Power distribution network flexible planning method considering operation safety

Abstract

The invention discloses a power distribution network flexible planning method considering operation safety, and relates to the technical field of power system planning, and the method comprises the following steps: building a wind-solar combined output model, and generating a power distribution network operation scene set; dividing feeder partitions based on the power distribution network operation scene set and the power distribution network topology, identifying key feeder segments by calculating fault weights, classifying the key feeder segments, and constructing a fault set; according to the fault set, establishing a flexible planning model by taking the minimum comprehensive cost as a target and taking the running state constraint as a constraint condition; and iteratively correcting the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met. The power distribution network flexible planning method is used for solving the key problem of complex calculation caused by difficulty in modeling of an N-1 criterion and too many fault scenes in traditional planning, and power distribution network flexible planning considering both safety and economy is realized.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and more specifically, to a flexible distribution network planning method that takes into account operational safety. Background Technology

[0002] Statistics show that a large number of power outages are caused by distribution network failures. Due to load growth and design flaws, most distribution networks fail to meet the N-1 safety criterion, resulting in low safety and reliability. As users' demands for power supply reliability increase, how to upgrade distribution networks to meet the N-1 safety criterion during the planning stage has become an important issue.

[0003] However, unlike transmission networks, distribution networks are characterized by closed-loop design and open-loop operation. When a branch is isolated due to a fault, load transfer needs to be completed through closed tie lines while maintaining radial operation. This change in topology makes it very difficult to directly and accurately embed N-1 security criterion constraints into the planning model. Existing methods typically consider the N-1 criterion by adding a large number of fault scenarios to the planning model, but with the large number of branches in a distribution network, considering the N-1 security criterion constraints for all branches simultaneously would generate a massive number of fault scenarios, leading to model complexity and high computational costs.

[0004] Furthermore, existing studies often fail to adequately consider the autocorrelation of wind and solar power output, as well as the cross-correlation between them, when modeling distributed generation, leading to discrepancies between the generated planning scenarios and actual operating conditions. Simultaneously, current planning methods tend to focus on nodal flexibility resources such as energy storage, while paying insufficient attention to network resources such as smart soft switches (SOPs) that can significantly improve the flexibility and resilience of distribution network operations.

[0005] The above-disclosed technical solutions have at least the following technical problems: It is difficult to accurately incorporate the N-1 safety criterion of the distribution network into the planning model within the acceptable range of computing resources; the correlation modeling of wind power and photovoltaic output is insufficient, affecting the accuracy of the planning scenario; and the potential of network-type flexible resources such as SOPs in improving the operational safety of the distribution network is not fully utilized.

[0006] To address the above problems, this invention proposes a solution. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a flexible distribution network planning method that considers operational safety. This method generates a set of distribution network operation scenarios by establishing a wind-solar combined output model, constructs a fault set based on key feeder segments identified by the power grid topology, establishes a planning model that considers the N-1 criterion, and employs iterative solution to address the problems of complex calculations caused by insufficient consideration of the correlation between wind and solar output in modeling and planning involving the N-1 criterion.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A flexible distribution network planning method considering operational safety includes the following steps: establishing a wind-solar combined output model and generating a distribution network operation scenario set; dividing the distribution network into feeder zones based on the distribution network operation scenario set and distribution network topology, identifying key feeder segments by calculating fault weights, classifying the key feeder segments, and constructing a fault set; establishing a flexible planning model based on the fault set, with the goal of minimizing overall cost and with operational state constraints as constraints; iteratively revising the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met.

[0009] In a preferred embodiment, the step of establishing a wind-solar combined output model and generating a set of distribution network operation scenarios includes: acquiring historical wind power data and historical photovoltaic data; introducing a Frank-Copula function to describe the cross-correlation between wind power and photovoltaic output; generating wind power output data based on historical wind power data using a preset wind power output prediction function, and generating photovoltaic output data by combining the cross-correlation between wind power and photovoltaic output with Rosenblatt transform; and using a fuzzy C-means clustering method to reduce the scenarios of the generated distributed generation output data to construct a set of distribution network operation scenarios.

[0010] In a preferred embodiment, the step of dividing feeder sections based on the distribution network operation scenario set and distribution network topology, identifying key feeder segments by calculating fault weights, classifying key feeder segments, and constructing a fault set includes: constructing a degree node set based on nodes in the distribution network with more than a preset number of connected branches and the connected nodes; dividing feeder sections based on degree nodes; calculating the fault weight of each feeder segment based on the feeder segment fault rate and the active power load loss of each feeder segment under normal operation scenarios obtained from historical data; classifying key feedback segments into TRBs or NRBs based on whether the N-1 safety criterion is met after a fault; and for each NRB, selecting the moment with the largest net power loss load as its fault moment, and constructing a fault set by using NRBs and their corresponding fault moments.

