solutions. S52: Obtain the potential solution parameters corresponding to the current nectar source location in the chaotic artificial bee colony algorithm, randomly select the solution parameters of another different nectar source location, and combine them with the chaotic sequence generated in step S51 to calculate the new solution parameters according to formula (32); S53: Substitute the new solution parameters obtained in step S52 into the constraint formula determined in step S3, and verify in turn whether the renewable energy constraint, microgrid power balance constraint and battery energy storage constraint are satisfied. S54: If the new solution parameters satisfy all the constraints of step S3, then the position corresponding to the new solution parameters is determined as the effective new nectar source position; If the conditions are not met, the new solution parameters are discarded, and the corresponding nectar source location is not generated.

8. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 6, characterized in that, If a potential solution in step S6 does not meet the algorithm's preset constraints, the scout bee will generate a new alternative nectar source location. The specific methods for ensuring that the potential solution corresponding to the new alternative nectar source location simultaneously meets the constraints described in step S3 include: S61: Determine the preset constraint judgment criteria for the chaotic artificial bee colony algorithm. The preset constraint is executed according to the predefined constraint value lim in formula (31). The judgment index is the fitness iteration improvement of the potential solution. (31) The number of worker bees corresponds to the number of solutions; The number of variables; S62: Substitute the high-quality potential solutions obtained by the roulette wheel selection into the preset constraint judgment criteria, compare the fitness improvement of its continuous iteration with the predefined constraint value lim of formula (31), and complete the preset constraint verification; S63: If the fitness improvement of the potential solution does not meet the predefined limit value lim of formula (31), then start the scout bee and generate the potential solution parameters corresponding to the new nectar source location according to the chaotic sequence generation rule of formula (33). S64: Substitute the parameters of the new potential solution generated by the scout bee into the constraints of step S3, and verify in turn whether they satisfy the renewable energy constraints, microgrid power balance constraints and battery energy storage constraints.

9. The method for optimal configuration of hybrid DC and AC microgrids based on chaotic artificial bee colony algorithm according to claim 1, characterized in that, The processing steps in step S6 if the potential solution does not meet the algorithm's preset constraints include: Discard potential solutions that do not meet the algorithm's preset constraints and their corresponding nectar source locations, and extract the parameter value boundaries corresponding to the invalid solutions as the basis for the parameter range of the scout bees to generate new solutions; Start the reconnaissance bee, generate a chaotic sequence based on the logarithmic mapping, and generate alternative new potential solution parameters according to formula (33) by combining the extracted parameter value boundaries; (33) Substitute the parameters of the new potential solution generated in step S612 into the constraints in step S3, and verify in turn whether the renewable energy constraints, microgrid power balance constraints, and battery energy storage constraints are met, thus confirming the physical feasibility of the new solution.

10. An electronic 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 program, it implements the steps of the hybrid DC and AC microgrid optimization configuration method based on the chaotic artificial bee colony algorithm as described in any one of claims 1 to 9.

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