WO2017071230A1 - Procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples - Google Patents

Procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples Download PDF

Info

Publication number
WO2017071230A1
WO2017071230A1 PCT/CN2016/085183 CN2016085183W WO2017071230A1 WO 2017071230 A1 WO2017071230 A1 WO 2017071230A1 CN 2016085183 W CN2016085183 W CN 2016085183W WO 2017071230 A1 WO2017071230 A1 WO 2017071230A1
Authority
WO
WIPO (PCT)
Prior art keywords
agent
station
power station
load
period
Prior art date
Application number
PCT/CN2016/085183
Other languages
English (en)
Chinese (zh)
Inventor
唐海东
芮钧
吴正义
Original Assignee
南京南瑞集团公司
国网电力科学研究院
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 南京南瑞集团公司, 国网电力科学研究院 filed Critical 南京南瑞集团公司
Publication of WO2017071230A1 publication Critical patent/WO2017071230A1/fr

Links

Images

Classifications

    • 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
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • 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

Definitions

  • the invention belongs to the technical field of testing, and particularly relates to a short-term optimal scheduling method for a hydropower station group based on task automatic distribution type multi-agent.
  • Agent intelligence technology in the computer field originated from a series of researches on distributed artificial intelligence conducted by researchers in the United States in the mid-1970s. They found that Agents can combine some simple information systems into a large overall system. Effectively improve its ability to handle complex problems, and improve the intelligence level of the overall system by defining appropriate coordination mechanisms. This results in an Agent system concept and method that is artificially intelligent and can passively sense problem processing information and can actively predict, analyze, and even actively seek solutions to support users in completing tasks.
  • Multi-Agent System refers to a combination of multiple executable Agent subsystems.
  • the multi-agent system can solve the target problem, modify its behavior as the environment changes, and can communicate, interact and coordinate with other Agents through the network to complete the same task.
  • each Agent is defined as a physical or abstract reality.
  • each agent individual is independent, can act on the individual and external environment changes, can manipulate part of the external environment change, and can reflect the changes of the external environment, and more It is important to communicate, interact, and coordinate with other Agents to accomplish the desired mission objectives. Therefore, Agent should have the characteristics of autonomy, interactivity, responsiveness and initiative.
  • the multi-agent system can simulate the group work of large organizations and can solve complex problems in the group by using its unique methods and methods. The structure of the multi-agent system is shown in Figure 1.
  • the solution models for short-term optimal scheduling of cascade hydropower stations are divided into two categories.
  • the first type is a water-powered model with a given The water is optimally distributed to produce more electric energy;
  • the second type is the electric water-fixing model, which is distributed to each power station based on the real-time load or load curve of the given hydropower station group, and then distributed to each unit, so that the entire hydropower station consumes water.
  • the first is the cascade center agent, which represents the entire cascade center and serves as the management coordination function.
  • the second is the power station agent, which has the basic data of the power station and can perform the start-stop arrangement and unit load optimization. There is an optimization algorithm in each agent. Under the cascade center, each power station is built as a power station agent.
  • the step agent receives the load scheduling information of the upper power grid and distributes the load to the power station agent through the optimization algorithm, and then waits for the power station agent to feedback the optimization result.
  • Each power station agent receives the load information assigned by the ladder agent, and then performs the startup and shutdown calculation and load distribution optimization for its power station, and then sends the result back to the ladder agent.
  • the step-level agent performs the operation update adjustment according to the data fed back by each power station agent, and then sends the load information to the power station agent; or when the optimization request is reached, it ends and no longer communicates.
  • the existing short-term optimal scheduling model solves either single-thread computing with a single computer, multi-thread parallel computing with a single computer, parallel computing with multiple computers, and multi-agent collaborative computing.
  • the calculation method of a single computer cannot meet the needs of calculation speed; the current multiple parallel calculations are simple division tasks, which can shorten the calculation time to a certain extent, but cannot be intelligently assigned tasks. Intelligent coordination, etc., resulting in unsatisfactory results.
  • the existing multi-agent collaborative computing is to plan the number of agents according to the number of hydropower stations. There is no significant difference between the parallel calculations and the traditional multi-machines, and the calculation speed cannot be improved.
  • the short-term optimal scheduling algorithm for hydropower station group is generally divided into two categories.
  • One is the classical algorithm used or its improved algorithm.
  • the classical algorithm such as dynamic programming needs to traverse all possible solutions for the optimization calculation of large hydropower stations. Therefore, it is prone to "dimensional disasters" and the calculation time is too long.
  • the other is the modern intelligent optimization algorithm used, which is studied by many researchers because of its fast calculation speed and fast convergence.
  • it since it is randomly generated from the initial solution and the iterative process has a large amount of random quantities, it can not guarantee 100% convergence. And easy to fall into local extremes. Therefore, the two types of algorithms have their own shortcomings.
  • the disadvantage of the classical algorithm and its improved algorithm is that when the number of units or the number of power stations increases, it is prone to "dimensionality disaster", resulting in a sharp increase in computing time.
  • the shortcoming of the modern intelligent optimization algorithm is that it cannot guarantee convergence every time, and it is easy to converge to local extremum rather than global extremum. Therefore, further research is needed.
  • the present invention provides a short-term optimal scheduling method for a hydropower station group based on task automatic distributed multi-agent.
  • the present invention focuses on improving the speed and operational efficiency of hydropower station group optimization scheduling, and solving the existing solving technology.
  • the problem of short-term optimal dispatching of large-scale hydropower stations cannot be satisfied.
  • the present invention will maximize the power generation efficiency of the entire hydropower station group, and is of great significance for promoting the development of optimal scheduling of cascade hydropower stations and improving the economic operation level.
  • a multi-agent hydropower station group short-term optimal scheduling method characterized in that it comprises the following steps,
  • Step (1) establish a short-term optimal scheduling model for hydropower stations
  • E is the sum of energy consumption of each power station;
  • T is the scheduling period;
  • K is the number of power stations;
  • Q k, t is the power generation flow of the kth power station t period;
  • P k, t is the kth power station t period Output;
  • H k,t is the head of the kth power station t period;
  • P t is the step load of the t period
  • P k,t is the output of the k power station t period
  • V k,t is the capacity of the kth power station in the t-th period
  • V k,t+1 is the storage capacity of the k-th power station in the t+1th period
  • Qin k,t is the kth
  • Qgen k, t is the power flow of the kth power station in the t-th period
  • Qdis k, t is the water flow of the k-th power station in the t-th period
  • ⁇ t is the time length of the unit period
  • Vmin k For the k-power station minimum storage capacity constraint and the integration of medium- and long-term planning constraints
  • Step (2) establishing a short-term optimal scheduling sub-model
  • Q is the sum of water consumption and flow in the T cycle of the power station
  • T is the scheduling period
  • n is the number of units
  • Qgen t i is the power generation flow of the unit at time t
  • N t i is the output of the unit at time t
  • H t is the net head at the time t of the power station
  • Qdis t is the water flow at the time of the power station t;
  • N s is the output of the cascade to the power station
  • N i is the output of the power station i
  • V t is the capacity of the power station in the t-th period
  • Qin t is the water flow of the power station in the t-th period
  • Qgen t is the power station in the t-th
  • ⁇ t is the length of time in the unit period
  • N i,min is the minimum output of the i unit
  • N i,max is the maximum output of the i unit
  • Qout max is the maximum output of the unit.
  • Flow Qout min is the minimum over flow of the unit.
  • Vmin is the minimum storage capacity constraint of the power station and the comprehensive minimum storage capacity constrained by the medium and long-term planning.
  • Vmax is the maximum storage capacity constraint of the power station and the comprehensive maximum storage capacity constrained by the medium and long-term planning;
  • Step (3) establishing an agent model according to the model in step (1) and step (2), the agent model includes an inter-station load distribution agent, an in-station start-stop agent, and an in-station load optimization agent.
