Distributed resource-assisted black start strategy for key nodes of the main network to improve resilience

Abstract

The present invention discloses a distributed resource-assisted black start strategy for key nodes of a main network for improving resilience, comprising: step 1, analyzing the output characteristics and control strategies of each DER in the distribution network, forming a feasibility assessment method for DER to support rapid recovery of key nodes of the main network, establishing a black start executable assessment index, and selecting a DER control strategy; step 2, based on the DER control strategy, establishing a joint optimization model for DER startup and grid reconstruction of the distribution network, and using a method combining a genetic algorithm and a Floyd algorithm to solve and obtain the optimal startup sequence of each DER, each DER is smoothly started and connected to the grid according to the optimal startup sequence, and aggregated to form a regional power supply; step 3, the regional power supply starts the auxiliary machines of the thermal power plant in batches, and starts the thermal power units after the auxiliary machines of the thermal power plant are fully and normally operated, and the restored thermal power units are successively connected to the surrounding distribution network loads, and provide startup power support for other thermal power plants, thereby gradually expanding the recovery range until the whole system is restored.

Description

Technical Field

[0001] The present invention relates to the field of power grids, and in particular to a distributed resource-assisted main network key node black start strategy for improving resilience. Background Art

[0002] The high proportion of new energy and large-scale access to power systems, the rapid development of AC / DC hybrid power distribution, and the increasing diversification of loads have led to the distribution network gradually becoming active, flexible, and multi-random. The situation and role it faces in the new power system will become more complex. At the same time, the power system is increasingly subject to internal and external security threats such as cascading failures, broadband oscillations, cyber attacks, human operation problems, and extreme natural disasters, posing new challenges to its safe operation.

[0003] As an important development direction of the future power system, resilient power grid has six key characteristics in a broad sense: perception, coordination, resilience, defense, recovery and learning. Among them, resilience is an important function to deal with extreme weather, natural disasters, and physical and cyber attacks that cause major power outages and minimize losses. It is necessary to tap the potential of multiple links to support the rapid recovery of power grid failures. However, with the gradual expansion of the scale of the power grid, the number and layout of traditional black start power sources can no longer meet the requirements of rapid recovery and reliability. Continuing to expand the layout of traditional black start power points is expensive and limited by the availability of energy resources, which to a certain extent restricts the economic efficiency of safe operation of the power grid. Therefore, how to achieve rapid recovery of power grid infrastructure in the face of internal and external risks based on existing resources, and to formulate a reasonable and effective black start plan with the goal of solving fault recovery problems has become an urgent need, which is of great significance to improving the resilience of the power grid and ensuring national energy security.

[0004] The black start scheme of power grid can be divided into three levels according to the scale and voltage level: microgrid, distribution network and transmission network. Conventional black start scheme is more oriented to the transmission network level, mainly using hydropower units with strong self-starting ability or large thermal power units with black start capability as black start power sources, gradually driving other units without black start capability to start, and finally restoring the entire power grid; the recovery of distribution network often needs to wait for the recovery of the upper power grid before supplying power to its key loads, resulting in long recovery time and insufficient recovery capacity. As the degree of coupling between the distribution network and the main grid becomes increasingly close, large-scale distributed generation (DG), electric vehicles, energy storage, microgrids and other diversified "source-load-storage" distributed energy resources (DER) are decentralized. Based on the advantages of DER such as low starting power, fast speed and flexible response, various DERs are aggregated to assist distribution network fault recovery or local self-healing to ensure power supply to important loads, and DER is used to quickly start up key nodes such as the main power supply and substation to provide auxiliary power and flexibility resource support, thereby accelerating the recovery process of the entire network, improving the resilience of the power grid in terms of resilience, and alleviating the problems of insufficient power supply and high cost for black start of the power grid. It has also become a new idea and direction for the formulation of black start plans under the background of "new power system" construction.

[0005] Domestic and foreign scholars have conducted preliminary research on DER-assisted microgrids and black start at the distribution network level. Some studies have established a multi-objective optimization model that considers both DG power generation and load importance for the DG start sequence in the black start of microgrids, and achieved coordinated allocation of DG start and load recovery timing. Some studies have focused on distributed resources such as photovoltaics, wind power, energy storage, electric vehicles, and microgrids as black start power sources to participate in the recovery of distribution network systems, using DER with black start capabilities to start uncontrollable DER, coordinate and optimize the recovery path and target grid, and gradually expand the recovery range while ensuring the power supply of important loads; among them, some studies have constructed a two-layer optimization model for grid reconstruction taking into account electric vehicle charging stations with the goal of maximizing the system's available power generation and minimizing the charging capacitance of the recovery path; some studies have analyzed the superiority of microgrids as black start power sources to support distribution network fault recovery, and established a grid reconstruction model for microgrids to support distribution network recovery with the goal of restoring the maximum power generation and minimum load loss in the system, and verified the advantages of the recovery scheme in terms of economy and reliability based on the New England 10-machine 39-node system. However, the above studies only focus on black starts at the microgrid or distribution network level, and have not yet considered the supporting role of distribution network resources for black starts at the transmission network level, which has limited impact on improving the overall resilience of the system.

[0006] The black start scheme for DER-assisted transmission network in distribution network usually includes three stages: power supply black start stage, grid reconstruction stage and load recovery stage. The existing research on black start of DER-assisted transmission network mainly focuses on the power supply black start stage, giving priority to starting DER with black start capability to provide starting power for large thermal power units in the transmission network that cannot start automatically, and saving time through parallel recovery of transmission network partitions. Some scholars use the photovoltaic storage combined power generation system as a black start power source to drive the asynchronous motor for the plant, and simulate the whole process of black start of the key nodes of the main network supported by DER. The developed photovoltaic storage system coordinated control strategy has strong engineering applicability; some studies propose to configure energy storage systems for wind farms to enable them to have black start capabilities, formulate strategies for rapid start of thermal power units in wind storage systems, and analyze the empty charging transmission lines, transformers and auxiliary machine start-up processes in detail through simulation; some studies optimize the black start scheme of the transmission network system, use micro gas turbines near power plants as new backup power sources for large units, improve the poor economy of backup diesel generator sets and insufficient black start power supply in urban power grids, and simulate and verify the black start strategy through the empty charging transformer and asynchronous motor start of micro gas turbines, and the black start strategy is feasible. The above-mentioned research has effectively reduced the transmission network fault recovery time by utilizing distribution network DER, but it mainly focuses on the analysis of the single problem of distribution network DER supporting the startup of large units in the main network during the recovery process. It only involves the safety verification and scheme evaluation of self-starting DER, empty charging lines and transformers, and starting power plant auxiliary equipment. It does not consider the DER startup sequence optimization, recovery path formulation, and target grid reconstruction processes during the recovery process. In the actual black start of the power grid, the three aspects are not independent of each other, but drive and support each other. It is necessary to comprehensively consider joint optimization to construct a dynamic full-process black start plan under the full cycle and multiple time scales. Summary of the invention

[0007] The present invention is conducted to solve the above-mentioned problem, and aims to provide a distributed resource-assisted main network key node black start strategy for improving resilience.

[0008] The present invention provides a distributed resource-assisted black start strategy for key nodes of the main network for improving resilience, which has the following characteristics and includes the following steps: Step 1, analyzing the output characteristics and control strategies of each DER in the distribution network, forming a feasibility assessment method for DER to support rapid recovery of key nodes of the main network, establishing a black start executable assessment index, evaluating the black start executable period within a day, and based on the assessment result, selecting a DER control strategy that meets the power supply characteristics and system recovery requirements for the black start executable period; Step 2, based on the DER control strategy, establishing a distribution network DER startup and grid reconstruction joint optimization model with minimum recovery time, maximum available power generation capacity and minimum recovery path weight as the target, and using a combination of genetic algorithm and Floyd algorithm to solve the optimal startup sequence of each DER, each DER is smoothly started and connected to the grid according to the optimal startup sequence, and aggregated to form a regional power supply; Step 3, the regional power supply starts the auxiliary machines of the thermal power plant in batches, and starts the thermal power units after the auxiliary machines of the thermal power plant are fully and normally operated. The restored thermal power units are successively connected to the surrounding distribution network loads, and provide startup power support for other thermal power plants, thereby gradually expanding the recovery range until the whole system is restored.

[0009] In the distributed resource-assisted main network key node black start strategy for resilience improvement provided by the present invention, it can also have the following characteristics: wherein, in step 2, wherein, in step 1, DER includes wind turbines, photovoltaics, energy storage and micro gas turbines. Step 1 includes the following steps: Step 1-1, predicting the available power generation capacity of DER in the future during the black start process in view of the output characteristics and spatiotemporal distribution uncertainties of multiple types of DER, using the confidence interval method to process the prediction error deviation, and obtaining the DER output prediction result of the distribution network system; Step 1-2, based on the DER output prediction result of the distribution network system, considering the two aspects of the minimum power demand for the auxiliary machine of the main network large unit and the continuous execution inclination of the black start, establish a black start executable evaluation index, evaluate the executable period of the black start within a day, and obtain the evaluation result; Step 1-3, based on the evaluation result, for the power outage time when the black start plan can be executed, select the DER control strategy that meets the power supply characteristics and system recovery requirements to ensure the successful startup and stable operation of the faulty system.

[0010] In the distributed resource-assisted main network key node black start strategy for resilience improvement provided by the present invention, there may also be such a feature: wherein, in step 1-1, the multi-type DER output characteristics include wind power output characteristics, photovoltaic unit output characteristics and energy storage operation characteristics. The energy storage operation characteristics consider the energy storage charging and discharging power constraints and the energy storage charge state constraints as shown below:

[0011] -P ES,N ≤P ES (t)≤P ES,N (1)

[0012] SOC ES,min ≤SOC ES (t)≤SOC ES,max (2)

[0013] Where P ES,N is the rated power of energy storage; SOC ES,max , SOC ES,min and SOC ES (t) are the upper and lower limits of the energy storage device’s state of charge and the energy storage state of charge during the black start process; SOC ES (t) The calculation formula can be expressed as:

[0014]

[0015] Where, δ is the energy storage self-discharge rate; η c , η d are the charge and discharge efficiency respectively; S is the charging and discharging power of energy storage at time t; s is the capacity of the energy storage device.

