WO2019006733A1 - 一种跨省互联水电站群长期联合调峰调度方法 - Google Patents

一种跨省互联水电站群长期联合调峰调度方法 Download PDF

Info

Publication number
WO2019006733A1
WO2019006733A1 PCT/CN2017/092122 CN2017092122W WO2019006733A1 WO 2019006733 A1 WO2019006733 A1 WO 2019006733A1 CN 2017092122 W CN2017092122 W CN 2017092122W WO 2019006733 A1 WO2019006733 A1 WO 2019006733A1
Authority
WO
WIPO (PCT)
Prior art keywords
power
hydropower
peak
provincial
station
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.)
Ceased
Application number
PCT/CN2017/092122
Other languages
English (en)
French (fr)
Inventor
申建建
程春田
孙立飞
苏承国
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Dalian University of Technology
Original Assignee
Dalian University of Technology
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 Dalian University of Technology filed Critical Dalian University of Technology
Priority to JP2019503558A priority Critical patent/JP6646182B2/ja
Priority to US16/322,829 priority patent/US10534327B2/en
Priority to PCT/CN2017/092122 priority patent/WO2019006733A1/zh
Publication of WO2019006733A1 publication Critical patent/WO2019006733A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/041Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a variable is automatically adjusted to optimise the performance
    • EFIXED CONSTRUCTIONS
    • E02HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02BHYDRAULIC ENGINEERING
    • E02B9/00Water-power plants; Layout, construction or equipment, methods of, or apparatus for, making same
    • E02B9/02Water-ways
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/04Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06QINFORMATION 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063Operations research, analysis or management
    • G06Q10/0631Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315Needs-based resource requirements planning or analysis
    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06QINFORMATION 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
    • G06Q50/00Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06Energy or water supply
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00Energy generation through renewable energy sources
    • Y02E10/20Hydro energy
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02EREDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70Smart grids as climate change mitigation technology in the energy generation sector
    • YGENERAL 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
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Definitions

