A method and system for improving the elasticity of distribution network based on distributed new energy aggregation

Abstract

The present invention discloses a method and system for improving the elasticity of a distribution network based on distributed new energy aggregation, which improves the role of distributed new energy units in improving the elasticity of distribution systems under the background of energy transformation. Probabilistic modeling is performed on the output and load data prediction errors of new energy units based on multivariate normal distribution; a typical output curve that is continuously updated over time is obtained by using fast forward scenario reduction; an aggregation model of energy storage systems and distributed new energy units is built; the aggregation characteristics corresponding to the aggregation model, i.e., the voltage frequency range and the active and reactive power output range, are input to the distribution network layer; the optimization goal of maximizing the recovery of key loads and various constraints are set; and the recovery capacity of the distribution system after a disaster is improved. Under the background of energy transformation, the present invention solves the uncertainty and volatility problems in the access of a high proportion of new energy to the distribution network, improves the key load recovery capacity of the distribution system after a disaster, and lays the foundation for solving the key outstanding problems of the elastic distribution network technology moving towards practical industrial applications.

Description

Technical Field

[0001] The present invention belongs to the technical field of improving the resilience of distribution systems, and specifically relates to a method and system for improving the elasticity of distribution networks based on distributed renewable energy aggregation. Background Art

[0002] At present, the redundancy of the distribution system is low and the control means are relatively scarce. Large-scale power outages occur frequently, which have a serious impact on production and life. It has become an urgent need for distribution network construction to build a flexible distribution network that can effectively resist various natural disasters and multi-equipment disturbances and can achieve rapid recovery after disasters and disturbances.

[0003] In the process of energy transformation, renewable energy such as wind power and photovoltaic power will become an important feature of distributed power distribution systems. Distributed renewable energy can work together with distributed energy storage to provide emergency power for critical loads to cope with power outages caused by natural disasters and malicious attacks.

[0004] However, most of the current research on technologies to improve the resilience of elastic distribution networks still has many shortcomings. The uncertainty of renewable energy output and load, changes in the control methods of renewable energy units and energy storage systems, and the interaction between distribution system operators and power systems have added more and more difficulties to load recovery decisions. In the existing post-disaster recovery problem, the configuration of distributed power sources in the distribution network is relatively simple, without considering the characteristics of large-scale renewable energy, energy storage and load aggregation, as well as the coordination of control modes between renewable energy and energy storage. In addition, the large-scale increase in decision variables makes the optimization solution process less efficient, and it is urgent to study efficient solution methods. Therefore, it is necessary to use a distributed resource management system to manage large-scale distributed power sources, and perform frequency and voltage control, establish a load recovery model for the interaction of distributed resource management system and distribution network decision information, and realize the rapid recovery of the distribution system after extreme events. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a distribution network elasticity improvement method and system based on distributed renewable energy aggregation in response to the above-mentioned deficiencies in the prior art, which is used to solve the technical problems of single distributed power supply configuration mode in the distribution network, coordination and cooperation of control modes between renewable energy and energy storage, and low solution efficiency.

[0006] The present invention adopts the following technical solutions:

[0007] A method for improving the elasticity of a distribution network based on distributed renewable energy aggregation includes the following steps:

[0008] S1. Use multivariate normal distribution to perform probability modeling on the forecast data of multiple distributed wind power, photovoltaic output and load data, add the sampled forecast error data to the forecast curve, and use the fast forward selection method to redistribute the probability of the scenarios and screen typical scenarios;

[0009] S2. Using the typical scenarios selected in step S1, establish an aggregation model of the distributed energy storage system and the new energy unit, and solve and obtain the aggregation characteristics of the aggregation model;

[0010] S3. Using the aggregation characteristics of the aggregation model obtained in step S2 as input parameters, with the goal of maximizing the load recovery under different weight coefficients, considering the network radiation topology constraints, flow constraints, node voltage / current constraints, and safety constraints, a multi-period distribution network load recovery model is established to solve the output of each distributed power source, the microgrid division situation and the load recovery results.

[0011] Specifically, step S1 is as follows:

[0012] S101. Probabilistic modeling is performed on the predicted output data of power output or load of multiple new energy units using multivariate normal distribution, and at each moment in the load recovery process, prediction error data is sampled to reflect the operational uncertainty of the power output and load of the new energy units;

[0013] S102, adding the prediction error data obtained in step S101 to the corresponding prediction curve to generate a time series scenario of power output and load of the new energy unit; finally, using the fast forward selection method to redistribute the probabilities of all scenarios and screen typical scenarios.

