Risk assessment method for power flow out-of-limit management strategy of power distribution network with high-proportion photovoltaic access

Abstract

The invention discloses a high-proportion photovoltaic access power distribution network power flow out-of-limit management strategy risk assessment method, and belongs to the technical field of power system risk assessment. The method comprises the steps of establishing a power distribution network flow out-of-limit risk index system according to an operation risk theory; calculating weight assignments of the indexes based on an improved analytic hierarchy process, an evaluation method and a subjective and objective combination method; and based on index weight assignment, carrying out risk sorting on the power flow out-of-limit management strategies by adopting an alternative strategy sorting method based on a compromise strategy. The power flow out-of-limit strategy risk assessment method can realize accurate power flow out-of-limit strategy risk assessment, and provides important support for safe and effective operation of a power distribution network power flow out-of-limit management strategy with high-proportion photovoltaic access.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network technology with a high proportion of photovoltaic access, and in particular to a risk assessment method for a power flow over-limit management strategy of a distribution network with a high proportion of photovoltaic access. Background Art

[0002] As the penetration of new source-load resources, such as photovoltaics and electric vehicles, increases within distribution networks, the probability of network power flow exceeding limits increases. Conventional measures, such as adjusting unit output or load power consumption plans, are no longer effective in addressing power flow exceeding limits in distribution networks with a high proportion of photovoltaic access. When developing management strategies for power flow exceeding limits in distribution networks with a high proportion of photovoltaic access, a reasonable risk assessment of the strategy is necessary to provide important support for subsequent improvement and optimization of the strategy, thereby improving the safety, economy, and stability of distribution networks with a high proportion of photovoltaic access.

[0003] However, in the existing technology, the relevant research on power flow over-limit management focuses more on proposing power flow over-limit management strategies in an indirect way based on the electricity price mechanism, and fails to conduct accurate risk assessment of the corresponding power flow over-limit management strategies. Summary of the Invention

[0004] In order to overcome the deficiencies of the prior art, the present invention aims to provide a risk assessment method for a power flow over-limit management strategy in a distribution network with a high proportion of photovoltaic access.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A risk assessment method for power flow over-limit management strategies in distribution networks with a high proportion of photovoltaic access is proposed, including:

[0007] Establish a distribution network power flow over-limit risk indicator system based on operation risk theory;

[0008] The weights of the indicators are calculated based on the improved analytic hierarchy process, critical method and subjective and objective combination method respectively;

[0009] The alternative strategy ranking method based on compromise strategy is used to rank the risk of tidal over-limit management strategies.

[0010] Preferably, a distribution network power flow over-limit risk indicator system is established based on the operational risk theory, including:

[0011] Analyze the operation risks of distribution network;

[0012] The distribution network flow over-limit risk index is established based on the risk influencing factors of the distribution network.

[0013] Preferably, the analysis of distribution network operation risks includes:

[0014] Clarify the definition of distribution network operation risks;

[0015] Analyze the “source-load” characteristics of distribution networks with a high proportion of photovoltaic access;

[0016] Based on the "source-load" characteristics of the distribution network with a high proportion of photovoltaic access, the distribution network operation risk types and risk factors are analyzed.

[0017] Preferably, establishing a distribution network power flow over-limit risk index includes:

[0018] Taking into account the combinable characteristics of the power flow over-limit risk of the distribution network with a high proportion of photovoltaic access at the spatial level, the risk indicators of the component layer are first established based on the operational risks such as node overvoltage operation risk and node undervoltage operation risk. These risk indicators are then superimposed according to the network topology characteristics to construct the power flow over-limit risk indicators of the distribution network layer.

[0019] Preferably, the weight assignment of the indicators is calculated based on the improved analytic hierarchy process, the critical method and the subjective and objective combination method, including:

[0020] The improved analytic hierarchy process is used to determine the subjective weights of each power flow over-limit risk index in the distribution network.

[0021] The critical method is used to calculate the objective weight of each power flow over-limit risk index of the distribution network;

[0022] Combining the two and based on the minimum discriminant information principle, the comprehensive weights of the risk indicators of each power flow exceeding the limit in the distribution network are obtained by solving the Lagrangian function.

[0023] Preferably, an improved hierarchical analysis method is used to determine the subjective weights of each power flow over-limit risk index of the distribution network, including:

[0024] Collect expert opinions to judge the relative importance of evaluation indicators;

[0025] The judgment matrix is ​​determined using the scaling construction method;

[0026] Calculate the subjective weight of the indicator.

[0027] Preferably, the critical method is used to calculate the objective weights of each power flow over-limit risk index of the distribution network, including:

[0028] Indicators are aligned;

[0029] Normalization of indicators;

[0030] Calculate the objective weight of the indicator.

[0031] Preferably, the comprehensive weights of the risk indicators of each power flow exceeding the limit in the distribution network are obtained by solving the Lagrangian function, including:

[0032] The subjective weight vector α is obtained by improving the analytic hierarchy process and the critical method respectively. i and the objective weight vector β i , considering the complementarity of the two weight determination methods, the two weights are combined to obtain the comprehensive weight γ i To make a comprehensive assessment of the risk of over-limit management strategy of power flow in distribution network with high proportion of photovoltaic access. i As close to α as possible i and β i Without giving preference to any one of them, the minimum discriminant information principle is used to γ i To obtain.

[0033] Preferably, the method uses a compromise-based alternative strategy ranking method to perform risk ranking on the power flow over-limit management strategies, including:

[0034] According to the comprehensive weight vector γ of each flow over-limit risk index of the distribution network i And the alternative strategy ranking method model based on the compromise strategy is used to evaluate the risks of the distribution network current over-limit management strategy with a high proportion of photovoltaic access. Finally, the risks of different strategies are ranked and the advantages and disadvantages are evaluated to help decision makers make the right choices and propose improvement plans.

