CN107895221B - Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment - Google Patents

Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment Download PDF

Info

Publication number
CN107895221B
CN107895221B CN201711013528.2A CN201711013528A CN107895221B CN 107895221 B CN107895221 B CN 107895221B CN 201711013528 A CN201711013528 A CN 201711013528A CN 107895221 B CN107895221 B CN 107895221B
Authority
CN
China
Prior art keywords
hydropower station
maintenance
medium
optimization
output
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201711013528.2A
Other languages
Chinese (zh)
Other versions
CN107895221A (en
Inventor
张粒子
刘方
唐成鹏
王昀昀
蒋燕
李秀峰
吴洋
涂启玉
王帮灿
周娜
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.)
Kunming Electric Power Transaction Center Co ltd
Beijing Weikenfolai Technology Co ltd
North China Electric Power University
Yunnan Power Grid Co Ltd
Original Assignee
Kunming Electric Power Transaction Center Co ltd
Beijing Weikenfolai Technology Co ltd
North China Electric Power University
Yunnan Power Grid Co Ltd
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 Kunming Electric Power Transaction Center Co ltd, Beijing Weikenfolai Technology Co ltd, North China Electric Power University, Yunnan Power Grid Co Ltd filed Critical Kunming Electric Power Transaction Center Co ltd
Priority to CN201711013528.2A priority Critical patent/CN107895221B/en
Publication of CN107895221A publication Critical patent/CN107895221A/en
Application granted granted Critical
Publication of CN107895221B publication Critical patent/CN107895221B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0206—Price or cost determination based on market factors
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S50/00—Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
    • Y04S50/14—Marketing, i.e. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Development Economics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Accounting & Taxation (AREA)
  • Game Theory and Decision Science (AREA)
  • Finance (AREA)
  • Health & Medical Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Educational Administration (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Operations Research (AREA)
  • Biophysics (AREA)
  • Quality & Reliability (AREA)
  • Data Mining & Analysis (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • General Health & Medical Sciences (AREA)
  • Genetics & Genomics (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Biomedical Technology (AREA)
  • Artificial Intelligence (AREA)
  • Mathematical Physics (AREA)
  • Physiology (AREA)
  • Molecular Biology (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Evolutionary Computation (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • Primary Health Care (AREA)

Abstract

The invention belongs to the technical field of scheduling and overhauling of cascade hydropower stations, and particularly relates to a medium-long-term scheduling and overhauling double-layer optimization method for cascade hydropower stations in a market environment. In order to reduce the influence of the step hydropower station maintenance plan on the operation income, the invention realizes the combined optimization of the medium-and-long-term dispatching and the maintenance plan by constructing a double-layer optimization model of the medium-and-long-term dispatching and the maintenance plan of the step hydropower station. In the process of medium-long term optimization scheduling, runoff and price prediction are used as references, decision is made according to the runoff and the price prediction, the output of the cascade hydropower stations in each time period is optimized by applying a genetic algorithm, and an optimal benefit space is searched; in the maintenance plan optimization process, the influence of the hydraulic power and electric power coupling relation of the upstream hydropower station and the downstream hydropower station on the maintenance plan is mainly considered, the intermediate result of the medium-long term scheduling is taken as a boundary condition, the minimum maintenance loss is taken as an optimization target, and the maintenance loss optimization result and the medium-long term power generation yield are merged into a total yield, so that the combined optimization is realized.

