Multi-control-target distributed power supply cluster division method based on day-ahead scheduling result

Abstract

The invention relates to a multi-control-target distributed power supply cluster division method based on a day-ahead scheduling result. The method comprises the following steps: S1, constructing a DGC structure performance index based on an electrical distance; s2, constructing a DGC scheduling flexibility supply and demand robust balance index based on day-ahead scheduling; s3, constructing a DGC reactive power reserve index based on day-ahead scheduling; s4, constructing a DGC division comprehensive index and a division algorithm; s5, example analysis is carried out; the method has the advantages that the robust balance index is formulated, the reactive power reserve index is formulated, the improved genetic algorithm is adopted, and a basis is provided for optimal scheduling.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid dispatching, and in particular relates to a method for dividing a multi-control target distributed power source cluster based on a day-ahead dispatching result. Background Art

[0002] When formulating the day-ahead dispatching plan considering the flexibility demand of power grid dispatching, due to the uncertainty of DPV output and load power, the day-ahead dispatching plan needs to be constantly revised in the actual operation process. However, the characteristics of large number and small capacity of DG make it difficult for centralized optimization dispatching to meet the requirements of computational efficiency on the intraday time scale. Therefore, cluster-based distributed optimization dispatching is a better choice. In order to achieve coordinated control of each cluster, it is necessary to formulate a reasonable cluster division strategy for different control objectives. Therefore, it is very necessary to provide a multi-control objective distributed power cluster division method based on the day-ahead dispatching results, which can formulate robust balance indicators, formulate reactive power reserve indicators, adopt improved genetic algorithms, and provide a basis for optimized dispatching. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a method for dividing distributed power clusters with multiple control objectives based on day-ahead scheduling results, which formulates robust balancing indicators, formulates reactive power reserve indicators, adopts an improved genetic algorithm, and provides a basis for optimizing scheduling.

[0004] The object of the present invention is achieved by: a method for dividing a distributed power supply cluster with multiple control targets based on a day-ahead scheduling result, the method comprising the following steps:

[0005] Step S1: constructing DGC structural performance index based on electrical distance;

[0006] Step S2: construct a robust balance index of supply and demand of DGC scheduling flexibility based on day-ahead scheduling;

[0007] Step S3: constructing a DGC reactive power reserve index based on day-ahead scheduling;

[0008] Step S4: construct DGC partition comprehensive index and partition algorithm;

[0009] Step S5: case analysis.

[0010] The step S1 of constructing the DGC structural performance index based on electrical distance is specifically as follows: In order to ensure the structural logic of the cluster, the modularity index is usually used for evaluation. For DGC, it is also necessary to ensure that the electrical coupling of its internal nodes is tight and the electrical coupling between DGCs is sparse. Therefore, the modularity index needs to be improved to be suitable for the DGC division in ADN. Combining reactive power-voltage sensitivity with active power-voltage sensitivity can more reasonably reflect the electrical relationship between nodes in ADN.

[0011] The reactive power-voltage sensitivity is specifically: According to the power system flow equation, it can be obtained: In the formula, ΔP and ΔQ are all power changes; Δθ and ΔV are voltage phase angle and amplitude changes. Transforming the above formula, we get: ΔV=S PV ΔP+S QV ·ΔQ (3), where S PV is the active-voltage sensitivity matrix, reflecting the impact of ΔP change on ΔV; S QV is the reactive power-voltage sensitivity matrix, which reflects the effect of ΔQ change ΔV on the generated active power-voltage sensitivity: The reactive power-voltage sensitivity expression is the same.

[0012] The electrical distance in step S1 is specifically: In the distribution network, the power change of any node will cause the voltage change of all nodes. Therefore, based on S PV Relationship between electrical distance L PV,ij And based on S QV Electrical distance L QV,ij for:

[0013]

[0014] The electrical distance is defined as: L can be defined ij is the edge weight in modularity, and the larger its value is, the sparser the electrical connection between the two nodes is. ij Perform appropriate processing to make it suitable for the modularity function, as shown in the following formula:

[0015] The step S2 of constructing the DGC dispatching flexibility supply and demand robust balance index based on day-ahead dispatching is specifically as follows: to consider the dispatching flexibility control demand, the distribution network is reasonably divided into DGCs, the DGC power autonomy capability is fully utilized, and the dispatching flexibility supply and demand robust balance rate is extended to the DGC scope, which specifically includes the following steps:

[0016] Step S2.1: Analyze the DGC scheduling flexibility robust demand interval and the DGC scheduling flexibility supply interval;

[0017] Step S2.2: DPV and load in DGC are the demand sources of DGC dispatch flexibility, and controllable DG is the supply source of DGC dispatch flexibility;

[0018] Step S2.3: Construct a robust balance indicator of DGC scheduling flexibility supply and demand for DGC partitioning.

