WO2022036778A1 - 一种输配协同负荷恢复优化控制方法及系统 - Google Patents
一种输配协同负荷恢复优化控制方法及系统 Download PDFInfo
- Publication number
- WO2022036778A1 WO2022036778A1 PCT/CN2020/114879 CN2020114879W WO2022036778A1 WO 2022036778 A1 WO2022036778 A1 WO 2022036778A1 CN 2020114879 W CN2020114879 W CN 2020114879W WO 2022036778 A1 WO2022036778 A1 WO 2022036778A1
- Authority
- WO
- WIPO (PCT)
- Prior art keywords
- transmission
- distribution
- load
- load recovery
- time
- Prior art date
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 116
- 238000011084 recovery Methods 0.000 title claims abstract description 111
- 230000005540 biological transmission Effects 0.000 title claims abstract description 104
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000005457 optimization Methods 0.000 claims abstract description 77
- 230000008878 coupling Effects 0.000 claims abstract description 29
- 238000010168 coupling process Methods 0.000 claims abstract description 29
- 238000005859 coupling reaction Methods 0.000 claims abstract description 29
- 238000012937 correction Methods 0.000 claims abstract description 10
- 230000008569 process Effects 0.000 claims description 16
- 238000012795 verification Methods 0.000 claims description 15
- 238000005096 rolling process Methods 0.000 claims description 13
- 238000003860 storage Methods 0.000 claims description 12
- 230000000694 effects Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 9
- 238000004590 computer program Methods 0.000 description 7
- 230000008859 change Effects 0.000 description 5
- 239000013598 vector Substances 0.000 description 5
- 238000012545 processing Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000003190 augmentative effect Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
-
- 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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- 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]
-
- 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
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
- Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
- Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
-
- 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
- Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
- Y04S20/20—End-user application control systems
- Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
Definitions
- the present disclosure belongs to the technical field of power system optimization control, and relates to a transmission and distribution coordinated load recovery optimization control method and system.
- Load restoration is a problem faced by the power system after a partial or total power outage.
- Traditional large-scale power grid load restoration research often focuses on the transmission network level, where the loads are actually load clusters at the distribution network level. Restricted by the "passive" characteristics of the load side, the distribution side obeys the dispatching of the transmission network and relies on the one-way power support of the transmission network level during the load recovery process.
- the distribution grid side has changed from "passive" to active.
- the distribution network side can provide timely power support to the large power grid through transmission and distribution recovery coordination, thereby further improving the resource utilization rate of the entire network and speeding up the load recovery process. Therefore, the research on load recovery needs to further transition from the large grid level to the multi-level and multi-regional transmission and distribution coupled grid.
- Transmission and distribution coordination load recovery is a long-term sequential decision-making problem, and traditional load recovery is realized through a time-step progressive process based on single-time-step optimization.
- single-time-step optimization is equivalent to local optimization.
- the decision based on single time step optimization does not consider the coupling between time steps and does not have the ability to coordinate decisions on a long time scale.
- the power supply side includes large-scale renewable energy clusters at the transmission grid level and a large number of distributed power sources at the distribution network level, and the load side includes uncertain load access points widely distributed in the system. Therefore, compared with the single-time-step optimization decision-making method, the decision-making process of transmission and distribution cooperative load restoration requires longer time-scale coordination.
- the present disclosure proposes a transmission and distribution coordinated load recovery optimization control method and system.
- the present disclosure is aimed at the prediction model of uncertain sources and the reconfiguration of the distribution network grid; the multi-time-step rolling optimization considering the inter-step coupling situation Feedback correction of optimization parameters and multi-time distribution; through the model predictive control method in transmission and distribution coordinated load recovery, learning from past experience is realized, future recovery conditions are considered, and the current recovery operation is strictly feasible.
- the present disclosure adopts the following technical solutions:
- a transmission and distribution coordinated load recovery optimization control method comprising the following steps:
- the load recovery optimization model is established
- the established load recovery optimization model is used to solve the problem, and the current time step strategy is extracted to implement the load recovery at the execution scale.