Description

Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment
Technical Field
The invention belongs to the technical field of scheduling and maintenance of cascade hydropower stations, and particularly relates to a medium-long-term scheduling and maintenance plan optimization method for cascade hydropower stations in a market environment.
Background
Through the development of 'drainage basin, cascade, rolling and comprehensive' for many years, extra large drainage basins and main flow cascade hydropower stations are gradually formed in the southwest area of China, and with the continuous promotion of electric power marketization construction, the trend that cascade hydropower stations participate in the electric power market and bid together with other types of power supplies becomes. The cascade hydropower stations are increased in stages and installed scale gradually, and the operation state of the cascade hydropower stations has increasingly obvious influence on the operation benefit of hydropower enterprises and the safety and stability of an electric power system. The operation mode of the hydropower station is limited by runoff change and reservoir capacity, and the maximum flexible output range is directly influenced by the unit maintenance plan, so that the electric energy supply capacity of the hydropower station is indirectly determined. Under the market environment, the water and electricity industry is as independent economic entity, and it will become main operation target to realize the biggest benefit, need synthesize when formulating the maintenance plan and consider various factors, for example: the economic benefit generated by timely maintenance; potential losses due to improper maintenance cycle scheduling include extra cost caused by excessive maintenance, outage risk loss caused by excessively long maintenance cycles, and the like. How to rationally arrange step hydropower station maintenance plan under market environment, reduce the influence to the operation income, effectively utilize the water and electricity resource, become the problem that the water and electricity enterprise needs to solve urgently. At present, scholars at home and abroad make intensive research on how a unit maintenance plan is formulated by a power generation enterprise, but most researches separately process a unit output plan and a maintenance plan, namely, the output plan formulation takes a given maintenance state as a premise, and the economic operation optimization space is limited to a certain extent. The idea of comprehensive optimization of unit maintenance and scheduling plan is provided in literature, decision is made according to medium-long term bilateral contract fixed capacity and price and short-term market price prediction, the comprehensive benefit is obviously improved, but a research object is mainly a conventional thermal power unit, and the method is lack of adaptability to hydropower stations which are limited in unit capacity and bear social functions such as flood control, irrigation and the like.
Disclosure of Invention
Aiming at the problems, the invention provides a double-layer optimization method for long-term scheduling and maintenance planning of a cascade hydropower station in a market environment, which comprises the following steps:
step 1: establishing a medium-long term scheduling and maintenance plan double-layer optimization model by taking the maximum profit as a target, wherein an objective function is expressed as:
maxRtotal＝RG+Loss
in the formula: rtotalFor total profit, RGAnticipating electricity sales revenue for long-and-medium-term electricity generation, LossRepairing expected losses for the hydroelectric generating set;
step 2: making a decision according to runoff and price prediction, optimizing the output of the cascade hydropower stations in each time period, and searching for an optimal benefit space;
and step 3: considering the influence of the coupling relation between the water power and the electric power of the upstream hydropower station and the downstream hydropower station on the maintenance plan and the characteristic that the output of the hydroelectric generating set is limited by runoff change, taking the intermediate result of optimizing the output of the cascade hydropower station in each time interval in the step 2 as the boundary condition of the optimization decision of the maintenance plan, and optimizing the maintenance plan of the cascade hydropower station by taking the minimum maintenance loss as the optimization target;
and 4, step 4: merging the intermediate result of optimizing the output of the cascade hydropower station in each time period in the step 2 with the maintenance planning optimization result in the step 3, and performing iterative optimization until the total yield is maximized.
The medium-long term scheduling and maintenance plan double-layer optimization model comprises a medium-long term optimization scheduling model and a maintenance plan optimization model, wherein the medium-long term optimization scheduling model is used for performing medium-long term scheduling optimization on the cascade hydropower station, and the maintenance plan optimization model is used for optimizing a maintenance plan of the cascade hydropower station.
The step 2 adopts a genetic algorithm to optimize the output of the cascade hydropower stations in each time period, and comprises the following specific steps:
1) randomly generating an initial chromosome population by setting parameters of a genetic algorithm, wherein the parameters comprise a cross rate, a variation rate and iteration times;
2) calculating the output and the electricity selling income of each hydropower station meeting the constraint through chromosome decoding;
3) compiling a maintenance plan according to the output of each power station, and calculating maintenance loss and individual fitness;
4) judging whether the iteration times are reached, if so, outputting the optimal solution, otherwise, returning to the step 2) to continue the chromosome decoding until the iteration times are met, and outputting the optimal solution.
And 3, carrying out maintenance plan optimization on the cascade hydropower station by adopting a 0-1 planning algorithm.
The minimum overhaul losses include overhaul gain losses and outage risk losses.
The medium and long term optimization scheduling model comprises the following steps:
an objective function:
Figure BDA0001445999240000031
Figure BDA0001445999240000032
Figure BDA0001445999240000033
in the formula, RGAnticipating the total income of electricity sale for medium-and long-term electricity generation; t, t respectively representing the total number and number of periods in the scheduling period; n, n are the total number and serial number of hydropower stations respectively; m, m are respectively the total number and serial number of the medium and long term bilateral contracts;
Figure BDA0001445999240000034
respectively, bilateral contract price and capacity;
Figure BDA0001445999240000035
respectively predicting the weighted average value of the coming price and the selling capacity of the market at the present day; pn,tThe output of the hydropower station n in the time period t is obtained; etanThe integrated average output coefficient of the hydropower station n;
Figure BDA0001445999240000036
Hn,trespectively averaging the generating capacity and the water purifying head of the hydropower station n in a time period t; Δ t is the optimized time granularity; zn,t、
Figure BDA0001445999240000037
Respectively determining a reservoir water level, a tail water level and a head loss of the hydropower station n in a time period t;
constraint conditions are as follows:
(1) reservoir level constraint:
Figure BDA0001445999240000038
(2) and (3) power generation flow restriction:
Figure BDA0001445999240000039
(3) and (3) restricting the downward flow:
Figure BDA00014459992400000310
(4) force restraint:
Figure BDA00014459992400000311
in the formula (I), the compound is shown in the specification,
Figure BDA0001445999240000041
respectively setting a lower limit and an upper limit of the reservoir water level of the hydropower station i in a time period t;
Figure BDA0001445999240000042
respectively setting a minimum limit value and a maximum limit value of the generated flow of the hydropower station i in a time period t;
Figure BDA0001445999240000043
the lower discharge upper limit value of the hydropower station i in the time period t;