[0016] In the distributed resource-assisted main network key node black start strategy for resilience improvement provided by the present invention, it can also have the following characteristics: wherein step 1-2 includes the following steps: step 1-2-1, confidence interval estimation: the actual output of the unit is distributed around the predicted value, and the actual output of the wind power and photovoltaic units in each period during the black start process is expressed as the sum of the predicted value and the prediction error, which is described as follows:

[0017]

[0018] Where P WT (t), P PV (t) respectively represent the actual power generation of wind power and photovoltaic units at time t, (t) respectively represent the predicted power of wind power and photovoltaic units at time t, Respectively represent the prediction errors of wind power and photovoltaic power generation at time t. The uncertainty processing method uses the confidence interval method to ensure that the scheme meets a certain confidence level. First, based on the predicted output values ​​of wind power and photovoltaic power generation units, the uncertainty model is used to fit the prediction error distribution in different power sections to obtain the probability density function. Secondly, based on the probability density function, the interval set with a cumulative probability greater than or equal to the confidence level 1-α is extracted. Finally, the interval with the shortest length under the same confidence level 1-α is selected from the interval set as the confidence interval under the predicted power. The confidence interval under the confidence level 1-α obtained based on the principle of the shortest confidence interval is as follows

[0019]

[0020] Where ΔP is the error corresponding to the power prediction value P, which conforms to the probability distribution F(ΔP); Step 1-2-2, the black start feasibility is evaluated using two evaluation indicators: the minimum power requirement for black start and the black start continuous execution inclination. The minimum power requirement for black start is P DER,min for:

[0021] P DER,min =(1+α)P M (6)

[0022] Where P M is the rated power of the auxiliary equipment of the thermal power unit to be started; the coefficient α includes the DER's own power consumption rate, line loss rate and a certain margin reserved, and the spinning reserve is reserved to cope with the uncertainty of the output of wind power and photovoltaic units. DER 、Black start minimum required power Q DER,min And the black start continuous execution inclination γ are expressed as:

[0023]

[0024] Where P DER (t) is the output of DER at time t. When γ is greater than or equal to 1, it indicates that the sustainable execution tendency is higher. When γ is less than 1, it indicates that the black start solution is more likely to be unexecutable.

[0025] In the distributed resource-assisted main network key node black start strategy for resilience improvement provided by the present invention, it can also have the following characteristics: wherein, in step 2, a joint optimization model of distribution network DER startup and grid reconstruction is established for the mutual driving and mutual supporting characteristics of DER startup sequence and recovery path. The process of solving the optimal startup sequence includes the following sub-steps: Step 2-1, select energy storage and micro gas turbines with self-starting capability and stronger regulation performance as black start power self-start, with the goal of shortest system recovery time, maximum available power generation capacity and minimum recovery path weight, optimize DER selection and DER startup sequence through genetic algorithm; Step 2-2, based on line importance evaluation, use the shortest path algorithm to optimize the path of DER startup sequence to obtain the shortest path, pass the shortest path back to the DER startup sequence optimization model, obtain the optimal startup sequence of each DER, and achieve global optimization.

[0026] In the distributed resource-assisted main network key node black start strategy for improving resilience provided by the present invention, the following features may also be provided: wherein, in step 2-1, the DER startup sequence optimization objectives are the minimum time, the maximum available power generation capacity, and the minimum weight of the DER recovery path to be started, as shown below:

[0027]

[0028] In the formula, Ψ BG and NBG are the sets of black start power supplies and non-black start power supplies started during the system network reconstruction phase, Ψ L is the set of all lines l in the recovery path; Z l is the restoration weight of the selected line l, P BG,n and P NBG,k The nth black start unit and the kth non-black start unit are the restored power outputs in this stage. The DER startup sequence optimization model is established with reference to the available power generation of DER at the time of power outage. The constraints in step 2-1 include DER output constraints, node voltage constraints, line thermal stability limit constraints, power balance constraints, thermal power unit hot start time constraints, distribution network topology radial structure constraints, and auxiliary motor self-start verification. The DER output constraints are:

[0029]

[0030] Where P Gn is the active power output of the activated DER, Q Gn is the reactive power output of the activated DER, Ψ G is the set of all DERs in the system. The node voltage constraint is:

[0031]

[0032] Where V i is the voltage amplitude of node i in the system, Ψ N is the set of all nodes in the system. The thermal stability limit constraint of the line is:

[0033]

[0034] Where P Ll is the active power transmitted in line l. The power balance constraint is:

[0035]

[0036] Where P M is the auxiliary power of thermal power unit, P Gn is the output of the activated DER, and the coefficient α includes the plant power consumption rate, line loss rate, and a certain margin reserved for the output prediction error of wind power and photovoltaic units. (1+α)P M To meet the minimum power demand required for black start. The hot start time constraint of thermal power units is:

[0037] 0≤T L ≤T max (16)

[0038] Where, T L T is the time it takes for the black start power supply to start and transmit power to the auxiliary equipment of the thermal power unit through the recovery path, max is the maximum time limit for hot start of thermal power units. The radial structure constraint of distribution network topology is:

[0039]

[0040] In the formula, z l is a binary variable for the switching state of branch l: 0 means the branch is disconnected, 1 means the branch is closed, n b 、n s They are the total number of nodes and the number of root nodes in the distribution network system. The self-starting verification of auxiliary motors is divided into voltage verification and capacity verification:

[0041]

[0042] Where U *1 U is the per unit value of the high voltage bus voltage used in the factory; *2 It is the per unit value of the factory low voltage bus voltage.

[0043] In the distributed resource-assisted main network key node black start strategy for improving resilience provided by the present invention, the following features may also be provided: wherein, in step 2-2, the line importance evaluation index includes line operation time weight setting, line recovery cost weight setting and line weight setting. In the line operation time weight setting, the operation time of each line is divided into optimistic estimation time A, pessimistic estimation time B and most likely estimation time M, and the operation time t required to restore a certain line l is t. l , its expected value E(t l ) and variance σ l They are:

[0044]

[0045] Where l∈Ψ L ,Ψ L is the set of all lines in the system. Assuming that the startup path of the unit at a certain node i consists of L lines, the mean and variance of the operation time required for the recovery path are:

[0046]

[0047] If T i If the probability of falling within the start-up time limit of the thermal power unit is high, it is considered that the thermal power unit can be included in the hot start plan. In the line recovery cost weight setting, the total charging capacitance on the recovery path of starting a certain unit i is expressed as:

[0048]

[0049] Set the line restoration cost weight to:

[0050] W l =C l +S l (twenty four)

[0051] In the formula, S l The cost of closing a switch for a line l. The line weight setting mainly considers two evaluation indicators: the time operation weight t and the recovery cost weight W. The weight Z of line l l Set to:

[0052] Z l =k1W l +k2t l (25)

[0053] In the formula, k1 and k2 are allocation coefficients, k1+k2=1. When the line time operation weight and the restoration cost weight are smaller, the line weight of the line will be smaller, and the line importance will be higher. In the process of restoring the path, the restoration line weight is smaller, that is, the line with higher line importance is preferred. In step 2-2, the optimization is performed with the minimum sum of the line weights in the restoration path as the goal, and the objective function is expressed as:

[0054]

[0055] In the dynamic optimization of the restoration path, when restoring the next section of the line based on the current status, the system network is divided into energized parts and de-energized parts, and the energized parts are aggregated into power supply points. From these power supply points, the optimal path to start the next target point is calculated, and the lines with higher importance are restored. The above steps are repeated until the thermal power plant is started.

[0056] In the distributed resource-assisted main network key node black start strategy for improving resilience provided by the present invention, the following features may also be provided: wherein, in step 2, the algorithm implementation process of using a method combining a genetic algorithm and a Floyd algorithm to solve the model is: based on the DER startup sequence randomly generated by the genetic algorithm, the Floyd algorithm is called to search for a recovery path for each DER startup sequence, and the genetic algorithm is called to select the optimal DER startup sequence and the recovery path under the sequence, thereby obtaining the global optimal solution for the target grid. The Floyd algorithm is sequentially S DER Medium S1~S N The node is used as the starting and ending point of the search to perform the recovery path search. The basic idea is: let L(S i ,S k ) represents point S i To point S k The shortest path, L(Sk ,S j ) represents point S k To point S j The shortest path, L(S i ,S j ) represents point S i To point S j Path, then point S i To point S j The shortest path is expressed as min(L(S i ,S k )+L(S k ,S j ),L(S i ,S j )). The basic idea of ​​the genetic algorithm is: assuming that there are N DER nodes and 1 main grid thermal power plant node Q in the system, the chromosome in the genetic algorithm adopts binary encoding, and the DER startup sequence is represented by a chromosome with a length of k*(N+1), that is, Chrom=[A1,A2,…,A N |A N+1 ], where A N+1 is a chromosome segment of length k, A1~A N Represents the start-up time of each DER, A N+1 The thermal power plant is set to be started last. The genetic algorithm first randomly generates a batch of initial chromosomes, and then converts the objective function of the DER startup order optimization model into the fitness function of the chromosome. Based on the fitness function value of each chromosome, the offspring chromosomes are generated through roulette random selection and crossover, mutation, recombination and other operation steps, and the excellent chromosomes of the parent generation are recorded and retained. The steps are repeated until the set conditions are met.

[0057] In the distributed resource-assisted main network key node black start strategy for improving resilience provided by the present invention, the following features may also be provided: wherein, in step 3, after various DERs in the distribution network are started and aggregated to form a regional black start power supply, the thermal power plant in the adjacent main network is started to provide power for the auxiliary equipment of the thermal power unit, and the power balance expression is:

[0058] P W (t)+P PV (t)+P ES (t)+P MT (t)-P load (t)=0 (27)

[0059] Where: P W (t) represents the fan output, P PV (t) represents the photovoltaic output, P ES (t) represents the energy storage output power, P MT(t) represents the fan output, P load (t) represents the auxiliary power of thermal power units. The excess and deficit of wind power and photovoltaic units compared with the auxiliary power are:

[0060] △P + (t)=|P W (t)+P PV (t)-P load (t)| (28)

[0061] △P - (t)=|P W (t)+P PV (t)-P load (t)| (29)

[0062] The coordinated control process of DER starting thermal power units is as follows: Step 3-1, at time t, DER starts to aggregate to form a regional power source; Step 3-2, the regional power source is put into the auxiliary equipment of the thermal power plant in sequence; Step 3-3, at the beginning of the black start, when the sum of the output of wind power and photovoltaic units is less than the auxiliary power, the wind power and photovoltaic units adopt the MPPT control strategy, and the remaining power shortage ΔP-(t) is borne by the energy storage and micro gas turbines. When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess ΔP + (t) less than 10%P PV At (t), the PV unit adopts MPPT control, and the wind turbine unit adopts load tracking control strategy to track the auxiliary power and the output change of the PV output shortage. + (t) greater than 10%P PV At (t), the photovoltaic unit adopts the voltage reduction control that deviates from the maximum power point, and the load reduction amount is 10%P PV (t), the wind turbine adopts a load following control strategy to track the auxiliary power and the output change of the photovoltaic output shortage; step 3-4, if the thermal power unit does not resume power generation, repeat steps 3-2 to 3-3 until the thermal power unit resumes power generation.