- the power grid structure of the transmission and distribution coupling system includes a multi-regional transmission network layer and a distribution network layer, the transmission network layer includes multiple interconnected transmission networks, and the distribution network layer includes multiple parallel distribution networks coupled with the transmission network. ;
- the transmission network level is based on a mesh network structure, including generator sets, transmission network equivalent loads and renewable energy clusters; the distribution network level is based on a radial network structure, including distribution network level loads and distributed power sources.
- the load recovery model of the transmission and distribution coupling system includes an objective function and constraints, and the objective function is the load active power of all transmission and distribution networks and the corresponding load weights and corresponding load access decision variables.
- the sum of the products is maximized, and the constraints include that the output of units in the transmission network, the output of renewable energy clusters, and the output of distributed power in the distribution network are all within the corresponding boundary conditions.
- the load recovery process is performed in a rolling manner, and the rolling execution steps include:
- the calculation process for calculating the source load threshold value includes: the source load threshold value depends on the sampled data, and when the sampled data comes from different time scales, the threshold value in the corresponding time range is obtained.
- the upper limit value of the load that may be reached is measured; for the uncertain source on the power supply side, the lower limit value given by the output of the uncertain source is measured.
- the specific process of performing parameter correction in combination with the past time step includes: using threshold values with sampled data of different time scales to verify the predicted value of the current time step.
- a transmission and distribution coordinated load recovery optimization control system comprising:
- the load recovery optimization modeling module is configured to establish a load recovery optimization model according to the power grid structure of the transmission and distribution coupling system
- the parameter prediction module is configured to determine the number of time steps corresponding to different time scales, and perform parameter prediction to obtain the threshold value of uncertain source load;
- the parameter update module is configured to perform parameter correction in combination with the past time step and update parameters and distribution network data in combination with the current situation;
- the multi-step optimization module is configured to use the established load recovery optimization model to solve based on the determined optimization parameters and system state, and extract the current time-step strategy to implement the load recovery at the execution scale.
- a computer-readable storage medium stores a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and execute the one transmission and distribution coordinated load recovery optimization control method.
- a terminal device comprising a processor and a computer-readable storage medium, where the processor is used to implement various instructions; the computer-readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded by the processor and executing the described one Transmission and distribution coordination load recovery optimization control method.
- the present disclosure optimizes the three links of prediction model, rolling optimization and feedback correction, takes into account the future recovery situation, ensures the strict feasibility of the current recovery operation, and can realize the multi-level and multi-region transmission and distribution coupled power grid under uncertain conditions. Long-time-scale coordination of load recovery.
- the present disclosure reduces the influence of uncertain source prediction errors during the load recovery process of the transmission and distribution coupled power grid through multi-time-step rolling prediction control, ensures the feasibility of the optimization strategy, and improves the utilization rate of renewable energy in the recovery control process.
- Figure 1 is a diagram of a multi-level and multi-region transmission and distribution coupling system
- Figure 2 is a schematic diagram of a model predictive control framework in load recovery
- Figure 3 is a schematic diagram of the multi-time-step load recovery optimization modeling principle
- FIG. 4 is a flow chart of the transmission and distribution cooperative load recovery control.
- a transmission and distribution coordinated load recovery optimization control method comprising the following steps:
- the transmission network layer includes multiple interconnected transmission networks; the distribution network layer includes multiple parallel distribution networks coupled with the transmission network.
- the transmission grid level is based on a mesh network structure, including traditional generator sets, transmission grid equivalent loads, large-scale renewable energy clusters, and traditional large-scale generator sets.
- the distribution network level is based on a radial network structure, including distribution network level loads and distributed power sources. Traditional generator sets, transmission grid equivalent loads, large-scale renewable energy clusters, and traditional large-scale generator sets. It is that each area of the distribution network level can interact with boundary information to realize a distributed optimization decision-making scheme.
