Alternating current and direct current hybrid distribution network distributed power supply and energy storage optimal configuration method based on non-dominated sorting genetic algorithm

Abstract

The invention relates to an AC / DC hybrid distribution network distributed power supply and energy storage optimal configuration method based on a non-dominated sorting genetic algorithm, the AC / DC hybrid distribution network is a distribution network power supply system entering a home in an AC / DC hybrid manner, and the method comprises the following steps: S1, establishing a distributed power supply optimal configuration model, and determining distributed power supply optimization variables; establishing multiple optimization objectives considering economic and environmental protection degrees, and designing constraint conditions and establishing a constraint equation according to operation stability conditions; s2, an energy storage optimization configuration model is established, energy storage optimization variables are determined, a variable set considering stationing optimization and capacity distribution is established, a system optimization target considering voltage support degree, power support degree and network loss is established, and a constraint equation is established according to operation stability conditions and design constraint conditions; and S3, carrying out optimization solution on the distributed power supply optimization configuration model and the energy storage optimization configuration model based on an improved non-dominated sorting genetic algorithm with an elitist strategy. The device has the beneficial effects of high economical efficiency, high operation reliability and good environmental protection property.

Description

[Technical Field]

[0001] This invention relates to the field of power system information technology, specifically to a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. [Background Technology]

[0002] AC distribution networks are currently the main form of distribution networks, but with the increasing integration of distributed power sources, energy storage devices, and new loads, DC distribution is gradually becoming a research hotspot. DC distribution networks eliminate the need for DC / AC converters, and the common DC bus voltage directly reflects the energy information of the distribution system, offering advantages such as more flexible control strategies. However, DC distribution networks also suffer from low inertia and poor stability. Compared to pure AC or DC power supply, AC / DC distribution networks inherit and combine the advantages of both forms. Compared to pure AC distribution, they have fewer converter stages, reducing distribution losses and simplifying the distribution structure. Compared to pure DC distribution, the distribution system has more AC inertia, improving system stability and reliability.

[0003] Compared to traditional AC distribution networks, hybrid AC / DC distribution networks can improve the efficiency of integrating distributed power sources, energy storage, and new loads. However, the widespread integration of renewable energy sources can exacerbate the peak-valley difference in the distribution network, increase system voltage and frequency fluctuations, and pose challenges to system operation and scheduling. On the one hand, the configuration of distributed power sources can be optimized based on their different generation characteristics to improve system performance indicators and reliability. On the other hand, in systems with a high proportion of renewable energy, the integration of energy storage devices can flexibly adjust power output, smooth intermittent energy power fluctuations from renewable energy sources, smooth peak and valley loads, improve voltage quality, and provide flexible power regulation.

[0004] Optimized configuration of distributed power sources and energy storage is crucial for achieving high-efficiency, high-economic, and high-reliability operation of hybrid AC / DC distribution networks. Besides considering system reliability and stability, optimized configuration of distributed power sources and energy storage also requires consideration of environmental friendliness and economic efficiency. Increasing the proportion of renewable energy input to reduce carbon emissions necessitates improving renewable energy utilization rates, reducing energy losses, and minimizing total lifecycle investment. Existing optimized configuration schemes for distributed power sources and energy storage often employ single-objective optimization, neglecting operational reliability and environmental objectives, and incorporating environmental indicators into economic objectives through cost adjustments; or they ignore economic indicators, focusing solely on system operational reliability modeling. However, realistic optimized configuration of distributed power sources and energy storage requires considering multiple objectives to obtain the optimal solution for multi-objective optimization. Furthermore, due to the limited research on hybrid AC / DC distribution networks, few studies have proposed optimized configuration methods for distributed power sources and energy storage specifically for these scenarios.

[0005] Non-dominated Sorting Genetic Algorithms (NSGA) are genetic algorithms based on the Pareto optimality concept. They evaluate and sort individuals through a non-dominated sorting mechanism and are mainly used to solve optimization problems with multiple objective functions, aiming to search for high-quality solutions that are suitable for multiple objectives. The NSGA process includes initializing the population, calculating the fitness values ​​of individuals, performing non-dominated sorting and crowding allocation, selecting superior individuals for crossover and mutation operations, and repeating these steps until the stopping condition is met.

[0006] This invention addresses the technical problem of incomplete modeling and difficulty in optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks. Based on a non-dominated sorting genetic algorithm, it improves the method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks. [Summary of the Invention]

[0007] The purpose of this invention is to propose an optimized configuration method for distributed power sources and energy storage that is economical, highly reliable, environmentally friendly, and suitable for AC / DC hybrid distribution networks.

[0008] To achieve the above objectives, the technical solution adopted by this invention is a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. The AC / DC hybrid distribution network is a power supply system with AC / DC connections to households, and includes the following steps:

[0009] S1. Establish a distributed power source optimization configuration model, determine the distributed power source optimization variables, establish multiple optimization objectives considering economic and environmental factors, and establish constraint equations based on operational stability conditions and design constraints.