[0011] In a preferred embodiment, the step of establishing a flexible programming model based on the fault set, with the goal of minimizing the overall cost and covering constraints under both normal and fault scenarios, specifically involves: using the minimization of the overall cost as the objective function, where the overall cost includes the total investment and maintenance cost of the smart soft switch, the system network loss cost, and the cost of purchasing electricity from the upstream power grid; establishing operating state constraints, which are divided into normal scenario operating constraints and fault scenario operating constraints; and transforming the flexible programming model into a mixed integer second-order cone programming model and solving it to obtain preliminary planning results.

[0012] In a preferred embodiment, the iterative correction of the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met specifically includes: Based on the preliminary planning results, the critical feeder segments initially classified as TRBs are sequentially examined to determine whether they meet the N-1 safety criterion when a fault occurs under the current planning scheme. If the examination fails, the TRB is reclassified as NRB, and the fault set is updated. It is then determined whether all TRBs have been examined and do not require reclassification. If so, the final planning scheme is output. If not, the process returns to the previous step, and the planning solution is re-performed based on the updated fault set.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. It overcomes the shortcomings of distributed generation modeling, which only considers the autocorrelation of wind power and photovoltaic output and cannot reflect the cross-correlation between the two. It avoids retaining the autocorrelation and cross-correlation of the original sequence when generating wind power and photovoltaic scenarios, making the planning scenario more realistic. 2. The definition of critical feeder segments is proposed and critical feeder segments are selected using weighted graphs, and they are divided into transferable critical feeder segments and non-transferable critical feeder segments; a flexible distribution network planning model considering the N-1 safety criterion is established using non-transferable critical feeder segments. 3. Considering the coupling between non-transferable key feeder sections and transferable key feeder sections, a planning-verification iterative algorithm was proposed to carry out flexible distribution network planning. Attached Figure Description

[0014] Figure 1 This is a schematic flowchart of a flexible distribution network planning method that takes into account operational safety, provided as an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0016] Example 1, Figure 1 This invention presents a flexible distribution network planning method that considers operational safety, comprising the following steps: S1, Establish a wind-solar combined output model and generate a set of power distribution network operation scenarios; S2, based on the set of distribution network operation scenarios and the distribution network topology, divides the feeder into zones, identifies key feeder segments by calculating fault weights, classifies the key feeder segments, and constructs a fault set; S3. Based on the fault set, a flexible programming model is established with the goal of minimizing the overall cost and with operational state constraints as the constraints. S4, iteratively revise the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met.

[0017] This embodiment proposes a flexible distribution network planning method that considers operational safety. By constructing a key feeder segment selection mechanism and an N-1 safety criterion planning model, combined with a planning-verification iterative algorithm, it achieves optimized configuration of intelligent soft switches. This method accurately identifies system vulnerabilities based on feeder partitioning and fault weight calculation, significantly reduces the scenario scale by utilizing fault sets, and ensures fault recovery capability through SOP control mode coordination and topology reconfiguration. It effectively solves the problems of difficult N-1 criterion modeling, high computational complexity, and insufficient consideration of wind and solar power output correlation in traditional planning. This invention significantly improves the operational safety of the distribution network while ensuring planning economy, providing a solution that balances computational efficiency and planning accuracy for distribution networks with a high proportion of distributed power sources.

[0018] S1, Establish a wind-solar combined output model and generate a set of power distribution network operation scenarios; In this embodiment, establishing a wind-solar combined output model and generating a set of distribution network operation scenarios includes: Obtain historical wind power data and historical photovoltaic data; The Frank-Copula function is introduced to describe the cross-correlation between wind power and solar power output; Based on historical wind power data, wind power output data is generated by a preset wind power output prediction function. Combined with the cross-correlation between wind power and photovoltaic output, photovoltaic output data is generated by Rosenblatt transformation. The fuzzy C-means clustering method is used to reduce the scenarios of the generated distributed generation output data and construct a set of distribution network operation scenarios.

[0019] In this embodiment, the Frank-Copula function is used to describe the cross-correlation between wind power and photovoltaic output, and the specific formula is as follows:

[0020] In the formula, θ is the parameter to be fitted; , These are the cumulative distribution function values ​​for wind power and photovoltaic power, respectively.

[0021] In this embodiment, the specific formula for generating photovoltaic power output data by combining Rosenblatt transformation is as follows:

[0022]

[0023] In the formula, , These are the cumulative distribution function values ​​for wind power and photovoltaic power, respectively. For wind power data, For wind power output data, θ is a random number between 0 and 1, θ is the parameter to be fitted, and t is the time period.

[0024] S2, based on the set of distribution network operation scenarios and the distribution network topology, divides the feeder into zones, identifies key feeder segments by calculating fault weights, classifies the key feeder segments, and constructs a fault set; In this embodiment, the step of dividing feeder sections based on the distribution network operation scenario set and distribution network topology, identifying key feeder segments by calculating fault weights, classifying key feeder segments, and constructing a fault set includes: Based on the number of nodes in the distribution network that have more than a preset number of connected branches and the number of connected nodes, a degree node set is constructed, and feeder partitions are divided based on the degree nodes. Based on the failure rate of feeder segments and the active power load loss of each feeder segment under normal operating conditions from the historical data, the failure weight of each feeder segment is calculated. Based on whether the N-1 safety criterion is met after a fault, critical feedback segments are classified as TRB or NRB. For each NRB, the moment with the largest net power loss load is selected as its fault moment, and a fault set is constructed by NRB and its corresponding fault moment.

[0025] In this embodiment, the specific formula for calculating the fault weight of each feeder segment is as follows:

[0026] In the formula: for Side road, This is a collection of normal scenarios; For time periods; A collection of branches in a power distribution network; for Failure rate of feeder sections in branch lines; branch road The active power load loss caused by a fault during time period t in a normal scenario s. for Fault weights of branches.

[0027] In this embodiment, the moment with the largest net power loss load is selected as the fault moment, and the specific formula is as follows:

[0028] In the formula, for Side road, This is the set of fault scenarios, i.e., the set of NRB fault scenarios; s and sc are the indices of normal scenarios and fault scenarios, respectively. The NRB fault corresponding to fault scenario sc; branch road The output of the DG installed in the power loss area after the fault; For fault scenario sc, the corresponding fault time. For time period sets, branch road The active power load loss caused by a fault during time period t in a normal scenario s, which is a moment in a normal scenario.

[0029] TRB stands for transferable critical feeder segment, while NRB stands for non-transferable critical feeder segment.

[0030] S3. Based on the fault set, a flexible programming model is established with the goal of minimizing the overall cost and with operational state constraints as the constraints. In this embodiment, the step of establishing a flexible planning model based on the fault set, with the objective of minimizing overall cost and covering constraints under both normal and fault scenarios, specifically involves: The objective function is to minimize the overall cost, which includes the total investment and maintenance cost of the smart soft switch, the system network loss cost, and the cost of purchasing electricity from the upstream power grid. Establish operational status constraints, which are divided into normal scenario operational constraints and fault scenario operational constraints. The flexible programming model is transformed into a mixed-integer second-order cone programming model and solved to obtain preliminary planning results.

[0031] In this embodiment, establishing constraints in the running state specifically means: Establish operational constraints under normal scenarios, including power flow constraints of distribution networks, operational constraints of smart soft switches, and safe operational constraints of node voltage and branch current; Establish operational constraints under fault scenarios, including NRB branch disconnection constraints, intelligent soft switch control mode coordination constraints, and distribution network radial topology operational constraints.

[0032] Among them, 1) Current constraint

[0033]

[0034]

[0035]

[0036] In the formula, Φ(i) and Ψ(i) are the sets of parent nodes and child nodes of node i, respectively; , These represent the active power and reactive power of branch ij during time period t in normal scenario s, respectively. , These represent the active and reactive power injected into node i by the DG during time period t in normal scenario s. , These represent the active power and reactive power injected into node i during time period t in a normal scenario s, respectively. , These represent the active and reactive loads of node i during time period t in normal scenario s. , Let be the active power and reactive power transmitted by the upstream power grid during time period t in normal scenario s, respectively; Is,t,ij and s,t,ij are the currents and squares of branch ij during time period t in normal scenario s, respectively; Us,t,i and s,t,i are the voltages and squares of node i during time period t in normal scenario s, respectively; rij and xij are the resistance and reactance of branch ij, respectively; M is a relatively large number, which is taken as 100 in this paper. This represents a 0-1 variable indicating the on / off state of branch ij during time period t in a normal scenario s. This indicates that the branch is closed. In normal scenarios, ordinary branches are all in a closed state, while connecting branches are in an open state.

