CN111799847B - Predictive control method of risk-considering two-stage random model of active power distribution network - Google Patents
Predictive control method of risk-considering two-stage random model of active power distribution network Download PDFInfo
- Publication number
- CN111799847B CN111799847B CN202010688381.2A CN202010688381A CN111799847B CN 111799847 B CN111799847 B CN 111799847B CN 202010688381 A CN202010688381 A CN 202010688381A CN 111799847 B CN111799847 B CN 111799847B
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power distribution
- oltc
- active
- active power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 111
- 238000000034 method Methods 0.000 title claims abstract description 65
- 238000005457 optimization Methods 0.000 claims abstract description 55
- 230000001105 regulatory effect Effects 0.000 claims abstract description 21
- 238000011217 control strategy Methods 0.000 claims abstract description 10
- 238000005070 sampling Methods 0.000 claims abstract description 10
- 238000005096 rolling process Methods 0.000 claims abstract description 7
- 230000008569 process Effects 0.000 claims description 25
- 230000009471 action Effects 0.000 claims description 21
- 230000008859 change Effects 0.000 claims description 12
- 238000007599 discharging Methods 0.000 claims description 9
- 230000009466 transformation Effects 0.000 claims description 8
- 230000005611 electricity Effects 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 7
- 206010063659 Aversion Diseases 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 6
- 238000000342 Monte Carlo simulation Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 230000001737 promoting effect Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 238000006467 substitution reaction Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000001419 dependent effect Effects 0.000 claims description 2
- 238000007619 statistical method Methods 0.000 claims description 2
- 238000011410 subtraction method Methods 0.000 claims description 2
- 238000011160 research Methods 0.000 description 9
- 238000007726 management method Methods 0.000 description 7
- 238000004364 calculation method Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 5
- 238000011156 evaluation Methods 0.000 description 4
- 230000004044 response Effects 0.000 description 4
- 230000032683 aging Effects 0.000 description 3
- 239000003990 capacitor Substances 0.000 description 3
- 238000004451 qualitative analysis Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000002347 injection Methods 0.000 description 2
- 239000007924 injection Substances 0.000 description 2
- 238000012954 risk control Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 238000012795 verification Methods 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
- 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
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- 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
- H02J3/48—Controlling the sharing of the in-phase component
-
- 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
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- 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
- H02J2300/20—The dispersed energy generation being of renewable origin
- H02J2300/22—The renewable source being solar energy
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Power Engineering (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a risk-related prediction control method for a two-stage stochastic model of an active power distribution network, which comprises the following steps of: 1) Photovoltaic output and load fluctuation are respectively designed to obey Beta distribution and normal distribution, a large-scale uncertain scene set is generated, and then the large-scale uncertain scene set is reduced; 2) Establishing an active and reactive power coordinated scheduling model of the active power distribution network by adopting a two-stage random optimization method; 3) An active and reactive power coordinated scheduling model of the active power distribution network is perfected; 4) Forming an optimal scheduling strategy; 5) At each sampling initial time t k Rolling solving an optimal scheduling strategy within a prediction period, wherein only [ t ] is performed k ,t k +T c ]Control strategy of each regulated resource in time period and is in [ t ] k ,t k +4T c ]The OLTC tap position and the CB gear are kept fixed in a time period, the method can flexibly adjust and quickly respond to an operation control object, and can effectively deal with the influence of errors between predicted values and actual values on an adjustment strategy so as to improve the voltage operation level of a system.
Description
Technical Field
The invention belongs to the technical field of power system automation, and relates to a risk-related predictive control method for a two-stage stochastic model of an active power distribution network.
Background
Active elements such as Distributed Generation (DG), electric vehicles and Energy Storage Systems (ESS) are widely connected, so that power flow of a power distribution network is changed from traditional one-way transmission to two-way flow, uncertainty is enhanced, power and voltage fluctuation of the active power distribution network are greatly improved, and a flexible and effective operation control means is more important. Traditional mechanical equipment such as an on-load tap changer (OLTC) and a Capacitor Bank (CB) has low regulation speed and limited action times. Devices such as DGs and ESS are generally connected with a power distribution network through novel power electronic devices, can quickly track power fluctuation and make timely adjustment, have the characteristics of flexible adjustment and quick response, and are a good operation control means. In the future, the boundary between Active Distribution Network (ADN) active scheduling and reactive power optimization becomes more fuzzy, and it is a necessary trend to comprehensively control various distributed energy sources and various active management means and realize comprehensive and efficient utilization of the energy sources in the whole network.
With the continuous increase of random operation conditions such as new energy power generation and power load change, an operation scheduling theory considering uncertainty factors becomes a current hot problem, and the research of an operation theory considering risks is not limited. An opportunity constraint method is generally adopted in the safe and economic dispatching operation research considering the risk, but the solving process of the opportunity constraint method is relatively complex. In order to visually describe the operation risk of the power distribution network, randomness and uncertainty factors are quantitatively processed, a more accurate and appropriate method is needed, and meanwhile, the efficiency of a calculation method of the method needs to be considered. In recent years, a number of scholars have extended the conditional value at risk (CVaR) method to the field of power system scheduling and operation research, and have made some technical breakthroughs. The risk measure is an analysis and estimation of risk level, and is one of the most important links in the risk management process, including measuring the probability of loss caused by various risks and the extent and degree of occurrence of the loss. Due to the fact that uncertainty of boundary conditions is enhanced, high-risk operation intervals under certain small-probability events can be formed in ADN operation control, and accurate evaluation of boundary risk of power distribution network scheduling operation under the condition of large-scale access of renewable energy sources is beneficial to reducing operation loss to the maximum extent and obtaining profit of system operation. In addition, in the actual operation process of the ADN, as the prediction uncertainty increases, the adaptability of the traditional day-ahead planning and scheduling strategy is weak, while Model Predictive Control (MPC) can realize rolling optimization and consider the dynamic performance of the system, and if the MPC can be applied to the operation cost management of the power distribution network, the control of the tail risk is expected to be further enhanced.
