Power distribution system power loss minimization method for optimizing distributed power supply position and capacity based on quantum genetic algorithm

Abstract

The invention provides a distributed generation (DG) optimal configuration method based on a quantum genetic algorithm, and aims to realize power loss minimization and voltage stability improvement of a power distribution system. The method comprises the following steps: firstly, collecting basic data of a power distribution network, including bus numbers, voltage grades, line impedance, bus load requirements, capacity limitation of distributed power supplies and the like; thirdly, initializing a group of populations containing different DG configuration schemes by adopting a random number generation algorithm, and carrying out iterative optimization on the populations through a quantum genetic algorithm; in the optimization process, a new generation of configuration schemes is generated by using quantum selection, crossover and mutation operations, and the power loss and the voltage level of each scheme are evaluated through a fitness function so as to gradually approach the optimal solution. After an optimal configuration scheme is obtained, different load conditions (such as load increase or decrease) are simulated so as to verify and optimize the adaptability of the configuration and ensure that the system can still maintain low power loss and stable voltage under load fluctuation. The finally output optimal DG configuration scheme comprises the bus position, the capacity and the optimized power loss and voltage curve, and can adapt to the dynamic change of the load. The method can effectively reduce the power loss of the power distribution system, improves the voltage stability, and provides technical support for constructing an efficient and reliable power distribution system.

Description

Technical Field

[0001] The present invention relates to the field of power distribution system optimization, and in particular to a method for optimizing the position and capacity of distributed power sources based on a quantum genetic algorithm to minimize the power loss of a power distribution system. Background Art

[0002] With the growth of global energy demand and the improvement of environmental awareness, traditional centralized power generation has gradually been supplemented by distributed generation (DG) systems. Distributed generation systems can reduce transmission losses, improve energy efficiency, and enhance the power supply reliability of distribution systems by directly connecting small-scale power generation units (such as solar energy, battery energy storage, wind power generation, etc.) to the distribution network. However, the configuration of distributed generation and its application in the distribution network still have the following pain points: 1) Improper DG location may cause the system power loss to increase instead of decrease; 2) Unreasonable DG location and capacity configuration may cause bus voltage to exceed the limit and affect voltage stability; 3) The load of the distribution system has significant time-varying characteristics, and the DG configuration needs to be able to adapt to load fluctuations to maintain stable system operation; 4) Optimizing the configuration of distributed power sources involves complex power flow calculations, especially in the case of multiple loads, which requires huge calculations. To solve the above problems, the present invention discloses a method for minimizing power loss in a distribution system based on a quantum genetic algorithm to optimize the location and capacity of distributed power sources. The method can greatly improve the search efficiency and global optimization capability through the selection, crossover and mutation operations of quantum bits during the optimization process, find the optimal or near-optimal DG configuration scheme in a shorter time, effectively reduce the power loss of the distribution system, enhance voltage stability, and adapt to multiple load conditions, thereby providing a distributed power generation optimization configuration scheme with strong adaptability and high operating efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a method for minimizing power loss in a distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources. The method can effectively reduce the power loss of the distribution system, stabilize the voltage profile, and adapt to system changes under different load conditions by optimizing the location and capacity of distributed power sources.

[0004] The technical solution adopted by the present invention is a method for minimizing power loss in a power distribution system by optimizing the location and capacity of distributed power sources based on a quantum genetic algorithm, which is specifically implemented in the following steps:

[0005] Step 1: First, collect the basic data of the distribution network, including the specific information of the busbar, line and distributed generation equipment (such as capacity and location, etc.), and initialize the voltage level and power flow of the power system. The purpose of this step is to obtain the power loss and voltage curve of the system in the initial state as a reference baseline for optimization.

[0006] Step 2: After obtaining the basic data, a random number generation algorithm is used to generate a set of initial populations, which contain several different DG configuration schemes. Each configuration scheme is called an "individual", in which the gene indicates whether DG is arranged on each bus. These individuals are substituted into the initial power system model to calculate their corresponding power loss and voltage curves, so as to provide a preliminary evaluation for subsequent optimization.

[0007] Step 3. After obtaining the initial population, the quantum genetic algorithm is used to iteratively optimize the population with the goal of minimizing power loss and keeping the voltage curve within the allowable range. The optimization process of the quantum genetic algorithm includes quantum selection, quantum crossover, and quantum mutation. First, quantum selection is based on the fitness function, and individuals with small power loss and stable voltage are preferentially selected to enter the next generation. Next, quantum crossover uses quantum superposition and cloning technology to combine the characteristics of high-fitness individuals to generate new individuals, thereby gradually optimizing the DG configuration. Finally, quantum mutation introduces perturbations through random Pauli gates to increase population diversity and avoid local optimality. In each generation, a new generation of configurations is generated through quantum selection, crossover, and mutation, and a better DG position and capacity solution is gradually obtained.

[0008] Step 4: After obtaining the preliminary optimized configuration, simulate different load conditions of the distribution system to verify the adaptability of the DG configuration. Specifically, set the load condition to increase or decrease by a certain percentage (e.g., 25%, 50%), and repeat the above quantum genetic algorithm optimization steps to ensure that the configuration scheme can still achieve minimum power loss and voltage stability under different load conditions. Record the optimal configuration under each load condition, and evaluate the power loss and voltage curve changes of the system under each load state.

[0009] Step 5. Finally, the optimal DG location and capacity configuration scheme is output, including the DG location and capacity of each bus and the optimized system power loss and voltage curve data. At the same time, corresponding optimization configurations are provided for different load conditions to ensure that the DG configuration can adapt to the dynamic changes of the load, thereby achieving stable and efficient power supply capabilities of the system. This method implements a dynamic optimization process based on quantum genetic algorithm, which enables the distribution system to maintain low power loss and good voltage stability under different load conditions.

[0010] The present invention is also characterized in that:

[0011] Step 1 is implemented as follows:

[0012] First, data reading and system initialization are performed. By reading the core data of the distribution network, the system can accurately represent the network structure. Specifically, the data includes: bus number and voltage level, impedance value of each line (resistance R iand reactance X i ), bus load (active power P i and reactive Q i ) and the rated power of distributed generation (DG) and the capacity limitations of the installation location. These data together construct the topology of the distribution network, ensure the connection relationship between the bus and the line is accurate, and provide a basis for subsequent power flow calculations and DG optimization.