[0014] Furthermore, in step S101, the prediction error data f X (x1,...,x n ) specifically:

[0015]

[0016] Among them, X i is the i-th prediction error, μ is the mean vector of all prediction errors, and Σ is the covariance matrix.

[0017] Furthermore, in step S102, the prediction error data is added to the corresponding prediction curve as follows:

[0018]

[0019] Among them, P i,s,t is the active power of the i-th renewable energy unit or load at time t and scenario s; is the predicted value corresponding to the i-th new energy unit or load at time t; E i,s,tis the prediction error data after sampling.

[0020] Specifically, step S2 is as follows:

[0021] S201, adopting a decentralized control strategy combined with droop control, multiple energy storage systems connected to the same node jointly support the voltage and frequency of the microgrid through droop control;

[0022] S202: Under the premise that the energy storage system supports voltage and frequency in step S201, multiple new energy units provide active power and reactive power to the microgrid through adaptive power control;

[0023] S203. Based on the control strategies of step S201 and step S202, a distributed resource management system is used to aggregate multiple distributed energy storage systems and new energy units, monitor the demand information of the upper distribution network, and allocate and adjust the power output of each configured new energy unit and energy storage; Cplex is used to solve the aggregation model of the energy storage system and the new energy unit to obtain the aggregation characteristics, that is, the active and reactive power range that can be provided, and the corresponding frequency and voltage range.

[0024] Further, in step S202, the active power of the nth new energy unit at time t and scenario s and reactive power They are:

[0025]

[0026]

[0027] in, is the control coefficient of the nth new energy unit; is the maximum active power of the nth new energy unit; f H is the high frequency threshold; is the frequency-active power control mode of the nth renewable energy unit, and are the maximum and minimum reactive power of the nth renewable energy unit at time t and scenario s respectively; U L and U H are the voltage low state threshold and high state threshold; and is the control coefficient; It is the voltage-reactive power control mode of the nth renewable energy unit.

[0028] Furthermore, in step S203, the aggregation model of multiple distributed energy storage systems and new energy units is expressed as:

[0029]

[0030]

[0031] in, and are the active power and reactive power of the distributed resource management system at node i, time t and scenario s respectively; and It is the decision variable of the new energy unit and energy storage, describing whether the new energy or energy storage is connected to the microgrid; and is the coefficient of the aggregation characteristic of fP; and is the coefficient of the UQ aggregation characteristic; K i and L i It is the collection of new energy units and energy storage in the distributed resource management system at node i.

[0032] Specifically, in step S3, the objective function of the multi-period distribution network load recovery model is:

[0033]

[0034] Among them, S is a typical scenario set of new energy units and loads, η s is the weight of scenario s, T is the time set of the optimization process, N is the node set in the distribution system, and λ i,t is a binary variable that determines whether to restore load i at time t, ω i is the weight of load i, is the active power required by the key load of node i in the distribution network at time t under scenario s.

[0035] Furthermore, the network radial topology constraints are:

[0036] α ij,t =β ij,t +β ji,t ,ij∈B I ,ji∈B II

[0037]

[0038]

[0039] Among them, B I and B II is the set of feeder branches in the distribution network, N is the set of nodes connected to the distribution network, G B is the set of synchronous machine power supply nodes in the distribution network, is a set of nodes containing new energy units and energy storage, α ij，t is the line state, β ij，t and βij，t The parent-child connection relationship of the nodes at both ends of the line is used for the branch transmission power equation;

[0040] The power flow constraints and node voltage / current constraints are:

[0041]

[0042]

[0043] -M(1-α ij,t )+(R ij P ij,s,t +X ij Q ij,s,t ) / U0≤U j,s,t -U i,s,t ,ij∈B I

[0044] M(1-α ij,t )+(R ij P ij,s,t +X ij Q ij,s,t ) / U0≥U j,s,t -U i,s,t ,ij∈B I

[0045] Among them, P ik,s,t (P ji,s,t ) and Q ik,s,t (Q ji,s,t ) are respectively the active and reactive power flows of the ik(ji) branch at time t and scenario s; P i,s,t and Q i,s,t is the power injection into node i; and are the active and reactive power of the load respectively; R ij and X ij is the resistance and reactance of branch ij; U i,s,t and U j,s,t is the voltage amplitude of nodes i and j, U0 is the rated voltage of the system; M is a real number with a large value;

[0046] The safety constraints are:

[0047] f min ≤f i,s,t ≤f max ,i∈N

[0048] U min ≤U i,s,t ≤U max ,i∈N

[0049]

[0050]

[0051] in, and are the maximum active power flow and minimum reactive power flow of the ij branch respectively;

[0052] The distributed generation constraints are:

[0053]

[0054]

[0055]

[0056] in, and are the actual active and reactive outputs of the synchronous machine power supply at node i, time t and scenario s respectively; and are the reference values ​​of active power and reactive power; and They are the maximum and minimum values ​​of active output and reactive output of the synchronous machine power supply respectively; and is the droop control coefficient of the synchronous machine power supply;

[0057]

[0058]

[0059] in, and They are the maximum and minimum values ​​of active output and reactive output of the node containing new energy units and energy storage at node i, time t and scenario s, respectively.

[0060] In a second aspect, an embodiment of the present invention provides a distribution network elasticity improvement system based on distributed new energy aggregation, including:

[0061] The screening module uses multivariate normal distribution to perform probability modeling on the forecast data of multiple distributed wind power, photovoltaic output and load data, adds the sampled forecast error data to the forecast curve, and uses the fast forward selection method to redistribute the probability of the scenarios and screen typical scenarios;

[0062] Aggregation module, which uses the typical scenarios selected by the screening module to establish the aggregation model of distributed energy storage system and new energy units, and solves the aggregation characteristics of the aggregation model;

[0063] The optimization module uses the aggregation characteristics of the aggregation model obtained by the aggregation module as input parameters, takes the maximum load recovery under different weight coefficients as the goal, considers the network radiation topology constraints, flow constraints, node voltage / current constraints, and safety constraints to establish a multi-period distribution network load recovery model, and solves the output of each distributed power source, microgrid division and load recovery results.

[0064] Compared with the prior art, the present invention has at least the following beneficial effects:

[0065] The present invention discloses a method for improving the elasticity of a distribution network based on distributed renewable energy aggregation. The method can fully consider the errors of prediction data of distributed renewable energy units such as photovoltaic and wind turbines, and obtain typical scenarios by using scenario reduction methods based on multivariate normal distribution modeling and considering the influence of spatial correlation. The method utilizes a distributed resource management system to manage the combination and coordinated control of multiple distributed renewable energy units and energy storage systems. The method performs probability modeling on the output and load prediction errors of distributed power sources, considers the coordinated control of distributed renewable energy units and energy storage systems in optimization decisions, establishes a multi-period distribution network load recovery model in which a multi-period distributed resource management system interacts with a distribution system, obtains a multi-period load recovery action sequence under the premise of considering multiple constraints, and obtains an optimal load recovery strategy.

[0066] Furthermore, by screening typical scenarios through the fast forward selection method in step S1, it is possible to achieve probabilistic modeling of distributed power output and load forecast errors, and take the uncertainty of dynamic changes into account in the multi-period distribution network load recovery model.

[0067] Furthermore, establishing the connection between the predicted value and the actual value of the distributed power generation can take into account the uncertainty of the distributed power generation prediction error and increase the accuracy of the model input variables.

[0068] Furthermore, the probability redistribution of all scenes can be used to reduce the scenes and screen out typical scenes by using fast forward selection.

[0069] Furthermore, through step S2, an aggregation model of the distributed energy storage system and the new energy unit is established, in which a master-slave collaborative control strategy based on droop control is designed.

[0070] Furthermore, the voltage and frequency fluctuations during the load recovery process are smoothed through coordinated control measures to meet the stringent frequency and voltage requirements of important loads.

[0071] Furthermore, the load-supporting capacity of the aggregated power generation resources is solved, that is, the active and reactive power that can be provided and the corresponding frequency and voltage range.

[0072] Furthermore, the aggregation characteristics in step S2 are used as input parameters to input the information of the distributed resource level into the distribution network level, thereby realizing information interaction between the two-layer optimization models.

[0073] Furthermore, various constraints are set so that the multi-period distribution network load recovery model satisfies the distribution network topology constraints, power flow constraints, distributed generation operation constraints and safety constraints.

[0074] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0075] In summary, the present invention effectively ensures and manages the collaborative cooperation of large-scale distributed renewable energy and energy storage, laying a foundation for the further application of distribution network resilience enhancement technology in industry.

[0076] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 The output characteristic diagram of the distributed new energy unit and energy storage system aggregation model in step S2 of the present invention;

[0078] Figure 2 This is a recovery result diagram obtained by the multi-period distribution network load recovery model in step S3 of the present invention;

[0079] Figure 3 This is a diagram of the dynamic microgrid partitioning results at a typical moment in step S3 of the present invention. DETAILED DESCRIPTION

[0080] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0081] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0082] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0083] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects are in an "or" relationship.

[0084] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0085] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0086] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0087] The present invention provides a method for improving the elasticity of a distribution network based on the aggregation of distributed renewable energy sources, performs probabilistic modeling on the output prediction error of distributed power sources, and considers the uncertainty of the output of distributed power sources in the optimization decision. An aggregation model of distributed renewable energy units and energy storage systems is established on the basis of considering master-slave collaborative control and droop control. On this basis, a multi-period distribution network load recovery model is established under the premise of considering constraints such as network radiation topology constraints, power output constraints, flow constraints, and safety constraints. This lays the foundation for the construction of a flexible distribution network under large-scale renewable energy access.

[0088] The present invention provides a method for improving the elasticity of a distribution network based on distributed new energy aggregation, comprising the following steps:

[0089] S1. Probabilistic modeling of multiple distributed wind power or photovoltaic output and load forecast data is performed. The forecast data is modeled using multivariate normal distribution, the sampled forecast error data is added to the corresponding forecast curve, and the probability of the scenarios is redistributed through the fast forward selection method to screen typical scenarios;

[0090] Specifically, it includes the collection and collation of distributed power and load forecast data, modeling of unit power output and load forecast data, sampling forecast error data, and scenario generation and reduction of distributed power output. The detailed steps are as follows:

[0091] S101, analyzing the correlation of the forecast data and modeling and sampling the forecast error data;

[0092] The specific method of sampling the prediction error data of distributed wind turbines, photovoltaics or loads using multivariate normal distribution is as follows:

[0093]

[0094] Among them, X i is the i-th prediction error, X i is the corresponding value of the i-th prediction error; μ is the average vector of all prediction errors, which is set to the zero vector; Σ is the covariance matrix, and |Σ| is the determinant of the matrix Σ.

[0095] The covariance parameter matrix in formula (1) is estimated using the historical data of the forecast error, that is, the forecast data minus the actual data; each element at (i, j) is calculated as follows:

[0096]

[0097] Among them, N is the length of historical data, X ik , X jk is the kth data value of the i-th and j-th prediction errors.

[0098] S102, generation and reduction of forecast scenarios for distributed power output and load;

[0099] The prediction error data is added to the corresponding prediction curve to generate a prediction curve that takes the prediction error into account, and the scene reduction is performed, specifically:

[0100]

[0101] Among them, P i,s,t is the active power of the i-th renewable energy unit or load at time t and scenario s; is the predicted value corresponding to time t; E i,s,t is the prediction error data after sampling.

[0102] In the recovery process, the probability density function of uncertainty in the near future is actually a set of normal distributions around the predicted values. After the scenario is generated, the number of scenarios is large, which will increase the amount of calculation for subsequent random optimization. Therefore, the scenario is usually reduced to select only representative scenarios and use the fast forward selection method to screen typical scenarios.

[0103] S2. Design a master-slave control coordination scheme for energy storage systems and new energy units based on droop control, use a distributed resource management system to aggregate multiple energy storage systems and new energy units, monitor the demand information of the upper distribution network, and use this information to allocate and adjust the power output of each new energy unit and energy storage configured therein;

[0104] This includes designing the control strategy of the energy storage system to support the frequency and voltage of the microgrid, designing the control scheme of the new energy unit to provide active and reactive power for the key loads, and using Cplex to solve the aggregation model of the energy storage system and the new energy unit to obtain the aggregation characteristics, as follows:

[0105] The control strategy of the energy storage system is as follows. Constraint (7) represents the constraints of active power and frequency, and constraint (8) represents the constraints of reactive power and voltage:

[0106]

[0107] Among them, f i,s,t , and are the frequency, active power and state of charge of the nth energy storage at node i, time t and scenario s respectively; f ref is the reference value of frequency, k ▽ and k Δ They are the droop coefficient, lift coefficient and drop coefficient respectively; S L and S HThey are the minimum value of the state of charge, the low state threshold and the high state threshold respectively; f max and f min is the maximum and minimum permissible frequencies of the microgrid; and is the maximum active power and minimum active power of the nth energy storage; It is the frequency-active power control mode of the nth energy storage.

[0108]

[0109] Among them, U i,s,t and are the voltage and reactive power of the nth energy storage at node i at time t and scenario s respectively; U ref is the voltage reference value; is the droop coefficient; U max and U min The maximum voltage amplitude and minimum voltage amplitude allowed for the AC bus; and is the maximum and minimum reactive power output of the nth energy storage.

[0110] The calculation process of the droop coefficient of the energy storage system is shown in constraints (9) and (10):

[0111]

[0112]

[0113] The control strategy of the new energy unit is as follows. Constraint (11) represents the constraints of active power and frequency, and constraint (12) represents the constraints of reactive power and voltage:

[0114]

[0115] in, is the active power of the nth renewable energy unit at time t and scenario s; is the control coefficient of the nth new energy unit; is the maximum active power of the nth new energy unit; f H is the high frequency threshold; It is the frequency-active power control mode of the nth renewable energy unit.

[0116]

[0117] in, and are the actual, maximum and minimum reactive power of the nth renewable energy unit at time t and scenario s respectively; U L and U Hare the voltage low state threshold and high state threshold; and is the control coefficient; It is the voltage-reactive power control mode of the nth renewable energy unit.

[0118] The calculation process of the control coefficient of the new energy unit is shown in constraints (13) and (14):

[0119]

[0120]

[0121] The distributed resource management system collects the power demand of key loads in the upper distribution network, monitors and distributes the power output of the lower distributed generation resources; the aggregation characteristics of the aggregation model of multiple distributed new energy units and energy storage systems can be found in Figure 1 , which is expressed as follows:

[0122]

[0123]

[0124] in, and are the active power and reactive power of the distributed resource management system at node i, time t and scenario s respectively; and It is the decision variable of the new energy unit and energy storage, describing whether the new energy or energy storage is connected to the microgrid; and is the coefficient of the aggregation characteristic of fP; and is the coefficient of the UQ aggregation characteristic; K i and L i It is the collection of new energy units and energy storage in the distributed resource management system at node i.

[0125] S3. Taking the maximum load recovery under different weight coefficients as the goal, a multi-period distribution network load recovery model is established by considering network radiation topology constraints, power flow constraints, safety constraints, and distributed power constraints, and optimized and solved using commercial solution software such as Cplex;

[0126] This includes setting the objective function and constraints, grading the loads of different importance, and defining the pick-up rates of loads of different importance to represent the recovery results. Please refer to Figure 2 , as follows:

[0127] Setting of objective function:

[0128]

[0129] Among them, S is a typical scenario set of new energy units and loads; η s is the weight of scenario s; T is the time set of the optimization process; N is the node set in the distribution system; λ i,t is a binary variable that determines whether to restore load i at time t; ω i is the weight of load i, indicating the priority of the load.

[0130] The constraints are set as:

[0131] Constraint (18) represents the network radial topology constraint:

[0132]

[0133] Among them, B I and B II is the set of feeder branches in the distribution network; N is the set of nodes connected to the distribution network; G B It is the set of synchronous machine power supply nodes in the distribution network; is a set of nodes containing new energy units and energy storage; α ij，t is the line status, 1 means the line is connected, 0 means the line is disconnected; β ij，t and β ji，t The parent-child connection relationship between the nodes at both ends of the line is used for the branch transmission power equation.

[0134] Constraints (19)-(21) represent power flow constraints and node voltage / current constraints:

[0135] Constraints (19)-(20) indicate that the power flowing into a node should be equal to the power flowing out of the node, as follows:

[0136]

[0137]

[0138] Constraint (21) limits the range of the voltage difference between the two ends of the energized line, as follows:

[0139]

[0140] Among them, P ik,s,t (P ji,s,t ) and Q ik,s,t (Q ji,s,t ) are respectively the active and reactive power flows of the ik(ji) branch at time t and scenario s; P i,s,t and Q i,s,t is the power injection into node i; and are the active and reactive power of the load respectively; R ij and Xij is the resistance and reactance of branch ij; U i,s,t and U j,s,t is the voltage amplitude of nodes i and j, U0 is the rated voltage of the system; M is a large real number, such as 10000;

[0141] The security constraints that need to be considered are shown in (22)-(23):

[0142]

[0143]

[0144] in, and are the maximum active power flow and minimum reactive power flow of the ij branch respectively;