Aiming at the active and reactive coordinated scheduling problem of ADN, more researches are carried out to comprehensively control various distributed energy sources and active management means so as to realize safe and economic operation of an active power distribution network, but the ADN has defects in the following aspects: firstly, response characteristics of control decision objects in the active power distribution network are different, and few researches relate to design of part of flexibly-adjusted and quickly-responded operation control objects (such as DGs, ESS and the like) as decision variables of the second stage, so that a two-stage optimized operation method is formulated. Secondly, a large amount of distributed energy is accessed, and the uncertainty brought to the safe, reliable and economic operation of the system also needs corresponding deep analysis and overall optimization. In the traditional research, the expected value of the operation cost is simply used as an objective function, the boundary risk is ignored, and the scientificity and comprehensiveness are lost. How to measure the tail risk of high loss during the operation of the active power distribution network under the uncertain operation condition still needs to be explored. Meanwhile, as the prediction time scale becomes longer, the prediction accuracy is reduced, as the prediction uncertainty increases, the fluctuation of the operation parameters of the active power distribution network and the external disturbance are more frequent, the operation strategy of the active power distribution network needs finer time granularity and more flexible time scale, and the deterministic modeling or simple rule control aiming at a typical mode possibly faces failure, so the operation level of the system voltage is seriously influenced.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a risk-related predictive control method of a two-stage random model of an active power distribution network.
In order to achieve the above purpose, the predictive control method for the two-stage stochastic model of the active power distribution network considering the risk of the invention comprises the following steps:
1) Considering the uncertainty and the randomness of photovoltaic output and load fluctuation, setting the photovoltaic output and the load fluctuation to respectively obey Beta distribution and normal distribution, utilizing Monte Carlo simulation in a set time scale, then carrying out repeated random sampling and statistical analysis on a simulation result to generate a large-scale uncertain scene set, and then reducing the large-scale uncertain scene set;
2) Establishing an active and reactive power coordinated scheduling model of the active power distribution network by adopting a two-stage random optimization method, wherein the whole optimization control process is modeled in stages, and in the first stage, the action state and the action quantity of slow-motion equipment in the active power distribution network are determined without considering uncertainty; in the second stage, adding uncertainty, introducing condition risk value to measure the boundary risk of the operation of the active power distribution network, and measuring the loss degree by taking economic loss as a measurement index;
3) Carrying out linear modeling processing on an OLTC (active load controller), a CB (circuit board), a PV (photovoltaic) inverter and an ESS (ESS) in the active power distribution network, and perfecting an active and reactive coordination scheduling model of the active power distribution network by considering the operation constraint condition of ADN (adaptive data network);
4) Solving an active and reactive power coordinated scheduling model of the active power distribution network to obtain an optimized decision variable of two stages, and determining an output plan of each regulated resource in a scheduling period to form an optimal scheduling strategy;
5) At intervals of time T c Starting rolling optimization, and at each sampling initial time t k At present, withThe sampling value of the moment is taken as an initial state, and the current prediction period T is aimed at N Rolling to solve the optimal scheduling strategy within the prediction period, wherein only t is executed k ,t k +T c ]Control strategy of each regulated resource in time period and at t k ,t k +4T c ]Keeping OLTC tap position and CB gear fixed in time period and waiting for next time t k+1 =t k +T c Comes and then shifts the time window backward by a time interval T c And completing the two-stage stochastic model prediction control for promoting the coordinated optimization operation of the active power distribution network.
The specific operation of the step 4) is as follows: converting the active and reactive power coordinated scheduling model of the active power distribution network obtained in the step 3) into a mixed integer second-order cone planning problem, calculating optimization decision variables of two stages by solving the mixed integer second-order cone optimization problem, and determining output plans of each regulated and controlled resource in a scheduling period to form an optimal scheduling strategy.
And in the step 1), a large-scale uncertain scene set is reduced by adopting a synchronous back substitution reduction method.
The specific operation of the step 2) is as follows:
an active and reactive power coordinated scheduling model of the active power distribution network is established by adopting a two-stage random optimization method, wherein the whole optimization control process is modeled in stages, and the objective function of the model considers the electricity purchasing cost of a main network, the DG electricity generation cost, the network loss cost and the operation and maintenance costs of OLTC, CB, SVC, PV inverter and ESS, wherein the objective function EC of the first stage SI Uncertainty is not considered, uncertainty is considered in the second phase, and the objective function EC of the second phase SD Scene-dependent changes;
wherein, the objective function of the first stage is:
wherein omega G Is a contact node set of a regional power distribution network and an active power distribution network, omega OLTC 、Ω CB 、Ω SVC And omega Inv For each regulating device candidate node set, c G 、c PV And c Loss Unit cost, c, for electricity purchase, PV generation and network loss of the active distribution network, respectively OLTC 、c CB 、c SVC 、c Inv And c ESS For the unit regulation cost of OLTC, CB, SVC, photovoltaic inverter and ESS in the active power distribution network,represents the exchange power of the distribution network and the active distribution network connecting line in the t period region,for the generated power of PV at node j during time t,for the loss of line ij during period t,andis a first stage control variable in which,andrespectively represents reactive power regulating variables corresponding to the t period SVC, the photovoltaic inverter, the CB and the ESS,for the gear change identification of the OLTC,andis a variable from 0 to 1, and is,andfor OLTC gear change identification whenThe gear value of the OLTC in the t-th period is smaller than the t-1 period,and withMeaning the same, T is the duration of the scheduling period.
The objective function for the second stage is:
where β is a given confidence level, and β ∈ (0, 1), π s And expressing the occurrence probability of the scene s, wherein rho is risk aversion degree, and is used for representing the aversion degree of power distribution network operating personnel to risks.
