New energy power system reliability evaluation method and system under multi-class short circuit ratio constraint

Abstract

The invention discloses a new energy power system reliability evaluation method and system under multi-class short-circuit ratio constraint. The method comprises the following steps: determining a short-circuit ratio model of each grid-connected bus and a short-circuit ratio model of each sending-end bus; power flow optimization models are established for different random faults, the power flow optimization models take the minimum system comprehensive cost under the different random faults as an objective function, and the power flow optimization models further comprise combined constraint conditions after the random faults and short-circuit ratio constraint conditions meeting the combined constraint conditions after the random faults; solving the power flow optimization model to obtain power flow control schemes under different random faults; the weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as a reliability evaluation index of the new energy power system, the direct current bearing strength and the new energy bearing strength are considered in the system reliability evaluation process, and the optimal power flow and the system stability are considered.

Description

Technical Field

[0001] The present invention relates to the technical field of power system reliability assessment, and in particular to a method and system for assessing the reliability of a power grid with a high proportion of renewable energy sending end by considering multiple types of short-circuit ratio constraints. Background Art

[0002] At present, a high proportion of renewable energy is concentrated in multiple DC sending-end power grids. The inertia and damping characteristics of local power grids show a downward trend, and the dynamic reactive power support capacity is insufficient. Its DC bearing strength and renewable energy bearing strength are insufficient, which easily leads to transient overvoltage and other problems. At the same time, the high proportion of renewable energy increases the difficulty of power grid power flow control. The power flow of the sending-end power grid flips day and night, and is blocked at each section. Considering the reverse distribution pattern of load and renewable energy, the risk of orderly power consumption after the failure of important equipment increases sharply. Some weak links in the sending-end power grid have become hard constraints that restrict load supply and new energy delivery, seriously affecting the reliability of the power grid. Therefore, it is urgent to realize the reliability assessment of the sending-end power grid with a high proportion of renewable energy.

[0003] In DL / T 2041-2019 "Guidelines for Assessment of the Carrying Capacity of Distributed Generation Access to the Grid", the grid carrying capacity level is divided based on the bus short-circuit current being less than the allowable short-circuit current limit under the maximum operating mode of the system. Therefore, the grid carrying capacity only considers the carrying capacity for renewable energy, and does not consider the carrying capacity of direct current. Moreover, the grid carrying capacity considers the situation under the maximum operating mode of the system, and does not involve the impact of the renewable energy carrying capacity and direct current carrying capacity caused by random failures of the grid and units. Therefore, based on the results of the grid carrying capacity division based on the existing technology, under the combined influence of the output volatility of renewable energy, the grid structure and the maneuverability of the unit investment and withdrawal, it will be impossible to take into account both the flow optimization and the system stability. This leads to the existing reliability assessment method of the high-proportion renewable energy sending-end power grid. After the output of renewable energy is blocked, it is difficult to balance the power and generate load limits. The flow optimization results will also be unreliable because the system cannot operate safely and stably. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a reliability assessment method and system for a new energy power system under multiple types of short-circuit ratio constraints. In the reliability assessment process of a power grid with a high proportion of new energy sending end, the DC carrying strength and the new energy carrying strength are considered, thereby ensuring that the system stability constraints are taken into account in the optimal power flow.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a reliability assessment method for a renewable energy power system under multiple short-circuit ratio constraints, which is applicable to a power grid with a high proportion of renewable energy, and includes:

[0007] Obtain the short-circuit capacity, active power and active power conversion factor of each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus;

[0008] A power flow optimization model is established for different random faults. The power flow optimization model takes the minimum comprehensive system cost under different random faults as the objective function, where the comprehensive system cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraint conditions after the random fault and the short-circuit ratio constraint conditions that meet the joint constraint conditions after the random fault; the power flow optimization model is solved to obtain the power flow control scheme under different random faults;

[0009] The weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as the reliability evaluation index of the new energy power system.

[0010] Preferably, after different random faults, based on the active power conversion factor, the short-circuit ratio model of the grid-connected bus i at time t satisfies the following relationship:

[0011]

[0012] Where, MRSCR S,i,t is the short-circuit ratio of the grid-connected bus i at time t, S ac,i,t is the short-circuit capacity of the grid-connected bus i at time t after various random faults, P RE,i,t , P RE,j,t are the active power injected into the grid-connected buses i and j by the new energy station set RE at time t, is the active power conversion factor between grid-connected buses i and j at time t after various random faults, where: is the mutual impedance between grid-connected buses i and j at time t after various random faults, is the self-impedance of grid-connected bus i at time t after various types of random faults, and n is the total number of grid-connected buses;

[0013] After different random faults, the short-circuit ratio model of the sending-end bus i at time t satisfies the following relationship:

[0014]

[0015] In the formula, SCR DC,i,t is the short-circuit ratio of the sending-end busbar i at time t, Q ac,i,t is the reactive power compensation capacity of the AC side of the sending bus i at time t; DC,i,tis the DC power injected into the sending-end bus i by the DC converter station set DC at time t.

[0016] Preferably, under different random faults, the load is reduced, the active power injected into the grid-connected bus is reduced, and the DC power injected into the sending-end bus is reduced. t Under this condition, the objective function of the power flow optimization model satisfies the following relationship:

[0017]

[0018] In the formula, f(x t ) is a single random fault x t The overall system cost under is the load reduction of node i at time t, where i∈L, L is the load set, is the reduction of DC power injected into the sending end bus i by the DC converter station set DC at time t, where i∈DC, is the reduction in active power injected into the grid-connected bus i by the new energy station set RE at time t, where i∈RE, α, β, and γ are the unit cost of load reduction after random faults, the unit cost of DC power reduction, and the unit cost of active power reduction, respectively.