Figure BDA0001445999240000044
the minimum and maximum output limits of the hydropower station i in the time period t are respectively.
The maintenance plan optimization model comprises the following steps:
an objective function:
Figure BDA0001445999240000045
Figure BDA0001445999240000046
Figure BDA0001445999240000047
Figure BDA0001445999240000048
Figure BDA0001445999240000049
in the formula (I), the compound is shown in the specification,
Figure BDA00014459992400000410
to an available capacity of
Figure BDA00014459992400000411
Loss of revenue in time;
Figure BDA00014459992400000412
is a hydropower station n unit gnLoss of outage risk; alpha is a weight coefficient;
Figure BDA00014459992400000413
the total capacity of n non-overhaul units of the hydropower station is obtained;
Figure BDA00014459992400000414
is in a maintenance state;
Figure BDA00014459992400000415
is a unit gnRated capacity of (d);
Figure BDA00014459992400000416
loss of revenue for maintenance; s, S and Pr (S) are respectively the total number of output typical scenes, the scene number and the probability of the cascade hydropower station without maintenance; t 'and T' are respectively scene time scale and time interval number;
Figure BDA00014459992400000417
representing the maintenance profit loss under a typical scene s;
Figure BDA00014459992400000418
clearing price for market time t' day before scene s;
Figure BDA00014459992400000419
respectively as a time period t' in n scenes s of a hydropower stationCalculating the output, and maintaining and adjusting the output;
Figure BDA00014459992400000420
risk loss for outage;
Figure BDA00014459992400000421
a maintenance interval;
Figure BDA00014459992400000422
the unit outage probability;
Figure BDA00014459992400000423
loss of one outage;
constraint conditions are as follows:
(1) and (4) maintenance capacity constraint:
Figure BDA0001445999240000051
Figure BDA0001445999240000052
in the formula, Gn、gnRespectively the total number and the serial number of the hydroelectric generator sets in the hydropower station n;
Figure BDA0001445999240000053
is in a maintenance state;
Figure BDA0001445999240000054
is a unit g in a hydropower station nnThe maximum output of (c); pn,tThe output of the hydropower station n in the time period t is obtained;
Figure BDA0001445999240000055
the maximum output of the hydropower station n; alpha is alphan,t、βn,tAll represent coupling coefficients;
(2) and (3) restricting the maintenance duration and continuity:
Figure BDA0001445999240000056
Figure BDA0001445999240000057
in the formula, D is the total overhaul time interval of the unit, and T, t respectively represents the total number and the number of the time intervals in the scheduling period;
(3) earliest, latest start of overhaul time constraint:
Figure BDA0001445999240000058
in the formula: t isearly、TlaterRespectively presetting earliest and latest overhaul time,
Figure BDA0001445999240000059
for maintenance intervals.
The hydraulic and electric coupling relation of the step upstream hydropower station and the step downstream hydropower station is as follows:
Figure BDA00014459992400000510
order to
Figure BDA00014459992400000511
Then P isn,t＝αn,t·Pn-1,t+βn,t
In the formula, Pn,tIs the output, eta, of the hydropower station n during the time period tnIs the comprehensive average output coefficient of the hydropower station n,
Figure BDA00014459992400000512
Hn,taverage generation capacity and clear water head, I, for hydropower station n in time period tn,tFor natural inflow of reservoir, alpha, in time period t of hydropower stationn,t、βn,tBoth represent coupling coefficients.
The invention has the beneficial effects that:
compared with the current output plan and maintenance plan combined optimization method, the invention provides a dual-layer optimization method for medium-and-long-term scheduling and maintenance plan of a cascade hydropower station in an electric power market environment, the method fully considers the characteristics of hydraulic power and electric power coupling of upstream and downstream hydropower stations and the limitation of the output of the hydroelectric generating set to runoff change, and constructs a dual-layer optimization model for medium-and-long-term scheduling and unit maintenance plan of the cascade hydropower station, wherein the outer layer of the model is used as an optimal layer for medium-and-long-term scheduling, and a decision is made according to runoff and price prediction to optimize the output of the cascade hydropower station in each time period and search an optimal benefit space. The inner layer of the model is used as an optimization layer of the maintenance plan, and the influence of the hydraulic power and electric power coupling relation of the upstream hydropower station and the downstream hydropower station on the maintenance plan is mainly considered so as to meet the actual operation requirement; merging and iteratively optimizing the maintenance plan optimization result and the power generation plan intermediate result, and realizing the combined optimization of the medium-term and long-term power generation plan and the maintenance plan.
Drawings
FIG. 1 is a medium-and-long-term dispatch and maintenance plan double-layer optimization framework;
FIG. 2 shows the estimated output, the overhaul adjusted output and the day-ahead market clearing price;
FIG. 3 is a graph of revenue variation resulting from unit maintenance;
FIG. 4 is a flow chart of genetic algorithm solving;
FIG. 5 is a medium and long term output plan optimization result;
FIG. 6 shows the variation of the water level of reservoir A;
FIG. 7 shows the results of the optimization of the year-round maintenance schedule;
FIG. 8 is the result of the horizontal year overhaul plan optimization;
FIG. 9 shows the optimization results of the dry year overhaul plan;
FIG. 10 is the result of an open-water annual overhaul plan optimization without consideration of hydraulic coupling;
fig. 11 shows the results of the horizontal year maintenance plan optimization (α ═ 0.4);
fig. 12 shows the results of the horizontal year maintenance plan optimization (α ═ 0.6);
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
In order to reduce the influence of a cascade hydropower station maintenance plan on operation income in an electric power market environment, the invention provides a medium-long term scheduling and maintenance plan optimization method for a cascade hydropower station in the market environment, which comprises the following steps:
step 1: establishing a medium-long term scheduling and maintenance plan double-layer optimization model;
step 2: making a decision according to runoff and price prediction, optimizing the output of the cascade hydropower stations in each time period, and searching for an optimal benefit space;
and step 3: considering the influence of the hydraulic and electric coupling relation of the upstream and downstream cascade hydropower stations on the maintenance plan and the characteristic that the output of the hydroelectric generating set is limited by runoff change, taking the optimization result of the step 2 as the boundary condition of the maintenance plan optimization decision, and optimizing the maintenance plan of the cascade hydropower stations by taking the minimum maintenance loss as the optimization target;
and 4, step 4: merging the optimization result of the step 2 and the maintenance plan optimization result of the step 3, and repeatedly performing iterative optimization until the total benefit is maximized.
Specifically, in the process of constructing the double-layer optimization model for long-term scheduling and overhaul of the cascade hydropower station, the following two factors are mainly considered:
(1) medium and long term dispatch for cascade hydropower stations
Under the market environment, the medium-term and long-term power generation planning electric quantity of a power generation enterprise mainly comprises two parts: 1) the medium-long term power supply contract signed by a large user and an electricity selling company can be one or more of a price difference contract, an electric future contract and a double-actual contract; 2) the electric quantity sold in the spot market is obtained by competitive bidding according to the running state of the unit and the market supply and demand situation. Therefore, when a long-term output plan is arranged in the cascade hydropower station, the medium-term and long-term power supply contract electric quantity is firstly met, and the surplus electric quantity is organized to participate in the spot market. The method is limited by the difficulties in large-scale economic storage of electric energy and the transmission capacity of a power grid, has strong fluctuation of the price of the spot market and large profit risk, and has obvious influence on the decision-making power generation plan of the power generation enterprise.