- the load recovery model based on the transmission and distribution coupling system is as follows:
- pTL,TSi and pDL ,DSi are the load active power vectors of transmission network i and distribution network i respectively; cTL,TSi and cDL ,DSi are the corresponding load weight vectors respectively; xTL,TSi , and x DL,DSi is the vector representing the corresponding load access decision variable; p G,TSi is the output variable of the traditional unit in the transmission network i; p RE,TSi is the output vector of the large-scale renewable energy cluster in the transmission network i; p DG, DSi are the distributed power output vectors in distribution network i; B TD, TSi and B DT, DSj are the transmission and distribution network boundary variables of transmission network i and distribution network i, respectively; B T, TSij , and B T , TSji are the boundary variables between transmission network i and transmission network j, respectively; B D, DSij , and B D, DSji are the boundary variables between distribution network i and
- the consistent coupling constraints in (4) are loosely decoupled using the augmented Lagrangian method.
- the constraint (4) in the original model is written as (5)-(7), and is added to the objective function in the form of a penalty function according to formula (8), so as to realize the relaxation of the consistency constraint.
- the load recovery model of the distributed multi-level and multi-region transmission and distribution coupling system corresponding to Fig. 1 is obtained.
- (9)-(10) represent the load recovery model of the distributed transmission network corresponding to each sub-region of the transmission network level in Fig. 1;
- (11)-(12) represent the distribution corresponding to each sub-region of the distribution network level in Fig. 1 A load recovery model of the distribution network.
- the rolling predictive control framework in load recovery will consider different time scales.
- t is the starting point of the current time step
- T m is the decision interval range of the current time step
- T e represents the optimization scale used for multi-time step optimization
- Tw is the prediction time scale of uncertain quantities.
- the prediction scale needs to be longer than the optimization scale.
- the actual implementation effect of the past time steps is used to perform feedback verification of the current decision parameters, and then based on the verification results, multi-time-step optimization considering the future recovery effect is realized.
- the current time step strategy corresponding to the multi-time step optimization result is extracted as the basis for the recovery operation of the current time step.
- the closed loop process rolls forward until load recovery is complete.
- the parameters involved in the decision-making and the recovery situation are first predicted and modeled in the prediction, and the feedback update is carried out in combination with the past experience; then, the future recovery effect is considered in the optimization scale based on the corrected parameters and the updated system state.
- the advantage of this method is that the next load recovery decision is made considering the future recovery effect and the current latest update information.
- the feedback verification based on the past time step enables the current latest information to be used more efficiently.
- the key to realizing the rolling predictive control framework is: 1) clarifying the distributed multi-time distribution optimization model; 2) clarifying the prediction scale parameters and feedback verification methods.
- the explicit distributed multi-time distribution optimization model is to establish a multi-time-step distributed load recovery model for the transmission and distribution coupling system.
- the model variables and constraints can be divided into two parts: independent variables and constraints at each time step and coupled variables and constraints at adjacent time steps.
- the multi-time-step load recovery optimization model of the transmission and distribution coupling system is shown in (13)-(16):
- the load recovery objective is updated from the maximum single-step load recovery amount to the longer time-scale multi-time-step load recovery maximum amount.
- Equation (17) reflects the characteristics of the multi-time-step model that requires response time coupling.
- the common variables between steps in the load recovery model of the transmission and distribution coupling system include the following six:
- Generator output variable During the load recovery process, the generator has basically started and continued to climb in the multi-time step process until the maximum output, and its output is a function related to the recovery time. Since the load recovery operation is time-limited, there is an upper limit on the available output of the generator at each time step. The available output of the generator at each time step is a variable between the initial value and the maximum value of the time step. In the multi-time step optimization decision-making process, the generator output of the strategy of the previous time step is the initial value of the next time step, so the generator output variable of the previous time step will appear in the generator output constraint of the next time step . 2) Load access decision variable: in the process of load recovery, the load will not be cut off again after the load is connected.