[0013] After completing the data reading, the system starts the initial power flow calculation and uses the forward-backward sweep method for analysis. This method is divided into two stages:

[0014] (1) Backward Sweep: Starting from the farthest bus in the system, calculate the current on each line step by step towards the power source. Initially, it is assumed that the voltage of each bus is equal to the source voltage. For the load current of each bus, the following formula can be used:

[0015]

[0016] Among them, I ldi represents the load current of the ith bus, P i and Q i are the active and reactive power requirements on the bus, V i is the voltage of the bus. During the backward scanning process, the current value in the line will be continuously updated according to the voltage value of the previous iteration until the current of all buses in the system is preliminarily distributed.

[0017] (2) Forward Sweep: Move forward from the power source point to the outside, gradually calculating and updating the voltage of each bus. i and current I Li Under the condition of , the voltage of each bus is calculated by the following formula:

[0018] V i =V u -Z i I Li (2)

[0019] Among them, V i is the voltage of the ith bus, V u is the voltage of the upper busbar, Z i is the line impedance,

[0020] I Li is the current in the line. By continuously advancing to the far-end bus of the system, the system can obtain the voltage distribution of each bus and complete the preliminary analysis of the voltage profile.

[0021] After completing the initial power flow calculation, the system also needs to verify the initial voltage and power distribution. During this process, check whether the voltage of each bus is within the specified allowable range (for example, 0.95 to 1.05pu). The calculation formula for the upper and lower voltage limits is as follows:

[0022]

[0023] Among them, V base is the reference voltage of the system.

[0024] At the same time, calculate the total power loss of the system. The total power loss calculation formula of the system is as follows:

[0025]

[0026] Among them, P loss is the total active power loss of the system, I i is the current of the ith line, R i is the resistance of the line.

[0027] Step 2 is specifically implemented as follows:

[0028] Based on the basic data collected in step 1, an initial population is generated, and the power loss and voltage curve of each individual are preliminarily calculated and evaluated. Each individual represents a distributed generation (DG) configuration scheme. The purpose of this step is to provide an initial solution set for the optimization of the quantum genetic algorithm.

[0029] 1. Use a random number generation algorithm to initialize a population consisting of multiple different distributed generation (DG) configuration schemes. Each configuration scheme is called an "individual", and the "gene" in it indicates whether DG is deployed on each bus. For example, setting a bus to 1 means that DG is deployed on the bus, and setting it to 0 means that DG is not deployed on the bus. Through this gene encoding method, each individual actually represents a specific DG location and capacity configuration scheme.

[0030] 2. Substitute each generated individual (i.e. a specific DG configuration scheme) into the power system model and calculate its power flow. For each individual configuration, calculate the net apparent power demand S of the system r , reflecting the difference between load power demand and DG power generation output.

[0031] The formula is as follows:

[0032] S r =S load -S DG (6)

[0033] Among them, S loadis the total apparent power demand of the current system load; S DG is the total apparent power output of the DG under the current individual configuration.

[0034] From the calculated net apparent power S r Extract the active part from the current configuration to represent the active power loss P DG,loss :

[0035] P DG,loss =real(S r ) (7)

[0036] Among them, real(S r ) is S r This value represents the power loss under the current configuration and serves as the basis for subsequent fitness evaluation.

[0037] During the power flow calculation, the voltage level of each bus is obtained. Ensure that the voltage of each bus is between 0.95 and 1.05 pu. If some bus voltages are outside this range, mark the individual configuration as a configuration that does not meet the voltage requirements.

[0038] 3. Calculate the initial fitness value of each individual for subsequent optimization of the quantum genetic algorithm. The fitness is mainly based on the active power loss P DG,loss The smaller the power loss, the higher the fitness of the configuration. If the voltage of a configuration is out of the specified range, its fitness value is set lower to reduce the probability of the configuration entering the next generation.

[0039] The definition of the initial fitness value Fitness(ψ) can be:

[0040]

[0041] In this way, in the initial population, individuals with smaller losses have higher fitness and thus have a higher probability of being selected in the subsequent optimization process.

[0042] Step 3 is implemented as follows:

[0043] First, define the objective function, with minimizing the total active power loss of the system as the optimization goal. The expression of the objective function is:

[0044]

[0045] Among them, P t is the total active power loss of the system, I i is the current of the ith bus, R iis the resistance of the line, and n represents the total number of lines in the system. The fitness function is used to evaluate the quality of individuals. The higher the fitness, the smaller the loss of the individual, which is more in line with the optimization goal.

[0046] The power balance relationship in the system is:

[0047]

[0048] Among them, P Gi Indicates the total active power output in the system, in kilowatts (kW); P DG Represents the total active power output of the distributed generation (DG) system in kilowatts (kW); P i Indicates the power demand of the load in the system (kW); P loss Indicates the system power loss.

[0049] The quantum genetic algorithm combines the characteristics of quantum computing and classical genetic algorithms, uses quantum registers to encode individuals, and simulates the evolutionary mechanism of classical genetic algorithms. Its core is to iteratively optimize the population through quantum selection, crossover, and mutation subroutines, gradually approaching the optimal solution to the problem.

[0050] 1. Initialization: First, set the algorithm parameters, including the population size n and the number of quantum bits of each individual c, where n is divisible by 4 and c is an even number. The algorithm represents the individual as a quantum state consisting of c quantum bits and encodes the individual in an independent quantum register. The fitness function uses the quantum Hamiltonian H P Represents that the fitness of each individual is its fitness in H P The optimization goal is to gradually evolve the population to the lowest energy state (i.e. H P ground state).

[0051] 2. Algorithm main loop: The algorithm main loop includes the following steps: quantum selection, quantum crossover and quantum mutation operations. This loop continues until the termination condition is met (for example, the maximum number of generations is reached or the fitness value no longer changes significantly). In the algorithm main program, it includes:

[0052] (1) Quantum selection subroutine: The quantum selection subroutine sorts the population by fitness value, retains individuals with higher fitness, and discards individuals with lower fitness. The specific implementation is as follows:

[0053] a) Define the ground state of the problem: Set the Hamiltonian H P The eigenbasis (i.e., the set of energy eigenstates) of the problem is defined as the ground state of the problem, denoted by ∣u k >. Each state |u k >Satisfy H P ∣u k >=∈ k ∣uk >, where ∈ k are eigenvalues, arranged in ascending order so that ∈ k ≤∈ k+1 .

[0054] b) Sorting network: Use the quantum sorting network to sort individuals by fitness. The sorting process includes the quantum comparison operator O CMP and the quantum exchange operator C SWAP Multi-layer combination.

