AC / DC power distribution system anticipated risk scene set construction and modeling coping method

Abstract

The invention discloses an AC / DC power distribution system anticipated risk scene set construction and modeling coping method, and relates to the technical field of power system steady-state analysis. The method comprises the following steps: traversing a power distribution network, receiving all adjustable active resource information and reactive resource information, and sorting the operation characteristics of the power distribution network; a 2n + 1 point estimation method probabilistic power flow and Cornish-Fisher series expansion is adopted, and an interval power flow algorithm based on a DistFlow power flow equation is adopted to comprehensively construct an AC / DC power distribution system operation risk assessment index. According to the method, the out-of-limit probability and the out-of-limit degree of node voltage and branch power are included, multi-dimensional factors are considered, an AC / DC power distribution system operation risk assessment system is proposed, the assessment result is more accurate, and the response method proposed by the invention realizes rapid and effective response to the risk scene of the AC / DC power distribution system, so that the risk risk of the AC / DC power distribution system is reduced. And the safe operation level of the AC-DC power distribution system is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of steady-state analysis of power systems, and in particular to a method for constructing a set of anticipated risk scenarios for AC and DC power distribution systems and a modeled response method. Background Art

[0002] In the field of distribution system risk assessment, the application scope of the traditional N-1 criterion is relatively limited, and it is mainly applicable to systems with simple structures and clear operating conditions. However, with the increasing complexity of power systems, especially the large-scale access of distributed energy and renewable energy, the traditional N-1 criterion is difficult to fully reflect the uncertainties in the system, including but not limited to the probability of equipment failure and load change patterns, which has led to significant limitations on its application in actual risk assessment.

[0003] In terms of constructing a set of anticipated risk scenarios, existing research focuses on optimizing the economic performance and safety and stability of the distribution system, and usually uses a deterministic model to describe the system state. However, this approach ignores the randomness and volatility of the output power of distributed renewable energy and user load demand. These uncertainties pose new challenges to the stable operation of AC and DC distribution systems. At present, the distribution system control strategies designed to deal with these risks are mainly aimed at static or quasi-static day-ahead scheduling plans. Their algorithm design fails to fully consider the characteristics of dynamic changes within the day, resulting in low control efficiency and slow response speed when dealing with sudden risk events, thereby weakening the inherent advantages of AC and DC distribution systems in flexibility and robustness. To this end, we propose a method for constructing a set of anticipated risk scenarios for AC and DC distribution systems and a modeled response method. Summary of the invention

[0004] The purpose of the present invention is to provide a method for constructing and patterning a set of anticipated risk scenarios for AC and DC power distribution systems, thereby achieving rapid and effective response to risk scenarios for AC and DC power distribution systems and effectively improving the safe operation level of AC and DC power distribution systems.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for constructing a set of anticipated risk scenarios for AC and DC power distribution systems and for modeling responses, comprising the following steps: Traverse the distribution network, receive all adjustable active and reactive resource information, and organize their operating characteristics; The 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion are adopted, and the interval power flow algorithm based on the DistFlow power flow equation is adopted to comprehensively construct the AC / DC distribution system operation risk assessment index; Sort out the potential risk elements of AC and DC power distribution systems, take into account the probability of occurrence of risk scenarios and the risk scenarios based on the system's expected risk scenario set, combine fuzzy comprehensive evaluation, and build an expected risk scenario response strategy library; Based on the Euclidean distance, a similarity function of expected risk scenarios is constructed, and the corresponding response measures in the response strategy library of the risk elements with the closest similarity are adopted to carry out patterned response to the risk scenarios.

[0006] Furthermore, the active resource information includes energy storage systems and controllable loads, and the reactive resource information includes on-load tap-changing transformers, photovoltaic inverters and reactive compensation capacitors.

[0007] Furthermore, the DistFlow power flow equation is linearized and expressed as follows: In the formula, and Node j The required active and reactive power, and Slave nodes i To Node j The active power and reactive power transmitted by the branch, and Slave nodes j To Node l Active power and reactive power transmitted by the branch, and Node i and nodes j The voltage amplitude, and Slave nodes i To Node j The impedance and inductance of the branch, and are the collections of nodes and branches in the distribution network, N and L are the number of nodes and the number of branches respectively; U sub corresponds to the voltage at the substation node; and Respectively represent the nodes j The collection of connected downstream and upstream nodes.