[0019] Preferably, the joint constraints after random failure are as follows:

[0020] 1) The node power balance constraint after random failure satisfies the following relationship:

[0021]

[0022] Where P G,i,t is the active power injected into node i by the conventional generator set G at time t, P i′i,t is the active power flowing from node i′ to node i at time t, P ii′,t is the active power flowing from node i to node i′ at time t, I i is the set of nodes directly connected to node i, is the reduction in active power injected into the grid-connected bus i by the new energy station set RE at time t, P L,i,t is the load power of node i at time t, is the load reduction of node i at time t, is the reduction of DC power injected into the sending-end bus i by the DC converter station set DC at time t; i∈I, I is the node set, t∈T, T is the evaluation period;

[0023] 2) The line power flow constraint condition after random faults satisfies the following relationship:

[0024] P ii′,t -B ii′ (δ i,t -δ i′,t )+M(1-x ii′,t )≥0 (7)

[0025] P ii′,t -B ii′ (δ i,t -δ i′,t )-M(1-x ii′,t )≥0 (8)

[0026] Where P ii′,t is the active power flowing from node i to node i′ at time t, B ii′ is the mutual admittance between node i and node i′, δ i,t and δ i′,t are the voltage phase angles between nodes i and i′ at time t, M is an arbitrarily large but non-infinite positive number, x ii′,t is the state quantity of the line between node i and node i′ at time t. ii′,t If it is 0, it means the line is out of service. ii′,t If it is 0, it means the line is in operation, i,i′∈I, I is the node set, t∈T, T is the evaluation period;

[0027] Combine x through M ii′,t To characterize the direction of line power flow after random faults;

[0028] 3) The line transmission capacity constraint after random failure satisfies the following relationship:

[0029] -P ii′,max x ii′,t ≤P ii′,t ≤P ii′,max x ii′,t (9)

[0030] Where P ii′,max is the maximum transmission power of the line between node i and node i′, i,i′∈I, I is the node set, t∈T, T is the evaluation period;

[0031] 4) The unit output constraint condition after random failure satisfies the following relationship:

[0032] P G,i,min x G,i,t ≤P G,i,t ≤P G,i,max x G,i,t (9)

[0033] Where P G,i,min and PG,i,max are the lower and upper limits of the active power injected into node i by the conventional generator set G at time t, respectively, G,i,t is the state quantity of the conventional unit set G connected to node i at time t. G,i,t If it is 0, it means that the conventional units are shut down collectively. G,i,t If it is 0, it means that the conventional unit set is in operation, i∈I, I is the node set, t∈T, T is the evaluation period;

[0034] 5) The load reduction constraint after random faults satisfies the following relationship:

[0035]

[0036] 6) The DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship:

[0037]

[0038] 7) The active power reduction constraint condition injected into the grid-connected bus after a random fault satisfies the following relationship:

[0039]

[0040] In the formula, x RE,i,t is the state quantity of the new energy station set RE connected to node i at time t. RE,i,t If it is 0, it means that the new energy stations are collectively shut down. RE,i,t If it is 0, it means that the new energy station cluster is in operation;

[0041] 8) The node phase angle constraint after random failure satisfies the following relationship:

[0042]

[0043] In the formula, δ i,t is the voltage phase angle of node i at time t, is the maximum voltage phase angle of node i at time t, is the voltage phase angle reference value of node i at time t.

[0044] Preferably, the short-circuit ratio constraint condition that satisfies the joint constraint condition after a random fault includes: a grid-connected bus short-circuit ratio constraint condition that satisfies the active power reduction constraint condition injected into the grid-connected bus after a random fault, and a sending-end bus short-circuit ratio constraint condition that satisfies the DC power reduction constraint condition injected into the sending-end bus after a random fault;

[0045] The details are as follows:

[0046] 1) When the active power reduction constraint condition injected into the grid-connected bus after a random fault is met, based on the short-circuit ratio model of the grid-connected bus i at time t, according to the active power reduction amount injected into the grid-connected bus after a random fault, the grid-connected bus short-circuit ratio constraint condition is established to meet the following relationship:

[0047] MRSCR′ S,i,t ≥MRSCR (16)

[0048] Where, MRSCR′ S,i,t The short-circuit ratio of the grid-connected bus i at time t when the active power injected into the grid-connected bus is reduced after a random fault, and MRSCR is the short-circuit ratio threshold of the grid-connected bus;

[0049] Among them, the short-circuit ratio of the grid-connected bus i at time t when the active power reduction constraint condition injected into the grid-connected bus after a random fault is met satisfies the following relationship:

[0050]

[0051] 2) When the DC power reduction constraint condition injected into the sending-end bus after a random fault is met, based on the short-circuit ratio model of the sending-end bus i at time t, according to the DC power reduction amount injected into the sending-end bus after a random fault, the sending-end bus short-circuit ratio constraint condition is established to meet the following relationship:

[0052] SCR′ DC,i,i ≥SCR (18)

[0053] In the formula, SCR′ DC,i,i is the short-circuit ratio of the sending-end bus i at time t that satisfies the DC power reduction constraint condition injected into the sending-end bus after a random fault, and SCR is the short-circuit ratio threshold of the sending-end bus;

[0054] Among them, the short-circuit ratio of the sending-end bus i at time t that meets the DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship:

[0055]

[0056] The grid-connected bus short-circuit ratio threshold is 2, and the sending-end bus short-circuit ratio threshold is 3.

[0057] Preferably, the weighted sum of the probability of occurrence of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as the reliability evaluation index of the new energy power system, including:

[0058] Based on the fault probability distribution of the renewable energy power system, the Monte Carlo sampling method is used to generate different random faults;

[0059] Single random failure xt Corresponding to a probability of occurrence Single random failure x t Corresponding power flow control solution and system comprehensive cost f(x t ); Based on a single random failure x t The probability of occurrence And the system comprehensive cost f(x t ), the reliability evaluation index of the new energy power system satisfies the following relationship:

[0060]

[0061] In the formula, F is the reliability evaluation index of the new energy power system, T is the total number of random failures, A single random fault x t The probability of occurrence, f(x t ) is a single random fault x t The overall system cost;

[0062] The smaller the reliability assessment index of the new energy power system is, the higher the reliability of the new energy power system is.