[0145] The distributed generation constraints are as shown in constraints (24)-(26):

[0146] Constraint (24) represents the upper and lower limits of active and reactive power that the synchronous machine power supply node needs to meet, as follows:

[0147]

[0148] Constraint (24) represents the droop control strategy satisfied by the synchronous machine power node, as follows:

[0149]

[0150] in, and are the actual active and reactive outputs of the synchronous machine power supply at node i, time t and scenario s respectively; and are the reference values ​​of active power and reactive power; and They are the maximum and minimum values ​​of active output and reactive output of the synchronous machine power supply respectively; and is the droop control coefficient of the synchronous machine power supply.

[0151] Constraint (26) represents the upper and lower limits of active and reactive power that nodes containing new energy units and energy storage need to meet, as follows:

[0152]

[0153] in, and They are the maximum and minimum values ​​of active output and reactive output of the node containing new energy units and energy storage at node i, time t and scenario s, respectively.

[0154] In order to quantitatively analyze the restoration degree of loads of different importance, the load pickup rate is defined to represent the proportion of restored power supply load to the total load, which is expressed as follows:

[0155]

[0156] Among them, z∈0,1,2,3,LSR z is the pickup rate of loads with different importance; N z is a node set of loads of different importance in the distribution system, z=1 represents the primary load, z=2 represents the secondary load, and z=3 represents the tertiary load.

[0157] In another embodiment of the present invention, a distribution network elasticity improvement system based on distributed new energy aggregation is provided. The system can be used to implement the above-mentioned distribution network elasticity improvement method based on distributed new energy aggregation. Specifically, the distribution network elasticity improvement system based on distributed new energy aggregation includes a screening module, an aggregation module and an optimization module.

[0158] Among them, the screening module uses multivariate normal distribution to perform probability modeling on the forecast data of distributed wind power, photovoltaic output and load data, adds the sampled forecast error data to the corresponding forecast curve, and uses the fast forward selection method to redistribute the probability of the scenarios to screen typical scenarios;

[0159] Aggregation module, which uses the typical scenarios selected by the screening module to establish the aggregation model of distributed energy storage system and new energy units, and solves the aggregation characteristics of the aggregation model;

[0160] The optimization module uses the aggregation characteristics of the aggregation model obtained by the aggregation module as input parameters, takes the maximum load recovery under different weight coefficients as the goal, considers the network radiation topology constraints, flow constraints, node voltage / current constraints, and safety constraints to establish a multi-period distribution network load recovery model, and solves the output of each distributed power source, microgrid division and load recovery results.

[0161] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0162] The present invention is applied to improve the resilience of elastic distribution networks after extreme events. After adopting the present invention, the frequency and voltage regulation capabilities of generator sets with uncertainty and volatility can be improved in distribution systems with a high ratio of new energy access, so that they can independently deliver power to microgrids, thereby maximizing the recovery of key loads in distribution systems after disasters. An example analysis was performed in the IEEE-37 node system, and the load recovery results obtained are as follows: Figure 2 It can be seen that the pick-up rate of the primary and secondary loads during the recovery process is higher than that of the tertiary load, which is in line with the objective function of the optimization problem and gives priority to powering the loads with high importance.

[0163] See also Figure 3 , three time periods are selected to show the division results of the dynamic microgrid during the recovery process. It can be seen that after adopting the present invention, the distributed new energy access nodes that originally did not have the ability to independently support the microgrid can independently supply power to the key loads in the microgrid under the action of the energy storage system, and adjust the frequency and voltage therein. While improving the new energy consumption capacity, the post-disaster recovery capacity of the distribution system is improved, providing power for more loads.

[0164] In summary, the present invention provides a method and system for improving the elasticity of a distribution network based on distributed renewable energy aggregation, which can improve the post-disaster recovery capability of the distribution system under the premise of large-scale and high-proportion renewable energy access. By probabilistically modeling the output prediction error of distributed power sources, reducing the impact of the uncertainty of distributed power output, and establishing an aggregation model of distributed renewable energy and energy storage systems, the critical load recovery capability of the distribution system is maximized under the premise of improving the voltage and frequency regulation capability of the renewable energy units, verifying the effectiveness of the proposed method.

[0165] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0166] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0169] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A method for improving the elasticity of a distribution network based on distributed renewable energy aggregation, characterized in that: The following steps are involved: S1. Use multivariate normal distribution to perform probability modeling on the forecast data of multiple distributed wind power, photovoltaic output and load data, add the sampled forecast error data to the forecast curve, and use the fast forward selection method to redistribute the probability of the scenarios and screen typical scenarios; S2. Using the typical scenarios selected in step S1, establish an aggregation model of the distributed energy storage system and the new energy unit, and solve and obtain the aggregation characteristics of the aggregation model. Step S2 is specifically as follows: S201, adopting a decentralized control strategy combined with droop control, multiple energy storage systems connected to the same node jointly support the voltage and frequency of the microgrid through droop control; S202: Under the premise that the energy storage system supports voltage and frequency in step S201, multiple new energy units provide active power and reactive power to the microgrid through adaptive power control. The active power of the nth new energy unit at time t and scenario s is and reactive power They are: in, is the control coefficient of the nth new energy unit; is the maximum active power of the nth new energy unit; f H is the high frequency threshold; is the frequency-active power control mode of the nth renewable energy unit, and are the maximum and minimum reactive power of the nth renewable energy unit at time t and scenario s respectively; U L and U H are the voltage low state threshold and high state threshold; and is the control coefficient; is the voltage-reactive power control mode of the nth renewable energy unit; S203, based on the control strategies of step S201 and step S202, a distributed resource management system is used to aggregate multiple distributed energy storage systems and new energy units, monitor the demand information of the upper distribution network, and allocate and adjust the power output of each configured new energy unit and energy storage; Cplex is used to solve the aggregation model of the energy storage system and the new energy unit to obtain the aggregation characteristics, that is, the active and reactive power ranges that can be provided, and the corresponding frequency and voltage ranges. The aggregation model of multiple distributed energy storage systems and new energy units is expressed as: in, and are the active power and reactive power of the distributed resource management system at node i, time t and scenario s respectively; and It is the decision variable of the new energy unit and energy storage, describing whether the new energy or energy storage is connected to the microgrid; and is the coefficient of the aggregation characteristic of fP; and is the coefficient of the UQ aggregation characteristic; K i and L i is the set of new energy units and energy storage in the distributed resource management system at node i; S3. Using the aggregation characteristics of the aggregation model obtained in step S2 as input parameters, with the goal of maximizing the load recovery under different weight coefficients, considering the network radiation topology constraints, flow constraints, node voltage / current constraints, and safety constraints, a multi-period distribution network load recovery model is established to solve the output of each distributed power source, the microgrid division situation and the load recovery results.

2. The method for improving the elasticity of distribution network based on distributed new energy aggregation according to claim 1 is characterized in that: Step S1 is specifically as follows: S101. Probabilistic modeling is performed on the predicted output data of power output or load of multiple new energy units using multivariate normal distribution, and at each moment in the load recovery process, prediction error data is sampled to reflect the operational uncertainty of the power output and load of the new energy units; S102, adding the prediction error data obtained in step S101 to the corresponding prediction curve to generate a time series scenario of power output and load of the new energy unit; finally, using the fast forward selection method to redistribute the probabilities of all scenarios and screen typical scenarios.

3. The method for improving the elasticity of distribution network based on distributed new energy aggregation according to claim 2 is characterized in that: In step S101, the prediction error data f X (x1,...,x n ) specifically: Among them, X i is the i-th prediction error, μ is the mean vector of all prediction errors, and Σ is the covariance matrix.

4. The method for improving the elasticity of distribution network based on distributed new energy aggregation according to claim 2 is characterized in that: In step S102, the prediction error data is added to the corresponding prediction curve as follows: Among them, P i,s,t is the active power of the i-th renewable energy unit or load at time t and scenario s; is the predicted value corresponding to the i-th new energy unit or load at time t; E i,s,t is the prediction error data after sampling.

5. The method for improving the elasticity of distribution network based on distributed new energy aggregation according to claim 1 is characterized in that: In step S3, the objective function of the multi-period distribution network load recovery model is: Among them, S is a typical scenario set of new energy units and loads, η s is the weight of scenario s, T is the time set of the optimization process, N is the node set in the distribution system, and λ i,t is a binary variable that determines whether to restore load i at time t, ω i is the weight of load i, is the active power required by the key load of node i in the distribution network at time t under scenario s.