(2) Step hydroelectric generating set maintenance plan
The unit maintenance plan is arranged in a low electricity price period as far as possible so as to reduce the revenue loss of the power generation enterprises. In addition, considering the limited output characteristics of the hydroelectric generating set, and the hydraulic and electric coupling characteristics of the upstream and downstream power stations, the following special considerations are required when the cascade hydroelectric station maintenance plan is arranged: 1) the device is arranged in a flat dry season with little water as much as possible so as to avoid water abandon caused by blocked output; 2) the maintenance of the upstream and downstream power stations needs to be matched with the residual power generation capacity, particularly the downstream hydropower station with weak regulation capacity, so that the problems that the maintenance and outage unit is too much and cannot absorb the upstream leakage flow to abandon water or the unit is too little and is idle and the like are solved.
In view of the above, the invention constructs a double-layer optimization model for medium-and-long-term scheduling and overhaul of the cascade hydropower station in a market environment, the model frame is shown in fig. 1, the outer layer of the frame is a medium-and-long-term scheduling optimization layer, the medium-and-long-term scheduling in the outer layer is a decision process of 'water and price electricity determination', the medium-and-long-term optimization scheduling takes runoff and price prediction as reference, decision is made according to the runoff and price prediction, under the premise of meeting comprehensive utilization of flood control, irrigation, shipping and the like, the output plan of the cascade hydropower station in each time period is optimized, and the optimal benefit space is searched, so that the benefit maximization is realized. Considering that the reservoir runoff is a continuous and non-stable random process, a typical scene method is applied, historical data are subjected to cluster analysis, and the runoff in the year is divided into a typical scene of a dry year, a flat year and a full-blown year to represent the true change characteristics of the runoff. The medium and long term contracts are usually signed 1 year or even several years ahead, the electric quantity and the price are fixed, the electricity price factor influencing the decision of the output plan is mainly the spot market price, and the invention selects the day-ahead market electricity price weighted mean value as the price reference factor. The inner layer of the frame is a maintenance plan optimization layer, and the influence of the hydraulic power and electric power coupling relation of the upstream hydropower station and the downstream hydropower station on the maintenance plan is mainly considered so as to meet the actual operation requirement; the maintenance plan optimization layer takes the minimum maintenance loss as a target, takes a medium-long term scheduling optimization intermediate result as a boundary condition, feeds back an inner layer maintenance loss optimization result to the outer layer, fuses a medium-long term scheduling optimization model fitness function, guides the outer layer optimizing direction, iterates repeatedly until the total yield is maximum, and realizes the combined optimization of the medium-long term power generation plan and the maintenance plan. Wherein minimizing service loss comprises: maintenance gain losses and outage risk losses. The maintenance income loss mainly considers the loss caused by the limitation of tracking the electricity price fluctuation in large-range variable-load operation of the cascade hydropower station in the market at the day before, and the profit loss is caused; the shutdown risk loss mainly considers the loss caused by the fact that the unit is in fault shutdown without maintenance after long-time operation, the generated maintenance cost and the electric quantity of the contract cannot be completed. The maintenance loss of revenue is schematically shown in fig. 2 and 3.
The combined optimization model of the medium-long term dispatching and the maintenance plan of the cascade hydropower station takes profit maximization as a target and consists of two parts of maximization electricity selling profit and minimization maintenance loss, and an objective function is expressed as follows:
maxRtotal＝RG+Loss (1)
in the formula: rtotalAs a total profit (one hundred million yuan), RGExpected electricity sales revenue (billions) for medium and long term electricity generation, LossExpected loss (billion yuan) for the maintenance of the hydroelectric generating set.
Specifically, the intermediate and long-term optimized scheduling model of the cascade hydropower station is as follows:
an objective function:
Figure BDA0001445999240000091
Figure BDA0001445999240000092
Figure BDA0001445999240000093
in the formula, RGThe total electricity selling income (hundred million yuan) is expected for medium-and long-term electricity generation; t, t respectively indicate the total number and number of periods in the scheduling period; n, n are the total number and serial number of hydropower stations respectively; m, m for total number and edition of medium and long term bilateral contractsNumber;
Figure BDA0001445999240000094
respectively, bilateral contract price (yuan/MW · h) and capacity (MW);
Figure BDA0001445999240000095
respectively predicting the weighted average value (Yuan/MW & h) of the coming market price and the selling capacity (MW); pn,tThe output of the hydropower station n in the time period t is obtained; etanThe comprehensive average output coefficient of the hydropower station n is obtained;
Figure BDA0001445999240000096
Hn,taverage generation of electricity (m) for hydropower station n in time period t3S) and a clean head (m); Δ t is the optimized time granularity; zn,t、
Figure BDA0001445999240000097
Figure BDA0001445999240000098
Respectively is the decision-making reservoir water level (m), the tail water level (m) and the head loss (m) of the hydropower station n in the time period t.
Flow rate S of lower drainage of hydropower stationn,tInvolving the flow of electricity generation
Figure BDA0001445999240000099
Flow of reclaimed water
Figure BDA00014459992400000910
The relationship with the change in the storage capacity is expressed as:
Figure BDA00014459992400000911
in the formula In,tIs the natural inflow (m3/s), V of the reservoir in the n period t of the hydropower stationn,t-1、Vn,tDecision storage capacity (billion m3) of the hydropower station n at the end of t-1 and t periods respectively; when water abandon occurs, i.e.
Figure BDA00014459992400000912
Then
Figure BDA00014459992400000913
Is the upper limit of the power generation flow
Figure BDA0001445999240000101
When there is no waste water, i.e.
Figure BDA0001445999240000102
The downward discharge flow is the power generation flow
Figure BDA0001445999240000103
Constraint conditions are as follows:
(1) reservoir level constraint:
Figure BDA0001445999240000104
(2) and (3) power generation flow restriction:
Figure BDA0001445999240000105
(3) and (3) restricting the downward flow:
Figure BDA0001445999240000106
(4) force restraint:
Figure BDA0001445999240000107
in the formula:
Figure BDA0001445999240000108
lower and upper reservoir level limits (m) for the hydropower station i in time t, respectively, typically
Figure BDA0001445999240000109
Taking a normal water storage level, setting the normal water storage level as a flood limit water level in a flood season,
Figure BDA00014459992400001010
taking a dead water level;
Figure BDA00014459992400001011
the minimum limit value and the maximum limit value (m) of the generating flow of the hydropower station i in the time interval t are respectively3/s)；
Figure BDA00014459992400001012
The lower limit value (m) of the discharge of the hydropower station i in the time period t3/s)；
Figure BDA00014459992400001013
The minimum limit value and the maximum limit value (MW) of the output of the hydropower station i in the time interval t are respectively
Figure BDA00014459992400001014
And setting medium and long term contract electric quantity resolving output. In addition, the water balance constraint of the upstream and downstream power stations and the end-period water level control constraint need to be considered.
Specifically, the step hydropower station maintenance plan optimization model is as follows:
an objective function:
Figure BDA00014459992400001015
Figure BDA00014459992400001016
Figure BDA00014459992400001017
Figure BDA00014459992400001018
Figure BDA00014459992400001019
in the formula (I), the compound is shown in the specification,
Figure BDA00014459992400001020
indicates an available capacity of
Figure BDA00014459992400001021
Loss of revenue (billions) over time;
Figure BDA00014459992400001022
is a hydropower station n unit gnOutage risk loss (billions); alpha is a weight coefficient;
Figure BDA00014459992400001023
is the total capacity (MW) of n non-overhaul units of the hydropower station,
Figure BDA00014459992400001024
in a maintenance state, 0 represents shutdown maintenance, and 1 represents normal operation;
Figure BDA00014459992400001025
is a unit gnRated capacity (MW).
Figure BDA0001445999240000111
For maintenance profit loss, S, S and Pr (S) are respectively the total number of output typical scenes, the scene number and the probability when the cascade hydropower station is not maintained; and T 'and T' are respectively a scene time scale and a time interval number, wherein T 'is 1d, and T' is 1 h.
Figure BDA0001445999240000112
Representing the loss of service revenue under a typical scenario s,
Figure BDA0001445999240000113
clearing the price for the market time t' before the day under the scene s;
Figure BDA0001445999240000114
respectively predicting output and overhauling and adjusting output for a time period t' under a scene s of the hydropower station n;
Figure BDA0001445999240000115
in order to risk the loss of the outage,
Figure BDA0001445999240000116
in order to maintain the time interval between overhauls,
Figure BDA0001445999240000117
the unit outage probability
Figure BDA0001445999240000118
Increasing progressively;
Figure BDA0001445999240000119
is lost in one outage.
Constraint conditions are as follows:
(1) and (4) maintenance capacity constraint:
Figure BDA00014459992400001110
Figure BDA00014459992400001111
the formula (15) restricts the overhaul capacity within the space range of the residual capacity of the medium-long term output plan; and (3) restricting the capacity of the non-overhaul unit of the downstream power station not to be lower than the generating capacity matched with the leakage flow and the interval inflow of the upstream power station by the formula (16).
(2) And (3) restricting the maintenance duration and continuity:
the hydroelectric generating set maintenance is completed within a specified duration, and the middle part of the hydroelectric generating set maintenance cannot be interrupted, namely:
Figure BDA00014459992400001112
Figure BDA00014459992400001113
in the formula, D is the total overhaul time interval of the unit;
(3) earliest, latest start of overhaul time constraint:
Figure BDA00014459992400001114
in the formula: t isearly、TlaterThe earliest and latest overhaul time are preset respectively to avoid excessive overhaul cost increase, failure in delivery of contract electric quantity and excessive risk of unit failure shutdown caused by too late overhaul.
In addition, overhaul resource constraints need to be considered to ensure that the resources required for overhaul are sufficient; and the overhaul is mutually exclusive and restrained, and the asynchronous overhaul and the like of the unit bearing special tasks are ensured.
Specifically, the hydraulic power and electric power coupling relationship of the upstream hydropower station and the downstream hydropower station of the stair is as follows:
because a large-reservoir-capacity tap hydropower station is usually built at the upstream in a step hydropower station development mode, and a plurality of hydropower stations with weak regulating capacity are arranged at the downstream, the functions of large-reservoir-capacity regulation, storage and compensation are fully exerted, but the power generation capacity of the downstream hydropower station in a medium-long time scale is highly dependent on upstream incoming water, and the hydraulic and electric coupling relationship influences the output plan and maintenance plan compilation of the step hydropower station. According to equation (3), the hydropower station n-1 leakage flow is expressed as:
Figure BDA0001445999240000121
the output force in the hydropower station n-warehousing balanced operation mode is as follows:
Figure BDA0001445999240000122
order:
Figure BDA0001445999240000123
then the power coupling relationship between the upstream hydropower station and the downstream hydropower station is obtained as follows:
Pn,t＝αn,t·Pn-1,t+βn,t (23)
in the formula, alphan,t、βn,tAre all coupling coefficients.
Specifically, because the optimized scheduling decision variables and the state variables of the cascade hydropower station are in implicit and nonlinear relations, specific functions are often difficult to describe accurately, and the intelligent algorithm has the advantages of flexible structure, strong mapping capability and no model limitation, and shows excellent performance in the optimized scheduling of cascade hydropower station groups. Because the maintenance plan optimization model built by the invention has the total of NXGnThe x T0-1 variables belong to a typical 0-1 planning model, and therefore, the method adopts a genetic algorithm in the outer-layer medium-long term optimization scheduling, adopts a 0-1 planning method in the inner-layer maintenance plan optimization, can converge to an optimal solution under the boundary condition of the outer-layer intermediate result, can directly use the formula (1) as a fitness function of a genetic algorithm, and avoids the separation of an outer-layer output plan and an inner-layer maintenance plan through repeated iteration until the optimization, thereby realizing the combined optimization. The method comprises the steps of firstly randomly generating an initial chromosome population by setting genetic algorithm parameters (including a crossing rate, a variation rate, iteration times and the like), then calculating the output of each hydropower station meeting the constraint through chromosome decoding, then calculating the electricity selling income, compiling a maintenance plan according to the output of each hydropower station, calculating the maintenance loss, calculating the individual fitness, judging whether the iteration times are reached, if so, outputting the optimal solution, otherwise, continuing chromosome decoding through roulette selection, multipoint crossing and multipoint variation until the iteration times are met, and outputting the optimal solution.
Example 1
Taking a 1-reservoir 3-step system composed of A, B, C hydropower stations from top to bottom in a certain step as an example, wherein the hydropower station A has annual regulation performance and is a leading hydropower station, the hydropower stations B, C are daily regulation, the operation parameters of each hydropower station are shown in table 1, and the unit numbers and the overhaul time intervals are shown in table 2; the optimization period is 1 year (11 months from the profit year to 10 months from the next year), and the time granularity is 1 week.
Table 1 individual regulated hydropower station operating parameters
Hydropower station Normal water storage level (m) Dead water level (m) Regulating storage capacity (billion m)3) Regulating performance Installed capacity (MW)
A 1880 1800 14.9 Year of year 120*4
B 1656 1650 0.0596 Day(s) 100*4
C 1340 1331 0.232 Day(s) 100*2+125*2
TABLE 2 maintenance intervals for each unit
Unit number Belonging to power station Installed capacity (MW) Maintenance interval (week)
1 A 120 48
2 A 120 40
3 A 120 36
4 A 120 34
5 B 100 46
6 B 100 44
7 B 100 38
8 B 100 32
9 C 100 48
10 C 100 40
11 C 125 42
13 C 125 38
In this embodiment, the medium-and-long-term scheduling and maintenance plan optimization results in the scenes of typical rich water years, open water years and dry water years are selected and analyzed, and the optimization results are shown in fig. 5 to 9, where fig. 5 is the medium-and-long-term output plan optimization result, fig. 6 is the water level change of the reservoir a corresponding to fig. 5, and fig. 7 to 9 are the typical maintenance plan optimization results of the rich, flat and dry years when the weight coefficient α is 0.5, respectively.
As can be seen from fig. 5 and 6, in a typical open water year of 1-25 weeks (11 months-4 months in the next year), the cascade hydropower station aims to ensure output and maintain high water head efficiency, the water level of the reservoir a is continuously eliminated due to insufficient water flow until the radial flow is suddenly increased along with the arrival of a flood season in 32 weeks (6 months), the water level begins to rise again, the normal water storage level is reached in 44 weeks (9 months), and the high water head is subsequently maintained for power generation. In contrast, the water quantity of the full water year is abundant, and the pre-flood bank is pushed to 21 weeks (3 months) before the flood to ensure that the full bank capacity is emptied before the flood; because water comes rapidly in the flood season, the reservoir water level still rises rapidly despite the full load of the hydropower station, the flood limit water level is reached in 36 weeks (7 months), the normal water storage level is maintained subsequently, and the water head benefit is good. In the dry water, the water is weak, the pre-dumping reservoir amplitude is obviously reduced, so that the problem of insufficient storage and hair shortage caused by insufficient runoff in the subsequent period is avoided, although the hair is not fully developed in most periods in the flood season, the water level rises slowly, and the water level is not close to the normal water storage level until 49 weeks (10 months).
According to the optimization result of the whole period, the hydropower station A has insufficient water flow in the non-flood period, the output is ensured by maintaining the water level of the properly-eliminated reservoir, the water level of the eliminated reservoir is recovered through water storage in the flood period, and the benefit of 'rich storage and dry compensation' of the adjustable reservoir is reflected. From the output, the power generation situation of each hydropower station in the non-flood period of 1-20 weeks is poor, the output slightly increases only in 9-13 weeks, which is caused by the fact that the load is small in winter and the peak electricity price is raised in the peak, the output is increased by the cascade hydropower and the profit is gained, the decision of runoff forecasting in annual power generation income is reflected in other time periods by taking the forecasting runoff as the main basis of power generation dispatching, and the decision is consistent with the actual situation.
As can be seen from fig. 7 to 9, the unit 1, the unit 5, and the unit 6 have a large maintenance time interval at the beginning of the cycle, the outage risk increases sharply with the maintenance time interval, and maintenance is preferentially arranged at 1 to 4 weeks with a slightly higher electricity price to reduce the failure outage risk. Comparing the maintenance schedule of the Fengshui year, the open water year and the dry water year can find that: most unit maintenance plans in the water-rich year are arranged at 1-8 weeks and 13-20 weeks, because a small load peak occurs in winter in 9-12 weeks, the market price is raised in the day ahead, the maintenance income loss is large, and 21-28 weeks are the pre-flood banking period, the power generation load is arranged to be heavy, and the residual capacity space is difficult to meet continuous maintenance; the maintenance arrangement of the unit in the dry season is relatively more dispersed, and the maintenance loss is reduced mainly in 0-8 weeks and 13-22 weeks with lower price and larger margin of generating capacity.
Considering that the hydraulic power coupling relation between the upstream hydropower station and the downstream hydropower station of the stair affects the output plan of the unit, the output plan cannot be ignored when the maintenance plan is made. FIG. 8 is a graph of the optimization of a maintenance schedule for a typical open water year without consideration of the hydraulic coupling between the upstream and downstream hydroelectric power plants. Fig. 8 is a result of optimization of a maintenance plan in consideration of the hydraulic coupling relationship between upstream and downstream power stations, and it can be seen from a comparison between fig. 10 and fig. 8 that, in the case of no consideration of the hydraulic coupling relationship between upstream and downstream power stations, the optimization result shown in fig. 10 is constrained by maintenance resources, and the distribution of maintenance capacity on a time scale hardly changes, but a case of centralized maintenance of a plurality of units of an individual power station occurs, for example: the hydropower station B is intensively arranged with the units 5, 6 and 7 for maintenance in 5-8 weeks, the power generation capacity is insufficient, and the water and electricity abandonment quantity reaches 0.1278 hundred million kW.h. For a hydropower station with excellent adjusting performance, runoff is weak in non-flood season, and water storage can be shut down for arranging maintenance in a short period, but for a hydropower station with weak adjusting capacity, the dependence on an upstream power station is high, and maintenance planning needs to preferentially consider the consumption of a non-maintenance unit on upstream power generation and drainage according to an upstream power station output plan and a hydraulic coupling relation between power stations so as to avoid water abandonment caused by improper maintenance scheduling time sequence or insufficient available capacity. Especially for cascade hydroelectric power stations with upstream and downstream power stations belonging to different operation bodies.
Fig. 11 and 12 show the optimization results of the maintenance schedule in a typical horizontal year with a weighting factor α of 0.4 and 0.6, respectively. As can be seen from fig. 11, when α is 0.4, that is, the proportion of the maintenance profit loss in the total loss is reduced, and the loss contribution rate of the outage risk is increased, 6 unit overhauls are scheduled in 0-8 periods in the overhaul plan schedule, and all unit overhauls are scheduled in 1-20 weeks; in fig. 12, when α is 0.6, 5 unit inspections are scheduled in a period of 0 to 8, and all unit inspections are completed in a period of 1 to 22. The smaller alpha is, the more important the decision maker is about the risk of unit failure shutdown, and the larger alpha is, the more serious the decision maker is about the influence of overhaul loss on the yield. Therefore, the decision maker is helped to reasonably compromise the operation reliability and the economy according to the market situation and the unit operation state when making the maintenance plan by adjusting the weight coefficient alpha.
Analysis results show that the double-layer optimization model established by the method is clear in structure and high in practicability. And the outer layer of the model makes a decision according to runoff and price prediction, optimizes the output of the cascade hydropower station in each time period by applying a genetic algorithm, and searches for an optimal benefit space. The influence of the hydraulic and electric coupling relation of the upstream hydropower station and the downstream hydropower station on the maintenance plan is mainly considered in the inner layer of the model so as to meet the actual operation requirement; and (3) taking the intermediate result of the medium-and-long-term scheduling optimization as a boundary condition, taking the minimum maintenance loss as an optimization target, integrating the maintenance loss and the medium-and-long-term power generation income into a total income, and using the total income as a fitness function of a genetic algorithm, thereby realizing the combined optimization of the medium-and-long-term power generation plan and the maintenance plan.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily made by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (6)

1. A double-layer optimization method for medium and long term scheduling and overhaul of a cascade hydropower station in a market environment is characterized by comprising the following steps:
step 1: establishing a medium-long term scheduling and maintenance plan double-layer optimization model by taking the maximum profit as a target, wherein an objective function is expressed as:
max Rtotal＝RG+Loss
in the formula: rtotalFor total profit, RGAnticipating electricity sales revenue for long-and-medium-term electricity generation, LossExpected loss for hydroelectric generating set overhaul;
step 2: making a decision according to runoff and price prediction, optimizing the output of the cascade hydropower stations in each time period, and searching for an optimal benefit space;
and step 3: considering the influence of the coupling relation between the water power and the electric power of the upstream hydropower station and the downstream hydropower station on the maintenance plan and the characteristic that the output of the hydroelectric generating set is limited by runoff change, taking the intermediate result of optimizing the output of the cascade hydropower station in each time period in the step 2 as the boundary condition of the optimization decision of the maintenance plan, and optimizing the maintenance plan of the cascade hydropower station by taking the minimum maintenance loss as the optimization target;
and 4, step 4: merging the intermediate result of optimizing the output of the cascade hydropower station in each time period in the step 2 with the maintenance plan optimization result in the step 3, and performing iterative optimization until the total yield is maximized;
the medium-long term scheduling and maintenance plan double-layer optimization model comprises a medium-long term optimization scheduling model and a maintenance plan optimization model, wherein the medium-long term optimization scheduling model is used for performing medium-long term scheduling optimization on the cascade hydropower station, and the maintenance plan optimization model is used for optimizing a maintenance plan of the cascade hydropower station;
the maintenance plan optimization model comprises the following steps:
an objective function:
Figure FDA0003156014980000021
Figure FDA0003156014980000022
Figure FDA0003156014980000023
Figure FDA0003156014980000024
Figure FDA0003156014980000025
in the formula (I), the compound is shown in the specification,
Figure FDA0003156014980000026
to an available capacity of
Figure FDA0003156014980000027
Loss of revenue in time;
Figure FDA0003156014980000028
is a hydropower station n unit gnLoss of outage risk; alpha is a weight coefficient;
Figure FDA0003156014980000029
the total capacity of n non-overhaul units of the hydropower station is obtained;
Figure FDA00031560149800000210
is in a maintenance state;
Figure FDA00031560149800000211
is a unit gnRated capacity of (d);
Figure FDA00031560149800000212
loss of revenue for maintenance; s, S and Pr (S) are respectively the total number of output typical scenes, the scene number and the probability of the cascade hydropower station without maintenance; t 'and T' are respectively a scene time scale and a time interval number;
Figure FDA00031560149800000213
representing the maintenance profit loss under a typical scene s;
Figure FDA00031560149800000214
clearing the price for the market time t' before the day under the scene s;
Figure FDA00031560149800000215
respectively predicting output and overhauling and adjusting output for a time period t' under a scene s of the hydropower station n;
Figure FDA00031560149800000216
risk loss for outage;
Figure FDA00031560149800000217
a maintenance interval;
Figure FDA00031560149800000218
the unit outage probability;
Figure FDA00031560149800000219
loss of one outage;
constraint conditions are as follows:
(1) and (4) maintenance capacity constraint:
Figure FDA00031560149800000220
Figure FDA00031560149800000221
in the formula, Gn、gnRespectively the total number and the serial number of the hydroelectric generator sets in the hydropower station n;
Figure FDA00031560149800000222
is in a maintenance state;
Figure FDA00031560149800000223
is a unit g in a hydropower station nnThe maximum output of (c); pn,tThe output of the hydropower station n in the time period t is obtained;
Figure FDA0003156014980000031
the maximum output of the hydropower station n; alpha is alphan,t、βn,tAll represent coupling coefficients;
(2) and (3) restricting the maintenance duration and continuity:
Figure FDA0003156014980000032
Figure FDA0003156014980000033
in the formula, D is the total overhaul time interval of the unit, and T, t respectively represents the total number and the number of the time intervals in the scheduling period;
(3) earliest, latest start of overhaul time constraint:
Figure FDA0003156014980000034
in the formula: t isearly、TlaterRespectively presetting earliest and latest overhaul time,
Figure FDA0003156014980000035
for maintenance intervals.
2. The double-layer optimization method for medium and long term scheduling and overhaul of a cascade hydropower station in a market environment according to claim 1, wherein the step 2 adopts a genetic algorithm to optimize the output of the cascade hydropower station in each time period, and comprises the following specific steps:
1) randomly generating an initial chromosome population by setting parameters of a genetic algorithm, wherein the parameters comprise a cross rate, a variation rate and iteration times;
2) calculating the output and the electricity selling income of each hydropower station meeting the constraint through chromosome decoding;
3) compiling a maintenance plan according to the output of each power station, and calculating maintenance loss and individual fitness;
4) judging whether the iteration times are reached, if so, outputting the optimal solution, otherwise, returning to the step 2) to continue the chromosome decoding until the iteration times are met, and outputting the optimal solution.
3. The double-layer optimization method for medium and long term dispatch and overhaul of a cascade hydropower station in a market environment according to claim 1, wherein the step 3 adopts a 0-1 planning algorithm to optimize an overhaul plan of the cascade hydropower station.
4. The method of claim 1, wherein the minimum service loss comprises a service gain loss and a down-risk loss.
5. The double-layer optimization method for medium-and-long-term dispatch and overhaul of a cascade hydropower station in a market environment according to claim 1, wherein the medium-and-long-term optimization dispatch model is as follows:
an objective function:
Figure FDA0003156014980000041
Figure FDA0003156014980000042
Figure FDA0003156014980000043
in the formula, RGFor generating electricity for medium and long periodsAnticipating the total revenue of electricity sales; t, t respectively indicate the total number and number of periods in the scheduling period; n, n are the total number and serial number of hydropower stations respectively; m, m are respectively the total number and serial number of the medium and long term bilateral contracts;
Figure FDA0003156014980000044
respectively, bilateral contract price and capacity;
Figure FDA0003156014980000045
respectively predicting the weighted average value of the coming price and the selling capacity of the market at the present day; pn,tThe output of the hydropower station n in the time period t is obtained; etanThe comprehensive average output coefficient of the hydropower station n is obtained;
Figure FDA0003156014980000046
Hn,trespectively averaging the generating capacity and the water purifying head of the hydropower station n in a time period t; Δ t is the optimized time granularity; zn,t、
Figure FDA0003156014980000047
Respectively determining a reservoir water level, a tail water level and a head loss of the hydropower station n in a time period t;
constraint conditions are as follows:
(1) reservoir level constraint:
Figure FDA0003156014980000048
(2) and (3) power generation flow restriction:
Figure FDA0003156014980000049
(3) and (3) restricting the downward flow:
Figure FDA00031560149800000410
(4) force restraint:
Figure FDA0003156014980000051
in the formula (I), the compound is shown in the specification,
Figure FDA0003156014980000052
respectively setting a lower limit and an upper limit of the reservoir water level of the hydropower station i in a time period t;
Figure FDA0003156014980000053
respectively setting a minimum limit value and a maximum limit value of the generating flow of the hydropower station i in a time period t;
Figure FDA0003156014980000054
the lower discharge upper limit value of the hydropower station i in the time period t;
Figure FDA0003156014980000055
the minimum and maximum output limits of the hydropower station i in the time period t are respectively.
6. The double-layer optimization method for long-term scheduling and overhaul of the cascade hydropower station in the market environment according to claim 1, wherein the hydraulic and electric coupling relationship between the cascade hydropower stations and the upstream hydropower stations is as follows:
Figure FDA0003156014980000056
order to
Figure FDA0003156014980000057
Then P isn,t＝αn,t·Pn-1,t+βn,t
In the formula, Pn,tIs the output, eta, of the hydropower station n during the time period tnIs the comprehensive average output coefficient of the hydropower station n,
Figure FDA0003156014980000058
Hn,taverage generation capacity and clear water head, I, for hydropower station n in time period tn,tIs the natural inflow of a reservoir in the hydropower station within n time period t,αn,t、βn,tboth represent coupling coefficients.
CN201711013528.2A 2017-10-25 2017-10-25 Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment Active CN107895221B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201711013528.2A CN107895221B (en) 2017-10-25 2017-10-25 Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201711013528.2A CN107895221B (en) 2017-10-25 2017-10-25 Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment

Publications (2)

Publication Number Publication Date
CN107895221A CN107895221A (en) 2018-04-10
CN107895221B true CN107895221B (en) 2021-09-28

Family

ID=61803766

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201711013528.2A Active CN107895221B (en) 2017-10-25 2017-10-25 Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment

Country Status (1)

Country Link
CN (1) CN107895221B (en)

Families Citing this family (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109657850B (en) * 2018-12-10 2023-07-18 国家电网有限公司西北分部 Medium-and-long-term step hydropower optimization scheduling method and device
CN109687534B (en) * 2019-03-07 2022-03-11 昆明电力交易中心有限责任公司 Power system generator set active power control method based on step water quantity matching
CN109902447A (en) * 2019-04-10 2019-06-18 华能澜沧江水电股份有限公司 A kind of huge Condition Maintenance for Hydraulic Power Plant model and construction method
CN110310032A (en) * 2019-06-26 2019-10-08 广州汇电云联互联网科技有限公司 A kind of automatic preparation method, device and storage medium for power plant operation plan
CN110826805B (en) * 2019-11-07 2022-10-21 广西电网有限责任公司 Low-water-head cascade hydropower station medium-term optimization scheduling method considering water unevenness
CN112862313B (en) * 2021-02-08 2023-02-28 山东大学 Generator set scheduling method and system based on medium-long term price difference contract electric quantity decomposition
CN115358134B (en) * 2021-03-17 2023-04-21 南京工业职业技术大学 River basin medium-long term runoff prediction method based on space-time granulation data scene model
CN113409163B (en) * 2021-07-15 2022-10-04 浙江大学 Functional recovery decision-making method of interdependent hydropower system based on flexible city
CN115438972B (en) * 2022-09-13 2023-10-31 中国长江电力股份有限公司 Cascade hydropower station joint optimization scheduling method considering electric power mutual-aid characteristics
CN116151807B (en) * 2023-04-18 2023-09-08 南方电网科学研究院有限责任公司 Method and device for determining overhauling of generator set and electronic equipment
CN117273413B (en) * 2023-11-23 2024-02-06 中国电建集团贵阳勘测设计研究院有限公司 Overhauling and arranging method for water-wind-solar-storage-base-regulated power supply unit

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102004835A (en) * 2010-11-26 2011-04-06 武汉大学 Power generation risk-oriented hydropower station optimal operation chart drawing method
CN102298731A (en) * 2010-06-25 2011-12-28 华东电网有限公司 Cascade reservoir short-term electricity generation optimal dispatching method considering comprehensive requirements of tide stemming water supply
CN104268682A (en) * 2014-09-15 2015-01-07 华北电力大学 Planning method and device for active power distribution network
CN106056236A (en) * 2016-05-19 2016-10-26 华能澜沧江水电股份有限公司 Hydropower station AGC combined output model and combined operation region calculation method
JP2017162044A (en) * 2016-03-08 2017-09-14 株式会社日立ソリューションズ東日本 Production planning device, production planning method and production planning program

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102298731A (en) * 2010-06-25 2011-12-28 华东电网有限公司 Cascade reservoir short-term electricity generation optimal dispatching method considering comprehensive requirements of tide stemming water supply
CN102004835A (en) * 2010-11-26 2011-04-06 武汉大学 Power generation risk-oriented hydropower station optimal operation chart drawing method
CN104268682A (en) * 2014-09-15 2015-01-07 华北电力大学 Planning method and device for active power distribution network
JP2017162044A (en) * 2016-03-08 2017-09-14 株式会社日立ソリューションズ東日本 Production planning device, production planning method and production planning program
CN106056236A (en) * 2016-05-19 2016-10-26 华能澜沧江水电股份有限公司 Hydropower station AGC combined output model and combined operation region calculation method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Joint optimization of production scheduling and machine group preventive maintenance;Lei Xiao等;《Reliability Engineering and System Safety 146 (2016)》;20151019;第68-78页 *
流域梯级水电站中长期调度计划与跨区域交易组合双层优化模型;刘方等;《中国电机工程学报》;20171017;第444-455页 *

Also Published As

Publication number Publication date
CN107895221A (en) 2018-04-10

Similar Documents

Publication Publication Date Title
CN107895221B (en) Medium-and-long-term scheduling and maintenance plan optimization method for cascade hydropower station in market environment
JP6736112B2 (en) A short-term practical scheduling method for ultra-large-scale hydropower stations
Liu et al. Optimal planning and investment benefit analysis of shared energy storage for electricity retailers
CN107301470B (en) Double-layer optimization method for power distribution network extension planning and optical storage location and volume fixing
Yuan et al. Stochastic optimization model for the short-term joint operation of photovoltaic power and hydropower plants based on chance-constrained programming
CN109256799B (en) New energy power system optimal scheduling method based on sample entropy
CN109636671B (en) Wind power consumption optimization strategy considering multi-level market linkage type
Yuansheng et al. A two-stage planning and optimization model for water-hydrogen integrated energy system with isolated grid
CN114336702B (en) Wind-solar storage station group power distribution collaborative optimization method based on double-layer random programming
CN110620397B (en) Peak regulation balance evaluation method for high-proportion renewable energy power system
CN110034561B (en) Wind power plant energy storage capacity optimization method based on cloud energy storage lease service
CN109726907A (en) Long-term output calculation method and system in a kind of GROUP OF HYDROPOWER STATIONS
CN111428970B (en) Large-scale hydropower station group cross-province capacity analysis model and solving method
CN110929428B (en) Method and system for determining power generation capacity adequacy of power system
CN110994606A (en) Multi-energy power supply capacity configuration method based on complex adaptive system theory
CN115841396A (en) Watershed cascade water-wind-solar complementary capacity stowage optimization and economic evaluation method
CN116418001A (en) Reservoir group multi-energy complementary scheduling method and system for coping with uncertainty of new energy
CN117557301A (en) Power market benefit evaluation model algorithm considering energy storage intervention
CN112598175A (en) Watershed type virtual power plant scheduling method
CN110826778A (en) Load characteristic optimization calculation method actively adapting to new energy development
CN115600757A (en) Coordination optimization method and system for offshore wind power sharing energy storage participation spot market trading
CN112736905B (en) Regional new energy capacity optimal configuration method and system
CN112257951B (en) Comprehensive energy system and power distribution company optimized operation method based on cooperative game
CN111612271B (en) Clean energy delivery planning and operation optimization method considering clean energy utilization rate
Yang et al. Locational marginal pricing-oriented transmission network expansion planning with consideration of line capacity sensitivity

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant