WO2019041857A1 - 一种基于场景分析的含分布式电源配电网运行状态预测方法 - Google Patents
一种基于场景分析的含分布式电源配电网运行状态预测方法 Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00002—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by monitoring
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/001—Methods to deal with contingencies, e.g. abnormalities, faults or failures
- H02J3/0012—Contingency detection
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/12—Monitoring or controlling equipment for energy generation units, e.g. distributed energy generation [DER] or load-side generation
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
- Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security
Definitions
- the invention belongs to the field situation sensing field of distribution network, and relates to a method for predicting the operating state of a distribution network, and more particularly to a method for predicting the running state of a distributed power distribution network based on scene analysis.
- Scene analysis is an effective method to solve random problems. By modeling the possible scenes, the uncertainty factors in the model are transformed into multiple deterministic scene problems, which reduces the difficulty of modeling and solving.
- the construction of the scene tree can provide a variety of expected scenarios compared to the single prediction results obtained by time series prediction.
- the scene analysis method can reflect the uncertainty of the system operation. At the same time, it can reflect the timing characteristics of the system operation, and apply the scenario analysis to the operational status prediction of the distributed power distribution network. It can be used and effective, and can fully utilize the distributed power history operation information and real-time operation information to the distribution network. Situational forecasting provides new ideas.
- the present invention provides a method for predicting the operating state of a distributed power distribution network based on scene analysis, and performs multi-scenario multi-scenario prediction of distributed power output information to give a distribution network operation for the next two hours. State change trend.
- the method for predicting the running state of a distributed power distribution network based on scenario analysis of the present invention comprises the following steps:
- Step 10) Obtain a power distribution system network architecture and historical operation information, where the historical operation information includes a distributed power history output sequence and historical demand information of each load point;
- Step 20 extracting a representative scene sequence segment of the distributed power source according to the distributed power history output sequence
- Step 30 performing historical similar scene matching by calculating a dynamic bending time distance between the real-time output sequence segment of the distributed power source and the representative scene sequence segment, and obtaining a multi-scene prediction result of the future single time section T 0 ;
- Step 40 establishing a future multi-time cross-section running scene tree according to the future single-time cross-section multi-scene prediction result
- Step 50 Deeply traverse the scenes in the multi-time cross-section operation scene tree in the future, separately perform distribution flow analysis for each scene, calculate the risk of over-limit of the line current of the distribution network, and the risk of exceeding the bus voltage, and obtain the continuous time section.
- the trend of the line current and bus voltage over-limit risk is the trend of the future operation state of the distributed power distribution network.
- step 10 the node number is traversed through the network, and each node type is acquired, and the distributed power access location is obtained, that is, the power distribution system network architecture is obtained.
- step 20 is as follows:
- Step 201) determining, according to the operating state prediction range of the distribution network, a distributed power source historical output sequence segment that needs to extract a representative scene sequence segment, the length of which is denoted as L; determining the required number of representative scene sequence segments M;
- Step 202) intercepting, in a distributed power history history output sequence, a time series segment whose length is L to be extracted from a representative scene sequence segment, and recording the number N as a scene set;
- Step 203) Calculate the probability of occurrence p(c i ) of each scene sequence segment in the scene set according to the following formula:
- c i represents the i-th scene sequence segment in the scene set, and i is the scene sequence segment number
- Step 204) For each scene sequence segment c i , calculate Kantorovich distance between it and other scene sequence segments according to the following formula, find the scene sequence segment closest to it and mark it in the scene set to form a minimum scene distance matrix KD, KD
- KD The matrix element KD(i) of the corresponding scene sequence segment c i is calculated according to the following formula:
- KD(i) min ⁇
- c j represents the jth scene sequence segment in the scene set, and j is the scene sequence segment number
- Step 205) For each scene sequence segment c i , multiply the corresponding minimum scene distance by the probability of the scene sequence segment, obtain the minimum scene probability distance corresponding to the scene sequence segment c i , and find the minimum probability in the scene set.
- the smallest scene sequence segment is taken as the culled scene sequence segment c*, and is removed from the scene set, and the scene sequence segment c* is culled as:
- Step 206) were excluded from the scene to find a sequence fragment c * latest scene sequence fragment c n, c n updated according to the probability p (c n):
- step 30 is as follows:
- Step 301) Step distributed power output based on the extracted sequence representative scene sequence fragment 20), calculation for distributed real-time dynamic bending output time series and the k-th representative scene sequence fragments from the DTW k;
- Step 302 taking the reciprocal of the dynamic bending time distance and normalizing the same, and obtaining the similarity between the real-time output sequence of the distributed power source and the representative scene sequence segment, and using the similarity as the probability of occurrence of the corresponding predicted scene,
- the k representative scene sequences and the corresponding dynamic bending time distance DTW k are used to calculate the future predicted value F k of the distributed power output sequence, and the M future predicted values constitute the multi-scene prediction result of the future single time section T 0 .
- step 40 is as follows:
- Step 402 Performing scene reduction on the multi-scene prediction result of the time section T′, setting the number of scene sequences M′ after the time section T′ is reduced, and calculating the Kantorovich distance between the U scene sequences to form a minimum scene distance matrix.
- the matrix element KD'(s) of the corresponding scene sequence c s in KD', KD' is calculated according to the following formula:
- KD'(s) min ⁇
- c s and c t respectively represent the sth and tth scene sequences of the distributed power source real-time output sequence set including the time section T predicted value F, and s and t are scene sequence numbers;
- Step 403 For each scene sequence c s , multiply the corresponding minimum scene distance by the probability of the scene sequence, obtain the minimum scene probability distance corresponding to the scene sequence c s , and find the minimum probability distance in the scene set is the smallest
- the scene sequence is taken as the culled scene sequence c ⁇ , which is removed from the scene set, and the scene sequence c ⁇ is culled as:
- c m is updated according to the probability p (c m):
- step 50 the specific process in step 50) is as follows:
- Step 501 Deeply traversing each scene in the future multi-time section running scene tree, that is, in each scene, the distributed power source predicted output value is regarded as a negative value load, and the distribution network power flow is calculated by using the forward pushback generation to obtain each line.
- Step 502 Based on the power flow calculation result, calculate the line overload value L OL , the line overload severity S OL (C/E), the voltage limit value L OV , and the bus over-voltage severity S OV according to the following formulas respectively ( C/E), get the risk of over-limit of the line current of the distribution network, OLR, bus voltage over-limit risk OVR:
- the line overload value L OL is:
- L represents the ratio of the current flowing through the line to its rated current
- the line overload severity is:
- NL is the number of lines in the whole network.
- the voltage limit L OV is:
- V is the node voltage standard value.
- the busbar overvoltage severity is:
- NP is the number of nodes in the whole network
- Step 503) Arranging the calculation result of step 502) from the time section T 0 to the nnth time section sequentially, and obtaining the trend of the line current and the bus voltage over-limit risk under the continuous time section, that is, the distributed power distribution network The trend of future operational status changes.
- the scene analysis method proposed by the invention makes full use of the distributed power supply historical output information and the real-time output information, and gives the ultra-short-term multi-scene prediction result of the distributed power supply in the next two hours, and builds a future multi-time section running scene tree.
- the trend analysis of each single scene provides various development trends of the distribution network operation status.
- the method proposed in this aspect pays attention to the possibility of occurrence of small probability scenes, and the trend of distribution network operation status after occurrence, which is conducive to more comprehensively carrying out situational awareness and risk warning of distribution network. .
- FIG. 1 is a schematic flow chart of a method according to an embodiment of the present invention.
- FIG. 2 is a structural diagram of an IEEE-33 node power distribution system with distributed power.
- FIG. 1 a scenario analysis method for distributed power distribution network operation state based on scene analysis
- FIG. 2 is an IEEE-33 node power distribution system with distributed power supply, given a balance in the network.
- the voltage amplitude and phase angle of the node, the load level of the PQ node, the voltage amplitude of the PV node, and the historical output information of the distributed power source connected to the system are known (the output data is recorded once every five minutes).
- Step 10) Obtain the power distribution system network structure, traverse the network to number the nodes, obtain each node type, and distribute the power access location, as shown in Figure 2; obtain the historical power output sequence of the distributed power supply and the historical demand information of each load point.
- Step 20 Extract a representative sequence of the distributed power supply output according to the distributed power history output sequence, and the specific steps are as follows:
- Step 203) Calculate the probability of occurrence p(c i ) of each scene sequence segment in the scene set according to the following formula:
- c i represents the ith scene sequence in the scene set, and i is the scene sequence number.
- Step 204) For each scene sequence segment c i , calculate Kantorovich distance between it and other scene sequence segments according to the following formula, find the scene sequence segment closest to it and mark it in the scene set to form a minimum scene distance matrix KD, KD The matrix element KD(i) corresponding to the scene sequence segment c i :
- KD(i) min ⁇
- c j represents the jth scene sequence segment in the scene set, and j is the scene sequence segment number.
- Step 205) For each scene sequence segment c i , multiply the corresponding minimum scene distance by the probability of the scene sequence segment, obtain the minimum scene probability distance corresponding to the scene sequence segment c i , and find the minimum probability in the scene set.
- the smallest scene sequence segment is taken as the culled scene sequence segment c*, and is removed from the scene set, and the scene sequence segment c* is culled as:
- Step 206) were excluded from the scene to find a sequence fragment c * latest scene sequence fragment c n, c n updated according to the probability p (c n):
- Step 30 Performing a historical similar scene matching by calculating a dynamic bending time distance between the real-time output sequence of the distributed power source and the representative scene, and obtaining a multi-scenario prediction result in the future single time section, the specific steps are as follows:
- Step 301) Calculate the dynamic bending time distance DTW k of the distributed power real-time output sequence R and the k-th representative scene sequence segment Q based on the representative scene sequence segments of the five distributed power output sequences extracted in step 20). Methods as below:
- Step 302 taking the reciprocal of the dynamic bending time distance and normalizing the same, and obtaining the similarity between the real-time output sequence of the distributed power source and the representative scene sequence segment, and using the similarity as the probability of occurrence of the corresponding predicted scene, k representative scene sequences and corresponding dynamic bending time distance DTW k calculate the output predicted value F k at 12:15 in the distributed power output sequence, and M future predicted values form the future single time section (June 1, 2017) 12:15) Multi-scene prediction results.
- step 40 according to the multi-scene prediction result, a future multi-time cross-section running scene tree is established, and the specific steps are as follows:
- the Kantorovich distance between the 25 scene sequences is calculated separately to form a minimum scene distance matrix KD', and the matrix element KD'(s) of the corresponding scene sequence c s in KD' is calculated according to the following formula:
- KD'(s) min ⁇
- c s and c t respectively represent the s and t scene sequences of the distributed power real-time output sequence set containing the multi-scene prediction result of the time section at 12:30 on June 1, 2017, and s and t are scene sequences. Numbering;.
- Step 403 For each scene sequence c s , multiply the corresponding minimum scene distance by the probability of the scene sequence, obtain the minimum scene probability distance corresponding to the scene sequence c s , and find the minimum probability distance in the scene set is the smallest
- the scene sequence is taken as the culled scene sequence c ⁇ , which is removed from the scene set, and the scene sequence c ⁇ is culled as:
- c m is updated according to the probability p (c m):
- step 50 the traversal of each scene in the multi-time section operation scenario tree is performed in depth, and the power flow analysis of the distribution network is sequentially performed for each scene, and the risk of over-limit of the line current of the distribution network and the risk of exceeding the bus voltage are obtained, and a continuous time section is obtained.
- the trend of the line current and the bus voltage over-limit risk is the trend of the future operation state of the distributed power distribution network.
- Step 501) Deeply traversing each scene in the multi-time section running scene tree in the future, and the single-time section multi-scene prediction value generated by the last prediction of the future multi-time section running scene tree is the starting point, and sequentially searches for the parent node, that is, the previous moment.
- the predicted value, up to the root node, generates a continuous time section in reverse.
- the predicted power output value of the distributed power supply is regarded as a negative value load
- the power flow of the distribution network is calculated by using the forward pushback generation to obtain the current and bus voltage conditions of each line;
- Initialization give the balanced node voltage and assign the initial value to other PQ nodes in the whole network.
- the PV node is reactively injected with initial power Q i (0) .
- Step 502 Calculate the line overload value L OL , the line overload severity S OL (C/E), the voltage limit value L OV , and the bus over-voltage severity S OV (C/E) in each scenario based on the power flow calculation result. , get the distribution line current over-limit risk OLR, bus voltage over-limit risk OVR:
- the line overload value L OL is:
- L is the ratio of the current flowing through the line to its rated current.
- the above formula reflects the overload value of a single line, and on this basis, defines the line overload risk.
- the line overload severity is:
- NL is the number of lines in the whole network.
- the voltage limit L OV is:
- V is the node voltage standard value.
- the above formula reflects the voltage limit of a single busbar, and on this basis, defines the voltage overload risk and evaluates the overall busbar overvoltage risk level of the area. Define the voltage across the bus.
- the risk severity function is S OV (C/E). When the bus voltage is 1.05 pu, the severity function takes 0; as the voltage exceeds the limit, the node voltage exceeds the risk severity.
- the busbar overvoltage severity is:
- NP is the number of nodes in the whole network.
- Step 503) Arranging the calculation result of step 502) from the time section T 0 to the nnth time section sequentially, and obtaining the trend of the line current and the bus voltage over-limit risk under the continuous time section, that is, the distributed power distribution network The trend of future operational status changes.
Abstract
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Claims (6)
- 一种基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,该方法包括如下步骤:步骤10)获取配电系统网络架构、历史运行信息,所述历史运行信息包括分布式电源历史出力序列、各负荷点历史需求信息;步骤20)根据分布式电源历史出力序列,提取分布式电源出力代表性场景序列片段;步骤30)通过计算分布式电源实时出力序列片段与代表性场景序列片段的动态弯曲时间距离进行历史相似场景匹配,得到未来单时间断面T 0的多场景预测结果;步骤40)根据所述未来单时间断面多场景预测结果,建立未来多时间断面运行场景树;步骤50)深度遍历未来多时间断面运行场景树中的各场景,针对各场景分别进行配电网潮流分析,计算配电网线路电流越限风险、母线电压越限风险,得到连续时间断面下的线路电流和母线电压越限风险的变化趋势,即为含分布式电源配电网未来运行状态变化趋势。
- 根据权利要求1所述的基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,所述步骤10)中通过遍历网络进行节点编号,获取各节点类型,分布式电源接入位置,即得到配电系统网络架构。