[0063] Functions and Effects of the Invention

[0064] According to the distributed resource-assisted black start strategy for key nodes of the main network for resilience improvement involved in the present invention, the startup steps are as follows: Step 1, analyze the output characteristics and control strategies of each DER in the distribution network, form a feasibility assessment method for DER to support rapid recovery of key nodes of the main network, establish a black start executable assessment index, evaluate the black start executable period within a day, and based on the assessment results, select a DER control strategy that meets the power supply characteristics and system recovery requirements for the black start executable period; Step 2, based on the DER control strategy, establish a distribution network DER startup and grid reconstruction joint optimization model with minimum recovery time, maximum available power generation capacity and minimum recovery path weight as the target, and use a combination of genetic algorithm and Floyd algorithm to solve the optimal startup sequence of each DER. Each DER starts and connects to the grid smoothly according to the optimal startup sequence, and aggregates to form a regional power supply; Step 3, the regional power supply starts the auxiliary machines of the thermal power plant in batches, and starts the thermal power units after the auxiliary machines of the thermal power plant are fully and normally operated. The restored thermal power units are gradually connected to the surrounding distribution network loads and provide startup power support for other thermal power plants, thereby gradually expanding the recovery range until the entire system is restored.

[0065] The above process evaluates the feasibility of black start within one day based on DER output prediction, establishes a grid reconstruction model for joint optimization of DER startup sequence and recovery path, and forms the optimal recovery plan for DER startup of the main grid thermal power plant in the distribution network system, ensuring that the thermal power units can be started safely as soon as possible within the hot start time limit, thereby improving the resilience of the power grid.

[0066] In addition, the distributed resource-assisted main grid key node black start strategy for improving resilience involved in the present invention significantly reduces the main grid power node recovery time, effectively improving the grid resilience and DER utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a diagram of a black start process of a main grid thermal power plant supported by a distribution network DER according to an embodiment of the present invention;

[0068] Figure 2 is a flow chart of path recovery in an embodiment of the present invention;

[0069] Figure 3 is a flowchart of an algorithm solution in an embodiment of the present invention;

[0070] Figure 4 is a flow chart of coordinated control of DER starting a thermal power unit in an embodiment of the present invention;

[0071] Figure 5 is a schematic diagram of a 35-node power distribution network system in a certain area in an embodiment of the present invention;

[0072] Figure 6 is a typical daily photovoltaic and wind power output diagram in an embodiment of the present invention;

[0073] Figure 7 is a daily curve diagram of the total DER power in an embodiment of the present invention;

[0074] Figure 8 is a black start continuous execution inclination diagram in an embodiment of the present invention;

[0075] Fig. 9 is a diagram of a grid reconstruction scheme for scenario I in an embodiment of the present invention;

[0076] Fig.10 is a curve diagram of DER and auxiliary machine power changes in scenario I in an embodiment of the present invention;

[0077] Fig.11 is a diagram of a grid reconstruction scheme for scenario II in an embodiment of the present invention;

[0078] Fig.12 is a curve diagram of DER and auxiliary machine power changes in scenario II in an embodiment of the present invention;

[0079] Fig.13 is a diagram of a grid reconstruction scheme for scenario III in an embodiment of the present invention; and

[0080] Fig.14 It is a curve diagram of DER and auxiliary machine power changes in scenario III in an embodiment of the present invention. DETAILED DESCRIPTION

[0081] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the following embodiments, combined with the accompanying drawings, specifically illustrate the distributed resource-assisted main network key node black start strategy for improving resilience of the present invention.

[0082] In this embodiment, a distributed resource-assisted main network key node black start strategy for improving resilience is provided.

[0083] Figure 1 This is a diagram of the black start process of the main grid thermal power plant supported by the distribution network DER in this embodiment.

[0084] like Figure 1 As shown, the distributed resource-assisted main network key node black start strategy for improving resilience involved in this embodiment includes the following steps:

[0085] Step S1, analyze the output characteristics and control strategies of wind turbines, photovoltaics, energy storage and micro gas turbines in the distribution network, form a feasibility assessment method for distributed resources to support the rapid recovery of key nodes in the main network, establish a black start executable assessment index, and evaluate the black start executable period within a day. Based on the assessment results, for the black start executable period, select a distributed resource control strategy that meets the power supply characteristics and system recovery requirements.

[0086] The uncertainty of spatiotemporal distribution of output of multiple types of DER, the differences in control strategies, and the inconsistency of output prediction deviations have greatly increased the difficulty and complexity of DER participating in black start. In this regard, this embodiment focuses on the three aspects of DER output volatility, prediction error uncertainty, and coordinated control strategy, and conducts an in-depth exploration of the feasibility of DER supporting the black start of the main grid. DER resources in the distribution network mainly include wind turbines, photovoltaics, energy storage, and micro gas turbines. Among them, energy storage and micro gas turbines have the characteristics of rapid self-starting, continuous and stable output power, and strong voltage and frequency regulation capabilities. They can be used as black start power supplies to provide stable reactive voltage and active frequency support for the system. The output of wind power and photovoltaic units is limited by the natural conditions of wind speed and light intensity, and has the characteristics of randomness, volatility, and uncontrollability. It is difficult to meet the requirements of frequency and voltage regulation. As a non-black start power supply, energy storage or micro gas turbines are required to provide starting power during startup.

[0087] Step S1 specifically includes the following steps:

[0088] Step S1-1, based on the output characteristics of multiple types of distributed resources and the uncertainty of spatiotemporal distribution, the available power generation capacity of distributed resources in the future during the black start process is predicted, and the prediction error deviation is processed using the confidence interval method to obtain the output prediction result of distributed resources in the distribution network system.

[0089] The output characteristics of multiple types of distributed resources include wind power output characteristics, photovoltaic unit output characteristics and energy storage operation characteristics.

[0090] A large number of studies and statistical data show that the average wind speed probability density follows the Weibull distribution, and the light intensity can be approximated as the Beta distribution. In this embodiment, the wind speed and light intensity prediction model, the probability density distribution and the cumulative distribution function of the output of distributed wind power and photovoltaic units refer to the existing technology.

[0091] The energy storage in the distribution network has a short startup time, and its state of charge (SOC) remains basically unchanged. During the black start process, it can maintain the stability of charging and discharging power, voltage and frequency, and provide voltage and frequency support for other power sources. However, when a blackout occurs, the state of charge (SOC) of the energy storage at time t is ES (t) is random, which has a significant impact on the energy storage power supply capacity during the black start process. The energy storage operation characteristics consider the energy storage charging and discharging power constraints and the energy storage charge state constraints as shown below:

[0092] -P ES,N ≤P ES (t)≤P ES,N (30)

[0093] SOC ES,min ≤SOCES (t)≤SOC ES,max (31)

[0094] Where P ES,N is the rated power of energy storage; SOC ES,max , SOC ES,min and SOC ES (t) are the upper and lower limits of the energy storage device’s state of charge and the energy storage state of charge during the black start process. ES (t) The calculation formula can be expressed as:

[0095]

[0096] Where, δ is the energy storage self-discharge rate; η c , η d are the charge and discharge efficiency respectively; S is the charging and discharging power of energy storage at time t; s is the capacity of the energy storage device.

[0097] Step S1-2, based on the prediction results of the distributed resource output of the distribution network system, taking into account the minimum power demand for auxiliary machine startup of large units in the main network and the continuous execution inclination of black start, establish a black start executable evaluation index, evaluate the black start executable period within a day, and obtain the evaluation result.

[0098] The time-varying regularity and randomness of wind power and photovoltaic unit output lead to significant differences in the available capacity of the starting power supply in the system at different time periods, and it is necessary to judge the feasibility of black start when a major power outage occurs at different times during the day. The basis for judging the feasibility of DER supporting the black start of the main grid thermal power plant mainly includes two aspects: 1) By predicting the output of wind power and photovoltaic units during the black start process, judge whether the DER power generation of the distribution network system meets the minimum black start power demand; 2) Evaluate the continuous effective output of DER during the black start period to judge the feasibility of the black start plan. At present, there are many output prediction methods for intermittent power sources such as wind and solar, but the prediction accuracy is still unsatisfactory. Among them, the average relative error of the short-term power generation prediction of wind turbines is as high as 15% to 30%, and the power generation prediction error of photovoltaic units is generally 10%-20%.

[0099] Step S1-2 includes the following steps:

[0100] Step S1-2-1, confidence interval estimation: A large amount of research data analysis shows that the output forecast error of wind power and photovoltaic units follows the mathematical distribution law to a certain extent. The Gaussian mixture probability distribution model can better describe the thick tail, peak and asymmetric characteristics of the output forecast error probability density distribution. The actual output of the unit is distributed around the predicted value. The actual output of wind power and photovoltaic units in each period during the black start process is expressed as the sum of the predicted value and the prediction error, which is described as follows:

[0101]

[0102] Where P WT (t), P PV (t) respectively represent the actual power generation of wind power and photovoltaic units at time t, They represent the predicted power of wind power and photovoltaic units at time t, They represent the prediction errors of wind power and photovoltaic power generation at time t respectively.

[0103] The uncertainty processing method uses the confidence interval method to ensure that the solution meets a certain confidence level. First, based on the output forecast values ​​of wind power and photovoltaic units, the uncertainty model is used to fit the forecast error distribution in different power sections to obtain the probability density function. Secondly, based on the probability density function, the interval set with a cumulative probability greater than or equal to the confidence level 1-α is extracted. Finally, the interval with the shortest length under the same confidence level 1-α is selected from the interval set as the confidence interval under the predicted power. The confidence interval under the confidence level 1-α obtained based on the shortest confidence interval principle is as follows:

[0104]

[0105] Where ΔP is the error corresponding to the power prediction value P, which conforms to the probability distribution F(ΔP).

[0106] In this embodiment, in order to avoid being too conservative in predicting the total output of DER, it is assumed that the probability that the output of all wind power and photovoltaic units is lower than the predicted power lower limit is extremely small. In order to simplify the calculation complexity, the predicted power lower limit is taken as the actual output of the unit. At the same time, a certain power margin is considered for uncertainty factors, and the feasibility of black start is evaluated.

[0107] Step S1-2-2, the black start of the main grid power supply supported by distributed resources cannot be completed instantly, but requires uninterrupted power supply support throughout the entire continuous process of restoring the power supply of the thermal power units. For the feasibility assessment of the black start plan of the main grid thermal power plant supported by DER within one day, a certain time margin should be fully considered, and 60 minutes should be taken as a continuous black start time period, which includes a series of steps such as DER startup, path recovery and auxiliary startup of thermal power units in the distribution network system. Since the resilience of the power grid after a fault directly affects the resilience level, the resilience level of the transmission and distribution network is judged by analyzing the executable time period of the black start plan. This embodiment uses two evaluation indicators, namely the minimum power demand for black start and the continuous execution inclination of black start, to evaluate the feasibility of black start.

[0108] Evaluation indicator 1: Black start minimum power requirement. The main load object during the black start process is the auxiliary equipment of the thermal power unit. The output power of the started DER in the distribution network should be greater than the load power. At the same time, it must also meet the uncertainty of DER's own power consumption, line losses, and wind power and photovoltaic unit output. The black start minimum power requirement is P 分布式资源,min for:

[0109] P DER,min =(1+α)P M (35)

[0110] Where P M is the rated power of the auxiliary equipment of the thermal power unit to be started; the coefficient α includes the distributed resource's own power consumption rate, line loss rate and a certain reserved margin, and the rotating reserve is reserved to cope with the uncertainty of the output of wind power and photovoltaic units.