  • the invention relates to the field of hydropower dispatching, in particular to a long-term joint peak shaving scheduling method for inter-provincial interconnected hydropower stations.
  • the complex scheduling requirements involving long-term and short-term multi-time scale coupling are the technical bottlenecks faced by China's large-scale hydropower transmission configuration, and there is a need for applicable theoretical methods and techniques.
  • the results of the invention rely on the National Natural Science Foundation's major program key support project (91547201) and the National Natural Science Foundation of China (51579029), and the invention is based on the long-term coordinated optimization of the Xi-Zhejiang high-pressure transmission and reception hydropower station group.
  • the technical problem to be solved by the present invention is to provide a long-term joint peak shaving scheduling method for inter-provincial interconnected hydropower stations, which can take into account the long-term electricity demand of the power system and the peaking demand of the typical day, and alleviate the short-term adjustment of the power system from a long-term perspective.
  • Peak pressure at the same time, according to the short-term peaking index of the power grid to guide the realization of the long-term distribution of hydropower long-term electricity, in order to improve the efficiency and accuracy of the long-term and short-term scheduling of hydropower stations.
  • a long-term joint peak regulation scheduling method for inter-provincial interconnected hydropower stations includes the following steps:
  • Step 1 Read the basic data, initialize the calculation parameters, including the operating conditions constraints and control bars of the inter-provincial interconnection hydropower station. Calculation parameters such as constraint, cross-region DC hydropower transmission line constraints, monthly average load of the power grid, and long-term typical daily load;
  • Step 2 Establish a long-term joint peaking optimization scheduling model for inter-provincial connected hydropower stations, which is optimized for the maximum power generation and the typical daily peak-to-peak difference of the peak period;
  • Step 3 Optimize the inter-provincial connection and delivery hydropower station group with the maximum conventional power generation as the target, and generate the initial solution of the model. At the same time, record the maximum R 0 of the peak-to-valley difference of the typical daily residual load of the grid during the dry period.
  • Step 7 The sending and receiving stations are grouped according to their own watersheds, and the total number of groups is N groups;
  • Step 9 Determine whether the hydropower station participating in the calculation is in a flood season. If yes, the calculation is prioritized. Otherwise, the calculation is performed after optimization of the power station in the flood season.
  • Step 10 Under the given target conditions, deal with the peak-to-valley rate constraint, the cross-region DC hydropower transmission line constraints, and other conventional hydropower station constraints, use the POA-DDDP algorithm to search the model two-stage optimal flow process;
  • the load method is used to process the typical sunrise force process of the two stages of power station t and t+1, and calculate the typical daily minimum load peak-to-valley difference R t and R t+1 of the two stages;
  • Step 12 Repeat steps 9-11 to determine whether there is a change in the water level of the power station in the adjacent two optimization results group. If not, go to step 13, otherwise, repeat step 12;
  • Step 13 Reduce the traffic search progress, determine whether the traffic search progress meets the progress requirements at this time, and if yes, go to step 9; otherwise, go to step 14;
  • Step 15 Determine whether there is a change in the current iteration of the water level process of each power station. If not, go to step 16; otherwise, go to step 6. ;
  • Step 17 If R max ⁇ R, output the current calculation scheduling scheme and the minimum load peak-to-valley difference rate constraint value R in the dry period as a feasible solution, and go to step 4; otherwise, the calculation ends.
  • the technical solution of the present invention can realize the following beneficial effects: the method proposed by the present invention can fully utilize the difference in hydrological characteristics of the hydropower stations at both ends of the trans-provincial power transmission hydropower station group, and effectively exert the compensation scheduling of the inter-provincial hydropower station group.
  • the role while taking into account the long-term electricity efficiency of the hydropower system and the demand for power balance.
  • the typical daily load characteristics of the power grid during dry season are fully considered. While the long-term power is distributed and coordinated, the typical daily residual peak-to-valley difference of the power grid is reduced, and the power generation of the hydropower system and the long-term peaking efficiency of the power grid are effectively improved.
  • inter-provincial hydropower station group coordination method proposed by the present invention has little influence on the operation mode of the existing out-of-area power transmission station, and provides an efficient and practical technical means for large-scale transportation of UHV DC hydropower across the province. .
  • Figure 1 is a schematic diagram of a typical sunrise force interval of a hydropower station in a dry season
  • FIG. 2 is a schematic diagram of a staged optimization sequence of a transmitting and receiving station
  • FIG. 3 is a schematic diagram of a POA-DDDP two-stage sub-question search process
  • Figure 4 is a plot of peak-to-valley difference-power generation distribution for a multi-scheme model with 50% typical interval flow.
  • Figure 5 is a comparison of the monthly power generation of a typical hydropower station group.
  • FIG. 6 is a comparison diagram of the output of the Xiluodu dispatching scheme and the actual dispatching scheme and the water level process of the typical scheme of the method of the present invention.
  • FIG. 7 is a comparison diagram of the output of the typical scheme of the beach pit scheduling scheme and the actual dispatching scheme and the water level process according to the method of the present invention.
  • the invention relates to a long-term joint peak regulation scheduling method for inter-provincial interconnected hydropower stations, and the present invention is further described below with reference to the accompanying drawings and examples.
  • the long-term optimal dispatching plan of inter-provincial connected hydropower stations is affected by many factors such as long-term and short-term load characteristics of the power grid, short-term constraints of inter-provincial transmission lines, hydropower station hydropower conditions, and unit maintenance.
  • the method of the invention mainly solves four key problems: firstly, how to establish a joint optimization scheduling model for inter-provincial connected hydropower stations; secondly, how to utilize the difference in dry season of inter-provincial interconnected hydropower stations to exert the scheduling characteristics of hydropower stations under different hydrological conditions;
  • the third is how to solve the typical daily constraints of inter-provincial power transmission stations and their transmission lines, to deal with the typical sunrise force problem of power transmission stations, and the fourth is how to efficiently search for the optimal water level process of hydropower stations.
  • the following four solutions to the problems are respectively explained.
  • the long-term power generation optimal dispatching of hydropower stations is based on the annual scheduling period, and the monthly or ten-day calculation period.
  • the long-term electricity optimization is realized by determining the optimal monthly average water level of each power station during the dispatching period. And the operating efficiency of the grid.
  • the inter-provincial power transmission and hydropower station and the receiving power station hydropower station group scheduling need to take into account the complex transmission constraints of the DC hydropower line based on the operational control conditions of the hydropower station group.
  • the space-time coupling of the model is more Tight, the solution is more difficult.
  • the model constructed by the invention adopts the largest power generation amount and the minimum daily peak-to-valley difference of the typical daily dry load period in the dry season, so as to balance the long-term power optimization of the hydropower station group and the scheduling requirement of short-term peak shaving.
  • the objective functions are represented as follows:
  • the largest amount of power generation the maximum power generation of the total hydropower system during the dispatch period.
  • the peak-to-valley difference of the residual load on the typical day of the dry season is the smallest: the peak-to-valley difference reflects the peaking difficulty of the daily load of the power system. If the peak-to-valley difference of the residual load is reduced, the peaking pressure of the other power sources such as thermal power and the load is smaller. On the contrary, the peaking pressure of the power supply is larger.
  • F 1 represents the target with the largest amount of power generation
  • F 2 represents the target with the smallest peak-to-valley difference of the residual daily load
  • m represents the plant number
  • M represents the total number of stations participating in the calculation
  • M 1 represents the province participating in the calculation.
  • the total number of power stations M 2 represents the total number of out-of-province power stations involved in the calculation
  • t represents the scheduling period number
  • ⁇ t represents the period of long-term scheduling
  • T represents the entire scheduling period
  • T 2 represents the set of dry months in the hydropower dispatching period
  • i represents the typical day
  • I represents the total length of the typical day period
  • Cday represents the typical daily load of the grid, in MW.
  • V m,t+1 V m,t +(Q m,t -q m,t -qd m,t ) ⁇ t (4)
  • V m,t represents the total water storage capacity of the power station m during the period t
  • Q m,t represents the inflow rate of the power station m during the period t
  • q m,t represents the power generation flow of the power station m during the period t
  • K represents The number of direct upstream power stations of the power station m
  • k represents the upstream power station number
  • In m t represents the interval flow of the power station m during the time period t
  • S k,t represents the outflow flow of the kth direct upstream power station of the power station m during the time period t
  • Qd m,t represents the abandoned water flow of the power station m during the period t
  • ⁇ t represents the hour of the t period
  • Z m,T represents the water level of the power station m at the end of the scheduling period; Indicates the upper limit of the power generation flow rate of the power station m during the time period t;
  • S m,t represents the outflow flow
  • E m,t and E m,t ' respectively represent the total electricity and demand electricity of the typical day of the power station m corresponding to the t-th scheduling period.
  • the way E m,t ' is determined is:
  • C t represents the average load demand for the tth scheduled month.
  • Pday m,i,t represents the output of the power station m at the typical day i corresponding to the time period t
  • the upper and lower limits of the output of the power station m at the typical day i corresponding to the time period t are respectively indicated.
  • ⁇ Pday m represents the output amplitude limit of the power station m.
  • v m represents the number of periods in which the maximum minimum delivery power of the power station m lasts for a minimum.
  • the fractional ratio indicates the upper and lower limits of the output of the DC hydropower transmission line at the time t corresponding to the typical day i.
  • the ⁇ P line m, t represents the typical daily power transmission variable limit of the DC hydropower transmission line.
  • the v line indicates the minimum duration of the minimum and minimum transmission power of the DC hydropower transmission line.
  • Inter-provincial connected hydropower stations span multiple provinces, and the transmission and reception stations have obvious differences in hydrological characteristics. They usually exhibit the characteristics of asynchronous during the dry period. In order to exert their complementary coordination ability, the hydropower stations are coordinated in the sub-component phase during the scheduling period. During the asynchronous period of the delivery station power station, the hydropower stations in the two places bear different working positions in the typical daily load. The power station in the flood season bears the base load, and the power station in the dry period bears the peak load, as shown in Figure 1.
  • the power station should exert its power capacity as much as possible, and it should not be restricted by the peak-to-valley difference.
  • the target should be the largest power generation; the power station in the dry period should reduce the typical daily load-to-valley difference of the power grid as the main target to adjust its own monthly. Electricity distribution, compensation for flood season power stations.
  • the power station is first put into calculation, and the typical daily residual load Cday i,t R after deducting the typical sunrise power of the flood season power station is calculated:
  • the typical daily residual load Cday i, t R is used as the load on the dry power station, and the residual power station is calculated and the residual load Cday i,t R2 is deducted from the typical sunrise power of the dry power station:
  • Cday i , t R1 represents the typical daily residual load minus the typical sunrise power of the flood season power station
  • Cday i, t represents the typical daily original load
  • Pday m, i, t represents the typical sunrise force of the mth power station
  • Cday i, t R2 means deduct all Typical daily residual load after a typical sunrise force at a power station.
  • the monthly power of the dry power station is redistributed, and the global target of the typical daily peaking is realized. Combined with the above steps, the solving sequence is as shown in Fig. 2.
  • the receiving hydropower station can determine its typical daily power generation according to formula (12), and then process the typical daily power plant constraints (11)-(15) according to the mature successive load shedding method to solve its typical sunrise force process.
  • the typical daily power process of the inter-provincial power transmission hydropower station is not only restricted by the power station itself, but also restricted by the (16)-(18) UHV DC transmission line.
  • the sequential shear load method is used to solve the typical sunrise force process, which cannot be satisfied at the same time. Two aspects of constraints.
  • the present invention reconstructs the typical daily load of the inter-provincial power transmission hydropower station group, so that the load naturally meets the UHV transmission line constraints, and then uses the successive load-shedding method to solve the typical sunrise force process of each power station to ensure the delivery of the power station group.
  • the typical daily total output satisfies the constraints (11)-(18) at the same time.
  • the 24-point power process value is ⁇ P 1, t + P group , P 2, t + P group , ..., P 24, t + P group ⁇ .
  • P ⁇ is used as the load-bearing hydropower station group to face the load, and the typical sunrise force process of each power transmission station is determined by the method of successive load shedding. At this time, the power transmission station can simultaneously meet the typical daily constraints of the power station and the typical daily constraints of the UHV line.
  • the adjustment calculation is carried out in the order of upstream and downstream: the power station in the group calculates the outflow flow in the t period, the flow rate adjustment in the t period, and the water level adjustment in the t+1 period; The power station with the change of the outbound flow of the group performs the water level adjustment in the t and t+1 periods.
  • the typical daily constraint of the processing power station and updating the typical daily negative output of the power station t and t+1 periods by the successive load shedding method, calculating the difference between the objective function and the penalty function value;
  • the optimization process of a long-term joint peak-shaving scheduling method for a complete inter-provincial connected hydropower station group can be expressed by the following steps:
  • Step 2 Establish a long-term joint peaking optimization scheduling model for inter-provincial connected hydropower stations, which is optimized for the maximum power generation and the typical daily peak-to-peak difference of the peak period;
  • Step 3 Optimize the inter-provincial connection and delivery hydropower station group with the maximum conventional power generation as the target, and generate the initial solution of the model. At the same time, record the maximum R 0 of the peak-to-valley difference of the typical daily residual load of the grid during the dry period.
  • Step 9 Determine whether the hydropower station participating in the calculation is in a flood season. If yes, the calculation is prioritized. Otherwise, the calculation is performed after optimization of the power station in the flood season.
  • Step 10 Under the given target conditions, deal with peak-to-valley rate constraint, cross-region DC hydropower transmission line constraints and other conventional hydropower station constraints, use POA-DDDP algorithm to search the model two-stage optimal flow process; The typical sunrise force process of the two stages of power station t and t+1 is processed, and the typical daily minimum load peak-to-valley rates R t and R t+1 are calculated.
  • Step 13 Reduce the traffic search progress, determine whether the traffic search progress meets the progress requirements at this time, and if yes, go to step 9; otherwise, go to step 14;
  • Step 17 If R max ⁇ R, output the current calculation scheduling scheme and the minimum load peak-to-valley difference rate constraint value R in the dry period as a feasible solution, and go to step 4; otherwise, the calculation ends.
  • Table 1 is the basic data table for participating in the calculation of the power station.
  • Table 2 is the model input with 50% typical interval flow, the multi-program scheduling result and the peak-to-valley difference table in the dry season, and Figure 4 is the model multi-program peak under the 50% typical interval flow.
  • the valley difference-power generation distribution map using the method proposed by the present invention, can formulate an optimal electric quantity scheduling scheme under different dry peak-to-valley difference rates as shown in Table 2 and FIG. 4, and provide various scheduling schemes for common scheduling. Personnel are preferred.
  • FIG. 5 compares the power generation process of two typical scheduling schemes formulated by the present invention, and compares it with the power generation process of the conventional scheduling scheme. It can be seen that the scheduling scheme developed by the method of the present invention has a significant change in the power generation capacity of the hydropower system during the dry period. The largest conventional power generation scheme has significantly increased the electricity consumption during the dry season, which has improved the peaking capacity of hydropower during the dry period. 6 and FIG.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Development Economics (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Educational Administration (AREA)
  • Artificial Intelligence (AREA)
  • Mechanical Engineering (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Remote Monitoring And Control Of Power-Distribution Networks (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