[0035] Beneficial Effects: The present invention provides a risk assessment method for power flow over-limit management strategies in distribution networks with a high proportion of photovoltaic access. This method establishes a distribution network power flow over-limit risk indicator system based on operational risk theory. The method also calculates indicator weights using an improved analytic hierarchy process, a critical thinking approach, and a subjective-objective combination approach. Based on the indicator weights, the method uses a compromise-based alternative strategy ranking method to rank the risks of power flow over-limit management strategies. This method enables accurate risk assessment of power flow over-limit strategies, providing important support for the safe and effective operation of power flow over-limit management strategies in distribution networks with a high proportion of photovoltaic access. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A flow chart of a method provided by an embodiment of the present invention;

[0037] Figure 2 A structural diagram of a distribution network with a high proportion of photovoltaic access provided by an embodiment of the present invention;

[0038] Figure 3 This is a risk assessment process for a power flow over-limit management strategy for a distribution network with a high proportion of photovoltaic access provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only 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 making creative efforts are within the scope of protection of the present invention.

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] Figure 1 A flow chart of the method provided in the embodiment of the present invention is shown in FIG. Figure 1 As shown, the present invention provides a risk assessment method for a power flow over-limit management strategy of a distribution network with a high proportion of photovoltaic access, comprising:

[0042] Step 100: Establish a distribution network power flow over-limit risk indicator system based on operation risk theory;

[0043] Step 200: Calculate the weights of the indicators based on the improved analytic hierarchy process, the critical method, and the subjective and objective combination method;

[0044] Step 300: Use the compromise-based alternative strategy ranking method to perform risk ranking on the power flow over-limit management strategies.

[0045] Specifically, this method establishes a distribution network power flow over-limit risk indicator system based on operational risk theory. This includes analyzing distribution network operational risks and establishing distribution network power flow over-limit risk indicators based on factors influencing distribution network risks. The analysis of distribution network operational risks includes clarifying the definition of distribution network operational risks and analyzing the "source-load" characteristics of distribution networks with a high proportion of photovoltaic access. The analysis then analyzes the distribution network operational risk types and risk factors based on these "source-load" characteristics.

[0046] This embodiment analyzes the distribution network operation risks as follows:

[0047] The purpose of power system risk assessment is to quantitatively analyze the uncertainties faced during the operation of the system. Risk refers to the combination of the possibility of a specific dangerous event and the consequences it causes. Power system risk assessment considers the possibility of the danger occurring, that is, the probability of the dangerous event occurring, and the consequences after the dangerous event occurs. Therefore, the operation risk of the distribution network refers to the combination of dangerous factors or specific events that affect the safe and stable operation of the distribution network and the adverse consequences caused by them. It is mathematically expressed as formula (1):

[0048] Risk(X t |Y t ,Ht )=Pro(X t )×Seve(Y t ,H t ) (1)

[0049] In the formula, Risk(X t |Y t ,H t ) is the risk indicator, where X t It represents the uncertainty events that may occur when the distribution network is running at time t, Y t and H t Respectively represent the operating status of the source side and load side of the distribution network at time t (branch current, node voltage, etc.). t ) is event X t The probability of occurrence, Seve(Y t ,H t ) is the distribution network at Y t and H t The consequences of an uncertain event occurring in the operating state, that is, the severity.

[0050] The distribution network with a high proportion of photovoltaic access uses cutting-edge science and technology to achieve flexible control and active management of resources such as distributed generation (DG), energy storage system (ESS), and flexible loads, as well as a benign interaction between "source-load". Figure 2 The focus of "active" is to improve the utilization efficiency of clean energy, reduce grid investment, and improve power quality through the effective use and control of resources on the source, grid, and load sides. The characteristics of the distribution network with a high proportion of photovoltaic access are manifested in the following three aspects:

[0051] (1) Source-side structural characteristics: DG can assist in the dispatching of distribution networks with a high proportion of photovoltaic access, rather than being limited to the traditional distribution method of "on-site load offset". In addition, the use of ESS can achieve more effective dispatching of DG, thereby improving the energy efficiency of the entire distribution network with a high proportion of photovoltaic access. With the access of various types of DG on the source side of the distribution network, such as micro gas turbines and photovoltaics, this diversified power access method can better adapt to the needs of different regions and users, while improving the flexibility and reliability of the distribution network with a high proportion of photovoltaic access. In addition, after DG was incorporated into the distribution network, the structure of the traditional distribution network underwent tremendous changes: the power flow began to change from unidirectional flow to bidirectional flow, the distribution network system began to change from passive to active, and the situation faced by power suppliers became increasingly complex.

[0052] As a new energy source, photovoltaics present several unique characteristics in distribution networks with a high penetration of photovoltaics. First, photovoltaic output fluctuates significantly, influenced by various factors such as weather and sunlight intensity. Therefore, it is necessary to monitor and predict its output, and to implement appropriate control strategies to ensure its stability. Second, photovoltaic output forecasting must consider the spatial distribution correlation within distribution networks with a high penetration of photovoltaics, so as to ensure reasonable scheduling in distribution network scheduling with a high penetration of photovoltaics. Furthermore, after photovoltaics are connected to the grid, coordinated control with flexible loads must be considered to fully utilize the advantages of photovoltaics while avoiding instability in distribution networks with a high penetration of photovoltaics. Finally, due to the strong interaction between photovoltaic power plants and the distribution network, their impact on distribution networks with a high penetration of photovoltaics must be considered, including in terms of power and voltage stability, to ensure the safe and stable operation of distribution networks with a high penetration of photovoltaics.

[0053] PV output forecasting errors are a common problem in distribution networks with a high PV penetration rate. This makes it difficult to accurately plan and schedule power generation and dispatch effectively, unlike with traditional thermal power plants. Whether output exceeds or falls below forecasts, these networks present varying degrees of risk, including economic losses for the power companies involved and the risk of system power flow exceeding limits. Regulations stipulate that for smaller PV power plants, if actual PV output exceeds forecasts, the full amount must be connected to the grid. Consequently, the power company must pay a fee to compensate for the costs of adjusting automatic generation control and reducing the output of generators with smaller peak-shaving capabilities. For larger PV power plants, if actual PV output exceeds forecasts, the excess must be discarded, effectively implementing "curtailment" measures to ensure that node voltages and line power in distribution networks with a high PV penetration rate do not create significant power flow exceeding limits. Furthermore, the power company must pay compensation to the PV power plant to ensure that its actual output meets the planned schedule. However, if actual PV output falls below forecasts, the power company must purchase electricity from the electricity market to make up the shortfall, posing significant financial risks. Due to the uncertainty of photovoltaic output, distribution networks with a high proportion of photovoltaic access may face a certain degree of economic loss risk and network current over-limit risk. This not only seriously affects the efficiency of power companies and greatly reduces the utilization rate of photovoltaic power generation, but more importantly, it poses a threat to the safe and stable operation of distribution networks with a high proportion of photovoltaic access.

