AC / DC hybrid distribution network planning method, medium and system

Abstract

The present invention discloses a method, medium, and system for planning an AC / DC hybrid distribution network, comprising: establishing a two-layer model for AC / DC hybrid distribution network planning, comprising an objective function of a lower operating layer and an objective function of an upper planning layer; determining constraints for the objective functions of the operating layer and the planning layer; solving the two-layer model under the constraints of the operating layer and the planning layer, outputting the optimal location and capacity of flexibility resources, and planning the AC / DC hybrid distribution network according to the optimal location and capacity of flexibility resources. The present invention improves system economy and the regulation capability of flexibility resources, thereby increasing the flexibility of the AC / DC hybrid distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of AC / DC hybrid distribution network planning, and in particular to an AC / DC hybrid distribution network planning method, medium and system. Background Art

[0002] The topology of AC / DC hybrid distribution network is as follows: Figure 1 As shown in the figure, the DC and AC distribution networks are connected via voltage source converters (VSCs) to form an AC / DC hybrid distribution network. The DC distribution network is connected to various DC loads, photovoltaic systems, and energy storage devices. The AC distribution network is connected to AC loads, wind turbines, and photovoltaic systems. Efficient scheduling of various ubiquitous flexibility resources enables safe and economical operation and efficient decision-making in the AC / DC hybrid distribution network.

[0003] The schematic diagram of the full-factor AC / DC distribution network including distributed power sources such as photovoltaic and wind power, AC / DC loads, and energy storage is as follows: Figure 2 As shown, its characteristic is that power electronic devices are the core link, so the AC / DC hybrid distribution network has a high degree of power electronic characteristics, which can greatly improve the stability, controllability and flexibility of the system; on the other hand, it is compatible with both AC and DC power forms, and while supplying power to the current AC load, it facilitates the access of a high proportion of renewable energy, reduces the complexity of the system, and can improve the efficiency of energy transmission.

[0004] The flexibility of a distribution network should be able to cope with the volatility and random uncertainty of individual or multiple units at different time scales, including the uncertainty of wind turbine and photovoltaic output, as well as the uncertainty of demand from new loads such as electric vehicles. Therefore, the flexibility of a distribution network has multidimensional characteristics: multiple time scales, bidirectional regulation, and multiple distribution spaces. The supply and demand balance of the power system can meet changes at different time scales. On the one hand, the dispatchable flexibility capacity of flexibility resources within the power system changes with the system's timing, and the parameters of flexibility resources also change in real time at different times of system operation. On the other hand, the response time and response speed of flexibility resources in the power system vary, and there is a significant difference between them and the flexibility demand. Therefore, considering flexibility based on the supply and demand balance in distribution network planning is of great significance. Summary of the Invention

[0005] The embodiments of the present invention provide a method, medium and system for planning an AC / DC hybrid distribution network to solve the problem that flexibility resources are not considered in the prior art of AC / DC hybrid distribution network planning.

[0006] In a first aspect, a method for planning an AC / DC hybrid distribution network is provided, comprising:

[0007] A two-layer model for AC / DC hybrid distribution network planning is established, which consists of the objective function of the lower operation layer and the objective function of the upper planning layer.

[0008] Determining the constraints of the objective functions of the operation layer and the planning layer respectively;

[0009] Solving the two-layer model under the constraints of the operation layer and the planning layer respectively, outputting the optimal location and capacity of the flexibility resources, so that the AC / DC hybrid distribution network is planned according to the optimal location and capacity of the flexibility resources;

[0010] Among them, the objective function of the operation layer is:

[0011] minF1=C F +C OM +C ENV +C DR +C G +C loss +C DRE ;

[0012] F1 represents the annual operating cost of the system, C F represents the fuel cost, C OM represents the flexible resource operation and maintenance cost, C ENV represents the cost of pollutant treatment, C DR represents the demand response load compensation cost, C G Indicates the main grid electricity purchase cost, C loss represents the network loss cost, C DRE Indicates the penalty fee for curtailing wind and solar power;

[0013] The objective function of the planning layer includes:

[0014] System comprehensive economic objective function:

[0015] minF2=F1+C inv ;