  • the inter-station load distribution agent immediately assigns the new next-day load curve to each power station according to a predetermined algorithm.
  • the station-initiated shutdown agent and the station-in-load optimization agent respectively calculate the start-stop plan for the power station unit, distribute the load, and distribute the load between stations.
  • the agent stores the result and continues to repeat the allocation until the result is satisfactory.
  • the inter-station load distribution agent is also used to delete the redundant agent and increase the agent to achieve the purpose of adapting to the optimization calculation;
  • the task of the start-stop agent in the station is to formulate a start-stop plan and find a feasible area of the unit that passes through the vibration zone at least.
  • the station-starting agent monitors whether each power station has a new load curve, and if the power station within the monitoring range is allocated new The load curve is immediately calculated and expanded;
  • the task of the station load optimization agent is to optimize the distribution load to each unit according to the minimum water consumption criterion according to the start-stop plan and the feasible area of the unit, and then the station load optimization agent sends the water consumption energy in the scheduling period to the station load distribution.
  • Step (4) the Agent algorithm library is built.
  • the algorithm library contains two kinds of algorithms, a classical algorithm and a heuristic intelligent optimization algorithm. Each calculation randomly selects a classical algorithm and an intelligent optimization algorithm, and performs parallel calculation to calculate the calculation result. Compare and then feed back The Agent algorithm library and the Agent algorithm library make good and bad statistics of the algorithm. The probability that the excellent algorithm is selected increases, the probability of the low efficiency algorithm is small, and finally it is gradually eliminated.
  • step (5) the Agent model established in the step (3) is invoked by the Agent algorithm library established in the step (4) to realize short-term optimal scheduling of the multi-agent hydropower station group.
  • the foregoing multi-agent short-term optimal scheduling method for a hydropower station group is characterized in that: the inter-station load distribution agent is set to one, and is set in an agent main container of a computer, wherein an agent platform is provided in the computer, and an agent is provided.
  • the main container computer is connected to several other computers, and the other computers have an Agent platform, and a multi-agent platform is formed with the Agent platform in the computer with the Agent main container, and the Agent main container also has a management agent.
  • the management agent is responsible for state management, agent management and startup of the main program of the entire multi-agent platform.
  • the corresponding agent container of the other computer has a sub-agent container, and the sub-agent container has an on-site shutdown agent and an internal load. Optimize the Agent.
  • the foregoing multi-agent hydropower station group short-term optimization scheduling method is characterized in that the multi-agent optimization process is performed when using the heuristic intelligent optimization algorithm,
  • the scale of the start-stop agent in a given station is N1, the size of the inter-station load distribution agent is N2, and the scale of the initial station-to-station load distribution scheme is M;
  • the station load distribution agent After the station load distribution agent receives the new next-day load curve, it randomly selects the initial load distribution plans of the power station, assigns its load curve to each power station, and then monitors whether each power station is allocated. If the distribution is finished, immediately According to the M plans and results issued last time, the M plans are updated, and then sent to the hydropower station, and so on, until the best solution for convergence is found;
  • the station When the station starts to stop the agent and monitors that a new load curve is assigned to the power station, it immediately starts the development of the start-stop plan, then finds the feasible area that passes through the minimum vibration zone, feeds back to the power station, and uses the pre-emption agent in the N1 station.
  • the treatment of the power station that is, for the M load schemes of a single power station, may be completed by a single station within the shutdown agent, or may be completed by multiple stations within the station, when an agent completes the task of a power station, then continue to scan the remaining The power station, if there is an unfinished task, immediately joins the execution task;
  • the station load optimization agent will immediately monitor the task and immediately plan for the power station.
  • the unit load is optimized and distributed, and the preemptive task is also used.
  • the idle station load optimization agent automatically monitors the load curve of each unit that is not assigned to the unit. Once it is monitored, it is immediately allocated to it, and then continues to monitor.
  • the present invention solves the two-dimensional model of short-term optimal scheduling of hydropower station group, the multi-agent technology multi-agent asynchronous parallel computing method is adopted, so the short-term scheduling calculation of large hydropower station group can be greatly shortened compared with the traditional calculation method. calculating time;
  • the Agent Since the Agent has an algorithm library intelligent selection algorithm strategy in the present invention, it is more intelligent than a single invariant algorithm, and the optimization effect is obtained.
  • the function of asynchronous parallel computing between classical algorithms and modern intelligent algorithms can make use of the advantages of modern intelligent algorithms on the one hand, and can make up for the shortcomings of intelligent algorithms by using classical algorithms. Therefore, the optimization effect of this method on short-term optimal scheduling of hydropower stations is more effective. Ok, the result is more reliable;
  • the agent Since the task is automatically assigned to the agent in the present invention, the agent is object-oriented rather than object-oriented in a similar solution, and has better adaptability to newly added power stations, faulty power stations, and new power plants in a hydropower station group, and the like.
  • the compiling agent In the face of the increase or decrease of the power station or the unit, it is necessary to rewrite the compiling agent, which may cause inconvenience, so the present invention has the advantage of adapting to the change of the power station or the unit.
  • Figure 1 is a schematic diagram of a multi-agent system
  • FIG. 3 is a structural diagram of optimized scheduling of a multi-agent cascade hydropower station according to the present invention.
  • FIG. 4 is a schematic diagram of operation of an in-station load distribution agent according to the present invention.
  • Figure 5 is a distribution diagram of a multi-agent platform of the present invention.
  • Figure 6 is a flow chart of the main program of the present invention.
  • FIG. 7 is a flowchart of an inter-station load distribution agent according to the present invention.
  • the present invention is directed to an electric water setting model.
  • a short-term optimal scheduling method for hydropower station group based on task automatic allocation multi-agent including the following steps
  • Step (1) establish a short-term optimal scheduling model for hydropower stations
  • E is the sum of energy consumption of each power station;
  • T is the scheduling period;
  • K is the number of power stations;
  • Q k, t is the power generation flow of the kth power station t period;
  • P k, t is the kth power station t period Output;
  • H k,t is the head of the kth power station t period;
  • P t is the step load of the t period
  • P k,t is the output of the k power station t period
  • V k,t is the capacity of the kth power station in the t-th period
  • V k,t+1 is the storage capacity of the k-th power station in the t+1th period
  • Qin k,t is the kth
  • Qgen k, t is the power flow of the kth power station in the t-th period
  • Qdis k, t is the water flow of the k-th power station in the t-th period
  • ⁇ t is the time length of the unit period
  • Vmin k For the k-power station minimum storage capacity constraint and the integration of medium- and long-term planning constraints
  • Step (2) establishing a short-term optimal scheduling sub-model
  • Q is the sum of water consumption and flow in the T cycle of the power station
  • T is the scheduling period
  • n is the number of units
  • Qgen t i is the power generation flow of the unit at time t
  • N t i is the output of the unit at time t
  • H t is the net head at the time t of the power station
  • Qdis t is the water flow at the time of the power station t;
  • N s is the output of the cascade to the power station
  • N i is the output of the power station i
  • V t is the capacity of the power station in the t-th period
  • Qin t is the water flow of the power station in the t-th period
  • Qgen t is the power station in the t-th
  • ⁇ t is the length of time in the unit period
  • N i,min is the minimum output of the i unit
  • N i,max is the maximum output of the i unit
  • Qout max is the maximum output of the unit.
  • Flow Qout min is the minimum over flow of the unit.
  • Vmin is the minimum storage capacity constraint of the power station and the comprehensive minimum storage capacity constrained by the medium and long-term planning.
  • Vmax is the maximum storage capacity constraint of the power station and the comprehensive maximum storage capacity constrained by the medium and long-term planning;
  • Step (3) according to the model in step (1) and step (2), the agent model is established, and the multi-agent architecture of the short-term optimal scheduling of the cascade hydropower station is designed as the agent network structure.