[0063] Preferably, based on the reliability evaluation index of the new energy power system, the reliability of multiple different types of distributed new energy power systems is compared, as follows:

[0064] The differences between different types of distributed renewable energy power systems include: different topological structures of distributed renewable energy power systems, different locations of grid-connected busbars for the collection of renewable energy stations, different locations of sending-end busbars for the collection of DC converter stations, different proportions of renewable energy connected to the system, and different loads connected to the system; among the reliability assessment indicators of various types of distributed renewable energy power systems, the distributed renewable energy power system corresponding to the minimum value of the reliability assessment indicator has the highest reliability.

[0065] Preferably, based on the reliability evaluation index of the new energy power system, the reliability of the new energy power system network before and after the adjustment of the planning scheme and the construction scheme is compared, as follows:

[0066] Before adjusting the planning and construction plans of the new energy power system network, determine the reliability assessment index of the system before adjustment; after adjusting the planning and construction plans of the new energy power system network, determine the reliability assessment index of the system after adjustment; if the reliability assessment index after adjustment is less than the reliability assessment index before adjustment, then it is determined that the reliability of the new energy power system network after the adjustment of the planning and construction plans is high.

[0067] Preferably, when comparing the reliability of multiple different types of distributed new energy power systems or comparing the reliability of the new energy power system network before and after the adjustment of the planning scheme and the construction scheme, in the iterative solution process, the iteration convergence is determined when the following relationship is satisfied:

[0068]

[0069] Where Var(F) is the variance of the reliability assessment index F of the new energy power system, and β is the variance coefficient of the reliability assessment index F of the new energy power system.

[0070] The present invention also proposes a reliability assessment system for a new energy power system under multiple types of short-circuit ratio constraints, which is suitable for a power grid with a high proportion of new energy at the sending end, including:

[0071] The short-circuit ratio model building module is used to obtain the short-circuit capacity, active power and active power conversion factor between each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus;

[0072] The power flow optimization module is used to establish a power flow optimization model for different random faults. The power flow optimization model takes the minimum system comprehensive cost under different random faults as the objective function, where the system comprehensive cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraint conditions after the random fault and the short-circuit ratio constraint conditions that meet the joint constraint conditions after the random fault; solve the power flow optimization model to obtain the power flow control scheme under different random faults;

[0073] The index generation module is used to take the weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault as the reliability evaluation index of the new energy power system.

[0074] The present invention is also a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0075] The present invention is also a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0076] The beneficial effects of the present invention are that, compared with the prior art, at least the method proposed in the present invention improves the traditional reliability assessment method for a high-proportion renewable energy sending-end power grid, and the proposed method takes into account the influence of the bus short-circuit ratio constraint of the DC sending-end converter station and the short-circuit ratio constraint of multiple renewable energy stations, and converts the reliability assessment method for a high-proportion renewable energy sending-end power grid considering multiple types of short-circuit ratio constraints into a linear programming problem, and can realize the reliability assessment of a high-proportion renewable energy sending-end power grid under a single random fault without using an iterative method. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a specific flow chart of the reliability assessment method for a high-proportion renewable energy sending-end power grid established by the present invention taking into account multiple types of short-circuit ratio constraints. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0079] The present invention proposes a reliability assessment method for a renewable energy power system under multiple short-circuit ratio constraints, which is applicable to a power grid with a high proportion of renewable energy. The renewable energy power grid includes multiple grid-connected buses connected to renewable energy stations and multiple sending-end buses connected to DC converter stations, such as Figure 1 As shown, including:

[0080] Step 1: Obtain the short-circuit capacity, active power and active power conversion factor between each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus.

[0081] Specifically, based on the fault probability distribution of the renewable energy power system, the Monte Carlo sampling method is used to generate different random faults, and the failure rate of each device in the renewable energy power system under each random fault is determined; in the embodiment, based on the failure rate of each component of the renewable energy sending-end power grid with a high proportion, the Monte Carlo module of the Matlab software is used to realize the generation of 10,000 random fault samplings of the renewable energy sending-end power grid with a high proportion, and determine the components and corresponding failure rates of each random fault in the renewable energy sending-end power grid with a high proportion; the basic parameters of the renewable energy sending-end power grid with a high proportion are established in the form of pre-entry into the PSD-BPA software, based on the single fault form obtained by sampling; different random faults are constituted by multiple different single fault forms.

[0082] Specifically, after different random faults, based on the active power conversion factor, the short-circuit ratio model of the grid-connected bus i at time t satisfies the following relationship:

[0083]

[0084] Where, MRSCR S,i,t is the short-circuit ratio of the grid-connected bus i at time t, S ac,i,t is the short-circuit capacity of the grid-connected bus i at time t after various random faults, P RE,i,t , P RE,j,t are the active power injected into the grid-connected buses i and j by the new energy station set RE at time t, is the active power conversion factor between grid-connected buses i and j at time t after various random faults, where: is the mutual impedance between grid-connected buses i and j at time t after various random faults, is the self-impedance of grid-connected bus i at time t after various types of random faults, and n is the total number of grid-connected buses;

[0085] Specifically, after different random faults, the short-circuit ratio model of the sending-end bus i at time t satisfies the following relationship:

[0086]

[0087] In the formula, SCR DC,i,t is the short-circuit ratio of the sending-end busbar i at time t, Q ac,i,t is the reactive power compensation capacity of the AC side of the sending bus i at time t; DC,i,t The DC power injected into the sending-end bus i by the DC converter station set DC at time t;

[0088] In the present invention, the active power conversion factor between each grid-connected bus is determined based on the mutual impedance and self-impedance of the grid-connected bus, and will not be affected by the active power reduction injected into the grid-connected bus by the new energy site and the DC power reduction injected into the sending-end bus by the DC converter station. Therefore, after each random fault, the mutual impedance and self-impedance of the grid-connected bus are recalculated to regenerate a new active power conversion factor, thereby determining the short-circuit ratios of different grid-connected buses, and performing tighter constraints in the solution space.