[0010] S2. Establish an energy storage optimization configuration model, determine energy storage optimization variables, establish a variable set considering site optimization and capacity allocation, establish a system optimization objective considering voltage support, power support and network loss, and establish constraint equations based on operating stability conditions and design constraints.

[0011] S3. An improved non-dominated sorting genetic algorithm with an elite strategy is used to optimize the distributed power supply configuration model and the energy storage configuration model. The optimal solution is selected based on the set membership weight value.

[0012] Preferably, step S1 specifically includes the following sub-steps:

[0013] S11. Describe the distributed power source optimization variables of the power distribution network system using mathematical language, including distributed power source type, distributed power source access location, and distributed power source access capacity, and model the distributed power source optimization variables.

[0014] S12. Describe the optimization objectives of the distributed power supply system using mathematical language, including the economic objectives and environmental objectives of the distributed power supply.

[0015] S13. Describe the distributed power supply system's distributed power source constraints using mathematical language, including distributed power source system operation constraints and distributed power source design constraints.

[0016] Preferably, step S11:

[0017] The distributed generation optimization variables are represented in set form as x=[T1,L1,N1,…,T n ,L n N n In the formula, T1~T n Indicates the type of distributed power source, L1 to L2. n Indicates the location of distributed power source connection, N1 to N n This represents the capacity of distributed power sources connected to the system, where n is the total number of distributed power sources connected to the system.

[0018] Preferably, step S12:

[0019] The economic objective of distributed power generation is to minimize the annual value cost of distributed power generation throughout its entire life cycle. The annual value cost of distributed power generation throughout its entire life cycle includes the annual value cost of the initial investment, operation and maintenance costs, fuel costs, and replacement costs of the equipment.

[0020] min C ann =C ini +C om +C f +C r

[0021] In the formula, C ann C represents the annualized cost over the entire lifecycle of a distributed power source. ini C represents the annual value cost of the initial investment in distributed power generation. om C represents the annual operating and maintenance cost of distributed power sources. f C represents the annual fuel cost of distributed power generation. r This indicates the annual replacement cost of distributed power sources such as equipment;

[0022] The annual value cost of initial investment in distributed power generation is calculated as follows: In the formula, n is the number of distributed power source types, and Cci Let P be the unit investment cost of the i-th power source. i Let μ be the capacity of the i-th power source. CRF (r,m) represents the annual capital recovery rate, r represents the discount rate, and m represents the project's total lifespan in years.

[0023] The annual operating and maintenance cost of distributed power sources is calculated as follows: In the formula, μ i This is the annual operation and maintenance cost coefficient for the equipment;

[0024] The annual fuel cost for distributed power generation is calculated as follows: In the formula, λ i E represents the fuel cost per unit of electricity generated by the i-th type of power source. i Let be the annual power generation of the i-th type of power source;

[0025] The annual replacement cost of distributed power equipment, etc. is calculated as follows: In the formula, k i The unit replacement cost for equipment of type i power supply;

[0026] The environmental goals of distributed power generation are to achieve the highest penetration rate of new energy sources and the lowest carbon emissions;

[0027]

[0028] In the formula, K ren P represents the penetration rate of new energy sources. ren,i For the power output of the i-th type of power source, P load This refers to the load power within the distribution network area.

[0029] Preferably, step S13:

[0030] The operating constraints of distributed generation are distributed generation power flow constraints and distributed generation node voltage offset constraints.

[0031] In the aforementioned AC / DC hybrid distribution network, both the AC distribution network and the DC distribution network need to meet their respective power flow constraints;

[0032] Power flow constraints of distributed generation in AC power distribution networks:

[0033]

[0034] In the formula, P Gi Let P be the active power of node i. Di U represents the active load power at node i. i U j G represents the voltage magnitudes at nodes i and j, respectively. ij B ij These are the real and imaginary parts of the admittance matrix, θ and θ', respectively. ijLet Q be the phase difference between the voltages at nodes i and j, m be the number of nodes in the AC distribution network system, and Q be the phase difference between the voltages at nodes i and j. Gi Let Q be the reactive power of node i. Di Let be the reactive load power of node i;

[0035] DC distribution network distributed power flow constraints:

[0036]

[0037] In the formula, P gi Let P be the DC power supply power of node i. di U is the DC load power of node i. i U j The voltage magnitudes at nodes i and j are respectively, Y ij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system.

[0038] Distributed generation node voltage offset constraints refer to the requirement that the node voltage deviation must meet certain requirements. In the formula, Let be the lower limit of the voltage at node i. This represents the upper limit of the voltage at node i;

[0039] Distributed power source design constraints include active power capacity constraints, distributed power source design capacity constraints, and AC / DC bus interconnection power constraints.

[0040] Active power capacity constraints require that the system has a certain active power reserve, i.e., ∑P i +P DE +P PV +P WT -P D ≥∈, where ∑P i For the power generation of synchronous machines and other power sources, P DE P is the power output of the diesel generator. PV For photovoltaic power generation, P WT P represents the power generation capacity of the wind turbine. D Where is the total load power, and ∈ is the active power reserve capacity;

[0041] The design capacity constraint for distributed power sources requires that the capacity of each distributed power source be within the upper and lower limits of the design capacity, i.e., P dis,min ≤P dis ≤P dis,max In the formula, P dis P refers to the total capacity of distributed power sources. dis,min P is the lower limit of the design capacity of the corresponding distributed power source. dis,max The upper limit of the design capacity for the corresponding distributed power source;

[0042] AC / DC bus interconnection power constraint is -P linemax ≤P line ≤P linemax In the formula, P line For power transmission between AC and DC bus tie lines, P linemax This represents the maximum transmission power of the AC / DC bus tie line.

[0043] Preferably, step S2 specifically includes the following sub-steps:

[0044] S21. Describe the energy storage optimization variables of the power distribution network system using mathematical language, including energy storage access capacity and energy storage access location, and model the energy storage optimization variables.

[0045] S22. Describe the energy storage optimization objectives of the power distribution network system using mathematical language, including energy storage economic objectives and energy storage operation stability objectives;

[0046] S23. Describe the energy storage constraints of the power distribution network system using mathematical language, including energy storage system operation constraints and energy storage design constraints.

[0047] Preferably, step S21:

[0048] The energy storage optimization variables are represented in set form as x=[E1,L1,…,E s ,L s In the formula, E1~E s Indicates the energy storage access capacity, L1 to L s This indicates the location of the energy storage connection, and s is the total number of energy storage units connected to the system.

[0049] Preferably, step S22:

[0050] The economic goal of energy storage is to minimize the annual cost of energy storage over its entire life cycle and network losses. The annual cost of energy storage over its entire life cycle includes the annual cost of initial investment, operation and maintenance costs, and replacement costs of energy storage equipment.

[0051] min C ann =C ini +C om +C r

[0052] In the formula, C ann C represents the annual value cost over the entire lifecycle of energy storage. ini C represents the annual value cost of the initial investment in energy storage. om C represents the annual operating and maintenance cost of energy storage. r This indicates the annual replacement cost of energy storage equipment, etc.

[0053]

[0054] In the formula, P loss For DC distribution network losses, U i U j The voltage magnitudes at nodes i and j are respectively, Y ij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system.

[0055] Energy storage operation stability targets include maximizing voltage support and power support;

[0056]

[0057] In the formula, I vf For comprehensive voltage support, N is the total number of nodes in the AC / DC distribution network, and V i Let V be the voltage magnitude at node i. n The node voltage rating, V max V is the upper limit of the node voltage. min P is the lower limit of the node voltage. i Let be the injected power at node i;

[0058] Power support is divided into positive power support and negative power support;

[0059]

[0060] In the formula, For positive power support, P represents the negative power support level, s represents the number of energy storage devices, and P represents the negative power support level. dcmax,i Let P be the maximum discharge power of the i-th energy storage device. cmax,i Let be the maximum charging power of the i-th energy storage device.

[0061] Preferably, step S23:

[0062] The constraints for energy storage operation are energy storage power flow constraints and energy storage node voltage offset constraints. The constraint equations are the same as those for distributed power source operation conditions in step S13, and the constraints will not be repeated.

[0063] Energy storage design constraints include energy storage charging and discharging power constraints and energy storage capacity constraints;

[0064] Energy storage charging and discharging power constraint is -P cmax ≤P ES ≤P dcmax In the formula, P ES For energy storage capacity, P cmax P is the maximum charging power for energy storage. dcmax This represents the maximum discharge power of the energy storage.

[0065] Energy storage capacity constraint is S SOCmin ≤SSOC (t)≤S SOCmax In the formula, S SOC (t) represents the state of charge of the energy storage battery, S SOCmin S is the minimum state of charge. SOCmax This represents the maximum value of the state of charge.

[0066] Preferably, step S3 uses the NSGA-II algorithm for distributed power source and energy storage optimization configuration, specifically including the following sub-steps:

[0067] S31. Determine the values ​​of each parameter in the algorithm, including the population size N. P Maximum number of generations G n Genetic factor F g , variable factor C r And the penalty factors w1 to w3, produce the first generation population;

[0068] S32. Calculate the objective function values ​​of each individual through AC / DC hybrid power flow calculation;

[0069] S33. Perform non-dominated sorting on the individuals in the population and calculate the crowding distance of each layer of individuals;

[0070] S34. Selection operation: Select N from the current population. P Two superior individuals are used to produce offspring;

[0071] S35, crossover and mutation operations, use the selected dominant individuals to generate offspring population, and calculate the objective function value of the offspring individuals through the power flow program;

[0072] S36. Merge the offspring population and the parent population into a new population, and perform non-dominated sorting and crowding calculation.

