CN109002936A - Consider the hydropower station Optimization Scheduling of flexibility - Google Patents

Consider the hydropower station Optimization Scheduling of flexibility Download PDF

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CN109002936A
CN109002936A CN201811043794.4A CN201811043794A CN109002936A CN 109002936 A CN109002936 A CN 109002936A CN 201811043794 A CN201811043794 A CN 201811043794A CN 109002936 A CN109002936 A CN 109002936A
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flexibility
period
maximum
formula
follows
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CN109002936B (en
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徐伟峰
刘攀
巩钰
刘旖旎
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Wuhan University WHU
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas 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

Abstract

The present invention provides a kind of hydropower station Optimization Schedulings for considering flexibility, power benefit and flexibility can be taken into account during optimizing scheduling of reservoir, it is characterized in that, the following steps are included: step 1 indicates the uncertainty of two Phase flow using flexibility index, two Phase flow is expressed as a range format；Step 2 establishes the double-goal optimal model for taking into account power benefit and flexibility: power benefit target is that total power generation is maximum under becoming a mandarin most bad situation in schedule periods, and flexibility target is that flexibility index is maximum；Step 3 solve double-goal optimal model, using leash law by flexibility targeted transformation be constraint condition, then solved using dynamic programming method；Or Noninferior Solution Set is sought using Multiobjective Intelligent algorithm.