The specific operation of the step 3) is as follows: and carrying out accurate linear modeling processing on OLTC, CB, SVC, PV inverters and ESS in the active power distribution network, and considering the operation constraint conditions of the active power distribution network so as to perfect an active and reactive power coordination scheduling model of the active power distribution network.
The voltage constraints of OLTC are:
wherein,is a voltage reference value, and is,andis the square of the upper and lower limits of the transformation ratio at both sides of the OLTC, l j,t Is the square of the OLTC transformation ratio, l j,t Is a discrete variable;
the action times constraint of OLTC is:
wherein,andis a variable from 0 to 1, indicating a change in the OLTC range whenThe OLTC gear value is larger than the gear at the t-1 time period in the t-th time period, otherwise, the OLTC gear value is smaller than the gear at the t-1 time period in the t-th time period; SR j For the maximum adjustment range of the OLTC gear,limiting the maximum action times of the OLTC in the T time period;
the constraints of the CB are:
the SVC constraints are:
wherein,andrespectively represent the upper and lower bounds of SVC reactive compensation output power
The constraints of the photovoltaic inverter are as follows:
the modeling process for ESS is represented as:
SOC i,T =SOC i,0
therein, SOC i,t For the state of charge, α, of an ESS connected at node i at time t i Is the self-discharge rate of the node;andrespectively representing the charging power and the discharging power of the ESS at the node,andthe charge-discharge efficiency is shown as follows,andrepresents the charging/discharging power upper limit value of the ESS at the node i,andis a binary variable used to indicate the charging and discharging states of the ESS.
The photovoltaic output is:
The safety constraints are:
wherein,andrespectively representing the upper and lower limit values of the current of branch ij during period t,andrespectively representing the upper and lower limits of the voltage at node j during time t.
The specific operation of the step 5) is as follows:
51 Based on historical data, predict the entire prediction period T N Load fluctuations and photovoltaic output conditions within, wherein uncertainty is not taken into account in the first phase;
52 Introduce uncertainty in the second stage, consider [ t ] k +T c ,t k +T N ]Generating N initial scenes by the load and photovoltaic output in a time period, and reducing the N initial scenes into N scenes;
53 T) is determined by solving a single deterministic mixed-integer second-order cone optimization problem while computing the two-stage optimization decision variables N The output plan of each regulated resource in the time period is formed to T N An optimal control strategy within a time period;
54 Execute [ t ] k ,t k +T c ]Optimal control strategy within a time period, and at [ t ] k ,t k +4T c ]Holding OLTC tap position and CB gear fixed for a period of time, etcWait for the next moment t k+1 =t k +T c Then the time window is shifted back by a time interval.
The invention has the following beneficial effects:
according to the prediction control method of the two-stage random model of the active power distribution network considering the risks, during specific operation, the whole optimization control process is modeled in stages, and in the first stage, uncertainty is not considered, and the action state and the action amount of slow-motion equipment in the active power distribution network are determined; in the second stage, uncertainty is added to reduce the action times of slow dynamic equipment, reduce the loss and aging cost of the slow dynamic equipment, fully exert the flexible adjustment characteristics of other equipment, simultaneously introduce conditional risk value to measure the boundary risk of the operation of the active power distribution network, measure the loss degree by taking economic loss as a measurement index, overcome the defects of qualitative analysis and subjective evaluation in the traditional research, and scientifically describe the tail risk and potential loss of the operation of the active power distribution network under the uncertain operation working condition by combining a continuous and scientific conditional risk value calculation method and a quantifiable analysis index. Finally, the model prediction control is applied to the ADN operation optimization considering the operation cost risk, the influence of the error between the predicted value and the actual value on the adjustment strategy can be effectively coped with, the boundary risk of the power distribution network scheduling operation under the large-scale access of renewable energy sources can be accurately evaluated, the operation loss is reduced to the maximum extent, and the system voltage operation level is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a system diagram of the present invention;
FIG. 2 is a schematic diagram of MPC based two-phase stochastic optimization.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Referring to fig. 1, the technical scheme adopted by the invention is as follows: the method comprises the steps of taking the operation cost of a power distribution network as a target, considering the limiting conditions of regulation and control equipment and the operation safety constraints of a power grid, controlling the regulation and control equipment in stages according to different response characteristics of an on-load tap changer (OLTC), a Capacitor Bank (CB), a Static Var Compensator (SVC), a Photovoltaic (PV) inverter and an ESS, considering the uncertainty of load and renewable energy, bringing risks into an optimization problem, introducing a CVaR (dynamic virtual random access controller) for risk management, establishing a two-stage random optimization model for considering the CVaR, integrating the two-stage random optimization model for considering the CVaR into a framework of an MPC (multimedia personal computer), and realizing rolling optimization solution. The method can realize the stable control of the voltage operation level of the power distribution network, overcomes the defects of qualitative analysis and subjective evaluation in the traditional research, and scientifically describes the tail risk and potential loss of the active power distribution network operation under the uncertain operation condition by combining a continuous and scientific calculation method and a quantifiable analysis index.
The two-stage stochastic model prediction control method for promoting coordinated optimization operation of the active power distribution network comprises the following steps of:
1) Considering the uncertainty and the randomness of photovoltaic output and load fluctuation, setting the photovoltaic output and the load fluctuation to respectively obey Beta distribution and normal distribution, utilizing Monte Carlo simulation in a certain time scale, repeatedly and randomly sampling and statistically analyzing a simulation result to generate a large-scale uncertain scene set, and then reducing the large-scale uncertain scene set by adopting a scene reduction technology;
the specific process of the step 1) is as follows:
considering uncertainty of photovoltaic output and load fluctuation, respectively adopting Beta distribution and normal distribution to carry out scene error generation, fusing generated error scene data and predicted scene data within a certain time scale to serve as real-time data which cannot be obtained temporarily in the verification process, wherein the total number of generated scenes is assumed to be N, and the probability corresponding to each scene is pi r R =1,2, \ 8230;, N. In order to achieve both of the calculation efficiency and the random variation characteristics of the data, it is necessary to extract setting characteristic information from the time-series data by using a scene reduction technique to form a typical scene.