一种跨省互联水电站群长期联合调峰调度方法,其特点是充分利用送受两端水电站水文特性差异,发挥跨省水电站群补偿调度作用,并兼顾枯水期电网典型日负荷特性,在协调分配水电长期电量的同时,提高水电对电网的高峰负荷调节能力。该方法包括:以发电量最大和枯期典型日峰谷差率最小为目标建立水电站群多目标优化模型,利用跨省区流域间水文特性和电源间调节性能差异,将电站按所处的出力区间分组,分阶段协调电站计算顺序;采用逐步优化和动态规划逐次逼近耦合算法优化送受端水电站群最优水位过程,并根据跨省直流联络线输送限制约束重构送端面临负荷,以逐次切负荷策略确定典型日出力过程,通过迭代优化得到水电站群长期电量分配方案和各月典型日电力过程。该方法可充分利用送受端水电站群补偿调节特性,有效响应电网长期电量需求和短期调峰需求,能够满足我国溪洛渡、锦屏等巨型水电站跨省跨区送电调度实际需要。

Description

一种跨省互联水电站群长期联合调峰调度方法 技术领域
本发明涉及水力发电调度领域,特别涉及一种跨省互联水电站群长期联合调峰调度方法。
背景技术
随着金沙江、雅砻江、澜沧江特大流域巨型水电站群集中并网发电,我国跨省区水电输送规模急剧扩大,最大可输送能力已超过6800万kW,如此大规模馈入水电对受端电网的影响作用明显增大,并与受端区域内水电构成了非常复杂的跨省互联水电系统,以协同满足受端电网电力供应和负荷调节等复杂任务需求,给受端电网调度管理带来新的挑战和难题。一方面,这些水电站群覆盖多个省份,在水文、地理、气象等方面存在巨大差异,且水库串、并联并存,调节性能多样,水力联系与电力联系相互耦合,大大增加了建模和求解难度;另一方面,跨省互联水电调度需要兼顾送受两端的电网和电站运行控制要求,如何在自身效益最大化的同时,兼顾电网长期电量协调和短期负荷调节需求,是跨省互联水电系统需要解决的新问题。
有关跨省跨区域水电调度的研究随着特高压直流水电输送规模的不断增大受到更多关注,部分研究针对单一流域梯级跨省送电问题,比如红水河干流送电广东和广西,金沙江下游送电广东、浙江等,开展了水电短期跨省调峰方法研究;另有个别研究针对直流水电在受端区域多个省级电网间的电力分配问题,开展了网省两级短期协调调峰方法研究,这些研究报道主要侧重于直流水电在受端区域内的网省协调或单一流域梯级的跨省短期调峰调度,鲜有研究将送端和受端电源统一进行考虑,更未涉及长、短期多时间尺度耦合的复杂调度需求,这一问题是我国水电大范围输送配置面临的技术瓶颈,亟需适用的理论方法和技术。本发明成果依托国家自然科学基金重大计划重点支持项目(91547201)和国家自然科学基金面上项目(51579029),以溪-浙特高压送受端水电站群长期协调优化问题为背景,发明了具有较强实用价值和广泛推广价值的一种跨省互联水电站群长期联合调峰调度方法。
发明内容
本发明要解决的技术问题是提供一种跨省互联水电站群长期联合调峰调度方法,该方法可兼顾电力系统的长期电量需求及典型日的调峰需求,从长期角度缓解了电力系统短期调峰压力,同时根据电网短期调峰指标指导实现水电长期电量的年内分配,以提升水电站长短期调度方案的制定效率和准确率。
本发明的技术方案为:
一种跨省互联水电站群长期联合调峰调度方法,包括以下步骤:
步骤1.读取基础数据,初始化计算参数,包括跨省互联水电站的运行条件约束及控制条 件约束、跨区直流水电输电线路约束、受电电网月平均负荷、长期典型日负荷等计算参数;
步骤2.建立跨省互联水电站群长期联合调峰优化调度模型,该模型以发电量最大和枯期典型日调峰峰谷差率最小为优化目标;
步骤3.以常规发电量最大为目标对跨省互联送受端水电站群进行优化计算,生成模型初始解,同时记录此时电网枯期各月典型日剩余负荷峰谷差率的最大值R0
步骤4.设置枯期最小负荷峰谷差率约束R=R0-0.01;
步骤5.令迭代次数k=0。
步骤6.令计算时段t=1,并定义T为调度期总时段数;
步骤7.将送受端电站按照所属流域分组,计分组总数为N组;
步骤8.若k>0;判断是否有电站两阶段水位过程发生变化。若有,则重置流量搜索步长,转步骤9。若无,则令t=t+1,若t>T,转步骤15,否则重复本步骤;
步骤9.判断参与计算的水电站是否处于汛期,若是,则优先进行计算,否则,在汛期电站优化后再进行计算。令计算优先级最高的电站组排序定义为n=0;
步骤10.在给定目标条件下,处理峰谷差率约束、跨区直流水电输电线路约束、以及其他常规水电站约束条件,采用POA-DDDP算法搜索模型两阶段最优流量过程;同时以逐次切负荷法处理电站t和t+1两阶段的典型日出力过程,计算两阶段典型日最小负荷峰谷差率Rt和Rt+1
步骤11.令n=n+1,若n<N,则重复步骤10,否则转步骤12;
步骤12.重复步骤9-11,判断相邻两次寻优结果组内电站水位过程是否有改变,若无,则转步骤13,否则,重复步骤12;
步骤13.缩小流量搜索进度,判断此时流量搜索进度是否符合进度要求,若是,转步骤9;否则,转步骤14;
步骤14.令t=t+1,若t>T,则令k=k+1,转步骤15;否则,转步骤8;
步骤15.判断各电站水位过程本轮迭代是否有改变,若无,转步骤16;否则,转步骤6。;
步骤16.统计各月枯期月份典型日最大峰谷差率Rmax=max{Rt′},t′包括所有枯期月份;
步骤17.若Rmax<R,则输出本次计算调度方案与枯期最小负荷峰谷差率约束值R,作为一个可行方案,并转步骤4;否则计算结束。
对比现有技术,实施本发明的技术方案,可实现如下有益效果:本发明提出的方法可充分利用跨省送电水电站群送受两端的水电站水文特性差异,有效发挥跨省水电站群补偿调度 作用,同时兼顾水电系统长期电量效益及受端电力平衡需求。并且充分考虑了枯水期电网典型日负荷特性,在协调分配长期电量的同时,降低了电网典型日余荷峰谷差率,有效提升了水电系统的发电量及电网长期调峰效益。且因本发明所提的跨省水电站群协调方法对现有的区外送电电站运行方式影响很小,为我国特高压直流水电跨区跨省大规模输送提供了一种高效实用的技术手段。
附图说明
图1是汛枯期水电站典型日出力区间示意图;
图2是送受端电站分阶段优化顺序示意图;
图3是POA-DDDP两阶段子问题搜索过程示意图;
图4是50%典型区间流量下模型多方案峰谷差率-发电量分布情况图。
图5是典型方案水电站群各月发电量对比图。
图6是本发明所提方法典型方案溪洛渡调度方案与实际调度方案出力及水位过程对比图。
图7是本发明所提方法典型方案滩坑调度方案与实际调度方案出力及水位过程对比图。
具体实施方式
本发明涉及一种跨省互联水电站群长期联合调峰调度方法,以下结合附图和实例对本发明作进一步的描述。
跨省互联水电站群长期优化调度计划在编制中过程受电网长短期负荷特性,跨省输电线路短期约束、水电站水情、机组检修等诸多因素影响。本发明方法主要解决四个方面的关键问题:一是如何建立跨省互联水电站群联合优化调度模型;二是如何利用跨省互联水电站群汛枯期差异,发挥不同水文情势下水电站的调度特性;三是如何解决跨省送电电站及其输电线路典型日约束,处理送电电站典型日出力问题,四是如何高效搜索水电站群最优水位过程,以下分别阐述四个方面问题的解决方案。
(a)跨省互联水电站群联合优化调度模型
一般而言,水电站群长期发电优化调度的目标是以年为调度周期,月或旬为计算时段,通过确定调度期内各电站的最优月平均水位过程实现长期电量优化,其调度结果涉及电站及电网的运行效益。与常规水电调度模型相比,跨省送电水电与受端电网水电站群调度在考虑送端水电站群运行控制条件的基础上,还需要兼顾直流水电联络线复杂输电限制约束,模型的时空耦合更加紧密,求解难度更大。本发明构造的模型采用发电量最大和枯水期典型日剩余负荷峰谷差率最小为目标,以兼顾水电站群长期电量优化和短期调峰的调度需求。目标函数分别表示如下:
发电量最大:以调度期内总水电系统发电量最大为目标。
Figure PCTCN2017092122-appb-000001
枯期典型日剩余负荷峰谷差率最小:峰谷差率体现了电力系统日内负荷的调峰难度,若剩余负荷峰谷差率减小,则火电等其他电源面临负荷的调峰压力越小;反之电源的调峰压力越大。
Figure PCTCN2017092122-appb-000002
式中:F1表示发电量最大的目标,F2表示枯期典型日剩余负荷峰谷差率最小的目标,m表示电站编号,M表示参与计算的电站总数,M1表示参与计算的省内电站总数,M2表示参与计算的省外电站总数,t表示调度时段编号,Δt表示长期调度的时段,T表示整个调度周期,T2表示水电调度周期内的枯期月份集合,i表示典型日时段标号,I表示典型日时段总长度,Cday表示电网典型日负荷,单位MW。
水电站长期约束
(1)末水位约束:Zm,T=Z'm               (3)
(2)水量平衡约束:Vm,t+1=Vm,t+(Qm,t-qm,t-qdm,tt      (4)
式中:
Figure PCTCN2017092122-appb-000003
(3)出库流量约束:
Figure PCTCN2017092122-appb-000004
(4)发电流量约束:
Figure PCTCN2017092122-appb-000005
(5)库水位约束:
Figure PCTCN2017092122-appb-000006
(6)电站出力约束:
Figure PCTCN2017092122-appb-000007
(7)泄流设备最大过流能力约束:
Figure PCTCN2017092122-appb-000008
式中:Vm,t表示电站m在时段t的总蓄水量;Qm,t表示电站m在时段t的入库流量,qm,t表示电站m在时段t的发电流量,K表示电站m的直接上游电站数,k表示上游电站编号,Inm,t表示电站m在时段t的区间流量,Sk,t表示电站m的第k个直接上游电站在时段t的出库流量;qdm,t表示电站m在时段t的弃水流量;Δt表示t时段小时数;Zm,T表示电站m在调度周期末的 水位;
Figure PCTCN2017092122-appb-000009
表示电站m在时段t的发电流量上限;Sm,t表示电站m在时段t的出库流量,
Figure PCTCN2017092122-appb-000010
Figure PCTCN2017092122-appb-000011
分别表示电站m在时段t的出库流量上下限;Zm,t表示电站m在时段t的出库水位,
Figure PCTCN2017092122-appb-000012
Figure PCTCN2017092122-appb-000013
分别表示电站m在时段t的库水位上下限;pm,t表示电站m在时段t的出力,
Figure PCTCN2017092122-appb-000014
Figure PCTCN2017092122-appb-000015
分别表示电站m在时段t的出力的上下限,
Figure PCTCN2017092122-appb-000016
表示电站m在时段t的最大泄流能力。
水电站典型日约束
(1)典型日电量约束
Em,t=Em,t′               (11)
式中Em,t和Em,t′分别表示电站m在第t个调度时段对应的典型日的总电量和需求电量。Em,t′的确定方式为:
Figure PCTCN2017092122-appb-000017
式中Ct表示第t个调度月份平均负荷需求。
(2)典型日出力功率上下限
Figure PCTCN2017092122-appb-000018
式中Pdaym,i,t表示电站m在时段t对应的典型日i时刻的出力,
Figure PCTCN2017092122-appb-000019
Figure PCTCN2017092122-appb-000020
分别表示电站m在时段t对应的典型日i时刻的出力上下限。
(3)典型日出力爬坡约束
|Pdaym,i,t-Pdaym,i-1,t|≤ΔPdaym            (14)
式中ΔPdaym表示电站m的出力变幅限制。
(4)典型日出力持续时间约束
(Pdaym,i-Δ+1,t-Pdaym,i-Δ,t)(Pdaym,i,t-Pdaym,i-1,t)≥0Δ=1,2,…vm        (15)
vm表示电站m最大最小输送功率最少持续的时段数。
直流输电线路典型日约束
(1)典型日输送功率上下限
Figure PCTCN2017092122-appb-000021
式中
Figure PCTCN2017092122-appb-000022
Figure PCTCN2017092122-appb-000023
分比表示直流水电输电线路在时段t对应典型日i时刻出力上下限。
(2)典型日送电功率变幅约束
Figure PCTCN2017092122-appb-000024
式中ΔP线m,t表示直流水电输电线路的典型日送电功率变幅限制。
(3)典型日送电功率持续时间约束
Figure PCTCN2017092122-appb-000025
式中v线表示直流水电输电线路最大最小输送功率最少持续的时段数
(b)考虑汛枯期差异的分阶段协调方法
跨省互联水电站群跨越多个省份,送受端电站具有明显的水文特性差异,通常呈现出汛枯期异步的特性,为发挥其互补协调能力,在调度周期内对水电站进行分组分阶段协调调度。在送受端电站汛枯期异步时段,两地水电站在典型日负荷中承担的工作位置不同,其中处于汛期的电站承担基荷,枯期电站承担峰荷,如附图1所示。
显然此阶段汛期电站应尽量发挥电量能力,不受峰谷差率约束限制,其目标应为发电量最大;枯期电站以降低电网典型日负荷峰谷差率为主要目标,以调节自身各月电量分配,补偿汛期电站。本文将汛期电站先投入计算,计算扣除汛期电站典型日出力后的典型日剩余负荷Cdayi,t R
Figure PCTCN2017092122-appb-000026
在此基础上,以典型日余荷Cdayi,t R作为枯期电站的面临负荷,对枯期电站进行试算,计算进一步扣除枯期电站典型日出力的余留负荷Cdayi,t R2
Figure PCTCN2017092122-appb-000027
此时枯期电站面临的典型日峰谷差率约束可表示为:
Figure PCTCN2017092122-appb-000028
式中i表示典型日时段编号,t表示月份编号,m表示电站编号,I表示典型日总时段数, R0表示峰谷差率,M表示汛期电站总数,M表示所有电站总数,Cdayi,t R1表示扣除汛期电站典型日出力的典型日剩余负荷,Cdayi,t表示典型日原始负荷,Pdaym,i,t表示第m座电站的典型日出力,Cdayi,t R2表示扣除所有电站典型日出力后的典型日剩余负荷。
通过谷差率约束重新分配枯期电站各月电量,实现典型日调峰的全局目标,结合上述步骤,求解顺序如附图2所示。
(c)跨省送电电站典型日约束处理方法
受端水电站可据式(12)确定其典型日发电量,进而根据成熟的逐次切负荷方法可处理典型日电站约束(11)-(15),求解其典型日出力过程。跨省送电水电站典型日电力过程除受电站自身约束外,还受式(16)-(18)特高压直流输电线路约束限制,直接采用逐次切负荷法求解其典型日出力过程,无法同时满足两方面约束。因此,本发明重构了跨省送电水电站群的典型日面临负荷,使面临负荷自然满足特高压输电线路约束,再用逐次切负荷法求解各电站典型日出力过程,以保证送端电站群典型日总出力同时满足约束(11)-(18),具体步骤如下:
(1)根据式(12)确定各送端电站典型日送电电量Em,t
(2)构造一个虚拟电站,替代送端水电站群,其典型日分配电量及各项约束表示如下:
典型日电量:Et=E1,t+E2,t+…+EM,t
虚拟电站出力上下限约束:
Figure PCTCN2017092122-appb-000029
虚拟电站出力爬坡约束:
Figure PCTCN2017092122-appb-000030
虚拟电站最大出力持续时间约束:
(Pday虚,i-Δ+1,t-Pday虚,i-Δ,t)(Pday虚,i,t-Pday虚,i-1,t)≥0,Δ=1,2,…v
式中
Figure PCTCN2017092122-appb-000031
表示虚拟电站最大最小输送功率最少持续的时段数。至此,该虚拟电站典型日出力过程可满足式所述特高压输电线路约束。
(3)以电网典型日负荷作为虚拟电站的面临负荷,采用逐次切负荷方法确定虚拟电站典型日出力过程{P1,t,P2,t,…,P24,t}。
(4)因送端水电站群调峰能力实际不如虚拟电站,故将虚拟电站的出力过程加上一个基数负荷,重构送端电站的面临负荷Pα,其24点电力过程值为{P1,t+P,P2,t+P,…,P24,t+P}。
(5)以Pα作为送端水电站群面临负荷,以逐次切负荷方法确定各送电电站典型日出力过 程,此时送电电站可同时满足电站典型日约束及特高压线路典型日约束。
(d)最优水位搜索策略
本文采用POA-DDDP算法求解跨省互联水电站群最优水位过程。POA-DDDP两阶段子问题搜索过程示意图如图4所示,具体优化步骤表示如下:
(1)令t=1,m=1,n=1;
(2)固定第m组第n座电站t时段初水位和t+1时段末水位,以t时段的出库流量
Figure PCTCN2017092122-appb-000032
作为决策变量,据所设初始流量离散步长ε,将第n组内各电站出库流量在
Figure PCTCN2017092122-appb-000033
上下进行离散,以各获取3个出库流量数值,记为
Figure PCTCN2017092122-appb-000034
组内所有电站
Figure PCTCN2017092122-appb-000035
构成3Mn个组合状态;
(3)依次在b=1,2,…,3Mn时按照上下游顺序进行调节计算:分组内的电站计算其t时段出库流量,t时段定流量调节,t+1时段定水位调节;分组外入库流量变化的电站进行t和t+1时段定水位调节。根据所述处理电站典型日约束,并以逐次切负荷方法更新电站t和t+1时段的典型日负出力,计算目标函数与惩罚函数值之差;
(4)令n=n+1,若n=该组总电站数N,则m=m+1转步骤5;否则转步骤2;
(5)若m=总电站组数M,则转步骤6;否则转步骤2;
(6)令ε=ε/2,若ε满足精度要求,则子问题搜索结束;否则转步骤1。
(e)总体求解方法步骤
结合上述关键问题的解决思路,一次完整的跨省互联水电站群长期联合调峰调度方法优化过程可通过如下步骤表述:
步骤1.读取基础数据,初始化计算参数,包括跨省互联水电站的运行条件约束及控制条件约束、跨区直流水电输电线路约束、受电电网月平均负荷、长期典型日负荷等计算参数;
步骤2.建立跨省互联水电站群长期联合调峰优化调度模型,该模型以发电量最大和枯期典型日调峰峰谷差率最小为优化目标;
步骤3.以常规发电量最大为目标对跨省互联送受端水电站群进行优化计算,生成模型初始解,同时记录此时电网枯期各月典型日剩余负荷峰谷差率的最大值R0
步骤4.设置枯期最小负荷峰谷差率约束R=R0-0.01;
步骤5.令迭代次数k=0。
步骤6.令计算时段t=1,并定义T为调度期总时段数;
步骤7.将送受端电站按照所属流域分组,计分组总数为N组;
步骤8.若k>0;判断是否有电站两阶段水位过程发生变化。若有,则重置流量搜索步长,转步骤9。若无,则令t=t+1,若t>T,转步骤15,否则重复本步骤;
步骤9.判断参与计算的水电站是否处于汛期,若是,则优先进行计算,否则,在汛期电站优化后再进行计算。令计算优先级最高的电站组排序定义为n=0;
步骤10.给定目标条件下,处理峰谷差率约束、跨区直流水电输电线路约束及其他常规水电站约束条件,采用POA-DDDP算法搜索模型两阶段最优流量过程;同时以逐次切负荷法处理电站t和t+1两阶段的典型日出力过程,计算两阶段典型日最小负荷峰谷差率Rt和Rt+1
步骤11.令n=n+1,若n<N,则重复步骤10,否则转步骤12;
步骤12.重复步骤9-11,判断相邻两次寻优结果组内电站水位过程是否有改变,若无,则转步骤13,否则,重复步骤12;
步骤13.缩小流量搜索进度,判断此时流量搜索进度是否符合进度要求,若是,转步骤9;否则,转步骤14;
步骤14.令t=t+1,若t>T,则令k=k+1,转步骤15;否则,转步骤8;
步骤15.判断各电站水位过程本轮迭代是否有改变,若无,转步骤16;否则,转步骤6。;
步骤16.统计各月枯期月份典型日最大峰谷差率Rmax=max{Rt′},t′包括所有枯期月份;
步骤17.若Rmax<R,则输出本次计算调度方案与枯期最小负荷峰谷差率约束值R,作为一个可行方案,并转步骤4;否则计算结束。
现以溪洛渡-浙江特高压直流输电工程送受端跨省互联水电站群为研究对象,采用本发明方法制作其调度方案。表1是参与计算电站的基础数据表,表2是以50%典型区间流量作为模型输入,多方案调度结果电量与枯水期峰谷差率表,图4是50%典型区间流量下模型多方案峰谷差率-发电量分布情况图,使用本发明所提的方法可制定如表2及图4所示的不同枯期峰谷差率下的最优电量调度方案,提供多种调度方案共调度人员优选。各方案兼顾了水电系统长期电量和电网调峰两方面的效益,调度人员可根据调度偏好,优选适用方法。图5优选了本发明制定的两个典型调度方案的发电过程,将其与常规调度方案的发电过程对比,可见本发明所提方法制定的调度方案水电系统枯期发电量有明显改变,相比常规发电量最大方案,枯期电量明显增加,提升了枯期水电的调峰能力。图6与图7表明本发明所提方案制定的调度方案主要改变了省内电站的水位出力过程,而省外送电电站水位出力过程变化不大,调度 执行难度较低,为我国特高压直流水电跨区跨省大规模输送提供了一种高效实用的技术手段。
表1 电站基础数据表
Figure PCTCN2017092122-appb-000036
表2 50%典型区间流量下多方案调度结果
Figure PCTCN2017092122-appb-000037

Claims (1)

  1. 一种跨省互联水电站群长期联合调峰调度方法,其特征在于,包括以下步骤:
    步骤1.读取基础数据,初始化计算参数,包括跨省互联水电站的运行条件约束及控制条件约束、跨区直流水电输电线路约束、受电电网月平均负荷、长期典型日负荷;
    步骤2.建立跨省互联水电站群长期联合调峰优化调度模型,该模型以发电量最大和枯期典型日调峰峰谷差率最小为优化目标;
    步骤3.以常规发电量最大为目标对跨省互联送受端水电站群进行优化计算,生成模型初始解,同时记录此时电网枯期各月典型日剩余负荷峰谷差率的最大值R0
    步骤4.设置枯期最小负荷峰谷差率约束R=R0-0.01;
    步骤5.令迭代次数k=0;
    步骤6.令计算时段t=1,并定义T为调度期总时段数;
    步骤7.将送受端电站按照所属流域分组,计分组总数为N组;
    步骤8.若k>0;判断是否有电站两阶段水位过程发生变化;若有,则重置流量搜索步长,转步骤9;若无,则令t=t+1,若t>T,转步骤15,否则重复本步骤;
    步骤9.判断参与计算的水电站是否处于汛期,若是,则优先进行计算,否则,在汛期电站优化后再进行计算;令计算优先级最高的电站组排序定义为n=0;
    步骤10.在给定目标条件下,处理峰谷差率约束、跨区直流水电输电线路约束及其他常规水电站约束条件,采用POA-DDDP算法搜索模型两阶段最优流量过程;同时以逐次切负荷法处理电站t和t+1两阶段的典型日出力过程,计算两阶段典型日最小负荷峰谷差率Rt和Rt+1
    步骤11.令n=n+1,若n<N,则重复步骤10,否则转步骤12;
    步骤12.重复步骤9-11,判断相邻两次寻优结果组内电站水位过程是否有改变,若无,则转步骤13,否则,重复步骤12;
    步骤13.缩小流量搜索进度,判断此时流量搜索进度是否符合进度要求,若是,转步骤9;否则,转步骤14;
    步骤14.令t=t+1,若t>T,则令k=k+1,转步骤15;否则,转步骤8;
    步骤15.判断各电站水位过程本轮迭代是否有改变,若无,转步骤16;否则,转步骤6;
    步骤16.统计各月枯期月份典型日最大峰谷差率Rmax=max{Rt′},t′包括所有枯期月份;
    步骤17.若Rmax<R,则输出本次计算调度方案与枯期最小负荷峰谷差率约束值R,作为一个可行方案,并转步骤4;否则计算结束。
PCT/CN2017/092122 2017-07-06 2017-07-06 一种跨省互联水电站群长期联合调峰调度方法 Ceased WO2019006733A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
JP2019503558A JP6646182B2 (ja) 2017-07-06 2017-07-06 省跨ぎ連通水力発電所群の長期間連合ピーキングスケジューリング方法
US16/322,829 US10534327B2 (en) 2017-07-06 2017-07-06 Method for long-term optimal operations of interprovincial hydropower system considering peak-shaving demands
PCT/CN2017/092122 WO2019006733A1 (zh) 2017-07-06 2017-07-06 一种跨省互联水电站群长期联合调峰调度方法