[0008] Furthermore, when conducting the AC / DC distribution system operation risk assessment, it is necessary to evaluate the importance of branches and nodes, including branch importance index and node importance index; (1) Branch importance index: Branch importance factor α ijThe factors affecting the branch include the location of the branch in the distribution network and the voltage level. In the actual evaluation work, it is determined according to the following formula: In the formula, α aij is the branch betweenness weight factor, is the branch voltage level weight factor; (2) Node importance index: Available node importance factor β i , determined according to the following formula: In the formula, β ai is the load level factor under the node, β bi is the voltage level weight factor, is the power factor required for the node load.

[0009] Furthermore, the AC / DC power distribution system operation risk assessment index includes voltage over-limit risk index and branch over-limit risk index; (1) Voltage over-limit risk indicator: Voltage over-limit penalty cost CEU Penalty cost based on voltage over-limit probability CEU.p Penalty cost for voltage over-limit CEU.l The specific expression is as follows: In the formula, and They are the voltage over-limit probability penalty cost and voltage over-limit degree penalty cost of the node respectively; and They are i The voltage over-limit probability and voltage over-limit degree of the node; and are the upper and lower thresholds of voltage over-limit probability, and They are the upper and lower thresholds of the voltage over-limit degree, is the set of all nodes, and are the voltage over-limit probability penalty cost and the voltage over-limit degree penalty cost, respectively. βi is the node importance factor; (2) Branch crossing risk indicator: Branch crossing penalty cost CES The penalty cost is calculated by the probability of branch crossing the limit CES.p The penalty cost of branch crossing the limit CES.l The specific expression is as follows: In the formula, and Branch ij The penalty cost of the probability of crossing the limit and the penalty cost of the degree of crossing the limit; and Branch ij The probability and degree of exceeding the limit; and are the upper and lower limit thresholds of the branch crossing probability, and They are the upper and lower thresholds of the branch crossing degree, is the set of all branches, and They are the branch crossing probability penalty cost and branch crossing degree penalty cost respectively. is the branch importance factor.

[0010] Furthermore, the probability characteristic analysis of risk elements in the expected risk scenario set introduces a motif based on clustering of load curves, and by analyzing the probability characteristics of risk elements, deeply explores the statistical laws of sudden change risk scenarios to calculate their occurrence probability; The motif refers to a high-frequency repeated, non-random basic network structure, including nodes and edges. By counting the number of occurrences and distribution of the motif, the fluctuations of load and renewable energy output can be effectively grasped, and the probability of occurrence of expected risk scenarios can be calculated. The calculation of the probability of occurrence of risk scenarios is to effectively calculate the probability of occurrence of expected risk scenarios by counting the number of occurrences and distribution of the models and understanding the fluctuations of load and new energy output within a certain time series.

[0011] Furthermore, the anticipated risk scenario set is constructed to include the anticipated risk scenario severity index Sev , specifically expressed as: In the formula, Sev m For risk scenarios m The severity index,C m For risk scenarios m The total operating risk, p m For risk scenarios m probability of occurrence.

[0012] Furthermore, the construction of the anticipated risk scenario response strategy library applies fuzzy comprehensive evaluation, which converts qualitative evaluation into quantitative evaluation according to the membership theory of fuzzy mathematics; The factor set of fuzzy comprehensive evaluation is constructed. The influence of each factor in the factor set on each state in the comment set has different degrees of fuzziness, that is, different membership degrees, which can be expressed by the membership matrix R as follows: In the formula, the matrix A is the factor set of fuzzy comprehensive evaluation, the matrix H is the comment set of fuzzy comprehensive evaluation, and the elements r ij represents the i-th factor A i For the jth comment H j The degree of membership.

[0013] Furthermore, based on the Euclidean distance, a similarity function of expected risk scenarios is constructed, and the similarity function expression is: In the formula, sim ij For risk scenarios i and risk scenarios j The similarity of and Risk scenarios i and risk scenarios j The first t elements, n is the length of the risk feature vector.