6. The method for improving the elasticity of distribution network based on distributed new energy aggregation according to claim 5 is characterized in that: The network spoke topology constraints are: a ij,t =b ij,t +b ji,t ,ij∈B I ,ji∈B II Among them, B I and B II is the set of feeder branches in the distribution network, N is the set of nodes connected to the distribution network, G B is the set of synchronous machine power supply nodes in the distribution network, is a set of nodes containing new energy units and energy storage, α ij , t is the line state, β ij , t and β ji , t The parent-child connection relationship of the nodes at both ends of the line is used for the branch transmission power equation; The power flow constraints and node voltage / current constraints are: -M(1-α ij,t )+(R ij P ij,s,t +X ij Q ij,s,t ) / U0≤U j,s,t -U i,s,t ,ij∈B I M(1-α ij,t )+(R ij P ij,s,t +X ij Q ij,s,t ) / U0≥U j,s,t -U i,s,t ,ij∈B I Among them, P ik,s,t (P ji,s,t ) and Q ik,s,t (Q ji,s,t ) are respectively the active and reactive power flows of the ik(ji) branch at time t and scenario s; P i,s,t and Q i,s,t is the power injection into node i; and are the active and reactive power of the load respectively; R ij and X ij is the resistance and reactance of branch ij; U i,s,t and U j,s,t is the voltage amplitude of nodes i and j, U0 is the rated voltage of the system; M is a large real number; The safety constraints are: f min ≤f i,s,t ≤f max ,i∈N IN min ≤U i,s,t ≤U max ,i∈N in, and are the maximum active power flow and minimum reactive power flow of the ij branch respectively; The distributed generation constraints are: in, and are the actual active and reactive outputs of the synchronous machine power supply at node i, time t and scenario s respectively; and are the reference values ​​of active power and reactive power; and They are the maximum and minimum values ​​of active output and reactive output of the synchronous machine power supply respectively; and is the droop control coefficient of the synchronous machine power supply; in, and They are the maximum and minimum values ​​of active output and reactive output of the node containing new energy units and energy storage at node i, time t and scenario s, respectively.

7. A distribution network elasticity improvement system based on distributed new energy aggregation, characterized in that: include: The screening module uses multivariate normal distribution to perform probability modeling on the forecast data of multiple distributed wind power, photovoltaic output and load data, adds the sampled forecast error data to the forecast curve, and uses the fast forward selection method to redistribute the probability of the scenarios and screen typical scenarios; The aggregation module uses the typical scenarios selected by the screening module to establish an aggregation model of distributed energy storage systems and new energy units, and solves the aggregation characteristics of the aggregation model, specifically: A decentralized control strategy combined with droop control is adopted. Multiple energy storage systems connected to the same node jointly support the voltage and frequency of the microgrid through droop control. Under the premise that the energy storage system supports voltage and frequency, multiple renewable energy units provide active power and reactive power to the microgrid through adaptive power control. The active power of the nth renewable energy unit at time t and scenario s is and reactive power They are: in, is the control coefficient of the nth new energy unit; is the maximum active power of the nth new energy unit; f H is the high frequency threshold; is the frequency-active power control mode of the nth renewable energy unit, and are the maximum and minimum reactive power of the nth renewable energy unit at time t and scenario s respectively; U L and U H are the voltage low state threshold and high state threshold; and is the control coefficient; is the voltage-reactive power control mode of the nth renewable energy unit; Based on the control strategy, the distributed resource management system is used to aggregate multiple distributed energy storage systems and new energy units, monitor the demand information of the upper distribution network, and allocate and adjust the power output of each configured new energy unit and energy storage; Cplex is used to solve the aggregation model of the energy storage system and the new energy unit to obtain the aggregation characteristics, that is, the range of active and reactive power that can be provided, as well as the corresponding frequency and voltage range. The aggregation model of multiple distributed energy storage systems and new energy units is expressed as: in, and are the active power and reactive power of the distributed resource management system at node i, time t and scenario s respectively; and It is the decision variable of the new energy unit and energy storage, describing whether the new energy or energy storage is connected to the microgrid; and is the coefficient of the aggregation characteristic of fP; and is the coefficient of the UQ aggregation characteristic; K i and L i is the set of new energy units and energy storage in the distributed resource management system at node i; The optimization module uses the aggregation characteristics of the aggregation model obtained by the aggregation module as input parameters, takes the maximum load recovery under different weight coefficients as the goal, considers the network radiation topology constraints, flow constraints, node voltage / current constraints, and safety constraints to establish a multi-period distribution network load recovery model, and solves the output of each distributed power source, microgrid division and load recovery results.

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