- the output of renewable energy will be adjusted according to the load access and the output of traditional units. Obviously, the application of different adjacent time steps will bring about changes in system power and thus affect the frequency. Therefore, the renewable energy application variables of the previous time step will participate in the calculation of the frequency offset of the subsequent time step.
- Transmission and distribution boundary variables The frequency regulation of the transmission and distribution coupling system mainly relies on the adjustment of the transmission network, but the distribution network will cause power changes in the large power grid through boundary power interaction. The boundary power change of the two time steps before and after will result in the incremental change of power originating from the distribution side. Therefore, the boundary power variable of the previous time step will appear in the frequency offset calculation of the subsequent time step.
- Regional boundary variable of transmission network Similar to the boundary variable of transmission and distribution, the regional boundary variable of transmission network will also affect the frequency safety of regional transmission network by causing the power change of regional large power grid, so the regional boundary power variable of transmission network of the previous time step will appear. in the frequency offset calculation of the subsequent time step. Note that when considering the coupling of transmission and distribution boundary variables, the coupling of load access on the distribution network side and the variation of distributed power sources will also be included, and the variables belonging to the distribution network side in constraint (17) do not need to be considered repeatedly.
- the augmented Lagrangian method based on equation (8) is further used to establish a multi-time-step distributed load recovery model of the transmission and distribution coupling system.
- (18)-(20) are the distributed regional power transmission network optimization models
- (21)-(23) are the distributed regional power distribution network optimization models.
- the explicit prediction scale parameters and feedback verification methods include:
- T is the total number of time points in a certain time period
- a threshold value with robust characteristics within a certain period of time can be given based on the sampled data.
- the threshold value can be obtained by various methods such as finding the boundary under a certain confidence or mining the implicit probability distribution, which will not be repeated here, only the source load threshold value is represented by ⁇ Li and ⁇ Si . Based on this, the parameters in rolling optimization are used in multi-time-step optimization decision-making through feedback verification.
- the predicted value thresholds used in the load recovery model predictive control method are ⁇ Li and ⁇ Si .
- ⁇ Li and ⁇ Si describe the uncertain worst case of load side and power side with the idea of robust measurement, respectively.
- the source load threshold depends on the sampled data When sampling data When coming from different time scales, the threshold value in the corresponding time range can be obtained. Based on the source-load threshold, the parameters of the uncertain source in the multi-time-step optimization process can be obtained.
- the threshold value ⁇ of sampled data with different time scales is used to form the verification formula in (25).
- e i,m+w-1 is the verification parameter for the m+w-1 time step.
- the threshold value of ⁇ m-wi, m-1 is the sampling value predicted for the m-1 time step at the mp time step ⁇ value.
- the verification parameter e i,m+w-1 is obtained from the weighted difference of these two values, indicating that the current time step is verified based on the historical situation.
- the ⁇ value of the uncertainty in the prediction scale Tw is checked.
- the sampling data of uncertain load nodes and power supply nodes at different time scales are used to obtain the corresponding node and For time step m, the measurement data of historical time step m-1 and the predicted data of time steps mW-1 to m-1 should be recorded to verify the current time step m and the future W time steps.
- Figure 4 shows the flow chart of the predictive control method of the transmission and distribution cooperative load recovery model.
- the model predictive control method is implemented.
- the parameter prediction is performed to obtain the threshold value of uncertain source load
- the parameter correction is carried out in combination with the past time step situation
- the parameters and distribution network data are updated in combination with the current situation.
- the established distributed load recovery optimization model is used to solve the problem, and the current time step strategy is extracted and implemented at the execution scale.
- the process rolls over, advancing in time steps, until full load recovery is complete.