[0055] Comparison operator O CMP It is used to determine the fitness of two adjacent registers. Its input is two register states |u k > i and |u k’ > i+1 and an auxiliary qubit |0> a , and adjust the auxiliary bit state according to the fitness:

[0056]

[0057] Controlled commutative operator C SWAP The register contents are swapped according to the status of the auxiliary bits to complete the sequencing:

[0058]

[0059] After many comparisons and exchanges, individuals with higher fitness are placed in the front, and individuals with lower fitness are placed in the back half and eliminated to achieve population selection.

[0060] (2) Quantum crossover subroutine: The purpose of the quantum crossover subroutine is to generate new individuals through quantum cloning and bit exchange. This operation imitates the gene recombination in the classical genetic algorithm.

[0061] a) Initialization of low-order registers: First, the low-order registers are set to the reference state ρ0 in order to receive the cloning results of individuals with higher fitness. Specifically, the population state after quantum selection can be expressed as:

[0062]

[0063] where tr low It means to perform trace operation on individuals with low fitness and retain individuals with high fitness.

[0064] b) Quantum cloning: Using a quantum cloning machine (QCM) to clone individuals with high fitness, the following two cloning methods can be selected:

[0065] Biomimicking Quantum Observation Cloning (BCQO): The reference state is set to |1>, and its cloning operator U(θ,ρ0) satisfies the following conditions:

[0066]

[0067] where θ is the observation operator.

[0068] · Universal Quantum Cloning Machine (UQCM): This cloning method has consistent cloning fidelity, and its cloning operator is defined as:

[0069]

[0070] in is the projection operator, and d is the dimension of the Hilbert space.

[0071] c) Quantum bit exchange: During the crossover process, the individual characteristics are reorganized by exchanging some quantum bits of the two low-order registers. The exchange process is represented by the following formula:

[0072]

[0073] Where S 2i+j,2i+c+j is the exchange operator, which is used to exchange the specified quantum bits.

[0074] (3) Quantum mutation subroutine: The quantum mutation subroutine is used to introduce moderate perturbations, increase population diversity, and avoid falling into local optimality.

[0075] a) Define mutation operation: Quantum mutation is achieved by applying a random Pauli gate (X, Y, Z) to each individual quantum bit. The mutation probability is set to p m , and define all possible mutation gate combinations U μ .

[0076] The mutation operator can be expressed as:

[0077]

[0078] in represents a possible Pauli gate, p μ represents the probability of each gate.

[0079] (4) Termination conditions and updates: The quantum mutation subroutine is used to introduce moderate disturbances, increase population diversity, and avoid falling into local optimality. At the end of each iteration, check whether the termination conditions are met, such as the maximum number of generations G or the fitness no longer changes significantly. Once the termination conditions are met, the algorithm stops and outputs the individual with the highest fitness in the population as the final solution, which is the current optimal DG configuration scheme. This optimal solution is the DG location and capacity configuration with the lowest power loss and the voltage level that meets the system requirements.

[0080] Step 4 is implemented as follows:

[0081] In order to ensure the adaptability of the optimized distributed generation (DG) configuration under different load conditions, dynamic load change simulation is performed. Specifically, by simulating load increase and decrease, the configuration location and capacity of DG are further verified and optimized to cope with the challenges brought by load fluctuations in the actual distribution system.

[0082] 1. Load change setting: In the load change simulation, the load of the distribution system is increased and decreased by a certain percentage. The load change ranges are usually ±25%, ±50%, ±75% and ±100%. These change ranges can cover the load fluctuations that the system may face and provide a sufficient test environment for verifying the robustness of the DG configuration.

[0083] 2. Dynamic optimization process: For each load change condition, repeat the quantum genetic algorithm optimization process defined in step 3. That is: reinitialize the population and substitute the changed load data into the power system model; optimize the configuration position and capacity of DG through quantum selection, quantum crossover and quantum mutation operations, and calculate the power loss and voltage level under each configuration; evaluate the fitness of each generation of optimized population to ensure that under different load conditions, the DG configuration scheme can still achieve power loss minimization and voltage curve stability.

[0084] 3. Adaptability evaluation and configuration adjustment: Under each load change condition, record the final optimized DG configuration position and capacity, and evaluate the system's power loss and voltage curve stability. Generally, an increase in load will lead to an increase in the volatility of the voltage curve, but through adaptive DG configuration, the power loss can be maintained at a low level and the voltage fluctuation can be kept within the allowable range; accordingly, when the load decreases, the voltage curve will become more stable and the power loss will be further reduced. Based on the adaptability evaluation results of different load conditions, the optimal DG configuration can be fine-tuned to further improve its robustness.

[0085] 4. Data recording and comparative analysis: Record the optimal configuration results and performance indicators (such as power loss, voltage fluctuation amplitude, etc.) under different load conditions, and compare and analyze them with the results under the initial load conditions. This analysis helps to summarize the performance of DG configuration under load changes and provide a reference for further system optimization.

[0086] 5. Final configuration scheme extraction: According to the optimization results under various load conditions, extract the DG configuration scheme that performs best under various load conditions, or synthesize the best schemes under different load conditions to ensure that the final scheme has good load adaptability in actual operation. The final scheme will achieve dynamic minimization of power loss while maintaining the stability of the voltage curve under different load conditions, providing support for the efficient and stable operation of the distribution system.

[0087] Step 5 is specifically implemented as follows:

[0088] Based on the results of the above optimization process and load change simulation, the final distributed generation (DG) configuration scheme is determined. The goal of this step is to output an optimal DG configuration that can minimize power loss and stabilize voltage under various load conditions to meet the efficient operation requirements of the actual distribution system.

[0089] 1. First, extract the DG configuration scheme with the best performance under various load conditions from the results of steps 3 and 4, including the specific DG installation location (bus number) and capacity. By comparing the optimal schemes under different load changes, select the configuration scheme with the most stable performance under various load conditions.

[0090] 2. Among the extracted optimal configurations, select the DG location and capacity combination that performs well under most load conditions as the final DG configuration solution. This final solution not only meets the requirements of optimal power loss and voltage curve stability under initial load conditions, but also maintains good adaptability when the load increases or decreases.

[0091] The final DG configuration plan will include the following specific parameters:

[0092] (1) DG location: the specific bus location of the optimal configuration.

[0093] (2) DG capacity: DG capacity in the optimal solution, in kW.