[0016] F2 represents the annual comprehensive cost of the system, C inv represents the investment cost of flexible resources, C inv,MT represents the unit capacity investment cost of micro gas turbine, C inv,ESS represents the unit capacity investment cost of power system energy storage equipment, P i,MT represents the installed capacity of the micro gas turbine at node i, E i,ESS represents the installed capacity of the power system energy storage equipment at node i, N MT Indicates the number of micro gas turbines to be installed, N ESS represents the number of energy storage devices to be installed in the power system, n represents the economic useful life of the flexible resource, r represents the discount rate; and,

[0017] System comprehensive security objective function:

[0018]

[0019] C SCS represents the comprehensive security index of the system, C LSS,s,t,l N represents the load safety power supply rate index of the lth transmission line failure within the total time τ of scenario s, s Indicates the number of scenes, N l represents the number of transmission lines, H represents the duration of safety analysis; node i is a node in the IEEE39 node, and its value range is 1-39;

[0020] The location and capacity of the flexibility resource are the micro gas turbine installation capacity at node i and the power system energy storage device installation capacity at node i.

[0021] In a second aspect, a computer-readable storage medium is provided, on which computer program instructions are stored; when the computer program instructions are executed by a processor, the AC / DC hybrid distribution network planning method as described in the embodiment of the first aspect is implemented.

[0022] In a third aspect, an AC / DC hybrid distribution network planning system is provided, comprising: a computer-readable storage medium as described in the embodiment of the second aspect above.

[0023] Thus, this embodiment of the present invention establishes a two-tier model for the joint optimization of flexible resource operation and planning by considering the supply and demand relationship of system flexibility. By incorporating the flexibility deficiency rate and comprehensive system security indicators for evaluation, the comprehensive configuration of multiple flexible resources can effectively reduce network losses and the system curtailment rate of wind and solar power, improve system economics and the regulation capacity of flexible resources, thereby enhancing the flexibility of AC / DC hybrid distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0025] Figure 1 It is an AC / DC hybrid distribution network topology;

[0026] Figure 2 It is a schematic diagram of a full-element AC and DC power distribution network;

[0027] Figure 3is a flow chart of a method for planning an AC / DC hybrid distribution network according to an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of the interactive relationship of the double-layer model according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the relationship between grid loss and wind and solar power curtailment rates under different technical solutions. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments derived by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0031] The core of the power system is power and electricity balance. This involves determining the installed capacity and required new capacity for the planned year based on the load demand, ensuring system reliability and ensuring the appropriate utilization hours of thermal power units. Traditional planning focuses solely on power and electricity balance, building new power plants to meet load growth and meet capacity requirements, without considering the need for flexibility. However, with the increasing proportion of renewable energy, system demands extend beyond simple load requirements. The need for flexibility has also become increasingly prominent, far exceeding the flexibility requirements of traditional power systems. Therefore, a dedicated analysis of the balance and matching of flexibility is necessary. Flexibility balance has become crucial and central to the safe and stable operation of the system.

[0032] Based on this, an embodiment of the present invention discloses a method for planning an AC / DC hybrid distribution network. This method considers the balance between supply and demand for flexibility. In this embodiment, microturbines (MTs), energy storage systems (ESSs), and demand response (DR) loads are used as flexibility resources. These three controllable resources all possess excellent flexibility regulation capabilities.

[0033] like Figure 3 As shown, the method includes the following steps:

[0034] Step S1: Establish a two-layer model for AC / DC hybrid distribution network planning consisting of the objective function of the lower operation layer and the objective function of the upper planning layer.

[0035] Specifically, the objective function of the operation layer is expressed as follows:

[0036] (1)minF1=C F +C OM +CENV +C DR +C G +C loss +C DRE .

[0037] Among them, F1 represents the annual operating cost of the system. F represents the fuel cost. C OM Indicates the flexible resource operation and maintenance cost. C ENV C represents the cost of pollutant treatment. DR C represents the demand response load compensation cost. G Indicates the cost of purchasing electricity from the main grid. C loss Represents the network loss cost. C DRE Represents the penalty fee for curtailing wind and solar power.

[0038] Specifically, the objective functions of the planning layer include:

[0039] 1. The comprehensive economic objective function of the system is specifically expressed by the following formula (2):

[0040] (2)minF2=F1+C inv .

[0041] Among them, F2 represents the annual comprehensive cost of the system. inv Represents the investment cost of flexible resources.

[0042] Specifically, C inv The following formula (3) is used for calculation:

[0043] (3)

[0044] Among them, C inv,MT C represents the unit capacity investment cost of micro gas turbine. inv,ESS P represents the unit capacity investment cost of power system energy storage equipment. i,MT represents the installed capacity of the micro gas turbine at node i, E i,ESS N represents the installed capacity of the power system energy storage equipment at node i. MT Indicates the number of micro gas turbines to be installed. N ESS represents the number of energy storage devices to be installed in the power system. n represents the economic useful life of the flexible resource. r represents the discount rate.

[0045] 2. The system comprehensive security objective function is specifically expressed by the following formula (4):

[0046] (4)

[0047] Among them, C SCS Represents the comprehensive security index of the system. C LSS,s,t,lIt represents the load safety power supply rate index of the lth transmission line failure within the total time τ of scenario s. s Indicates the number of scenes. N l represents the number of transmission lines. H represents the duration of the safety analysis. In a specific embodiment, H = 4h.

[0048] The planning layer of this embodiment of the present invention also incorporates the aforementioned system comprehensive security index to assess the load's ability to safely power supply N-1 faults within a total time τ for each scenario in the system security analysis, thereby evaluating the overall system security. This index describes the system's ability to maintain continuous power supply to the system load by forming an island from the internal power supply in the event of a branch failure.

[0049] Step S2: Determine the constraints of the objective functions of the operation layer and the planning layer respectively.

[0050] Specifically, the constraints of the objective function of the operation layer include:

[0051] 1. The demand response load change limit constraint is shown in formula (6):

[0052] (6)

[0053] Among them, P load,t Represents the user load at time t. min Indicates the lower limit of the load removal response coefficient within the response period. max Indicates the upper limit of the load removal response coefficient within the response period. β min Indicates the lower limit of the load response coefficient during the response period. max Indicates the upper limit of the load response coefficient during the response period, P L,cut,t P represents the load removed by the demand response load at time t. L,add,t represents the load increased by the demand response load at time t.

[0054] In a specific embodiment, α min =0,α max =0.5,β min =0,β max =0.5.

[0055] 2. Flexible operation index constraints are shown in formula (7):

[0056] (7)f loss <σ.

[0057] Among them, f loss represents the system flexibility deficiency rate. σ represents the upper limit of the system flexibility deficiency rate.

[0058] Specifically, the system flexibility deficiency rate f lossThe formula is shown in formula (8):

[0059] (8)

[0060] Where ξ represents the weight coefficient. Indicates that the system increases the flexibility deficiency rate. Indicates that the system lowers the flexibility deficiency rate. Represents the sum of the system's upward supply in all scenarios. Represents the sum of the system's downward supply in all scenarios.

[0061] If the system flexibility supply is greater than the flexibility demand, then the flexibility is sufficient; otherwise, the flexibility is insufficient. In order to more intuitively quantify the flexibility supply and demand relationship, the embodiment of the present invention introduces the flexibility deficiency rate as a system flexibility evaluation indicator. Formula (8) unifies the system upward flexibility deficiency rate and the system downward flexibility deficiency rate into the system flexibility deficiency rate. Specifically, the formulas for the system upward flexibility deficiency rate and the system downward flexibility deficiency rate are shown in Formula (9):

[0062] (9)

[0063] Among them, p s represents the probability of occurrence of scenario s. τ represents the total time of occurrence of scenario s. t D,down Represents the sum of downward flexibility requirements in all scenarios. t D,up Represents the sum of upward flexibility requirements in all scenarios. represents the upward flexibility supply of the microturbine at time t. represents the microturbine's downward flexibility supply at time t. Indicates the flexibility supply of power system energy storage equipment at time t. Indicates that the power system energy storage equipment adjusts down the flexibility supply at time t. represents the demand response load upward flexibility supply at time t. represents the demand response load reduction flexibility supply at time t.

[0064] Specifically, the formula for the micro gas turbine upward flexibility supply and the micro gas turbine downward flexibility supply is shown in formula (10):

[0065] (10)

[0066] Among them, R up Indicates the power regulation rate of the micro gas turbine. down Indicates the power rate of the micro gas turbine under regulation. Indicates the maximum output power of the micro gas turbine. Indicates the minimum output power of the micro gas turbine. MT,t represents the output of the micro gas turbine at time t. ΔT represents the time interval.

[0067] Specifically, the formula for the upward flexibility supply of power system energy storage equipment and the downward flexibility supply of power system energy storage equipment are shown in formula (11):

[0068] (11)

[0069] Among them, P dis,max Indicates the maximum discharge power. ch,max Indicates the maximum charging power. E ESS Indicates the rated capacity of the energy storage equipment in the power system. η dis Indicates the discharge efficiency of the power system energy storage equipment. η ch Indicates the charging efficiency of the power system energy storage equipment. SOC max Indicates the maximum state of charge of the power system energy storage device. SOC min Indicates the minimum state of charge of the energy storage device in the power system. SOC t Indicates the state of charge of the power system energy storage equipment at time t.

[0070] Specifically, the formula for demand response load upward flexibility supply and demand response load downward flexibility supply is shown in formula (12):

[0071] (12)

[0072] Among them, F L,cut,t represents the power consumption of the demand response load removed at time t, F L,add,t It represents the power consumption of the demand response load increase at time t.

[0073] 3. Flexible Resource Output Constraints

[0074] Among them, the flexibility resource output constraint includes the output of the power system energy storage equipment and micro gas turbines to quantify the flexibility supply capacity of the AC / DC hybrid distribution network, as shown in formula (13):

[0075] (13)

[0076] Where I is the node set of power system energy storage equipment and micro gas turbines, They represent the upward and downward adjustment of flexibility supply at time t respectively; are the upper and lower limit outputs of the flexibility resource at time t, P i t The actual contribution of flexibility resources at time t, are the ramp-up and ramp-down rates of each flexibility resource, and Δt represents the time interval. Specifically, the constraints of the objective function at the planning layer include:

[0077] 1. Flexible resource capacity constraint is shown in formula (14):

[0078] (14)

[0079] in, represents the maximum installed capacity of the microturbine at node i. represents the maximum installed capacity of the power system energy storage equipment at node i.

[0080] 2. The comprehensive security constraints of the system are shown in formula (15):

[0081] (15)C SCS ≥η.

[0082] Where η represents the comprehensive safety limit of the system.

[0083] 3. AC Regional Flow Constraints

[0084] The power flow constraint is to transform the nonlinear terms in the original DisFlow power flow model in the AC region through second-order cone relaxation to obtain a convex linear power flow constraint, as shown in Equations (16), (17), (18), and (19).

[0085] (16)

[0086] (17)

[0087] (18)

[0088] (19)

[0089] Among them, r ij 、x ij They represent the resistance and reactance between nodes i→j, respectively; u(j) and v(j) represent the branch node sets with node j as the end and the beginning, respectively; They represent the total active and reactive power injected at node j respectively; denote the active and reactive power of the flexibility resource at node j respectively; They represent the active and reactive power flowing through branch i→j at time t respectively; They represent the active and reactive power flowing from the main grid to the distribution grid at time t respectively; Represents branch current and the voltage at node i the square of represents the voltage at node j the square of They represent the conventional load and interruptible load at node j at time t respectively; They represent the normal load and interruptible load at node j at time t respectively.

[0090] 4. DC Regional Flow Constraints

[0091] The DC region power flow equation is similar to that of the AC region after linearization, as shown in Equation (20):

[0092] (20)

[0093] in, They represent the charging and discharging power of the energy storage equipment in the power system at node i at time t respectively; Represents the actual output of distributed photovoltaic power at node j at time t.

[0094] 5. Operation constraints of energy storage equipment in power systems, as shown in equations (21) and (22):

[0095] (twenty one)

[0096] (twenty two)

[0097] in, They represent the maximum charging and discharging power of the energy storage device in the power system at node i at time t respectively; They represent the charging and discharging states of the energy storage equipment in the power system at the node constraint point i at time t, 0 represents charging and 1 represents discharging; E i,t 、 They represent the power storage device capacity and the maximum allowable capacity of the power system at node i at time t respectively; E i,t+1 represents the amount of energy storage equipment in the power system at node i at time t+1; η i,ch ,η i,dis Represent the ESS charging and discharging efficiency, They represent the charging and discharging power of the energy storage equipment in the power system at node i at time t respectively.

[0098] 6. Interaction power constraint with the main grid, as shown in formula (23):

[0099] (twenty three)

[0100] in, They represent the upper and lower limits of the active power injected into substation node i; They represent the upper and lower limits of reactive power injected into substation node i, They represent the active and reactive power flowing from the main network to node i in the distribution network at time t respectively.

[0101] 7. Safety constraints, as shown in formula (24):

[0102] (twenty four)

[0103] Among them, U i,max 、U i,min They represent the upper and lower limits of safe operation of voltage at node i respectively; They represent the active and reactive power transmitted by the AC branch i→j at time t respectively; It represents the active power transmitted by DC branch i→j at time t; Respectively represent the maximum transmission capacity of AC and DC branches i→j; U i represents the voltage at node i.

[0104] 8. The upper and lower limit constraints of the rated power and rated capacity of the power system energy storage equipment are shown in formula (25):

[0105] (25)

[0106] Among them, P ESS,max 、P ESS,min Respectively represent the upper and lower limits of the rated power of the power system energy storage equipment; E ESS,max 、E ESS,min Respectively represent the upper and lower limits of the rated capacity of the power system energy storage equipment; P ESS,n Indicates the power of the power system energy storage equipment, E ESS,n Indicates the rated capacity of the power system energy storage equipment.

[0107] Step S3: Solve the two-layer model under the constraints of the operation layer and the planning layer respectively, output the optimal location and capacity of the flexibility resources, and plan the AC / DC hybrid distribution network according to the optimal location and capacity of the flexibility resources.

[0108] The location and capacity of the flexibility resource are the micro gas turbine installation capacity of node i and the power system energy storage device installation capacity of node i. That is, the method of the embodiment of the present invention ultimately obtains the micro gas turbine installation capacity and power system energy storage device installation capacity planned at a specific node, so that planning can be carried out accordingly.

[0109] Particle swarm optimization (PSO) is an optimization algorithm that uses particles to simulate the predation behavior of birds, continuously updates the position and velocity of particles, and searches for the optimal solution of the target. This algorithm is widely used due to its fast convergence speed and strong search capability. Based on this, the algorithm for solving the two-layer model in the embodiment of the present invention is an improved particle swarm algorithm, which is an improved particle swarm algorithm that uses inertia weight factors and learning factors for adjustment, which can further optimize the particle search capability and improve the convergence of the operation. The improved particle swarm algorithm used in the embodiment of the present invention is the improved particle swarm algorithm disclosed in the following literature: Xu Yan, Zhang Hui, Sun Yizhou. Active distribution network fault recovery strategy based on variant particle swarm algorithm [J]. Electric Power Automation Equipment, 2021, 41(12): 45-53.

[0110] In the process of solving the problem using the improved particle swarm algorithm, the location and capacity of the flexible resources are used as the location and velocity of the particles in the improved particle swarm algorithm.

[0111] The specific process of this step is as follows:

[0112] (1) Initialize the location and capacity of flexibility resources.

[0113] (2) Taking the output of flexible resources as the decision variable, under the constraints of the objective function of the operation layer, the objective function of the operation layer is solved to obtain the annual operating cost of the system.

[0114] The k-means clustering algorithm was used to process historical data on wind and solar loads. The random characteristics of wind and solar loads were deterministically described using multiple typical scenarios across different seasons and climates. This provided the initial parameters for the case analysis, including AC and DC distribution network voltage levels, total active load, total reactive load, VSC capacity, and data related to distributed photovoltaic and distributed wind power. This data was fed into the algorithm for the corresponding solution, resulting in the system's annual operating cost and flexible resource output.

[0115] (3) The annual operating cost of the system is returned to the planning layer. The location and capacity of the flexibility resources are used as decision variables. Under the constraints of the objective function of the planning layer, the objective function of the planning layer is solved to obtain the updated location and capacity of the flexibility resources.

[0116] (4) Repeat the above steps (2) and (3) until the convergence condition is met, and output the optimal location and capacity of the flexibility resource.

[0117] The convergence condition is that the difference between the previous and next iteration values ​​reaches the iteration accuracy or the number of iterations reaches the maximum number. The number of iterations can be determined based on experience.

[0118] The decision variable of the operation layer is the output of the flexibility resource. The objective function is optimized by optimizing the output of the flexibility resource.

[0119] In the above calculation process, the operation layer uses the system flexibility deficiency rate as the flexible operation evaluation index and the optimal annual comprehensive operation cost of the system as the objective function to optimize the output of flexible resources. The planning layer also uses the optimal annual comprehensive cost of the system as the goal to conduct a comprehensive planning of the location and capacity of flexible resources. The operation layer feeds back the annual operating cost obtained by optimizing the output of flexible resources in each period to the planning layer. The planning layer and the operation layer interact with each other and jointly decide on the optimal configuration plan of flexible resources. The interactive relationship is as follows: Figure 4 shown.

[0120] In order to verify the effectiveness of the three flexible resources of the micro gas turbine, power system energy storage equipment and demand response load in improving system performance in the technical solution of the embodiment of the present invention, the following three schemes are set for verification: 1) System flexibility is not considered, that is, no flexible resources are involved, and σ and η constraints are not taken into account; 2) Flexible resources are only a two-layer model of micro gas turbine and demand response load, and σ and η constraints are not taken into account; 3) Flexible resources are a two-layer model of micro gas turbine, power system energy storage equipment and demand response load, and σ and η constraints are not taken into account. The above schemes are solved to obtain the relationship between the amount of system wind and solar power curtailment and the amount of network loss under the three schemes. Figure 5 .

[0121] Depend on Figure 5 As can be seen, Scheme 2) reduces grid losses by 20.9% and wind and solar curtailment by 8.8% compared to Scheme 1. Scheme 3) further reduces grid losses by 23.2% and wind and solar curtailment by 5.9% compared to Scheme 2. These results demonstrate that deploying flexible resources in distribution networks with a high proportion of distributed renewable energy (DRE) can effectively improve DRE utilization and reduce grid losses. Furthermore, when multiple flexible resources are comprehensively utilized, the distribution network can minimize grid losses and wind and solar curtailment, improving system performance.

[0122] An embodiment of the present invention further discloses a computer-readable storage medium having computer program instructions stored thereon; when the computer program instructions are executed by a processor, the AC / DC hybrid distribution network planning method as described in the above embodiment is implemented.

[0123] An embodiment of the present invention further discloses an AC / DC hybrid distribution network planning system, comprising: a computer-readable storage medium as described in the above embodiment.

[0124] In summary, the present invention establishes a two-tier model for the joint optimization of flexible resource operation and planning by considering the supply and demand relationship of system flexibility. This model incorporates the flexibility deficiency rate and comprehensive system security indicators for evaluation. By comprehensively configuring multiple flexible resources, network losses and system curtailment rates can be effectively reduced, improving system economics and the regulation capacity of flexible resources, thereby enhancing the flexibility of hybrid AC / DC distribution networks.

[0125] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for planning an AC / DC hybrid distribution network, characterized in that: include: A two-layer model for AC / DC hybrid distribution network planning is established, which consists of the objective function of the lower operation layer and the objective function of the upper planning layer. Determining the constraints of the objective functions of the operation layer and the planning layer respectively; Solving the two-layer model under the constraints of the operation layer and the planning layer respectively, outputting the optimal location and capacity of the flexibility resources, so that the AC / DC hybrid distribution network is planned according to the optimal location and capacity of the flexibility resources; Among them, the objective function of the operation layer is: ； Indicates the annual operating cost of the system, represents the fuel cost, represents the flexible resource operation and maintenance cost, represents the cost of pollutant treatment, represents the demand response load compensation cost, Indicates the main grid electricity purchase cost, represents the network loss cost, Indicates the penalty fee for curtailing wind and solar power; The objective function of the planning layer includes: System comprehensive economic objective function: ； Indicates the annual comprehensive cost of the system, represents the investment cost of flexible resources, , represents the unit capacity investment cost of micro gas turbine, represents the unit capacity investment cost of power system energy storage equipment, Representation node i installed capacity of microturbines, Representation node i The installed capacity of energy storage equipment in the power system, Indicates the number of micro gas turbines to be installed, Indicates the number of energy storage devices to be installed in the power system, n Indicates the economic useful life of flexible resources, r represents the discount rate; and, System comprehensive security objective function: ； represents the comprehensive security index of the system, Representation scene s Total time of occurrence τ Neidi l load safety power supply rate index for transmission line failures, Indicates the number of scenes, represents the number of transmission lines, H Indicates the duration of security analysis; The location and capacity of the flexibility resource are nodes i Microturbine installation capacity and nodes i The constraints of the objective function of the operation layer include: Demand response load change limit constraints: ,in, express t User load at any moment, Indicates the lower limit of the load response coefficient removed during the response period. Indicates the upper limit of the load response coefficient removed during the response period. Indicates the lower limit of the load response coefficient during the response period. Indicates the upper limit of the load response coefficient within the response period. express t The load removed by demand response load at any moment, express t The load of moment-to-moment demand response load increase; Flexible operation indicator constraints: ,in, Indicates the system flexibility deficiency rate, Indicates the upper limit of the system flexibility deficiency rate; The output constraints of flexible resources include the output of power system energy storage equipment and micro gas turbines, and the constraints on the flexibility supply capacity of AC / DC hybrid distribution networks: ； Where I is the node set of power system energy storage equipment and micro gas turbines, 、 They represent the upward and downward adjustment of flexibility supply at time t respectively; 、 are the upper and lower limit outputs of the flexibility resource at time t, The actual contribution of flexibility resources at time t, 、 are the climbing and sliding rates of each flexibility resource, Indicates a time interval.

2. The AC / DC hybrid distribution network planning method according to claim 1, characterized in that: The formula for the system flexibility deficiency rate is: ； in, represents the weight coefficient, Indicates that the system increases the flexibility deficiency rate, Indicates that the system lowers the flexibility deficiency rate, Represents the sum of the system's upward supply in all scenarios, Represents the sum of the system's downward supply in all scenarios.

3. The AC / DC hybrid distribution network planning method according to claim 2, characterized in that: The formula for calculating the system's upward flexibility deficiency rate and the system's downward flexibility deficiency rate is: ； in, Representation scene s The probability of occurrence, Representation scene s The total time it occurred, represents the sum of downward flexibility requirements in all scenarios, represents the sum of upward flexibility requirements in all scenarios, express t Microturbines provide flexibility at all times, express t Microturbines provide flexibility when turning down supplies, express t The power system energy storage equipment is constantly increasing its flexibility supply, express t The power system energy storage equipment reduces the flexibility supply at all times, express t Moment-to-moment demand response load adjustment flexibility supply, express t Moment-to-moment demand response load reduction flexibility supply.

4. The AC / DC hybrid distribution network planning method according to claim 3, characterized in that: The formulas for the micro-turbine upward flexibility supply and the micro-turbine downward flexibility supply include: ； in, represents the power regulation rate on the micro gas turbine, represents the power rate of the micro gas turbine under regulation, represents the maximum output power of the micro gas turbine, represents the minimum output power of the micro gas turbine, express t The output of the micro gas turbine at all times, Indicates a time interval; The formula for the upward flexibility supply of the power system energy storage device and the downward flexibility supply of the power system energy storage device include: ； in, Indicates the maximum discharge power, Indicates the maximum charging power. Indicates the rated capacity of the energy storage equipment in the power system. represents the discharge efficiency of the power system energy storage equipment, represents the charging efficiency of the power system energy storage equipment, Indicates the maximum state of charge of the power system energy storage equipment, Indicates the minimum state of charge of the power system energy storage equipment, express t The state of charge of energy storage equipment in the power system at all times; The formulas for demand response load upward flexibility supply and demand response load downward flexibility supply include: ； in, express t The power consumption of the momentary demand response load removal, express t The power consumption of the load is increased at any time.

5. The AC / DC hybrid distribution network planning method according to claim 1, characterized in that: The constraints of the objective function of the planning layer include: Flexible resource capacity constraints: ,in, Representation node i The maximum installed capacity of the microturbine, Representation node i The maximum installed capacity of power system energy storage equipment; System comprehensive security constraints: ,in, Indicates the comprehensive safety limit of the system; AC area power flow constraints: ； ； ； ； in, 、 Respectively represent the resistance and reactance between nodes i→j; 、 They represent the branch node sets with node j as the end and the beginning respectively; 、 They represent the total active and reactive power injected at node j respectively; 、 denote the active and reactive power of the flexibility resource at node j respectively; 、 They represent the active and reactive power flowing through branch i→j at time t respectively; 、 They represent the active and reactive power flowing from the main grid to the distribution grid at time t respectively; 、 Represents branch current and the voltage at node i the square of represents the voltage at node j the square of 、 They represent the conventional load and interruptible load at node j at time t respectively; 、 They represent the conventional load and interruptible load at node j at time t respectively; DC regional power flow constraints: ； in, 、 They represent the charging and discharging power of the energy storage equipment in the power system at node i at time t respectively; represents the actual output of distributed photovoltaic power at node j at time t; Operational constraints of power system energy storage equipment: ； ； in, 、 They represent the maximum charging and discharging power of the energy storage device in the power system at node i at time t respectively; 、 They represent the charging and discharging states of the power system energy storage equipment at the node constraint point i at time t, 0 represents charging and 1 represents discharging; 、 They represent the power storage device capacity and the maximum allowable capacity at node i in the power system at time t respectively; represents the amount of energy storage equipment in the power system at node i at time t+1; 、 Respectively represent the charging and discharging efficiency of the power system energy storage equipment, 、 They represent the charging and discharging power of the energy storage device in the power system at node i at time t respectively; Interacting with the main network to control power constraints: ； in, 、 They represent the upper and lower limits of the active power injected into substation node i; 、 They represent the upper and lower limits of reactive power injected into substation node i, 、 They represent the active and reactive power of node i flowing from the main network to the distribution network at time t respectively; Security constraints: ； in, 、 They represent the upper and lower limits of safe operation of voltage at node i respectively; 、 They represent the active and reactive power transmitted by the AC branch i→j at time t respectively; It represents the active power transmitted by DC branch i→j at time t; 、 Respectively represent the maximum transmission capacity of AC and DC branches i→j; represents the voltage at node i; Upper and lower limits of rated power and rated capacity of power system energy storage equipment: ； in, 、 Respectively represent the upper and lower limits of the rated power of the power system energy storage equipment; 、 Respectively represent the upper and lower limits of the rated capacity of the power system energy storage equipment; Indicates the power of the power system energy storage equipment, Indicates the rated capacity of the power system energy storage equipment.

6. The AC / DC hybrid distribution network planning method according to claim 1, characterized in that: The algorithm for solving the two-layer model is an improved particle swarm algorithm. During the solution process using the improved particle swarm algorithm, the position and capacity of the flexibility resource are used as the position and speed of the particles of the improved particle swarm algorithm.

7. The AC / DC hybrid distribution network planning method according to claim 6, characterized in that: The step of solving the two-layer model comprises: Initialize the location and capacity of flexibility resources; Taking the output of flexible resources as the decision variable, under the constraints of the objective function of the operation layer, the objective function of the operation layer is solved to obtain the annual operation cost of the system; Return the annual system operating cost to the planning layer, use the location and capacity of the flexible resources as decision variables, and solve the planning layer's objective function under the constraints of the planning layer's objective function to obtain the updated location and capacity of the flexible resources; Repeat the above steps of solving the objective function of the operation layer and the steps of solving the objective function of the planning layer until the convergence conditions are met and the optimal location and capacity of the flexible resources are output.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, the AC / DC hybrid distribution network planning method according to any one of claims 1 to 7 is implemented.

9. An AC / DC hybrid distribution network planning system, characterized in that: include: The computer-readable storage medium of claim 8.

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