- a transmission and distribution coordinated load recovery optimization control system comprising:
- the load recovery optimization modeling module is configured to establish a load recovery optimization model according to the power grid structure of the transmission and distribution coupling system
- the parameter prediction module is configured to determine the number of time steps corresponding to different time scales, and perform parameter prediction to obtain the threshold value of uncertain source load;
- the parameter update module is configured to perform parameter correction in combination with the past time step and update parameters and distribution network data in combination with the current situation;
- the multi-step optimization module is configured to use the established load recovery optimization model to solve based on the determined optimization parameters and system state, and extract the current time-step strategy to implement the load recovery at the execution scale.
- a computer-readable storage medium stores a plurality of instructions, wherein the instructions are adapted to be loaded by a processor of a terminal device and execute the one transmission and distribution coordinated load recovery optimization control method.
- a terminal device comprising a processor and a computer-readable storage medium, where the processor is used to implement various instructions; the computer-readable storage medium is used to store a plurality of instructions, the instructions are suitable for being loaded by the processor and executing the described one Transmission and distribution coordination load recovery optimization control method.
- embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media having computer-usable program code embodied therein, including but not limited to disk storage, CD-ROM, optical storage, and the like.
- These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions
- the apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
Description
Claims (10)
- 一种输配协同负荷恢复优化控制方法,其特征是:包括以下步骤:依据输配耦合系统电网结构,建立负荷恢复优化模型;确定不同时间尺度对应的时步数,进行参数预测获得不确定源荷门槛值;结合过去时步情况进行参数修正并结合当下情形进行参数及配网网络数据更新;基于确定的优化参数和系统状态,利用建立的负荷恢复优化模型进行求解,并抽取当前时步策略在执行尺度实施,进行负荷恢复。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:输配耦合系统电网结构包括多区域的输电网层和配网层,输电网层包含多个互联输电网,配网层包括与输电网耦合的多个并行配电网;输电网层基于网状网络结构,包含发电机组、输电网等效负荷和可再生能源集群;配网层基于辐射状网络结构,包含配网层级负荷以及分布式电源。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:输配耦合系统的负荷恢复模型包括目标函数和约束条件,所述目标函数为所有输电网和配电网的负荷有功功率与相应负荷权重及相应负荷接入决策变量的乘积之和最大化,约束条件包括输电网中机组出力、可再生能源集群出力、配电网中的分布式电源出力均在 对应的边界条件内。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:负荷恢复过程是滚动执行的,随时步推进,滚动执行步骤包括:利用过去时步的实际实施效果进行当前决策参数的反馈校验,基于校验结果实现考虑未来恢复效果的多时步优化;抽取多时步优化结果中对应的当前时步策略,作为当前时步的恢复操作依据。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:计算源荷门槛值的计算过程包括:源荷门槛值取决于采样数据,当采样数据来自不同的时间尺度时,得到相应的时间范围内的门槛值。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:对于负荷侧不确定源,衡量可能达到的负荷量上限值;对于电源侧不确定源,衡量不确定源出力给出的下限值。
- 如权利要求1所述的一种输配协同负荷恢复优化控制方法,其特征是:结合过去时步情况进行参数修正的具体过程包括:使用具有不同时间尺度的采样数据的门槛值来校验当前时步的预测值。
- 一种输配协同负荷恢复优化控制系统,其特征是:包括:负荷恢复优化建模模块,被配置为依据输配耦合系统电网结构,建立负荷恢复优化模型;参数预测模块,被配置为确定不同时间尺度对应的时步数,进行 参数预测获得不确定源荷门槛值;参数更新模块,被配置为结合过去时步情况进行参数修正并结合当下情形进行参数及配网网络数据更新;多步优化模块,被配置为基于确定的优化参数和系统状态,利用建立的负荷恢复优化模型进行求解,并抽取当前时步策略在执行尺度实施,进行负荷恢复。
- 一种计算机可读存储介质,其特征是:其中存储有多条指令,所述指令适于由终端设备的处理器加载并执行权利要求1-7中任一项所述一种输配协同负荷恢复优化控制方法。
- 一种终端设备,其特征是:包括处理器和计算机可读存储介质,处理器用于实现各指令;计算机可读存储介质用于存储多条指令,所述指令适于由处理器加载并执行权利要求1-7中任一项所述的一种输配协同负荷恢复优化控制方法。
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010848672.3A CN112003277B (zh) | 2020-08-21 | 2020-08-21 | 一种输配协同负荷恢复优化控制方法及系统 |
CN202010848672.3 | 2020-08-21 |
Publications (1)
Publication Number | Publication Date |
---|---|
WO2022036778A1 true WO2022036778A1 (zh) | 2022-02-24 |
Family
ID=73473038
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
PCT/CN2020/114879 WO2022036778A1 (zh) | 2020-08-21 | 2020-09-11 | 一种输配协同负荷恢复优化控制方法及系统 |
Country Status (2)
Country | Link |
---|---|
CN (1) | CN112003277B (zh) |
WO (1) | WO2022036778A1 (zh) |
Cited By (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114638433A (zh) * | 2022-03-28 | 2022-06-17 | 国网湖北省电力有限公司电力科学研究院 | 一种考虑风电不确定性的负荷恢复分布鲁棒优化方法 |
CN115036963A (zh) * | 2022-04-14 | 2022-09-09 | 东南大学 | 一种提升配电网韧性的两阶段需求响应策略 |
CN115167142A (zh) * | 2022-07-29 | 2022-10-11 | 华能伊敏煤电有限责任公司 | 一种多热源供热机组联合控制方法、系统、设备及存储介质 |
CN115800275A (zh) * | 2023-02-08 | 2023-03-14 | 国网浙江省电力有限公司宁波供电公司 | 电力平衡调控配电方法、系统、设备及存储介质 |
CN115833115A (zh) * | 2023-02-03 | 2023-03-21 | 南方电网数字电网研究院有限公司 | 多时间尺度分配模型的分布式资源边缘控制方法及装置 |
CN115995815A (zh) * | 2023-03-23 | 2023-04-21 | 国网山西省电力公司电力科学研究院 | 一种基于多模块嵌套迭代的负荷故障恢复方法 |
CN116780529A (zh) * | 2023-06-30 | 2023-09-19 | 国网北京市电力公司 | 一种配电网故障恢复方法、装置、设备及介质 |
CN117394353A (zh) * | 2023-12-08 | 2024-01-12 | 国网天津市电力公司电力科学研究院 | 一种配电网负荷转供与恢复方法及装置 |
CN117595231A (zh) * | 2023-10-20 | 2024-02-23 | 国网安徽省电力有限公司六安供电公司 | 一种智能电网配网管理系统及其方法 |
CN117595231B (zh) * | 2023-10-20 | 2024-05-31 | 国网安徽省电力有限公司六安供电公司 | 一种智能电网配网管理系统及其方法 |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114156847A (zh) * | 2021-12-08 | 2022-03-08 | 国网辽宁省电力有限公司朝阳供电公司 | 一种配电网接地故障混合优化决策处理方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104156774A (zh) * | 2013-05-31 | 2014-11-19 | 贵州电网公司电力调度控制中心 | 一种考虑了相邻系统的电力支援方法 |
CN108493930A (zh) * | 2018-03-30 | 2018-09-04 | 国网江苏省电力有限公司 | 计及风电接入的负荷恢复两阶段优化方法 |
CN108988322A (zh) * | 2018-06-30 | 2018-12-11 | 南京理工大学 | 考虑系统时变性的微网运行策略优化方法 |
CN110994598A (zh) * | 2019-11-26 | 2020-04-10 | 国家电网有限公司 | 一种多目标电网故障恢复方法及装置 |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103577892A (zh) * | 2013-10-30 | 2014-02-12 | 河海大学 | 一种智能配电系统递进式调度方法 |
CN107994551A (zh) * | 2017-11-22 | 2018-05-04 | 国电南瑞科技股份有限公司 | 基于输配协同的故障快速恢复处理方法 |
CN109560547A (zh) * | 2019-01-15 | 2019-04-02 | 广东电网有限责任公司 | 一种考虑输配协同的主动配电网n-1安全评估方法 |
CN110232475B (zh) * | 2019-05-29 | 2021-07-13 | 广东电网有限责任公司 | 一种分布式输电网配电网协同经济调度方法 |
CN111092455A (zh) * | 2019-11-19 | 2020-05-01 | 国网江苏省电力有限公司电力科学研究院 | 一种储能系统与已恢复机组联合运行的负荷恢复优化方法 |
CN111047115B (zh) * | 2019-12-30 | 2022-09-20 | 国电南瑞科技股份有限公司 | 一种地区电网恢复控制优化决策方法、系统及存储介质 |
-
2020
- 2020-08-21 CN CN202010848672.3A patent/CN112003277B/zh active Active
- 2020-09-11 WO PCT/CN2020/114879 patent/WO2022036778A1/zh active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104156774A (zh) * | 2013-05-31 | 2014-11-19 | 贵州电网公司电力调度控制中心 | 一种考虑了相邻系统的电力支援方法 |
CN108493930A (zh) * | 2018-03-30 | 2018-09-04 | 国网江苏省电力有限公司 | 计及风电接入的负荷恢复两阶段优化方法 |
CN108988322A (zh) * | 2018-06-30 | 2018-12-11 | 南京理工大学 | 考虑系统时变性的微网运行策略优化方法 |
CN110994598A (zh) * | 2019-11-26 | 2020-04-10 | 国家电网有限公司 | 一种多目标电网故障恢复方法及装置 |
Non-Patent Citations (1)
Title |
---|
ZHAO JIN; LIU YAO; WANG HONGTAO; WU QIUWEI: "Receding horizon load restoration for coupled transmission and distribution system considering load-source uncertainty", INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, vol. 116, 17 September 2019 (2019-09-17), GB , pages 1 - 14, XP085895815, ISSN: 0142-0615, DOI: 10.1016/j.ijepes.2019.105517 * |
Cited By (14)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114638433B (zh) * | 2022-03-28 | 2024-05-31 | 国网湖北省电力有限公司电力科学研究院 | 一种考虑风电不确定性的负荷恢复分布鲁棒优化方法 |
CN114638433A (zh) * | 2022-03-28 | 2022-06-17 | 国网湖北省电力有限公司电力科学研究院 | 一种考虑风电不确定性的负荷恢复分布鲁棒优化方法 |
CN115036963A (zh) * | 2022-04-14 | 2022-09-09 | 东南大学 | 一种提升配电网韧性的两阶段需求响应策略 |
CN115036963B (zh) * | 2022-04-14 | 2023-12-15 | 东南大学 | 一种提升配电网韧性的两阶段需求响应策略 |
CN115167142A (zh) * | 2022-07-29 | 2022-10-11 | 华能伊敏煤电有限责任公司 | 一种多热源供热机组联合控制方法、系统、设备及存储介质 |
CN115833115A (zh) * | 2023-02-03 | 2023-03-21 | 南方电网数字电网研究院有限公司 | 多时间尺度分配模型的分布式资源边缘控制方法及装置 |
CN115800275A (zh) * | 2023-02-08 | 2023-03-14 | 国网浙江省电力有限公司宁波供电公司 | 电力平衡调控配电方法、系统、设备及存储介质 |
CN115995815A (zh) * | 2023-03-23 | 2023-04-21 | 国网山西省电力公司电力科学研究院 | 一种基于多模块嵌套迭代的负荷故障恢复方法 |
CN116780529A (zh) * | 2023-06-30 | 2023-09-19 | 国网北京市电力公司 | 一种配电网故障恢复方法、装置、设备及介质 |
CN116780529B (zh) * | 2023-06-30 | 2024-06-11 | 国网北京市电力公司 | 一种配电网故障恢复方法、装置、设备及介质 |
CN117595231A (zh) * | 2023-10-20 | 2024-02-23 | 国网安徽省电力有限公司六安供电公司 | 一种智能电网配网管理系统及其方法 |
CN117595231B (zh) * | 2023-10-20 | 2024-05-31 | 国网安徽省电力有限公司六安供电公司 | 一种智能电网配网管理系统及其方法 |