Aiming at the selection problem of the typical scene, the invention adopts the synchronous back-substitution subtraction method to reduce the scale of the typical scene, the number of the reduced scenes is n, and the corresponding probability is pi s ,s=1,2,…,n。
2) Establishing an active and reactive power coordinated scheduling model of the active power distribution network by adopting a two-stage random optimization method, wherein the whole control process is modeled in stages, and in the first stage, the action state and the action quantity of slow-motion equipment in the system are determined without considering uncertainty; in the second stage, adding uncertainty, introducing condition risk value to measure boundary risk of Active Distribution Network (ADN) operation, and measuring loss degree by taking economic loss as a measurement index;
the specific process of the step 2) is as follows:
an active and reactive power coordinated scheduling model of the active power distribution network is established by adopting a two-stage random optimization method, wherein the whole control process is modeled in stages, and the economic operation objective function of the active power distribution network takes the electricity purchasing cost of a main network, the DG power generation cost, the network loss cost and the operation and maintenance costs of OLTC, CB, SVC, PV inverters and ESS into consideration. Objective function EC of the first stage SI Uncertainty is not considered, independent of scene changes (SI), and in the second phase uncertainty, its objective function EC, is considered SD Depending on the scene change (SD).
The objective function of the first stage optimization is:
wherein omega G Is a set of contact nodes, omega, of the regional distribution network and the main network OLTC 、Ω CB 、Ω SVC And omega Inv For each regulating device candidate node set, c G 、c PV And c Loss Unit cost for electricity purchase, PV generation and network loss of the main network, respectively, c OLTC 、c CB 、c SVC 、c Inv And c ESS For the unit regulation cost of OLTC, CB, SVC, photovoltaic inverter and ESS,representing the power exchange between the distribution network and the main network connecting line in the t period,for the generated power of PV at node j during time t,for the loss of line ij during period t,andis a first stage control variable in which,andrespectively representing reactive power regulating quantities corresponding to the t period SVC, the photovoltaic inverter, the CB and the ESS,is the gear change identification of the OLTC,anda variable of 0-1 for OLTC range change identification whenThe stage value of OLTC in the t-th period is smaller than the t-1 period,and withMeaning the same, T is the duration of the scheduling period.
The construction process of the optimization objective function at the second stage comprises the following steps:
because uncertain factors are introduced in the optimization process of the second stage, an expected value of an objective function is generally taken as an optimization target, and compared with a deterministic method for accurately predicting the DG output magnitude, the method has certain advantages, but other influence parameters for characterizing cost distribution are often ignored, the expected cost value can present certain distribution characteristics, wherein the possibility of high cost in some cases is high, from the perspective of risk control, a certain risk measurement method needs to be adopted to measure the possibility magnitude of loss caused by various risks and the range and degree of loss occurrence, and the construction of the initial objective function of the second stage is as follows:
wherein,andis a controlled variable for the second stage, wherein, respectively representing reactive power regulating variables corresponding to the SVC, the photovoltaic inverter and the ESS in a t period under a scene s,representing the power exchange between the power distribution network and the main network connecting line in the t period area under the scene s,for the generated power of PV at node j at time t under scene s,the network loss of the branch ij in the t period under the scene s is shown.
On the basis of expected cost, the method brings risk into an optimization problem, applies CVaR to risk measurement of the operation cost of the power distribution network, avoids thick tail phenomenon in cost distribution, realizes effective management and control of risk, adjusts deviation of control strategies, enables the adaptability of the strategies to be stronger, and enables given confidence coefficient beta to be E (0, 1) and VaR β The probability that the operation cost is less than xi is larger than the corresponding threshold value when beta, xi is a variable, vaR β Is defined as:
CVaR β indicating that the operating cost of the distribution network exceeds VaR β Average running cost in terms of value, wherein,
wherein omega s For uncertain scene sets, furthermore, continuous non-negative auxiliary variables eta are introduced S A value of equal toCVaR β Can be expressed as:
thus, considering the influence of uncertainty factors, the final objective function of the second stage optimization, which takes into account the running cost risk, is represented as:
where β is a given confidence level, and β ∈ (0, 1), π s The probability of occurrence of a scene s is represented, rho is risk aversion degree, rho is used for representing the aversion degree of power distribution network operating personnel to risks, the larger rho is, the more tendency of the operating personnel to avoid the risks is represented, and the decision is relatively conservative.
3) And carrying out linear modeling processing on the OLTC, CB, PV inverter, ESS and other controllable resources in the ADN, and considering the operation constraint conditions of the ADN to perfect an active and reactive power coordination scheduling model of the active power distribution network.
The specific process of the step 3) is as follows:
the method comprises the following steps of carrying out accurate linear modeling processing on OLTC, CB, SVC and PV inverters and ESS in the ADN, considering other operation constraint conditions of the ADN, perfecting an active and reactive power coordination scheduling model of the active power distribution network, and expressing detailed modeling processes and constraint conditions of active management elements as follows:
OLTC
in the operation process of the power distribution network, after the transformer substation is additionally provided with the OLTC, the voltage of the node of the transformer substation can be adjusted, and the voltage constraint of the OLTC is as follows:
wherein,is a voltage reference value, and is,andis the square of the upper and lower limits of the transformation ratio at both sides of the OLTC, l j,t Is the square of the OLTC transformation ratio, l j,t For discrete variables, for clarity, let l j,t Further processed into a form containing a variable of 0-1, i.e.
Wherein r is j,d Represents the increment of the square of the transformation ratio of the adjacent gears of the OLTC, namely the difference between the square of the transformation ratio of the gear D and the D-1, D is the gear set of the OLTC,being 0-1 auxiliary variable, since OLTC is limited by equipment wear and aging, the number of OLTC actions needs to be necessarily constrained, namely:
wherein,andis a variable from 0 to 1, indicating a change in OLTC gear whenThe OLTC gear value is larger than the gear at the t-1 time period in the t period, otherwise, the OLTC gear value is smaller than the gear at the t-1 time period in the t period; SR j For the maximum adjustment range of the OLTC gear,the maximum limit action number of OLTC in the T period.
Capacitor bank
The CB reactive compensation quantity is only related to the number of groups to which the CB reactive compensation quantity is put, and the relation between the CB reactive compensation quantity and the node voltage is not considered temporarily, and in practical application, the CB reactive compensation quantity is a discrete variable and is as follows:
wherein,the number of groups put into reactive compensation for the CB accessed to the node j at the time t,in the case of a discrete variable, the number of discrete variables,the maximum number of access CB groups for node j,the reactive compensation power that each group of CB can provide is shown, similar to OLTC, and is limited by the loss of CB equipment, and the action times constraint of adding the CB is as follows:
the constraints of CB are therefore:
wherein,is an auxiliary variable of 0-1, similar toAnda variable of 0-1, indicating a change in the number of CB input groups, ifThe number of CB commissioning groups is increased during the t-th period compared to the t-1 period.
Static reactive compensator
SVC can be continuously adjusted, and the SVC has the following constraints:
wherein,andrespectively representing the upper and lower bounds of the SVC reactive compensation output power.
Photovoltaic inverter
The available reactive power support capacity of the photovoltaic inverter is determined by the apparent power capacity and the active power output of the current photovoltaic, and the constraint conditions of the photovoltaic inverter are as follows:
ESS
the modeling process for ESS can be represented as:
SOC i,T =SOC i,0
therein, SOC i,t Is the state of charge (SOC) of the ESS connected to node i at time t, α i Is the self-discharge rate of the node;andrespectively representing the charging power and the discharging power of the ESS at the node,andthe charge-discharge efficiency is shown in the figure,andrepresents the upper limit value of the charging and discharging power of the ESS at the node i,andis a binary variable used to indicate the charging and discharging state of the ESS.
The photovoltaic output is:
The safety constraints are:
wherein,andrespectively representing the upper and lower limit values of the current of branch ij during period t,andrespectively representing the upper and lower limits of the voltage at node j during time t.
4) Carrying out convex relaxation on a power flow equation based on a second-order cone relaxation technology, converting the active and reactive power coordinated scheduling model of the active power distribution network obtained in the step 3) into a mixed integer second-order cone planning problem, calculating optimization decision variables of two stages by solving the mixed integer second-order cone optimization problem, and determining an output plan of each regulated and controlled resource in a scheduling period;
the specific process of the step 4) is as follows:
aiming at a radial distribution network, the invention adopts a branch flow model, photovoltaic power generation is taken as a representative of renewable energy to construct the branch flow model, and then the branch flow form of an active and reactive power coordinated scheduling model of the active distribution network is expressed as follows:
wherein, delta (j) Representing a set of branch end nodes, pi, with j as head-end node (j) Represents a set of branch head nodes with j as an end node, and Ω is a set of all nodes in the network, Ω Line For all line sets in the network, I ij,t Representing the corresponding branch current at time t, r ij Representing the resistance, x, of branch ij ij Representing the reactance value, b, on branch ij j Susceptance, P, representing node j j,t And Q j,t Respectively representing the active and reactive injection power of the node j in the period t,the active injection quantity of the upper-level power grid of the node j in the period t is represented,the active output value of the photovoltaic power generation is shown, andrespectively representing the idle work output by nodes j of a superior power grid, CB, SVC and photovoltaic inverter in the t-th period, P ij,t And Q ij,t Respectively representing the active and reactive power on branch ij during time t,andrespectively representing the active load and the reactive load of a node j in a period t;
order:
then, the branch load flow form of the active and reactive power coordinated scheduling model of the active power distribution network is expressed as follows:
therefore, the active and reactive power coordinated scheduling model of the active power distribution network is converted into a mixed integer second-order cone planning problem, the optimization decision variables of the two stages are calculated by solving the mixed integer second-order cone optimization problem, and the output plans of various regulated and controlled resources in the scheduling period are determined.
5) At intervals of time T c Starting rolling optimization, and at each sampling initial time t k Taking the sampling value at the current moment as the initial state, aiming at the current prediction period T N Rolling solution of the predicted result of (2)Predicting an optimal scheduling strategy within a cycle, wherein only [ t ] is performed k ,t k +T c ]Control strategy of each regulated resource in time period and at t k ,t k +4T c ]Keeping OLTC tap position and CB tap position fixed for a period of time until the next time t k+1 =t k +T c The time window is shifted backwards by a time interval T c And repeating the process.
Referring to fig. 2, the specific process of step 5) is:
the MPC is applied to ADN operation optimization considering the operation cost risk, so that the boundary risk of the distribution network scheduling operation under the large-scale access of the renewable energy sources can be accurately evaluated, the operation loss can be reduced to the maximum extent, and the profit of system operation can be obtained. The invention divides the two-stage random optimization into a static optimization stage (first stage) which is advanced by 1h and a prediction rolling optimization stage (second stage) which is advanced by 15min based on an MPC framework, and the coordination relationship between the two stages is shown in FIG. 2.
It should be noted that the tap position of the OLTC and the adjustment amount of the CB are defined as first-stage variables, and the SVC, the photovoltaic inverter, and the ESS can be adjusted in two stages. The two-stage coordination optimization is carried out by integrating the adjusting performance of various control devices, so that the effect of smoothly adjusting reactive power can be realized, the frequent action of related devices can be avoided, and the specific optimization steps in the two stages can be summarized as follows:
51 Based on historical data, predict the entire prediction period T N Load fluctuations and photovoltaic output conditions within, uncertainty is not taken into account in the first phase;
52 Introduce uncertainty in the second phase, consider [ t ] k +T c ,t k +T N ]Load and photovoltaic output (T) over a period of time c =15 min), N initial scenes are generated, and then the scenes are reduced to N, so as to improve the calculation efficiency.
53 T) is determined by solving a single deterministic mixed-integer second-order cone optimization problem while computing the two-stage optimization decision variables N Planning the output of each regulated resource in a time period;
54 ) holdLine [ t ] k ,t k +T c ]Control strategy of each regulated resource in time period and is in [ t ] k ,t k +4T c ]Keeping OLTC tap position and CB gear fixed in time period, and waiting for next time t k+1 =t k +T c The time window is shifted backward by a time interval and the process is repeated.
According to the invention, the action states and action quantities of slow-motion devices (OLTC and CB) are set as decision variables of a first stage by combining the response characteristics of different control devices, and operation control objects (SVC, inverter and ESS) capable of flexibly adjusting and quickly responding are designed as decision variables of a second stage, so that a two-stage optimized operation method is formulated, the action times of the devices such as OLTC and CB are effectively reduced, the loss and aging cost of the devices are reduced, and the flexible adjustment characteristics of the SVC, the inverter and the ESS are more fully exerted.
In addition, the CVaR is introduced to carry out risk control on the optimization target, so that the boundary risk of the operation of the active power distribution network is measured, the loss degree is measured by taking the economic loss as a measurement index, the defects of qualitative analysis and subjective evaluation in the traditional research are overcome, and the tail risk and the potential loss of the operation of the active power distribution network under the uncertain operation condition are scientifically described by combining a continuous and scientific condition risk value calculation method and a quantifiable analysis index.
Finally, the MPC strategy is applied to ADN operation optimization considering the operation cost risk, the influence of the error between the predicted value and the actual value on the adjustment strategy is effectively coped with, the boundary risk of the distribution network scheduling operation under the large-scale access of renewable energy sources is favorably and accurately evaluated, the operation loss is reduced to the maximum extent, and the profit of system operation is obtained.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A prediction control method of a risk-considering two-stage stochastic model of an active power distribution network is characterized by comprising the following steps:
1) Considering the uncertainty and the randomness of photovoltaic output and load fluctuation, respectively setting the photovoltaic output and the load fluctuation to obey Beta distribution and normal distribution, performing repeated random sampling and statistical analysis on a simulation result by using Monte Carlo simulation in a set time scale to generate a large-scale uncertain scene set, and then reducing the large-scale uncertain scene set;
2) Establishing an active and reactive power coordinated scheduling model of the active power distribution network by adopting a two-stage random optimization method, wherein the whole optimization control process is modeled in stages, and in the first stage, the action state and the action quantity of slow-motion equipment in the active power distribution network are determined without considering uncertainty; in the second stage, adding uncertainty, introducing condition risk value to measure the boundary risk of the operation of the active power distribution network, and measuring the loss degree by taking economic loss as a measurement index;
3) Carrying out linear modeling processing on an OLTC (active load controller), a CB (circuit board), a PV (photovoltaic) inverter and an ESS (ESS) in the active power distribution network, and perfecting an active and reactive coordination scheduling model of the active power distribution network by considering the operation constraint condition of ADN (adaptive data network);
4) Solving an active and reactive power coordinated scheduling model of the active power distribution network to obtain an optimized decision variable of two stages, and determining an output plan of each regulated resource in a scheduling period to form an optimal scheduling strategy;
5) At intervals of time T c Starting rolling optimization, and at each sampling initial time t k Taking the sampling value at the current moment as the initial state, aiming at the current prediction period T N Rolling to solve the optimal scheduling strategy within the prediction period, wherein only [ t ] is performed k ,t k +T c ]Control strategy of each regulated resource in time period and at t k ,t k +4T c ]Holding OLTC tap position and CB tap fixed for a period of time and waiting for the nextTime t k+1 =t k +T c Comes and then shifts the time window backward by a time interval T c Completing two-stage random model prediction control for promoting coordinated optimization operation of the active power distribution network;
the specific operation of step 2) is as follows:
an active and reactive power coordinated scheduling model of the active power distribution network is established by adopting a two-stage random optimization method, wherein the whole optimization control process is modeled in stages, and the objective function of the model considers the electricity purchasing cost of a main network, the DG power generation cost, the network loss cost and the operation and maintenance costs of OLTC, CB, SVC, PV inverter and ESS, wherein the objective function EC of the first stage SI Uncertainty is not considered, uncertainty is considered in the second phase, and the objective function EC of the second phase SD Scene-dependent changes;
wherein, the objective function of the first stage is:
wherein omega G Is a contact node set of a regional power distribution network and an active power distribution network, omega OLTC 、Ω CB 、Ω SVC And omega Inv For each regulating device candidate node set, c G 、c PV And c Loss Unit cost for electricity purchase, PV power generation and network loss of active distribution network, respectively, c OLTC 、c CB 、c SVC 、c Inv And c ESS For the unit regulation cost of OLTC, CB, SVC, photovoltaic inverter and ESS in the active power distribution network,represents the exchange power of the distribution network and the active distribution network connecting line in the t period region,for the generated power of PV at node j during time t,for the loss of line ij during period t,andis a first stage control variable in which,andrespectively representing reactive power regulating quantities corresponding to the t period SVC, the photovoltaic inverter, the CB and the ESS,is the gear change identification of the OLTC,andis a variable of 0 to 1, and is,andfor OLTC gear change identification whenThe gear value of the OLTC in the T-th time period is smaller than that in the T-1 time period, and T is the duration of the scheduling cycle;
the objective function for the second stage is:
where β is a given confidence level, and β ∈ (0, 1), π s And representing the occurrence probability of the scene s, wherein rho is risk aversion degree, and is used for representing the aversion degree of the power distribution network operating personnel to the risks.
2. The predictive control method for the risk-considering two-stage stochastic model of the active power distribution network according to claim 1, wherein the specific operation of step 4) is: converting the active and reactive power coordinated scheduling model of the active power distribution network obtained in the step 3) into a mixed integer second-order cone planning problem, calculating optimization decision variables of two stages by solving the mixed integer second-order cone optimization problem, and determining output plans of each regulated and controlled resource in a scheduling period to form an optimal scheduling strategy.
3. The predictive control method for the two-stage stochastic model of the active power distribution network considering the risks as claimed in claim 1, wherein the large-scale uncertain scene set is reduced by a synchronous back-substitution subtraction method in step 1).
4. The predictive control method for the risk-considering two-stage stochastic model of the active power distribution network according to claim 1, wherein the specific operations in step 3) are as follows: and carrying out accurate linear modeling processing on OLTC, CB, SVC, PV inverters and ESS in the active power distribution network, and considering the operation constraint conditions of the active power distribution network so as to perfect an active and reactive power coordination scheduling model of the active power distribution network.
5. The predictive control method for the risk-taking account of the two-stage stochastic model of the active power distribution network according to claim 4, wherein the voltage constraint of the OLTC is as follows:
wherein,is a voltage reference value, and is,andis the square of the upper and lower limits of the transformation ratio at both sides of the OLTC, l j,t Is the square of the OLTC transformation ratio, l j,t Is a discrete variable;
the number of actions of OLTC is constrained to:
wherein,andis a variable from 0 to 1, indicating a change in the OLTC range whenThe OLTC gear value is larger than the gear at the t-1 time period in the t-th time period, otherwise, the OLTC gear value is smaller than the gear at the t-1 time period in the t-th time period; SR j For the maximum adjustment range of the OLTC gear,limiting the maximum action times of the OLTC in the T time period;
the constraint conditions of the CB are as follows:
the SVC constraints are:
wherein,andrespectively representing the upper and lower bounds of SVC reactive compensation output power
The constraint conditions of the photovoltaic inverter are as follows:
6. the predictive control method for the two-stage stochastic model of the active power distribution network taking into account the risk of claim 4, wherein the modeling process of the ESS is represented as:
SOC i,T =SOC i,0
therein, SOC i,t For the state of charge, α, of an ESS connected to node i at time t i Is the self-discharge rate of the node;andrespectively representing the charging power and the discharging power of the ESS at the node,andthe charge-discharge efficiency is shown as follows,andrepresents the upper limit value of the charging and discharging power of the ESS at the node i,andis a binary variable used to indicate the charging and discharging states of the ESS.
8. The predictive control method for the risk-aware two-stage stochastic model of the active power distribution network according to claim 4, wherein the safety constraints are:
9. The predictive control method for the risk-considering two-stage stochastic model of the active power distribution network according to claim 1, wherein the specific operation of step 5) is:
51 Based on historical data, predict the entire prediction period T N Load fluctuations and photovoltaic output conditions, wherein uncertainty is not considered in the first phase;
52 Introduce uncertainty in the second stage, consider [ t ] k +T c ,t k +T N ]Generating N initial scenes by the load and photovoltaic output in a time period, and reducing the N initial scenes into N scenes;
53 T) is determined by solving a single deterministic mixed-integer second-order cone optimization problem while computing the two-stage optimization decision variables N The output plan of each regulated resource in the time period is formed to T N An optimal control strategy within a time period;
54 Execute [ t ] k ,t k +T c ]Optimal control strategy within a time period, and at [ t ] k ,t k +4T c ]Keeping OLTC tap position and CB gear fixed in time period, and waiting for next time t k+1 =t k +T c Then the time window is shifted back by one time interval.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010688381.2A CN111799847B (en) | 2020-07-16 | 2020-07-16 | Predictive control method of risk-considering two-stage random model of active power distribution network |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010688381.2A CN111799847B (en) | 2020-07-16 | 2020-07-16 | Predictive control method of risk-considering two-stage random model of active power distribution network |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111799847A CN111799847A (en) | 2020-10-20 |
CN111799847B true CN111799847B (en) | 2022-11-22 |
Family
ID=72807475
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010688381.2A Active CN111799847B (en) | 2020-07-16 | 2020-07-16 | Predictive control method of risk-considering two-stage random model of active power distribution network |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111799847B (en) |
Families Citing this family (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113162060B (en) * | 2021-03-17 | 2022-10-04 | 武汉工程大学 | Opportunity constraint optimization-based active power distribution network two-stage reactive power regulation method |
CN113516278B (en) * | 2021-04-26 | 2023-08-22 | 山东大学 | Active power distribution network multi-time scale active and reactive power coordination optimization scheduling method and system |
CN113095951B (en) * | 2021-05-08 | 2023-09-22 | 西安科技大学 | Intelligent gas extraction regulation and control method, equipment, device and storage medium |
CN113364043A (en) * | 2021-05-08 | 2021-09-07 | 国网浙江省电力有限公司丽水供电公司 | Micro-grid group optimization method based on condition risk value |
CN113110561B (en) * | 2021-05-24 | 2021-11-12 | 四川大学 | Random distribution robust optimization-based SMPC algorithm for maintaining formation of satellite |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106712031A (en) * | 2017-02-10 | 2017-05-24 | 东南大学 | Sequence-robustness optimizing dispatching system and dispatching method of active power distribution network considering uncertainty |