- 根据权利要求1所述的基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,所述步骤20)的具体流程如下:步骤201)根据配电网运行状态预测范围确定需要提取代表性场景序列片段的分布式电源历史出力序列片段,其长度记作L;确定需要的代表性场景序列片段个数M;步骤202)在分布式电源历史出力序列中截取拟提取代表性场景序列片段的长度为L的时间序列片段,记其数量为N,构成场景集;步骤203)根据下式计算场景集中每个场景序列片段的出现概率p(c i):式中,c i表示场景集中的第i个场景序列片段,i为场景序列片段编号;步骤204)对于每个场景序列片段c i,根据下式计算其与其他场景序列片段之间的Kantorovich距离,找到距离其最近的场景序列片段并在场景集中标记,形成最小场景距离矩阵KD,KD中对应场景序列片段c i的矩阵元素KD(i)根据下式计算:KD(i)=min{||c i-c j|| 2,j∈[1,2,3,...N],j≠i},i∈[1,2,3,...N]其中,c j表示场景集中的第j个场景序列片段,j为场景序列片段编号;步骤205)对于每个场景序列片段c i,将其对应的最小场景距离与该场景序列片段的概率相乘,求得场景序列片段c i对应的最小场景概率距离,并找出场景集中最小概率距离最小的场景序列片段作为被剔除场景序列片段c*,将其从场景集中剔除,被剔除场景序列片段c*为:c*=min{KD(i)*p(i)|i∈[1,2,3,...N]}步骤206)寻找到距离被剔除场景序列片段c*最近的场景序列片段c n,根据下式更新c n的概率p(c n):p(c n)=p(c*)+p(c n)步骤207)令场景序列片段总数N=N-1,如果更新后的场景序列片段总数N=M,则结束步骤20),否则返回步骤204)。
- 根据权利要求1、2或3所述的基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,所述步骤30)的具体流程如下:步骤301)基于步骤20)提取的分布式电源出力序列代表性场景序列片段,计算分布式电源实时出力序列与第k个代表性场景序列片段的动态弯曲时间距离DTW k;步骤302)取动态弯曲时间距离的倒数并对其进行归一化处理,得到分布式电源实时出力序列与代表性场景序列片段的相似度,将该相似度作为对应预测场景出现的概率,由第k个代表性场景序列及对应的动态弯曲时间距离DTW k计算分布式电源出力序列的未来预测值F k,M个未来预测值组成未来单时间断面T 0的多场景预测结果。
- 根据权利要求1、2或3所述的基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,所述步骤40)的具体流程如下:步骤401)将步骤30)产生的未来单时间断面T 0的多场景预测结果纳入分布式电源实时出力序列,按照与步骤30)相同的方式得到下一时间断面T’=T 0+Δt的多场景预测结果,共U=M 2个,Δt为预测间隔;步骤402)针对时间断面T’的多场景预测结果,进行场景削减,设定时间断面T’削减后的场景序列数M’,分别计算U个场景序列之间的Kantorovich距离,形成最小场景距离矩阵KD’,KD’中对应场景序列c s的矩阵元素KD’(s)根据下式计算:KD'(s)=min{||c s-c t|| 2,t∈[1,2,3,...M 2],t≠s},s∈[1,2,3,...M 2]其中,c s和c t分别表示包含时间断面T预测值F的分布式电源实时出力序列集中的第s和第t个场景序列,s和t为场景序列编号;步骤403)对于每个场景序列c s,将其对应的最小场景距离与该场景序列的概率相乘,求得场景序列c s对应的最小场景概率距离,并找出场景集中最小概率距离最小的场景序列作为被剔除场景序列c^,将其从场景集中剔除,被剔除场景序列c^为:c^=min{KD'(s)*p(s)|s∈[1,2,3,...M 2]}寻找到距离被剔除场景序列c^最近的场景序列c m,根据下式更新c m的概率p(c m):p(c m)=p(c ^)+p(c m)步骤404)令场景总数U=U-1,如果更新后的场景总数U=M’,则进入步骤405),否则返回步骤402);步骤405)如果T’=T 0+n*Δt,则将各时间断面预测结果依时间先后排列,生成未来多时间断面运行场景树,并结束步骤40),否则令T=T’,T’=T+Δt,M=M’,返回步骤401),其中n为需要预测的时间断面个数。
- 根据权利要求1、2或3所述的基于场景分析的含分布式电源配电网运行状态预测方法,其特征在于,所述步骤50)中的具体流程如下:步骤501)深度遍历未来多时间断面运行场景树中的各场景,即在各场景下,将分布式电源预测出力值视为负值负荷,利用前推回代计算配电网潮流,得到各线路电流和母线电压情况;步骤502)基于潮流计算结果,分别根据以下公式计算各场景下的线路过载值L OL、线路过载严重度S OL(C/E)、电压越限值L OV、母线过压严重度S OV(C/E),得到配电网线路电流越限风险OLR、母线电压越限风险OVR:线路过载值L OL为:L OL=L-0.8其中,L表示流过线路的电流占其额定电流的比例;线路过载严重度为:线路电流越限风险OLR:其中,NL为全网线路数。电压越限值L OV为:L OV=|1.05-V|其中,V为节点电压标幺值。母线过压严重度为:母线电压越限风险OVR:其中,NP为全网节点数;步骤503)将步骤502)计算结果自时间断面T 0至第nn个时间断面依次排列,得到连续时间断面下的线路电流和母线电压越限风险的变化趋势,即为含分布式电源配电网未来运行状态变化趋势。
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