[0111] Evaluation index 2: Black start continuous execution inclination. In order to ensure the smooth startup of the auxiliary equipment of the thermal power unit during the black start process, DER needs to continuously and effectively provide startup power within a certain period of time. For this purpose, an evaluation index for the black start continuous execution inclination is established, that is, the continuous power supply of DER within 60 minutes should be greater than the total auxiliary load demand during the startup of the main grid unit. Distributed resource power generation during the black start process Q 分布式资源 、Black start minimum required power Q 分布式资源,min And the black start continuous execution inclination γ are expressed as:

[0112]

[0113] Where P 分布式资源 (t) is the output of distributed resources at time t. When γ is greater than or equal to 1, it indicates that the sustainable execution tendency is higher. When γ is less than 1, it indicates that the black start solution is more likely to be unexecutable.

[0114] Step S1-3, based on the evaluation results, for the power outage time when the black start solution can be executed, a distributed resource control strategy that meets the power supply characteristics and system recovery requirements is selected to ensure that the faulty system is successfully started and runs stably.

[0115] Step S2, based on the distributed resource control strategy, in view of the mutual driving and mutual supporting characteristics of the distributed resource startup sequence and the recovery path, a joint optimization model of distribution network distributed resource startup and grid reconstruction is established with the minimum recovery time, the maximum available power generation capacity and the minimum recovery path weight as the target, and a method combining genetic algorithm and Floyd algorithm is used to solve the optimal startup sequence of each distributed resource. Each distributed resource is smoothly started and connected to the grid according to the optimal startup sequence, and aggregated to form a regional power supply.

[0116] The main task of grid reconstruction is to restore the main grid thermal power plant as soon as possible, initially establish a relatively stable grid structure, and lay the foundation for the comprehensive recovery of the system in the next stage. This embodiment proposes a joint optimization method for the DER startup sequence and the recovery path sequence for the system recovery strategy in the grid reconstruction stage, which to a certain extent avoids the problem of separation of the two optimization links of determining the target grid and searching for the recovery path. First, the startup sequence of each DER is determined with the goal of restoring the maximum available power generation capacity of the system as quickly as possible and minimizing the weight of the recovery path of the DER to be started; at the same time, based on the power startup sequence generated by the DER optimization model, the recovery path for starting the DER and supplying power to the auxiliary equipment of the thermal power unit is determined with the goal of minimizing the sum of the weights of the paths; finally, a system recovery strategy for the grid reconstruction stage is formed.

[0117] In step S2, the process of solving the optimal startup sequence includes the following sub-steps:

[0118] Step S2-1, select energy storage and micro gas turbines with self-starting capability and stronger regulation performance as black start power supply self-starting, with the goal of shortest system recovery time, maximum available power generation capacity and minimum recovery path weight, and optimize distributed resource selection and distributed resource startup sequence through genetic algorithm.

[0119] Arranging a reasonable DER startup sequence during the black start process and coordinating different types of power sources will help increase the available power generation capacity and improve the reliability and continuity of the startup power supply, thereby accelerating the system recovery process. At the same time, starting the power source with strong adjustable capability first can increase the black start success rate and ensure the safe and stable operation of the restored system. Compared with traditional black start units, DER has the characteristics of short startup time, rapid response, and flexible control. According to the DER self-starting capability and output characteristics, the optimal DER startup sequence must meet the following principles:

[0120] 1) DERs with strong black start capabilities are prioritized for startup. DERs can be divided into black start power sources and non-black start power sources based on their black start capabilities. DERs with strong black start capabilities are prioritized for self-starting, while non-black start power sources need to be supported by black start power sources to start and connect to the grid in turn.

[0121] 2) Prioritize the startup of DERs with large available capacity. The goal in the early stage of system recovery is to obtain as much available generation capacity as possible to provide greater startup power for the system.

[0122] 3) Prioritize the startup of DERs with small path weights. When arranging the startup sequence of non-black start power supplies, factors that affect the path weights, such as line charging capacitance, number of switch operations, and path recovery time, need to be considered.

[0123] 4) Give priority to starting DERs with frequency and voltage regulation capabilities. DERs need to have certain frequency and voltage regulation capabilities to maintain stable system operation. During the black start process, different DERs adopt different control strategies according to local conditions to ensure the stability of system voltage and frequency.

[0124] The DER startup sequence will affect the final path recovery plan. If only a single type of DER startup sequence is considered, the optimal path recovery plan only represents the local optimum under the DER startup sequence rather than the global optimum. Therefore, it is necessary to consider and jointly optimize the DER startup sequence and the recovery path. The goal of the initial black start is to obtain as much available power generation capacity as possible to provide the system with greater startup power. The coupling relationship between the DER startup sequence and the recovery path is comprehensively considered, and the optimization goal of the distributed resource startup sequence is to minimize time, maximize available power generation capacity, and meet the minimum weight of the recovery path of the distributed resources to be started, as shown below:

[0125]

[0126] In the formula, Ψ BG and NBG are the sets of black start power supplies and non-black start power supplies started during the system network reconstruction phase, Ψ L is the set of all lines l in the recovery path; Z l is the restoration weight of the selected line l, P BG,n and P NBG,k They are the restored power outputs of the nth black start unit and the kth non-black start unit in this stage respectively.

[0127] The uncertainty of wind power and photovoltaic unit output will affect the selection of DER startup sequence during the black start process. If the black start period is [0, T], there exists t1∈[0, T], t2∈[0, T], and t1≠t2. If the wind power and photovoltaic unit output fluctuates greatly at t1 and t2, different recovery strategies will appear at the two moments, and the grid structure finally established will also be significantly different. Therefore, when the DER output fluctuates greatly in the [0, T] period, it is easy to cause the DER startup sequence and the target grid structure to change frequently, the line switch operation is frequent, and the auxiliary equipment of the thermal power unit cannot obtain a continuous and stable power supply. In order to ensure the smooth progress of the black start process and improve the stability of the recovery system, this embodiment uses the available power generation of DER at the time of power outage as a reference to establish a DER startup sequence optimization model.

[0128] The constraints include distributed resource output constraints, node voltage constraints, line thermal stability limit constraints, power balance constraints, thermal power unit hot start time constraints, distribution network topology radial structure constraints, and auxiliary motor self-start verification.

[0129] The output constraints of distributed resources are:

[0130]

[0131] Where P Gn is the active power output of the activated distributed resources, Q Gn is the reactive power output of the activated distributed resources, G A collection of all distributed resources in the system.

[0132] The node voltage constraints are:

[0133]

[0134] Where V i is the voltage amplitude of node i in the system, Ψ N A collection of all nodes in the system.

[0135] The thermal stability limit constraint of the line is:

[0136]

[0137] Where P Ll is the active power transmitted in line l.

[0138] In this embodiment, the main load in the black start solution is the auxiliary equipment of the thermal power unit. The thermal power unit can only be started when the DER output meets the auxiliary power demand. However, the wind and solar output is random and the photovoltaic start can only be performed during the day. Therefore, the DER and auxiliary power in the black start should meet the following constraints:

[0139]

[0140] Where P M is the auxiliary power of thermal power unit, P Gn is the output of the activated distributed resources. The coefficient α includes the power consumption rate of the plant, the line loss rate, and a certain margin reserved for the output prediction error of wind power and photovoltaic units. (1+α)P M To meet the minimum power requirement for black start.

[0141] The hot start time constraint of thermal power units is:

[0142] 0≤T L ≤T max (45)

[0143] Where, T L T is the time it takes for the black start power supply to start and transmit power to the auxiliary equipment of the thermal power unit through the recovery path, max The maximum time limit for hot start of thermal power units.

[0144] Considering the protection setting of the distribution network and the requirement of small short-circuit current, the distribution network is generally required to operate in a radial manner.

[30] , that is, there is no "loop" and "island" topology in the system, and the distribution network topology radial structure constraint is expressed as:

[0145]

[0146] In the formula, z l is a binary variable for the switching state of branch l: 0 means the branch is disconnected, 1 means the branch is closed, n b 、n s They are the total number of nodes and the number of root nodes in the distribution network system. The radial network can be abstracted as a "tree" structure in graph theory. Each feeder is represented as a tree. The root node corresponds to the root of the tree, the node corresponds to the node of the tree, and the branch corresponds to the edge of the tree. At the same time, it must satisfy "total number of edges = number of nodes - number of roots".

[0147] The self-starting verification for auxiliary motors can be divided into voltage verification and capacity verification. Since the starting current of the auxiliary motor can reach 4 to 7 times the rated current when starting, the auxiliary motor startup moment is equivalent to a short circuit, and the residual voltage of the plant bus is verified. The requirements for the self-starting bus voltage verification of the motor are: the minimum voltage of the high-voltage bus for the self-starting motor powered by the plant high-voltage bus is 60% of the rated voltage; the minimum voltage of the low-voltage bus for the self-starting motor powered by the plant low-voltage bus and the high-voltage bus in series is 55% of the rated voltage.

[0148]

[0149] Where U *1U is the per unit value of the high voltage bus voltage used in the factory; *2 It is the per unit value of the factory low voltage bus voltage.

[0150] Step S2-2, based on the line importance evaluation, the shortest path algorithm is used to optimize the path of the distributed resource startup sequence to obtain the shortest path, and the shortest path is passed back to the distributed resource startup sequence optimization model to obtain the optimal startup sequence of each distributed resource to achieve global optimization.

[0151] For a given DER startup sequence, multiple different system recovery paths can be obtained, and the recovery grids established thereby will also be different, resulting in differences in line operation time, charging capacitance, and number of switch closing operations during the recovery path. Selecting a reasonable recovery path can effectively shorten the system recovery time and ensure the safe and stable execution of the black start process. When establishing the target grid, this embodiment comprehensively considers the influence of three aspects, namely, line operation time, charging capacitance, and number of switch closing operations, on line weights, evaluates the importance of lines in the system by setting line weights, establishes an optimization model, and obtains a recovery path consisting of lines with relatively higher importance.

[0152] The indicators for line importance evaluation include line operation time weight setting, line restoration cost weight setting and line weight setting.

[0153] Line operation time weight setting: During the black start process, the operation time required for line recovery is uncertain. The operation time evaluation standard for each line is set to the time operation weight t, and t is used to measure the time it takes for the line to be charged from the beginning to the end. In the actual system, based on the operator's experience, the operation time of each line is generally divided into optimistic estimation time A, pessimistic estimation time B and most likely estimation time M. Then the actual time required to restore each line is beta distributed between A and B. For the operation time t required to restore a certain line l l , its expected value E(t l ) and variance σ l They are:

[0154]

[0155] Where l∈Ψ L ,Ψ L is the set of all lines in the system. Assuming that the startup path of the unit at a certain node i consists of L lines, the mean and variance of the operation time required for the recovery path are:

[0156]

[0157]

[0158] If Ti If the probability of falling within the start-up time limit of the thermal power unit is high, it is considered that the thermal power unit can be included in the hot start plan.

