Resilient distribution system evaluation system with high penetration distributed generation

Abstract

The resilient distribution network evaluation system with high penetration of distributed generation belongs to the field of distribution network planning. It includes the quantitative analysis of high penetration, which quantifies the impact of high penetration of distributed generation on the distribution network. In order to ensure the power supply capacity of important loads under extreme events such as typhoon weather, the rationality index of distributed generation location, the rationality index of power supply structure, the load loss index, the load normal operation time index, the line fault probability index and the topology connectivity index are established respectively. The combination weight is calculated by subjective and objective methods. Finally, the approximate ideal solution ranking method is used to give the evaluation results of different distribution network resilience. The comprehensive evaluation system of the resilient distribution network with high penetration of distributed generation is established, which can reasonably evaluate the system and guide the planning of the resilient distribution network with high penetration of distributed generation. It can be widely used in the field of comprehensive evaluation of distribution network.

Description

Technical Field

[0001] This invention belongs to the field of comprehensive evaluation of distribution networks, and in particular relates to a comprehensive evaluation method for resilient distribution networks containing high-penetration distributed power sources. Background Technology

[0002] The integration of distributed power sources can help the distribution network meet more load demands. However, as its penetration rate increases, how to improve the stable power supply capability of the distribution network becomes a key issue. This reflects the impact of high-penetration distributed power sources on resilient distribution networks. Under extreme events such as typhoon disasters, the uncertainty of distribution network failures, load losses, and wind and solar power output is significant. How to characterize their impact on resilient distribution networks has certain guiding significance for the rational planning and operation of resilient distribution networks.

[0003] A comprehensive evaluation index system for the resilience of distribution networks with high-penetration distributed generation is an important standard for measuring the ability of resilient distribution networks to cope with extreme events (typhoons, storm surges), and it is also key to determining whether the planning and direction of resilient distribution networks after the integration of high-penetration distributed generation can be systematically, scientifically, and accurately guided. In the research on resilient distribution network evaluation systems, there are relatively few studies that comprehensively consider both high penetration and resilience indicators; further research is needed on evaluation index systems.

[0004] For example, patent application CN 113868585 A, published on December 31, 2021, discloses a method and system for comprehensive resilience assessment of distribution networks. The method includes: acquiring distribution network parameters; conducting an assessment based on a pre-set comprehensive resilience assessment system; the system includes primary and secondary evaluation indicators, with each primary indicator having corresponding secondary indicators; the primary indicators include perception, adaptability, defense, resilience, coordination, and learning; and, based on the assessment results of each secondary indicator, a comprehensive calculation is performed according to the weights of each secondary indicator and the primary indicator to obtain the comprehensive resilience assessment result of the distribution network. Compared with existing technologies, this solution targets six key characteristics of resilient power grids, focusing on three functions: distribution network situational awareness, disturbance response, and self-improvement capabilities. It can establish a more comprehensive and refined assessment system under resilience requirements, improving the accuracy and reliability of the assessment results.

[0005] For example, patent application CN 113868586 A, published on December 31, 2021, also discloses a multi-dimensional, multi-level resilient power grid assessment method and system. This includes: acquiring equipment parameters, distribution network parameters, and transmission network parameters for resilient power grids in equipment, distribution network, and transmission network scenarios respectively; and conducting power grid indicator assessment through a three-layer assessment indicator system to obtain a comprehensive assessment result. The three-layer assessment indicator system includes a comprehensive assessment indicator layer, a six-dimensional assessment indicator layer, and a refined assessment indicator layer, arranged sequentially. The comprehensive assessment indicator layer includes equipment-level, distribution network-level, and transmission network-level indicators. Each indicator in the comprehensive assessment indicator layer corresponds to a six-dimensional assessment indicator layer, which includes resilience, defensive capability, resilient capability, sensing capability, collaborative capability, and learning capability. Each indicator in the six-dimensional assessment indicator layer corresponds to a refined assessment indicator layer. Compared with existing technologies, this invention helps to establish a more refined assessment indicator system and improve the accuracy of resilient power grid assessment.