[0094] (3) Power loss and voltage curve: Under the optimal configuration, the system's power loss value and voltage curve ensure that the voltage is between the specified upper and lower limits (0.95 to 1.05 pu).

[0095] 3. For different load change conditions (such as ±25%, ±50%, etc.), output the DG configuration recommendations that perform best under the conditions, so that they can be flexibly adjusted in actual operation. For example, if the system load increases to a certain extent, the DG configuration can be adjusted accordingly based on the optimal solution under this load condition, thereby minimizing power loss and stabilizing the voltage curve.

[0096] The beneficial effects of the present invention are as follows: a distributed generation (DG) optimization configuration method based on a quantum genetic algorithm is proposed, which realizes the minimization of power loss and the improvement of voltage stability in the distribution system. The method can significantly reduce the power loss of the system, and the optimized DG configuration scheme improves the overall energy utilization efficiency by scientifically selecting the location and capacity. At the same time, the present invention sets voltage restriction conditions and dynamic load simulation during the optimization process to ensure that the voltage curve fluctuates within the allowable range and meets the power quality requirements of the user end. Through load change simulation and adaptability verification, the configuration scheme finally outputted shows high stability and adaptability under various load conditions, which improves the operational reliability and flexibility of the system under load fluctuations. In addition, by effectively reducing the power loss in the distribution network, the present invention helps to reduce the demand for fossil fuels and greenhouse gas emissions, thereby achieving a more environmentally friendly power supply. Therefore, the present invention provides an efficient, stable and environmentally friendly distributed power generation optimization scheme for distribution systems, which provides technical support for building a modern smart grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 is a schematic diagram of a 6-node radial power distribution system of the present invention;

[0098] Figure 2 It is an overall flow chart of a method for minimizing power loss in a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources in the present invention;

[0099] Figure 3 It is an IEEE-34 node standard calculation example wiring diagram of the present invention;

[0100] Figure 4 This is a result diagram of system line loss and voltage level before optimization of the present invention;

[0101] Figure 5 It is a result diagram of line loss and voltage level after DG grid connection optimization of the present invention; DETAILED DESCRIPTION

[0102] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0103] The present invention provides a method for minimizing power loss in a power distribution system by optimizing the position and capacity of distributed power sources based on a quantum genetic algorithm, which is specifically implemented according to the following steps:

[0104] Step 1 is specifically implemented as follows: First, data is read and the system is initialized. By reading the core data of the distribution network, the system can accurately represent the network structure. Specifically, the data includes: the number and voltage level of the busbar, the impedance value of each line (resistance R i and reactance X i ), bus load (active power P i and reactive Q i ) and the rated power of distributed generation (DG) and the capacity limitations of the installation location. These data together construct the topology of the distribution network, ensure the connection relationship between the bus and the line is accurate, and provide a basis for subsequent power flow calculations and DG optimization.

[0105] After completing the data reading, the system starts the initial power flow calculation and uses the forward-backward sweep method for analysis. This method is divided into two stages:

[0106] (1) Backward Sweep: Starting from the farthest bus in the system, calculate the current on each line step by step towards the power source. Initially, it is assumed that the voltage of each bus is equal to the source voltage. For the load current of each bus, the following formula can be used:

[0107]

[0108] Among them, I ldi represents the load current of the ith bus, P i and Q i are the active and reactive power requirements on the bus, V i is the voltage of the bus. During the backward scanning process, the current value in the line will be continuously updated according to the voltage value of the previous iteration until the current of all buses in the system is preliminarily distributed.

[0109] (2) Forward Sweep: Move forward from the power source point to the outside, gradually calculating and updating the voltage of each bus. i and current I Li Under the condition of , the voltage of each bus is calculated by the following formula:

[0110] V i =V u -Z i I Li (2)

[0111] Among them, V i is the voltage of the ith bus, V u is the voltage of the upper busbar, Z iis the line impedance,

[0112] I Li is the current in the line. By continuously advancing to the far-end bus of the system, the system can obtain the voltage distribution of each bus and complete the preliminary analysis of the voltage profile.

[0113] After completing the initial power flow calculation, the system also needs to verify the initial voltage and power distribution. During this process, check whether the voltage of each bus is within the specified allowable range (for example, 0.95 to 1.05pu). The calculation formula for the upper and lower voltage limits is as follows:

[0114]

[0115] Among them, V base is the reference voltage of the system.

[0116] At the same time, calculate the total power loss of the system. The total power loss calculation formula of the system is as follows:

[0117]

[0118] Among them, P loss is the total active power loss of the system, I i is the current of the ith line, R i is the resistance of the line.

[0119] by Figure 1 As an example, the radial power distribution system shown in the figure can be

[0120] and DLF matrix.

[0121] (1) BBIC Matrix (Branch-Branch Incidence Current Matrix) The matrix expresses the relationship between the branch currents through Kirchhoff's current law (KCL). Figure 1 In a 6-node system, the BBIC matrix can be constructed in the form of:

[0122]

[0123] Right now:

[0124] [B]=[BBIC][I] (7)

[0125] Where [B] is the branch current matrix and [I] is the busbar injection current matrix.

[0126] (2) The BCBV (Branch-Current Bus-Voltage Matrix) matrix is ​​used to represent the relationship between branch current and bus voltage. The voltage change can be expressed using the BCBV matrix as:

[0127]

[0128] Right now:

[0129] [ΔV]=[BCBV][B] (9) where ΔV represents the voltage difference matrix.

[0130] (3) The DLF matrix (Distribution Load Flow Matrix) combines the BBIC and BCBV matrices to form the relationship between voltage and injection current, which is used to simplify the load flow calculation:

[0131] [ΔV]=[BCBV][BBIC][I]=[DLF][I] (10)

[0132] The DLF matrix can directly convert the injected current into the bus voltage change, providing an efficient method for voltage level evaluation during power flow calculation and optimization.

[0133] Step 2 is specifically implemented as follows: Based on the basic data collected in step 1, an initial population is generated, and the power loss and voltage curve of each individual are preliminarily calculated and evaluated.

[0134] Each individual represents a distributed generation (DG) configuration scheme. The purpose of this step is to provide an initial solution set for the optimization of the quantum genetic algorithm.

[0135] 1. Use a random number generation algorithm to initialize a population consisting of multiple different distributed generation (DG) configuration schemes. Each configuration scheme is called an "individual", and the "gene" in it indicates whether DG is deployed on each bus. For example, setting a bus to 1 means that DG is deployed on the bus, and setting it to 0 means that DG is not deployed on the bus. Through this gene encoding method, each individual actually represents a specific DG location and capacity configuration scheme.

[0136] 2. Substitute each generated individual (i.e. a specific DG configuration scheme) into the power system model and calculate its power flow. For each individual configuration, calculate the net apparent power demand S of the system r , reflecting the difference between load power demand and DG power generation output.

[0137] The formula is as follows:

[0138] S r =S load-S DG (11)

[0139] Among them, S load is the total apparent power demand of the current system load; S DG is the total apparent power output of the DG under the current individual configuration.

[0140] From the calculated net apparent power S r Extract the active part from the current configuration to represent the active power loss P DG,loss :

[0141] P DG,loss =real(S r ) (12)

[0142] Among them, real(S r ) is S r This value represents the power loss under the current configuration and serves as the basis for subsequent fitness evaluation.

[0143] During the power flow calculation, the voltage level of each bus is obtained. Ensure that the voltage of each bus is between 0.95 and 1.05 pu. If some bus voltages are outside this range, mark the individual configuration as a configuration that does not meet the voltage requirements.

[0144] 3. Calculate the initial fitness value of each individual for subsequent optimization of the quantum genetic algorithm. The fitness is mainly based on the active power loss P DG,loss The smaller the power loss, the higher the fitness of the configuration. If the voltage of a configuration is out of the specified range, its fitness value is set lower to reduce the probability of the configuration entering the next generation.

[0145] The definition of the initial fitness value Fitness(ψ) can be:

[0146]

[0147] In this way, in the initial population, individuals with smaller losses have higher fitness and thus have a higher probability of being selected in the subsequent optimization process.

[0148] Step 3 is specifically implemented as follows: First, define the objective function, with minimizing the total active power loss of the system as the optimization goal. The expression of the objective function is:

[0149]

[0150] Among them, P t is the total active power loss of the system, I i is the current of the ith bus, R iis the resistance of the line, and n represents the total number of lines in the system. The fitness function is used to evaluate the quality of individuals. The higher the fitness, the smaller the loss of the individual, which is more in line with the optimization goal.

[0151] The power balance relationship in the system is:

[0152]

[0153] Among them, P Gi Indicates the total active power output in the system, in kilowatts (kW); P DG Represents the total active power output of the distributed generation (DG) system in kilowatts (kW); P i Indicates the power demand of the load in the system (kW); P loss Indicates the system power loss.

[0154] The quantum genetic algorithm combines the characteristics of quantum computing and classical genetic algorithms, uses quantum registers to encode individuals, and simulates the evolutionary mechanism of classical genetic algorithms. Its core is to iteratively optimize the population through quantum selection, crossover, and mutation subroutines, gradually approaching the optimal solution to the problem.

[0155] 1. Initialization: First, set the algorithm parameters, including the population size n and the number of quantum bits of each individual c, where n is divisible by 4 and c is an even number. The algorithm represents the individual as a quantum state consisting of c quantum bits and encodes the individual in an independent quantum register. The fitness function uses the quantum Hamiltonian H P Represents that the fitness of each individual is its fitness in H P The optimization goal is to gradually evolve the population to the lowest energy state (i.e. H P ground state).

[0156] 2. Algorithm main loop: The algorithm main loop includes the following steps: quantum selection, quantum crossover and quantum mutation operations. This loop continues until the termination condition is met (for example, the maximum number of generations is reached or the fitness value no longer changes significantly). In the algorithm main program, it includes:

[0157] (1) Quantum selection subroutine: The quantum selection subroutine sorts the population by fitness value, retains individuals with higher fitness, and discards individuals with lower fitness. The specific implementation is as follows:

[0158] a) Define the ground state of the problem: Set the Hamiltonian H P The eigenbasis (i.e., the set of energy eigenstates) of the problem is defined as the ground state of the problem, denoted by ∣u k >. Each state |u k >Satisfy H P ∣u k >=∈ k ∣uk >, where ∈ k are eigenvalues, arranged in ascending order so that ∈ k ≤∈ k+1 .

[0159] b) Sorting network: Use the quantum sorting network to sort individuals by fitness. The sorting process includes the quantum comparison operator O CMP and the quantum exchange operator C SWAP Multi-layer combination.

[0160] Comparison operator O CMP It is used to determine the fitness of two adjacent registers. Its input is two register states |u k > i and |u k’ > i+1 and an auxiliary qubit |0> a , and adjust the auxiliary bit state according to the fitness:

[0161]

[0162] Controlled commutative operator C SWAP The register contents are swapped according to the status of the auxiliary bits to complete the sequencing:

[0163]

[0164] After many comparisons and exchanges, individuals with higher fitness are placed in the front, and individuals with lower fitness are placed in the back half and eliminated to achieve population selection.

[0165] (2) Quantum crossover subroutine: The purpose of the quantum crossover subroutine is to generate new individuals through quantum cloning and bit exchange. This operation imitates the gene recombination in the classical genetic algorithm.

[0166] a) Initialization of low-order registers: First, the low-order registers are set to the reference state ρ0 in order to receive the cloning results of individuals with higher fitness. Specifically, the population state after quantum selection can be expressed as:

[0167]

[0168] where tr low It means to perform trace operation on individuals with low fitness and retain individuals with high fitness.

[0169] b) Quantum cloning: Using a quantum cloning machine (QCM) to clone individuals with high fitness, the following two cloning methods can be selected:

[0170] Biomimicking Quantum Observation Cloning (BCQO): The reference state is set to |1>, and its cloning operator U(θ,ρ0) satisfies the following conditions:

[0171]

[0172] where θ is the observation operator.

[0173] · Universal Quantum Cloning Machine (UQCM): This cloning method has consistent cloning fidelity, and its cloning operator is defined as:

[0174]

[0175] in is the projection operator, and d is the dimension of the Hilbert space.

[0176] c) Quantum bit exchange: During the crossover process, the individual characteristics are reorganized by exchanging some quantum bits of the two low-order registers. The exchange process is represented by the following formula:

[0177]

[0178] Where S 2i+j,2i+c+j is the exchange operator, which is used to exchange the specified quantum bits.

[0179] (3) Quantum mutation subroutine: The quantum mutation subroutine is used to introduce moderate perturbations, increase population diversity, and avoid falling into local optimality.