Description

Consider the hydropower station Optimization Scheduling of flexibility
Technical field
The invention belongs to reservoir operation fields, and in particular to a kind of hydropower station Optimization Scheduling for considering flexibility.
Technical background
Developing hydroelectric generation is to alleviate future source of energy crisis, improves an important measure of energy resource structure.Reservoir optimizing tune Degree is the Regulation capacity according to reservoir, and outbound runoff process is adjusted using Optimized Operation theory, thus realize flood control, The maximization of the comprehensive benefits such as power generation, irrigation, shipping.But reservoir inflow used in reservoir actual schedule process is by forecasting It obtains, therefore is the uncertainty for being considered as two Phase flow process in optimizing scheduling of reservoir.The flexibility of reservoir operation is (soft Property) refer to that reservoir copes with the adaptability of uncertain factor (two Phase flow uncertainty etc.), improves the flexibility of scheduling process The influence that uncertain factor runs reservoir can be substantially reduced.Flexibility and power benefit are taken into account in optimizing scheduling of reservoir It has important practical significance.
Traditional certainty Optimal Operation Model fails to fully consider the uncertainty of two Phase flow, and Stochastic Optimization Model It is overly dependent upon the hypothesis to uncertain factor distribution pattern and distribution function.However due to the shadow of climate change and mankind's activity Ring, the distribution pattern and distribution function of two Phase flow will increasingly it is difficult to predict.Therefore, how in the uncertain feelings of reservoir inflow Hydropower station amount is improved under condition and flexibility is a problem to be solved.
Summary of the invention
The present invention is to carry out to solve the above-mentioned problems, and it is an object of the present invention to provide a kind of hydropower station for considering flexibility Optimization Scheduling can take into account power benefit and flexibility during optimizing scheduling of reservoir.
The present invention to achieve the goals above, uses following scheme:
The present invention provides a kind of hydropower station Optimization Schedulings for considering flexibility, which is characterized in that including following Step:
Step 1 indicates the uncertainty of two Phase flow using flexibility index, and two Phase flow is expressed as a section shape Formula；
Two Phase flow expression formula are as follows:
In formula: number of segment when T is reservoir operation；ItFor the reservoir inflow of period t；For the reservoir inflow predicted value of period t；WithFor maximum offset；μ is flexibility index；
Step 2 establishes the double-goal optimal model for taking into account power benefit and flexibility；
Power benefit target is that total power generation is maximum under becoming a mandarin most bad situation in schedule periods, calculating formula are as follows:
Flexibility target is that flexibility index is maximum, expression formula are as follows:
max μ
In formula, NtFor the power output of period t；Δ t is Period Length；
Constraint condition are as follows:
In formula, vt+1And vtStorage capacity when respectively period t+1 and period t start；qtFor period t storage outflow；vmaxAnd vmin The minimum and maximum storage capacity respectively allowed；qmaxAnd qminThe minimum and maximum storage outflow respectively allowed；NmaxAnd NminPoint It Wei not minimum and maximum power output；
Step 3 solve double-goal optimal model, using leash law by flexibility targeted transformation be constraint condition, then make It is solved with dynamic programming method；Or Noninferior Solution Set is sought using Multiobjective Intelligent algorithm.
The hydropower station Optimization Scheduling provided by the invention for considering flexibility, can also have the feature that in step In rapid one: firstly, the two Phase flow process following using mathematical statistical model or physical model prediction Then, the maximum offset of reservoir inflow is setWithFinally, reservoir inflow is expressed using ratio of slenderness μ (0≤μ≤1) For range format.
The hydropower station Optimization Scheduling provided by the invention for considering flexibility, can also have the feature that in step In rapid two:
Nt=min (Nc,Ne)
Ne=fNh(ht)
Zd(q)=aqb+c
Zu(v)=fzv(v)
In formula: NcCalculate the power output of gained period t；NeFor forced partial outage；htFor the head of period t；ZuAnd ZdWhen respectively The upstream and downstream water level of section t；Hl is the head loss of period t；qemaxFor maximum generation flow；A, b, c and k are constant.
The hydropower station Optimization Scheduling provided by the invention for considering flexibility, can also have the feature that in step In rapid three: firstly, multi-objective problem is converted to single-objective problem, specific method is to convert constraint: 0≤μ for flexibility index ≤ 1, discrete form are as follows: μ=0,0.1 ..., 1；Then, simplify maximum generating watt objective function, specific method is to close to power output It differentiates in stream out:
Ne=fNh(ht)=mht+ n=m (Zu-Zd(q)-hl)+n
N′e=-mabqb-1< 0
When upstream water level determines, period power output shows the rule for first increasing and subtracting afterwards with stream increase is gone out, since the period goes out to flow With become a mandarin that there are linear relationships:
qt=It+(vt-vt+1)/Δt
Period power output shows the rule for first increasing and subtracting afterwards with becoming a mandarin to increase, and objective function can simplify at this time are as follows:
In formula: ImIt becomes a mandarin interval border for period t；
Finally, when optimizing scheduling using Dynamic Programming, two stages recurrence equation are as follows:
Ft(vt)=max [ft(qt)+Ft+1(vt+1)]
In formula: ft() is the utility function of period t reservoir, Ft() is the cumulative maximum utility function of period T to t.
The action and effect of invention
The present invention has fully considered the uncertain feature of reservoir inflow, can be the reservoir inflow forecasting inaccuracy the case where Under, power generation and actual motion that flexible, the stable reservoir operation process of one kind is used to instruct reservoir can be still provided.
Detailed description of the invention
Fig. 1 is the flow chart of the hydropower station Optimization Scheduling of consideration flexibility involved in the embodiment of the present invention.
Specific embodiment
Below in conjunction with attached drawing to the present invention relates to the considerations of flexibility hydropower station Optimization Scheduling carry out in detail Explanation.
<embodiment>
As shown in Figure 1, the hydropower station Optimization Scheduling for considering flexibility provided by the present embodiment includes following step It is rapid:
Two Phase flow is expressed as range format using ratio of slenderness by step 1:
Firstly, the two Phase flow process following using mathematical statistical model or physical model prediction
Then, the maximum offset of reservoir inflow is setWith
Finally, indicating the uncertainty of two Phase flow using ratio of slenderness μ (0≤μ≤1), reservoir inflow is expressed as area Between form, expression formula are as follows:
In formula: number of segment when T is reservoir operation；ItFor the reservoir inflow of period t；For the reservoir inflow predicted value of period t；WithFor maximum offset；μ is flexibility index.
Step 2 establishes the double-goal optimal model for taking into account power benefit and flexibility:
Power benefit target is that total power generation is maximum under becoming a mandarin most bad situation in schedule periods, calculating formula are as follows:
Flexibility target is that flexibility index is maximum, expression formula are as follows:
max μ
In formula, NtFor the power output of period t；Δ t is Period Length.
The constraint condition that the double-goal optimal model established considers has: water balance constraint, storage capacity constraint, storage outflow Constraint, units limits, maximum generation traffic constraints etc., corresponding expression formula are as follows:
In formula, vt+1And vtStorage capacity when respectively period t+1 and period t start；qtFor period t storage outflow；vmaxAnd vmin The minimum and maximum storage capacity respectively allowed；qmaxAnd qminThe minimum and maximum storage outflow respectively allowed；NmaxAnd NminPoint It Wei not minimum and maximum power output.
Wherein, the practical output calculation formula in power station are as follows:
Nt=min (Nc,Ne)
Ne=fNh(ht)
Zd(q)=aqb+c
Zu(v)=fzv(v)
In formula: NcCalculate the power output of gained period t；NeFor forced partial outage；htFor the head of period t；ZuAnd ZdWhen respectively The upstream and downstream water level of section t；Hl is the head loss of period t；qemaxFor maximum generation flow；A, b, c and k are constant.
Step 3 solve double-goal optimal model: using leash law by flexibility targeted transformation be constraint condition, then make It is solved with dynamic programming method；Or Noninferior Solution Set is sought using Multiobjective Intelligent algorithm.
Firstly, multi-objective problem is converted to single-objective problem, specific method is to convert constraint: 0≤μ for flexibility index ≤ 1, discrete form are as follows: μ=0,0.1 ..., 1；
Then, simplify maximum generating watt objective function, specific method is verifying when reservoir upstream water level is constant, generated energy The rule for first increasing and subtracting afterwards is showed with becoming a mandarin to increase, objective function can simplify at this time are as follows:
In formula: ImIt becomes a mandarin interval border for period t；
Finally, optimizing scheduling, two stages recurrence equation using Dynamic Programming are as follows:
Ft(vt)=max [ft(qt)+Ft+1(vt+1)]
In formula: ft() is the utility function of period t reservoir, Ft() is the cumulative maximum utility function of period T to t.
Above embodiments are only the illustration done to technical solution of the present invention.It is according to the present invention to consider flexibly Property hydropower station Optimization Scheduling be not merely defined in described content in the embodiment above, but wanted with right It asks subject to limited range.Any modification or benefit that those skilled in the art of the invention are made on the basis of the embodiment It fills or equivalence replacement, all in claim range claimed of the invention.