[0054] The integration of ESS into the grid changes the direction of power flow in traditional distribution networks. From the original one-way flow from the "source" side to the "load" side, ESS now operates under grid control and can reverse power flow to distribution networks with a high proportion of photovoltaic power. This diversifies the direction of system power flow, potentially causing network power flow over-limit issues. However, through appropriate scheduling, ESS can also effectively mitigate the risk of power flow over-limit issues in distribution networks with a high proportion of photovoltaic power. With the continuous development of energy storage technology, the emergence of new energy storage devices such as household distributed energy storage devices and portable energy storage devices can effectively reduce the peak-to-valley variation in distribution networks with a high proportion of photovoltaic power, alleviate power supply pressure during peak hours, and provide comprehensive guarantees for safe and stable operation.

[0055] (2) Grid-side structural characteristics: The network topology of the distribution network with a high proportion of photovoltaic access can be flexibly adjusted according to actual needs. With the large-scale utilization of clean energy and the rapid development of flexible loads, the traditional distribution network grid structure can no longer meet the current flexible and changeable needs. By controlling the segmented switches and interconnecting switches in the distribution network with a high proportion of photovoltaic access, the network topology can be flexibly adjusted to achieve the purpose of controlling the power supply range and improving the system flow distribution. This can not only reduce investment, but also better meet the current demand for the distribution network grid structure. In addition, with the emergence of new power electronic devices such as intelligent distribution soft switches and new voltage source converters, the optimization potential of the network topology for the system flow is being continuously explored.

[0056] (3) Load-side structural characteristics: Flexible loads can be actively dispatched. With the rapid development of smart industries such as smart grids, smart loads, and smart home appliances, loads such as electric vehicles (EVs), lighting loads, and household electric heating can be used as flexible loads in distribution networks with a high proportion of photovoltaic access and participate in the system dispatch. There are generally two types of dispatching methods. One is to allow users to actively adjust their electricity consumption behavior to a certain extent according to their personal electricity consumption preferences under different electricity price environments through time-of-use pricing, responsive subsidies, etc. The other is that the dispatching department of the distribution network with a high proportion of photovoltaic access directly adjusts the load according to the actual operation of the system to change the electricity demand and supply situation. The above-mentioned flexible loads not only meet people's needs for a comfortable and convenient life, but also can be used as a flexible dispatching method to achieve the requirements of the distribution network with a high proportion of photovoltaic access for safe and stable operation of the system.

[0057] It is further pointed out that the operational risks of distribution networks can be divided into intrinsic risks and external risks according to their causes. Intrinsic risks refer to uncertain factors that affect the safe and stable operation of the system, such as the operating conditions of the equipment in the system, certain characteristics of the internal components of the system, etc.; external risks refer to natural uncertain factors that are not controllable by humans, such as severe typhoons, hail and other weather conditions, and the activities of birds and animals around the system. This embodiment mainly considers the intrinsic operational risks of distribution networks with a high proportion of photovoltaic access. Based on the "source-load" characteristic analysis, the operational risks of distribution networks with a high proportion of photovoltaic access can be summarized as shown in Table 1:

[0058] Table 1 Operational risks of distribution networks with high proportion of photovoltaic access

[0059]

[0060] In summary, the idea behind distribution network operation risk assessment is: first determine the scenarios to be analyzed, analyze the problems in each scenario and establish risk indicators, determine the weights of the risk indicators, calculate the comprehensive risk value based on the weights, and finally divide the risk levels. The specific steps are as follows:

[0061] Step 1: Determine the scenario to be analyzed: This mainly involves modeling the distribution network with a high proportion of photovoltaic access and obtaining system power flow information.

[0062] Step 2: Scenario Analysis: Analyze the risks that may affect the safe and stable operation of the distribution network with a high proportion of PV access, such as node overvoltage and line active power exceeding the limit.

[0063] Step 3: Establish risk indicators: Based on scenario analysis, establish corresponding risk assessment indicators for distribution network operation with a high proportion of PV access. Through quantitative calculation of these indicators, the overall operational performance of the system can be evaluated. Therefore, reasonable indicators are particularly important, as they form the basis and basis for the assessment.

[0064] Step 4: Determine indicator weights: After establishing the operational risk assessment indicators for distribution networks with a high proportion of PV access, it is necessary to weigh the weights of each indicator, considering that each indicator has varying impacts on operational risk events. The weights of each indicator are determined through quantitative analysis.

[0065] Step 5: Calculate the comprehensive risk value: After determining the indicator weights, perform weighted summation on the established risk indicators according to the weights to obtain the comprehensive risk value of the distribution network operation with a high proportion of photovoltaic access.

[0066] Step 6: Classify the risk levels: After calculating the comprehensive risk value of the distribution network operation with a high proportion of photovoltaic access, the final judgment on the operation risk of the distribution network with a high proportion of photovoltaic access can be made according to certain specific evaluation standards or evaluation rules, that is, the risk level can be divided.

[0067] Combining the above evaluation ideas, the risk assessment process of the distribution network power flow over-limit management strategy with high proportion of photovoltaic access in the present invention is obtained, as follows: Figure 3 shown.

[0068] Preferably, the distribution network current over-limit risk index is established according to the operation risk theory, including taking into account that the current over-limit risk of the distribution network with a high proportion of photovoltaic access has a combinable characteristic at the spatial level, firstly, the component layer risk index is established based on the operation risks such as node overvoltage operation risk and node low voltage operation risk, and then these risk indicators are superimposed according to the network topology characteristics to construct the distribution network layer current over-limit risk index.