[0089] In the present invention, the short-circuit ratio of the sending-end busbar is used to characterize the DC carrying strength of the high-proportion new energy sending-end power grid, and the short-circuit ratio of each grid-connected busbar is used to characterize the new energy carrying strength of the high-proportion new energy sending-end power grid, and the DC carrying strength and the new energy carrying strength are not affected by DC power reduction and active power reduction. In addition, the DC carrying strength and the new energy carrying strength are not only the operating mode of the system at the maximum, but also the operating mode after random failures, so the DC carrying strength and the new energy carrying strength are more stringent than the normal operating mode of the system.

[0090] Under the same AC system grid and conventional unit startup mode, changes in the output of the new energy station injected into the grid-connected bus of the new energy station and changes in the DC power transmission power injected into the sending-end bus of the DC converter station will not cause changes in the active power conversion factor between the grid-connected buses of the new energy station. Therefore, after each random fault, based on the short-circuit ratio model of the grid-connected bus of the new energy station, the short-circuit capacity provided by the AC system to the grid-connected bus of the new energy station and the output of the new energy station injected into the grid-connected bus of the new energy station are used to determine the active power conversion factor between the grid-connected buses of the new energy station; also based on the sending-end bus of the DC converter station The short-circuit ratio model is used to determine the reactive compensation amount on the AC side of the sending-end bus of the DC converter station by utilizing the short-circuit capacity provided by the AC system to the grid-connected bus of the new energy station and the DC power injected into the sending-end bus of the DC converter station. The short-circuit ratio, short-circuit capacity, reactive compensation amount on the AC side of the sending-end bus of the DC converter station and the active power conversion factor between the grid-connected buses of the new energy station under various fault modes are used as known constants in the reliability assessment of the new energy power system, which is convenient for establishing the short-circuit ratio constraints on the grid-connected bus of the new energy station and the DC converter station when considering the DC power reduction and the obstruction of the new energy output.

[0091] Step 2, establish a power flow optimization model for different random faults. The power flow optimization model takes the minimum comprehensive system cost under different random faults as the objective function, where the comprehensive system cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraints after the random fault and the short-circuit ratio constraints that meet the joint constraints after the random fault; solve the power flow optimization model to obtain the power flow control scheme under different random faults.

[0092] Specifically, under different random faults, the load is reduced, the active power injected into the grid-connected bus is reduced, and the DC power injected into the sending-end bus is reduced to achieve power flow optimization control; single random fault x t Under this condition, the objective function of the power flow optimization model satisfies the following relationship:

[0093]

[0094] In the formula, f(x t ) is a single random fault x t The overall system cost under is the load reduction of node i at time t, where i∈L, L is the load set, is the reduction of DC power injected into the sending end bus i by the DC converter station set DC at time t, where i∈DC, is the reduction of active power injected into the grid-connected bus i by the set of new energy stations RE at time t, where i∈RE, α, β, and γ are the unit cost of load reduction after random faults, the unit cost of DC power reduction, and the unit cost of active power reduction, respectively;

[0095] In the embodiment, α is the electricity price for large users at the DC sending end, β is the electricity price for large users at the DC receiving end, and γ is the grid-connected electricity price for new energy.

[0096] The power flow optimization model also includes joint constraints after random faults, as follows:

[0097] 1) The node power balance constraint after random failure satisfies the following relationship:

[0098]

[0099] Where P G,i,t is the active power injected into node i by the conventional generator set G at time t, P i′i,t is the active power flowing from node i′ to node i at time t, P ii′,t is the active power flowing from node i to node i′ at time t, I i is the set of nodes directly connected to node i, is the reduction in active power injected into the grid-connected bus i by the new energy station set RE at time t, P L,i,t is the load power of node i at time t, is the load reduction of node i at time t, is the reduction of DC power injected into the sending-end bus i by the DC converter station set DC at time t; i∈I, I is the node set, t∈T, T is the evaluation period;

[0100] 2) The line power flow constraint condition after random faults satisfies the following relationship:

[0101] P ii′,t -B ii′ (δ i,t -δ i′,t )+M(1-x ii′,t )≥0 (7)

[0102] Pii′,t -B ii′ (δ i,t -δ i′,t )-M(1-x ii′,t )≥0 (8)

[0103] Where P ii′,t is the active power flowing from node i to node i′ at time t, B ii′ is the mutual admittance between node i and node i′, δ i,t and δ i′,t are the voltage phase angles between nodes i and i′ at time t, M is an arbitrarily large but non-infinite positive number, x ii′,t is the state quantity of the line between node i and node i′ at time t. ii′,t If it is 0, it means the line is out of service. ii′,t If it is 0, it means the line is in operation, i,i′∈I, I is the node set, t∈T, T is the evaluation period;

[0104] Combine x through M ii′,t To characterize the direction of line power flow after random faults;

[0105] 3) The line transmission capacity constraint after random failure satisfies the following relationship:

[0106] -P ii′,max x ii′,t ≤P ii′,t ≤P ii′,max x ii′,t (9)

[0107] Where P ii′,max is the maximum transmission power of the line between node i and node i′, i,i′∈I, I is the node set, t∈T, T is the evaluation period;

[0108] 4) The unit output constraint condition after random failure satisfies the following relationship:

[0109] P G,i,min x G,i,t ≤P G,i,t ≤P G,i,max x G,i,t (9)