[0037] 2) Smart Soft Switch (SOP) Constraints During normal operation of the distribution network, one end of the SOP operates in PQ mode and the other end operates in VdcQ mode. Its control variables are the active power and reactive power transmitted at both ends of the SOP. Specific constraints include capacity constraints, power balance constraints, etc., as follows:

[0038]

[0039]

[0040] In the formula, For 0-1 variables, when =1 indicates that the SOP is installed on the ij branch; otherwise, it is not installed. The minimum unit capacity of SOP; It is a non-negative integer.

[0041] 3) Safety operation constraints To ensure the safe operation of the distribution network, both node voltage and branch current must be within permissible ranges, as follows:

[0042] In the formula, , These are the upper and lower limits of the voltage at distribution network nodes; This represents the maximum allowable current for the branch.

[0043] In fault scenarios, the planning model includes branch disconnection constraints, SOP control mode coordination constraints, radial topology constraints, and other constraints, as detailed below: 1) Branch line disconnection constraint In a fault scenario, the corresponding NRB feeder segment is in an open state, as follows:

[0044] In the formula: For fault scenario sc The on / off state of branch ij during time period. This indicates that in the fault scenario sc During the time period, branch ij is in an open state.

[0045] 2) SOP control mode coordination constraints When a distribution network fault occurs, the prerequisite for a Standard Operating Procedure (SOP) to provide voltage support to the power-loss area is that one end is connected to the normally supplied area and the other end is connected to the power-loss area. Even if multiple SOPs meet this requirement simultaneously, only one SOP will have its VSC (Voltage Controller) at the end connected to the power-loss area operating in Vf mode to provide voltage support to the power-loss area, as follows:

[0046]

[0047] ) In the formula, O(sc) is the set of nodes within the power-loss region; Auxiliary variables of 0-1; For 0-1 variables, when When, it indicates that in the fault scenario sc When the SOP installed on the ij branch is switched to voltage / frequency operating mode at its i terminal to provide voltage support, it indicates that the SOP does not need to switch operating modes; U0 is the fault side voltage constraint value when the SOP power supply is restored.

[0048] 3) Radial topological constraints When a fault occurs in the distribution network, power can be transferred to the affected areas using tie lines while maintaining radial operation, as follows: (1).

[0049] (2).

[0050] (3).

[0051] (4).

[0052] In the formula, The variable is 0-1, representing the direction of power flow. Indicates the fault scenario sc The power flow of branch ij during time period flows from node i to node j; Equation (2) indicates that each node has only one power source node; Equation (3) indicates that substation nodes have no power source nodes; Equations (1) to (3) restrict the distribution network topology to be radial; Equation (4) indicates that when branch ij is equipped with SOP, in fault scenario sc During the period of distribution network topology reconfiguration, this branch is in a closed state.

[0053] S4, iteratively revise the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met.

[0054] In this embodiment, the iterative correction of the feeder segment classification and planning scheme ensures that the N-1 safety criterion is met, specifically as follows: Based on the preliminary planning results, the critical feeder segments initially classified as TRB are sequentially examined to determine whether they meet the N-1 safety criterion when a fault occurs under the current planning scheme. If the examination fails, the TRB is reclassified as NRB, and the fault set is updated. Determine whether all TRBs have been inspected and do not require reclassification; if yes, output the final planning solution; otherwise, return to the previous step and re-solve the planning problem based on the updated fault set.

[0055] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0056] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0057] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0058] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0059] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0060] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A flexible planning method for distribution networks considering operational safety, characterized in that, Includes the following steps: Establish a wind-solar combined output model and generate a set of power distribution network operation scenarios; Based on the set of distribution network operation scenarios and the distribution network topology division of feeder zones, key feeder segments are identified by calculating fault weights, and key feeder segments are classified to construct a fault set; Based on the fault set, a flexible programming model is established with the goal of minimizing overall cost and with operational state constraints as the conditions. Iteratively revise the feeder segment classification and planning scheme to ensure that the N-1 safety criterion is met.

2. The flexible distribution network planning method considering operational safety according to claim 1, characterized in that, The establishment of the wind-solar combined output model and the generation of a set of distribution network operation scenarios include: Obtain historical wind power data and historical photovoltaic data; The Frank-Copula function is introduced to describe the cross-correlation between wind power and solar power output; Based on historical wind power data, wind power output data is generated by a preset wind power output prediction function. Combined with the cross-correlation between wind power and photovoltaic output, photovoltaic output data is generated by Rosenblatt transformation. The fuzzy C-means clustering method is used to reduce the scenarios of the generated distributed generation output data and construct a set of distribution network operation scenarios.

3. The flexible distribution network planning method considering operational safety according to claim 2, characterized in that, The method involves dividing feeder sections based on a set of distribution network operation scenarios and distribution network topology, identifying key feeder segments by calculating fault weights, classifying these key feeder segments, and constructing a fault set, including: Based on the number of nodes in the distribution network that have more than a preset number of connected branches and the number of connected nodes, a degree node set is constructed, and feeder partitions are divided based on the degree nodes. Based on the failure rate of feeder segments and the active power load loss of each feeder segment under normal operating conditions from the historical data, the failure weight of each feeder segment is calculated. Based on whether the N-1 safety criterion is met after a fault, critical feedback segments are classified as TRB or NRB. For each NRB, the moment with the largest net power loss load is selected as its fault moment, and a fault set is constructed by NRB and its corresponding fault moment.

4. The flexible distribution network planning method considering operational safety according to claim 3, characterized in that, Based on the fault set, and with the objective of minimizing overall cost, a flexible planning model is established, encompassing constraints under both normal and fault scenarios. Specifically: The objective function is to minimize the overall cost, which includes the total investment and maintenance cost of the smart soft switch, the system network loss cost, and the cost of purchasing electricity from the upstream power grid. Establish operational status constraints, which are divided into normal scenario operational constraints and fault scenario operational constraints. The flexible programming model is transformed into a mixed-integer second-order cone programming model and solved to obtain preliminary planning results.

5. The flexible distribution network planning method considering operational safety according to claim 4, characterized in that, The iterative correction of the feeder segment classification and planning scheme ensures that the N-1 safety criterion is met, specifically as follows: Based on the preliminary planning results, the critical feeder segments initially classified as TRB are sequentially examined to determine whether they meet the N-1 safety criterion when a fault occurs under the current planning scheme. If the examination fails, the TRB is reclassified as NRB, and the fault set is updated. Determine whether all TRBs have been inspected and do not require reclassification; If yes, output the final planning solution; otherwise, return to the previous step and re-solve the planning problem based on the updated fault set.

6. The flexible distribution network planning method considering operational safety according to claim 5, characterized in that, The Frank-Copula function describes the correlation between wind power and solar power output, and the specific formula is as follows: In the formula, θ is the parameter to be fitted; , These are the cumulative distribution function values ​​for wind power and photovoltaic power, respectively.

7. The flexible distribution network planning method considering operational safety according to claim 6, characterized in that, The specific formula for generating photovoltaic output data by combining Rosenblatt transformation is as follows: In the formula, , These are the cumulative distribution function values ​​for wind power and photovoltaic power, respectively. For wind power data, For wind power output data, θ is a random number between 0 and 1, θ is the parameter to be fitted, and t is the time period.

8. The flexible distribution network planning method considering operational safety according to claim 7, characterized in that, The specific formula for calculating the fault weight of each feeder segment is as follows: In the formula: This is a collection of normal scenarios; For time periods; A collection of branches in a power distribution network; For the first Failure rate of feeder segments in each branch; branch road The active power load loss caused by a fault during time period t in a normal scenario s. for Fault weights of branches.

9. The flexible distribution network planning method considering operational safety according to claim 8, characterized in that, The time when the net power loss load is the largest is selected as the fault time, and the specific formula is as follows: In the formula, A set of fault scenarios, This is a set of normal scenarios, where s and sc are the indices of normal and fault scenarios, respectively. The NRB fault corresponding to fault scenario sc; branch road The output of the DG installed in the power loss area after the fault; For fault scenario sc, the corresponding fault time. For time period sets, branch road The active power load loss caused by a fault during time period t in a normal scenario s.

10. The flexible distribution network planning method considering operational safety according to claim 9, characterized in that, The establishment of constraints in the operational state specifically includes: Establish operational constraints under normal scenarios, including power flow constraints of distribution networks, operational constraints of smart soft switches, and safe operational constraints of node voltage and branch current; Establish operational constraints under fault scenarios, including NRB branch disconnection constraints, intelligent soft switch control mode coordination constraints, and distribution network radial topology operational constraints.

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