  • the model for the solution similar to the present invention is for the hydropower station, and one hydropower station establishes an agent. If the new power station is newly installed, it is necessary to newly compile and compile the cascade agent and the new power station agent. Similarly, if a new unit is added to a power station, the whole program will also be caused. In addition, the computing power of a single power station agent is limited, and the speed of the single power station agent cannot be completed.
  • the present invention aims at the problem that the cascade center and each hydropower station are multi-agent environments, and establish an inter-station load distribution agent, an in-station start-stop agent, and an in-station load optimization.
  • Agent This not only facilitates the increase and decrease of the power station and the unit without affecting the deployment of the agent, but also increases the progress of the task completion by adding an agent, so that the total calculation time is reduced.
  • the station-initiated shutdown agent and the station-in-load optimization agent respectively calculate the start-stop plan for the power station unit and distribute the load, and then the station The load distribution agent stores the result and continues to repeat the allocation until the result is satisfactory.
  • the inter-station load distribution agent is also used to delete some redundant agents and add some agents to achieve the purpose of adapting to the optimization calculation;
  • the task is to distribute load between stations, the pursuit of minimum water consumption, the less water consumption, the better the effect, so the agent is designed as an effect agent;
  • the two tasks that need to be completed in the station start-stop agent formulate the start-stop plan and find the feasible domain that traverses the vibration zone at least.
  • the task of the start-stop agent in the station is to monitor whether each power station has a new load curve. If the power station within the monitoring range is assigned a new load curve, it will be immediately calculated.
  • the specific process is to formulate a start-stop plan based on the start-stop rule, and then find the optimal feasible domain according to the different feasible domain combinations of each unit. If there is no feasible domain, re-establish the start-stop plan;
  • the task of the station load optimization agent is to optimize the distribution load to each unit according to the minimum water consumption criterion according to the start-stop plan and the feasible area of the unit, and then the station load optimization agent sends the water consumption energy in the scheduling period to the station load distribution.
  • Agent the stop and stop agent in the station is reactive, and the load optimization agent in the station is effective;
  • Step (4) establishing an agent algorithm library
  • the algorithm library includes two types of algorithms, a classical algorithm and a heuristic intelligent optimization algorithm, each The second calculation randomly selects a classical algorithm and an intelligent optimization algorithm, performs parallel calculation, compares the calculation results, and then feeds back to the Agent algorithm library.
  • the Agent algorithm library performs the algorithm's pros and cons statistics, and the excellent algorithm is selected. Large, inefficient algorithms are less likely to be selected and eventually phase out;
  • step (5) the Agent model established in the step (3) is invoked by the Agent algorithm library established in the step (4) to realize short-term optimal scheduling of the multi-agent hydropower station group.
  • the multi-agent model structure is shown in Figure 3. It consists of an inter-station load distribution agent, multiple in-station shutdown agents, multiple in-station load optimization agents, and a power station environment. Each agent communicates with each other through the network.
  • the power station and the cascade center are multi-agent monitoring environments, and each agent completes the corresponding task by monitoring the corresponding targets and collaborative cooperation.
  • the scale of the start-stop agent in a given station is N1, the size of the inter-station load distribution agent is N2, and the scale of the initial station-to-station load distribution scheme is M;
  • the station load distribution agent After the station load distribution agent receives the new next-day load curve, it randomly selects the initial load distribution plans of the power station, assigns its load curve to each power station, and then monitors whether each power station is allocated. If the distribution is finished, immediately According to the M plans and results issued last time, the M plans are updated, and then sent to the hydropower station, and so on, until the best solution for convergence is found;
  • the station When the station starts to stop the agent and monitors that a new load curve is assigned to the power station, it immediately starts the development of the start-stop plan, then finds the feasible area that passes through the minimum vibration zone, feeds back to the power station, and uses the pre-emption agent in the N1 station.
  • the treatment of the power station may be completed by a single station within the shutdown agent, or may be completed by multiple stations within the station, as shown in Figure 4, when an agent completes the task of a power station , then continue to scan the remaining power stations, if there are unfinished tasks, immediately join the execution task;
  • the station load optimization agent will immediately monitor the task and immediately plan for the power station.
  • the unit load is optimized and distributed, and the preemptive task is also used.
  • the idle station load optimization agent automatically monitors the load curve of each unit that is not assigned to the unit. Once it is monitored, it is immediately allocated to it, and then continues to monitor. As shown in Figure 4, the calculation of each agent to the power station is random.
  • the joint optimization operation of multi-agent is similar to a group of bee feeding processes.
  • the queen bee coordinates and publishes the task.
  • the worker bees search for flowers in a certain area, and once they are found, they immediately eat pollen. It is possible that many bees pick a flower or there may be only one bee. If you finish eating flowers, look for the next flower. Repeat the loop all the time.
  • the inter-station load distribution agent is set to one, and is set in the agent main container of the computer.
  • the computer has an agent platform, and the computer with the agent main container is connected to several other computers, and the other computers are provided with an agent.
  • Platform, and The Agent platform in the computer with the Agent main container forms a multi-agent platform, and the Agent main container also has a management agent, which is responsible for state management, Agent management and startup of the main program of the entire multi-agent platform, and the like.
  • the corresponding agent container in the computer's Agent platform is provided, and the sub-agent container is provided with an in-site shutdown agent and an in-station load optimization agent.
  • the multi-agent system can run on any computer operating system, as long as the Agent platform is installed on the system, the Agent can be run.
  • the Agent runs on the platform and does not depend on the operating system, so it only needs to be programmed once.
  • the entire Multi-Agent system needs a main container.
  • the main container has a management agent for managing and coordinating Agent containers in other computers.
  • the hardware structure of the multi-agent joint operation is shown in Figure 5.
  • the management agent is responsible for state management, agent management, and startup of the main program of the entire multi-agent platform.
  • the startup flowchart of the main program is shown in Fig. 6.
  • an inter-station load distribution agent In the main container of the agent, an inter-station load distribution agent is arranged, and in the agent container of other platforms, an in-station agent, that is, an in-station stop agent and an in-station load optimization agent are arranged.
  • the calculation process of the main program and each agent is shown in Fig. 6, Fig. 7, and Fig. 8.
  • the Agent is always there, so it should constantly monitor the target.
  • the invention proposes a parallel computing method based on task automatic allocation type multi-agent technology in short-term optimal scheduling of hydropower station groups.
  • the multi-agent applied to the hydropower station group short-term optimization scheduling method can be used for short-term optimal scheduling of cascade or basin hydropower station group.
  • the agent is designed to complete the self-running software of a certain task, and independently complete each part of the optimization process in a preemptive manner. task.
  • Multiple Agents can perform arithmetic processing on the same hydropower station at the same time to achieve the purpose of shortening the solution time.
  • the increase in the number of hydropower station power stations can be solved by adding computers and agents, and the total calculation time is rarely increased.
  • multi-agents integrate multiple algorithms to form an algorithm library.
  • the algorithm library adopts the idea of survival of the fittest, and the probability that the algorithm is selected increases or decreases with the evaluation of the calculation optimization effect.
  • the combination of classical and modern intelligent optimization algorithms and asynchronous parallel computing are adopted to prevent the intelligent optimization algorithm from having few solutions.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Water Supply & Treatment (AREA)
  • Development Economics (AREA)
  • Game Theory and Decision Science (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

La présente invention concerne un procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples, mettant l'accent sur l'amélioration de la vitesse de résolution de la planification optimale et de l'efficacité de fonctionnement d'un groupe de stations hydroélectriques, ce qui résout le problème créé par la technique de résolution actuelle qui ne peut pas assurer la planification optimale à court terme d'un groupe de stations hydroélectriques à grande échelle. Cette invention peut maximiser les avantages de production d'énergie de l'ensemble du groupe de stations hydroélectriques, et avoir une importance particulière dans l'accroissement du développement de la planification optique de stations hydroélectriques en cascade et dans l'amélioration du niveau de développement économique.
PCT/CN2016/085183 2015-10-30 2016-06-08 Procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples WO2017071230A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201510728777.4 2015-10-30
CN201510728777.4A CN105225017B (zh) 2015-10-30 2015-10-30 一种多Agent的水电站群短期优化调度方法

Publications (1)

Publication Number Publication Date
WO2017071230A1 true WO2017071230A1 (fr) 2017-05-04

Family

ID=54993973

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2016/085183 WO2017071230A1 (fr) 2015-10-30 2016-06-08 Procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples

Country Status (2)

Country Link
CN (1) CN105225017B (fr)
WO (1) WO2017071230A1 (fr)

Cited By (53)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108984825A (zh) * 2018-06-01 2018-12-11 中国电力科学研究院有限公司 一种水力发电系统建模方法和系统
CN109767051A (zh) * 2018-10-26 2019-05-17 国网天津市电力公司 基于大数据分析的变压器停电计划安排方法
CN109829611A (zh) * 2018-12-24 2019-05-31 长江勘测规划设计研究有限责任公司 基于防洪库容动态分配的梯级优化调度方法
CN109886543A (zh) * 2019-01-16 2019-06-14 河南大学 基于均匀设计的水库优化调度方法
CN109933892A (zh) * 2019-03-12 2019-06-25 中国电建集团中南勘测设计研究院有限公司 一种水温模型电站下泄流量边界条件的修正方法
CN110163508A (zh) * 2019-05-23 2019-08-23 上海申瑞继保电气有限公司 计量区域用电需量的错峰计算方法
CN110188912A (zh) * 2019-02-28 2019-08-30 西安理工大学 基于改进花粉算法的地表水和地下水联合调度优化方法
CN110288133A (zh) * 2019-06-06 2019-09-27 国网湖北省电力有限公司孝感供电公司 基于远景年饱和负荷分布图的规划变电站自动选址方法
CN110472825A (zh) * 2019-07-09 2019-11-19 贵州黔源电力股份有限公司 一种多级调度机构协调的梯级水电站实时调度弃水削减方法
CN110533244A (zh) * 2019-08-28 2019-12-03 重庆大学 一种梯级水坝优化调度方法、系统与计算机可读存储介质
CN110705784A (zh) * 2019-09-29 2020-01-17 河南郑大水利科技有限公司 一种径流式水电站优化运行评价方法
CN110766210A (zh) * 2019-10-12 2020-02-07 华中科技大学 一种梯级水库群短期优化调度方法与系统
CN110782281A (zh) * 2019-10-23 2020-02-11 四川大学 一种多业主梯级电站流域电量转移的日前市场出清方法
CN110912200A (zh) * 2019-10-21 2020-03-24 贵州电网有限责任公司 一种梯级水电站优化调度系统、方法及安全电网系统
CN111030123A (zh) * 2019-12-31 2020-04-17 东北大学 一种基于边缘计算的多智能体负荷调控方法
CN111080157A (zh) * 2019-12-26 2020-04-28 大连理工大学 一种梯级水电站排磷量的调度方法和系统
CN111080152A (zh) * 2019-12-23 2020-04-28 华中科技大学 一种梯级水库发电调度补偿电量分配方法
CN111126847A (zh) * 2019-12-24 2020-05-08 华中科技大学 耦合河道水动力过程的梯级水库短期优化调度方法和系统
CN111126693A (zh) * 2019-12-20 2020-05-08 华中科技大学 基于上游水库运行对下游电站发电能力影响的调度方法
CN111126709A (zh) * 2019-12-27 2020-05-08 中国南方电网有限责任公司 调度出力的确定方法、装置、计算机设备和存储介质
CN111342461A (zh) * 2020-03-30 2020-06-26 国网福建省电力有限公司 一种考虑网架动态重构的配电网优化调度方法及系统
CN111476477A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 基于发电效益目标的梯级水电站中长期优化调度方法
CN111476475A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 多约束条件下梯级水电站短期优化调度方法
CN111476474A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 梯级水电站减少弃水量的调度方法
CN111932021A (zh) * 2020-08-17 2020-11-13 浙江财经大学 一种再制造系统调度方法
CN112234604A (zh) * 2020-09-10 2021-01-15 西安交通大学 一种多能互补电源基地优化配置方法、存储介质及设备
CN112907078A (zh) * 2021-02-20 2021-06-04 中国电力科学研究院有限公司 水电耦合的市场出清方法、系统、设备及可读存储介质
CN113255974A (zh) * 2021-05-10 2021-08-13 四川华能宝兴河水电有限责任公司 一种梯级水电站联合调度负荷分配的方法
CN113554320A (zh) * 2021-07-27 2021-10-26 西安热工研究院有限公司 一种基于供热经济性最优的全厂热、电负荷分配方法
CN113705899A (zh) * 2021-08-30 2021-11-26 武汉大学 水库优化调度最优决策与效益的找寻方法
CN113779768A (zh) * 2021-08-16 2021-12-10 西安交通大学 模型构建方法、电子设备及存储介质
CN113780629A (zh) * 2021-08-16 2021-12-10 西安交通大学 梯级水电调度模型优化方法、装置、电子设备及存储介质
CN113820952A (zh) * 2021-07-26 2021-12-21 国网新源控股有限公司 抽水蓄能电站导叶关闭规律的优化方法及装置
CN114021933A (zh) * 2021-10-29 2022-02-08 华中科技大学 基于动态泄流法的梯级水库群溃坝应急调度方法及系统
CN114021902A (zh) * 2021-10-15 2022-02-08 华中科技大学 基于动态收索与离散机制的动态规划降维的水库调度方法
CN114154404A (zh) * 2021-11-22 2022-03-08 大连理工大学 一种利用观测数据推断相邻水电站运行状态及参数的方法
CN114336740A (zh) * 2021-12-15 2022-04-12 湖北清江水电开发有限责任公司 水电站机组孤网运行成组调节系统及方法
CN114677064A (zh) * 2022-05-27 2022-06-28 长江水利委员会水文局 一种耦合最优性与稳定性的梯级水库调度决策支持方法
CN115439027A (zh) * 2022-11-08 2022-12-06 大唐乡城唐电水电开发有限公司 一种梯级水电站负荷优化调度方法、装置、设备和介质
CN115470998A (zh) * 2022-09-23 2022-12-13 上海交通大学 港口冷箱负荷群用电一致性分层优化调度方法与系统
CN115619189A (zh) * 2022-11-09 2023-01-17 中国南方电网有限责任公司 考虑梯级水电弃水流量限制的弃水调度方法及装置
CN115860224A (zh) * 2022-12-06 2023-03-28 国网四川省电力公司电力科学研究院 一种电力数据多约束优化方法及系统
CN116341852A (zh) * 2023-03-27 2023-06-27 湖北清江水电开发有限责任公司 一种用于水电厂的多机组负荷分配方法
CN116467562A (zh) * 2023-04-20 2023-07-21 武汉大学 水电站耗水率特性曲线的确定方法及设备
CN116565947A (zh) * 2023-04-26 2023-08-08 武汉大学 水电站日调峰能力确定方法及设备
CN116562572A (zh) * 2023-05-14 2023-08-08 中国长江电力股份有限公司 一种梯级水电站群年度计划电量曲线分解方法
CN116703134A (zh) * 2023-08-10 2023-09-05 长江勘测规划设计研究有限责任公司 一种大型跨流域调水水库的多目标调度方法及系统
CN116780657A (zh) * 2023-08-17 2023-09-19 长江三峡集团实业发展(北京)有限公司 一种水风蓄互补发电系统的调度方法、装置、设备及介质
CN116993130A (zh) * 2023-09-26 2023-11-03 华电电力科学研究院有限公司 梯级水电站短期发电调度方法、装置、设备和存储介质
CN117236478A (zh) * 2023-06-01 2023-12-15 南京航空航天大学 基于Transformer改进深度强化学习的多目标多水库调度优化方法
CN117454674A (zh) * 2023-12-25 2024-01-26 长江水利委员会水文局 一种水电站实时生态流量智能动态调控方法
CN117674266A (zh) * 2024-01-31 2024-03-08 国电南瑞科技股份有限公司 一种梯级水电与光伏协同运行的超前预测控制方法及系统
CN117674293A (zh) * 2023-12-07 2024-03-08 华能西藏雅鲁藏布江水电开发投资有限公司 一种梯级水电站的长期发电优化调度方法及装置

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105225017B (zh) * 2015-10-30 2019-02-26 南京南瑞集团公司 一种多Agent的水电站群短期优化调度方法
CN105809272B (zh) * 2016-02-25 2019-04-23 大连理工大学 一种基于数据挖掘的梯级水电站群指令调度优化方法
CN108932588B (zh) * 2018-06-29 2021-04-02 华中科技大学 一种前后端分离的水电站群优化调度系统及方法
CN109636015B (zh) * 2018-11-28 2023-04-07 国网甘肃省电力公司电力科学研究院 一种梯级水电虚拟抽蓄电站调度方法
CN111476407B (zh) * 2020-03-25 2021-06-15 云南电网有限责任公司 联合风电光伏电站的梯级水电站中长期隐随机调度方法
CN111612268B (zh) * 2020-05-28 2021-11-30 国家电网公司西南分部 考虑市场交易的龙头水库水电集群运行优化方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102855591A (zh) * 2012-08-14 2013-01-02 贵州乌江水电开发有限责任公司 梯级水库群短期联合发电优化调度方法及系统
CN103020742A (zh) * 2012-12-27 2013-04-03 大连理工大学 具有多限制运行区的梯级水电站群短期优化调度方法
CN105225017A (zh) * 2015-10-30 2016-01-06 南京南瑞集团公司 一种多Agent的水电站群短期优化调度方法

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1996022648A1 (fr) * 1995-01-18 1996-07-25 British Telecommunications Plc Systeme de repondeur telephonique
CN103093282B (zh) * 2012-12-27 2016-06-22 贵州乌江水电开发有限责任公司 一种水电站群期末蓄能最大短期优化调度方法
CN104123589B (zh) * 2014-06-24 2015-04-15 华中科技大学 一种梯级水电站短期优化调度方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102855591A (zh) * 2012-08-14 2013-01-02 贵州乌江水电开发有限责任公司 梯级水库群短期联合发电优化调度方法及系统
CN103020742A (zh) * 2012-12-27 2013-04-03 大连理工大学 具有多限制运行区的梯级水电站群短期优化调度方法
CN105225017A (zh) * 2015-10-30 2016-01-06 南京南瑞集团公司 一种多Agent的水电站群短期优化调度方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
MEI, YADONG ET AL.: "huanghe22 shangyou42 tiji21 shuidianzhan344 duanqi31 youhua14 diaodu44 moshi24 ji2 diedai24 jiefa33", JOURNAL OF HYDROELECTRIC ENGINEERING, 25 June 2000 (2000-06-25) *
SUN, HAITAO.: "jiyu12 duo1 Agent dedianchang43 youhua14 yunxing42 yanjiu21", CHINA MASTER'S THESES FULL-TEXT DATABASE, vol. II, no. 11, 15 November 2009 (2009-11-15) *

Cited By (92)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108984825A (zh) * 2018-06-01 2018-12-11 中国电力科学研究院有限公司 一种水力发电系统建模方法和系统
CN109767051A (zh) * 2018-10-26 2019-05-17 国网天津市电力公司 基于大数据分析的变压器停电计划安排方法
CN109767051B (zh) * 2018-10-26 2022-12-09 国网天津市电力公司 基于大数据分析的变压器停电计划安排方法
CN109829611A (zh) * 2018-12-24 2019-05-31 长江勘测规划设计研究有限责任公司 基于防洪库容动态分配的梯级优化调度方法
CN109829611B (zh) * 2018-12-24 2023-06-27 长江勘测规划设计研究有限责任公司 基于防洪库容动态分配的梯级优化调度方法
CN109886543B (zh) * 2019-01-16 2022-09-16 河南大学 基于均匀设计的水库优化调度方法
CN109886543A (zh) * 2019-01-16 2019-06-14 河南大学 基于均匀设计的水库优化调度方法
CN110188912A (zh) * 2019-02-28 2019-08-30 西安理工大学 基于改进花粉算法的地表水和地下水联合调度优化方法
CN110188912B (zh) * 2019-02-28 2023-01-24 西安理工大学 基于改进花粉算法的地表水和地下水联合调度优化方法
CN109933892A (zh) * 2019-03-12 2019-06-25 中国电建集团中南勘测设计研究院有限公司 一种水温模型电站下泄流量边界条件的修正方法
CN109933892B (zh) * 2019-03-12 2023-04-07 中国电建集团中南勘测设计研究院有限公司 一种水温模型电站下泄流量边界条件的修正方法
CN110163508A (zh) * 2019-05-23 2019-08-23 上海申瑞继保电气有限公司 计量区域用电需量的错峰计算方法
CN110163508B (zh) * 2019-05-23 2023-05-02 上海申瑞继保电气有限公司 计量区域用电需量的错峰计算方法
CN110288133B (zh) * 2019-06-06 2023-03-24 国网湖北省电力有限公司孝感供电公司 基于远景年饱和负荷分布图的规划变电站自动选址方法
CN110288133A (zh) * 2019-06-06 2019-09-27 国网湖北省电力有限公司孝感供电公司 基于远景年饱和负荷分布图的规划变电站自动选址方法
CN110472825A (zh) * 2019-07-09 2019-11-19 贵州黔源电力股份有限公司 一种多级调度机构协调的梯级水电站实时调度弃水削减方法
CN110533244A (zh) * 2019-08-28 2019-12-03 重庆大学 一种梯级水坝优化调度方法、系统与计算机可读存储介质
CN110705784B (zh) * 2019-09-29 2023-04-07 河南郑大水利科技有限公司 一种径流式水电站优化运行评价方法
CN110705784A (zh) * 2019-09-29 2020-01-17 河南郑大水利科技有限公司 一种径流式水电站优化运行评价方法
CN110766210A (zh) * 2019-10-12 2020-02-07 华中科技大学 一种梯级水库群短期优化调度方法与系统
CN110766210B (zh) * 2019-10-12 2022-04-29 华中科技大学 一种梯级水库群短期优化调度方法与系统
CN110912200A (zh) * 2019-10-21 2020-03-24 贵州电网有限责任公司 一种梯级水电站优化调度系统、方法及安全电网系统
CN110912200B (zh) * 2019-10-21 2022-09-23 贵州电网有限责任公司 一种梯级水电站优化调度系统、方法及安全电网系统
CN110782281A (zh) * 2019-10-23 2020-02-11 四川大学 一种多业主梯级电站流域电量转移的日前市场出清方法
CN110782281B (zh) * 2019-10-23 2022-06-07 四川大学 一种多业主梯级电站流域电量转移的日前市场出清方法
CN111126693A (zh) * 2019-12-20 2020-05-08 华中科技大学 基于上游水库运行对下游电站发电能力影响的调度方法
CN111126693B (zh) * 2019-12-20 2022-11-11 华中科技大学 基于上游水库运行对下游电站发电能力影响的调度方法
CN111080152A (zh) * 2019-12-23 2020-04-28 华中科技大学 一种梯级水库发电调度补偿电量分配方法
CN111126847A (zh) * 2019-12-24 2020-05-08 华中科技大学 耦合河道水动力过程的梯级水库短期优化调度方法和系统
CN111126847B (zh) * 2019-12-24 2022-08-02 华中科技大学 耦合河道水动力过程的梯级水库短期优化调度方法和系统
CN111080157B (zh) * 2019-12-26 2023-04-07 大连理工大学 一种梯级水电站排磷量的调度方法和系统
CN111080157A (zh) * 2019-12-26 2020-04-28 大连理工大学 一种梯级水电站排磷量的调度方法和系统
CN111126709B (zh) * 2019-12-27 2023-04-25 中国南方电网有限责任公司 调度出力的确定方法、装置、计算机设备和存储介质
CN111126709A (zh) * 2019-12-27 2020-05-08 中国南方电网有限责任公司 调度出力的确定方法、装置、计算机设备和存储介质
CN111030123B (zh) * 2019-12-31 2023-04-28 东北大学 一种基于边缘计算的多智能体负荷调控方法
CN111030123A (zh) * 2019-12-31 2020-04-17 东北大学 一种基于边缘计算的多智能体负荷调控方法
CN111342461A (zh) * 2020-03-30 2020-06-26 国网福建省电力有限公司 一种考虑网架动态重构的配电网优化调度方法及系统
CN111342461B (zh) * 2020-03-30 2022-08-05 国网福建省电力有限公司 一种考虑网架动态重构的配电网优化调度方法及系统
CN111476475B (zh) * 2020-04-01 2023-10-13 贵州黔源电力股份有限公司 多约束条件下梯级水电站短期优化调度方法
CN111476474A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 梯级水电站减少弃水量的调度方法
CN111476475A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 多约束条件下梯级水电站短期优化调度方法
CN111476477A (zh) * 2020-04-01 2020-07-31 贵州黔源电力股份有限公司 基于发电效益目标的梯级水电站中长期优化调度方法
CN111476474B (zh) * 2020-04-01 2023-10-13 贵州黔源电力股份有限公司 梯级水电站减少弃水量的调度方法
CN111932021B (zh) * 2020-08-17 2023-06-27 浙江财经大学 一种再制造系统调度方法
CN111932021A (zh) * 2020-08-17 2020-11-13 浙江财经大学 一种再制造系统调度方法
CN112234604B (zh) * 2020-09-10 2023-04-28 西安交通大学 一种多能互补电源基地优化配置方法、存储介质及设备
CN112234604A (zh) * 2020-09-10 2021-01-15 西安交通大学 一种多能互补电源基地优化配置方法、存储介质及设备
CN112907078A (zh) * 2021-02-20 2021-06-04 中国电力科学研究院有限公司 水电耦合的市场出清方法、系统、设备及可读存储介质
CN113255974B (zh) * 2021-05-10 2023-09-15 四川华能宝兴河水电有限责任公司 一种梯级水电站联合调度负荷分配的方法
CN113255974A (zh) * 2021-05-10 2021-08-13 四川华能宝兴河水电有限责任公司 一种梯级水电站联合调度负荷分配的方法
CN113820952A (zh) * 2021-07-26 2021-12-21 国网新源控股有限公司 抽水蓄能电站导叶关闭规律的优化方法及装置
CN113554320A (zh) * 2021-07-27 2021-10-26 西安热工研究院有限公司 一种基于供热经济性最优的全厂热、电负荷分配方法
CN113779768A (zh) * 2021-08-16 2021-12-10 西安交通大学 模型构建方法、电子设备及存储介质
CN113779768B (zh) * 2021-08-16 2024-05-17 西安交通大学 一种需求响应模型构建方法、电子设备及存储介质
CN113780629A (zh) * 2021-08-16 2021-12-10 西安交通大学 梯级水电调度模型优化方法、装置、电子设备及存储介质
CN113780629B (zh) * 2021-08-16 2024-04-12 西安交通大学 梯级水电调度模型优化方法、装置、电子设备及存储介质
CN113705899A (zh) * 2021-08-30 2021-11-26 武汉大学 水库优化调度最优决策与效益的找寻方法
CN113705899B (zh) * 2021-08-30 2023-08-04 武汉大学 水库优化调度最优决策与效益的找寻方法
CN114021902A (zh) * 2021-10-15 2022-02-08 华中科技大学 基于动态收索与离散机制的动态规划降维的水库调度方法
CN114021902B (zh) * 2021-10-15 2024-03-19 华中科技大学 基于动态收索与离散机制的动态规划降维的水库调度方法
CN114021933A (zh) * 2021-10-29 2022-02-08 华中科技大学 基于动态泄流法的梯级水库群溃坝应急调度方法及系统
CN114154404B (zh) * 2021-11-22 2024-05-21 大连理工大学 一种利用观测数据推断相邻水电站运行状态及参数的方法
CN114154404A (zh) * 2021-11-22 2022-03-08 大连理工大学 一种利用观测数据推断相邻水电站运行状态及参数的方法
CN114336740B (zh) * 2021-12-15 2023-10-27 湖北清江水电开发有限责任公司 水电站机组孤网运行成组调节系统及方法
CN114336740A (zh) * 2021-12-15 2022-04-12 湖北清江水电开发有限责任公司 水电站机组孤网运行成组调节系统及方法
CN114677064A (zh) * 2022-05-27 2022-06-28 长江水利委员会水文局 一种耦合最优性与稳定性的梯级水库调度决策支持方法
CN115470998B (zh) * 2022-09-23 2024-02-02 上海交通大学 港口冷箱负荷群用电一致性分层优化调度方法与系统
CN115470998A (zh) * 2022-09-23 2022-12-13 上海交通大学 港口冷箱负荷群用电一致性分层优化调度方法与系统
CN115439027A (zh) * 2022-11-08 2022-12-06 大唐乡城唐电水电开发有限公司 一种梯级水电站负荷优化调度方法、装置、设备和介质
CN115619189A (zh) * 2022-11-09 2023-01-17 中国南方电网有限责任公司 考虑梯级水电弃水流量限制的弃水调度方法及装置
CN115619189B (zh) * 2022-11-09 2023-11-14 中国南方电网有限责任公司 考虑梯级水电弃水流量限制的弃水调度方法及装置
CN115860224A (zh) * 2022-12-06 2023-03-28 国网四川省电力公司电力科学研究院 一种电力数据多约束优化方法及系统
CN116341852B (zh) * 2023-03-27 2024-04-26 湖北清江水电开发有限责任公司 一种用于水电厂的多机组负荷分配方法
CN116341852A (zh) * 2023-03-27 2023-06-27 湖北清江水电开发有限责任公司 一种用于水电厂的多机组负荷分配方法
CN116467562A (zh) * 2023-04-20 2023-07-21 武汉大学 水电站耗水率特性曲线的确定方法及设备
CN116565947B (zh) * 2023-04-26 2024-04-19 武汉大学 水电站日调峰能力确定方法及设备
CN116565947A (zh) * 2023-04-26 2023-08-08 武汉大学 水电站日调峰能力确定方法及设备
CN116562572A (zh) * 2023-05-14 2023-08-08 中国长江电力股份有限公司 一种梯级水电站群年度计划电量曲线分解方法
CN116562572B (zh) * 2023-05-14 2024-03-12 中国长江电力股份有限公司 一种梯级水电站群年度计划电量曲线分解方法
CN117236478B (zh) * 2023-06-01 2024-04-26 南京航空航天大学 基于Transformer改进深度强化学习的多目标多水库调度优化方法
CN117236478A (zh) * 2023-06-01 2023-12-15 南京航空航天大学 基于Transformer改进深度强化学习的多目标多水库调度优化方法
CN116703134A (zh) * 2023-08-10 2023-09-05 长江勘测规划设计研究有限责任公司 一种大型跨流域调水水库的多目标调度方法及系统
CN116703134B (zh) * 2023-08-10 2023-11-10 长江勘测规划设计研究有限责任公司 大型跨流域调水水库的多目标调度方法及系统
CN116780657A (zh) * 2023-08-17 2023-09-19 长江三峡集团实业发展(北京)有限公司 一种水风蓄互补发电系统的调度方法、装置、设备及介质
CN116780657B (zh) * 2023-08-17 2024-01-19 长江三峡集团实业发展(北京)有限公司 一种水风蓄互补发电系统的调度方法、装置、设备及介质
CN116993130B (zh) * 2023-09-26 2024-02-06 华电电力科学研究院有限公司 梯级水电站短期发电调度方法、装置、设备和存储介质
CN116993130A (zh) * 2023-09-26 2023-11-03 华电电力科学研究院有限公司 梯级水电站短期发电调度方法、装置、设备和存储介质
CN117674293A (zh) * 2023-12-07 2024-03-08 华能西藏雅鲁藏布江水电开发投资有限公司 一种梯级水电站的长期发电优化调度方法及装置
CN117454674B (zh) * 2023-12-25 2024-04-09 长江水利委员会水文局 一种水电站实时生态流量智能动态调控方法
CN117454674A (zh) * 2023-12-25 2024-01-26 长江水利委员会水文局 一种水电站实时生态流量智能动态调控方法
CN117674266B (zh) * 2024-01-31 2024-04-26 国电南瑞科技股份有限公司 一种梯级水电与光伏协同运行的超前预测控制方法及系统
CN117674266A (zh) * 2024-01-31 2024-03-08 国电南瑞科技股份有限公司 一种梯级水电与光伏协同运行的超前预测控制方法及系统