[0159] During the grid reconstruction phase, excessive charging capacitance in the recovery path will affect system safety, and the reactive power generated by line no-load or light-load will cause line overvoltage. Therefore, charging capacitance is one of the important factors affecting the line recovery cost weight, and the line charging capacitance needs to be taken into consideration. When selecting the power transmission path for starting the auxiliary equipment of the thermal power unit, a solution with relatively small line charging capacitance is selected to reduce the reactive power generated on the power transmission path. The total charging capacitance on the recovery path for starting a unit i can be expressed as:

[0160]

[0161] Set the line recovery cost weight W l As an indicator to measure the difficulty of a line l in the recovery process, if only the line charging capacitance is considered as an influencing factor to set the recovery cost weight W of each line, it is easy to cause the recovery path composed of multiple lines with relatively small charging capacitance to be better than the recovery path composed of a single line. In order to avoid giving priority to paths composed of too many lines and causing too many line switch operations, the line recovery cost weight is set as:

[0162] W l =C l +S l (53)

[0163] In the formula, S l The cost of closing the switch for a line l. Since circuit breakers are usually installed at both ends of the line, closing the switch is required to restore the line. Therefore, it is assumed that the S l Similarly, by changing S l The value can adjust the influence of line charging capacitance and switch operation times on the optimization of the recovery path.

[0164] Line weight setting: In this embodiment, the line weight Z is set mainly considering two evaluation indicators: time operation weight t and recovery cost weight W. l Set to:

[0165] Z l =k1W l +k2t l (54)

[0166] In the formula, k1 and k2 are allocation coefficients, k1+k2=1. When the line time operation weight and the recovery cost weight are smaller, the line weight of the line will be smaller, and the line importance will be higher. In the process of restoring the path selection, the line with a smaller restoration line weight, that is, a line with a higher line importance, is preferentially selected.

[0167] When determining the line to be restored, the importance of line operation time, charging capacitance, and the number of switch closing operations are comprehensively considered, that is, it is required to take into account both efficiency and risk when establishing the skeleton network. The larger the scale of the reconstructed grid, the more important lines and power sources will be restored, but the final system recovery time will also be longer. At the same time, due to the increase in the number of restored lines, the charging capacitance and the number of switch operations will also increase, making the risk of black start failure greater. Therefore, this embodiment uses the minimum sum of line weights in the recovery path as the goal for optimization, and the objective function is expressed as:

[0168]

[0169] The constraints of this model are the same as those of the DER startup sequence optimization model.

[0170] Figure 2 This is a flow chart of path recovery in this embodiment.

[0171] like Figure 2 As shown, during the grid reconstruction process, as some lines are charged and restored, the importance of the remaining power-off lines will change, and it is necessary to re-determine the importance of the power-off lines to avoid failure in the final restoration path optimization due to repeated calculation of the restored lines. In this regard, this embodiment proposes a dynamic rating indicator for line importance - dynamic line importance. During the path recovery process, when restoring the next section of the line based on the current state, the system network is divided into a live part and a power-off part, and the live part is aggregated into a power source point. The power source point calculates the optimal path to start the next target point, and restores the lines with higher importance. Repeat the above steps until the thermal power plant is started.

[0172] The grid reconstruction problem in this embodiment is a joint optimization problem that considers the mutual influence between the DER startup sequence and the recovery path. The model can be solved by combining the genetic algorithm with the shortest path algorithm. The algorithm is implemented as follows:

[0173] This embodiment uses a method combining a genetic algorithm with a Floyd algorithm to optimize the grid reconstruction model. Based on the DER startup sequence randomly generated by the genetic algorithm, the Floyd algorithm is called to search for a recovery path for each DER startup sequence. At the same time, the genetic algorithm is called to select the optimal DER startup sequence and the recovery path under the sequence, thereby obtaining the global optimal solution for the target grid.

[0174] 1) Local optimization algorithm based on Floyd algorithm

[0175] Assume that the startup sequence of each DER in the distribution network is known to be S DER ={S1,S2,…,S N}, first use the shortest path method to find S1~S N Search for the recovery path, then aggregate the started DERs into power points and search for power supply paths for the auxiliary machines, and finally obtain an optimal target grid. Since there is an optimal recovery path for each DER startup sequence, only considering the recovery path optimization and ignoring the DER startup sequence optimization can only obtain a local optimal solution.

[0176] This embodiment uses the classic shortest path algorithm, the Floyd algorithm, to optimize the recovery path. DER Medium S1~S N The node is used as the starting and ending point of the search to perform recovery path search. The core idea of ​​the algorithm is: Let L(S i ,S k ) represents point S i To point S k The shortest path, L(S k ,S j ) represents point S k To point S j The shortest path, L(S i ,S j ) represents point S i To point S j Path, then point S i To point S j The shortest path can be expressed as min(L(S i ,S k )+L(S k ,S j ),L(S i ,S j )).

[0177] 2) Global optimization algorithm based on genetic algorithm

[0178] It is known that the DER startup sequence affects the final recovery grid. When optimizing the recovery path, the DER startup sequence needs to be optimized as well. In this way, the optimal recovery path under the optimal DER startup sequence can be obtained, and the global optimal solution of the target grid can be obtained.

[0179] This embodiment uses a genetic algorithm to globally optimize the DER startup sequence. The basic idea of ​​the algorithm is: Assume that there are N DER nodes and one main grid thermal power plant node Q in the system. The chromosome in the genetic algorithm uses a binary encoding method, so the DER startup sequence can be represented by a chromosome with a length of k*(N+1), that is, Chrom=[A1,A2,…,AN |A N+1 ], where A N+1 is a chromosome segment of length k, A1~A N Represents the start-up time of each DER, A N+1 The thermal power plant is set to start last. The algorithm first randomly generates a batch of initial chromosomes, then converts the objective function of the DER startup order optimization model into the fitness function of the chromosome, and generates offspring chromosomes based on the fitness function value of each chromosome through roulette random selection and crossover, mutation, recombination and other operation steps, and records and retains the excellent chromosomes of the parent generation, and repeats the steps until the set conditions are met.

[0180] Figure 3 It is the algorithm solution flow chart in this embodiment.

[0181] Step S3, the regional power supply starts the auxiliary equipment of the thermal power plant in batches, and starts the thermal power units after the auxiliary equipment of the thermal power plant is fully and normally operated. The restored thermal power units are gradually connected to the surrounding distribution network loads and provide starting power support for other thermal power plants, thereby gradually expanding the recovery scope until the whole system is restored.

[0182] During the black start process of the key nodes of the main grid supported by DER, energy storage is selected as the main power source to provide voltage and frequency support for the system. Overcharging and over-discharging of energy storage will affect the execution of the black start process of the thermal power plant supported by DER. From the perspective of coordinated control, limiting the charging and discharging depth of energy storage to a reasonable range will be conducive to the smooth black start. Therefore, it is necessary to select a reasonable DER control strategy based on the characteristics of various power sources and system recovery requirements, and control the power generation output of the power source in each period of the black start to reduce the required energy storage capacity, so as to avoid black start failures caused by overcharging and over-discharging of energy storage. Therefore, a reasonable DER control strategy is a necessary condition for the successful restart and stable operation of the faulty system.

[0183] After various DERs in the distribution network are started and aggregated to form a regional black start power supply, the thermal power plants in the adjacent main grid are started to provide power for the auxiliary equipment of the thermal power units. The power balance expression is:

[0184] P W (t)+P PV (t)+P ES (t)+P MT (t)-P load (t)=0 (56)

[0185] Where P W (t) represents the fan output, P PV (t) represents the photovoltaic output, P ES (t) represents the energy storage output power, P MT (t) represents the fan output, P load(t) represents the auxiliary power of thermal power unit,

[0186] The excess and deficit of wind power and photovoltaic power generation compared with auxiliary power are as follows:

[0187] △P + (t)=|P W (t)+P PV (t)-P load (t)| (57)

[0188] △P - (t)=|P W (t)+P PV (t)-P load (t)| (58)

[0189] Combined control of load tracking and MPPT: Photovoltaic units use MPPT control and load reduction control, and wind turbines use a combination of MPPT control and load tracking control

[28] , see the specific control process principle Figure 4 At the beginning of black start, when the sum of the output of wind power and photovoltaic units is less than the auxiliary power, both wind power and photovoltaic units adopt MPPT control strategy to ensure that they provide as much output as possible. The remaining power shortage ΔP-(t) is borne by energy storage and micro gas turbines to improve the utilization rate of wind power and photovoltaics, reduce the required energy storage capacity, and avoid black start failure due to insufficient remaining discharge capacity in the energy storage device. When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess ΔP + (t) less than 10%P PV At (t), the PV unit adopts MPPT control, and the wind turbine unit adopts load tracking control strategy to track the auxiliary power and the output change of the PV output shortage. + (t) greater than 10%P PV At (t), the photovoltaic unit adopts the voltage reduction control that deviates from the maximum power point, and the load reduction amount is 10%P PV (t), the wind turbine adopts a load following control strategy to track the output changes of the auxiliary machine power and the photovoltaic output shortage.

[0190] Figure 4 This is a coordinated control flow chart of DER starting thermal power units in this embodiment.

[0191] like Figure 4 As shown in the figure, the process of coordinated control of distributed resource starting thermal power units is:

[0192] Step S3-1, at time t, distributed resources start to aggregate to form a regional power supply;

[0193] Step S3-2, the regional power supply is put into the auxiliary equipment of the thermal power plant in sequence;

[0194] Step S3-3, at the initial stage of black start, when the sum of the output of wind power and photovoltaic units is less than the auxiliary power, both wind power and photovoltaic units adopt the MPPT control strategy, and the remaining power shortage ΔP-(t) is borne by the energy storage and micro gas turbine.

[0195] When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess amount ΔP + (t) less than 10%P PV At (t), the PV unit adopts MPPT control, and the wind turbine unit adopts load following control strategy to track the auxiliary power and the output change of the PV output shortage.

[0196] When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess amount ΔP + (t) greater than 10%P PV At (t), the photovoltaic unit adopts the voltage reduction control that deviates from the maximum power point, and the load reduction amount is 10%P PV (t), the wind turbine adopts a load following control strategy to track the output changes of the auxiliary power and the photovoltaic output shortage;

[0197] Step S3-4: If the thermal power unit has not resumed power generation, repeat steps 3-2 to 3-3 until the thermal power unit resumes power generation.

[0198] V / F control: In V / F (constant voltage and frequency) control, the inverter can establish a stable reference voltage and frequency according to the control requirements. The energy storage and micro gas turbine power supply in the system should choose V / F control to achieve self-starting. When starting other power supplies and putting the auxiliary equipment of the thermal power unit into use, it can smooth the system power fluctuation and provide voltage and frequency support for the system. When there are multiple DERs with frequency and voltage regulation capabilities such as energy storage and micro gas turbines during the black start process, multiple inverter control strategies with droop characteristics can be selected to avoid a large number of endless power adjustment operations due to subtle differences between different inverter controls.

[0199] In this embodiment, a 35-node main distribution network system in a certain area is taken as an example to analyze the black start model and method of DER-assisted main network key nodes.