CN117394353B (zh) * | 2023-12-08 | 2024-05-14 | 国网天津市电力公司电力科学研究院 | 一种配电网负荷转供与恢复方法及装置 |
CN117394353A (zh) * | 2023-12-08 | 2024-01-12 | 国网天津市电力公司电力科学研究院 | 一种配电网负荷转供与恢复方法及装置 |
Also Published As
Publication number | Publication date |
---|---|
CN112003277A (zh) | 2020-11-27 |
CN112003277B (zh) | 2021-11-30 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
WO2022036778A1 (zh) | 一种输配协同负荷恢复优化控制方法及系统 | |
WO2021238505A1 (zh) | 基于联邦学习的区域光伏功率概率预测方法及协同调控系统 | |
CN110298138B (zh) | 一种综合能源系统优化方法、装置、设备及可读存储介质 | |
CN110930016A (zh) | 一种基于深度q学习的梯级水库随机优化调度方法 | |
CN113128793A (zh) | 一种基于多源数据融合的光伏功率组合预测方法及系统 | |
CN103390116A (zh) | 采用分步方式的光伏电站发电功率预测方法 | |
CN105631528A (zh) | 一种基于nsga-ii和近似动态规划的多目标动态最优潮流求解方法 | |
CN103683337A (zh) | 一种互联电网cps指令动态分配优化方法 | |
Zhang et al. | An efficient multi-objective adaptive differential evolution with chaotic neuron network and its application on long-term hydropower operation with considering ecological environment problem | |
CN116914747B (zh) | 电力用户侧负荷预测方法及系统 | |
CN105809349A (zh) | 一种考虑来水相关性梯级水电站群的调度方法 | |
CN109921420A (zh) | 弹性配电网恢复力提升方法、装置及终端设备 | |
CN106600055A (zh) | 一种基于自激励门限自回归模型的风速预测方法 | |
CN116894504A (zh) | 一种风电集群功率超短期预测模型建立方法 | |
CN113139341A (zh) | 基于联邦集成学习的电量需求预测方法和系统 | |
CN115345380A (zh) | 一种基于人工智能的新能源消纳电力调度方法 | |
CN108108837B (zh) | 一种地区新能源电源结构优化预测方法和系统 | |
CN116307287B (zh) | 一种光伏发电有效时段的预测方法、系统及预测终端 | |
CN111799793A (zh) | 一种源网荷协同的输电网规划方法与系统 | |
CN116756498A (zh) | 一种基于lstm和分位数回归的径流概率预测算法 | |
CN116667424A (zh) | 一种基于韧性提升的配电网二阶段鲁棒故障恢复方法 | |
CN112200366B (zh) | 负荷预测方法、装置、电子设备及可读存储介质 | |
CN114759579A (zh) | 一种基于数据驱动的电网有功优化控制系统、方法和介质 | |
Kaneda et al. | Optimal management of storage for offsetting solar power uncertainty using multistage stochastic programming | |
Gianluigi et al. | The innovative FlexPlan methodology to reap the benefits of including storage and load flexibility in grid planning: methodology and regional study cases |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
121 | Ep: the epo has been informed by wipo that ep was designated in this application |
Ref document number: 20949982 Country of ref document: EP Kind code of ref document: A1 |
|
NENP | Non-entry into the national phase |
Ref country code: DE |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 20949982 Country of ref document: EP Kind code of ref document: A1 |
|
122 | Ep: pct application non-entry in european phase |
Ref document number: 20949982 Country of ref document: EP Kind code of ref document: A1 |
|
32PN | Ep: public notification in the ep bulletin as address of the adressee cannot be established |
Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 07/12/2023) |