[0073] S37. Select N from the new population. P Select outstanding individuals to serve as the next generation of the species;

[0074] S38. Perform conditional judgment: If the maximum number of generations has been reached, proceed to step S39; otherwise, return to step S34 and loop.

[0075] S39. Calculate the membership weights and select the optimal solution.

[0076] This invention provides a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. Compared with existing technologies, this method offers the following advantages: Addressing the technical problem of incomplete modeling and difficulty in optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks for multiple objectives, this invention establishes an optimization model for distributed power sources and energy storage in AC / DC hybrid distribution networks and uses a non-dominated sorting genetic algorithm to solve the optimization model. This effectively solves the problem of optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks, optimizing the system configuration at the levels of economy, environmental protection, and system operational stability, thereby achieving a balanced and optimal configuration for each objective. This provides a feasible method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks. [Attached Image Description]

[0077] Figure 1 This is a schematic diagram of the Pareto optimal solution set, which is a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm.

[0078] Figure 2 This is a schematic diagram illustrating the execution of an optimization algorithm for a distributed power supply and energy storage optimization configuration method for AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm.

[0079] Figure 3 This is a flowchart of an algorithm for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm.

Detailed Implementation Methods

[0080] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0081] Example

[0082] This embodiment implements a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm.

[0083] In this embodiment, the optimized configuration method uses an improved non-dominated sorting genetic algorithm with an elite strategy. It is a multi-objective optimization algorithm based on genetic algorithms. The AC / DC hybrid distribution network is a power supply system that connects AC and DC to households. The optimized configuration method for distributed power sources and energy storage achieves the goals of optimal economy, environmental protection, and reliability of the hybrid distribution network system by using multiple energy forms, different configuration quantities, and different configuration locations.

[0084] Specifically,

[0085] Step 1: Establish a distributed power source optimization configuration model, determine the optimization variables, establish multiple optimization objectives that consider economic and environmental factors, and establish constraint equations based on operational stability conditions and design constraints.

[0086] 1A. Use mathematical language to describe the system optimization variables, including distributed power source type, access location, and access capacity, and model the optimization variables.

[0087] The optimization variables are represented in set form as x = [T1, L1, N1, ..., T]. n ,L n N n In the formula, T1~T n Indicates the type of distributed power source, which can be photovoltaic, wind power, or other types of distributed power sources, L1 to L2. n Indicates the access location, N1 to N n This represents the access capacity, where n is the total number of distributed power sources connected to the system.

[0088] 1B. Use mathematical language to describe the system optimization objectives, including economic objectives and environmental objectives.

[0089] The economic objective is to minimize the annual cost over the entire life cycle, while the environmental objective is to maximize the penetration rate of new energy sources and minimize carbon emissions.

[0090] The life-cycle cost of equal annual value includes equipment investment costs, operation and maintenance costs, fuel costs, and equipment replacement costs. The economic objective can be expressed by the following mathematical expression.

[0091] min C ann =C ini +C om +C f +C r

[0092] In the formula, C ann C represents the annual cost over the entire life cycle. ini C represents the annual value cost of the initial investment. om C represents the annual operating and maintenance costs. f Indicates annual fuel cost, C r This indicates the cost of replacing equipment at its annual value.

[0093] The specific calculations for each fee are as follows:

[0094] The annual value cost of the initial investment is calculated as follows: In the formula, n is the number of distributed power source types, and C ci Let P be the unit investment cost of the i-th power source. iLet μ be the capacity of the i-th power source. CRF (r,m) represents the annual capital recovery rate, r represents the discount rate, and m represents the project's total lifespan.

[0095] Annual operation and maintenance costs are calculated as follows: In the formula, μ i This is the annual operation and maintenance cost coefficient for the equipment.

[0096] Annual fuel costs are calculated as follows: In the formula, λ i E represents the fuel cost per unit of electricity generated by the i-th type of power source. i Let be the annual power generation of the i-th type of power source.

[0097] The annual value replacement cost of equipment, etc. is calculated as follows: In the formula, k i The unit replacement cost for equipment of type i power supply.

[0098] Environmental protection goals can be represented by the following mathematical expression.

[0099]

[0100] In the formula, K ren P represents the penetration rate of new energy sources. ren,i For the power output of the i-th type of power source, P load This refers to the load power within the distribution network area.

[0101] 1C. Use mathematical language to describe system constraints, including system operation constraints and design constraints.

[0102] The main operational constraints are power flow constraints and node voltage offset constraints.

[0103] In a hybrid AC / DC distribution network, both the AC and DC distribution networks need to meet their respective power flow constraints.

[0104] The power flow constraints of the AC distribution network are shown in the following formula.

[0105]

[0106] In the formula, P Gi Let P be the active power of node i. Di U represents the active load power at node i. i U j G represents the voltage magnitudes at nodes i and j, respectively. ij B ij These are the real and imaginary parts of the admittance matrix, θ and θ', respectively. ij Let Q be the phase difference between the voltages at nodes i and j, m be the number of nodes in the AC distribution network system, and Q be the phase difference between the voltages at nodes i and j. Gi Let Q be the reactive power of node i.Di Let be the reactive load power of node i.