[0180] a) Define mutation operation: Quantum mutation is achieved by applying a random Pauli gate (X, Y, Z) to each individual quantum bit. The mutation probability is set to p m , and define all possible mutation gate combinations U μ .

[0181] The mutation operator can be expressed as:

[0182]

[0183] in represents a possible Pauli gate, p μ represents the probability of each gate.

[0184] (4) Termination conditions and updates: The quantum mutation subroutine is used to introduce moderate disturbances, increase population diversity, and avoid falling into local optimality. At the end of each iteration, check whether the termination conditions are met, such as the maximum number of generations G or the fitness no longer changes significantly. Once the termination conditions are met, the algorithm stops and outputs the individual with the highest fitness in the population as the final solution, which is the current optimal DG configuration scheme. This optimal solution is the DG location and capacity configuration with the lowest power loss and the voltage level that meets the system requirements.

[0185] Step 4 is specifically implemented as follows: In order to ensure the adaptability of the optimized distributed generation (DG) configuration under different load conditions, a dynamic load change simulation is performed. Specifically, by simulating load increase and decrease, the configuration location and capacity of DG are further verified and optimized to cope with the challenges brought by load fluctuations in the actual distribution system.

[0186] 1. Load change setting: In the load change simulation, the load of the distribution system is increased and decreased by a certain percentage. The load change ranges are usually ±25%, ±50%, ±75% and ±100%. These change ranges can cover the load fluctuations that the system may face and provide a sufficient test environment for verifying the robustness of the DG configuration.

[0187] 2. Dynamic optimization process: For each load change condition, repeat the quantum genetic algorithm optimization process defined in step 3. That is: reinitialize the population and substitute the changed load data into the power system model; optimize the configuration position and capacity of DG through selection, crossover and mutation operations, and calculate the power loss and voltage level under each configuration; evaluate the fitness of each generation of optimized population to ensure that under different load conditions, the DG configuration scheme can still achieve power loss minimization and voltage curve stability.

[0188] 3. Adaptability evaluation and configuration adjustment: Under each load change condition, record the final optimized DG configuration position and capacity, and evaluate the system's power loss and voltage curve stability. Generally, an increase in load will lead to an increase in the volatility of the voltage curve, but through adaptive DG configuration, the power loss can be maintained at a low level and the voltage fluctuation can be kept within the allowable range; accordingly, when the load decreases, the voltage curve will become more stable and the power loss will be further reduced. Based on the adaptability evaluation results of different load conditions, the optimal DG configuration can be fine-tuned to further improve its robustness.

[0189] 4. Data recording and comparative analysis: Record the optimal configuration results and performance indicators (such as power loss, voltage fluctuation amplitude, etc.) under different load conditions, and compare and analyze them with the results under the initial load conditions. This analysis helps to summarize the performance of DG configuration under load changes and provide a reference for further system optimization.

[0190] 5. Final configuration scheme extraction: According to the optimization results under various load conditions, extract the DG configuration scheme that performs best under various load conditions, or synthesize the best schemes under different load conditions to ensure that the final scheme has good load adaptability in actual operation. The final scheme will achieve dynamic minimization of power loss while maintaining the stability of the voltage curve under different load conditions, providing support for the efficient and stable operation of the distribution system.

[0191] Step 5 is specifically implemented as follows: Based on the results of the above optimization process and load change simulation, the final distributed generation (DG) configuration scheme is determined. The goal of this step is to output an optimal DG configuration that can minimize power loss and stabilize voltage under various load conditions to meet the efficient operation requirements of the actual distribution system.

[0192] 1. First, extract the DG configuration scheme with the best performance under various load conditions from the results of steps 3 and 4, including the specific DG installation location (bus number) and capacity. By comparing the optimal schemes under different load changes, select the configuration scheme with the most stable performance under various load conditions.

[0193] 2. Among the extracted optimal configurations, select the DG location and capacity combination that performs well under most load conditions as the final DG configuration solution. This final solution not only meets the requirements of optimal power loss and voltage curve stability under initial load conditions, but also maintains good adaptability when the load increases or decreases.

[0194] The final DG configuration plan will include the following specific parameters:

[0195] DG location: The specific bus location of the optimal configuration.

[0196] DG capacity: DG capacity in the optimal solution, in kW.

[0197] Power loss and voltage curve: Under the optimal configuration, the system's power loss value and voltage curve ensure that the voltage is between the specified upper and lower limits (0.95 to 1.05 pu).

[0198] 3. For different load change conditions (such as ±25%, ±50%, etc.), output the DG configuration recommendations that perform best under the conditions, so that they can be flexibly adjusted in actual operation. For example, if the system load increases to a certain extent, the DG configuration can be adjusted accordingly based on the optimal solution under this load condition, thereby minimizing power loss and stabilizing the voltage curve.

[0199] The overall flow chart is as follows Figure 2 shown.

[0200] Example

[0201] The present invention performs relevant simulation verification based on the IEEE-34 node system, and its single-line diagram and wiring method are as follows Figure 3 As shown. The rated voltage of the network is 24.9kV, and the active load and reactive load are 1769kW and 1044kVAR respectively. Considering the diverse characteristics of nonlinear loads in the distribution network, three independent harmonic sources, namely, Electric vehicle charging stations (EVC), adjustable speed drives (ASD) and variable-frequency drives (VFD), are connected to simulate various nonlinear loads. In order to facilitate comparative analysis, the system parameters of the example before optimization are first introduced. The specific results are shown in the figure below. Figure 4 As shown. The left axis represents active power loss, which corresponds to the bar graph below, indicating the active power loss of each line in the system. The table below also lists the system bus loss and line loss rate. The right axis represents the voltage level of the system before optimization. The table above lists the maximum positive deviation, maximum negative deviation and mean deviation of the system voltage at this time.

[0202] The optimization scheme for access to the distribution network is shown in Table 1, where the DG grid connection point and capacity are 854 and 532.656kW respectively. The distribution of line loss and voltage of the optimized system is shown in Table 1. Figure 5 shown.

[0203] Table 1 Grid connection point optimization plan

[0204]

[0205] Depend on Figure 5 It can be seen that after adopting the access optimization solution, the total system loss is 191.884kW and the line loss rate is 9.5%. At this time, the main line losses are concentrated on L3, L5, L6, L14, L27 and L32. In terms of voltage distribution, the maximum positive and negative deviations and the mean voltage deviation are 0.049pu, -0.049pu and 0.042pu respectively.