[0006] However, none of the above technical solutions take into account the evaluation of distribution networks with high penetration distributed power sources, fail to reflect the impact of high penetration distributed power sources on resilient distribution networks, and cannot meet the actual status and development trend of distributed power source access. Summary of the Invention

[0007] The technical problem to be solved by this invention is to provide an evaluation system for resilient distribution networks with high penetration distributed generation. It takes into account the impact of high penetration distributed generation on resilient distribution networks, reflects its influence, provides a reasonable assessment of resilient distribution networks with high penetration distributed generation, and improves upon the shortcomings of existing research in this area. It can systematically, scientifically, and accurately guide the planning and direction of resilient distribution networks after the integration of high penetration distributed generation.

[0008] The technical solution of this invention is to provide an evaluation system for resilient distribution networks with high penetration of distributed power sources, characterized by the following steps:

[0009] Step 1: Establish various indicators, including distributed power availability, distributed power absorption rate, and active power balance.

[0010] Step 2: To ensure the power supply capacity of the distribution network to important loads under extreme events such as typhoons, various resilience indicators are established based on the impact of typhoon disasters, including indicators for the rationality of distributed power source location, the rationality of power supply structure, load loss, load uptime, line fault probability, and topology connectivity.

[0011] Step 3: First, calculate the combined weights using subjective and objective methods. Then, use the approximation ideal solution ranking method to give the different distribution network resilience assessment results, so as to facilitate the scientific and accurate guidance of the planning of resilient distribution networks after the integration of high-penetration distributed power sources.

[0012] Specifically, the indicators established in step 1 include distributed power availability, distributed power absorption rate, and active power balance. Each indicator is as follows:

[0013] (1) Availability index of intermittent distributed power sources:

[0014] The λ1 index is mainly used to reflect the power generation capacity of a photovoltaic power station. The numerator represents the actual power generation of the intermittent distributed power source under multiple conditions, including environmental factors and its own operating characteristics, within the evaluation period T. The denominator represents the rated output of the intermittent distributed power source. This index is used to intuitively evaluate the power generation capacity of intermittent distributed power sources. Its formula is as follows:

[0015]

[0016] In the formula, T is the evaluation period, 24 hours; P(t) is the actual output curve of the intermittent distributed power source; P DN The rated output of the intermittent distributed power source;

[0017] (2) Distributed power absorption rate index:

[0018] To minimize the curtailment of solar power caused by high-penetration distributed generation (DG) access to the distribution network, a DG absorption rate index is proposed. This index reflects the distribution network's ability to absorb the output power of DG, and can be expressed as:

[0019]

[0020] In the formula, λ2 is the distributed power absorption rate; P dg-Σ The rated power of distributed power sources connected to the distribution network; P max This represents the maximum load power of the distribution network.

[0021] (3) Active power balance index:

[0022] To better evaluate the power generation level of distribution networks with high-penetration distributed generation, it is required that power generation and load within the distribution network be matched. On the one hand, the maximum power generation capacity of generators must meet the maximum load; on the other hand, to cope with load reduction caused by extreme events, the minimum stable output of generators must be less than the minimum load.

[0023]

[0024] In the formula, P L,max and P L,min These represent the maximum and minimum loads of the distribution network, respectively; P G,max and P G,min These are the maximum and minimum power of the generators in the distribution network, which include both uninterrupted distributed power sources and intermittent distributed power sources.

[0025] Specifically, the resilience indicators established in step 2 include distributed power source location rationality indicators, power supply structure rationality indicators, load loss indicators, load uptime indicators, line fault probability indicators, and topology connectivity indicators. Each indicator is as follows:

[0026] (1) Resilience index f1, distributed power source location rationality index: The connection location of distributed power sources will affect the power supply characteristics of each load. The shorter the distance between the load node and the power supply path, the easier it is to meet the power supply needs of the load when a line fault occurs, and therefore the higher the network resilience. Under the same power supply path, distributed power sources should be connected near more important loads with higher power supply quality requirements to ensure that the network and important load nodes can quickly restore power supply. Under the constraint of the maximum access capacity of distributed power sources, the more distributed power sources are connected, the higher the distribution network resilience.