[0069] Specifically, this embodiment first establishes component-level risk indicators:

[0070] The combination of the possibility (probability) of a specific dangerous event for safe and stable operation and the resulting consequences (severity) can be quantitatively evaluated to determine the level of system operation. The over-limit risk of the power flow in the distribution network with a high proportion of photovoltaic access considered in this invention is mainly a comprehensive consideration of the probability of occurrence of events such as node voltage over-limit and line power overload caused by the uncertainty of individual photovoltaic output and the severity of their consequences, which can be expressed as formula (2):

[0071] Risk(Y t )=Pro(X i,t )×Seve(X i,t |Y t ) (2)

[0072] Where, X i,t is the i-th event that may occur at time t in a distribution network with a high proportion of photovoltaic access, including node voltage exceeding the limit, line power overload, etc. i,t ) is event X i,t The probability of occurrence, Seve(X i,t |Y t ) is the distribution network with a high proportion of photovoltaic access and the operating condition on the source side is Y t Event X occurs i,t The severity of the aftermath.

[0073] (1) Node overvoltage operation risk

[0074] If the per-unit value of the node voltage in a distribution network with a high proportion of photovoltaic access exceeds the deviation range of 5%, it is considered to be out of limit. When a node voltage in a distribution network with a high proportion of photovoltaic access exceeds the upper limit, it will generate a node overvoltage operation risk. The relevant function is as follows:

[0075]

[0076] Where, is the probability of voltage exceeding the upper limit at the i-th node of the distribution network with a high proportion of photovoltaic access. Ω is the set of scenarios to be analyzed. σ s is the probability of each scenario. It is the overvoltage operation risk indicator, and its value is 1 or 0 depending on whether the overvoltage event occurs or not. i,s is the voltage of node i in the sth scenario; N is the number of nodes in the distribution network with high proportion of photovoltaic access; Seve vh (V i,s ) is the severity function of the voltage exceeding the upper limit event. V max The upper limit of the node voltage of the distribution network system with a high proportion of photovoltaic access is 1.05pu; is the risk function of the voltage exceeding the upper limit event.

[0077] (2) Risk of low voltage operation at nodes

[0078] Similar to the node overvoltage operation risk, when a node voltage exceeds the lower limit event in a distribution network with a high proportion of photovoltaic access, a node undervoltage operation risk will occur. The relevant function is as follows:

[0079]

[0080] Where, is the probability of a voltage lower limit event occurring at the i-th node of the distribution network with a high proportion of photovoltaic access. It is a low voltage operation risk indicator. It is 1 when the voltage exceeds the lower limit event, otherwise it is 0. vl (V i,s ) is the severity function of the voltage crossing the lower limit event. V min The lower limit of the node voltage of the distribution network system with a high proportion of photovoltaic access is 0.95pu; is the risk function of the voltage crossing the lower limit event.

[0081] (3) Risk of line overload operation

[0082] Each line in a distribution network with a high proportion of photovoltaic access has a fixed transmission capacity limit. When the power carried by the line exceeds this limit, there is a risk of line overload operation. The relevant function is as follows:

[0083]

[0084] Where, is the probability of an active power limit exceeding event occurring on the kth line of the distribution network with a high proportion of photovoltaic access. is the line overload operation risk indicator, which is 1 when a line overload event occurs, and 0 otherwise. L is the number of lines in the distribution network with a high proportion of photovoltaic access; Seve p (P k,t,s ) is the severity function of the line overload event. k,t,s P is the active power of line k in the sth determined scenario at time t. k,max is the maximum active power allowed to be carried by line k, i.e., line capacity; is the risk function of line overload events.

[0085] Furthermore, this embodiment constructs a power flow over-limit risk indicator at the distribution network layer based on the component layer risk indicators.

[0086] (1) Overvoltage risk in distribution network

[0087] The main research object of the present invention is the current over-limit management strategy for distribution networks with a high proportion of photovoltaic access. One of the two main factors that affect the current over-limit of distribution networks with a high proportion of photovoltaic access is voltage. Therefore, it is necessary to establish a distribution network layer current over-limit risk index related to node voltage. After photovoltaic power generation is connected to the grid, the voltage of the distribution network with a high proportion of photovoltaic access will exceed the limit. The voltage exceeding the upper limit means that the excessively high voltage will cause a certain degree of damage to the devices and equipment in the distribution network, while the voltage exceeding the lower limit means that the excessively low voltage cannot provide sufficient support for the normal operation of the distribution network, resulting in power loss. When the severity of the two is too great, the distribution network with a high proportion of photovoltaic access will suffer from current over-limit risks at different levels of influence. Therefore, it is very necessary to establish a distribution network overvoltage risk index.

[0088] The overvoltage operation risk of the distribution network is obtained by comprehensively considering the power characteristics and network characteristics of the distribution network nodes and accumulating the overvoltage risk of each node according to the node weight coefficient, as shown in Equations (12) and (13):

[0089]

[0090] Where, ω i is the weight coefficient of node i. i is the number of branches connected to node i. i is the active power of node i; The risk of overvoltage operation in the distribution network.

[0091] (2) Low voltage risk in distribution network

[0092] Similarly, the low voltage operation risk of the distribution network is obtained by comprehensively considering the power characteristics and network characteristics of the distribution network nodes and accumulating the low voltage risk of each node according to the node weight coefficient, as shown in formula (14):

[0093]

[0094] Where, Risk of low voltage operation of distribution network.

[0095] (3) Risk of overload of distribution network lines

[0096] Another major factor affecting power flow exceeding limits in distribution networks with a high PV penetration rate is power. When the power carried by a line in a distribution network with a high PV penetration rate exceeds the maximum allowable limit, line overload occurs. This creates the risk of power flow exceeding limits and can also accelerate insulation aging or overheat internal devices and equipment, posing a threat to the safe and stable operation of the system. Therefore, establishing a distribution network line overload risk indicator is crucial. Unlike the distribution network voltage overload risk indicator, the power of a distribution network line only addresses the risk of exceeding the line power flow upper limit. The line power flow upper limit in a distribution network with a high PV penetration rate varies significantly depending on the network topology and related parameter settings.

[0097] Similar to the previous two distribution network risk indicators, the overload operation risk of the distribution network line is obtained by comprehensively considering the voltage characteristics and network characteristics of the distribution network branches and accumulating the overload risks of each branch line according to the branch weight coefficient, as shown in Equations (15) and (16):

[0098]

[0099] Where, ω k is the weight coefficient of branch k. i Similar, Link k is the number of nodes connected by branch k. k is the voltage of branch k. The risk of overload operation of distribution network lines.