Claims

1. A method for minimizing power loss in a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources, characterized in that: Follow the steps below to implement it: Step 1: First, collect the basic data of the distribution network, including the specific information of the busbar, line and distributed generation equipment (such as capacity and location, etc.), and initialize the voltage level and power flow of the power system. The purpose of this step is to obtain the power loss and voltage curve of the system in the initial state as a reference baseline for optimization. Step 2: After obtaining the basic data, a random number generation algorithm is used to generate a set of initial populations, which contain several different DG configuration schemes. Each configuration scheme is called an "individual", in which the gene indicates whether DG is arranged on each bus. These individuals are substituted into the initial power system model to calculate their corresponding power loss and voltage curves, so as to provide a preliminary evaluation for subsequent optimization. Step 3. After obtaining the initial population, the quantum genetic algorithm is used to iteratively optimize the population with the goal of minimizing power loss and keeping the voltage curve within the allowable range. The optimization process of the quantum genetic algorithm includes quantum selection, quantum crossover, and quantum mutation. First, quantum selection is based on the fitness function, and individuals with small power loss and stable voltage are preferentially selected to enter the next generation. Next, quantum crossover uses quantum superposition and cloning technology to combine the characteristics of high-fitness individuals to generate new individuals, thereby gradually optimizing the DG configuration. Finally, quantum mutation introduces perturbations through random Pauli gates to increase population diversity and avoid local optimality. In each generation, a new generation of configurations is generated through quantum selection, crossover, and mutation, and a better DG position and capacity solution is gradually obtained. Step 4: After obtaining the preliminary optimized configuration, simulate different load conditions of the distribution system to verify the adaptability of the DG configuration. Specifically, set the load condition to increase or decrease by a certain percentage (e.g., 25%, 50%), and repeat the above quantum genetic algorithm optimization steps to ensure that the configuration scheme can still achieve minimum power loss and voltage stability under different load conditions. Record the optimal configuration under each load condition, and evaluate the power loss and voltage curve changes of the system under each load state. Step 5. Finally, the optimal DG location and capacity configuration scheme is output, including the DG location and capacity of each bus and the optimized system power loss and voltage curve data. At the same time, corresponding optimization configurations are provided for different load conditions to ensure that the DG configuration can adapt to the dynamic changes of the load, thereby achieving stable and efficient power supply capabilities of the system. This method implements a dynamic optimization process based on quantum genetic algorithm, which enables the distribution system to maintain low power loss and good voltage stability under different load conditions.

2. According to claim 1, a method for minimizing power loss in a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources is characterized in that: The step 1 is specifically implemented as follows: First, data reading and system initialization are performed. By reading the core data of the distribution network, the system can accurately represent the network structure. Specifically, the data includes: bus number and voltage level, impedance value of each line (resistance R i and reactance X i ), bus load (active power P i and reactive Q i ) and the capacity limitations of the rated power and installation location of distributed generation (DG). After completing the data reading, the system starts the initial power flow calculation and uses the forward-backward sweep method for analysis. This method is divided into two stages: (1) Backward Sweep: Starting from the farthest bus in the system, calculate the current on each line step by step towards the power source. Initially, it is assumed that the voltage of each bus is equal to the source voltage. For the load current of each bus, the following formula can be used: Among them, I ldi represents the load current of the ith bus, P i and Q i are the active and reactive power requirements on the bus, V i is the voltage of the bus. During the backward scanning process, the current value in the line will be continuously updated according to the voltage value of the previous iteration until the current of all buses in the system is preliminarily distributed. (2) Forward Sweep: Move forward from the power point to the outside, gradually calculating and updating the voltage of each bus. i and current I Li Under the condition of , the voltage of each bus is calculated by the following formula: V i =V u -Z i I Li (2) Among them, V i is the voltage of the ith bus, V u is the voltage of the upper busbar, Z i is the line impedance, I Li is the current in the line. By continuously advancing to the far-end bus of the system, the system can obtain the voltage distribution of each bus and complete the preliminary analysis of the voltage profile. After completing the initial power flow calculation, the system also needs to verify the initial voltage and power distribution. During this process, check whether the voltage of each bus is within the specified allowable range (for example, 0.95 to 1.05pu). The calculation formula for the upper and lower voltage limits is as follows: Among them, V base is the reference voltage of the system. At the same time, calculate the total power loss of the system. The total power loss calculation formula of the system is as follows: Among them, P loss is the total active power loss of the system, I i is the current of the ith line, R i is the resistance of the line.

3. According to the method for minimizing power loss of a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources in claim 1, it is characterized in that the step 2 is specifically implemented as follows: Based on the basic data collected in step 1, an initial population is generated, and the power loss and voltage curve of each individual are preliminarily calculated and evaluated. Each individual represents a distributed generation (DG) configuration scheme. The purpose of this step is to provide an initial solution set for the optimization of the quantum genetic algorithm.

1. Use a random number generation algorithm to initialize a population consisting of multiple different distributed generation (DG) configuration schemes. Each configuration scheme is called an "individual", and the "gene" in it indicates whether DG is deployed on each bus. For example, setting a bus to 1 means that DG is deployed on the bus, and setting it to 0 means that DG is not deployed on the bus. Through this gene encoding method, each individual actually represents a specific DG location and capacity configuration scheme.

2. Substitute each generated individual (i.e. a specific DG configuration scheme) into the power system model and calculate its power flow. For each individual configuration, calculate the net apparent power demand S of the system r , reflecting the difference between load power demand and DG power generation output. The formula is as follows: S r =S load -S DG (6) Among them, S load is the total apparent power demand of the current system load; S DG is the total apparent power output of the DG under the current individual configuration. From the calculated net apparent power S r Extract the active part from the current configuration to represent the active power loss P DG,loss : P DG,loss =real(S r ) (7) Among them, real(S r ) is S r This value represents the power loss under the current configuration and serves as the basis for subsequent fitness evaluation. During the power flow calculation, the voltage level of each bus is obtained. Ensure that the voltage of each bus is between 0.95 and 1.05 pu. If some bus voltages are outside this range, mark the individual configuration as a configuration that does not meet the voltage requirements.