[0014] According to another aspect of the present invention, the present invention provides a system for constructing a set of anticipated risk scenarios for AC and DC power distribution systems and for modeling responses, which is used to implement the above-mentioned method for constructing a set of anticipated risk scenarios for AC and DC power distribution systems and for modeling responses, and is characterized in that it includes: A receiving module is used to traverse the power distribution network, receive all adjustable active resource information and reactive resource information, and organize their operating characteristics; The first building block is used to comprehensively construct the AC / DC distribution system operation risk assessment index by using the 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion and the interval power flow algorithm based on the DistFlow power flow equation; The second building module is used to sort out the potential risk elements of the AC and DC power distribution system, taking into account the probability of occurrence of risk scenarios and the risk scenarios based on the system's expected risk scenario set, combined with fuzzy comprehensive evaluation, to build an expected risk scenario response strategy library; The response output module is used to construct a similarity function of expected risk scenarios based on Euclidean distance, take the response measures corresponding to the risk elements with the closest similarity in the response strategy library, and conduct a patterned response to the risk scenarios.

[0015] The present invention has at least the following beneficial effects: The present invention includes the over-limit probability and over-limit degree of node voltage and branch power, and proposes to build an AC / DC distribution system operation risk assessment system considering multi-dimensional factors. The assessment result is more accurate, and the response method proposed in the present invention realizes rapid and effective response to risk scenarios of AC / DC distribution systems, effectively improving the safe operation level of AC / DC distribution systems.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the process of the method of the present invention; Figure 2 It is a schematic diagram of an AC / DC system containing controllable resources of the present invention; Figure 3 It is a schematic diagram of the cluster center curve of the present invention; Figure 4 It is a schematic diagram of load classification of the AC / DC system of the present invention; Figure 5 It is a schematic diagram of typical risk elements in the risk scenarios envisioned by the present invention; Figure 6 This is a schematic diagram of similarity analysis of risk scenario 2 of the present invention; Figure 7 It is a schematic diagram of similarity analysis of risk scenarios three of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0019] Embodiment 1: See also Figure 1-Figure 7 The present invention provides a technical solution: a method for constructing a set of anticipated risk scenarios for AC and DC power distribution systems and a method for modeling response, comprising the following steps: S1. Traverse the distribution network, receive all adjustable active resource information and reactive resource information, and organize their operating characteristics; It should be noted that active resource information includes energy storage systems and controllable loads, and reactive resource information includes on-load tap-changing transformers, photovoltaic inverters, and reactive compensation capacitors; S2. Adopting the 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion, and the interval power flow algorithm based on the DistFlow power flow equation, comprehensively construct the AC / DC distribution system operation risk assessment index; S21. According to the technical solution of this embodiment, the DistFlow power flow equation is linearized and expressed as follows: In the formula, and Node j The required active and reactive power, and Slave nodes i To Node j The active power and reactive power transmitted by the branch, and Slave nodes j To Node l Active power and reactive power transmitted by the branch, and Node i and nodes j The voltage amplitude, and Slave nodes i To Node j The impedance and inductance of the branch, and are the collections of nodes and branches in the distribution network, N and L are the number of nodes and the number of branches respectively; U sub corresponds to the voltage at the substation node; and Respectively represent the nodes j The set of connected downstream and upstream nodes; S22. Further, when conducting the AC / DC power distribution system operation risk assessment, it is necessary to evaluate the importance of branches and nodes, including branch importance index and node