[0027] (2) Resilience index f2, power supply structure rationality index: The access of distributed power sources has changed the power supply structure, from single-source centralized to multi-source distributed. Load nodes are powered by multiple distributed power sources at the same time. When some facilities are damaged, the remaining distributed power sources can still continue to ensure the power supply to the load, reducing the occurrence of power outage accidents. A reasonable power supply structure can enhance the resilience of the distribution network under extreme weather conditions;

[0028] The specific resilience indices f1 and f2 are described by the following formulas: n load nodes in the network can be powered by η power supply nodes, and the various power sources in the power supply nodes are distributed and connected to the loads, then;

[0029]

[0030]

[0031]

[0032]

[0033] In the formula: P i S represents the load power of load node i in the network; j The total power supplied to power node j; The power factor is denoted by m, and the number of distributed power sources connected to this power node is denoted by u′. m,jFor single-supply power supply reliability; v k For the reliability of the power supply line; P Ri For the reliability of power supply to the load; M Pi To meet the power supply quality requirements of the load, M Pi =3,2,1 represent the values ​​for Class I, Class II, and Class III loads, respectively; L ij Let L be the distance from node i to j. ij =1, 2, 3 correspond to long, moderate, and short power supply distances, respectively; w i Weights for electricity load nodes;

[0034] (3) The resilience index f3 is used to reflect the resilience of the distribution network by the proportion of the load level missing area under extreme weather conditions to the area before the fault occurred. It also takes into account the time required for the system to return to normal and the magnitude of the fault loss during the disaster.

[0035]

[0036] In the formula, T0 represents the time when the distribution network is affected by extreme weather, which can be understood as t1 to t4; L(t) represents the actual load curve when extreme weather causes a large-scale failure; TL(t) represents the target load curve when the system is running without faults. This indicator can intuitively reflect the power supply capacity of the system under extreme conditions. The stronger the power supply capacity, the more stable the system is under extreme conditions and the higher its resilience.

[0037] (4) The resilience index f4 is the ratio of the normal operating time of all loads during the entire typhoon process to the duration of the disaster's impact. It reflects the resilience of the distribution network from the perspectives of extreme weather impact and load normal operating time.

[0038]

[0039] Where: n a T represents the total number of nodes; i,U T represents the normal operating time of node i's load during typhoon weather; i,FR The time during which node i's flexibility resources supply power to the load during typhoon weather; T i,D The time during which node i's flexibility resources are not supplying power to the load during typhoon weather; θ t and ε t These are the evaluation parameters for the relationship between flexible resource supply time, outage time, and cost;

[0040] In addition, for power distribution line faults caused by typhoons, resilience indices f5 and f6 were proposed to address the impact of typhoons.

[0041] (5) Resilience index f5, line fault probability index: For the same distribution network, the higher the overall transmission line fault probability, the weaker the ability to maintain load power supply during typhoon weather, and the smaller its resilience. The average fault probability and maximum fault probability of the distribution network are used to reflect the fault probability of the distribution network:

[0042]

[0043] In the formula, j is the line number; p j Let n be the probability of failure for line j; j The total number of distribution network lines; maxp j This represents the highest probability of a fault in a power distribution network line.

[0044] (6) Resilience index f6, topology connectivity index: The ability of a distribution network to maintain connectivity after a branch line is disconnected during typhoon weather reflects the reliability of the distribution network structure. For a given distribution network, if disconnecting a line results in an unconnected area, the connectivity of the distribution network is considered weakened. The topology connectivity index can be reflected by the line fault probability and the number of power-loss buses and load after disconnection.

[0045]

[0046] In the formula, n loss,j and P loss,j These represent the number of nodes lost and the load lost after line j was disconnected, respectively; p j P represents the probability of failure for line j. L This represents the total load of the distribution network.

[0047] Specifically, in step 3, the combined weights are first calculated using subjective and objective methods, and then the ranking method for approximating the ideal solution is used to give the results of different distribution network resilience assessments.

[0048] Furthermore, the method of ranking different distribution network resilience assessment results using the approximation ideal solution ranking method includes the following steps:

[0049] 1) Construct a weighted index decision matrix w represents the overall weight of each indicator;

[0050] 2) Determine the positive and negative ideal solutions. Positive ideal solution The negative ideal solution is composed of data where all indicators are at their maximum values. It consists of data where all indicators are at their minimum values;

[0051] 3) Calculate the distance from the evaluated scheme to the positive ideal solution set. Distance to the negative ideal solution set The calculation formula is:

[0052]

[0053] 4) Based on step 3, rank the schemes to be evaluated.

[0054] The evaluation system for resilient distribution networks with high penetration distributed power sources described in this invention establishes a comprehensive evaluation system for resilient distribution networks with high penetration distributed power sources, enabling reasonable assessment and thus helping to guide the planning of resilient distribution networks after the integration of high penetration distributed power sources.

[0055] Compared with the prior art, the advantages of the present invention are:

[0056] The method described in this patented technical solution can provide a reasonable assessment of resilient distribution networks with high-penetration distributed power sources, improve the shortcomings of existing research in this area, and systematically, scientifically, and accurately guide the planning and direction of resilient distribution networks after the integration of high-penetration distributed power sources. Attached Figure Description

[0057] Figure 1 This is a flowchart of the comprehensive evaluation method of this patent.

[0058] Figure 2 This is a flowchart illustrating the EWM-IAHP-TOPSIS algorithm of this patent. Detailed Implementation

[0059] The invention will now be further described with reference to the accompanying drawings.

[0060] The integration of distributed power sources can help the distribution network meet more load demands. However, as its penetration rate increases, how to improve the stable power supply capability of the distribution network becomes a key issue. This reflects the impact of highly penetrated distributed power sources on resilient distribution networks. Under extreme events, the uncertainty of distribution network faults, load losses, and wind and solar power output is significant. Characterizing their impact on resilient distribution networks provides guidance for the rational planning and operation of resilient distribution networks.

[0061] The technical solution of this invention provides a comprehensive evaluation method for resilient distribution networks containing highly penetrated distributed power sources, specifically including the following steps:

[0062] Step 1: In order to quantify the impact of high-penetration distributed power sources on the distribution network, we establish distributed power source availability rate, distributed power source absorption rate, and active power balance index respectively.

[0063] Step 2: To ensure the power supply capacity of the distribution network to important loads under extreme events such as typhoons, establish indicators for the rationality of distributed power source location, the rationality of power supply structure, load loss, load normal operation time, line fault probability, and topology connectivity, respectively, based on the impact of typhoon disasters.

[0064] Step 3: In order to better obtain the weight of each indicator to the target layer and to give a reasonable resilience assessment for different distribution networks, we first use subjective and objective methods to calculate their combined weights, and then use the Approximation Ideal Solution Ranking Method (TOPSIS) to give the resilience assessment results of different distribution networks, so as to facilitate the scientific and accurate guidance of the system for planning resilient distribution networks after the access of high-penetration distributed power sources.

[0065] The specific details of step 1:

[0066] Step 1 establishes various indicators for high penetration, specifically including distributed power availability, distributed power absorption rate, and active power balance. These indicators are as follows:

[0067] (1) Availability index of intermittent distributed power sources

[0068] The λ1 index is mainly used to reflect the power generation capacity of a photovoltaic power station. The numerator represents the actual power generation of the intermittent photovoltaic (DG) under multiple conditions, including environmental factors and its own operating characteristics, within the evaluation period T. The denominator represents the rated output of the intermittent DG. This index can provide a relatively intuitive evaluation of the power generation capacity of intermittent DG.

[0069]

[0070] In the formula, T is the evaluation period, 24 hours; P(t) is the actual output curve of the intermittent DG; P DN Rated output of intermittent DG (photovoltaic) power generation, in kW.

[0071] (2) Distributed power absorption rate index

[0072] To minimize the curtailment of solar power caused by high-penetration distributed generation (DEP) connections to the distribution network, a DEP absorption rate index is proposed. This index reflects the distribution network's ability to absorb the output power of DEP, and can be expressed as:

[0073]

[0074] In the formula, λ2 is the distributed power absorption rate; P dg-Σ The rated power of distributed power sources connected to the distribution network; P max This represents the maximum load power of the distribution network.

[0075] (3) Active power balance index

[0076] To better evaluate the power generation level of distribution networks with high-penetration distributed power sources, it is required that the power generation and load within the distribution network be matched. On the one hand, the maximum power generation capacity of the generators must meet the maximum load; on the other hand, in order to cope with load reduction caused by extreme events, the minimum stable output of the generators must be less than the minimum load.

[0077]

[0078] In the formula, P L,max and P L,min These represent the maximum and minimum loads of the distribution network, respectively; P G,max and P G,min These represent the maximum and minimum power of the generator in the distribution network, respectively. Here, the generator includes both reliable DG and unreliable DG (photovoltaic).

[0079] Step 2 details:

[0080] Step 2 establishes various resilience indicators, specifically including indicators for the rationality of distributed power source location, the rationality of power supply structure, load loss, load uptime, line fault probability, and topology connectivity; the specific indicators for each are as follows:

[0081] (1) Resilience index f1, distributed generation location rationality index: The connection location of DG will affect the power supply characteristics of each load. The shorter the distance between the load node and the power supply path, the easier it is to meet the power supply needs of the load when a line fault occurs, and therefore the higher the network resilience. Under the same power supply path, DG should be connected near more important loads with higher power supply quality requirements to ensure that the network and important load nodes can quickly restore power supply. Under the constraint of the maximum DG access capacity, the more DGs connected, the higher the distribution network resilience.

[0082] (2) Resilience index f2, power supply structure rationality index: The access of distributed generation has changed the power supply structure, from single-source centralized to multi-source decentralized. Load nodes are powered by multiple DGs at the same time. When some facilities are damaged, the remaining distributed generation can still continue to ensure the power supply to the load, reducing the occurrence of power outage accidents. A reasonable power supply structure can enhance the resilience of the distribution network under extreme weather conditions.

[0083] The specific resilience indices f1 and f2 are described by the following formulas: In a network, n load nodes can be powered by η power supply nodes, and the various power sources within the power supply nodes are distributed among the loads. Therefore:

[0084]

[0085]

[0086]

[0087]

[0088] In the formula: P i S represents the load power of load node i in the network; j The total power supplied to power node j; The power factor is m; the number of DGs connected to this power node is u′. m,j For single-supply power supply reliability; v k For the reliability of the power supply line; P Ri For the reliability of power supply to the load; M Pi Power supply quality requirements for the load (M) Pi =3,2,1 are the values ​​for Class I, Class II, and Class III loads, respectively; L ij The distance from node i to j (L ij =1, 2, 3 correspond to long, moderate, and short power supply distances respectively; w i The weights of the electricity load nodes.

[0089] (3) The resilience index f3 is the ratio of the area of ​​load level loss under extreme weather conditions to the area before the fault occurred to reflect the resilience of the distribution network. It also takes into account the time required for the system to return to normal and the magnitude of the fault loss during the disaster.

[0090]

[0091] In the formula, T0 represents the time when the distribution network is affected by extreme weather, which can be understood as t1 to t4; L(t) represents the actual load curve when extreme weather causes a large-scale failure; TL(t) represents the target load curve when the system is running without faults. This indicator can intuitively reflect the power supply capacity of the system under extreme conditions. The stronger the power supply capacity, the more stable the system is under extreme conditions and the higher its resilience.

[0092] (4) The resilience index f4 is the ratio of the normal operating time of all loads during the entire typhoon process to the duration of the disaster's impact. It reflects the resilience of the distribution network from the perspective of the impact of extreme weather and the normal operating time of loads.

[0093]

[0094] Where: n a T represents the total number of nodes; i,U T represents the normal operating time of node i's load during typhoon weather; i,FR The time during which node i's flexibility resources supply power to the load during typhoon weather; T i,D The time during which node i's flexibility resources are not supplying power to the load during typhoon weather; θt and ε t These are the evaluation parameters for the relationship between the power supply time and outage time of flexible resources and costs.

[0095] In addition, resilience indices f5 and f6 were proposed to address power grid transmission line faults caused by typhoons.

[0096] (5) Resilience index f5, line fault probability index: For the same distribution network, the higher the overall transmission line fault probability, the weaker the ability to maintain load power supply during typhoon weather, and the smaller its resilience. The average fault probability and maximum fault probability of the distribution network are used to reflect the fault probability of the distribution network.

[0097]

[0098] In the formula, j is the line number; p j Let n be the probability of failure for line j; j The total number of distribution network lines; maxp j This represents the highest probability of a fault in a power distribution network line.

[0099] (6) Resilience index f6, topology connectivity index: The ability of a distribution network to maintain connectivity after a branch line is disconnected during typhoon weather reflects the stability of the distribution network structure. For a given distribution network, if disconnecting a line results in an unconnected area, the connectivity of the distribution network is considered weakened. The topology connectivity index can be reflected by the line fault probability and the number of power-loss buses and load after disconnection:

[0100]

[0101] In the formula, n loss,j and P loss,j These represent the number of nodes lost and the load lost after line j was disconnected, respectively; p j P represents the probability of failure for line j. L This represents the total load of the distribution network.

[0102] Step 3 details:

[0103] Step 3 first calculates the combined weights using both subjective and objective methods, and then applies the Top-Optimal Solution Ranking Method (TOPSIS) to determine the relative strength of different distribution networks. Specifically:

[0104] (1) Objective weighting method - entropy weighting method

[0105] Table 1. Steps for solving the entropy weight method

[0106]

[0107]

[0108] (2) Subjective weighting method - improved analytic hierarchy process:

[0109] Table 2. Improved Hierarchical Analysis Solution Steps

[0110]

[0111]

[0112] (3) Calculate its combined weights:

[0113] The improved AHP method is used to subjectively determine weights, while the entropy method is used to determine weights from objective information data. The final weights are determined by combining the subjectively and objectively determined weights. The expression for the combined weights is as follows:

[0114]

[0115] In the formula, α j and β j The weights of the j-th attribute are obtained using the AHP method and the entropy method, respectively, where n is the number of attributes.

[0116] (4) The TOPSIS solution yields varying results for different distribution network resilience assessments:

[0117] Table 3 TOPSIS Solution Steps

[0118]

[0119] Example:

[0120] This example only explains the toughness index; the calculation of the high permeability index is similar.

[0121] (1) Before calculation, at least two types of distribution network data or schemes are required. Here, we take the IEEE 33-node distribution network system as an example to illustrate and analyze the five different resilience enhancement schemes.

[0122] Table 4 Toughness Enhancement Schemes

[0123]

[0124] (2) Then, based on the data of each group of schemes and the formula for calculating the resilience index, calculate the scores of each resilience index for each group of schemes or distribution network data. It should be noted that f1-f4 are positive indicators, and f5 and f6 are negative indicators. The specific scores are shown in the table below.

[0125] Table 5 Toughness Index Values

[0126]

[0127] (3) Determination of the weights of the EWM-IAHP comprehensive index:

[0128] 1. The entropy weight method calculates the weight vector w1 for the resilience index's contribution to the target layer's resilience improvement as follows:

[0129] w1=(0.1393, 0.1830, 0.1154, 0.1313, 0.3218, 0.1094)

[0130] 2. The steps for solving the various schemes and index weights using the improved analytic hierarchy process are as follows:

[0131] (2.1) Importance ranking:

[0132] The importance of each resilience index in the criterion layer relative to the target layer is ranked as follows: f3 = f5 > f4 = f1 > f2 > f6.

[0133] Based on the specific values ​​of the evaluation indicators in each toughness enhancement scheme, the importance ranking of each scheme at the scheme level to each toughness indicator at the criterion level can be obtained as shown in the table below:

[0134] Table 6 ranks the importance of each scheme at the scheme layer to each resilience index at the criterion layer.

[0135]

[0136]

[0137] (2.2) Construct the comparison matrix:

[0138] Based on the importance ranking of each resilience index in the criterion layer to the target layer, the comparison matrix of each resilience index in the criterion layer to the target layer can be obtained, as shown in the table below:

[0139] Table 7. Comparison matrix of each resilience index according to its importance.

[0140]

[0141] (2.3) Find the near-perfect consistency matrix of the judgment matrix corresponding to the comparison matrix:

[0142] Table 8. Optimal Consistency Matrix of the Comparison Matrix Based on Importance of Each Toughness Indicator

[0143]

[0144] (2.4) Calculate the weight vector:

[0145] The weight vector w2 for the improvement in resilience of each resilience index in the criterion layer relative to the resilience of the target layer can be calculated as follows:

[0146] w2=(0.1026, 0.0391, 0.3668, 0.1026, 0.3668, 0.0220)

[0147] 3. Calculate the combined weight w3:

[0148] w3=(0.0723, 0.0362, 0.2141, 0.0681, 0.5971, 0.0122)

[0149] (4) TOPSIS provides a ranking of the toughness improvement of different formulations:

[0150] Input the specific scores of each scheme, the combined weight w3, and the positive and negative indicators.

[0151] Based on the program, the different schemes are ranked, and the relative magnitude of the resilience improvement of the five resilience improvement strategies is ranked as follows: Scheme 5 > Scheme 4 > Scheme 2 > Scheme 3 > Scheme 1.

[0152] The technical solution of this invention takes into account the impact of high-penetration distributed power sources on resilient distribution networks, and reflects its impact on resilient distribution networks. It can provide a reasonable assessment of resilient distribution networks containing high-penetration distributed power sources, and systematically and accurately guide the planning and direction of resilient distribution networks after the integration of high-penetration distributed power sources.

[0153] This invention can be widely used in the field of comprehensive evaluation of power distribution networks.

Claims

1. An evaluation system for resilient distribution networks with high-penetration distributed generation, characterized by: Includes the following steps: Step 1: Establish various indicators, including distributed power availability, distributed power absorption rate, and active power balance. Step 2: To ensure the power supply capacity of the distribution network to important loads under extreme events, including typhoons, various resilience indicators are established for the impact of typhoon disasters, including indicators for the rationality of distributed power source location, the rationality of power supply structure, load loss, load uptime, line fault probability, and topology connectivity. Step 3: First, calculate the combined weights using subjective and objective methods, and then use the approximation ideal solution ranking method to give the different distribution network resilience assessment results, so as to facilitate the scientific and accurate guidance of the planning of resilient distribution networks after the access of high-penetration distributed power sources. The resilience indicators established in step 2 specifically include distributed power source location rationality indicators, power supply structure rationality indicators, load loss indicators, load uptime indicators, line fault probability indicators, and topology connectivity indicators. Each indicator is as follows: (1) Resilience index Distributed power source location rationality index: The access location of distributed power sources will affect the power supply characteristics of each load. The shorter the distance between the load node and the power supply path, the easier it is to meet the power supply needs of the load in the event of a line fault, and therefore the higher the network resilience. Under the same power supply path, distributed power sources should be connected near more important loads with higher power quality requirements to ensure that the network and important load nodes can quickly restore power supply. Under the constraint of the maximum access capacity of distributed power sources, the more distributed power sources are connected, the higher the resilience of the distribution network. (2) Resilience index Power supply structure rationality index: The access of distributed power sources changes the power supply structure from single-source centralized to multi-source decentralized; load nodes are powered by multiple distributed power sources at the same time. When some facilities are damaged, the remaining distributed power sources can still continue to ensure the power supply to the load, reducing the occurrence of power outage accidents. A rational power supply structure can enhance the resilience of the distribution network under extreme weather conditions. Specific resilience indicators , The formula is described as follows: In the network Each load node can be determined by Each power node supplies power, and various power sources within the power nodes are distributed and connected to the loads in a decentralized manner. In the formula: For load nodes in the network The load power; For power nodes Total power supply; Power factor; The number of distributed power sources connected to this power node; For single-power supply reliability; For the reliability of power supply lines; To ensure the reliability of power supply to the load; To meet the power supply quality requirements of the load, The values ​​are for Class I, Class II, and Class III loads, respectively. For nodes arrive distance, The corresponding power supply distances are long, moderate, and short, respectively; Weights for electricity load nodes; (3) Resilience index The resilience of the distribution network is reflected by the proportion of the load level loss area under extreme weather conditions to the area before the fault occurred, while also taking into account the time required for the system to return to normal and the magnitude of the fault loss during the disaster. In the formula, This indicates the duration of the impact of extreme weather on the power distribution network, which can be understood as... ; This represents the actual load curve when extreme weather causes large-scale failures. This represents the target load curve when the system is running without faults. This indicator can intuitively reflect the system's power supply capacity under extreme conditions. The stronger the power supply capacity, the more stable the system is under extreme conditions and the higher its resilience. (4) Resilience index The ratio of the normal operating time of all loads during the entire typhoon process to the duration of the disaster's impact is used to reflect the resilience of the power distribution network from the perspectives of extreme weather impact and load normal operating time. In the formula: The total number of nodes; Nodes during typhoon weather The load uptime; Nodes during typhoon weather The time that flexible resources provide power to the load; Nodes during typhoon weather The time during which flexible resources do not supply power to the load; and These are the evaluation parameters for the relationship between flexible resource supply time, outage time, and cost; In addition, resilience indicators for typhoon impacts were proposed to address power grid transmission line faults caused by typhoons. and ; (5) Resilience index Line fault probability index: For the same distribution network, the higher the overall transmission line fault probability, the weaker the ability to maintain load power supply during typhoon weather, and the lower its resilience. The average fault probability and maximum fault probability of the distribution network are used to reflect the fault probability of the distribution network. In the formula, Line number; For the line The probability of failure; This represents the total number of lines in the distribution network. This represents the highest probability of a fault in a distribution network line. (6) Resilience index Topology connectivity index: The ability of a distribution network to maintain connectivity after a branch line is disconnected during typhoon weather reflects the reliability of the distribution network structure. For a given distribution network, if disconnecting a line results in an unconnected area, the connectivity of the distribution network is considered weakened. The topology connectivity index can be reflected by the probability of line faults and the number of power-loss buses and load after disconnection. In the formula, and The lines are respectively The number of nodes lost and the load after the disconnection; For the line The probability of failure; This represents the total load of the distribution network.

2. The evaluation system for resilient distribution networks with high penetration distributed power sources according to claim 1, characterized in that: The indicators established in step 1 specifically include distributed power availability, distributed power absorption rate, and active power balance. Each indicator is as follows: (1) Availability index of intermittent distributed power sources: This indicator is mainly used to reflect the power generation capacity of a photovoltaic power station. The numerator represents the actual power generation of the intermittent distributed power source under multiple conditions, including environmental factors and its own operating characteristics, within the evaluation period T. The denominator represents the rated output of the intermittent distributed power source. This indicator is used to intuitively evaluate the power generation capacity of intermittent distributed power sources. Its formula is as follows: In the formula, The evaluation period is 24 hours. The actual output curve of the intermittent distributed power source; The rated output of the intermittent distributed power source; (2) Distributed power absorption rate index: To minimize the curtailment of solar power caused by high-penetration distributed generation (DG) access to the distribution network, a DG absorption rate index is proposed. This index reflects the distribution network's ability to absorb the output power of DG, and can be expressed as: In the formula, This refers to the distributed power absorption rate. The rated power of distributed power sources connected to the distribution network; This represents the maximum load power of the distribution network. (3) Active power balance index: To better evaluate the power generation level of distribution networks with high-penetration distributed generation, it is required that power generation and load within the distribution network be matched. On the one hand, the maximum power generation capacity of generators must meet the maximum load; on the other hand, to cope with load reduction caused by extreme events, the minimum stable output of generators must be less than the minimum load. In the formula, and These are the maximum and minimum loads of the distribution network, respectively. and These are the maximum and minimum power of the generators in the distribution network, which include both uninterrupted distributed power sources and intermittent distributed power sources.

3. The evaluation system for resilient distribution networks with high-penetration distributed power sources according to claim 1, characterized in that: In step 3, the combined weights are first calculated using subjective and objective methods, and then the ranking method for approximating the ideal solution is used to give the results of different distribution network resilience assessments.

4. The evaluation system for resilient distribution networks with high-penetration distributed power sources according to claim 3, characterized in that: The method of ranking different distribution network resilience assessment results using the approximation ideal solution ranking method includes the following steps: 1) Construct a weighted index decision matrix , The overall weight of each indicator; 2) Determine the positive and negative ideal solutions; positive ideal solution The negative ideal solution is composed of data where all indicators are at their maximum values. It consists of data that are all minimum values ​​among all indicators; 3) Calculate the distance from the evaluated scheme to the positive ideal solution set. Distance to the negative ideal solution set The calculation formula is: 4) Based on step 3, rank the schemes to be evaluated.

5. The evaluation system for resilient distribution networks with high penetration distributed power sources according to claim 1, characterized in that: The evaluation system for resilient distribution networks with high penetration distributed power sources establishes a comprehensive evaluation system for resilient distribution networks with high penetration distributed power sources, enabling reasonable assessment and thus helping to guide the planning of resilient distribution networks after the integration of high penetration distributed power sources.

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