[0110] Where P G,i,min and P G,i,max are the lower and upper limits of the active power injected into node i by the conventional generator set G at time t, respectively, G,i,t is the state quantity of the conventional unit set G connected to node i at time t. G,i,t If it is 0, it means that the conventional units are shut down collectively. G,i,tIf it is 0, it means that the conventional unit set is in operation, i∈I, I is the node set, t∈T, T is the evaluation period;

[0111] 5) The load reduction constraint after random faults satisfies the following relationship:

[0112]

[0113] 6) The DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship:

[0114]

[0115] 7) The active power reduction constraint condition injected into the grid-connected bus after a random fault satisfies the following relationship:

[0116]

[0117] In the formula, x RE,i,t is the state quantity of the new energy station set RE connected to node i at time t. RE,i,t If it is 0, it means that the new energy stations are collectively shut down. RE,i,t If it is 0, it means that the new energy station cluster is in operation;

[0118] 8) The node phase angle constraint after random failure satisfies the following relationship:

[0119]

[0120] In the formula, δ i,t is the voltage phase angle of node i at time t, δ i max is the maximum voltage phase angle of node i at time t, is the voltage phase angle reference value of node i at time t;

[0121] The power flow optimization model also includes: short-circuit ratio constraints that meet the joint constraints after random faults, including: grid-connected bus short-circuit ratio constraints that meet the active power reduction constraints injected into the grid-connected bus after random faults, and sending-end bus short-circuit ratio constraints that meet the DC power reduction constraints injected into the sending-end bus after random faults, as follows:

[0122] 1) When the active power reduction constraint condition injected into the grid-connected bus after a random fault is met, based on the short-circuit ratio model of the grid-connected bus i at time t, according to the active power reduction amount injected into the grid-connected bus after a random fault, the grid-connected bus short-circuit ratio constraint condition is established to meet the following relationship:

[0123] MRSCR′ S,i,t ≥MRSCR (16)

[0124] Where, MRSCR′ S,i,t The short circuit ratio of the grid-connected bus i at time t when the active power reduction constraint condition injected into the grid-connected bus after a random fault is satisfied, MRSCR is the short circuit ratio threshold of the grid-connected bus, and the value in the embodiment is 2;

[0125] Among them, the short-circuit ratio of the grid-connected bus i at time t when the active power reduction constraint condition injected into the grid-connected bus after a random fault is met satisfies the following relationship:

[0126]

[0127] 2) When the DC power reduction constraint condition injected into the sending-end bus after a random fault is met, based on the short-circuit ratio model of the sending-end bus i at time t, according to the DC power reduction amount injected into the sending-end bus after a random fault, the sending-end bus short-circuit ratio constraint condition is established to meet the following relationship:

[0128] SCR′ DC,i,i ≥SCR (18)

[0129] In the formula, SCR′ DC,i,i The short-circuit ratio of the sending-end bus i at time t that satisfies the DC power reduction constraint condition injected into the sending-end bus after a random fault, SCR is the short-circuit ratio threshold of the sending-end bus, and the value in the embodiment is 3;

[0130] Among them, the short-circuit ratio of the sending-end bus i at time t that meets the DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship:

[0131]

[0132] In the embodiment, a power flow optimization model is formed by the objective function, the joint constraint condition after the random fault, and the short circuit ratio constraint condition that satisfies the joint constraint condition after the random fault; the power flow optimization model is solved to obtain a power flow control scheme;

[0133] The present invention adopts the minimum system comprehensive cost brought by reduction under random faults as the objective function, and considers the system joint constraints after random faults and the short-circuit ratio constraints that satisfy the joint constraints after random faults to jointly obtain the optimal power flow control scheme; compared with the existing technology that only considers the minimum system comprehensive cost brought by reduction under random faults and the system joint constraints, the power flow control scheme determined by the present invention ensures the high reliability of power supply and new energy consumption of the sending-end power grid load, while ensuring the high reliability of the power receiving capacity of the receiving-end power grid, taking into account the optimal power flow and system stability, avoiding the problem of difficult power balance and load limitation caused by the obstruction of new energy output due to the decrease in grid strength, and realizing power balance control under stability constraints. When facing new power systems, the power flow control scheme obtained based on the present invention can effectively evaluate the reliability of the sending-end power grid with a high proportion of new energy.

[0134] In the embodiment, the short circuit ratio constraint condition satisfying the joint constraint condition after the random fault is linearized to satisfy the following relationship:

[0135]

[0136] The optimal power flow model of a high-proportion renewable energy power grid sending end considering multiple types of short-circuit ratio constraints is an inequality model with a lower bound. The present invention realizes the transformation from nonlinear multiple types of short-circuit ratio constraints of power grids to linear inequality constraints with lower bounds expressed in short-circuit capacity, active power, reactive compensation, and active power factor, simplifies the amount of calculation of repeated short-circuit ratios, and adds them to the reliability assessment model. The linear programming problem of the optimal power flow can be realized through the optimization method, and the reliability assessment of the high-proportion renewable energy power grid sending end under a single random fault can be realized without the iterative method, so that it can achieve the optimal power flow and the short-circuit ratios of multiple stations meet the requirements.

[0137] Step 3: The weighted sum of the probability of occurrence of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as the reliability evaluation index of the new energy power system.

[0138] Random faults are generated by sampling from the fault probability distribution of the renewable energy power system using the Monte Carlo sampling method, so a single random fault x t Corresponding to a probability of occurrence Follow steps 1 to 3 to get a single random fault x t Corresponding power flow control solution and system comprehensive cost f(x t ); Based on a single random failure x t The probability of occurrence And the system comprehensive cost f(x t ), the reliability evaluation index of the new energy power system satisfies the following relationship:

[0139]

[0140] In the formula, F is the reliability evaluation index of the new energy power system, T is the total number of random failures, A single random fault x t The probability of occurrence, f(x t ) is a single random fault x t The overall system cost;

[0141] The smaller the reliability assessment index of the new energy power system, the higher the reliability of the new energy power system. In the reliability assessment process of the power grid with a high proportion of new energy, the DC load strength and the new energy load strength are considered to ensure that the system stability constraints are taken into account in the optimal power flow.

[0142] The method proposed in the present invention can also compare the reliabilities of multiple different types of distributed renewable energy power systems. The differences between different types of distributed renewable energy power systems include but are not limited to: different topological structures of distributed renewable energy power systems, different grid-connected bus locations of renewable energy stations, different sending-end bus locations of DC converter stations, different proportions of renewable energy connected to the system, and different loads connected to the system; among the reliability evaluation indicators of various types of distributed renewable energy power systems, the distributed renewable energy power system corresponding to the minimum value of the reliability evaluation indicator has the highest reliability.

[0143] The method proposed in the present invention can also compare the reliability of the new energy power system network before and after the planning scheme and the construction scheme are adjusted. Specifically, before adjusting the planning scheme and the construction scheme of the new energy power system network, the method proposed in the present invention is used to determine the reliability evaluation index of the system before adjustment; after adjusting the planning scheme and the construction scheme of the new energy power system network, the method proposed in the present invention is used to determine the reliability evaluation index of the system after adjustment; if the reliability evaluation index after adjustment is less than the reliability evaluation index before adjustment, it is determined that adjusting the planning scheme and the construction scheme of the new energy power system network is helpful to improve the stability and reliability of the system, otherwise it is not conducive to the stability of the system.

[0144] When the method proposed in the present invention is used to compare the reliability of multiple different types of distributed new energy power systems or to compare the reliability of the new energy power system network before and after the adjustment of the planning scheme and the construction scheme, in the iterative solution process, the iteration convergence is determined when the following relationship is satisfied:

[0145]

[0146] Where, Var(F) is the variance of the reliability assessment index F of the new energy power system, and β is the variance coefficient of the reliability assessment index F of the new energy power system;

[0147] For a certain actual high-proportion renewable energy sending-end power grid, the loads at nodes 6 and 8 in the IEEE-RTS24 node system are converted into DC sending-end nodes, and the conventional generators at nodes 1 and 2 in the IEEE-RTS24 node system are converted into renewable energy nodes. Based on the failure rate of each component in the system, the Monte Carlo module of Matlab software is used to generate 10,000 random fault samples of the high-proportion renewable energy sending-end power grid, and the components and corresponding failure rates of each random fault in the high-proportion renewable energy sending-end power grid are determined. Here, the first-order to third-order fault forms of line N-1, line N-2, line N-1 and unit N-1, line N-3, line N-2 and unit N-1, line N-1 and unit N-2, unit N-1, unit N-2, and unit N-3 are considered.

[0148] For a certain actual high-proportion renewable energy transmission-end power grid, an electromechanical transient simulation model of the system is established on the PSD-BPA power system analysis software platform. Based on the single fault form obtained by sampling, the short-circuit ratio calculation program in the PSD-BPA software is called to calculate the short-circuit ratio, short-circuit capacity and reactive power compensation on the AC side of the converter station busbar under the fault form. The results are shown in Tables 1 and 2. At the same time, the active power conversion factor λ between renewable energy stations is obtained. RE,ij,t As known data.

[0149] Table 1. Short-circuit ratio, short-circuit capacity and reactive power compensation of the DC converter station busbar on the AC side under a random fault

[0150] node Short circuit ratio Short circuit capacity / MVA Reactive power compensation / Mvar DC power transmission power / MW 6 1.74 840 318 300 8 1.69 597 259 200

[0151] Table 2 Short-circuit ratio and short-circuit capacity of multiple stations at renewable energy grid connection points under a random fault

[0152] node Short circuit ratio of multiple stations Short circuit capacity / MVA New energy output / MW 1 1.42 644 120 2 1.56 671 100

[0153] For the convenience of comparison, the reliability assessment of the power grid at the sending end of the new energy with a high proportion, which considers the load reduction cost, DC reduction cost, the cost of the output obstruction of new energy, and multiple types of short-circuit ratio constraints, is regarded as scenario 1, and the reliability result is obtained based on the method proposed in the embodiment of the present invention. The reliability assessment of the power grid at the sending end of the new energy with a high proportion, which only considers the load reduction cost and multiple types of short-circuit ratio constraints, is regarded as scenario 2. The reliability assessment of the power grid at the sending end of the new energy with a high proportion, which considers the load reduction cost, DC reduction cost, the cost of the output obstruction of new energy, and ignores the short-circuit ratio constraint, is regarded as scenario 3, and the reliability assessment result is obtained using the traditional reliability assessment method.

[0154] The calculation results of the three scenarios are shown in Table 3 and Table 4:

[0155] Table 3 Calculation results of short-circuit ratio optimization under a random fault

[0156]

[0157] It can be seen from Table 3 that due to the consideration of multiple types of short-circuit ratio constraints in scenarios 1 and 2, all types of short-circuit ratios of each busbar in the reliability evaluation results under a certain random fault meet the requirements; while in scenario 3, all types of short-circuit ratios of each busbar do not meet the requirements and cannot meet the requirements of power grid safety and stability, so no comparison will be made later.

[0158] Table 4 Calculation results under various scenarios

[0159]

[0160]

[0161] It can be seen from Table 4 that compared with Scenario 2, the comprehensive reliability cost in Scenario 1 is lower, considering the load reduction cost, DC reduction cost and the cost of obstructed new energy output.