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2017/092122 WO2019006733A1 (zh) 2017-07-06 2017-07-06 一种跨省互联水电站群长期联合调峰调度方法

Publications (1)

Publication Number Publication Date
WO2019006733A1 true WO2019006733A1 (zh) 2019-01-10

Family

ID=64950487

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2017/092122 Ceased WO2019006733A1 (zh) 2017-07-06 2017-07-06 一种跨省互联水电站群长期联合调峰调度方法

Country Status (3)

Country Link
US (1) US10534327B2 (zh)
JP (1) JP6646182B2 (zh)
WO (1) WO2019006733A1 (zh)

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110570078A (zh) * 2019-07-22 2019-12-13 广东工业大学 一种基于可能性c-均值聚类的微电网中小水电站发电量计算方法
CN110717688A (zh) * 2019-10-16 2020-01-21 云南电网有限责任公司 考虑新能源出力不确定性的水风光短期联合优化调度方法
CN110991810A (zh) * 2019-11-07 2020-04-10 四川大学 一种考虑水光蓄互补的区域联合体两阶段经济调度方法
CN110991733A (zh) * 2019-11-29 2020-04-10 国网吉林省电力有限公司电力科学研究院 一种电力系统省间调峰需求和调峰能力评估分析方法
CN111539153A (zh) * 2020-04-07 2020-08-14 长江水利委员会长江科学院 一种基于预构泥沙信息库的水沙联合优化调度方法
CN111832900A (zh) * 2020-06-15 2020-10-27 华中科技大学 一种多年调节水库年末消落水位动态控制方法
CN111859620A (zh) * 2020-06-17 2020-10-30 扬州大学 一种基于机组转速的泵站单机组变速优化运行方法
CN112434876A (zh) * 2020-12-03 2021-03-02 华中科技大学 一种水电站调峰调度方法
CN112633578A (zh) * 2020-12-24 2021-04-09 国电电力发展股份有限公司和禹水电开发公司 一种引调水工程影响下梯级水库群优化调度方法
CN113033953A (zh) * 2021-02-07 2021-06-25 国网浙江省电力有限公司金华供电公司 一种基于大数据的用户侧需求响应决策建议方法
CN113240546A (zh) * 2021-05-11 2021-08-10 国网湖南省电力有限公司 密集水电地区的机组月度调度方法
CN113590676A (zh) * 2021-06-29 2021-11-02 长江水利委员会水文局 基于梯级联合等效防洪库容的流域防洪调度方法及系统
CN113627762A (zh) * 2021-07-30 2021-11-09 国网山西省电力公司电力科学研究院 一种基于激励电价的虚拟电厂调峰方法
CN114548603A (zh) * 2022-04-02 2022-05-27 国电南瑞南京控制系统有限公司 一种年度电量校核计算方法
CN115425697A (zh) * 2022-09-19 2022-12-02 中国南方电网有限责任公司 基于交替方向乘子法的分布式跨区跨省调度方法及系统
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
CN115456667A (zh) * 2022-09-02 2022-12-09 浙江大学 基于输电通道分配的电量分配方法、装置、设备及介质
CN116363134A (zh) * 2023-06-01 2023-06-30 深圳海清智元科技股份有限公司 煤与矸石的识别与分割方法、装置及电子设备
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
CN120046956A (zh) * 2025-04-24 2025-05-27 清华大学 虚拟电厂多时间尺度优化配置方法及装置
CN120542789A (zh) * 2025-05-08 2025-08-26 中山大学 一种基于水文模拟的水库调度方法和系统

Families Citing this family (63)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110120685B (zh) * 2019-05-23 2023-04-07 国家电网公司西南分部 高水电比重系统中梯级水电群与风光电站协调调峰方法
CN110472824B (zh) * 2019-07-09 2023-04-07 贵州黔源电力股份有限公司 一种考虑调峰需求的梯级水电站短期多目标优化调度方法
CN110707760B (zh) * 2019-07-09 2021-11-16 中国电力科学研究院有限公司 一种基于常规电源开机获取新能源临界占比的方法及系统
CN110348740B (zh) * 2019-07-12 2021-01-12 华能四川水电有限公司 基于大数据的多流域水电站集群发电计划系统
CN110365057B (zh) * 2019-08-14 2022-12-06 南方电网科学研究院有限责任公司 基于强化学习的分布式能源参与配电网调峰调度优化方法
CN110490472A (zh) * 2019-08-21 2019-11-22 四川中鼎科技有限公司 一种基于物联网的水电站群技术监督智能服务系统、方法、终端及存储介质
CN110751365B (zh) * 2019-09-11 2022-03-25 华中科技大学 一种梯级水库群多目标均衡调度方法与系统
CN111191868B (zh) * 2019-09-26 2022-04-29 深圳市东深电子股份有限公司 一种基于动态规划模型的水闸优化调度方法
CN110852565B (zh) * 2019-10-10 2023-05-12 国家电网有限公司 一种考虑不同功能属性的输电网网架规划方法
CN110867902B (zh) * 2019-10-15 2023-05-16 东北大学 基于发电预测的微电网分布式电源去中心优化运行方法
CN110854933B (zh) * 2019-11-26 2023-05-02 三峡大学 利用灵活性资源的月度机组组合优化方法
CN110929947B (zh) * 2019-11-29 2022-03-18 广东电网有限责任公司广州供电局 储能配置方法、装置、计算机设备和存储介质
CN110867857B (zh) * 2019-11-29 2021-10-15 沈阳工业大学 一种基于风储联合系统的调峰机组组合的优化方法
CN110929959B (zh) * 2019-12-11 2024-05-07 国网安徽省电力有限公司 一种电网局部深度调峰费用分摊方法及装置
CN111222711B (zh) * 2020-01-16 2022-04-08 大连理工大学 一种基于指标联动分析的电力系统调峰调度多目标优化方法
CN111428970B (zh) * 2020-03-05 2023-05-05 三峡大学 一种大规模水电站群跨省外送能力分析模型及求解方法
CN111353649B (zh) * 2020-03-06 2022-03-29 大连理工大学 一种基于kl展开的大规模水电站群优化调度降维方法
CN111461419B (zh) * 2020-03-25 2023-05-30 长江水利委员会长江科学院 一种通航河流水库水沙联合调度方案生成方法
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
CN111583064B (zh) * 2020-05-11 2022-09-09 国网四川省电力公司电力科学研究院 基于动态时间规整的负荷生产时段检测方法和存储介质
CN111709605B (zh) * 2020-05-19 2023-06-06 成都大汇智联科技有限公司 一种基于多重反调节作用的水库电站调峰能力评估方法
CN111612270B (zh) * 2020-05-28 2022-04-12 国家电网公司西南分部 考虑龙头水库适应性的清洁能源外送规划及运行优化方法
CN111612267B (zh) * 2020-05-28 2022-07-01 国家电网公司西南分部 考虑远景水平年的水电集群送出网架优化方法
CN111817320B (zh) * 2020-06-04 2021-12-07 国网宁夏电力有限公司经济技术研究院 计及蒸发影响的抽水蓄能电站调峰能力分析方法
CN111799778A (zh) * 2020-06-11 2020-10-20 国网山东省电力公司经济技术研究院 一种计及调峰需求的含风电电力系统储能容量优化方法
CN113962419B (zh) * 2020-07-20 2024-05-31 浙江大学 基于改进多目标布谷鸟搜索算法的热电联产机组负荷优化分配方法
CN111934362B (zh) * 2020-07-29 2022-11-15 国网甘肃省电力公司电力科学研究院 一种可再生能源的波动特性的多电源协调优化调峰方法
CN114069601B (zh) * 2020-08-04 2025-07-04 国家能源投资集团有限责任公司 协同电厂深度调峰的储能系统的配置方法和设备
CN112186813B (zh) * 2020-09-30 2022-07-29 中国南方电网有限责任公司 一种电力系统区域电网调度方法、系统、装置及存储介质
CN112311018A (zh) * 2020-10-12 2021-02-02 国网甘肃省电力公司电力科学研究院 一种配套调峰电源调节和多源协调调峰方法
CN112383097B (zh) * 2020-11-02 2025-02-25 湖南江河机电自动化设备股份有限公司 基于水位变化速率的自动优化发电控制系统
CN112818549B (zh) * 2021-02-05 2022-09-30 四川大学 一种水电站负荷优化分配的分级降维动态规划方法
CN112785087B (zh) * 2021-02-22 2022-02-01 中国水利水电科学研究院 一种考虑水力响应特性的跨流域调水工程旬水量优化调度计划编制方法
CN113065980B (zh) * 2021-03-23 2022-07-12 水利部海河水利委员会水资源保护科学研究所 一种面向河流生态需水的多水源优化配置方法
CN113112082B (zh) * 2021-04-21 2023-04-28 上海电力大学 一种针对分布式系统的两阶段运行优化方法
CN113205273B (zh) * 2021-05-20 2024-03-29 国网山西省电力公司经济技术研究院 一种计及区外电能交易的低碳化电源规划方法及系统
CN114389313A (zh) * 2021-12-03 2022-04-22 国网上海市电力公司 一种虚拟电厂参与的电网深度调峰调度方法、装置和介质
CN114417625B (zh) * 2022-01-24 2024-03-26 太原理工大学 一种考虑风气互补特性的季节储能解决方法
CN114781682B (zh) * 2022-03-01 2023-10-27 中国长江电力股份有限公司 基于变库容法的水库调峰增加出力坝前水位变化预测方法
CN114759611B (zh) * 2022-04-07 2025-02-07 天生桥一级水电开发有限责任公司水力发电厂 一种水电厂厂级自动发电控制系统的负荷分配方法
CN114925893B (zh) * 2022-05-09 2024-06-04 重庆大学 一种考虑源荷时序模拟与水库月间协调的跨省跨区全年购电策略分层优化方法
CN115409234B (zh) * 2022-06-06 2023-10-27 中国长江电力股份有限公司 一种基于混合算法的梯级水电站优化调度模型求解方法
CN115099494B (zh) * 2022-06-27 2024-07-16 中国南方电网有限责任公司 兼顾调峰与通航的梯级水电站多目标调度的milp方法
CN115438852B (zh) * 2022-08-31 2025-10-03 三峡大学 一种梯级水电站群短期调峰实用化求解方法
CN115514015A (zh) * 2022-09-23 2022-12-23 中国南方电网有限责任公司 一种煤电机组日电量生成方法、装置及设备
CN115619102B (zh) * 2022-11-14 2023-04-07 中国能源建设集团湖南省电力设计院有限公司 一种基于新能源弃电率的电气计算校核方法
CN116205331A (zh) * 2022-12-14 2023-06-02 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) 一种分时电价的含燃煤自备电厂企业发用电策略优化方法
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
CN116742639B (zh) * 2023-04-28 2024-02-20 国家电投集团江西峡江发电有限公司 一种日调节水电站发电优化运行方法及系统
CN116822900B (zh) * 2023-07-14 2025-07-25 中国长江三峡集团有限公司 一种耦合可行域与并行计算的随机动态规划算法
CN117060454B (zh) * 2023-08-14 2025-03-28 上海大学 一种考虑电氢协同运行的配电网两阶段协调电压控制方法
CN116757446B (zh) * 2023-08-14 2023-10-31 华中科技大学 基于改进粒子群算法的梯级水电站调度方法及系统
CN116760122B (zh) * 2023-08-21 2023-12-26 国网浙江省电力有限公司宁波供电公司 虚拟电厂资源管控方法、装置、计算机设备及存储介质
CN116882718B (zh) * 2023-09-08 2023-12-01 湖南大学 高温干旱天气下配电网和流域网灵活性资源聚合调控方法
CN117013535B (zh) * 2023-09-28 2023-12-26 长江勘测规划设计研究有限责任公司 一种兼顾生态调度需求的水风光火互补容量配置方法
CN117433111A (zh) * 2023-10-16 2024-01-23 广东电网有限责任公司广州供电局 考虑空调集群需求响应策略的负荷预测方法
CN117874470B (zh) * 2024-03-11 2024-06-07 中电装备山东电子有限公司 一种专变采集终端监测数据分析处理方法
CN118396262B (zh) * 2024-03-20 2025-09-05 西安理工大学 多主体混合式抽水蓄能电站联合调度下的利益分配方法
CN119253767A (zh) * 2024-12-03 2025-01-03 河海大学 基于分布鲁棒的日前调峰调度方法、装置、设备及介质
CN119582286B (zh) * 2025-02-08 2025-05-27 四川大学 兼顾调峰与风险规避的混合式抽蓄优化调度方法及系统
CN120582113B (zh) * 2025-08-01 2025-10-17 华电环球(北京)贸易发展有限公司 一种发电调度方法及相关装置
CN120657867B (zh) * 2025-08-15 2025-11-28 国网上海市电力公司 一种虚拟电厂参与超大规模电网的优化调度方法及系统
CN121503950A (zh) * 2025-09-25 2026-02-10 国家电网有限公司华东分部 省间电力调度方法、装置、计算机设备及可读存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102034159A (zh) * 2010-12-21 2011-04-27 福建省电力有限公司 大规模跨流域水电站群智能调度系统
CN104063808A (zh) * 2014-06-27 2014-09-24 大连理工大学 一种跨省送电梯级水电站群调峰调度两阶段搜索方法
CN104537445A (zh) * 2015-01-13 2015-04-22 大连理工大学 一种网省两级多电源短期协调调峰方法
CN106786790A (zh) * 2016-11-19 2017-05-31 国网浙江省电力公司 一种含水气煤核电的省级电网长期多电源协调调度方法

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH11285152A (ja) * 1998-03-27 1999-10-15 Hitachi Ltd 電力需給調整装置及びその方法
US9811061B1 (en) * 2001-05-18 2017-11-07 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption
JP2006260004A (ja) * 2005-03-16 2006-09-28 Tokyo Electric Power Co Inc:The 潜在出力算出装置、方法及びコンピュータプログラム
US8396572B2 (en) * 2009-09-11 2013-03-12 Siemens Corporation System and method for energy plant optimization using mixed integer-linear programming
US8626352B2 (en) * 2011-01-26 2014-01-07 Avista Corporation Hydroelectric power optimization service
US9026257B2 (en) * 2011-10-06 2015-05-05 Avista Corporation Real-time optimization of hydropower generation facilities
US9098876B2 (en) * 2013-05-06 2015-08-04 Viridity Energy, Inc. Facilitating revenue generation from wholesale electricity markets based on a self-tuning energy asset model
WO2015031581A1 (en) * 2013-08-28 2015-03-05 Robert Bosch Gmbh System and method for energy asset sizing and optimal dispatch
CN103942728B (zh) * 2014-04-11 2017-02-08 武汉大学 梯级水电站群日发电计划编制方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102034159A (zh) * 2010-12-21 2011-04-27 福建省电力有限公司 大规模跨流域水电站群智能调度系统
CN104063808A (zh) * 2014-06-27 2014-09-24 大连理工大学 一种跨省送电梯级水电站群调峰调度两阶段搜索方法
CN104537445A (zh) * 2015-01-13 2015-04-22 大连理工大学 一种网省两级多电源短期协调调峰方法
CN106786790A (zh) * 2016-11-19 2017-05-31 国网浙江省电力公司 一种含水气煤核电的省级电网长期多电源协调调度方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
WU, XINYU: "Long-Term Hydropower Optimal Operation Model for Peak Load Regulating of Typical Days", SHOWCASING THE FUTURE : WORLD ENVIRONMENTAL AND WATER RESOURCES CONGRESS (EWRI 2013) ; CINCINNATI, OHIO, USA, 19 - 23 MAY 2013 ; [PROCEEDINGS OF THE 2013 CONGRESS ; INCLUDING VARIOUS SYMPOSIA], vol. 3, 31 May 2013 (2013-05-31), pages 2190 - 2199, XP009519903, ISBN: 9780784412947, DOI: 10.1061/9780784412947.216 *

Cited By (33)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110570078A (zh) * 2019-07-22 2019-12-13 广东工业大学 一种基于可能性c-均值聚类的微电网中小水电站发电量计算方法
CN110570078B (zh) * 2019-07-22 2022-10-04 广东工业大学 一种基于可能性c-均值聚类的微电网中小水电站发电量计算方法
CN110717688A (zh) * 2019-10-16 2020-01-21 云南电网有限责任公司 考虑新能源出力不确定性的水风光短期联合优化调度方法
CN110717688B (zh) * 2019-10-16 2022-07-01 云南电网有限责任公司 考虑新能源出力不确定性的水风光短期联合优化调度方法
CN110991810A (zh) * 2019-11-07 2020-04-10 四川大学 一种考虑水光蓄互补的区域联合体两阶段经济调度方法
CN110991733A (zh) * 2019-11-29 2020-04-10 国网吉林省电力有限公司电力科学研究院 一种电力系统省间调峰需求和调峰能力评估分析方法
CN110991733B (zh) * 2019-11-29 2022-06-14 国网吉林省电力有限公司电力科学研究院 一种电力系统省间调峰需求和调峰能力评估分析方法
CN111539153B (zh) * 2020-04-07 2023-12-08 长江水利委员会长江科学院 一种基于预构泥沙信息库的水沙联合优化调度方法
CN111539153A (zh) * 2020-04-07 2020-08-14 长江水利委员会长江科学院 一种基于预构泥沙信息库的水沙联合优化调度方法
CN111832900A (zh) * 2020-06-15 2020-10-27 华中科技大学 一种多年调节水库年末消落水位动态控制方法
CN111832900B (zh) * 2020-06-15 2024-02-09 华中科技大学 一种多年调节水库年末消落水位动态控制方法
CN111859620A (zh) * 2020-06-17 2020-10-30 扬州大学 一种基于机组转速的泵站单机组变速优化运行方法
CN111859620B (zh) * 2020-06-17 2024-02-23 扬州大学 一种基于机组转速的泵站单机组变速优化运行方法
CN112434876B (zh) * 2020-12-03 2023-10-20 华中科技大学 一种水电站调峰调度方法
CN112434876A (zh) * 2020-12-03 2021-03-02 华中科技大学 一种水电站调峰调度方法
CN112633578A (zh) * 2020-12-24 2021-04-09 国电电力发展股份有限公司和禹水电开发公司 一种引调水工程影响下梯级水库群优化调度方法
CN113033953A (zh) * 2021-02-07 2021-06-25 国网浙江省电力有限公司金华供电公司 一种基于大数据的用户侧需求响应决策建议方法
CN113033953B (zh) * 2021-02-07 2023-08-25 国网浙江省电力有限公司金华供电公司 一种基于大数据的用户侧需求响应决策建议方法
CN113240546B (zh) * 2021-05-11 2022-05-20 国网湖南省电力有限公司 密集水电地区的机组月度调度方法
CN113240546A (zh) * 2021-05-11 2021-08-10 国网湖南省电力有限公司 密集水电地区的机组月度调度方法
CN113590676A (zh) * 2021-06-29 2021-11-02 长江水利委员会水文局 基于梯级联合等效防洪库容的流域防洪调度方法及系统
CN113627762A (zh) * 2021-07-30 2021-11-09 国网山西省电力公司电力科学研究院 一种基于激励电价的虚拟电厂调峰方法
CN113627762B (zh) * 2021-07-30 2024-03-22 国网山西省电力公司电力科学研究院 一种基于激励电价的虚拟电厂调峰方法
CN114548603A (zh) * 2022-04-02 2022-05-27 国电南瑞南京控制系统有限公司 一种年度电量校核计算方法
CN115456667A (zh) * 2022-09-02 2022-12-09 浙江大学 基于输电通道分配的电量分配方法、装置、设备及介质
CN115425697A (zh) * 2022-09-19 2022-12-02 中国南方电网有限责任公司 基于交替方向乘子法的分布式跨区跨省调度方法及系统
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
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
CN116363134B (zh) * 2023-06-01 2023-09-05 深圳海清智元科技股份有限公司 煤与矸石的识别与分割方法、装置及电子设备
CN116363134A (zh) * 2023-06-01 2023-06-30 深圳海清智元科技股份有限公司 煤与矸石的识别与分割方法、装置及电子设备
CN120046956A (zh) * 2025-04-24 2025-05-27 清华大学 虚拟电厂多时间尺度优化配置方法及装置
CN120542789A (zh) * 2025-05-08 2025-08-26 中山大学 一种基于水文模拟的水库调度方法和系统

Also Published As

Publication number Publication date
US20190187637A1 (en) 2019-06-20
US10534327B2 (en) 2020-01-14
JP6646182B2 (ja) 2020-02-14
JP2019525705A (ja) 2019-09-05

Similar Documents

Publication Publication Date Title
WO2019006733A1 (zh) 一种跨省互联水电站群长期联合调峰调度方法
CN107274302B (zh) 一种跨省互联水电站群长期联合调峰调度方法
CN106981888B (zh) 基于多源互补的风蓄水火电力系统的多目标动态调度方法
CN107910883B (zh) 基于抽水蓄能电站修正时序负荷曲线的随机生产模拟方法
CN106786790A (zh) 一种含水气煤核电的省级电网长期多电源协调调度方法
CN116667395B (zh) 基于梯级水电改造的水风光蓄互补泵站容量配置方法
CN104268653B (zh) 基于集束径流预报的梯级水库优化调度方法
CN106682810B (zh) 巨型水电站动态投产下跨流域梯级水电站群长期运行方法
CN118381124B (zh) 支撑新能源灵活性的梯级龙头水电站水位预测与调控方法
CN110350523A (zh) 基于需求响应的多能源互补优化调度方法
CN105260801B (zh) 一种水电富集电网大规模电站群长期电力电量平衡分析方法
CN105427017B (zh) 一种水电富集电网特大规模电站群短期计划编制方法
CN116526469A (zh) 一种水风光互补系统长期随机动态调度方法
CN110994606B (zh) 一种基于复杂适应系统理论的多能源电源容量配置方法
Yue et al. Dispatch optimization study of hybrid pumped storage-wind-photovoltaic system considering seasonal factors
CN118297491B (zh) 考虑水电站与光伏电站嵌套调度的光伏容量计算方法
CN117526446A (zh) 梯级水风光多能互补发电系统风光容量双层优化配置方法
CN118539520B (zh) 一种基于启发式可行解策略的水风光蓄多能互补运行模拟智能优化方法
CN119582286B (zh) 兼顾调峰与风险规避的混合式抽蓄优化调度方法及系统
CN115271244A (zh) 一种基于两阶段分布鲁棒优化的梯级水电站短期调峰模型
CN117013535A (zh) 一种兼顾生态调度需求的水风光火互补容量配置方法
CN119315639A (zh) 考虑混合式抽蓄电站季调节特性的水风光蓄互补调度方法
CN107992980A (zh) 一种耦合相对目标接近度和边际分析原理的梯级水电站多目标优化调度方法
CN110783927B (zh) 多时间尺度交直流配电网调度方法及装置
CN110826806B (zh) 一种结合聚合水库和模拟优化的梯级水电站优化调度规则制定方法

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2019503558

Country of ref document: JP

Kind code of ref document: A

121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 17916653

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: 17916653

Country of ref document: EP

Kind code of ref document: A1