[0100] (4) Risk of load loss in distribution network

[0101] As shown in the introduction to the first three distribution network risk indicators, exceeding the node voltage or line power limits in a distribution network with a high proportion of PV integration can cause varying degrees of damage to the system and trigger the risk of power flow exceeding limits. When the severity of an exceeding limit event exceeds a certain level, load shedding is necessary to ensure safe and stable system operation, which manifests as a distribution network load loss risk.

[0102] It can be seen that the load-loss operation risk of the distribution network is the maximum load shedding value caused by the above three over-limit events, as shown in equations (17) to (20):

[0103]

[0104] Where, and They are the load-loss operation risks of the distribution network caused by exceeding the node voltage and line power limits; and are the load reduction ratios when the node voltage and line power exceed the limit, respectively. Their values ​​are usually calculated from the severity function of the corresponding limit-exceeding event; The risk of distribution network operating without load.

[0105] The comprehensive power flow over-limit operation risk of the distribution network is the result of the superposition of the four power flow over-limit risk indicators mentioned above, as shown in formula (21):

[0106]

[0107] Where, It is the risk of over-limit operation of the comprehensive power flow of the distribution network.

[0108] Preferably, the weight assignment of the indicators is calculated based on the improved analytic hierarchy process, the critical method and the subjective and objective combination method, including: using the improved analytic hierarchy process (AHP) to determine the subjective weight of each power flow over-limit risk indicator of the distribution network; using the critical method (CriteriaImportanceThroughIntercrieriaCorrelation, CRITIC) to calculate the objective weight of each power flow over-limit risk indicator of the distribution network; combining the two and according to the minimum discriminant information principle, by solving the Lagrangian function to obtain the comprehensive weight of each power flow over-limit risk indicator of the distribution network.

[0109] Preferably, an improved hierarchical analysis method is used to determine the subjective weights of each power flow over-limit risk indicator of the distribution network, including collecting expert opinions to judge the relative importance of the evaluation indicators; using the scaling construction method to determine the judgment matrix; and then calculating the subjective weights of the indicators. Specifically, the subjective weight calculation method of this embodiment is as follows:

[0110] After analyzing the operational risks of distribution networks with a high proportion of photovoltaic access, distribution network current over-limit risk indicators and comprehensive current over-limit risk indicators were established from the component layer to the distribution network layer. Taking into account that the impact of each indicator on the operational risk of distribution networks with a high proportion of photovoltaic access varies, it is necessary to weigh the weight of each indicator. When the traditional 1 to 9 scale AHP method is used to calculate the subjective weight of each indicator, the relative importance of each indicator is not easy to judge. If the resulting judgment matrix cannot pass the consistency test, a contradictory indicator system will be obtained, resulting in difficulties in the evaluation process. To solve this problem, the present invention adopts an improved AHP method to determine the subjective weight of each indicator, collects expert opinions to judge the relative importance of the evaluation indicators, and then uses the scaling construction method to determine the judgment matrix.

[0111] The specific steps of subjective weighting are as follows:

[0112] 1) Rank the indicators based on expert opinions.

[0113] Assume there are n evaluation indicators x1, x2, ..., x n Collect the reference opinions of experts on the relative importance of each evaluation index, rank the importance of each evaluation index according to the reference opinions of experts under the unified standard in the hierarchical model, and subjectively rank the evaluation indexes according to the principle of decreasing importance to obtain the form x1≥x2≥…≥x n evaluation results.

[0114] 2) Get the scale value of the indicator.

[0115] Compare the importance of two adjacent evaluation indicators, determine the scale value according to Table 2 and record it as c i Similarly, the scale values ​​c1≥c2≥…≥c between all adjacent evaluation indicators can be obtained n-1 .

[0116] Table 2 Meaning of scale values

[0117]

[0118] 3) Construct the judgment matrix of indicators.

[0119] According to the transferability of importance, other scale values ​​in the judgment matrix R are obtained, and the corresponding judgment matrix R is obtained, as shown in formula (22). The judgment matrix R satisfies the following conditions: (1) r ij >0;(2)r ii =1;(3)r ij =1 / r ji ;(4)r ij =r ik r kj .

[0120] Among them, r ij is the scale value of evaluation index i relative to evaluation index j.

[0121]

[0122] 4) Calculation of subjective weights of indicators.

[0123] The judgment matrix R obtained by the above method meets the consistency requirements and does not need to be tested. The subjective weight of each evaluation indicator can be calculated based on R. The normalized calculation formula is shown in formula (23):

[0124]

[0125] Where, α i is the weight value of the evaluation index i. is the geometric mean of the total scale value of evaluation index i. This can be used to quantitatively determine the subjective weight of each flow over-limit risk index of the distribution network.

[0126] Preferably, the objective weight of each power flow over-limit risk index of the distribution network is calculated using a critical method, including index aligning, index normalization and calculation of objective weight of the index. Specifically, the objective weight calculation method of this embodiment is as follows:

[0127] Subjective weights are weights assigned to the risk indicators of each flow over-limit risk in the distribution network based on the subjective judgment and experience of experts. The determination of weights lacks objective basis, and in some cases, subjective judgment may even produce certain deviations. In order to overcome the limitations of subjective weights, the present invention adds objective weights based on the CRITIC method to the weights of evaluation indicators. This method is an objective weighting method based on evaluation indicator data, comprehensively considering the differences between various strategies and the conflicts between various evaluation indicators. The resulting weight calculation results are more objective and reasonable.

[0128] Assuming there are m strategies, each with n corresponding evaluation indicators, the evaluation matrix J can be used to determine the relationship between the evaluation indicators and evaluate different strategies. The evaluation matrix can better interpret the advantages and disadvantages of the strategy, thereby helping decision makers make the right choice and propose improvement plans. The evaluation matrix can be expressed as Equation (24):

[0129]

[0130] Where z nm Represents the evaluation value of the nth evaluation indicator corresponding to the mth strategy.

[0131] The specific steps of objective weighting are as follows:

[0132] S1: Indicators are moving in the same direction.