[0019] The DGC scheduling flexibility robust demand interval in step S2.1 is specifically: DGC net load scenario calculation: Where: is the kth net load forecasting scenario of the cith DGC at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; the expression of the day-ahead prediction value of the DGC net load is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; They are the load at time t and DPV prediction scenario respectively; the DGC scheduling flexibility demand scenario expression is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; In the formula, F need,ci is the robust interval of the scheduling flexibility requirement of the ci-th DGC; is the upper bound of the robust interval of scheduling flexibility requirement of the ci-th DGC; is the lower bound of the robust interval of scheduling flexibility requirement for the ci-th DGC.

[0020] The DGC scheduling flexibility supply interval in step S2.1 is: the upper and lower bounds of the ESS scheduling flexibility supply in the ci-th DGC at time t are: Where, NSci is the ESS set in the ci-th DGC; E si (t) is the day-ahead dispatch capacity of the si-th ESS in the ci-th DGC at time t; E si,max 、E si,min , η si are the maximum charging power, maximum discharging power, maximum capacity, minimum capacity and charging and discharging efficiency of the si-th ESS in the ci-th DGC respectively; the upper and lower bounds of the MGT scheduling flexibility supply in the ci-th DGC at time t are: Where NGci is the set of all MGTs in the ci-th DGC; P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; u gi (t) is the start / stop state 0-1 variable of the gi-th MGT at time t; τ is the power adjustment time scale; are the maximum and minimum outputs of the gi-th MGT respectively; the dispatch flexibility supply capacity range is: In the formula, is the ci-th DGC scheduling flexibility supply interval at time t, They are respectively the upper and lower bounds of the scheduling flexibility supply interval of the ci-th DGC at time t.

[0021] The step S2.3 of constructing the DGC scheduling flexibility supply and demand robust balance index for DGC division is specifically: the DGC scheduling flexibility supply and demand robust balance rate of each DGC scheduling flexibility supply and demand balance is: In the formula, are the upper and lower bounds of the robust interval of the scheduling flexibility requirement of the ci-th DGC, respectively; are the upper and lower bounds of the supply and demand balance interval of the ci-th DGC scheduling flexibility, respectively. The calculation formula is: In the formula, are the upper and lower bounds of the dispatch flexibility supply of the ci-th DGC respectively; the dispatch flexibility supply and demand robust balance index of all DGCs in the distribution network is summed and normalized to obtain the DGC dispatch flexibility supply and demand robust balance index: In the formula, S ci,bal is the robust balance indicator of supply and demand for scheduling flexibility of the ci-th DGC; Nc is the set of all DGCs; cn is the total number of divided DGCs.

[0022] The construction of the DGC reactive power reserve index based on day-ahead scheduling in step S3 is specifically as follows: a method of matching reactive load with reactive power supply capacity within a cluster is adopted, network partitioning is performed in combination with modularity considering voltage sensitivity, and a DGC reactive power reserve coefficient is proposed from the perspective of active power and reactive power-voltage sensitivity to measure the degree of change in DGC reactive power demand caused by the active power change of each DGC. Finally, a DGC reactive power reserve index is proposed based on the DGC reactive power reserve coefficient to guide the reasonable allocation of reactive power reserves in each DGC during the DGC division process, so that there is sufficient reactive power reserve in each divided cluster.

[0023] The construction of the DGC partition comprehensive index and partition algorithm in step S4 is specifically: using an improved genetic algorithm to partition the DGC, that is, using the adjacency matrix of the distribution network topology structure as the encoding chromosome for encoding, and using an advanced crossover mutation mechanism to make the algorithm suitable for DGC partition while having a faster solution efficiency.

[0024] Beneficial effects of the present invention: The present invention is a multi-control target distributed power cluster division method based on the day-ahead scheduling result. In use, the present invention introduces the scheduling flexibility supply and demand robust balance rate into the cluster division for the ADN scheduling flexibility control target, and performs DGC division on the distribution network in combination with the modularity index; the present invention proposes a reactive reserve coefficient for the ADN reactive power control target through a sensitivity analysis method to measure the risk of node voltage exceeding the limit caused by DPV and load power uncertainty in the cluster, analyzes the reactive energy storage capacity in the cluster, and proposes a DGC reactive reserve index in combination with the DGC reactive reserve coefficient to measure the ability of the cluster to resist the node voltage exceeding the limit caused by DPV and load power uncertainty in the cluster, and finally combines the index with the modularity index to perform DGC division on the distribution network; the present invention takes into account the situation that the active and reactive power of the distribution network system are not decoupled, considers the reactive control and scheduling flexibility control targets, and performs cluster division on the distribution network in combination with the modularity index, the DGC scheduling flexibility supply and demand robust balance index and the DGC reactive reserve index; the present invention has the advantages of formulating a robust balance index, formulating a reactive reserve index, adopting an improved genetic algorithm, and providing a basis for optimizing scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flow chart of the present invention.

[0026] Figure 2 This is a schematic diagram of the cluster division results of Solution 1 of the present invention.

[0027] Figure 3 This is a schematic diagram of the cluster division results of Solution 2 of the present invention.

[0028] Figure 4 This is a schematic diagram of the cluster division results of solution three of the present invention.

[0029] Figure 5 This is a schematic diagram of the cluster division results of scheme 4 of the present invention.

[0030] Figure 6 This is a schematic diagram of the cluster division results of scheme 5 of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Example 1

[0033] like Figure 1-6 As shown, a method for dividing a distributed power generation cluster with multiple control objectives based on day-ahead scheduling results comprises the following steps:

[0034] Step S1: constructing DGC structural performance index based on electrical distance;

[0035] Step S2: construct a robust balance index of supply and demand of DGC scheduling flexibility based on day-ahead scheduling;

[0036] Step S3: constructing a DGC reactive power reserve index based on day-ahead scheduling;

[0037] Step S4: construct DGC partition comprehensive index and partition algorithm;

[0038] Step S5: case analysis.

[0039] The construction of the DGC reactive power reserve index based on day-ahead scheduling in step S3 is specifically as follows: a method of matching reactive load with reactive power supply capacity within a cluster is adopted, network partitioning is performed in combination with modularity considering voltage sensitivity, and a DGC reactive power reserve coefficient is proposed from the perspective of active power and reactive power-voltage sensitivity to measure the degree of change in DGC reactive power demand caused by the active power change of each DGC. Finally, a DGC reactive power reserve index is proposed based on the DGC reactive power reserve coefficient to guide the reasonable allocation of reactive power reserves in each DGC during the DGC division process, so that there is sufficient reactive power reserve in each divided cluster.

[0040] In this embodiment, due to the uncertainty of DPV output, there will be large node voltage fluctuations in the distribution network during actual operation. In order to maintain the voltage stability of the system and the rapid response of the partition control, each cluster needs to be tightly coupled. For economic considerations, the long-distance transmission of reactive power should be reduced. Not only does the distribution network need to have sufficient reactive power reserves, but the reactive power reserves also need to be reasonably allocated during the cluster division process. At present, the method of matching the reactive load with the reactive power supply capacity within the cluster is often used, and the network is partitioned in combination with the modularity considering voltage sensitivity. However, the cluster division result obtained by only considering the reactive load does not consider the voltage fluctuations caused by DPV and load uncertainty. The obtained cluster division result is not entirely reasonable, and the changes in reactive power demand caused by DPV and load uncertainty need to be considered. Therefore, it is necessary to analyze the DGC reactive power reserve capacity, and from the perspective of active power and reactive power-voltage sensitivity, a DGC reactive power reserve coefficient is proposed to measure the degree of change in DGC reactive power demand caused by the active power change of each DGC. Finally, a DGC reactive power reserve index is proposed based on the DGC reactive power reserve coefficient to guide the reasonable allocation of reactive power reserves in each DGC during the DGC division process, so that there is sufficient reactive power reserve in each divided cluster.

[0041] The construction of the DGC partition comprehensive index and partition algorithm in step S4 is specifically: using an improved genetic algorithm to partition the DGC, that is, using the adjacency matrix of the distribution network topology structure as the encoding chromosome for encoding, and using an advanced crossover mutation mechanism to make the algorithm suitable for DGC partition while having a faster solution efficiency.

[0042] In this embodiment, in order to improve the ability of DGC to cope with the dispatch flexibility requirements brought about by the uncertainty of DPV output and load power and give full play to the autonomy level within the DGC group, it is necessary to construct a comprehensive index for DGC division that considers multiple control objectives; combine the modularity index, the DGC dispatch flexibility supply and demand robust balance index, and the DGC reactive reserve index to evaluate the structural logic and required functionality of the divided DGC;

[0043] At present, the research on cluster partitioning algorithms mainly focuses on community discovery algorithms, clustering algorithms and modern intelligent algorithms. Both community discovery algorithms and clustering algorithms need to specify the number of clusters to be partitioned. Facing complex distribution networks, it is difficult to achieve global optimization. Modern intelligent algorithms simulate the laws of evolution in nature and approach the global optimal solution through generations of iterations of genetic, crossover and mutation mechanisms. Therefore, the representative genetic algorithm in modern intelligent algorithms can be used to partition DGC.

[0044] However, conventional genetic algorithms use binary coding, which has problems such as coding difficulties in DGC partitioning. Therefore, it is necessary to improve the coding method of genetic algorithms. The adjacency matrix of the distribution network topology structure is used to encode the encoding chromosomes. At the same time, advanced crossover and mutation mechanisms are used to make the algorithm suitable for DGC partitioning while having faster solution efficiency.

[0045] The present invention is a distributed power cluster partitioning method with multiple control targets based on day-ahead scheduling results. In use, the present invention introduces the scheduling flexibility supply and demand robust balance rate into the cluster partitioning for the ADN scheduling flexibility control target, and divides the distribution network into DGCs in combination with the modularity index. The DGCs partitioned in this way not only have better structural performance, but also have stronger scheduling flexibility supply and demand robust balance capabilities for the DPV output and day-ahead load forecast errors within the cluster, and each cluster has the maximum ability to balance internal net load fluctuations. The present invention proposes a reactive reserve coefficient through a sensitivity analysis method for the ADN reactive power control target to measure the risk of node voltage over-limit caused by DPV and load power uncertainty within the cluster, analyzes the reactive energy storage capacity within the cluster, and proposes a DGC reactive reserve index in combination with the DGC reactive reserve coefficient to measure the cluster's ability to resist node voltage over-limit caused by DPV and load power uncertainty within the cluster. The limited capacity is finally obtained, and the index is combined with the modularity index to divide the distribution network into DGCs; the DGC divided in this way not only has better structural performance, but also can reasonably allocate the reactive power reserve of the distribution network and improve the voltage autonomy of the DGC; the present invention takes into account the situation that the active and reactive power of the distribution network system are not decoupled, considers the reactive power control and scheduling flexibility control targets, and combines the modularity index, the DGC scheduling flexibility supply and demand robust balance index and the DGC reactive power reserve index to divide the distribution network into clusters; the clusters divided by comprehensively considering the two control targets have better structural performance, as well as better power autonomy and reactive power autonomy to deal with DPV and load power uncertainty. The proposed cluster division method provides a theoretical basis for intraday distributed scheduling based on clusters; the present invention has the advantages of formulating robust balance indicators, formulating reactive power reserve indicators, adopting improved genetic algorithms, and providing a basis for optimizing scheduling.

[0046] Example 2

[0047] like Figure 1-6 As shown, a method for dividing a distributed power generation cluster with multiple control objectives based on day-ahead scheduling results comprises the following steps:

[0048] Step S1: constructing DGC structural performance index based on electrical distance;

[0049] Step S2: construct a robust balance index of supply and demand of DGC scheduling flexibility based on day-ahead scheduling;

[0050] Step S3: constructing a DGC reactive power reserve index based on day-ahead scheduling;

[0051] Step S4: construct DGC partition comprehensive index and partition algorithm;

[0052] Step S5: case analysis.

[0053] The step S1 of constructing the DGC structural performance index based on electrical distance is specifically as follows: In order to ensure the structural logic of the cluster, the modularity index is usually used for evaluation. For DGC, it is also necessary to ensure that the electrical coupling of its internal nodes is tight and the electrical coupling between DGCs is sparse. Therefore, the modularity index needs to be improved to be suitable for the DGC division in ADN. Combining reactive power-voltage sensitivity with active power-voltage sensitivity can more reasonably reflect the electrical relationship between nodes in ADN.

[0054] The reactive power-voltage sensitivity is specifically: According to the power system flow equation, it can be obtained: In the formula, ΔP and ΔQ are all power changes; Δθ and ΔV are voltage phase angle and amplitude changes. Transforming the above formula, we get: ΔV=S PV ΔP+S QV ·ΔQ (3), where S PV is the active-voltage sensitivity matrix, reflecting the impact of ΔP change on ΔV; S QV is the reactive power-voltage sensitivity matrix, which reflects the effect of ΔQ change ΔV on the generated active power-voltage sensitivity: The reactive power-voltage sensitivity expression is the same.

[0055] The electrical distance in step S1 is specifically: In the distribution network, the power change of any node will cause the voltage change of all nodes. Therefore, based on S PV Relationship between electrical distance L PV,ij And based on S QV Electrical distance L QV,ij for:

[0056]

[0057] The electrical distance is defined as: L can be defined ij is the edge weight in modularity, and the larger its value is, the sparser the electrical connection between the two nodes is. ij Perform appropriate processing to make it suitable for the modularity function, as shown in the following formula:

[0058] The step S2 of constructing the DGC dispatching flexibility supply and demand robust balance index based on day-ahead dispatching is specifically as follows: to consider the dispatching flexibility control demand, the distribution network is reasonably divided into DGCs, the DGC power autonomy capability is fully utilized, and the dispatching flexibility supply and demand robust balance rate is extended to the DGC scope, which specifically includes the following steps:

[0059] Step S2.1: Analyze the DGC scheduling flexibility robust demand interval and the DGC scheduling flexibility supply interval;

[0060] Step S2.2: DPV and load in DGC are the demand sources of DGC dispatch flexibility, and controllable DG is the supply source of DGC dispatch flexibility;

[0061] Step S2.3: Construct a robust balance indicator of DGC scheduling flexibility supply and demand for DGC partitioning.

[0062] The DGC scheduling flexibility robust demand interval in step S2.1 is specifically: DGC net load scenario calculation: Where: is the kth net load forecasting scenario of the cith DGC at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; the expression of the day-ahead prediction value of the DGC net load is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; They are the load at time t and DPV prediction scenario respectively; the DGC scheduling flexibility demand scenario expression is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; In the formula, F need,ci is the robust interval of the scheduling flexibility requirement of the ci-th DGC; is the upper bound of the robust interval of scheduling flexibility requirement of the ci-th DGC; is the lower bound of the robust interval of scheduling flexibility requirement for the ci-th DGC.

[0063] The DGC scheduling flexibility supply interval in step S2.1 is: the upper and lower bounds of the ESS scheduling flexibility supply in the ci-th DGC at time t are: Where, NSci is the ESS set in the ci-th DGC; E si (t) is the day-ahead dispatch capacity of the si-th ESS in the ci-th DGC at time t; E si,max 、E si,min , η siare the maximum charging power, maximum discharging power, maximum capacity, minimum capacity and charging and discharging efficiency of the si-th ESS in the ci-th DGC respectively; the upper and lower bounds of the MGT scheduling flexibility supply in the ci-th DGC at time t are: Where NGci is the set of all MGTs in the ci-th DGC; P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; u gi (t) is the start / stop state 0-1 variable of the gi-th MGT at time t; τ is the power adjustment time scale; are the maximum and minimum outputs of the gi-th MGT respectively; the dispatch flexibility supply capacity range is: In the formula, is the ci-th DGC scheduling flexibility supply interval at time t, They are respectively the upper and lower bounds of the scheduling flexibility supply interval of the ci-th DGC at time t.

[0064] The step S2.3 of constructing the DGC scheduling flexibility supply and demand robust balance index for DGC division is specifically: the DGC scheduling flexibility supply and demand robust balance rate of each DGC scheduling flexibility supply and demand balance is: In the formula, are the upper and lower bounds of the robust interval of the scheduling flexibility requirement of the ci-th DGC, respectively; are the upper and lower bounds of the supply and demand balance interval of the ci-th DGC scheduling flexibility, respectively. The calculation formula is: In the formula, are the upper and lower bounds of the dispatch flexibility supply of the ci-th DGC respectively; the dispatch flexibility supply and demand robust balance index of all DGCs in the distribution network is summed and normalized to obtain the DGC dispatch flexibility supply and demand robust balance index: In the formula, S ci,bal is the robust balance indicator of supply and demand for scheduling flexibility of the ci-th DGC; Nc is the set of all DGCs; cn is the total number of divided DGCs.

[0065] In this embodiment, the example analysis is as follows: taking the improved IEEE33 node system as an example, based on the day-ahead scheduling scheme 3, cluster division is performed using the above-mentioned DGC division comprehensive index considering scheduling flexibility and the distributed power cluster division comprehensive index considering voltage control, and an improved genetic algorithm is used, with the adjacency matrix as the chromosome, the population number is set to 100, and the maximum number of iterations is 500;

[0066] 1): Analysis of DGC partition results considering only the robust balance of supply and demand for scheduling flexibility:

[0067] Day-ahead dispatch does not consider the EVCS energy storage characteristics, but considers the dispatch flexibility, supply and demand robust balance penalty cost. On this basis, three cluster division schemes are set:

[0068] Solution 1: Only consider the modularity index for cluster division, that is, w1 = 1, w2 = 0, w3 = 0, w4 = 0;

[0069] Solution 2: Only consider the scheduling flexibility supply and demand robust balance indicators for cluster division, that is, w1 = 0, w2 = 1, w3 = 0, w4 = 0;

[0070] Solution 3: Cluster division is performed by taking into account the cluster modularity index and the scheduling flexibility supply and demand robust balance index, w1=1, w2=1, w3=0, w4=0;

[0071] The cluster division results of each scheme are as follows Figure 2-4 As shown, PV represents that the node is connected to DPV; S represents that the node is connected to ESS equipment; G represents that the node is connected to MGT; SVC represents that the node is connected to continuous reactive power compensation equipment; CB represents that the node is connected to discrete reactive power compensation equipment;

[0072] Scheme 1 only considers the modularity index, and the number of DGCs after division is the largest, and the structural performance is stronger; Scheme 2 only considers the dispatch flexibility supply and demand robust balance index, and only two DGCs are obtained, the overall structural strength is weak, and the distributed control performance cannot be exerted; Scheme 3 takes into account both modularity and dispatch flexibility supply and demand robust balance indicators, and divides the distribution network into 4 DGCs. The structural strength is slightly weaker than Scheme 1, but the difference is not large, and it still maintains a strong structural strength;

[0073] 2): Analysis of DGC division results considering only reactive reserve capacity:

[0074] Considering the reactive power reserve capacity index and modularity index, cluster division scheme 4 is formulated:

[0075] Scheme 4: Cluster division is performed by taking into account both the cluster modularity index and the DGC reactive power reserve index, and the following weights are assigned to different indicators: w1 = 1, w2 = 0, w3 = 1, w4 = 1; the cluster division results of scheme 4 are as follows Figure 5 As shown;

[0076] Table 1 Cluster division index results of scheme 1 and scheme 4

[0077]

[0078]

[0079] Scheme 1 only considers the modularity index, and after the division, the number of DGCs is the largest, and the structural performance is stronger; Scheme 4 takes into account both modularity and reactive power reserve indexes, divides the distribution network into 4 clusters, and the structural strength is slightly weaker than Scheme 1;

[0080] From the modularity index results, it can be further found that the cluster structure performance of Scheme 1 is stronger, but there is only a small gap with Scheme 4, and the reactive power reserve of each cluster is obviously unreasonable. There is no reactive power reserve in clusters ② and ④, and there is no ability to deal with DPV and load uncertainty in the cluster; while the reactive power reserve allocation of each cluster in Scheme 4 is more reasonable. Each cluster has a reactive power regulation device and has a certain reactive power regulation capability. Only cluster ④ has less inductive reactive power reserve. This is because in the day-ahead dispatch, the SVC equipment installed at node 30 in cluster ④ has a large output and can provide less inductive reactive power reserve. More continuous reactive power compensation equipment can be installed in cluster ④ to increase reactive power reserve.

[0081] 3): Analysis of DGC partition results considering multiple control objectives:

[0082] Considering the active and reactive power optimization control objectives, cluster division scheme 5 is formulated: the following weights are assigned to different indicators, w1 = 1, w2 = 1, w3 = 1, w4 = 1, such as Figure 6 As shown;

[0083] According to the partitioning results, it can be found that schemes 3, 4, and 5 all take modularity into consideration, and the number of DGCs after partitioning is 4. That is to say, by adopting the modularity index proposed in the present invention, a partitioning result with better structural performance can be obtained without presetting the number of clusters. According to Table 2, the modularity indexes of each scheme are not much different, and their values ​​are all greater than 0.5. The cluster structure performance is better, the electrical coupling of nodes in the cluster is tighter, and the structural performance is good.

[0084] Table 2 Results of cluster division indicators for schemes 3, 4 and 5

[0085]

[0086]

[0087] Further analysis of the functional index results of the scheme cluster division is shown in Table 2. For the reactive power control target, compared with scheme 4, the inductive and capacitive reactive power reserve indicators of scheme 5 are not much different. However, the supply and demand balancing capabilities of each scheduling flexibility of scheme 4 show that cluster ① is significantly stronger than other clusters, which will cause cluster ① to be able to cope with more extreme scenarios while other clusters have weaker capabilities to cope with power changes, which is not conducive to the autonomy of each cluster. In other words, not considering the scheduling flexibility control target but only considering the reactive power control target cannot significantly improve the reactive power balancing capability of each cluster, but will cause an imbalance in the scheduling flexibility supply and demand balancing capability of the cluster, bringing difficulties to the distributed coordinated control of the cluster.

[0088] The present invention is a method for dividing distributed power clusters with multiple control targets based on day-ahead dispatching results. The present invention adopts a modularity index based on active power and reactive power-voltage sensitivity. On the basis of day-ahead dispatching, a DGC dispatching flexibility supply and demand robust balance index considering active power control targets and a DGC reactive power reserve index considering reactive power control targets are respectively formulated. For the cluster division algorithm, an improved genetic algorithm suitable for DGC division is adopted. Finally, cluster division is performed based on case analysis to verify the effectiveness of the proposed DGC division index, providing a theoretical basis for intraday distributed rolling optimization dispatching based on DGC. In use, the present invention introduces the dispatching flexibility supply and demand robust balance rate into cluster division for ADN dispatching flexibility control targets, and performs DGC division on the distribution network in combination with the modularity index. The DGC divided in this way not only has better structural performance, but also has stronger dispatching flexibility supply and demand robust balance capability for DPV output and load day-ahead forecast errors within the group, and each cluster has the greatest ability to balance internal net load fluctuations. For the ADN reactive power control target, the present invention proposes a reactive power reserve coefficient through sensitivity analysis to measure the DPV and load within the cluster. The risk of node voltage exceeding the limit caused by load power uncertainty is analyzed, and the reactive energy storage capacity in the cluster is analyzed. Combined with the DGC reactive reserve coefficient, a DGC reactive reserve index is proposed to measure the cluster's ability to resist node voltage exceeding the limit caused by DPV and load power uncertainty in the cluster. Finally, this index is combined with the modularity index to divide the distribution network into DGCs; the DGC divided in this way not only has better structural performance, but also can reasonably allocate the reactive reserve of the distribution network and improve the DGC voltage autonomy; the present invention takes into account the situation that the active and reactive power of the distribution network system are not decoupled, considers the control objectives of reactive control and scheduling flexibility, and combines the modularity index, the DGC scheduling flexibility supply and demand robust balance index and the DGC reactive reserve index to divide the distribution network into clusters; the clusters divided by comprehensively considering the two control objectives have better structural performance, as well as better power autonomy and reactive autonomy to deal with DPV and load power uncertainty. The proposed cluster division method provides a theoretical basis for intraday distributed scheduling based on clusters; the present invention has the advantages of formulating robust balance indicators, formulating reactive reserve indicators, adopting improved genetic algorithms, and providing a basis for optimized scheduling.

Claims

1. A multi-control objective distributed power generation cluster partitioning method based on day-ahead scheduling results, characterized by: The method comprises the following steps: Step S1: constructing DGC structural performance index based on electrical distance; Step S2: construct a robust balance index of supply and demand of DGC scheduling flexibility based on day-ahead scheduling; Step S3: constructing a DGC reactive power reserve index based on day-ahead scheduling; Step S4: construct DGC partition comprehensive index and partition algorithm; Step S5: case analysis.

2. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 1, characterized in that: The step S1 of constructing the DGC structural performance index based on electrical distance is specifically as follows: In order to ensure the structural logic of the cluster, the modularity index is usually used for evaluation. For DGC, it is also necessary to ensure that the electrical coupling of its internal nodes is tight and the electrical coupling between DGCs is sparse. Therefore, the modularity index needs to be improved to be suitable for the DGC division in ADN. Combining reactive power-voltage sensitivity with active power-voltage sensitivity can more reasonably reflect the electrical relationship between nodes in ADN.

3. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 2, characterized in that: The reactive power-voltage sensitivity is specifically: According to the power system flow equation, it can be obtained: In the formula, ΔP and ΔQ are all power changes; Δθ and ΔV are voltage phase angle and amplitude changes. Transforming the above formula, we get: ΔV=S PV ΔP+S QV ΔQ(3), where S PV is the active-voltage sensitivity matrix, reflecting the impact of ΔP change on ΔV; S QV is the reactive power-voltage sensitivity matrix, which reflects the effect of ΔQ change ΔV on the generated active power-voltage sensitivity: The reactive power-voltage sensitivity expression is the same.

4. The method for dividing distributed power generation clusters with multiple control targets based on day-ahead scheduling results according to claim 2, characterized in that: The electrical distance in step S1 is specifically: In the distribution network, the power change of any node will cause the voltage change of all nodes. Therefore, based on S PV Relationship between electrical distance L PV,ij And based on S QV Electrical distance L QV,ij for: The electrical distance is defined as: L can be defined ij is the edge weight in modularity, and the larger its value is, the sparser the electrical connection between the two nodes is. ij Perform appropriate processing to make it suitable for the modularity function, as shown in the following formula:

5. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 1, characterized in that: The step S2 of constructing the DGC dispatching flexibility supply and demand robust balance index based on day-ahead dispatching is specifically as follows: to consider the dispatching flexibility control demand, the distribution network is reasonably divided into DGCs, the DGC power autonomy capability is fully utilized, and the dispatching flexibility supply and demand robust balance rate is extended to the DGC scope, which specifically includes the following steps: Step S2.1: Analyze the DGC scheduling flexibility robust demand interval and the DGC scheduling flexibility supply interval; Step S2.2: DPV and load in DGC are the demand sources of DGC dispatch flexibility, and controllable DG is the supply source of DGC dispatch flexibility; Step S2.3: Construct a robust balance indicator of DGC scheduling flexibility supply and demand for DGC partitioning.

6. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 5, characterized in that: The DGC scheduling flexibility robust demand interval in step S2.1 is specifically: DGC net load scenario calculation: Where: is the kth net load forecasting scenario of the cith DGC at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; is the klth load forecast scenario at time t; is the kpth DPV prediction scenario at time t; NLci and NDPVci are the load node set and DPV set in the cith DGC respectively; the expression of the day-ahead prediction value of the DGC net load is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; They are the load at time t and DPV prediction scenario respectively; the DGC scheduling flexibility demand scenario expression is: In the formula, is the day-ahead forecast value of the net load of the ci-th DGC at time t; In the formula, F need,ci is the robust interval of the scheduling flexibility requirement of the ci-th DGC; is the upper bound of the robust interval of scheduling flexibility requirement of the ci-th DGC; is the lower bound of the robust interval of scheduling flexibility requirement for the ci-th DGC.

7. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 6, characterized in that: The DGC scheduling flexibility supply interval in step S2.1 is: the upper and lower bounds of the ESS scheduling flexibility supply in the ci-th DGC at time t are: Where, NSci is the ESS set in the ci-th DGC; E si (t) is the day-ahead dispatch capacity of the si-th ESS in the ci-th DGC at time t; E si,max 、E si,min , η si are the maximum charging power, maximum discharging power, maximum capacity, minimum capacity and charging and discharging efficiency of the si-th ESS in the ci-th DGC respectively; the upper and lower bounds of the MGT scheduling flexibility supply in the ci-th DGC at time t are: Where NGci is the set of all MGTs in the ci-th DGC; P gi (t) is the day-ahead dispatch value of the gi-th MGT at time t; ΔP gi is the maximum climbing power of the gi-th MGT at time t; u gi (t) is the start / stop state 0-1 variable of the gi-th MGT at time t; τ is the power adjustment time scale; are the maximum and minimum outputs of the gi-th MGT respectively; the dispatch flexibility supply capacity range is: In the formula, is the ci-th DGC scheduling flexibility supply interval at time t, They are respectively the upper and lower bounds of the scheduling flexibility supply interval of the ci-th DGC at time t.

8. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 5, characterized in that: The step S2.3 of constructing the DGC scheduling flexibility supply and demand robust balance index for DGC division is specifically: the DGC scheduling flexibility supply and demand robust balance rate of each DGC scheduling flexibility supply and demand balance is: In the formula, are the upper and lower bounds of the robust interval of the scheduling flexibility requirement of the ci-th DGC, respectively; are the upper and lower bounds of the supply and demand balance interval of the ci-th DGC scheduling flexibility, respectively. The calculation formula is: In the formula, are the upper and lower bounds of the dispatch flexibility supply of the ci-th DGC respectively; the dispatch flexibility supply and demand robust balance index of all DGCs in the distribution network is summed and normalized to obtain the DGC dispatch flexibility supply and demand robust balance index: In the formula, S ci,bal is the robust balance indicator of supply and demand for scheduling flexibility of the ci-th DGC; Nc is the set of all DGCs; cn is the total number of divided DGCs.

9. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 1, characterized in that: The construction of the DGC reactive power reserve index based on day-ahead scheduling in step S3 is specifically as follows: a method of matching reactive load with reactive power supply capacity within a cluster is adopted, network partitioning is performed in combination with modularity considering voltage sensitivity, and a DGC reactive power reserve coefficient is proposed from the perspective of active power and reactive power-voltage sensitivity to measure the degree of change in DGC reactive power demand caused by the active power change of each DGC. Finally, a DGC reactive power reserve index is proposed based on the DGC reactive power reserve coefficient to guide the reasonable allocation of reactive power reserves in each DGC during the DGC division process, so that there is sufficient reactive power reserve in each divided cluster.

10. The method for dividing distributed power generation clusters with multiple control objectives based on day-ahead scheduling results according to claim 1, characterized in that: The construction of the DGC partition comprehensive index and partition algorithm in step S4 is specifically: using an improved genetic algorithm to partition the DGC, that is, using the adjacency matrix of the distribution network topology structure as the encoding chromosome for encoding, and using an advanced crossover mutation mechanism to make the algorithm suitable for DGC partition while having a faster solution efficiency.

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