An ac-dc power distribution network energy storage capacity multi-objective optimization configuration method

Abstract

The present application relates to a kind of AC-DC distribution network energy storage capacity multi-objective optimization configuration method, comprising: obtaining the target information in distribution network area, constructs AC-DC hybrid power flow calculation basic model, wherein the target information includes the wind and light data of distribution network, load data and network frame parameter data;According to the AC-DC hybrid power flow calculation basic model, the response constraint of flexible load is classified and the dynamic relationship model of each type of load is established;According to the dynamic relationship model, establish objective function and constraint condition;According to the objective function and constraint condition, the optimal energy storage capacity configuration scheme is obtained by using whale optimization and multi-objective differential evolution hybrid algorithm to optimize the optimal energy storage capacity configuration of AC-DC flexible distribution network.The present application can realize the collaborative optimization of flexible load and energy storage, effectively reduce the energy storage capacity configuration demand, improve the adaptability of system to wind and light fluctuation.

Description

Technical Field

[0001] This invention belongs to the field of AC / DC distribution network energy storage capacity optimization configuration technology, specifically relating to a multi-objective optimization configuration method for AC / DC distribution network energy storage capacity. Background Technology

[0002] Currently, large-scale grid connection of distributed renewable energy sources such as wind power and photovoltaics has become the core path for energy transformation. AC / DC distribution networks, with their advantages of high compatibility with renewable energy and low power loss, are gradually becoming the mainstream topology for future distribution networks. However, the intermittency, volatility, and randomness of distributed renewable energy sources not only increase the difficulty of voltage fluctuations, frequency fluctuations, and power balance in the distribution network, but may also cause problems such as control conflicts in AC / DC converters and uneven power flow distribution, severely restricting the absorption of renewable energy and the operational stability of the distribution network.

[0003] Energy storage systems are a key supporting technology for mitigating renewable energy fluctuations, optimizing power flow distribution, and improving power supply reliability. Their optimized configuration directly determines the safe operation of AC / DC distribution networks. In recent years, China's energy storage industry has accelerated its development; however, traditional energy storage configuration methods often focus on single cost or reliability targets, failing to consider the adjustability of flexible loads. Flexible loads, through demand response mechanisms, can achieve coordinated interaction between energy sources, grids, loads, and storage, reducing the configuration requirements of energy storage systems while improving the system's adaptability to renewable energy fluctuations.

[0004] AC / DC distribution networks exhibit complex topological characteristics such as converter constraints and AC / DC side power interaction limitations. The spatiotemporal regulation uncertainty of flexible loads, coupled with the randomness of renewable energy output, significantly increases the difficulty of collaborative modeling of multi-source heterogeneous elements. Furthermore, there are complex coupling relationships between flexible load response priorities, energy storage charging and discharging constraints, and AC / DC power flow security constraints.

[0005] In view of the above problems, the present invention constructs a multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks, which comprehensively considers the energy storage system capacity, total network loss, and the adaptability of flexible load electricity demand, thereby selecting the optimal energy storage capacity optimization configuration scheme.

[0007] To achieve the above objectives, the present invention provides the following solution: A multi-objective optimization method for energy storage capacity allocation in AC / DC distribution networks includes: Obtain target information within the distribution network area and construct a basic model for AC / DC hybrid power flow calculation. The target information includes wind and solar data, load data, and grid parameter data of the distribution network. Based on the aforementioned AC / DC hybrid power flow calculation model, flexible loads are classified and response constraints and dynamic relationship models for each type of load are established. Based on the dynamic relationship model, establish the objective function and constraints; Based on the objective function and constraints, a hybrid algorithm combining whale optimization and multi-objective differential evolution is used to optimize the energy storage configuration of AC / DC flexible distribution networks and obtain the optimal energy storage capacity configuration scheme.

[0008] Optional, dynamic relationship models for various types of loads include: dynamic relationship models corresponding to shiftable loads and loads that can be reduced; The response constraints for various load types include: minimum continuous reduction time constraint, maximum continuous reduction time constraint, and reduction number constraint.

[0009] Optionally, the objective function can be established by: Objective function of energy storage system capacity : ; in, The start time of maximum charging or discharging; The maximum charge / discharge end time; n represents the time from the start of the maximum charge / discharge. The number of time intervals between the end time and the end time. The time interval between two moments; For the first Energy storage system The charging / discharging power at any given moment; The number of energy storage systems; Objective function of total network loss in the system : ; in, This represents the total number of branches in the distribution network. branch road exist The system always experiences active power loss; Objective function of flexible load electricity demand adaptability : ; in, The influencing factor of the transferable load, This is to reduce the impact of load factors; For the first The actual translation time of a movable load. For the first Total power consumption time of each transferable load; For the first The actual reduction time for load that can be reduced. For the first The total electricity usage time for which load can be reduced.

[0010] Optionally, the constraints include: AC distribution network power flow constraints, DC distribution network power flow constraints, system safe operation constraints, converter power interaction constraints, new energy output constraints, energy storage operation constraints, and flexible load response constraints.

[0011] Optionally, the optimization of energy storage configuration in AC / DC flexible distribution networks can be achieved using a hybrid algorithm combining whale optimization and multi-objective differential evolution, including: S1. A hybrid initialization strategy is adopted to generate an initial population, wherein individuals in the population represent energy storage installation nodes, rated capacity, and charging / discharging strategies. S2. A hybrid mutation strategy is adopted to generate mutated individuals, wherein the hybrid mutation strategy includes differential mutation combining differential evolution algorithm, shrinking encirclement and spiral update of whale optimization algorithm; S3. Based on the objective function and constraints, a hybrid crossover strategy and a hybrid selection strategy are used to perform crossover and selection operations on the mutated individuals to obtain the globally optimal individual in the current population and store it in the elite archive. S4. When the number of iterations reaches the preset value but not the maximum value, a new global optimal individual is selected from the elite archive and returned to S2. When the number of iterations does not reach the preset value but not the maximum value, S2 is returned based on the current optimal individual. If the number of iterations reaches the maximum value, the optimal energy storage capacity configuration scheme is output.

[0012] Optionally, a hybrid initialization strategy can be used to generate the initial population, including: The first set of initial individuals is generated by random and uniform initialization using a multi-objective differential evolution algorithm. The second set of initial individuals is generated by randomly searching and initializing based on the whale optimization algorithm; The initial population is obtained based on the first part of the initial individuals and the second part of the initial individuals.

[0013] Optionally, a hybrid mutation strategy can be used to generate mutated individuals, including: When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the whale optimization algorithm is used to simulate the spiral movement of humpback whales around their prey to generate the mutant individuals, where rand(·) is a random number generation function; When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the mutated individuals are generated according to the scaling factor, wherein the scaling factor decreases as the control parameter decreases; When the control parameters of the whale optimization algorithm are less than or equal to the preset value, the parent generation is selected from the elite subpopulation, and the whale optimization algorithm is used to shrink and surround the simulated prey to generate the mutant individual.

[0014] Optionally, a hybrid crossover strategy may be employed to perform crossover operations on the mutated individuals, including: For the aforementioned variant individuals and parent individuals, experimental individuals are generated using a binomial crossover method; If the experimental individuals do not meet the constraints, the position of the experimental individuals is updated and optimized using the whale optimization algorithm to obtain the optimized experimental population.

[0015] Optionally, a hybrid selection strategy may be employed to select the mutated individuals, including: The parent population and the experimental population are merged, and the decision variables of individuals that violate the decision variable constraints are adjusted using the whale optimization algorithm to obtain the processed population. According to the objective function, individuals in the processed population are sorted in a non-dominated order, and crowding distance is calculated based on the global best individual in the current population. The selection operation is performed based on the non-dominated sorting results and the crowding distance.

[0016] Optionally, storing the globally optimal individual in the current population in the elite archive includes: Individuals that meet all constraints and belong to non-dominant level 1 are added to the elite archive as the global best individuals in the current population. If the archive size exceeds the threshold, the Euclidean distance between each individual in the elite archive and the global best individual in the current population is calculated. Individuals with close distances are retained, and dense individuals are deleted according to the crowding distance.

[0017] The beneficial effects of this invention are as follows: This invention takes the AC / DC power flow constraints, system safety operation constraints, converter (VSC) power interaction constraints, energy storage operation constraints, flexible load response constraints, and new energy output constraints as core constraints of AC / DC distribution networks. It comprehensively considers the energy storage system capacity, total system network loss, and the adaptability rate of flexible load electricity demand to build a three-objective function. It uses a hybrid algorithm of whale optimization and multi-objective differential evolution to solve for the optimal installation node, rated capacity, rated power, and charging and discharging strategy of energy storage adapted to AC / DC flexible distribution networks. At the same time, it determines the optimal response scheme of flexible loads, realizes the synergistic optimization of flexible loads and energy storage, effectively reduces the energy storage capacity configuration requirements and improves the system's adaptability to wind and solar fluctuations, and fully leverages the advantages of AC / DC distribution networks in being compatible with new energy sources. It has high engineering practical value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating a multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to an embodiment of the present invention. Figure 2 This is a diagram of the IEEE 33 AC / DC interconnected distribution network structure according to an embodiment of the present invention; Figure 3 This is a diagram showing the daily load demand of the system according to an embodiment of the present invention; Figure 4 This is a diagram showing the wind and solar power output of an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0022] like Figure 1 As shown in the figure, this embodiment proposes a multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks, including: Obtain target information within the distribution network area and construct a basic model for AC / DC hybrid power flow calculation. The target information includes wind and solar data, load data, and grid parameter data of the distribution network. Based on the AC / DC hybrid power flow calculation model, flexible loads are classified and response constraints and dynamic relationship models for each type of load are established. Based on the dynamic relationship model, establish the objective function and constraints; Based on the objective function and constraints, a hybrid algorithm combining whale optimization and multi-objective differential evolution is used to optimize the energy storage configuration of AC / DC flexible distribution networks and obtain the optimal energy storage capacity configuration scheme.

[0023] Furthermore, the construction of the basic model for AC / DC hybrid power flow calculation includes: clarifying the AC / DC hybrid grid structure and control strategy containing AC / DC converters, determining the core parameters of grid components, converters, etc., and constructing the basic model for AC / DC hybrid power flow calculation; The mathematical model of an AC / DC interconnected distribution network is expressed as follows: VSC (Voltage Source Converter) is one of the core devices in AC / DC interconnected distribution networks. It is usually deployed at the connection between AC and DC lines. It can quickly adjust the active and reactive power of the system and can achieve bidirectional power flow transmission by changing the current direction, providing key technical support for the flexible operation of AC / DC distribution networks.

[0024] In the steady-state model of VSC, the AC side voltage at time t, Equal to DC side voltage, and VSC modulation index Functions: ; In the above formula, This indicates the DC voltage utilization rate.

[0025] VSC AC side reference voltage DC side reference voltage The relationship between them is: ; The relationship between the per-unit voltage values ​​of the AC side and DC side of the VSC under a unit modulation index is as follows: ; VSC AC active power With reactive power The calculation is as follows: ; ; In the above formula, This indicates the conversion efficiency between AC and DC power in the VSC. This represents the power factor angle of the VSC.

[0026] Furthermore, the dynamic relationship models for various types of loads include: dynamic relationship models corresponding to shiftable loads and loads that can be reduced; The response constraints for various load types include: minimum continuous reduction time constraint, maximum continuous reduction time constraint, and reduction number constraint.

[0027] Specifically, the classification and model description of flexible loads are as follows: (1) Transferable load: Slewable loads refer to loads whose power supply time can be varied according to plan, mainly including washing machines, water heaters, and disinfection cabinets. Their power consumption can span multiple scheduling cycles. Assuming a unit scheduling period of 1 hour, for a certain slewable load... Power distribution vector before scheduling As shown in the following formula: ; In the above formula: Represents the start time, in hours; Represents the duration.

[0028] Assume the translationable interval is Since the entire system needs to be translated, we need to consider... The start time and duration are represented by 0-1 variables. express The translation state at a certain time period, 0 represents No translation occurs, 1 indicates from The time period begins to shift. Then the set of the initial time periods... for: ; like If the load remains unchanged; if ,and but From the start time period Shift to the starting time period of The power distribution vector is: ; No. The actual translation time for each movable load is: .

[0029] (2) Load can be reduced: Reduceable loads are loads that can withstand certain interruptions, power reductions, or reduced operating times, and can be partially or completely reduced based on supply and demand. Unlike shiftable or transferable loads, reduceable loads reduce the user's electricity consumption. (Using 0-1 variables) Indicates that the load can be reduced. During a certain period of time The reduction status, 0 indicates no reduction, 1 indicates reduction exist If the time slot is reduced, then after participating in scheduling... The power during the time period is: ; In the above formula: represent Load reduction factor for the time period ; represent Before participating in scheduling Power during a given time period.

[0030] No. The total reduction time for each load that can be reduced within the scheduling cycle is: ; To ensure user satisfaction with electricity usage, constraints need to be placed on the timing and frequency of power cuts.

[0031] 1) Minimum consecutive reduction time constraint: ; 2) Maximum continuous reduction time constraint: ; 3) Reduction count constraint: ; ; In the above formula: , These represent the minimum and maximum continuous reduction times, respectively, in hours (h). This represents the maximum number of cuts.

[0032] Furthermore, establishing the objective function includes: Objective function of energy storage system capacity : ; in, The start time of maximum charging or discharging; The maximum charge / discharge end time; n represents the time from the start of the maximum charge / discharge. The number of time intervals between the end time and the end time. The time interval between two moments; For the first Energy storage system The charging / discharging power at any given moment; The number of energy storage systems; Objective function of total network loss in the system : ; in, This represents the total number of branches in the distribution network. branch road exist The system always experiences active power loss; Objective function of flexible load electricity demand adaptability : ; in, The influencing factor of the transferable load, This is to reduce the impact of load factors; For the first The actual translation time of a movable load. For the first Total power consumption time of each transferable load; For the first The actual reduction time of the load reduction, For the first The total electricity usage duration for which load can be reduced The number of loads that can be moved. To reduce the amount of load.

[0033] Furthermore, the established constraints include: AC distribution network power flow constraints, DC distribution network power flow constraints, system safety operation constraints, converter (VSC) power interaction constraints, new energy output constraints, energy storage operation constraints, and flexible load constraints.

[0034] Specifically, power flow constraints in AC distribution networks: ; ; ; ; In the above formula, , These are the AC network lines and Active power transmitted at the first end, , These are the AC network lines and Reactive power is transmitted at the first end; , These are the AC network lines Resistance and reactance; , These are nodes in the communication network. and nodes Voltage; express Time Node The actual active power injected into the upwind system; Represents the node at time t The actual active power injected into the photovoltaic system; and They represent the nodes at time t, respectively. The active and reactive power consumed by the load; and These represent the discharge and charging power of the energy storage device at node i at time t, respectively. , The nodes at time t are respectively This can reduce the actual active and reactive power of the load; and They are respectively Time Node Active and reactive power purchased from higher authorities; , The node at time t The active and reactive power flowing into the AC power grid from the connected VSC.

[0035] DC distribution network power flow constraints: ; ; ; In the above formula, , DC network lines and The active power transmitted at the headend; DC network lines The resistance; , These are nodes in a DC network. and nodes Voltage; express Time Node The actual active power injected into the upwind system; express Time Node The actual active power injected into the photovoltaic system; Represents a node The active power consumed by the load; and These represent the discharge and charging power of the energy storage device on node i, respectively; For nodes The actual reduction in active power that can reduce the load; for Time Node The active power flowing into the DC grid from the connected VSC.

[0036] System security operation constraints: ; ; In the above formula, For communication nodes exist Voltage at any given moment; , These are the upper and lower limits of the voltage amplitude at AC node i; DC node exist Voltage at any given moment; , These are the upper and lower limits of the voltage amplitude at DC node i; For communication branch road exist Current at any given moment; This is the upper limit of the current transmitted in the AC branch; DC branch exist Current at any given moment; This is the upper limit of the current transmitted in the DC branch.

[0037] VSC power interaction constraints: ; ; ; In the above formula, For the first A VSC in The voltage amplitude of each node within a given time period; , The first Upper and lower limits of the voltage amplitude of each internal node in a VSC; For the first A VSC in The current amplitude of each line within a given time period; This is the upper limit of the amplitude of the internal circuit current in the VSC. , for Time of the first Power measured by each VSC AC; This represents the maximum operating capacity.

[0038] Constraints on new energy output: ; ; In the above formula, and These represent the maximum output power of the photovoltaic and wind turbine generators during time period t, respectively.

[0039] Energy storage operation constraints: ; ; ; ; In the above formula, and Both are 0-1 variables, when At that time, , indicating the first Energy storage device The period is the discharge state; when At that time, , indicating the first Energy storage device The period is during the charging phase; , , , The first The maximum and minimum power for discharging and charging of an energy storage device; and The charging and discharging efficiencies of the energy storage device are respectively. For the first Energy storage device Capacity of a time period; and The first Minimum and maximum capacity of each energy storage device; and For the first The capacity of an energy storage device at the start and end of a single charge / discharge cycle.

[0040] Flexible load response constraints: The constraints on shiftable and load-reducible loads are those that constrain the timing and number of reductions.

[0041] Furthermore, the optimization of energy storage configuration in AC / DC flexible distribution networks using a hybrid algorithm combining whale optimization and multi-objective differential evolution includes: S1. A hybrid initialization strategy is adopted to generate an initial population, wherein individuals in the population represent energy storage installation nodes, rated capacity, and charging / discharging strategies. S2. A hybrid mutation strategy is adopted to generate mutated individuals, wherein the hybrid mutation strategy includes differential mutation combining differential evolution algorithm, shrinking encirclement and spiral update of whale optimization algorithm; S3. Based on the objective function and constraints, a hybrid crossover strategy and a hybrid selection strategy are used to perform crossover and selection operations on the mutated individuals to obtain the globally optimal individual in the current population and store it in the elite archive. S4. When the number of iterations reaches the preset value but not the maximum value, a new global optimal individual is selected from the elite archive and returned to S2. When the number of iterations does not reach the preset value but not the maximum value, S2 is returned based on the current optimal individual. If the number of iterations reaches the maximum value, the optimal energy storage capacity configuration scheme is output.

[0042] Specifically, the Whale Optimization Algorithm (WOA) achieves optimization through three strategies: shrinking encirclement, spiral update, and random search. It boasts strong local exploitation capabilities and fast convergence speed, but it is prone to getting trapped in local optima in multi-objective scenarios and suffers from insufficient solution diversity. The Multi-Objective Differential Evolution (MODE) algorithm, with its global search capabilities through differential mutation and crossover operations, is widely used in multi-objective optimization, but it suffers from insufficient convergence accuracy in later stages and poor uniformity of solution distribution. This paper embeds the core optimization operator of WOA into the evolutionary framework of MODE, coordinates the search behavior of the two algorithms through an adaptive strategy, and combines non-dominated sorting and crowding distance to maintain the external archive, forming a hybrid optimization system.

[0043] Furthermore, a hybrid initialization strategy is adopted to generate the initial population, including: The first set of initial individuals is generated by random and uniform initialization using a multi-objective differential evolution algorithm. The second set of initial individuals is generated by randomly searching and initializing based on the whale optimization algorithm; The initial population is obtained based on the first part of the initial individuals and the second part of the initial individuals.

[0044] Specifically, the mixed population initialization strategy includes: For decision variables such as energy storage installation nodes, energy storage capacity, shiftable load periods, and number of load reductions, values ​​are randomly generated according to the constraint boundaries of the power grid model to obtain the initial population. The population is initialized using a hybrid initialization strategy, as detailed below: 1) 60% of individuals are randomly and uniformly initialized based on MODE, and each decision variable satisfies: ; In the above formula, For interval Uniformly random numbers within; Indicates the first The first individual One decision variable; Indicates the first The lower bound of each decision variable; Indicates the first The upper limit of each decision variable; The total population size; The total number of decision variables included for each individual.

[0045] 2) 40% of individuals are initialized using a WOA random search, and each decision variable satisfies: .

[0046] Furthermore, by employing a hybrid mutation strategy, mutated individuals are generated, including: When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the whale optimization algorithm is used to simulate the spiral movement of humpback whales around their prey to generate the mutant individuals, where rand(·) is a random number generation function; When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the mutated individuals are generated according to the scaling factor, wherein the scaling factor decreases as the control parameter decreases; When the control parameters of the whale optimization algorithm are less than or equal to the preset value, the parent generation is selected from the elite subpopulation, and the whale optimization algorithm is used to shrink and surround the simulated prey to generate the mutant individual.

[0047] Specifically, hybrid mutation strategies: The mutation phase needs to address the challenges of solving power grid models, such as AC / DC power flow coupling and the coordination of energy storage and flexible loads. Through iterative adjustments to the mutation mode, the algorithm can both explore multi-variable combinations and focus on the optimal region. The hybrid mutation strategy uses differential mutation of the DE (Device Optimization) to ensure population diversity, and WOA (Waistboard Analysis) with shrinking encirclement and spiral updates to achieve elite-driven development, dynamically balancing global exploration and local exploitation, and dynamically selecting the mutation mode according to the iterative phase.

[0048] 1) Early stage of iteration (WOA control parameters) hour): when To address the coupling relationships in the power grid model, a humpback whale spiral movement is simulated, allowing individuals to move closer to the current globally optimal individual. WOA spiral update is used to simulate the humpback whale's spiral movement around its prey, generating mutant individuals. : ; In the above formula, The globally optimal individual in the current population; spiral shape parameter ; For random numbers in the range [-1, 1] For the first A primitive individual.

[0049] when To address the issue of multivariable, high-dimensional data in power grid models, diverse individuals are generated to avoid getting trapped in local optima. DE / rand / 1 mutation is used to generate variant individuals. DE / rand / 1 is one of the most classic mutation strategies in differential evolution algorithms. Its core is to randomly select three different individuals and then introduce a new search direction through a set of difference vectors, thereby ensuring population diversity and preventing the algorithm from getting trapped in local optima. The specific formula is as follows: ; In the above formula, , , For randomly selected distinct individuals; scaling factor Follow Decreasing: .

[0050] 2) Later stage of iteration (WOA control parameters) hour): At this point, the algorithm needs to precisely satisfy the core constraints of the power grid model and optimize the three objective functions. First, parents are selected from the elite subpopulation to ensure that the mutation starting point conforms to the core constraints of the power grid. Then, a WOA (Warranty Overlap) simulation is used to simulate predator approach, where individuals are guided to move closer to the optimal power flow solution, generating mutated individuals. : ; ; In the above formula, A is the step size control parameter for the contraction encirclement.

[0051] Simultaneously, the elite-ordinary subpopulation mutation of the MODE is integrated, using elite individuals to guide the local search and improve convergence speed. That is, the optimization of ordinary individuals is guided by elite individuals, and the results are passed to ordinary individuals through a difference vector, ensuring that all individuals in the later stages of iteration conform to the needs of the power grid model for renewable energy consumption and load smoothing. Individuals are selected from the elite subpopulation (top 30% of individuals). Individuals were selected from the middle 30% of the ordinary subpopulation. To supplement the difference vector: ; In the above formula, This represents the final mutated individual after the fusion of elite and ordinary subpopulations; This represents the initial mutated individuals generated by the WOA contraction enclosure; Furthermore, a hybrid crossover strategy is employed to perform crossover operations on the mutated individuals, including: For the aforementioned variant individuals and parent individuals, experimental individuals are generated using a binomial crossover method; If the experimental individuals do not meet the constraints, the position of the experimental individuals is updated and optimized using the whale optimization algorithm to obtain the optimized experimental population.

[0052] Specifically, a hybrid crossover strategy: During the crossover phase, it is necessary to avoid disrupting the coupling relationships of related variables in the power grid model, and at the same time, repair individuals that may violate constraints after the crossover, corresponding to the power flow constraints and flexible load constraints of the aforementioned power grid model. The crossover operation of MODE is responsible for population information recombination, and the fine-tuning of WOA can enhance the local optimization ability of individuals after the crossover, avoiding the destruction of high-quality genes by the crossover operation.

[0053] 1) Binomial crossover: For strongly correlated variables such as the energy storage installation node and its VSC power, and the flexible load shifting period and the corresponding node's load power, the same cross-dimensional processing is used. For individual variations... With parental individuals Experimental individuals were generated using a binomial crossover method. It is necessary to generate test individuals for each dimension through binomial cross-validation. : ; ; In the above formula, the crossover rate With control parameters Adaptive adjustment promotes diversity in the early stages and preserves high-quality genes in the later stages; For random dimensions; Indicates the first The first experimental individual One decision variable; Indicates the first The first mutant individual One decision variable.

[0054] 2) WOA Spiral Local Optimization Experimental individuals generated after crossover operation If constraints such as AC / DC distribution network power flow constraints, converter (VSC) power interaction, and the timing and frequency of load shedding are violated, fine-tuning is performed via WOA spiral updates. This involves updating the individual test cases after the crossover. If its fitness does not improve, introduce WOA spiral update to optimize its position: ; In the above formula, A random number in the range [-1, 1] is used to ensure that the individual... The optimal area is guided closer to improve the accuracy of local development.

[0055] Furthermore, a hybrid selection strategy is employed to select the mutated individuals, including: The parent population and the experimental population are merged, and the decision variables of individuals that violate the decision variable constraints are adjusted using the whale optimization algorithm to obtain the processed population. According to the objective function, individuals in the processed population are sorted in a non-dominated order, and crowding distance is calculated based on the global best individual in the current population. The selection operation is performed based on the non-dominated sorting results and the crowding distance.

[0056] Specifically, a hybrid selection strategy: The selection phase should use the three objective function values ​​and constraints of the power grid model as evaluation criteria to select the better individuals to enter the next generation.

[0057] 1) Population merging and constraint handling: Merging parent populations With the experimental population For each decision variable of each individual who violates the constraints Follow these steps to process the constraints: First, determine if a constraint is violated. If or If the variable violates the constraint, then the variable is in violation of the constraint. Then, the logic of WOA (World of Optimals) towards the globally optimal individual is introduced. If a variable is below the lower bound, it moves from the lower bound... Initially, adjust towards the variable corresponding to the optimal individual; if the variable is higher than the upper limit, adjust from the upper limit. Initially, adjustments are made towards the corresponding variable of the optimal individual.

[0058] In the above formula, It is the globally optimal individual. The One decision variable; For the first The lower bound of each decision variable; For the first The upper limit of each decision variable.

[0059] 2) Non-dominated sorting: Pareto hierarchical sorting is performed according to the three objective functions of the power grid model. Individuals with smaller energy storage capacity, lower network loss and higher adaptability to the electricity demand of flexible loads are prioritized to ensure that the sorting results are consistent with the optimization objectives of the power grid model.

[0060] 3) Improved calculation of congestion distance: When calculating individual density, the Global Optimal Individual (WOA) is introduced. To mitigate the impact of this, we ensure that the retained individuals are not only evenly distributed but also close to the ideal operating state of the power grid model, thus avoiding the loss of high-quality solutions. ; In the above formula, For the first One objective function; , These are the global maximum and global minimum values ​​of the objective function, respectively.

[0061] Furthermore, storing the globally optimal individual in the current population in the elite archive includes: Individuals that meet all constraints and belong to non-dominant level 1 are added to the elite archive as the global best individuals in the current population. If the archive size exceeds the threshold, the Euclidean distance between each individual in the elite archive and the global best individual in the current population is calculated. Individuals with close distances are retained, and dense individuals are deleted according to the crowding distance.

[0062] Specifically, the maintenance of hybrid elite save files includes: The elite archive stores the best Pareto solutions from previous generations. It must store the individuals that satisfy all grid constraints and have the best three-objective function values, forming a candidate optimization library for the grid model. WOA-based elite guidance is incorporated into the elite archive to improve its quality.

[0063] 1) Archive update After each iteration, only individuals that satisfy all constraints and belong to non-dominant level 1 are added to the archive. If the archive size exceeds the threshold, it is updated according to the following rules: First, calculate the relationship between each individual in the archive and... The Euclidean distance is used to prioritize retaining individuals that are close to each other, thus improving the convergence of the solution. Then, for individuals that are close to each other, dense individuals are removed according to the improved crowding distance to ensure the diversity of the solution.

[0064] 2) WOA Elite Refresh Every 10 iterations, new elites are selected from the elite archive. , use new Guide the next iteration to avoid the population getting stuck in local convergence.

[0065] For this embodiment, the improved IEEE 33 AC / DC interconnected distribution network is as follows: Figure 2 As shown, the system's base power is 10 MVA, the AC base voltage is 12.66 kV, the DC base voltage is 20.67 kV, the node voltage tolerance is 0.9~1.1 pu, and the VSC efficiency is set to 95%. The daily system load demand is as follows: Figure 3 As shown, the power output of the wind and solar power is as follows Figure 4As shown in Table 1, the load installation capacity parameters that can be moved in the system are shown in Table 1. The load reduction capacity is set to 20% of the load at the installation node, with a maximum allowable reduction count of 15 and an allowable number of consecutive reductions of 5. The population size is set to 100 during algorithm iteration, the maximum number of iterations is 30, the scaling factor for differential mutation is set to 0.5, and the probability of binomial crossover is set to 0.6.

[0066] Table 1 To investigate the rationality of the proposed model and the impact of energy storage devices and flexible loads on the operation of the distribution network, the following four cases are used for comparative analysis: Case 1: No energy storage devices are configured, and flexible loads do not participate in grid dispatch. Case 2: Configure energy storage devices so that flexible loads do not participate in grid dispatch.

[0067] Case 3: Configuring energy storage devices can reduce load and participate in grid dispatch.

[0068] Case 4: Configure energy storage devices to reduce load and shift loads to participate in grid dispatch simultaneously.

[0069] The optimal energy storage capacity configuration scheme obtained by adopting the multi-objective optimization configuration method of AC / DC distribution network according to the present invention is shown in Table 2. A comparison of Case 1 and Case 2 shows that when flexible loads do not participate in grid dispatch, configuring energy storage devices can reduce the total network loss of the system and slightly improve the electricity demand adaptation rate of flexible loads. A comparison of Case 2 and Case 3 shows that after the loads that can be reduced participate in dispatch, configuring energy storage devices can not only reduce the total network loss of the system by 132kW, but also improve the electricity demand adaptation rate of flexible loads by 13.1%; the configuration capacity of the energy storage devices in Case 3 is also much smaller than that in Case 2. A comparison of Case 3 and Case 4 shows that when configuring energy storage devices, the simultaneous participation of both loads that can be reduced and loads that can be shifted in grid dispatch can not only reduce the total network loss of the system by 213kW, but also improve the electricity demand adaptation rate of flexible loads by 14.7%; at the same time, the configuration capacity of the energy storage devices in Case 4 is also smaller than that in Case 3.

[0070] Table 2 In summary, using a hybrid algorithm combining whale optimization and multi-objective differential evolution to optimize the energy storage capacity of AC / DC flexible distribution networks can effectively reduce the total network loss and improve the adaptability of flexible loads to electricity demand. This provides guidance for accelerating the construction of AC / DC flexible distribution networks in practical engineering.

[0071] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A multi-objective optimization method for energy storage capacity in AC / DC distribution networks, characterized in that, include: Obtain target information within the distribution network area and construct a basic model for AC / DC hybrid power flow calculation. The target information includes wind and solar data, load data, and grid parameter data of the distribution network. Based on the aforementioned AC / DC hybrid power flow calculation model, flexible loads are classified and response constraints and dynamic relationship models for each type of load are established. Based on the dynamic relationship model, establish the objective function and constraints; Based on the objective function and constraints, a hybrid algorithm combining whale optimization and multi-objective differential evolution is used to optimize the energy storage configuration of AC / DC flexible distribution networks and obtain the optimal energy storage capacity configuration scheme.

2. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 1, characterized in that, The dynamic relationship models for various types of loads include: dynamic relationship models corresponding to shiftable loads and loads that can be reduced; The response constraints for various load types include: minimum continuous reduction time constraint, maximum continuous reduction time constraint, and reduction number constraint.

3. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 1, characterized in that, The objective function includes: Objective function of energy storage system capacity : ； in, The start time of maximum charging or discharging; The maximum charge / discharge end time; n represents the time from the start of the maximum charge / discharge. The number of time intervals between the end time and the end time. The time interval between two moments; For the first Energy storage system The charging / discharging power at any given moment; The number of energy storage systems; Objective function of total network loss in the system : ； in, This represents the total number of branches in the distribution network. branch road exist The system always experiences active power loss; Objective function of flexible load electricity demand adaptability : ； in, The influencing factor of the transferable load, This is to reduce the impact of load factors; For the first The actual translation time of a movable load. For the first Total power consumption time of each transferable load; For the first The actual reduction time for load that can be reduced. For the first The total electricity usage time for which load can be reduced.

4. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 1, characterized in that, The constraints include: AC distribution network power flow constraints, DC distribution network power flow constraints, system safe operation constraints, converter power interaction constraints, new energy output constraints, energy storage operation constraints, and flexible load response constraints.

5. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 1, characterized in that, The optimization of energy storage configuration in AC / DC flexible distribution networks is carried out using a hybrid algorithm combining whale optimization and multi-objective differential evolution, including: S1. A hybrid initialization strategy is adopted to generate an initial population, wherein individuals in the population represent energy storage installation nodes, rated capacity, and charging / discharging strategies. S2. A hybrid mutation strategy is adopted to generate mutated individuals, wherein the hybrid mutation strategy includes differential mutation combining differential evolution algorithm, shrinking encirclement and spiral update of whale optimization algorithm; S3. Based on the objective function and constraints, a hybrid crossover strategy and a hybrid selection strategy are used to perform crossover and selection operations on the mutated individuals to obtain the globally optimal individual in the current population and store it in the elite archive. S4. When the number of iterations reaches the preset value but not the maximum value, a new global optimal individual is selected from the elite archive and returned to S2. When the number of iterations does not reach the preset value but not the maximum value, S2 is returned based on the current optimal individual. If the number of iterations reaches the maximum value, the optimal energy storage capacity configuration scheme is output.

6. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 5, characterized in that, The initial population is generated using a hybrid initialization strategy, including: The first set of initial individuals is generated by random and uniform initialization using a multi-objective differential evolution algorithm. The second set of initial individuals is generated by randomly searching and initializing based on the whale optimization algorithm; The initial population is obtained based on the first part of the initial individuals and the second part of the initial individuals.

7. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 5, characterized in that, Using a hybrid mutation strategy, the generated mutant individuals include: When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the whale optimization algorithm is used to simulate the spiral movement of humpback whales around their prey to generate the mutant individuals, where rand(·) is a random number generation function; When the control parameters of the whale optimization algorithm are greater than the preset value and At that time, the mutated individuals are generated according to the scaling factor, wherein the scaling factor decreases as the control parameter decreases; When the control parameters of the whale optimization algorithm are less than or equal to the preset value, the parent generation is selected from the elite subpopulation, and the whale optimization algorithm is used to shrink and surround the simulated prey to generate the mutant individual.

8. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 5, characterized in that, The crossover operation on the mutant individuals using a hybrid crossover strategy includes: For the aforementioned variant individuals and parent individuals, experimental individuals are generated using a binomial crossover method; If the experimental individuals do not meet the constraints, the position of the experimental individuals is updated and optimized using the whale optimization algorithm to obtain the optimized experimental population.

9. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 8, characterized in that, The selection operation on the mutated individuals using a hybrid selection strategy includes: The parent population and the experimental population are merged, and the decision variables of individuals that violate the decision variable constraints are adjusted using the whale optimization algorithm to obtain the processed population. According to the objective function, individuals in the processed population are sorted in a non-dominated order, and crowding distance is calculated based on the global best individual in the current population. The selection operation is performed based on the non-dominated sorting results and the crowding distance.

10. The multi-objective optimization configuration method for energy storage capacity in AC / DC distribution networks according to claim 8, characterized in that, Storing the globally optimal individual in the current population to the elite archive includes: Individuals that meet all constraints and belong to non-dominant level 1 are added to the elite archive as the global best individuals in the current population. If the archive size exceeds the threshold, the Euclidean distance between each individual in the elite archive and the global best individual in the current population is calculated. Individuals with close distances are retained, and dense individuals are deleted according to the crowding distance.

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