3. Calculate the initial fitness value of each individual for subsequent optimization of the quantum genetic algorithm. The fitness is mainly based on the active power loss P DG,loss The smaller the power loss, the higher the fitness of the configuration. If the voltage of a configuration is out of the specified range, its fitness value is set lower to reduce the probability of the configuration entering the next generation. The definition of the initial fitness value Fitness(ψ) can be:

4. According to the method for minimizing power loss of a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources in claim 1, it is characterized in that the step 3 is specifically implemented as follows: First, define the objective function, with minimizing the total active power loss of the system as the optimization goal. The expression of the objective function is: Among them, P t is the total active power loss of the system, I i is the current of the ith bus, R i is the resistance of the line, and n is the total number of lines in the system The power balance relationship in the system is: Among them, P Gi Indicates the total active power output in the system, in kilowatts (kW); P DG Represents the total active power output of the distributed generation (DG) system in kilowatts (kW); P i Indicates the power demand of the load in the system (kW); P loss Indicates the system power loss.

1. Initialization: First, set the algorithm parameters, including the population size n and the number of quantum bits of each individual c, where n is divisible by 4 and c is an even number. The algorithm represents the individual as a quantum state consisting of c quantum bits and encodes the individual in an independent quantum register. The fitness function uses the quantum Hamiltonian H P Represents that the fitness of each individual is its fitness in H P The optimization goal is to make the population gradually evolve to the lowest energy state (i.e. H P ground state).

2. Algorithm main loop: The algorithm main loop includes the following steps: quantum selection, quantum crossover and quantum mutation operations. This loop continues until the termination condition is met. In the algorithm main program, it includes: (1) Quantum selection subroutine: The quantum selection subroutine sorts the population by fitness value, retains individuals with higher fitness, and discards individuals with lower fitness. The specific implementation is as follows: a) Define the ground state of the problem: Set the Hamiltonian H P The eigenbasis (i.e., the set of energy eigenstates) of the problem is defined as the ground state of the problem, denoted by ∣u k >. Each state |u k >Satisfy H P ∣u k >=∈ k ∣u k >, where ∈ k is the eigenvalue, arranged in ascending order, so that ∈ k ≤∈ k+1 . b) Sorting network: Use the quantum sorting network to sort individuals by fitness. The sorting process includes the quantum comparison operator O CMP and the quantum exchange operator C SWAP Multi-layer combination. ●Comparison Operator O CMP It is used to determine the fitness of two adjacent registers. Its input is two register states |u k > i and |u k’ > i+1 and an auxiliary qubit |0> a , and adjust the auxiliary bit state according to the fitness: Controlled commutative operator C SWAP The register contents are swapped according to the status of the auxiliary bits to complete the sequencing: After multiple comparisons and exchanges, individuals with higher fitness are placed in the front, and individuals with lower fitness are placed in the back half and eliminated. (2) Quantum crossover subroutine: a) Initialization of low-order registers: First, set the low-order registers to the reference state ρ0. Specifically, the population state after quantum selection can be expressed as: where tr low It means to perform trace operation on individuals with low fitness and retain individuals with high fitness. b) Quantum cloning: Using a quantum cloning machine (QCM) to clone individuals with high fitness, the following two cloning methods can be selected: Biomimicry Quantum Observation Cloning (BCQO): The reference state is set to |1>, and its cloning operator U(θ,ρ0) satisfies the following conditions: where θ is the observation operator. · Universal Quantum Cloning Machine (UQCM): This cloning method has consistent cloning fidelity, and its cloning operator is defined as: in is the projection operator, and d is the dimension of the Hilbert space. c) Quantum bit exchange: During the crossover process, the individual characteristics are reorganized by exchanging some quantum bits of the two low-order registers. The exchange process is represented by the following formula: Where S 2i+j,2i+c+j is the exchange operator, which is used to exchange the specified quantum bits. (3) Quantum mutation subroutine: a) Define mutation operation: Quantum mutation is achieved by applying a random Pauli gate (X, Y, Z) to each individual quantum bit. The mutation probability is set to p m , and define all possible mutation gate combinations U μ . The mutation operator can be expressed as: in represents a possible Pauli gate, p μ represents the probability of each gate. (4) Termination conditions and updates: At the end of each iteration, check whether the termination conditions are met, such as the maximum number of generations G or the fitness no longer changes significantly. Once the termination conditions are met, the algorithm stops and outputs the individual with the highest fitness in the population as the final solution, which is the current optimal DG configuration scheme. This optimal solution is the DG location and capacity configuration with the minimum power loss and the voltage level that meets the system requirements.

5. According to the method for minimizing power loss of a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources in claim 1, it is characterized in that step 4 is specifically implemented as follows:

1. Load change setting: In the load change simulation, the load of the distribution system is increased and decreased by a certain percentage. The selected load change ranges are ±25%, ±50%, ±75% and ±100%.

2. Dynamic optimization process: For each load change condition, repeat the quantum genetic algorithm optimization process defined in step 3. That is: reinitialize the population, substitute the changed load data into the power system model; optimize the configuration position and capacity of DG through selection, crossover and mutation operations, and calculate the power loss and voltage level under each configuration.

3. Adaptability assessment and configuration adjustment: Under each load change condition, record the final optimized DG configuration position and capacity, and evaluate the system's power loss and voltage curve stability.

4. Data recording and comparative analysis: Record the optimal configuration results and performance indicators (such as power loss, voltage fluctuation amplitude, etc.) under different load conditions, and compare and analyze them with the results under the initial load conditions.

5. Final configuration scheme extraction: Based on the optimization results under various load conditions, extract the DG configuration scheme that performs best under various load conditions, or synthesize the best schemes under different load conditions to ensure that the final scheme has good load adaptability in actual operation.

6. The method for minimizing power loss in a power distribution system based on quantum genetic algorithm to optimize the location and capacity of distributed power sources according to claim 1, characterized in that: The step 5 is specifically implemented as follows: Based on the results of the aforementioned optimization process and load variation simulation, the final distributed generation (DG) configuration scheme is determined.

1. First, from the results of steps 3 and 4, extract the DG configuration scheme that performs best under various load conditions, including the specific DG installation location (bus number) and capacity.

2. Among the extracted optimal configurations, select the DG location and capacity combination that performs well under most load conditions as the final DG configuration solution. The final DG configuration plan will include the following specific parameters: (1) DG location: the specific bus location of the optimal configuration. (2) DG capacity: DG capacity in the optimal solution, in kW. (3) Power loss and voltage curve: Under the optimal configuration, the system's power loss value and voltage curve ensure that the voltage is between the specified upper and lower limits (0.95 to 1.05 pu).

3. For different load change conditions (such as ±25%, ±50%, etc.), output the DG configuration recommendation that performs best under the condition.

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