[0200] Figure 5 It is a schematic diagram of a 35-node distribution network system in a certain area in this embodiment.

[0201] like Figure 5 As shown, the solid line and the dotted line represent the lines with the switches closed and open respectively under normal operation. All line parameters in the system are shown in Table 1 (using the standard line type in pandapower v2.6.0).

[0202] Table 1 shows all the line parameters in the system.

[0203] Table 1 Line parameters

[0204]

[0205]

[0206] The target load in the system to be restored is the auxiliary equipment of the 100MW thermal power unit, the total capacity of the auxiliary equipment is 3MW, and the black start duration is set to 60min. The transient problems such as the excitation inrush current generated by the substation during the black start process and the impact current generated when the asynchronous motor is started are not considered in this embodiment. DERs are connected at nodes 4, 11, 14, 20, 29, and 31, which are DER1, DER2, DER3, DER4, DER5, and DER6, respectively. The initial SOC of the energy storage is set to 50%, so that it can continue to supply power for 1h at the maximum discharge power under full discharge conditions. Node 0 represents the auxiliary equipment of the thermal power plant connected to the 10kV bus. The specific DER and auxiliary equipment detailed parameters are shown in Tables 2 and 3.

[0207] Table 2 is the DER parameter table in the distribution network.

[0208] Table 3 is the main auxiliary machine parameters.

[0209] Table 2 DER parameters in distribution network

[0210]

[0211] Tab.2main auxiliary machine parameters

[0212]

[0213] The uncertainty factor processing method in step S1 is used to process the DER output forecast deviation, and the wind power and photovoltaic unit power generation forecast curves with a 5-minute time granularity on a typical day in the region are obtained as follows: Figure 6 According to the DER daily output curve, the feasibility of black start in a typical day is divided into time periods, as shown in Figure 2. Figure 7 shown.

[0214] Figure 6 It is a typical daily photovoltaic and wind power output diagram in the embodiment of the present invention.

[0215] Figure 7 DER total power daily curve diagram in an embodiment of the present invention.

[0216] Based on the above black start feasibility evaluation method, considering the influence of line loss, power consumption of the plant itself and uncertain factors, P DER,min Take 3.5MW as the value, and get the black start duration inclination at each power outage time in a day as follows: Figure 8 shown.

[0217] Figure 8 It is a black start continuous execution inclination diagram in this embodiment.

[0218] like Figure 8 As shown in the figure, it can be seen that the system resilience varies greatly at different times. The higher the execution inclination, the higher the probability that the system can successfully achieve black start during this period, and the stronger its resilience. Figures 6 to 8 According to the analysis, the black start-inoperable periods can be estimated to be 03:30-07:30 and 16:00-19:00, accounting for about 29% of the whole day. When planning the distribution system, the proportion of black start-executable periods throughout the day can be increased by rationally allocating distributed resources, thereby improving the resilience of the transmission and distribution network. Based on the above analysis, it can be seen that since the output level of DER in the distribution network system varies greatly at different power outage times, it will directly affect whether the thermal power plant can be successfully started. In addition, if the output of DER fluctuates greatly during the system black start execution period, it will lead to a decrease in the execution inclination. It is necessary to select different time domain scenarios in a day to conduct an in-depth analysis of the DER-assisted thermal power plant black start solution.

[0219] Scenario I: The power outage occurred at 09:00, in a suitable wind / solar environment, with the available photovoltaic power generation capacity of approximately 39% (a total of 0.702MW) and the available wind power generation capacity of approximately 41% (a total of 0.902MW).

[0220] Scenario II: The power outage occurred at 12:00, in a weak wind and strong light environment. The available photovoltaic power generation capacity was as high as 80% (a total of 1.44MW), and the available wind power generation capacity was reduced to 22% (a total of 0.484MW).

[0221] Scenario III: The power outage occurred at 23:00 in a strong wind and dark environment. The available photovoltaic power generation capacity was 0%, and the available wind power generation capacity was as high as about 80% (a total of 1.76MW).

[0222] For the grid reconstruction optimization model built in this embodiment, the genetic algorithm is combined with the Floyd algorithm to solve the DER-assisted thermal power plant startup scheme under different scenarios. The number of genetic algorithm populations is set to 50, the number of iterations is 200, and the minimum output of the black start power supply supporting the safe and stable startup of the auxiliary machine is determined to be 3.5MW, considering the plant power consumption, transmission line loss and reserved margin. The black start optimization schemes under three scenarios are as follows: Fig. 9 , 11 , as shown in 13.

[0223] Fig. 9 This is a diagram of the grid reconstruction plan for Scenario I in this embodiment.

[0224] Fig.11 This is a diagram of the grid reconstruction plan for Scenario II in this embodiment.

[0225] Fig.13 This is a diagram of the grid reconstruction plan for scenario III in this embodiment.

[0226] like Fig. 9 , 11 , 13, where green represents started DER and restored lines. Then, based on the control strategy of DER in each black start stage, the output change of DER when starting the auxiliary equipment of the thermal power plant within the black start execution period T = 60min is analyzed, and the power change curves of DER and auxiliary equipment under different scenarios are obtained as follows: Fig.10 , 12 , as shown in 14.

[0227] Fig.10 It is a curve diagram of DER and auxiliary machine power changes in scenario I in this embodiment.

[0228] Fig.12 It is a curve diagram of DER and auxiliary machine power changes in scenario II in this embodiment.

[0229] Fig.14 It is a curve diagram of DER and auxiliary machine power changes in scenario III in this embodiment.

[0230] Comparative analysis of grid reconstruction solutions under three types of power outage scenarios shows that DER4 at node 20 is selected for priority self-start. The fast charging and discharging of energy storage and reactive voltage regulation capabilities can provide stable voltage and frequency support for other power sources to successfully start and connect to the grid. When the line is lightly loaded and charging the path to be restored, it takes on a large charging power to prevent the node voltage from exceeding the limit. According to the evaluation and analysis of DER characteristics, the good black start support characteristics of energy storage make it the first choice for black start power supply.

[0231] 1) Scenario I Black Start Solution

[0232] For the grid reconstruction scheme in scenario I, Fig. 9As shown in Table 4, first, DER4 (energy storage) at node 20 is arranged to start automatically to support stable power output and frequency and voltage regulation; then, based on the minimum black start power constraint, the sequence of other DERs to be started is determined, which are DER3, DER6, DER5 and DER1 in sequence. Since the power outage occurred at 09:00 in the morning, the output of photovoltaic and wind power was moderate, and the difference in the available capacity of photovoltaic and wind turbines in the system was not obvious. The startup sequence of the two mainly considered the size of the recovery path weight. The main goal in the initial stage of black start was to provide sufficient power to the system as soon as possible with the minimum recovery path weight. Based on this principle, the tie switch 20-16 was closed to restore DER3, which was closest to DER4 and had a large available power generation capacity. The restored power sources were aggregated into one power source point. It was determined that the recovery path weight of DER6 from the power source point was the smallest at this time. Therefore, the micro gas turbine DER6 with stable output power and larger capacity was started through the optimal recovery path. The remaining photovoltaic units DER1 and DER2 with small available output and large recovery path weight and wind turbine DER5 were connected to the grid in turn according to the startup optimization principle. The DERs that were successfully started and aggregated were put into lines 3-4, 2-3, 1-2, and 0-1 through node 4 to supply power to the 10kV busbar of the auxiliary equipment of the thermal power unit, gradually restoring the auxiliary equipment and starting the thermal power unit. Finally, the total operation time to establish the target grid was about 19 minutes, and the total available power of the recovery system in scenario I was about 3.804 MW.

[0233] Table 4 is the grid reconstruction plan table for scenario I.

[0234] Table 4 Scenario I grid reconstruction solution

[0235]

[0236] In scenario I, from 0 to 19 minutes, each DER is started and the required path is restored. The subsequent period is the auxiliary machine startup process of the thermal power plant, such as Fig.10As shown. First, at 19 minutes, the electric feed water pump is put into operation with a capacity of 1.4MW. At this time, the available output of photovoltaic and wind turbines is about 1.614MW to meet the power demand of auxiliary machines. The photovoltaic adopts the voltage load reduction control that deviates from the maximum power point, and the wind turbine adopts the load tracking control strategy to track the auxiliary power and the photovoltaic output shortage to adjust the output. Since the output of photovoltaic and wind turbines has met the load requirements, there is no need to call energy storage or micro gas turbines to increase power during this period. At 30 minutes, the circulating water pump is put into operation with a capacity of 0.5MW, and the total load of the auxiliary machines is 1.9MW. At this time, the available output of photovoltaic and wind turbines is about 1.656MW, which is less than the power of the auxiliary machines. Therefore, both photovoltaic and wind turbines adopt the MPPT control strategy to maximize the use of wind and light resources. The power difference is borne by the micro gas turbine to avoid the failure of black start due to insufficient load in the subsequent period caused by the call of energy storage. At 39 minutes, the coal mill and induced draft fan are put into operation with a total of 0.9MW. At this time, due to the increase in load, the micro gas turbine has reached its maximum output power, and it is necessary to call energy storage discharge to combine with other DERs to supply power to the load. Finally, at 49 minutes, the condensate pump and the jet pump were put into operation, and the auxiliary equipment of the thermal power plant was successfully started.

[0237] 2) Scenario II Black Start Solution

[0238] For the grid reconstruction scheme in scenario II, Fig.11 As shown in Table 5, since the power outage occurred at 12:00 noon in a weak wind and strong light environment, the photovoltaic power generation was much higher than the wind turbines at this time, and the photovoltaic power generation had a higher starting priority than the wind turbines in the system. Different from the grid reconstruction scheme in scenario I, the energy storage at node 20 did not give priority to starting the wind turbine at node 14 after starting, but started the micro gas turbine DER6 with higher available capacity and more stable output by putting into lines 16-15 and 32-31 and closing the tie switches 20-16 and 15-32. Then, based on the optimization goal of providing sufficient power to the system with the minimum recovery path weight, the wind turbine DER3, photovoltaic units DER1 and DER2 were started in sequence. The wind turbine DER5, which was far away from the auxiliary equipment of the thermal power plant, was abandoned as the power source to be started because of its large recovery path weight and low available power generation capacity. Finally, the recovery path 4-3-2-1-0 provided the starting power for the auxiliary equipment of the thermal power plant. The total operation time of the grid reconstruction in this scenario was about 15 minutes, and the total available power of the restored system was about 3.948MW.

[0239] Table 5 is the grid reconstruction plan table for scenario II.