Claims (4)

1. a kind of hydropower station Optimization Scheduling for considering flexibility, which comprises the following steps:
Step 1 indicates the uncertainty of two Phase flow using flexibility index, and two Phase flow is expressed as a range format；
Two Phase flow expression formula are as follows:
In formula: number of segment when T is reservoir operation；ItFor the reservoir inflow of period t；For the reservoir inflow predicted value of period t；WithFor maximum offset；μ is flexibility index；
Step 2 establishes the double-goal optimal model for taking into account power benefit and flexibility；
Power benefit target is that total power generation is maximum under becoming a mandarin most bad situation in schedule periods, calculating formula are as follows:
Flexibility target is that flexibility index is maximum, expression formula are as follows:
maxμ
In formula, NtFor the power output of period t；Δ t is Period Length；
Constraint condition are as follows:
In formula, vt+1And vtStorage capacity when respectively period t+1 and period t start；qtFor period t storage outflow；vmaxAnd vminRespectively For the minimum and maximum storage capacity of permission；qmaxAnd qminThe minimum and maximum storage outflow respectively allowed；NmaxAnd NminRespectively Minimum and maximum power output；
Step 3 solves double-goal optimal model, the use of leash law by flexibility targeted transformation is constraint condition, then using dynamic State planing method solves；Or Noninferior Solution Set is sought using Multiobjective Intelligent algorithm.
2. the hydropower station Optimization Scheduling according to claim 1 for considering flexibility, it is characterised in that:
Wherein, in step 1: firstly, the two Phase flow process following using mathematical statistical model or physical model predictionThen, the maximum offset of reservoir inflow is setWithFinally, using ratio of slenderness μ (0≤μ≤ 1) reservoir inflow is expressed as range format.
3. the hydropower station Optimization Scheduling according to claim 1 for considering flexibility, it is characterised in that:
Wherein, in step 2:
Nt=min (Nc,Ne)
Ne=fNh(ht)
Zd(q)=aqb+c
Zu(v)=fzv(v)
In formula: NcCalculate the power output of gained period t；NeFor forced partial outage；htFor the head of period t；ZuAnd ZdRespectively period t's Upstream and downstream water level；Hl is the head loss of period t；qemaxFor maximum generation flow；A, b, c and k are constant.
4. the hydropower station Optimization Scheduling according to claim 1 for considering flexibility, it is characterised in that:
Wherein, in step 3: firstly, multi-objective problem is converted to single-objective problem, specific method is to turn flexibility index Turn to constraint: 0≤μ≤1, discrete form are as follows: μ=0,0.1 ..., 1；Then, simplify maximum generating watt objective function, specific side Method is to differentiate to power output about stream out:
Ne=fNh(ht)=mht+ n=m (Zu-Zd(q)-hl)+n
N′e=-mabqb-1< 0
When upstream water level determines, period power output shows the rule for first increasing and subtracting afterwards with stream increase is gone out, since the period goes out to flow and enter There are linear relationships for stream:
qt=It+(vt-vt+1)/Δt
Period power output shows the rule for first increasing and subtracting afterwards with becoming a mandarin to increase, and objective function can simplify at this time are as follows:
In formula: ImIt becomes a mandarin interval border for period t；
Finally, when optimizing scheduling using Dynamic Programming, two stages recurrence equation are as follows:
Ft(vt)=max [ft(qt)+Ft+1(vt+1)]
In formula: ft() is the utility function of period t reservoir, Ft() is the cumulative maximum utility function of period T to t.
CN201811043794.4A 2018-09-07 2018-09-07 Reservoir power generation optimal scheduling method considering flexibility Active CN109002936B (en)

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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105976101A (en) * 2016-04-29 2016-09-28 武汉大学 Prediction-decision making coupled reservoir operation method based on SVM (Support Vector Machine) and DPY (Dynamic Programming modified by Yang Guang)
US20180024514A1 (en) * 2001-05-18 2018-01-25 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20180024514A1 (en) * 2001-05-18 2018-01-25 The Energy Authority, Inc. Method for management and optimization of hydropower generation and consumption
CN105976101A (en) * 2016-04-29 2016-09-28 武汉大学 Prediction-decision making coupled reservoir operation method based on SVM (Support Vector Machine) and DPY (Dynamic Programming modified by Yang Guang)

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
杨光等: "基于决策因子选择的梯级水库多目标优化调度规则研究", 《水利学报》 *
王海鹏等: "小水电集中上网地区无功电压影响与分析", 《中国农村水利水电》 *

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