importance index; S221. Branch importance index: Branch importance factor α ij The factors affecting the branch include the location of the branch in the distribution network and the voltage level. In the actual evaluation work, it is determined according to the following formula: In the formula, α aij is the branch betweenness weight factor, is the branch voltage level weight factor; S222. Node importance index Available node importance factor β i , determined according to the following formula: In the formula, β ai is the load level factor under the node, β bi is the voltage level weight factor, is the power factor required for node load; S23. It should be further explained that the AC / DC power distribution system operation risk assessment indicators include voltage over-limit risk indicators and branch over-limit risk indicators; S231. Voltage over-limit risk indicator: Voltage over-limit penalty cost CEU Penalty cost based on voltage over-limit probability CEU.p Penalty cost for voltage over-limit CEU.l The specific expression is as follows: In the formula, and They are the voltage over-limit probability penalty cost and voltage over-limit degree penalty cost of the node respectively; and They are i The voltage over-limit probability and voltage over-limit degree of the node; and are the upper and lower thresholds of voltage over-limit probability, and They are the upper and lower thresholds of the voltage over-limit degree, is the set of all nodes, and are the voltage over-limit probability penalty cost and the voltage over-limit degree penalty cost, respectively. βi is the node importance factor; S232. Branch crossing risk indicator: Branch crossing penalty cost CES The penalty cost is calculated by the probability of branch crossing the limit CES.p The penalty cost of branch crossing the limit CES.l The specific expression is as follows: In the formula, and Branch ij The penalty cost of the probability of crossing the limit and the penalty cost of the degree of crossing the limit; and Branch ij The probability and degree of exceeding the limit; and are the upper and lower limit thresholds of the branch crossing probability, and They are the upper and lower thresholds of the branch crossing degree, is the set of all branches, and They are the branch crossing probability penalty cost and branch crossing degree penalty cost respectively. is the branch importance factor; S3. Sort out the potential risk elements of AC and DC power distribution systems, take into account the probability of occurrence of risk scenarios and the risk scenarios based on the system expected risk scenario set, combine fuzzy comprehensive evaluation, and build an expected risk scenario response strategy library; S31. Probabilistic characteristic analysis of risk elements in the expected risk scenario set. Based on clustering the load curve, the model is introduced to analyze the probabilistic characteristics of risk elements and deeply explore the statistical laws of sudden risk scenarios to calculate their occurrence probability; A motif refers to a high-frequency, non-random basic network structure, including nodes and edges. By counting the number of occurrences and distribution of motifs, we can effectively grasp the fluctuations in load and renewable energy output, and then calculate the probability of occurrence of expected risk scenarios. The probability of occurrence of risk scenarios is calculated by counting the number of occurrences and distribution of the models, and understanding the fluctuations of load and renewable energy output within a certain time series, so as to effectively calculate the probability of occurrence of expected risk scenarios. S32. Construction of the anticipated risk scenario set including the anticipated risk scenario severity index Sev , specifically expressed as: In the formula, Sev m For risk scenarios m The severity index, C m For risk scenarios m The total operating risk, p m For risk scenarios m The probability of occurrence; S33. Construct a response strategy library for anticipated risk scenarios and apply the fuzzy comprehensive evaluation method; Fuzzy comprehensive evaluation is a comprehensive evaluation method based on fuzzy mathematics. It converts qualitative evaluation into quantitative evaluation according to the membership theory of fuzzy mathematics. It constructs a factor set of fuzzy comprehensive evaluation. The influence of each factor in the factor set on each state in the comment set has different degrees of fuzziness, that is, different memberships, which can be expressed by the membership matrix R as follows: In the formula, the matrix A is the factor set of fuzzy comprehensive evaluation, the matrix H is the comment set of fuzzy comprehensive evaluation, and the elements r ij represents the i-th factor A i For the jth comment H j The degree of membership.

[0020] S4. Construct a similarity function for expected risk scenarios based on Euclidean distance, adopt the corresponding response measures of the risk elements with the closest similarity in the response strategy library, and conduct a patterned response to the risk scenarios; S41. Based on the Euclidean distance, a similarity function of the expected risk scenario is constructed. The similarity function expression is: In the formula, sim ij For risk scenarios i and risk scenarios j The similarity of and Risk scenariosi and risk scenarios j The first t elements, n is the length of the risk feature vector; Euclidean distance is a distance quantification method in mathematics, statistics, and computer science. It is the true distance between two points in space. The Euclidean distance in two-dimensional space is the straight line segment distance between the two points. Euclidean distance is widely used in clustering, similarity analysis and other fields.

[0021] Next, the technical solution of the present invention is further described in conjunction with specific embodiments: A comparative experiment was conducted based on the following three risk scenarios: Risk scenario 1: The load power demand of nodes 13 and 14 increases by 30%; Risk scenario 2: Wind power output decreases by 18%, the load on node 30 increases by 15%, and the load on node 5 increases by 35%; Risk scenario three: PV output decreases by 10%, the load on node 15 increases by 20%, and the load on node 27 increases by 20%; Figure 1 : is a schematic diagram of the method flow of this embodiment, which introduces the basic steps of the method of the present invention: Step 1, traverse the distribution network, receive all adjustable active resource information and reactive resource information, and organize their operating characteristics; Step 2: Using the 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion, and the interval power flow algorithm based on the DistFlow power flow equation, comprehensively determine the AC / DC distribution system operation risk assessment indicators; Step 3: Sort out the potential risk elements of the AC and DC power distribution systems, consider the probability of occurrence of risk scenarios and the impact of risk scenarios, and build a set of anticipated risk scenarios; Step 4: Based on the system expected risk scenario set and combined with fuzzy comprehensive evaluation, a library of response strategies for expected risk scenarios is constructed; Step 5: Based on the Euclidean distance, construct a similarity function for the expected risk scenarios, take the corresponding response measures in the response strategy library for the risk elements with the closest similarity, and conduct a patterned response to the risk scenarios.

[0022] Figure 2 The system used is an improved AC / DC power distribution system based on the IEEE-33 node system; 1) In the following AC / DC distribution system, the load is assumed to obey the normal distribution with a variance of 1 / 10 of the expected value. Nodes 9 and 17 are connected to wind power with a rated power of 1200kW, respectively. The cut-in wind speed is 3m / s and the rated wind speed is 12m / s. The corresponding shape parameter of the two-parameter Weibull distribution is 4.23 and the scale parameter is 8.34. Nodes 4 and D6 are connected to photovoltaic power with a rated power of 590kW, respectively. In the Beta distribution, α is 0.662 and β is 1.779. The improved AC / DC distribution system has the following topology: Figure 2 As shown; 2) Cluster the daily load curves into cluster 1, cluster 2, cluster 3, cluster 4 and cluster 5. The load clustering results are as follows: Figure 3 As shown; 3) Based on the clustering results of the load curve in step 2), the uncertain power flow analysis is performed on load mutations, photovoltaic and wind power output mutations, and VSC failures to calculate the operating risks under each risk scenario, and combined with the probability of occurrence, the risk scenario severity index is obtained, from which concentrated risk elements are screened out. The risk elements are arranged according to the index to generate a set of expected risk scenarios for the AC and DC distribution system. This embodiment screened out a total of 146 risk scenarios, showing 10 typical risk elements and their risk indicators, probability of occurrence, and severity indicators, such as Figure 5 As shown; 4) The 146 risk element scenarios in the expected risk scenario set in step 3) are brought into the solution one by one to obtain the action status of each controllable resource. The corresponding response strategies for some risk elements are shown in Table 1: Table 1 Anticipated risk scenario response strategy library 5) Based on the expected risk scenario sets and corresponding response strategies generated in steps 3) and 4), three different risk scenarios were selected for model response strategy generation. The penalty costs of risk violations in each scenario are shown in Table 2: Table 2 Penalty cost of risk exceeding limit in each scenario 6) The penalty cost of each risk in step 5) is used to calculate the penalty cost score of each risk scenario based on the fuzzy comprehensive evaluation method. The global operation status scores under the three scenarios are shown in Table 3: Table 3 Global operation status scores 7) Based on the global operation status scores of each scenario in step 6), the risk feature vector of the risk scenario and the element risk feature of the risk scenario set are analyzed for similarity based on the Euclidean distance. Since the global operation status score of scenario 1 is 80 points, it is in a risky state but does not need to take a patterned response. Further observation can be made. The similarity analysis diagram of scenario 2 and scenario 3 is shown in the figure below: Figure 6-Figure 7 As shown, Figure 6 Wind power output suddenly dropped by 20%. Figure 7 The load on 8 nodes suddenly increased by 40%; the corresponding response measures in the response strategy library for the risk elements with the closest similarity were adopted, and the risk scenarios were responded to in a patterned manner. After taking the corresponding response measures, the operation risk of the distribution system was reduced to 0.

[0023] To sum up, the technical solution of this embodiment includes the over-limit probability and over-limit degree of node voltage and branch power, and proposes to build an AC / DC distribution system operation risk assessment system considering multi-dimensional factors. The assessment result is more accurate, and the response method proposed in this embodiment realizes the rapid and effective response to the risk scenarios of the AC / DC distribution system, and effectively improves the safe operation level of the AC / DC distribution system.

[0024] Embodiment 2: This embodiment provides a system for constructing a set of anticipated risk scenarios for an AC / DC power distribution system and for modeling responses, which is used to implement the method for constructing a set of anticipated risk scenarios for an AC / DC power distribution system and for modeling responses described in the first embodiment, including: A receiving module is used to traverse the power distribution network, receive all adjustable active resource information and reactive resource information, and organize their operating characteristics; The first building block is used to comprehensively construct the AC / DC distribution system operation risk assessment index by using the 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion and the interval power flow algorithm based on the DistFlow power flow equation; The second building module is used to sort out the potential risk elements of the AC and DC power distribution system, taking into account the probability of occurrence of risk scenarios and the risk scenarios based on the system's expected risk scenario set, combined with fuzzy comprehensive evaluation, to build an expected risk scenario response strategy library; The response output module is used to construct a similarity function of expected risk scenarios based on Euclidean distance, take the response measures corresponding to the risk elements with the closest similarity in the response strategy library, and conduct a patterned response to the risk scenarios.

[0025] Specifically, the above-mentioned receiving module, the first building module, the second building module and the countermeasure output module can be embedded in a computer processing system. The computer calls the above-mentioned modules to complete the task of patterning the response to the risk scenarios of the distribution system according to the above-mentioned multi-mobile intelligent body anti-shake automatic aiming method; the above-mentioned receiving module, the first building module, the second building module and the countermeasure output module can perform operations according to the specific steps given in the method for constructing and patterning the anticipated risk scenario set of the AC / DC distribution system.

[0026] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the receiving module can be a separately established processing element, or it can be integrated in a certain chip of the above device. In addition, it can also be stored in the memory of the above device in the form of program code, and a certain processing element of the above device calls and executes the functions of the above signal processing module. The implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each module above can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0027] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more digital singnal processors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processors that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0028] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0029] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0030] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0031] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. Construction of anticipated risk scenario set and model-based response method for AC and DC power distribution systems, characterized by: The following steps are involved: Traverse the distribution network, receive all adjustable active and reactive resource information, and organize their operating characteristics; The 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion are adopted, and the interval power flow algorithm based on the DistFlow power flow equation is adopted to comprehensively construct the AC / DC distribution system operation risk assessment index; Sort out the potential risk elements of AC and DC power distribution systems, take into account the probability of occurrence of risk scenarios and the risk scenarios based on the system's expected risk scenario set, combine fuzzy comprehensive evaluation, and build an expected risk scenario response strategy library; Based on the Euclidean distance, a similarity function of expected risk scenarios is constructed, and the corresponding response measures in the response strategy library of the risk elements with the closest similarity are adopted to carry out patterned response to the risk scenarios.

2. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 1 is characterized in that: The active resource information includes energy storage systems and controllable loads, and the reactive resource information includes on-load tap-changing transformers, photovoltaic inverters and reactive compensation capacitors.

3. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 2 is characterized in that: The DistFlow power flow equation is linearized and expressed as: In the formula, and Node j The required active and reactive power, and Slave nodes i To Node j The active power and reactive power transmitted by the branch, and Slave nodes j To Node l Active power and reactive power transmitted by the branch, and Node i and nodes j The voltage amplitude, and Slave nodes i To Node j The impedance and inductance of the branch, and are the collections of nodes and branches in the distribution network, N and L are the number of nodes and the number of branches respectively; U sub corresponds to the voltage at the substation node; and Respectively represent the nodes j The collection of connected downstream and upstream nodes.

4. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 3 is characterized by: When conducting an AC / DC distribution system operation risk assessment, it is necessary to evaluate the importance of branches and nodes, including branch importance indicators and node importance indicators; (1) Branch importance index: Branch importance factor α ij The factors affecting the branch include the location of the branch in the distribution network and the voltage level. In the actual evaluation work, it is determined according to the following formula: In the formula, α aij is the branch betweenness weight factor, is the branch voltage level weight factor; (2) Node importance index can be divided into node importance factor β i , determined according to the following formula: In the formula, β ai is the load level factor under the node, β bi is the voltage level weight factor, is the power factor required for the node load.

5. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 1 is characterized in that: The AC / DC power distribution system operation risk assessment index includes a voltage over-limit risk index and a branch over-limit risk index; (1) Voltage over-limit risk indicator: Voltage over-limit penalty cost CEU Penalty cost based on voltage over-limit probability CEU.p Penalty cost for voltage over-limit CEU.l The specific expression is as follows: In the formula, and They are the voltage over-limit probability penalty cost and voltage over-limit degree penalty cost of the node respectively; and They are i The voltage over-limit probability and voltage over-limit degree of the node; and are the upper and lower thresholds of voltage over-limit probability, and They are the upper and lower thresholds of the voltage over-limit degree, is the set of all nodes, and are the voltage over-limit probability penalty cost and the voltage over-limit degree penalty cost, respectively. βi is the node importance factor; (2) Branch crossing risk indicator: Branch crossing penalty cost CES The penalty cost is calculated by the probability of branch crossing the limit CES.p The penalty cost of branch crossing the limit CES.l The specific expression is as follows: In the formula, and Branch ij The penalty cost of the probability of crossing the limit and the penalty cost of the degree of crossing the limit; and Branch ij The probability and degree of exceeding the limit; and are the upper and lower limit thresholds of the branch crossing probability, and They are the upper and lower thresholds of the branch crossing degree, is the set of all branches, and They are the branch crossing probability penalty cost and branch crossing degree penalty cost respectively. is the branch importance factor.

6. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 1 is characterized in that: The probability characteristic analysis of risk elements in the expected risk scenario set introduces a model based on clustering of load curves, and deeply explores the statistical laws of sudden risk scenarios by analyzing the probability characteristics of risk elements to calculate their occurrence probability; The motif refers to a high-frequency repeated, non-random basic network structure, including nodes and edges. By counting the number of occurrences and distribution of the motif, the fluctuations of load and renewable energy output can be effectively grasped, and the probability of occurrence of expected risk scenarios can be calculated. The calculation of the probability of occurrence of risk scenarios is to effectively calculate the probability of occurrence of expected risk scenarios by counting the number of occurrences and distribution of the models and understanding the fluctuations of load and new energy output within a certain time series.

7. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 6 is characterized in that: The anticipated risk scenario set construction includes the anticipated risk scenario severity index Sev , specifically expressed as: In the formula, Sev m For risk scenarios m The severity index, C m For risk scenarios m The total operating risk, p m For risk scenarios m probability of occurrence.

8. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 1 is characterized in that: The construction of the anticipated risk scenario response strategy library applies fuzzy comprehensive evaluation, which converts qualitative evaluation into quantitative evaluation based on the membership theory of fuzzy mathematics; The factor set of fuzzy comprehensive evaluation is constructed. The influence of each factor in the factor set on each state in the comment set has different degrees of fuzziness, that is, different membership degrees, which can be expressed by the membership matrix R as follows: In the formula, the matrix A is the factor set of fuzzy comprehensive evaluation, the matrix H is the comment set of fuzzy comprehensive evaluation, and the elements r ij represents the i-th factor A i For the jth comment H j The degree of membership.

9. The method for constructing and modeling the anticipated risk scenario set for AC and DC power distribution systems according to claim 1 is characterized in that: Based on the Euclidean distance, a similarity function of expected risk scenarios is constructed. The similarity function expression is: In the formula, For risk scenarios i and risk scenarios j The similarity of and Risk scenarios i and risk scenarios j The first t elements, n is the length of the risk feature vector.

10. A system for constructing a set of anticipated risk scenarios and patterned responses for AC and DC power distribution systems, used to implement a method for constructing a set of anticipated risk scenarios and patterned responses for AC and DC power distribution systems as claimed in any one of claims 1 to 9, characterized in that: include: A receiving module is used to traverse the power distribution network, receive all adjustable active resource information and reactive resource information, and organize their operating characteristics; The first building block is used to comprehensively construct the AC / DC distribution system operation risk assessment index by using the 2n+1 point estimation method probability power flow and Cornish-Fisher series expansion and the interval power flow algorithm based on the DistFlow power flow equation; The second building module is used to sort out the potential risk elements of the AC and DC power distribution system, taking into account the probability of occurrence of risk scenarios and the risk scenarios based on the system's expected risk scenario set, combined with fuzzy comprehensive evaluation, to build an expected risk scenario response strategy library; The response output module is used to construct a similarity function of expected risk scenarios based on Euclidean distance, take the response measures corresponding to the risk elements with the closest similarity in the response strategy library, and conduct a patterned response to the risk scenarios.

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