[0240] Table 5 Scenario II network reconstruction plan

[0241]

[0242]

[0243] In scenario II, within 0 to 15 minutes, the grid is reconstructed based on the determined DER startup sequence and the optimal recovery path. Fig.12 As shown in the figure, the remaining period is the auxiliary start-up process of the thermal power plant. First, at 15 minutes, the electric feed water pump is put into operation with a capacity of 1.4MW. At this time, the available power of the photovoltaic unit is sufficient, which is 1.458MW, while the available power of the wind turbine unit is less, which is 0.322MW. The auxiliary load is mainly borne by the photovoltaic unit. According to the control strategy, the photovoltaic unit adopts the voltage load reduction control that deviates from the maximum power point, and the wind turbine unit adopts the load tracking control strategy to track the output of the auxiliary power and the photovoltaic output shortage. Since the output of the photovoltaic and wind turbine units has met the load requirements, there is no need to call for energy storage or micro gas turbines to increase power during this period. At 26 minutes, the circulating water pump is put into operation with a capacity of 0.5MW, and the total load of the auxiliary machine is 1.9MW. At this time, the available output of the photovoltaic and wind turbine units is approximately less than the input auxiliary power. Therefore, the output of the photovoltaic and wind turbine units adopts the MPPT control strategy, and the remaining power difference is borne by the micro gas turbine, reducing the required energy storage capacity. At 37 minutes and 48 minutes, the coal mill, induced draft fan, condensate pump and jet pump are put into operation respectively. At this time, the power supply control strategy is similar to that of Scenario I. As the load increases, the photovoltaic, wind turbine and micro gas turbine all reach their maximum output power during this period, requiring energy storage discharge to make up for the power difference. Finally, the auxiliary equipment of the thermal power plant is successfully started.

[0244] 3) Scenario III Black Start Solution

[0245] For the grid reconstruction scheme in scenario III, Fig.13 As shown in Table 6, since the power outage occurred at 23:00 at night in a strong wind and no light environment, the available capacity of the photovoltaic unit was 0 and could not participate in the black start, while the available output of the wind turbine was relatively high, about 1.76MW. Based on the optimization goal of providing sufficient power to the system with the minimum recovery path weight, the optimal DER startup sequence and recovery path are obtained. First, the energy storage DER4 with frequency and voltage regulation capabilities is started, and then the wind turbine DER3 with higher available capacity is started through the recovery path 20-16-15-14, and then the micro gas turbine DER6, which is closer to the restored power source and has a more stable output, is started. However, at this time, the system's available power generation capacity has not yet reached the minimum requirement of 3.5MW for black start, and it is necessary to consider starting the wind turbine DER5, which is farther away from the auxiliary machine, to obtain more power. The final available power sources are DER4, DER3, DER6 and DER5. The total operation time of grid reconstruction in this scenario is about 17 minutes, and the total available power output is about 3.96MW.

[0246] Table 6 is the grid reconstruction plan table for scenario III.

[0247] Table 6 Scenario III Network Reconstruction Solution

[0248]

[0249] In scenario III, from 0 to 17 minutes, the target grid is established through grid reconstruction and optimization model. Fig.14 The remaining period shown is the auxiliary machine startup process of the thermal power plant. First, at 17 minutes, the electric feed water pump is put into operation with a capacity of 1.4MW. At this time, the available output of the photovoltaic unit is 0MW, while the available output of the wind turbine unit is more, which is 1.76MW. Therefore, the auxiliary machine load is mainly borne by the wind turbine unit. The wind turbine unit adopts a load tracking control strategy to track the output of the auxiliary machine power change. Since the output of the wind turbine unit has met the load requirements at this time, there is no need to call energy storage or micro gas turbine to increase power. At 28 minutes, the circulating water pump is put into operation with a capacity of 0.5MW, and the total load of the auxiliary machine is 1.9MW. At this time, the available output of the wind turbine unit is slightly lower than the power of the auxiliary machine. Therefore, the wind turbine units all adopt the MPPT control strategy to make full use of wind power resources. The remaining power difference is borne by the micro gas turbine, reducing the required energy storage capacity. At 39 minutes and 50 minutes, the coal mill, induced draft fan, condensate pump, and jet pump are put into operation respectively. At this time, as the load increases, the wind turbine unit and the micro gas turbine both reach the maximum output power, and energy storage discharge is required to make up for the power difference. Finally, the auxiliary machine of the thermal power plant is successfully started.

[0250] From the above results, it can be seen that, obviously, the schemes of using distributed power sources in the distribution network to assist the black start of the main grid will be different depending on the time of power outage, and the corresponding transmission and distribution network resilience will also be different. The recovery speed will be faster when the available capacity of DER is more sufficient during the black start period. The resilience level of the power grid can be improved by reasonably allocating the proportion and coordination of wind power, photovoltaic units and energy storage, and micro gas turbines.

[0251] The grid reconstruction strategies under the above three different scenarios are as follows: Fig. 9 , 11 As shown in Figure 13, through comparative analysis, it can be seen that the output of wind power and photovoltaic units varies greatly due to weather changes, while the available power generation output of energy storage and micro gas turbines is relatively stable. Therefore, in the recovery plans formulated for the three scenarios using the model and method proposed in this embodiment, priority is given to starting power sources such as energy storage and micro gas turbines with relatively stable output. For intermittent power sources such as wind power and photovoltaics, whether to start them first depends on weather changes. Fig.10 , 12 As can be seen from Figures 14, for the three scenarios, different distribution network DER support black start plans for the main grid thermal power plant are formulated to complete the startup of the thermal power plant auxiliary equipment. In the process of starting the auxiliary equipment, the uncertainty of wind power and photovoltaic unit output is comprehensively considered to ensure that the wind power and photovoltaic power generation output are fully utilized to supply power to the thermal power plant auxiliary equipment, reduce the number of energy storage charge and discharge switching and the required energy storage capacity, and avoid black start failure due to insufficient remaining energy storage charge.

[0252] The auxiliary machine start-up verification of the thermal power plant is carried out according to the black start scheme. Since the starting current of the asynchronous motor is too large, which can reach 4 to 7 times the rated current, the voltage of the power port will drop significantly during the start-up. The auxiliary motor start-up moment can be regarded as a short circuit for calculation. The power supply to the auxiliary machine in the distribution network system is regarded as an infinite power supply, that is, the bus voltage is 1. After calculation, the plant high-voltage bus is 0.682>0.60, and the plant low-voltage bus is 0.559>0.55. Finally, it is verified that the auxiliary machine can be successfully started in the black start period (T=60min) under the three scenarios, which proves the effectiveness of the proposed model and method.

[0253] Functions and Effects of the Embodiments

[0254] According to the distributed resource-assisted black start strategy for key nodes of the main network for resilience improvement involved in this embodiment, the startup steps are as follows: Step 1: Analyze the output characteristics and control strategies of each DER in the distribution network, form a feasibility assessment method for DER to support rapid recovery of key nodes of the main network, establish a black start executable assessment index, evaluate the black start executable period within a day, and based on the assessment results, select a DER control strategy that meets the power supply characteristics and system recovery requirements for the black start executable period; Step 2: Based on the DER control strategy, establish a distribution network DER startup and grid reconstruction joint optimization model with minimum recovery time, maximum available power generation capacity and minimum recovery path weight as the target, and use a combination of genetic algorithm and Floyd algorithm to solve the optimal startup sequence of each DER. Each DER starts and connects to the grid smoothly according to the optimal startup sequence, and aggregates to form a regional power supply; Step 3: The regional power supply starts the auxiliary machines of the thermal power plant in batches, and starts the thermal power units after the auxiliary machines of the thermal power plant are fully and normally operated. The restored thermal power units are gradually connected to the surrounding distribution network loads and provide startup power support for other thermal power plants, thereby gradually expanding the recovery range until the entire system is restored.

[0255] The above process evaluates the feasibility of black start within one day based on DER output prediction, establishes a grid reconstruction model for joint optimization of DER startup sequence and recovery path, and forms the optimal recovery plan for DER startup of the main grid thermal power plant in the distribution network system, ensuring that the thermal power units can be started safely as soon as possible within the hot start time limit, thereby improving the resilience of the power grid.

[0256] In addition, the distributed resource-assisted main grid key node black start strategy for improving resilience involved in this embodiment significantly reduces the main grid power node recovery time, effectively improving the grid resilience and DER utilization efficiency.

[0257] The above-mentioned embodiments are preferred examples of the present invention and are not intended to limit the protection scope of the present invention.

Claims

1. A distributed resource-assisted black start strategy for key nodes of the main network for improving resilience, characterized in that: The following steps are involved: Step 1: Analyze the output characteristics and control strategies of each distributed resource in the distribution network, form a feasibility assessment method for distributed resources to support the rapid recovery of key nodes in the main network, establish a black start executable assessment index, and evaluate the black start executable period within a day. Based on the assessment results, for the black start executable period, select a distributed resource control strategy that meets the power supply characteristics and system recovery requirements; Step 2: Based on the distributed resource control strategy, a joint optimization model for the startup of distributed resources and grid reconstruction of the distribution network is established with the minimum recovery time, the maximum available power generation capacity and the minimum recovery path weight as the target, and a method combining a genetic algorithm and a Floyd algorithm is used to solve the optimal startup sequence of each distributed resource. Each of the distributed resources is smoothly started and connected to the grid according to the optimal startup sequence, and aggregated to form a regional power supply. Step 3: The regional power supply starts the auxiliary equipment of the thermal power plant in batches, and starts the thermal power units after the auxiliary equipment of the thermal power plant is fully and normally operated. The thermal power units after recovery are gradually connected to the surrounding distribution network loads and provide starting power support for other thermal power plants, thereby gradually expanding the recovery scope until the whole system is restored. Among them, in step 2, according to the characteristics that the distributed resource startup sequence and the recovery path drive and support each other, a joint optimization model of the distribution network distributed resource startup and the grid reconstruction is established, The process of solving the optimal startup sequence includes the following sub-steps: Step 2-1, selecting energy storage and micro gas turbines with self-starting capability and stronger regulation performance as black start power supply self-starting, with the goal of shortest system recovery time, maximum available power generation capacity and minimum recovery path weight, and optimizing the distributed resource selection and distributed resource startup sequence through the genetic algorithm; Step 2-2, based on the line importance evaluation, the shortest path algorithm is used to optimize the path of the distributed resource startup sequence to obtain the shortest path, and the shortest path is passed back to the distributed resource startup sequence optimization model to obtain the optimal startup sequence of each distributed resource to achieve global optimization. In step 2, the algorithm implementation process of using the genetic algorithm combined with the Floyd algorithm to solve the model is as follows: Based on the distributed resource startup sequence randomly generated by the genetic algorithm, the Floyd algorithm is called to search for a recovery path for each distributed resource startup sequence, and the genetic algorithm is called to select the optimal distributed resource startup sequence and the recovery path under the sequence, thereby obtaining the global optimal solution of the target grid. The Floyd algorithm is sequentially divided into S 分布式资源 Medium S1~S N The node is used as the starting and ending point of the search to perform the recovery path search. The basic idea is: let L(S i ,S k ) represents point S i To point S k The shortest path, L(S k ,S j ) represents point S k To point S j The shortest path, L(S i ,S j ) represents point S i To point S j Path, then point S i To point S j The shortest path is expressed as min(L(S i ,S k )+L(S k ,S j ),L(S i ,S j )), The basic idea of ​​the genetic algorithm is: assuming that there are N distributed resource nodes and one main grid thermal power plant node Q in the system, the chromosome in the genetic algorithm adopts a binary encoding method, and the distributed resource startup sequence is represented by a chromosome with a length of k*(N+1), that is, Chrom=[A1,A2,…,A N |A N+1 ], where A N+1 is a chromosome segment of length k, A1~A N Represents the start time of each distributed resource, A N+1 The thermal power plant is set to be started last. The genetic algorithm first randomly generates a batch of initial chromosomes, then converts the objective function of the distributed resource startup order optimization model into the fitness function of the chromosome, and generates offspring chromosomes based on the fitness function value of each chromosome through roulette random selection and crossover, mutation, recombination and other operation steps, and records and retains the excellent chromosomes of the parent generation, and repeats the steps until the set conditions are met.

2. According to claim 1, a distributed resource-assisted main network key node black start strategy for improving resilience, Features: Wherein, in step 1, the distributed resources include wind turbines, photovoltaics, energy storage and micro gas turbines, Step 1 includes the following steps: Step 1-1, according to the output characteristics and spatiotemporal distribution uncertainty of multiple types of distributed resources, the available power generation capacity of distributed resources in the future period during the black start process is predicted, and the prediction error deviation is processed by the confidence interval method to obtain the output prediction result of distributed resources in the distribution network system; Step 1-2, based on the output prediction result of the distributed resources of the distribution network system, considering the two aspects of the minimum power demand for the auxiliary machine startup of the large-scale units in the main network and the continuous execution inclination of the black start, establish a black start executable evaluation index, evaluate the black start executable period within a day, and obtain an evaluation result; Step 1-3, based on the evaluation result, for the power outage time when the black start solution can be executed, select a distributed resource control strategy that meets the power supply characteristics and system recovery requirements to ensure that the faulty system is successfully started and runs stably.

3. According to claim 2, a distributed resource-assisted main network key node black start strategy for improving resilience, Features: Wherein, in step 1-1, the output characteristics of the multi-type distributed resources include wind power output characteristics, photovoltaic unit output characteristics and energy storage operation characteristics, The energy storage operation characteristics taking into account the energy storage charging and discharging power constraints and the energy storage charge state constraints are as follows: -P ES,N ≤P ES (t)≤P ES,N (1) SOC ES,min ≤SOC ES (t)≤SOC ES,max (2) Where P ES,N is the rated power of energy storage; SOC ES,max , SOC ES,min and SOC ES (t) are the upper and lower limits of the energy storage device’s state of charge and the energy storage state of charge during the black start process; SOC ES (t) The calculation formula can be expressed as: Where, δ is the energy storage self-discharge rate; η c , η d are the charge and discharge efficiency respectively; S is the charging and discharging power of energy storage at time t; s is the capacity of the energy storage device.

4. According to claim 2, a distributed resource-assisted main network key node black start strategy for improving resilience is characterized by: in, Step 1-2 includes the following steps: Step 1-2-1, confidence interval estimation: The actual output of the unit is distributed around the predicted value. The actual output of wind power and photovoltaic units in each period during the black start process is expressed as the sum of the predicted value and the prediction error, which is described as follows: Where P WT (t), P PV (t) respectively represent the actual power generation of wind power and photovoltaic units at time t, They represent the predicted power of wind power and photovoltaic units at time t, They represent the prediction errors of wind power and photovoltaic power generation at time t, The uncertainty processing method uses the confidence interval method to ensure that the scheme meets a certain confidence level. First, based on the output forecast values ​​of wind power and photovoltaic units, the uncertainty model is used to fit the prediction error distribution in different power sections to obtain the probability density function. Secondly, based on the probability density function, the interval set with a cumulative probability greater than or equal to the confidence level 1-α is extracted. Finally, the interval with the shortest length under the same confidence level 1-α is selected from the interval set as the confidence interval under the predicted power. The confidence interval obtained based on the shortest confidence interval principle under the confidence level 1-α is as follows Where ΔP is the error corresponding to the power prediction value P, which conforms to the probability distribution F(ΔP); Step 1-2-2, using two evaluation indicators, namely, the minimum black start power requirement and the black start continuous execution inclination, to evaluate the feasibility of black start. The minimum black start power requirement is P 分布式资源,min for: P DER,min =(1+α)P M (6) Where P M is the rated power of the auxiliary equipment of the thermal power unit to be started; the coefficient α includes the power consumption rate of the distributed resources themselves, the line loss rate and a certain margin reserved, and the rotating reserve is reserved to cope with the uncertainty of the output of wind power and photovoltaic units. Distributed resource power generation Q during the black start process 分布式资源 、Black start minimum required power Q 分布式资源,min And the black start continuous execution inclination γ is respectively expressed as: Where P 分布式资源 (t) is the output of distributed resources at time t. When γ is greater than or equal to 1, it indicates that the sustainable execution tendency is higher. When γ is less than 1, it indicates that the black start solution is more likely to be unexecutable.

5. According to claim 1, a distributed resource-assisted main network key node black start strategy for improving resilience is characterized by: in, In step 2-1, the optimization objectives of the distributed resource startup sequence are the minimum time, the maximum available power generation capacity, and the minimum weight of the recovery path of the distributed resources to be started, as shown below: In the formula, Ψ BG and NBG are the sets of black start power supplies and non-black start power supplies started during the system network reconstruction phase, Ψ L is the set of all lines l in the recovery path; Z l is the restoration weight of the selected line l, P BG,n and P NBG,k are the restored power outputs of the nth black start unit and the kth non-black start unit in this stage, Taking the available power generation of distributed resources at the time of power outage as a reference, an optimization model for the startup sequence of distributed resources is established. The constraints in step 2-1 include distributed resource output constraints, node voltage constraints, line thermal stability limit constraints, power balance constraints, thermal power unit hot start time constraints, distribution network topology radial structure constraints, and auxiliary motor self-start verification. The distributed resource output constraint is: Where P Gn is the active power output of the activated distributed resources, Q Gn is the reactive power output of the activated distributed resources, Ψ G It is the collection of all distributed resources in the system. The node voltage constraint is: In i min ≤V i ≤V i max i∈ψ N (13) Where V i is the voltage amplitude of node i in the system, Ψ N is the set of all nodes in the system. The thermal stability limit constraint of the line is: Where P Ll is the active power transmitted in line l, The power balance constraint is: Where P M is the auxiliary power of thermal power unit, P Gn is the output of the activated distributed resources. The coefficient α includes the power consumption rate of the plant, the line loss rate, and a certain margin reserved for the output prediction error of wind power and photovoltaic units. (1+α)P M In order to meet the minimum power requirement for black start, The thermal power unit hot start time constraint is: 0≤T L ≤T max (16) Where, T L T is the time it takes for the black start power supply to start and transmit power to the auxiliary equipment of the thermal power unit through the recovery path, max is the maximum time limit for hot start of thermal power units, The distribution network topology radial structure constraint is: In the formula, z l is a binary variable for the switching state of branch l: 0 means the branch is disconnected, 1 means the branch is closed, n b 、n s are the total number of nodes and the number of root nodes in the distribution network system, The self-starting verification of the auxiliary motor is divided into voltage verification and capacity verification: Where U *1 U is the per unit value of the high voltage bus voltage used in the factory; *2 It is the per unit value of the factory low voltage bus voltage.

6. According to claim 1, a distributed resource-assisted main network key node black start strategy for improving resilience is characterized by: in, In step 2-2, the indicators for evaluating the importance of the line include setting the line operation time weight, setting the line restoration cost weight, and setting the line weight. In the line operation time weight setting, the operation time of each line is divided into optimistic estimation time A, pessimistic estimation time B and most likely estimation time M. The operation time t required to restore a line l is l , its expected value E(t l ) and variance σ l They are: Where l∈Ψ L ,Ψ L is the set of all lines in the system. Assuming that the startup path of the unit at a certain node i consists of L lines, the mean and variance of the operation time required for the recovery path are: If T i If the probability of falling within the start-up time limit of the thermal power unit is high, it is considered that the thermal power unit can be included in the hot start plan. In the line restoration cost weight setting, the total charging capacitance on the restoration path of starting a certain unit i is expressed as: Set the line restoration cost weight to: W l =C l +S l (24) In the formula, S l The cost of closing a switch for a line l, The line weight setting mainly considers two evaluation indicators: time operation weight t and recovery cost weight W. The weight Z of line l is l Set to: From l =k1W l +k2t l (25) In the formula, k1 and k2 are allocation coefficients, k1+k2=1. When the line time operation weight and the restoration cost weight are smaller, the line weight of the line will be smaller, and the line importance will be higher. In the process of restoring the path selection, the line with smaller restoration line weight, that is, the line with higher line importance, is preferentially selected. In step 2-2, the optimization is performed with the goal of minimizing the sum of line weights in the restoration path. The objective function is expressed as: In the dynamic optimization of the restoration path, when restoring the next section of the line based on the current status, the system network is divided into energized parts and de-energized parts, and the energized parts are aggregated into power supply points. From these power supply points, the optimal path to start the next target point is calculated, and the lines with higher importance are restored. The above steps are repeated until the thermal power plant is started.

7. According to claim 1, a distributed resource-assisted main network key node black start strategy for improving resilience is characterized by: in, In step 3, after various distributed resources in the distribution network are started and aggregated to form a regional black start power supply, the thermal power plant in the adjacent main grid is started to provide power for the auxiliary equipment of the thermal power unit. The power balance expression is: P W (t)+P PV (t)+P ES (t)+P MT (t)-P load (t)=0 (27) Where P W (t) represents the fan output, P PV (t) represents the photovoltaic output, P ES (t) represents the energy storage output power, P MT (t) represents the fan output, P load (t) represents the auxiliary power of thermal power unit, The excess and deficit of wind power and photovoltaic power generation compared with auxiliary power are as follows: △P + (t)=|P W (t)+P PV (t)-P load (t)| (28) △P_(t)=|P W (t)+P PV (t)-P load (t)| (29) The process of coordinated control of distributed resource startup of thermal power units is as follows: Step 3-1, at time t, distributed resources start to aggregate to form a regional power supply; Step 3-2, the regional power sources are sequentially put into the auxiliary equipment of the thermal power plant; Step 3-3: At the beginning of black start, when the sum of the output of wind power and photovoltaic units is less than the auxiliary power, both wind power and photovoltaic units adopt MPPT control strategy, and the remaining power shortage ΔP-(t) is borne by energy storage and micro gas turbines. When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess amount ΔP + (t) less than 10%P PV At (t), the PV unit adopts MPPT control, and the wind turbine unit adopts load following control strategy to track the auxiliary power and the output change of the PV output shortage. When the sum of the output of wind power and photovoltaic units is greater than the auxiliary power and the excess amount ΔP + (t) greater than 10%P PV At (t), the photovoltaic unit adopts the voltage reduction control that deviates from the maximum power point, and the load reduction amount is 10%P PV (t), the wind turbine adopts a load following control strategy to track the output changes of the auxiliary power and the photovoltaic output shortage; Step 3-4: If the thermal power unit has not resumed power generation, repeat steps 3-2 to 3-3 until the thermal power unit resumes power generation.

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