CN109687510A (en) * | 2018-12-11 | 2019-04-26 | 东南大学 | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method |
CN109861204A (en) * | 2018-12-18 | 2019-06-07 | 青岛理工大学 | Active power distribution network cooperative control system and method based on model predictive control |
CN109873447A (en) * | 2019-02-19 | 2019-06-11 | 国网江苏省电力有限公司南京供电分公司 | A kind of multi-source collaboration active-idle regulation method of the more time stages of active distribution network |
Family Cites Families (11)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103544656B (en) * | 2013-10-24 | 2016-08-17 | 清华大学 | A kind of active power distribution network operational control method towards minimum carbon emission |
CN104866924A (en) * | 2015-05-26 | 2015-08-26 | 清华大学 | Active power distribution network planning and operation combined optimization method |
CN104993522B (en) * | 2015-06-30 | 2018-01-19 | 中国电力科学研究院 | A kind of active distribution network Multiple Time Scales coordination optimization dispatching method based on MPC |
CN105550766A (en) * | 2015-12-04 | 2016-05-04 | 山东大学 | Micro-grid robustness multi-target operation optimization method containing renewable energy resources |
CN106786806B (en) * | 2016-12-15 | 2023-06-06 | 国网江苏省电力公司南京供电公司 | Active and reactive coordination control method for power distribution network based on model predictive control |
CN107147152B (en) * | 2017-06-15 | 2019-12-10 | 广东工业大学 | new energy power distribution network multi-type active and reactive power source collaborative optimization configuration method and system |
CN108448585B (en) * | 2018-03-29 | 2019-09-27 | 清华大学 | A kind of electric network swim equation solution method of linearization based on data-driven |
CN110148969B (en) * | 2019-03-26 | 2023-07-21 | 上海电力学院 | Active power distribution network optimized operation method based on model predictive control technology |
CN110009262A (en) * | 2019-04-28 | 2019-07-12 | 国网福建省电力有限公司福州供电公司 | A kind of a few days ago-in a few days two stages Optimization Scheduling of active distribution network |
CN110460036B (en) * | 2019-05-10 | 2021-08-24 | 四川大学 | Distributed optimization method for alternating current-direct current power distribution network considering wind power uncertainty |
CN110298138B (en) * | 2019-07-09 | 2024-01-05 | 南方电网科学研究院有限责任公司 | Comprehensive energy system optimization method, device, equipment and readable storage medium |
-
2020
- 2020-07-16 CN CN202010688381.2A patent/CN111799847B/en active Active
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN106712031A (en) * | 2017-02-10 | 2017-05-24 | 东南大学 | Sequence-robustness optimizing dispatching system and dispatching method of active power distribution network considering uncertainty |
CN109687510A (en) * | 2018-12-11 | 2019-04-26 | 东南大学 | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method |
CN109861204A (en) * | 2018-12-18 | 2019-06-07 | 青岛理工大学 | Active power distribution network cooperative control system and method based on model predictive control |
CN109873447A (en) * | 2019-02-19 | 2019-06-11 | 国网江苏省电力有限公司南京供电分公司 | A kind of multi-source collaboration active-idle regulation method of the more time stages of active distribution network |
Also Published As
Publication number | Publication date |
---|---|
CN111799847A (en) | 2020-10-20 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111799847B (en) | Predictive control method of risk-considering two-stage random model of active power distribution network | |
Wang et al. | Adaptive control for energy storage systems in households with photovoltaic modules | |
Mohamed et al. | Real-time energy management scheme for hybrid renewable energy systems in smart grid applications | |
Chua et al. | A novel fuzzy control algorithm for reducing the peak demands using energy storage system | |
CN110581571A (en) | dynamic optimization scheduling method for active power distribution network | |
Wang et al. | Underfrequency load shedding scheme for islanded microgrids considering objective and subjective weight of loads | |
CN110912205A (en) | Dynamic scheduling optimization method for wind power system operation standby based on scene set | |
Lu et al. | Data-driven stochastic programming for energy storage system planning in high PV-penetrated distribution network | |
Wang et al. | Two-stage full-data processing for microgrid planning with high penetrations of renewable energy sources | |
Habib et al. | Evaluation of the effect of high penetration of renewable energy sources (RES) on system frequency regulation using stochastic risk assessment technique (an approach based on improved cumulant) | |
CN113516278A (en) | Active power distribution network multi-time scale active and reactive power coordinated optimization scheduling method and system | |
Dong et al. | Optimal scheduling framework of electricity-gas-heat integrated energy system based on asynchronous advantage actor-critic algorithm | |
CN110867907B (en) | Power system scheduling method based on multi-type power generation resource homogenization | |
Wang et al. | Multi-objective robust optimization of hybrid AC/DC distribution networks considering flexible interconnection devices | |
Yang et al. | Tracking-dispatch of a combined wind-storage system based on model predictive control and two-layer fuzzy control strategy | |
Laouafi et al. | An evaluation of conventional and computational intelligence methods for medium and long-term load forecasting in Algeria | |
Liu et al. | A comprehensive control strategy for photovoltaic virtual synchronous generator considering frequency regulation capability | |
CN113255982A (en) | Medium-long term optimized scheduling method for wind-light-water complementary system | |
Liu et al. | An AGC dynamic optimization method based on proximal policy optimization | |
Farjah | Proposing an efficient wind forecasting agent using adaptive MFDFA | |
Eltohamy et al. | Power system flexibility metrics review with high penetration of variable renewable generation | |
Tsao et al. | Integrated voltage control and maintenance insurance planning for distribution networks considering uncertainties | |
CN113013884B (en) | Three-section type reactive voltage control method for photovoltaic power distribution system with high permeability | |
CN115663791A (en) | Intelligent power distribution network multi-target adaptive scheduling method based on time-varying property of operating environment | |
Sahoo et al. | Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |