Alternating current and direct current hybrid microgrid control method based on multi-target collaborative topological optimization

Abstract

The invention discloses an AC / DC hybrid micro-grid control method based on multi-objective collaborative topological optimization, and the method comprises the steps: determining a multi-objective optimization function containing economical efficiency, reliability and stability, and related constraint conditions through constructing an AC / DC hybrid micro-grid system model; the method comprises the following steps: collecting operation data of a micro-grid in real time, performing turnover crossover variation by applying an improved genetic algorithm, performing collaborative optimization on a topological structure and operation parameters of the micro-grid in combination with a topological optimization strategy, and screening and executing an optimal control strategy; an intelligent topology reconstruction technology is introduced, an intelligent change-over switch is utilized to dynamically adjust the connection relation between the hybrid main network and each sub-microgrid, a topology path with the minimum energy loss is preferentially selected, dynamic feedback adjustment is performed, and orderly consumption of new energy and efficient operation of the system are realized. According to the method, multi-performance index collaborative optimization of the micro-grid is realized, the adaptability to topological changes and uncertain factors is enhanced, the operation performance of the micro-grid can be effectively improved, and the method is widely applied to various alternating current and direct current hybrid micro-grid systems.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of microgrid control, and particularly relates to a control method for AC-DC hybrid microgrid based on multi-objective collaborative topology optimization, which can be applied to the field of distributed energy systems and the like, and realizes efficient and stable operation of the AC-DC hybrid microgrid. BACKGROUND

[0002] With the rapid development of distributed energy (such as solar photovoltaic, wind power, etc.) and the continuous progress of energy storage technology, the AC-DC hybrid microgrid has attracted widespread attention and application in the energy field due to its ability to simultaneously meet the power supply needs of AC and DC loads, effectively integrate different types of energy resources, and reduce energy conversion losses. However, due to the complex and variable topology structure of the AC-DC hybrid microgrid, which contains multiple types of power sources, loads and energy storage devices, and the influence of various uncertain factors such as light intensity, wind speed, load fluctuation and the like during operation, the microgrid has problems such as power imbalance, voltage and frequency fluctuation, poor system stability and the like during operation.

[0003] Currently, the control method for the AC-DC hybrid microgrid mostly optimizes control for a single target, such as only considering reducing operating costs or improving power supply reliability, and it is difficult to simultaneously achieve optimization of multiple important performance indicators such as economy, reliability and stability of the microgrid under complex and variable operating conditions. In addition, the traditional control method lacks an effective dynamic adjustment mechanism when dealing with changes in the topology structure of the microgrid, and cannot optimize the topology structure of the microgrid in real time according to the actual operating conditions, which limits the operating efficiency and performance of the microgrid. SUMMARY

[0004] In view of the above problems in the prior art, the present application aims to provide a control method for an AC-DC hybrid microgrid based on multi-objective collaborative topology optimization, which optimizes the topology structure and operating parameters of the AC-DC hybrid microgrid through multi-objective collaboration, introduces intelligent switches to realize topology changes, and achieves comprehensive improvement of multiple targets such as economy, reliability and stability of the microgrid under different operating conditions, while enhancing the adaptability of the microgrid to topology changes and improving the overall operating performance of the microgrid.

[0005] The present application provides the following technical solution: a control method for an AC-DC hybrid microgrid based on multi-objective collaborative topology optimization, based on a constructed AC-DC hybrid microgrid system model, comprising the following steps: S1, a multi-objective optimization model is constructed, with the minimum total system loss, the minimum voltage deviation and the highest reliability as the targets, a multi-objective function is established, and the constraint conditions are determined; in order to solve the multi-objective optimization problem, a weighted summation method is used to convert the multi-objective into a single-objective fitness function; S2, adopt improved genetic algorithm to solve multi-objective function: write the target function, constraint condition and switch state of the multi-objective optimization model in step 1 into the improved genetic algorithm, encode the switch to form an initial population, then select, mutate and iterate the population, and finally obtain the optimal solution; S3, convert the optimal switch state in the AC-DC hybrid microgrid system model into actual control instructions to obtain the optimal operation strategy and topology structure; S4, adopt a feasibility repair strategy to avoid load power failure; S5, real-time monitor the operation state of the AC-DC hybrid microgrid, compare the actual operation data with the optimized operation strategy and topology structure, if the loss and voltage deviation calculation values in the actual operation are greater than or less than the calculation values of the optimal solution in the algorithm, and the error exceeds 10%, then re-execute the data acquisition, multi-objective optimization and topology adjustment steps.

[0006] Further, the target function of the multi-objective optimization model is as follows: ; represents the total loss of the system, including line loss and converter loss, represents the voltage deviation, represents the system reliability, , , represents the weight coefficient, satisfying .

[0007] Further, the constraint conditions of the multi-objective optimization model are as follows: a. Power balance: the sum of the output power of several power sources and the output power of energy storage is equal to the sum of the load power and the loss power: ; b. Voltage constraint: the voltage of each bus is controlled within the limit range; ; c. Topology constraint: Connectivity(s) is a topology connectivity function, which takes the value of 1 when and only when the topology structure formed by the switch state s is connected: .

[0008] Further, in S2, to avoid falling into local optimum, match system physical constraints, and reduce the generation of invalid solutions, the algorithm is improved in two places for mutation and selection steps: (1) Adopt tournament selection, randomly select three individuals to calculate and compare the fitness values; (2) Individual encoding is one-to-one mapped with physical topology, and random trigger mutation is performed on non-critical switches, and when mutation is performed, i positions of non-critical switches are randomly selected for flip mutation.

[0009] The specific process of solving the multi-objective function by using the improved genetic algorithm is as follows: a. Generate an initial population N: each gene position of each individual corresponds to a switch state (1 represents closing, and 0 represents opening). If the total number of switches in the system is w, of which v are critical switches, the length of the individual is w, and v positions are designated as critical switch positions, and the remaining (w-v) positions are non-critical switch positions. The v critical positions are forced to be 1, and the remaining positions are randomly generated combinations to obtain an initial candidate individual; b. Calculate the fitness value of each individual according to the objective function minF, and select the individual with the optimal fitness value from the current population; randomly select multiple individuals from the population, calculate the fitness value of each individual and sort them, and select the individual with the optimal fitness value to enter the offspring; repeat this process until N individuals are selected to form a new population.

[0010] c. Flip mutation operation is performed on the population to ensure that the switches on which the non-stop load is connected remain closed, and gene exchange is performed on other switch positions, i.e., from 0 to 1 (representing the switch position is converted from open to closed), or from 1 to 0 (representing the switch position is converted from closed to open).

[0011] d. Set the maximum number of iterations T, and stop the calculation when the number of iterations reaches T, and output the optimal solution, i.e., the optimal switch state combination.

[0012] Among them, the critical switch is the switch on which the non-stop load is connected, and the non-critical switch is the switch other than the critical switch.

[0013] Further, the specific process of S4 is as follows: When the load loses power after flip mutation, the corresponding power supply switch closest to the load is forced to be closed.

[0014] By using the above-mentioned technology, compared with the prior art, the beneficial effects of the present application are as follows: 1) The present application can realize multi-performance index collaborative optimization: by constructing a multi-objective optimization function including economy, reliability and stability, the performance requirements of different dimensions of the microgrid can be considered at the same time; on the basis of ensuring power supply reliability and stable operation of the system, the economic cost is optimized, such as reasonable scheduling of distributed power generation, energy storage charging and discharging, to realize the optimal comprehensive benefit of the microgrid. 2) The application can adapt to topological changes and uncertainties: intelligent topological reconfiguration technology is introduced to dynamically adjust the connection relationship of the hybrid main network and the sub-microgrid. In the face of distributed power output fluctuation, load random variation and other uncertainties, the topological path can be quickly switched, the path with the minimum energy loss is preferentially selected to ensure stable operation of the system and enhance the anti-interference and adaptive ability of the microgrid. 3) The application can optimize the energy transmission path from the topological level, reduce energy loss, effectively improve the overall operation efficiency and performance of the microgrid, and adapt to various AC / DC hybrid microgrid system application scenarios; the feasibility repair strategy is adopted to improve the operation reliability of the microgrid and avoid short circuit or load power failure. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The figure is a schematic diagram of the frame structure of the application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical scheme and advantages of the application more clear and explicit, the application will be further described in detail below in combination with the drawings and examples in the specification. It should be understood that the specific examples described herein are only used to explain the application and not to limit the application.

[0017] On the contrary, the application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the application defined by the claims. Further, in order to make the public have a better understanding of the application, some specific details are described in detail in the following detailed description of the application. The application can also be completely understood without the description of these details by those skilled in the art.

[0018] An AC / DC hybrid microgrid control method based on multi-objective cooperative topology optimization is realized based on a constructed AC / DC hybrid microgrid system model, and the model construction process is as follows: An AC / DC hybrid microgrid system model containing an AC sub-microgrid and a DC sub-microgrid is established, wherein the AC sub-microgrid contains AC distributed power, AC load, AC bus, AC circuit breaker and other devices; the DC sub-microgrid contains DC distributed power (such as solar photovoltaic array, fuel cell, etc.), DC load, DC bus, DC circuit breaker and other devices; the AC sub-microgrid and the DC sub-microgrid are connected through a bidirectional converter to realize bidirectional flow of energy, and a switch is used to realize line on-off. The working characteristics and operating parameters of each device in the system are modeled, including the output power characteristics of the power supply, the power demand characteristics of the load, the conversion efficiency of the converter, etc.

[0019] The specific steps of the control method are as follows: (1) A multi-objective optimization model is constructed, the minimum system total loss, the minimum voltage deviation and the highest reliability are taken as the objectives, the objective function is established, and the constraint conditions are determined: ; ; ; ; wherein: , , represents the weight coefficient, satisfying ; represents the total system loss percentage, including line loss and converter loss; represents the voltage deviation, is the voltage reference value; represents the system reliability; represents the failure rate of the mth component; represents the operating time of the mth component; the optimal solution of this multi-objective problem represents the opening and closing state of each intelligent switch.

[0020] The constraint conditions are as follows: a. Power balance: the sum of the output power of several power sources and the output power of energy storage is equal to the sum of the load power and the loss power: ; b. Voltage constraint: the voltage of each bus is controlled within the limit range: ; c. Topology constraint: Connectivity(s) is the topology connectivity function, which takes the value of 1 only when the topology structure formed by the switch state s is connected: .

[0021] (2) Improved genetic algorithm is used to solve the multi-objective function: a. Generate an initial population: each gene bit corresponds to a switch, 1 represents closed and 0 represents open. Ensure that the state of the key switch (AC power supply switch and DC power supply switch) is 1 (closed).

[0022] b. Calculate the fitness value of each individual according to the objective function minF, and select the individual with the optimal fitness value from the current population.

[0023] c. Perform flip mutation operation on the population, only exchange genes at non-key bits, and ensure that the key switch is always closed. Randomly select a non-key bit to reverse, 0 to 1, 1 to 0, recalculate the fitness value, and keep the optimal individual. Ensure that the updated topology meets the network connectivity and power flow constraints.

[0024] d. Stop when the maximum number of iterations is met.

[0025] (3) Convert the optimal switch state into actual control instructions to obtain the optimal operation strategy and topology structure.

[0026] (4) Adopt a feasibility repair strategy. If the load loses power after the flip mutation, the switch closest to the load is forced to close.

[0027] (5) Real-time monitor the operation state of the AC-DC hybrid microgrid, compare the actual operation data with the optimized operation strategy and topology structure, and if there is a deviation, re-execute the data acquisition, multi-objective optimization and topology adjustment steps.

[0028] The application determines a multi-objective optimization function including economy, reliability and stability and related constraint conditions by constructing an AC-DC hybrid microgrid system model, collects microgrid operation data in real time, uses an improved genetic algorithm to perform flip crossover mutation, cooperatively optimizes the microgrid topology structure and operation parameters in combination with a topology optimization strategy, selects an optimal control strategy and executes it, introduces an intelligent topology reconfiguration technology, dynamically adjusts the connection relationship between the hybrid main grid and each sub-microgrid by using intelligent switching switches, preferentially selects a topology path with minimum energy loss, dynamically feedback adjusts, realizes orderly consumption of new energy and efficient operation of the system, and realizes multi-performance index cooperative optimization of the microgrid, enhances the adaptability to topology changes and uncertain factors, can effectively improve the operation performance of the microgrid, and is widely applicable to various AC-DC hybrid microgrid systems.

[0029] Embodiment: 1, construct an AC-DC hybrid microgrid system, as shown in Figure 1 Intelligent switches S1 are arranged between AC buses M1 and M2, intelligent switches S2 are arranged between DC buses L1 and L2, an AC / DC bidirectional converter S3 is arranged between the AC bus M1 and the DC bus L1, an AC / DC bidirectional converter S4 is arranged between the DC bus L2 and the system, an intelligent switch S5 is arranged between the AC bus M2 and the system, an intelligent switch S6 is arranged on the AC bus M1, an intelligent switch S7 is arranged on the AC bus M2, an intelligent switch S8 is arranged on the DC bus L1, an intelligent switch S9 is arranged on the DC bus L2, an energy storage converter S10 is arranged between the DC bus L2 and an energy storage battery, and a photovoltaic converter S11 is arranged between the DC bus L1 and a photovoltaic device. Each switch has two states of 0 (off) and 1 (on).

[0030] 2, deploy various sensors at each key node of the microgrid to collect operation data, including electrical parameters such as voltage, current, power and frequency, and provide data support for optimization calculation.

[0031] 3. Improved Genetic Algorithm Initialization: The state combinations of smart switches and converters, S=[S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11], correspond to different topological connections and represent a control strategy. For each individual (switch combination), a binary code (0 or 1) is randomly generated to ensure that the state of critical switches is 1 (closed). For example, the important load switches S6 and S7 are set to state 1. Solutions that do not meet the topological constraints are checked and repaired to ensure that each load node has at least one power supply path.

[0032] 4. Fitness Function Calculation: Each switch combination represents a system topology. The total system loss under each structure is calculated, including line losses, conversion losses, and transformer losses. The deviation of each node voltage from the reference voltage is calculated. Based on the failure rate of each component and the switch states, system reliability is calculated, thereby determining the fitness function value. Matrix operations are used to check whether the topology, voltage, and power meet the constraints, and the constraints are verified. The individual with the optimal fitness value is selected. If the constraints are not met, the individual mutation process is initiated, and the switch states are updated.

[0033] 5. Update the switching state through individual flip-mutation. For example, if an individual in the initial population is [1,1,1,1,1,1,1,1,1,1,1], arbitrarily select a non-critical position for flip-mutation to [1,1,1,1,1,1,1,1,1,1,0]. When the topology corresponding to an individual changes, recalculate the optimized operating parameters under that topology to ensure coordinated optimization of the topology and parameters. For example, if the state of the intelligent switching switch changes, i.e., the microgrid topology path is adjusted, the system loss, voltage deviation, and system reliability are recalculated simultaneously, and the fitness function is calculated. At the same time, the constraints are checked, and if they are not met, the individual is discarded.

[0034] 6. Stop when the maximum number of iterations is met, and apply the switch combination state with the optimal fitness value to the system.

[0035] 7. The algorithm outputs the circuit breaker states for the optimal topology as [0,0,0,0,1,0,1,1,1,1,1]. In the control system, the circuit breakers for the photovoltaic sub-microgrid (S8, S11), energy storage sub-microgrid (S9, S10), and AC microgrid (S5, S7) are closed, while the other circuit breakers are open.

[0036] 8. If the photovoltaic power generation in the photovoltaic sub-microgrid suddenly drops to 0 at this time, the S11 photovoltaic converter will be forced to disconnect, and the power switch closest to the S8 load switch, i.e., S2, will be closed.

[0037] 9. After operation, the micro-grid power flow is calculated once every 30 minutes, the power flow calculation data (voltage, current, power, etc.) is substituted into the objective function to calculate the total loss and voltage deviation rate of the system. If the error between the calculation results of the optimal solution in the algorithm and the actual operation loss and voltage deviation calculation value exceeds 10%, the data acquisition, multi-objective optimization and topology adjustment steps are re-executed.

[0038] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A control method of AC / DC hybrid microgrid based on multi-objective cooperative topology optimization, characterized in that, Based on the constructed AC / DC hybrid microgrid system model, the following steps are included: S1, a multi-objective optimization model is constructed, with the minimum system loss, minimum voltage deviation and highest reliability as the target, a multi-objective function is established, and the constraint condition is determined; S2, the improved genetic algorithm is used to solve the multi-objective function: the information of the multi-objective optimization model in step 1 is written into the improved genetic algorithm, the initial population is formed by coding the switch, and the population is selected, mutated and iterated to get the optimal solution; S3, the optimal switch state in the AC / DC hybrid microgrid system model is converted into actual control instruction to get the optimal operation strategy and topology structure; S4, the feasibility repair strategy is used to avoid load power failure; S5, the running state of the AC / DC hybrid microgrid is monitored in real time, the actual running data is compared with the optimized operation strategy and topology structure, if the loss and voltage deviation calculation value in the actual running is greater than or less than the calculation value of the optimal solution in the algorithm, and the error is more than 10%, then the data acquisition, multi-objective optimization and topology adjustment steps are executed again. 2.The AC / DC hybrid microgrid control method based on multi-objective cooperative topology optimization of claim 1, wherein, The objective function of the multi-objective optimization model is as follows: ； representing the total system loss, including line loss and converter loss, representing the voltage deviation, representing the system reliability, , , representing the weight coefficient, satisfying . 3.The AC / DC hybrid microgrid control method based on multi-objective cooperative topology optimization of claim 2, wherein, The constraint condition of the multi-objective optimization model is as follows: a. Power balance: the sum of the output power of several power sources and the output power of energy storage is equal to the sum of the load power and the loss power: ； b. Voltage constraint: the voltage of each bus is controlled within the limit range; ； c. Topology constraint: Connectivity(s) is the topology connectivity function, which takes the value of 1 when and only when the topology structure formed by the switch state s is connected: 。 4.The AC / DC hybrid microgrid control method based on multi-objective collaborative topology optimization of claim 1, wherein, In S2, the improvement of genetic algorithm is as follows: 1) Tournament selection is used, and a plurality of individuals are randomly selected for fitness value calculation and comparison; 2) Individual coding is one-to-one mapped with physical topology, and non-critical switch is randomly triggered to mutate, and i positions of non-critical switch are randomly selected for flip mutation when mutating; The specific process of solving the multi-objective function by using the improved genetic algorithm is as follows: a. Generate initialization population N: each gene bit corresponds to a switch; b. Calculate the fitness value of each individual according to the objective function minF, and select the individual with the optimal fitness value from the current population; c. Flip mutation operation is performed on the population to ensure that the critical switch remains closed, and gene exchange is performed on the non-critical switch; d. Set the maximum iteration number T, stop calculation when the iteration number reaches T, and output the optimal solution, i.e. the optimal switch state combination; Wherein, the critical switch is the switch on the line of non-power failure load, and the non-critical switch is the switch other than the critical switch.

5. The AC / DC hybrid microgrid control method based on multi-objective collaborative topology optimization according to claim 1, characterized in that, The specific process of S4 is as follows: When the load loses power after flip mutation, the corresponding power switch of the load is forced to close.

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