[0107] The DC power flow constraint is shown in the following equation.

[0108]

[0109] In the formula, P gi Let P be the DC power supply power of node i. di U is the DC load power of node i. i U j The voltage magnitudes at nodes i and j are respectively, Y ij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system.

[0110] The node voltage deviation needs to meet certain requirements. In the formula, Let be the lower limit of the voltage at node i. is the upper limit of the voltage at node i.

[0111] Design constraints include active power capacity constraints, distributed power source design capacity constraints, and AC / DC bus interconnection power constraints.

[0112] Active power capacity constraints require that the system has a certain active power reserve, i.e., ∑P i +P DE +P PV +P WT -P D ≥∈, where ∑P i For the power generation of synchronous machines and other power sources, P DE P is the power output of the diesel generator. PV For photovoltaic power generation, P WT P represents the power generation capacity of the wind turbine. D ∈ represents the total load power, and ∈ represents the active power reserve capacity.

[0113] The design capacity constraint for distributed power sources requires that the capacity of each distributed power source be within the upper and lower limits of the design capacity, i.e., P dis,min ≤P dis ≤P dis,max In the formula, P dis P refers to the capacity of all distributed power sources, such as photovoltaics and wind turbines. dis,min P is the lower limit of the design capacity of the corresponding distributed power source. dis,max The upper limit of the design capacity for the corresponding distributed power source.

[0114] AC / DC bus interconnection power constraint is -P linemax ≤P line ≤P linemax In the formula, P lineFor power transmission between AC and DC bus tie lines, P linemax This represents the maximum transmission power of the AC / DC bus tie line.

[0115] Step 2: Establish an energy storage optimization configuration model, determine optimization variables, establish a variable set considering site optimization and capacity allocation, establish a system optimization objective considering voltage support, power support and network loss, and establish constraint equations based on operating stability conditions and design constraints.

[0116] 2A. Use mathematical language to describe the system optimization variables, including energy storage access capacity and access location, and model the optimization variables.

[0117] The optimization variables are represented in set form as x = [E1, L1, ..., E...]. s ,L s In the formula, E1~E s Indicates the energy storage access capacity, L1 to L s The location of the connection is indicated by s, which represents the total number of energy storage units connected to the system.

[0118] 2B. Use mathematical language to describe the system optimization objectives, including economic objectives and operational stability objectives.

[0119] The economic objectives are to minimize the annual cost and network loss over the entire life cycle, while the operational stability objectives include maximizing voltage and power support.

[0120] The total life-cycle equivalent annual cost includes equipment investment costs, operation and maintenance costs, and equipment replacement costs. The minimum target for total life-cycle equivalent annual cost can be expressed by the following mathematical expression.

[0121] min C ann =C ini +C om +C r

[0122] In the formula, C ann C represents the annual cost over the entire life cycle. ini C represents the annual value cost of the initial investment. om C represents the annual operating and maintenance costs. r This indicates the cost of replacing equipment at its annual value.

[0123] The minimum network loss target can be expressed by the following mathematical expression:

[0124]

[0125] In the formula, P loss For DC distribution network losses, U i U j The voltage magnitudes at nodes i and j are respectively, Yij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system.

[0126] The voltage support target can be expressed by the following mathematical expression:

[0127]

[0128] In the formula, I vf For comprehensive voltage support, N is the total number of nodes in the AC / DC distribution network, and V i Let V be the voltage magnitude at node i. n The node voltage rating, V max V is the upper limit of the node voltage. min P is the lower limit of the node voltage. i Let be the injected power at node i.

[0129] I vf The larger the value, the better the overall voltage level of the system. vf The maximum value is 1.

[0130] Power support targets can be divided into positive power support and negative power support, expressed by the following mathematical expression:

[0131]

[0132] In the formula, For positive power support, P represents the negative power support level, s represents the number of energy storage devices, and P represents the negative power support level. dcmax,i Let P be the maximum discharge power of the i-th energy storage device. cmax,i Let be the maximum charging power of the i-th energy storage device.

[0133] 2C. Use mathematical language to describe system constraints, including system operation constraints and design constraints.

[0134] The main operating constraints are power flow constraints and node voltage offset constraints. The constraint equations are the same as those in step S13, so the constraints do not need to be repeated.

[0135] The design constraints take into account the energy storage charging and discharging power and capacity constraints.

[0136] Energy storage charging and discharging power constraint is -P cmax ≤P ES ≤P dcmax In the formula, P ES For energy storage capacity, P cmax P is the maximum charging power for energy storage. dcmax This represents the maximum discharge power of the energy storage.

[0137] Energy storage capacity constraint is SSOCmin ≤S SOC (t)≤S SOCmax In the formula, S SOC (t) represents the state of charge of the energy storage battery, S SOCmin S is the minimum state of charge. SOCmax This represents the maximum value of the state of charge.

[0138] Step 3: Based on the improved non-dominated sorting genetic algorithm with elite strategy, optimize the distributed power source and energy storage configuration model, and select the optimal solution according to the set membership weight value.

[0139] For multi-objective optimization problems, since there is generally no consistency among the various sub-objectives, improving one objective often weakens the performance of others. In other words, multiple objectives are mutually constrained, making it impossible for all objectives to reach their optimal state simultaneously. Therefore, solving multi-objective optimization problems requires trade-offs and compromises among different alphabets, yielding a set of solutions rather than a single globally optimal solution. The NSGA-II algorithm obtains a set of non-dominated solutions, also known as the Pareto optimal solution set, through non-dominated sorting.

[0140] Figure 1 This is a schematic diagram of the Pareto optimal solution set, a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. (See diagram for example.) Figure 1 As shown, this represents the Pareto optimal solution set considering the minimization of two objectives F1 and F2 simultaneously.

[0141] After each generation of poor mutation operations, the newly generated offspring population Q is merged with the parent population P to form a new population R. Then, population R is subjected to non-dominated sorting to obtain a series of non-dominated layers F. i The crowding degree of each individual in each layer is calculated. Since the population R is a set of parent and offspring populations, the resulting non-dominated layer F1 is the set of the best individuals in the entire population. Then, according to the crowding degree comparison operator, the best N individuals are selected sequentially to form a new generation population. Through generations of genetic mutation operations, the obtained solution set continuously approaches the Pareto optimal solution. After obtaining the Pareto optimal solution, a membership function is set for each objective function to represent the degree of acceptance of optimization for each objective. Weights are assigned to each objective based on their importance, and the optimal solution is selected using a membership degree weighting function.

[0142] Figure 2 This is a schematic diagram illustrating the execution of an optimization algorithm for distributed power sources and energy storage in AC / DC hybrid distribution networks, based on a non-dominated sorting genetic algorithm. Figure 2 As shown, the main steps for optimizing the configuration of distributed power sources and energy storage using the NSGA-II algorithm are as follows:

[0143] 1) Determine the values ​​of each parameter in the algorithm, including the population size N. P Maximum number of generations G n Genetic factor F g , variable factor C r And the penalty factors w1 to w3, to produce the first generation population (also called the parent population relative to the next generation population);

[0144] 2) Calculate the objective function values ​​of each individual component through AC / DC hybrid power flow calculation;

[0145] 3) Perform non-dominated sorting on the individuals in the population and calculate the crowding distance for each layer of individuals;

[0146] 4) Selection operation: Select N from the current population. P Two superior individuals are used to produce offspring;

[0147] 5) Crossover and mutation operations are used to generate offspring populations from the selected dominant individuals, and the objective function values ​​of the offspring individuals are calculated through the power flow program.

[0148] 6) Merge the offspring population and the parent population into a new population, and perform non-dominated sorting and crowding calculation;

[0149] 7) Select N from the new population P Select outstanding individuals to serve as the next generation of the species;

[0150] 8) Perform a conditional check: If the maximum number of generations has been reached, proceed to step 9); otherwise, return to step 4) and repeat.

[0151] 9) Calculate the membership weights and select the optimal solution.

[0152] Figure 3 This is a flowchart illustrating an algorithm for optimizing the allocation of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. Figure 3 As shown, the optimal configuration of power distribution networks needs to consider economy, environmental protection, and system operational stability. Under multi-objective conditions, system optimization configuration is quite complex. This embodiment inherits the advantage of time-domain Kron simplification being applicable to dynamic and heterogeneous networks, while restricting the selection method of the null space basis vectors. This results in a network dynamics simplification method that is applicable to both dynamic and heterogeneous networks and can obtain a physical model. Based on a non-dominated sorting genetic algorithm, this embodiment designs a method for the optimal configuration of distributed power sources and energy storage suitable for AC / DC hybrid distribution networks.

[0153] This embodiment presents a method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm. It mainly consists of three key steps: establishing a distributed power source optimization model, establishing an energy storage optimization model, and solving the optimization problem using a non-dominated sorting genetic algorithm. The resulting method effectively addresses the optimization configuration problem of distributed power sources and energy storage in AC / DC hybrid distribution networks. It optimizes the system configuration at three levels: economic efficiency, environmental friendliness, and system operational stability, thereby achieving a balanced and optimal configuration for each objective. This provides a feasible method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks.

[0154] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM).

[0155] The above description is only a preferred embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and additions without departing from the principle of the present invention, and these improvements and additions should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm, wherein the AC / DC hybrid distribution network is a power supply system with AC / DC connections to households, characterized in that... Includes the following steps: S1. Establish a distributed power source optimization configuration model, determine the distributed power source optimization variables, establish multiple optimization objectives considering economic and environmental factors, and establish constraint equations based on operational stability conditions and design constraints. S2. Establish an energy storage optimization configuration model, determine energy storage optimization variables, establish a variable set considering site optimization and capacity allocation, establish a system optimization objective considering voltage support, power support and network loss, and establish constraint equations based on operating stability conditions and design constraints. S3. An improved non-dominated sorting genetic algorithm with an elite strategy is used to optimize the distributed power supply configuration model and the energy storage configuration model. The optimal solution is selected based on the set membership weight value.

2. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm as described in claim 1, characterized in that... Step S1 specifically includes the following sub-steps: S11. Describe the distributed power source optimization variables of the power distribution network system using mathematical language, including distributed power source type, distributed power source access location, and distributed power source access capacity, and model the distributed power source optimization variables. S12. Describe the optimization objectives of the distributed power supply system using mathematical language, including the economic objectives and environmental objectives of the distributed power supply. S13. Describe the distributed power supply system's distributed power source constraints using mathematical language, including distributed power source system operation constraints and distributed power source design constraints.

3. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm as described in claim 2, characterized in that... Step S11: The distributed generation optimization variables are represented in set form as x=[T1,L1,N1,…,T n ,L n N n In the formula, T1~T n Indicates the type of distributed power source, L1 to N. n Indicates the location of distributed power source connection, N1 to N n This represents the capacity of distributed power sources connected to the system, where n is the total number of distributed power sources connected to the system.

4. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm as described in claim 3, characterized in that... Step S12: The economic objective of distributed power generation is to minimize the annual value cost of distributed power generation throughout its entire life cycle. The annual value cost of distributed power generation throughout its entire life cycle includes the annual value cost of the initial investment, the operation and maintenance costs, the fuel costs, and the annual value replacement costs of the equipment. minC ann =C ini +C om +C f +C r In the formula, C ann C represents the annualized cost over the entire lifecycle of a distributed power source. ini C represents the annual value cost of the initial investment in distributed power generation. om C represents the annual operating and maintenance cost of distributed power sources. f C represents the annual fuel cost of distributed power generation. r This indicates the annual replacement cost of distributed power sources such as equipment; The annual value cost of initial investment in distributed power generation is calculated as follows: In the formula, n is the number of distributed power source types, and C ci Let P be the unit investment cost of the i-th power source. i Let μ be the capacity of the i-th power source. CRF (r,m) represents the annual capital recovery rate, r represents the discount rate, and m represents the project's total lifespan in years. The annual operating and maintenance cost of distributed power sources is calculated as follows: In the formula, μ i This is the annual operation and maintenance cost coefficient for the equipment; The annual fuel cost for distributed power generation is calculated as follows: In the formula, λ i E represents the fuel cost per unit of electricity generated by the i-th type of power source. i Let be the annual power generation of the i-th type of power source; The annual replacement cost of distributed power equipment, etc. is calculated as follows: In the formula, k i The unit replacement cost for equipment of type i power supply; The environmental goals of distributed power generation are to achieve the highest penetration rate of new energy sources and the lowest carbon emissions; In the formula, K ren P represents the penetration rate of new energy sources. ren,i For the power output of the i-th type of power source, P load This refers to the load power within the distribution network area.

5. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm according to claim 4, characterized in that... Step S13: The operating constraints of distributed generation are distributed generation power flow constraints and distributed generation node voltage offset constraints. In the aforementioned AC / DC hybrid distribution network, both the AC distribution network and the DC distribution network need to meet their respective power flow constraints; Power flow constraints of distributed generation in AC power distribution networks: In the formula, P Gi Let P be the active power of node i. Di U represents the active load power at node i. i U j G represents the voltage magnitudes at nodes i and j, respectively. ij B ij These are the real and imaginary parts of the admittance matrix, θ and θ', respectively. ij Let Q be the phase difference between the voltages at nodes i and j, m be the number of nodes in the AC distribution network system, and Q be the phase difference between the voltages at nodes i and j. Gi Let Q be the reactive power of node i. Di Let be the reactive load power of node i; DC distribution network distributed power flow constraints: In the formula, P gi Let P be the DC power supply power of node i. di U is the DC load power of node i. i U j The voltage magnitudes at nodes i and j are respectively, Y ij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system. Distributed generation node voltage offset constraints refer to the requirement that the node voltage deviation must meet certain requirements. In the formula, Let be the lower limit of the voltage at node i. This represents the upper limit of the voltage at node i; Distributed power source design constraints include active power capacity constraints, distributed power source design capacity constraints, and AC / DC bus interconnection power constraints. Active power capacity constraints require that the system has a certain active power reserve, i.e., ∑P i +P DE +P PV +P WT -P D ≥∈, where ∑P i For the power generation of synchronous machines and other power sources, P DE P is the power output of the diesel generator. PV For photovoltaic power generation, P WT P represents the power generation capacity of the wind turbine. D Where is the total load power, and ∈ is the active power reserve capacity; The design capacity constraint for distributed power sources requires that the capacity of each distributed power source be within the upper and lower limits of the design capacity, i.e., P dis,min ≤P dis ≤P dis,max In the formula, P dis P refers to the total capacity of distributed power sources. dis,min P is the lower limit of the design capacity of the corresponding distributed power source. dis,max The upper limit of the design capacity for the corresponding distributed power source; AC / DC bus interconnection power constraint is -P linemax ≤P line ≤P linemax In the formula, P line For power transmission between AC and DC bus tie lines, P linemax This represents the maximum transmission power of the AC / DC bus tie line.

6. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm according to claim 5, characterized in that... Step S2 specifically includes the following sub-steps: S21. Describe the energy storage optimization variables of the power distribution network system using mathematical language, including energy storage access capacity and energy storage access location, and model the energy storage optimization variables. S22. Describe the energy storage optimization objectives of the power distribution network system using mathematical language, including energy storage economic objectives and energy storage operation stability objectives; S23. Describe the energy storage constraints of the power distribution network system using mathematical language, including energy storage system operation constraints and energy storage design constraints.

7. A method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm, as described in claim 6, is characterized in that... Step S21: The energy storage optimization variables are represented in set form as x=[E1,L1,…,E s ,L s In the formula, E1~E s Indicates the energy storage access capacity, L1 to L s This indicates the location of the energy storage connection, and s is the total number of energy storage units connected to the system.

8. The method for optimal configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm according to claim 7, characterized in that... Step S22: The economic goal of energy storage is to minimize the annual cost of energy storage over its entire life cycle and network losses. The annual cost of energy storage over its entire life cycle includes the annual cost of initial investment, operation and maintenance costs, and replacement costs of energy storage equipment. my C ann =C ini +C om +C r In the formula, C ann C represents the annual value cost over the entire lifecycle of energy storage. ini C represents the annual value cost of the initial investment in energy storage. om C represents the annual operating and maintenance cost of energy storage. r This indicates the annual replacement cost of energy storage equipment, etc. In the formula, P loss For DC distribution network losses, U i U j The voltage magnitudes at nodes i and j are respectively, Y ij Let l be the line admittance connecting nodes i and j, and l be the number of nodes in the DC distribution network system. Energy storage operation stability targets include maximizing voltage support and power support; In the formula, I vf For comprehensive voltage support, N is the total number of nodes in the AC / DC distribution network, and V i Let V be the voltage magnitude at node i. n The node voltage rating, V max V is the upper limit of the node voltage. min P is the lower limit of the node voltage. i Let be the injected power at node i; Power support is divided into positive power support and negative power support; In the formula, For positive power support, P represents the negative power support level, s represents the number of energy storage devices, and P represents the negative power support level. dcmax,i Let P be the maximum discharge power of the i-th energy storage device. cmax,i Let be the maximum charging power of the i-th energy storage device.

9. A method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm, as described in claim 8, is characterized in that... Step S23: The constraints for energy storage operation are energy storage power flow constraints and energy storage node voltage offset constraints. The constraint equations are the same as those for distributed power source operation conditions in step S13, and the constraints will not be repeated. Energy storage design constraints include energy storage charging and discharging power constraints and energy storage capacity constraints; Energy storage charging and discharging power constraint is -P cmax ≤P ES ≤P dcmax In the formula, P ES For energy storage capacity, P cmax P is the maximum charging power for energy storage. dcmax This represents the maximum discharge power of the energy storage. Energy storage capacity constraint is S SOCmin ≤S SOC (t)≤S SOCmax In the formula, S SOC (t) represents the state of charge of the energy storage battery, S SOCmin S is the minimum state of charge. SOCmax This represents the maximum value of the state of charge.

10. A method for optimizing the configuration of distributed power sources and energy storage in AC / DC hybrid distribution networks based on a non-dominated sorting genetic algorithm, as described in claim 11, is characterized in that... Step S3 uses the NSGA-II algorithm to optimize the configuration of distributed power sources and energy storage, specifically including the following sub-steps: S31. Determine the values ​​of each parameter in the algorithm, including the population size N. P Maximum number of generations G n Genetic factor F g , variable factor C r And the penalty factors w1 to w3, produce the first generation population; S32. Calculate the objective function values ​​of each individual through AC / DC hybrid power flow calculation; S33. Perform non-dominated sorting on the individuals in the population and calculate the crowding distance of each layer of individuals; S34. Selection operation: Select N from the current population. P Two superior individuals are used to produce offspring; S35, crossover and mutation operations, use the selected dominant individuals to generate offspring population, and calculate the objective function value of the offspring individuals through the power flow program; S36. Merge the offspring population and the parent population into a new population, and perform non-dominated sorting and crowding calculation. S37. Select N from the new population. P Select outstanding individuals to serve as the next generation of the species; S38. Perform conditional judgment: If the maximum number of generations has been reached, proceed to step S39; otherwise, return to step S34 and loop. S39. Calculate the membership weights and select the optimal solution.

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