[0133] The index values ​​of evaluation indicators are divided into two directions: positive and negative. A positive indicator indicates that the larger the positive index value, the better the effect, while a negative indicator indicates that the smaller the negative index value, the better the effect. The coexistence of both will greatly complicate the calculation of the evaluation indicator system, so negative indicators are usually converted into positive indicators by aligning them. The conversion formula is shown in Equation (25):

[0134]

[0135] Where x′ ij is the positive evaluation index after the same direction processing. P is the coordination coefficient, which is generally set to 0.1. max|J i| is the maximum value of evaluation index i, that is, the maximum value of row i in the evaluation matrix J. The evaluation matrix after index homogeneity processing is denoted as J′.

[0136] S2: Indicator normalization.

[0137] Since the meanings of the evaluation indicators in J′ are different and the units used are also different, it is necessary to normalize the evaluation indicators to standardize and unify the standards of the evaluation indicators. The normalization formula is shown in formula (26):

[0138]

[0139] Where x″ ij is the evaluation index after normalization, and the corresponding standard evaluation matrix is ​​expressed as J″.

[0140] S3: Calculation of objective weights of indicators.

[0141] In the CRITIC method, the same evaluation indicator may obtain different values ​​in different decisions or strategies. The standard deviation of the evaluation indicator is used to represent this difference. There may be certain conflicts between different evaluation indicators. In order to characterize this conflict, the relationship coefficient between the evaluation indicators is used to construct a quantitative expression. Different evaluation indicators contain different amounts of information. The above two expressions are used to construct a quantitative expression to represent the amount of information. i and the relationship coefficient ρ between each evaluation index ij It can be obtained through the standard evaluation matrix J″, taking into account s i and ρ ij Get the amount of information G contained in each evaluation index i , respectively expressed as follows:

[0142]

[0143] Where, is the average value of the evaluation index i, that is, the average value of the elements in the i-th row of the standard evaluation matrix J″; Cov(J i ″,J j ″) is the covariance between the elements in the i-th row and the j-th row in the standard evaluation matrix J″. When the two are positively correlated, its value is greater than zero, otherwise it is less than zero, and when there is no correlation, its value is zero; It is the quantitative value of the conflict between evaluation index i and other evaluation indicators.

[0144] After completing the calculation of the above formula, the final objective weight β can be obtained i , as shown in formula (30), G i The larger it is, the greater the objective weight of the evaluation index i is, and the more important the index is.

[0145]

[0146] Preferably, the comprehensive weights of the risk indicators of each power flow exceeding the limit in the distribution network are obtained by solving the Lagrangian function, including:

[0147] The subjective weight vector α is obtained by improving the AHP method and the CRITIC method respectively. i and the objective weight vector β i , considering the complementarity of the two weight determination methods, the two weights are combined to obtain the comprehensive weight γ i To make a comprehensive assessment of the risk of over-limit management strategy of power flow in distribution network with high proportion of photovoltaic access. i As close to α as possible i and β i Without giving preference to any one of them, the minimum discriminant information principle is used to γ i To obtain.

[0148] Specifically, the comprehensive calculation method of this embodiment is as follows:

[0149] The objective function is as follows:

[0150]

[0151] First, construct the Lagrangian function of the objective function as follows:

[0152]

[0153] Taking the partial derivative of the Lagrangian function:

[0154]

[0155] By solving the simultaneous equations, the result is shown in formula (34):

[0156]

[0157] The comprehensive weight vector γ of each evaluation index is:

[0158] γ=[γ1,γ2,…,γ n ] T (35)

[0159] Preferably, the risk ranking of the power flow over-limit management strategies is performed using the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), which includes:

[0160] According to the comprehensive weight vector γ of each flow over-limit risk index of the distribution network i And the alternative strategy ranking method model based on the compromise strategy is used to evaluate the risks of the distribution network current over-limit management strategy with a high proportion of photovoltaic access. Finally, the risks of different strategies are ranked and the advantages and disadvantages are evaluated to help decision makers make the right choices and propose improvement plans.

[0161] Specifically, this embodiment first defines the specific steps of the alternative strategy ranking method based on the compromise strategy:

[0162] The MARCOS method is a multi-criteria group decision-making approach. Specifically, it defines a utility function for determining alternative strategies based on the relationship between ideal and non-ideal strategies, or between ideal solutions (AI) and non-ideal solutions (AAI). This utility function represents the relative utility of alternative strategies relative to ideal and non-ideal strategies. Alternative strategies are ranked based on their risk. The ranking is based on the distance between the alternative strategy and the AI ​​and AAI. The optimal strategy is the one closest to the AI ​​and furthest from the AAI.

[0163] The specific steps of the MARCOS method are as follows:

[0164] (1) Initial decision matrix As shown in Equation (36), it contains m alternative strategies and n evaluation criteria. In the case of group decision-making, the alternative strategies are evaluated according to the evaluation criteria by collecting expert opinions.

[0165]

[0166] (2) Forming an extended initial decision matrix As shown in formula (37). This step is to m×n It is obtained by adding two rows of elements to the basis, namely AAI and AI.

[0167]

[0168] Where, This is the last alternative strategy to be considered in risk assessment. is the top alternative strategy for risk assessment. According to the nature of different assessment criteria, AAI and AI can be expressed as Equations (38) and (39), respectively.

[0169]

[0170] In the formula, B is a subset of the benefit index, and the larger its value, the better; C is a subset of the cost index, and the lower its value, the better.

[0171] (3) Yes Normalization processing is performed, and the normalization formulas are shown in Equations (40) and (41).

[0172]

[0173] Where n ij is the element in the matrix obtained after normalization; and Both Elements in .

[0174] (4) By ij The elements v of the weighted matrix can be obtained by multiplying them with γ ij .

[0175] (5) Calculate the alternative strategy K through equations (42) and (43) i Utility relative to AAI and AI.

[0176]

[0177] Where, is the sum of the elements in the i-th row of the weighted matrix; Sum aai and Sum ai are the sum of the elements in the aai-th row and the ai-th row of the weighted matrix respectively.

[0178] (6) Based on and Determine the final utility function f(K i ), as shown in formula (44):

[0179]

[0180] Where, and are the utility functions related to AAI and AI, respectively, as shown in Equations (45) and (46).

[0181]

[0182] (7) According to each alternative strategy f(K i ) values ​​to perform risk ranking, and implement risk assessment of the power flow over-limit management strategy for distribution networks with a high proportion of photovoltaic access.

[0183] Furthermore, this embodiment uses the MARCOS method to evaluate the risks of different power flow over-limit management strategies according to the comprehensive weights, and completes the risk ranking of different strategies.

[0184] The research object is the power flow over-limit management strategy for distribution networks with a high proportion of photovoltaic access, considering the uncertainty of photovoltaic power generation. The evaluation case is as follows:

[0185] Strategy 1: Flow over-limit management strategy without flow over-limit management.

[0186] Strategy 2: A power flow over-limit management strategy that only considers interruptible loads, time-shiftable loads, and line power flow capacity constraints, but does not consider node voltage over-limits.

[0187] Strategy 3: Only considers the V2G mode and energy storage of electric vehicles, as well as the line power flow capacity constraints, and does not consider the power flow over-limit management strategy of node voltage over-limit.

[0188] Strategy 4: Considering interruptible loads, time-shiftable loads, V2G mode of electric vehicles and energy storage, as well as line power flow capacity constraints, and considering the power flow over-limit management strategy of node voltage over-limit.

[0189] While conducting a power flow over-limit risk analysis on a distribution network system with a high proportion of photovoltaic access, a power flow over-limit risk index and a comprehensive power flow over-limit risk index at the distribution network layer were constructed. A combination of subjective and objective methods was used to determine the comprehensive weight of each index. Based on the comprehensive weight, the MARCOS method was used to evaluate the risks of different power flow over-limit management strategies, and the risk ranking of different strategies was completed.

[0190] (1) Analysis of risk indicators of each power flow exceeding the limit in the distribution network

[0191] Table 3 shows the calculation results of the distribution network's power flow over-limit risk indicators under the four strategies. Because all four strategies take into account the line power flow capacity constraints of the distribution network with a high proportion of photovoltaic access, or because the line capacity parameters of the IEEE 33-node high-proportion photovoltaic access distribution network system are adequately set, the indicators for line overload risk in the distribution network are all very small, and their impact can be completely ignored. The main risks are overvoltage operation, undervoltage operation, and load loss operation.

[0192] Table 3. Risk indicators of power flow exceeding the limit for each distribution network under different strategies.

[0193]

[0194] Comparing the comprehensive power flow over-limit operation risk indicators under the four strategies shows that Strategy 4, a power flow over-limit management strategy that considers interruptible loads, time-shiftable loads, V2G mode for electric vehicles, and energy storage, has the lowest comprehensive power flow over-limit operation risk. The next highest risk is Strategy 2, a power flow over-limit management strategy that considers interruptible loads and time-shiftable loads. The highest risk is achieved by Strategy 3, a power flow over-limit management strategy that considers V2G mode for electric vehicles and energy storage.

[0195] Compared to Strategy 1 (a power flow over-limit management strategy without power flow over-limit management), the three power flow over-limit management strategies for distribution networks with high PV penetration rates proposed in this invention (Strategies 2 through 4) all have varying degrees of effectiveness in addressing the power flow over-limit issue in distribution networks with high PV penetration rates. Strategy 2 reduces the power flow over-limit risk by 0.283013772%, Strategy 3 by 0.076320263%, and Strategy 4 by 0.531707209%. The corresponding power flow over-limit risk reduction percentages for Strategies 2 through 4 are 16.10%, 4.34%, and 30.25%, respectively. This indicates that the power flow over-limit management strategy for distribution networks with high PV penetration rates proposed in this invention (Strategy 4) is the most effective in addressing the power flow over-limit issue in distribution networks with high PV penetration rates.

[0196] In addition, through the current over-limit risk assessment of the distribution network with a high proportion of photovoltaic access, it was found that photovoltaic access will have a certain impact on the stability of the system. During the operation, it is necessary to further strengthen the monitoring of voltage and load conditions to achieve effective control of the safety and stability of the distribution network with a high proportion of photovoltaic access.

[0197] (2) Comprehensive weight analysis of tidal over-limit risk assessment indicators based on subjective and objective factors

[0198] Step 1: Calculate subjective weights.

[0199] First, we combine the opinions of power experts to judge the relative importance of the distribution network flow over-limit risk assessment indicators, and subjectively sort the indicators according to the principle of decreasing importance to obtain the preliminary assessment results of overvoltage risk > low voltage risk > load loss risk. Using the principle of hierarchical analysis, we compare the importance of the indicators in pairs according to Table 2. Assuming r 12 =1.6000, r 23 =1.4000, and the judgment matrix R is obtained according to formula (22).

[0200]

[0201] The subjective weights of each indicator are calculated from the matrix R according to formula (23): α1 = 0.20661, α2 = 0.33058, α3 = 0.46281. Because the improved AHP method is used, no consistency test is required.

[0202] Step 2: Calculate objective weights.

[0203] The evaluation matrix J is obtained by formula (24). According to the CRITIC method and using formula (30), the objective weight of each indicator is calculated: β1 = 0.32972, β2 = 0.16633, β3 = 0.50395.

[0204]

[0205] Step 3: Calculate the comprehensive weight.

[0206] Combining the subjective weight with the objective weight, and using formula (34) to calculate the comprehensive weight, we can get the corresponding comprehensive weight vector γ = [0.33938 0.24105 0.41957] T This result is the combined weighted value of the three distribution network power flow over-limit risk indicators (overvoltage operation risk, undervoltage operation risk, and load loss operation risk) obtained by combining the improved AHP method and the CRITIC method. The final statistical results of the three indicators at different weights are summarized in Table 4.

[0207] Table 4 Calculation results of different weights of evaluation indicators

[0208]

[0209] (3) Risk ranking analysis of distribution network flow over-limit management strategies for high-proportion photovoltaic access based on the MARCOS method

[0210] Based on the weight results obtained in Table 4, the utility function values ​​of strategies 1 to 4 are calculated, and the calculated results are used as risk assessment values ​​to rank the risk of the alternative strategies. The final statistical utility function values ​​of the four alternative strategies under comprehensive weights, subjective weights, and objective weights are shown in Table 5.

[0211] Table 5 Utility function value results of alternative strategies under different weights

[0212]

[0213] As shown in Table 5, the results of risk assessment based on the MARCOS method are as follows: the risk values ​​from high to low are strategy 1 > strategy 3 > strategy 2 > strategy 4, and the risk assessment values ​​are 0.7935, 0.7649, 0.6467, and 0.5941 respectively; the results of risk assessment based on the improved AHP method are as follows: the risk values ​​from high to low are strategy 1 > strategy 3 > strategy 2 > strategy 4, and the risk assessment values ​​are 0.7689, 0.7505, 0.6431, and 0.6287 respectively; the results of risk assessment based on the CRITIC method are as follows: the risk values ​​from high to low are strategy 1 > strategy 3 > strategy 2 > strategy 4, and the risk assessment values ​​are 0.7985, 0.7708, 0.6309, and 0.6087 respectively.

[0214] It can be seen that the risk ranking results obtained by different methods are the same, among which the results obtained by using comprehensive weights and objective weights are closer, indicating that the ranking of the two methods is more reasonable, reflecting both the combination of subjective and objective factors and the objective factors based on evaluation data, while making the weight assignment of evaluation indicators more flexible. Among the results obtained by all methods. The alternative strategy with the highest risk is Strategy 1, that is, the flow over-limit management strategy without flow over-limit management, which is consistent with its comparison strategy with the other three flow over-limit management strategies (Strategies 2 to 4), verifying the effectiveness of the risk assessment of the flow over-limit management strategy of the distribution network with a high proportion of photovoltaic access by the MARCOS method. The alternative strategy with the lowest risk is Strategy 4, that is, the flow over-limit management strategy of the distribution network with a high proportion of photovoltaic access proposed by the present invention, which shows the rationality and effectiveness of the strategy.

[0215] The risk assessment method for the power flow over-limit management strategy of the distribution network with a high proportion of photovoltaic access described in the present invention is a method for ranking the risks of the power system based on the analytic hierarchy process-critical method-alternative strategy based on a compromise strategy. On the basis of theoretical analysis of the power flow over-limit problem of the distribution network with a high proportion of photovoltaic access under study, a power flow over-limit risk index system was established from the component layer to the distribution network layer. Secondly, the characteristics of the indicator weight assignment method and the specific calculation process are explained, including the subjective weight assignment method based on the improved analytic hierarchy process, the objective weight assignment method based on critical method and the comprehensive weight assignment method based on the first two. Finally, the characteristics and specific calculation process of the alternative strategy ranking method based on the compromise strategy are introduced. Through this method and combined with different weight assignments, a simulation verification of the risk assessment of the power flow over-limit management strategy of the distribution network with a high proportion of photovoltaic access is carried out.

[0216] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part. For those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and scope of application. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A risk assessment method for power flow over-limit management strategy of distribution network with high proportion of photovoltaic access, characterized by: include: Establish a distribution network power flow over-limit risk indicator system based on operation risk theory; The weights of the indicators are calculated based on the improved analytic hierarchy process, critical method and subjective and objective combination method respectively; The alternative strategy ranking method based on compromise strategy is used to rank the risk of tidal over-limit management strategies.

2. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 1 is characterized in that: The establishment of the distribution network current over-limit risk index system includes analyzing the distribution network operation risk and establishing the distribution network current over-limit risk index based on the distribution network risk influencing factors, including distribution network overvoltage risk, distribution network undervoltage risk, distribution network line overload risk and distribution network load loss risk.

3. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 2 is characterized in that: Analyze the distribution network operation risks, including: Clarify the definition of distribution network operation risks; Analyze the "source-load" characteristics of distribution networks with a high proportion of photovoltaic access; Based on the "source-load" characteristics of distribution networks with a high proportion of photovoltaic access, the types and risk factors of distribution network operation risks are analyzed.

4. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 2 is characterized in that: Establishing distribution network flow over-limit risk indicators includes: Taking into account the combinable characteristics of the power flow over-limit risk of the distribution network with a high proportion of photovoltaic access at the spatial level, the risk indicators of the component layer are first established based on the operational risks including node overvoltage operation risk and node undervoltage operation risk. These risk indicators are then superimposed according to the network topology characteristics to construct the power flow over-limit risk indicators of the distribution network layer.

5. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 1 is characterized in that: The weights of the indicators are calculated based on the improved analytic hierarchy process, critical method and subjective and objective combination method, including: The improved analytic hierarchy process is used to determine the subjective weights of each power flow over-limit risk index in the distribution network. The critical method is used to calculate the objective weight of each power flow over-limit risk index of the distribution network; Combining the two and based on the minimum discriminant information principle, the comprehensive weights of the risk indicators of each power flow exceeding the limit in the distribution network are obtained by solving the Lagrangian function.

6. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 5 is characterized in that: The improved analytic hierarchy process is used to determine the subjective weights of each power flow over-limit risk index of the distribution network, including: Collect expert opinions to judge the relative importance of evaluation indicators; The judgment matrix is ​​determined using the scaling construction method; Calculate the subjective weight of the indicator.

7. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 5 is characterized in that: The critical method is used to calculate the objective weights of each power flow over-limit risk index of the distribution network, including: Indicators are aligned; Normalization of indicators; Calculate the objective weight of the indicator.

8. The method for risk assessment of power flow over-limit management strategies for distribution networks with a high proportion of photovoltaic access according to any one of claims 6 or 7, wherein the method combines subjective weights obtained by a tomographic analysis method with objective weights obtained by a critical approach, and based on the minimum discriminant information principle, obtains the comprehensive weights of various power flow over-limit risk indicators of the distribution network by solving the Lagrangian function, including: The subjective weight vector α is obtained by improving the analytic hierarchy process and the critical method respectively. i and the objective weight vector β i , considering the complementarity of the two weight determination methods, the two weights are combined to obtain the comprehensive weight γ i To assess the risk of over-limit management strategy for power flow in distribution networks with high proportion of photovoltaic access; In order to make γ i As close to α as possible i and β i Without giving preference to any one of them, the minimum discriminant information principle is used to γ i To obtain.

9. The method for risk assessment of power flow over-limit management strategy for distribution network with high proportion of photovoltaic access according to claim 1 is characterized in that: The alternative strategy ranking method based on compromise strategy is used to rank the risk of power flow over-limit management strategy, including the comprehensive weight vector γ of each power flow over-limit risk index of distribution network. i And the alternative strategy ranking method model based on the compromise strategy is used to evaluate the risk of the distribution network flow over-limit management strategy with a high proportion of photovoltaic access. Finally, the risks of different strategies are ranked to complete the risk assessment of the power system.

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