[0162] The present invention also proposes a reliability assessment system for a new energy power system under multiple types of short-circuit ratio constraints, which is suitable for a power grid with a high proportion of new energy at the sending end, including:

[0163] The short-circuit ratio model building module is used to obtain the short-circuit capacity, active power and active power conversion factor between each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus;

[0164] The power flow optimization module is used to establish a power flow optimization model for different random faults. The power flow optimization model takes the minimum system comprehensive cost under different random faults as the objective function, where the system comprehensive cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraint conditions after the random fault and the short-circuit ratio constraint conditions that meet the joint constraint conditions after the random fault; solve the power flow optimization model to obtain the power flow control scheme under different random faults;

[0165] The index generation module is used to take the weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault as the reliability evaluation index of the new energy power system.

[0166] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0167] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0168] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0169] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A reliability assessment method for a renewable energy power system under multiple short-circuit ratio constraints, applicable to a high-proportion renewable energy sending-end power grid, characterized in that: include: Obtain the short-circuit capacity, active power and active power conversion factor of each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus; A power flow optimization model is established for different random faults. The power flow optimization model takes the minimum comprehensive system cost under different random faults as the objective function, where the comprehensive system cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraint conditions after the random fault and the short-circuit ratio constraint conditions that meet the joint constraint conditions after the random fault; the power flow optimization model is solved to obtain the power flow control scheme under different random faults; The weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as the reliability evaluation index of the new energy power system.

2. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 1 is characterized in that: After different random faults, based on the active power conversion factor, the short-circuit ratio model of the grid-connected bus i at time t satisfies the following relationship: Where, MRSCR S,i,t is the short-circuit ratio of the grid-connected bus i at time t, S ac,i,t is the short-circuit capacity of the grid-connected bus i at time t after various random faults, P RE,i,t , P RE,j,t are the active power injected into the grid-connected buses i and j by the new energy station set RE at time t, is the active power conversion factor between grid-connected buses i and j at time t after various random faults, where: is the mutual impedance between grid-connected buses i and j at time t after various random faults, is the self-impedance of grid-connected bus i at time t after various types of random faults, and n is the total number of grid-connected buses; After different random faults, the short-circuit ratio model of the sending-end bus i at time t satisfies the following relationship: In the formula, SCR DC,i,t is the short-circuit ratio of the sending-end busbar i at time t, Q ac,i,t is the reactive power compensation capacity of the AC side of the sending bus i at time t; DC,i,t is the DC power injected into the sending-end bus i by the DC converter station set DC at time t.

3. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 1 is characterized in that: Under different random faults, the load is reduced, the active power injected into the grid-connected bus is reduced, and the DC power injected into the sending-end bus is reduced. t Under this condition, the objective function of the power flow optimization model satisfies the following relationship: In the formula, f(x t ) is a single random fault x t The overall system cost under is the load reduction of node i at time t, where i∈L, L is the load set, is the reduction of DC power injected into the sending end bus i by the DC converter station set DC at time t, where i∈DC, is the reduction in active power injected into the grid-connected bus i by the renewable energy station set RE at time t, where i∈RE, α, β, and γ are the unit cost of load reduction after random faults, the unit cost of DC power reduction, and the unit cost of active power reduction, respectively.

4. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 1 is characterized in that: The joint constraints after random failures are as follows: 1) The node power balance constraint after random failure satisfies the following relationship: Where P G,i,t is the active power injected into node i by the conventional generator set G at time t, P i′i,t is the active power flowing from node i′ to node i at time t, P ii′,t is the active power flowing from node i to node i′ at time t, I i is the set of nodes directly connected to node i, is the reduction in active power injected into the grid-connected bus i by the new energy station set RE at time t, P L,i,t is the load power of node i at time t, is the load reduction of node i at time t, is the reduction of DC power injected into the sending-end bus i by the DC converter station set DC at time t; i∈I, I is the node set, t∈T, T is the evaluation period; 2) The line power flow constraint condition after random faults satisfies the following relationship: P ii′,t -B ii′ (d i,t -d i′,t )+M(1-x ii′,t )≥0 (7) P ii′,t -B ii′ (d i,t -d i′,t )-M(1-x ii′,t )≥0 (8) Where P ii′,t is the active power flowing from node i to node i′ at time t, B ii′ is the mutual admittance between node i and node i′, δ i,t and δ i′,t are the voltage phase angles between nodes i and i′ at time t, M is an arbitrarily large but non-infinite positive number, x ii′,t is the state quantity of the line between node i and node i′ at time t. ii′,t If it is 0, it means the line is out of service. ii′,t If it is 0, it means the line is in operation, i,i′∈I, I is the node set, t∈T, T is the evaluation period; Combine x through M ii′,t To characterize the direction of line power flow after random faults; 3) The line transmission capacity constraint after random failure satisfies the following relationship: -P ii′,max x ii′,t ≤P ii′,t ≤P ii′,max x ii′,t (9) Where P ii′,max is the maximum transmission power of the line between node i and node i′, i,i′∈I, I is the node set, t∈T, T is the evaluation period; 4) The unit output constraint condition after random failure satisfies the following relationship: P G,i,min x G,i,t ≤P G,i,t ≤P G,i,max x G,i,t (9) Where P G,i,min and P G,i,max are the lower and upper limits of the active power injected into node i by the conventional generator set G at time t, respectively, G,i,t is the state quantity of the conventional unit set G connected to node i at time t. G,i,t If it is 0, it means that the conventional units are shut down collectively. G,i,t If it is 0, it means that the conventional unit set is in operation, i∈I, I is the node set, t∈T, T is the evaluation period; 5) The load reduction constraint after random faults satisfies the following relationship: 6) The DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship: 7) The active power reduction constraint condition injected into the grid-connected bus after a random fault satisfies the following relationship: In the formula, x RE,i,t is the state quantity of the new energy station set RE connected to node i at time t. RE,i,t If it is 0, it means that the new energy stations are collectively shut down. RE,i,t If it is 0, it means that the new energy station cluster is in operation; 8) The node phase angle constraint after random failure satisfies the following relationship: In the formula, δ i,t is the voltage phase angle of node i at time t, is the maximum voltage phase angle of node i at time t, is the voltage phase angle reference value of node i at time t.

5. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 1 is characterized in that: The short-circuit ratio constraint condition that satisfies the joint constraint condition after the random fault includes: the short-circuit ratio constraint condition of the grid-connected bus that satisfies the active power reduction constraint condition injected into the grid-connected bus after the random fault, and the short-circuit ratio constraint condition of the sending-end bus that satisfies the DC power reduction constraint condition injected into the sending-end bus after the random fault; The details are as follows: 1) When the active power reduction constraint condition injected into the grid-connected bus after a random fault is met, based on the short-circuit ratio model of the grid-connected bus i at time t, according to the active power reduction amount injected into the grid-connected bus after a random fault, the grid-connected bus short-circuit ratio constraint condition is established to meet the following relationship: MRSCR′ S,i,t ≥MRSCR(16) Where, MRSCR′ S,i,t The short-circuit ratio of the grid-connected bus i at time t when the active power injected into the grid-connected bus is reduced after a random fault, and MRSCR is the short-circuit ratio threshold of the grid-connected bus; Among them, the short-circuit ratio of the grid-connected bus i at time t when the active power reduction constraint condition injected into the grid-connected bus after a random fault is met satisfies the following relationship: 2) When the DC power reduction constraint condition injected into the sending-end bus after a random fault is met, based on the short-circuit ratio model of the sending-end bus i at time t, according to the DC power reduction amount injected into the sending-end bus after a random fault, the sending-end bus short-circuit ratio constraint condition is established to meet the following relationship: SCR′ DC,i,i ≥SCR (18) In the formula, SCR′ DC,i,i is the short-circuit ratio of the sending-end bus i at time t that satisfies the DC power reduction constraint condition injected into the sending-end bus after a random fault, and SCR is the short-circuit ratio threshold of the sending-end bus; Among them, the short-circuit ratio of the sending-end bus i at time t that meets the DC power reduction constraint condition injected into the sending-end bus after a random fault satisfies the following relationship: The grid-connected bus short-circuit ratio threshold is 2, and the sending-end bus short-circuit ratio threshold is 3.

6. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 1 is characterized in that: The weighted sum of the probability of occurrence of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault is used as the reliability evaluation index of the new energy power system, including: Based on the fault probability distribution of the renewable energy power system, the Monte Carlo sampling method is used to generate different random faults; Single random failure x t Corresponding to a probability of occurrence Single random failure x t Corresponding power flow control solution and system comprehensive cost f(x t ); Based on a single random failure x t The probability of occurrence And the system comprehensive cost f(x t ), the reliability evaluation index of the new energy power system satisfies the following relationship: In the formula, F is the reliability evaluation index of the new energy power system, T is the total number of random failures, A single random fault x t The probability of occurrence, f(x t ) is a single random fault x t The overall system cost; The smaller the reliability assessment index of the new energy power system is, the higher the reliability of the new energy power system is.

7. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 6 is characterized in that: Based on the reliability evaluation index of new energy power system, the reliability of multiple different types of distributed new energy power systems is compared as follows: The differences between different types of distributed renewable energy power systems include: different topological structures of distributed renewable energy power systems, different locations of grid-connected busbars for the collection of renewable energy stations, different locations of sending-end busbars for the collection of DC converter stations, different proportions of renewable energy connected to the system, and different loads connected to the system; among the reliability assessment indicators of various types of distributed renewable energy power systems, the distributed renewable energy power system corresponding to the minimum value of the reliability assessment indicator has the highest reliability.

8. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 7 is characterized in that: Based on the reliability evaluation index of the new energy power system, the reliability of the new energy power system network before and after the adjustment of the planning scheme and the construction scheme is compared as follows: Before adjusting the planning and construction plans of the new energy power system network, determine the reliability assessment index of the system before adjustment; after adjusting the planning and construction plans of the new energy power system network, determine the reliability assessment index of the system after adjustment; if the reliability assessment index after adjustment is less than the reliability assessment index before adjustment, then it is determined that the reliability of the new energy power system network after the adjustment of the planning and construction plans is high.

9. The reliability assessment method of a new energy power system under multiple types of short-circuit ratio constraints according to claim 8 is characterized in that: When comparing the reliability of multiple different types of distributed renewable energy power systems or comparing the reliability of renewable energy power system networks before and after the adjustment of planning and construction plans, the iteration converges when the following relationship is satisfied during the iterative solution process: Where Var(F) is the variance of the reliability assessment index F of the new energy power system, and β is the variance coefficient of the reliability assessment index F of the new energy power system.

10. A reliability assessment system for renewable energy power systems under multiple short-circuit ratio constraints, suitable for power grids with a high proportion of renewable energy, characterized in that: include: The short-circuit ratio model building module is used to obtain the short-circuit capacity, active power and active power conversion factor between each grid-connected bus after different random faults to determine the short-circuit ratio model of each grid-connected bus; obtain the reactive compensation capacity and DC power of the sending-end bus after different random faults to determine the short-circuit ratio model of each sending-end bus; The power flow optimization module is used to establish a power flow optimization model for different random faults. The power flow optimization model takes the minimum system comprehensive cost under different random faults as the objective function, where the system comprehensive cost includes: load reduction cost, DC power reduction cost injected into the sending end bus and active power reduction cost injected into the grid-connected bus; the power flow optimization model also includes the joint constraint conditions after the random fault and the short-circuit ratio constraint conditions that meet the joint constraint conditions after the random fault; solve the power flow optimization model to obtain the power flow control scheme under different random faults; The index generation module is used to take the weighted sum of the occurrence probability of each random fault and the system comprehensive cost corresponding to the power flow control scheme under each random fault as the reliability evaluation index of the new energy power system.

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