Also Published As

Publication number Publication date
CN105225017A (zh) 2016-01-06
CN105225017B (zh) 2019-02-26

Similar Documents

Publication Publication Date Title
WO2017071230A1 (fr) Procédé de planification optimale à court terme d'un groupe de stations hydroélectriques à agents multiples
CN102097866B (zh) 中长期机组组合优化方法
CN104377826B (zh) 一种主动配电网控制策略及方法
CN112072641A (zh) 一种源网荷储柔性协调控制和运行优化方法
Changtian et al. Energy-aware genetic algorithms for task scheduling in cloud computing
CN110957717A (zh) 一种多电源电力系统多目标日前优化调度方法
CN112103943A (zh) 日前电力现货市场出清的安全校核方法、装置及存储介质
MacDougall et al. Mitigation of wind power fluctuations by intelligent response of demand and distributed generation
CN103699938A (zh) 一种含抽水蓄能电站的电力系统发电计划制定方法
CN104934970A (zh) 基于合作博弈动态联盟结构划分的互联微网经济调度方法
CN113162055B (zh) 一种储能与新能源电站无功电压的协调控制方法及系统
CN103455729A (zh) 一种光储并网联合发电调度值的计算方法
CN114140022A (zh) 一种多虚拟电厂分布式动态经济调度方法及系统
CN109286208A (zh) 一种综合能源系统调度方法和系统
CN108053083B (zh) 一种水库水电站非汛期联合优化发电调度方法
Ma et al. Long-term coordination for hydro-thermal-wind-solar hybrid energy system of provincial power grid
Lohi et al. Analysis and review of effectiveness of metaheuristics in task scheduling process with delineating machine learning as suitable alternative
Jin et al. Research on energy management of microgrid in power supply system using deep reinforcement learning
CN111799793A (zh) 一种源网荷协同的输电网规划方法与系统
CN113837449B (zh) 虚拟电厂参与的电网系统集中优化调度方法
Hoogsteen et al. On the scalability of decentralized energy management using profile steering
CN115347583A (zh) 一种基于多智能体的能源互联网功率指令分配方法及系统
CN110611335B (zh) 一种考虑电力系统和信息系统联合调度的方法及装置
CN115879676A (zh) 一种冷链参与电力辅助服务市场的调控方法及装置
CN112381333A (zh) 一种基于分布式改进蝙蝠算法的微电网优化方法

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: 16858673

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: 16858673

Country of ref document: EP

Kind code of ref document: A1

122 Ep: pct application non-entry in european phase

Ref document number: 16858673

Country of ref document: EP

Kind code of ref document: A1