WO2019174039A1 - 一种超大规模水电站群短期实用化调度方法 - Google Patents

一种超大规模水电站群短期实用化调度方法 Download PDF

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WO2019174039A1
WO2019174039A1 PCT/CN2018/079307 CN2018079307W WO2019174039A1 WO 2019174039 A1 WO2019174039 A1 WO 2019174039A1 CN 2018079307 W CN2018079307 W CN 2018079307W WO 2019174039 A1 WO2019174039 A1 WO 2019174039A1
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power
output
power station
load
station
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French (fr)
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申建建
程春田
曹瑞
申乾倩
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大连理工大学
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Priority to JP2019556320A priority patent/JP6736112B2/ja
Priority to US16/644,431 priority patent/US11221594B2/en
Publication of WO2019174039A1 publication Critical patent/WO2019174039A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/041—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a variable is automatically adjusted to optimise the performance
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • E—FIXED CONSTRUCTIONS
    • E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
    • E02B—HYDRAULIC ENGINEERING
    • E02B9/00—Water-power plants; Layout, construction or equipment, methods of, or apparatus for, making same
    • 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"
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • 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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/20—Hydro energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • 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
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Definitions

  • the invention relates to the field of hydropower dispatching operation, in particular to a short-term practical dispatching method for a large-scale hydropower station group.
  • the large-scale development of China's hydropower has achieved a qualitative leap, forming a group of giant cascade hydropower stations with a capacity of over 10 million kilowatts in the vast river basins such as the Minjiang River, Jinsha River, Yalong River and Hongshui River, and through UHV AC and DC.
  • the interconnection constitutes a super-large-scale hydropower system, with a high proportion of hydropower provincial power grids with an installed capacity of over 80 million kilowatts and a multi-million-watt regional power grid.
  • the difficulty of dispatching such a large-scale hydropower system and the previous small-scale or single-stage cascade hydropower stations are not available. Same day.
  • the large-scale river basin cascade hydropower stations have a large scale, and the dimensional disaster problems are prominent, and most of them are high-head and giant generator sets. They not only face the short-term output climbing limit, the refined control of the associated time period, the minimum duration of opening and closing, and other traditional operations. Constraints, and there are also multiple levels of nested section constraints and differential power transmission requirements caused by complex grid-connected structures. The power and hydraulic connections are coupled to each other, which constitutes increasingly fine control conditions and increases scheduling difficulty.
  • the present invention proposes a short-term practical dispatching method for ultra-large-scale hydropower stations, and applies the test of 162 hydropower stations of Yunnan Power Grid as engineering background.
  • the results show that the present invention can quickly provide reasonable hydropower.
  • the scheduling results meet the timeliness and practical requirements of the actual operation of the power grid.
  • the technical problem to be solved by the invention is the problem of "dimensionality disaster” and the practicality of the results of the super-large-scale hydropower station group optimization scheduling, and the results can be classified and dispatched according to the problem characteristics and the power station characteristics, and the large-system group iterative solution is realized, and the "dimensionality" is significantly alleviated.
  • the power station and grid demand and practical experience can be used to effectively simplify the power generation dispatching mode, reduce the number of hydropower stations, reduce the difficulty of modeling and solving, and improve the availability and practicability of the results.
  • a short-term practical scheduling method for a large-scale hydropower station group includes the following steps:
  • hydropower stations are divided into three categories: fixed dispatch mode power stations, medium and small watershed cascade hydropower stations, large-scale cascade hydropower stations;
  • p d is the output adjustment step size
  • pr i is the maximum output increase and decrease allowed for the i-stage power station during the adjacent period
  • p′ i,t is the output of the i- time station t period, p i,t , They are the average output and output upper limit of the t period of the i power station
  • El i is the water abandonment
  • ⁇ t is the number of hours in the t period
  • t, T are the number and total number of the scheduling period respectively
  • [t 1 , t 2 ] is T i Min consecutive time periods with the largest load value
  • T i min is the minimum duration of the output force extreme value
  • Step 1 Optimize the output of the unbalanced power station: Use the formula (7) to construct the peak-shaping optimization model, and use the variable-scale method to solve the model;
  • minF 1 is the minimum power station load distribution
  • R t is the grid residual load after t-stage after hydropower station peak shaving
  • L t is the grid load demand during the t period
  • the power-carrying process of each power station is determined by the method of successive load-cutting; repeating step 2, the power of the power station is updated to the next block power. Determine the corresponding output process until all the block power calculations are completed and accumulate the final power output.
  • the invention has the beneficial effects that the present invention combines the scheduling features of the system hierarchy, the spatial attributes, the task requirements, the planning specificity and the like to perform the dimensional reduction solution of the hydropower station classification, and proposes the water supply correction strategy based on the peak regulation response and the load balancing reduction strategy in the low valley period.
  • the present invention can quickly provide reasonable hydropower scheduling results, meet the timeliness and practical requirements of the actual operation of the power grid, and provide an efficient solution for the super-large hydropower system scheduling in southeastern China. New technology approach.
  • Figure 1 is a schematic diagram of the balance calculation result of the entire power grid
  • Figure 2 (a) is a schematic diagram of the calculation results of the fixed-discharge mode power station- ⁇ ;
  • Fig. 2(b) is a schematic diagram showing the calculation result of the water level of the fixed dispatching mode power station- ⁇ ;
  • Fig. 2(c) is a schematic diagram showing the calculation results of the power generation of the fixed dispatching mode power station-liyuan
  • Fig. 2(d) is a schematic diagram showing the calculation result of the water level of the power station-liyuan in the fixed dispatch mode
  • Fig. 2(e) is a schematic diagram showing the calculation result of the output of the power station-utility bridge in the fixed dispatch mode
  • Fig. 2(f) is a schematic diagram showing the calculation result of the water level of the power station-utility bridge in the fixed dispatch mode
  • Figure 2 (g) is a schematic diagram of the calculation results of the output of the fixed dispatch mode power station-Songshan estuary
  • Figure 2 (h) is a schematic diagram of the water level calculation result of the fixed dispatch mode power station-Songshan estuary
  • Figure 3 is a schematic diagram showing the load distribution results of the Xiqiao River cascade power station
  • Figure 4 is a schematic diagram of the total output process of a hydropower station participating in an optimization calculation
  • Figure 5 (a) is a schematic diagram of the balance power plant - Manwan output process
  • Figure 5(b) is a schematic diagram of the process of the balanced power station-Dachaoshan output.
  • hydropower stations can be classified and grouped according to the hierarchical characteristics of the problem, natural space attributes, task requirements, scale characteristics, planning specificity, etc., and different modeling methods and solving strategies are selected to solve the problem. Quickly solve and get the purpose of the calculation results that meet the actual requirements of the project.
  • the short-term scheduling of hydropower enriched power grid large-scale hydropower station group is proposed.
  • the power grid hydropower station is classified and grouped and solved according to the scheduling characteristics, which reduces the optimization calculation scale.
  • the first type is a fixed-distribution mode power station.
  • the first type is a fixed-distribution mode power station.
  • the actual plan is planned, when the system load demand, power supply, and control requirements of the second day are not changed much, it is not necessary to significantly adjust the power station plan or scheduling arrangement of the previous day. It is beneficial to the implementation of the plan or to ensure the safe operation of the power grid, power station and unit. This can basically determine the dispatching mode of the power station, such as electric water, water and electricity, etc., greatly reducing the number of hydropower stations involved in the optimization, only need to Plan to check and fine-tune power stations that have no water and other special needs;
  • the second category is cascade hydropower stations in small and medium-sized basins.
  • Hydropower enriched power grids usually include many small and medium-sized watershed cascades, which are characterized by small number of stages and small scale. Power generation dispatching has relatively small impact on the overall grid. Therefore, when formulating such power generation plans, the power grid It is often regarded as a whole, that is, a virtual power station, and given the total power generation output process of the cascade, and then the load distribution is optimized by the cascade power station to further reduce the calculation scale;
  • the third type is large-scale cascade hydropower stations. These power plants have better regulation performance and large installed scale.
  • the power grid generally determines the daily power generation or water level control conditions of each power station according to the medium and long-term dispatch control requirements, and considers the system load. Modeling and optimization calculations, peaking and other requirements, when the plant size is still large, group iterative optimization can be used to reduce the number of single-optimized power plants.
  • the first type is a fixed-scheduling power station, such as a fixed-planning output process or generating electricity according to specific dispatching instructions.
  • Such power plants need to conduct water condition check analysis, and adopt suitable according to water level limit, water abandonment, and empty space.
  • the strategy corrects the initial scheduling method or the power generation output plan to ensure the feasibility of the results.
  • a water-based correction strategy based on peak-shaping response is proposed to appropriately increase the planned power consumption to avoid or reduce the abandonment of water;
  • a trough load balancing reduction strategy is proposed to appropriately reduce the planned power and ensure the feasibility of the output.
  • the water-saving check is performed on a time-by-period basis. If there is any time period Ql i, t > 0, the water disposal correction is required.
  • an iterative abandonment water correction strategy is proposed. The specific steps are as follows:
  • Step 1 Determine the water abandonment period t'.
  • the time-by-time traversal is used to judge whether there is water in the power station m. If Ql i,t ⁇ 0,1 ⁇ t ⁇ T, the water disposal correction ends; otherwise, the last water disposal period is marked as t'.
  • Step 2 Mark the time period set in [0, t'] as ⁇ , and remove the invalid period in [0, t'] (that is, the power station output or power generation flow, and the time when the reservoir water level reaches the upper limit boundary). ⁇ If the collection is empty, if it is, the water removal correction ends, otherwise it will go to the next step.
  • Step 3 Estimate the amount of water discarded.
  • the total abandonment power of the power station i is calculated by the following formula.
  • Ql i,t is the abandonment flow rate of the power station of the t period i
  • r i,t is the power consumption rate of the power station of the t period i.
  • Step 4 Mark the peak load period [t 1 , t 2 ].
  • the continuous time range of the maximum load value of T i min is selected from the time set ⁇ , which is denoted as [t 1 , t 2 ].
  • Step 5 Determine the output adjustment step size.
  • the force adjustment step size is determined using equation (9).
  • Step 6 Correct the power output of the power station. Use equation (10) to correct the power output of the power station from t 1 to t 2 , and calculate the force for the period of t 1 to T according to the following method, determine the corresponding power generation flow, reservoir water level and other results, and return to step 1.
  • the calculation method of the output force for any time period t, taking the power station output as the control target, using the maximum and minimum power generation flow as the boundary conditions, using the dichotomy method for iterative search until the calculated force and the given output satisfy the convergence accuracy; during the search process Calculate the calculated force using equation (11).
  • f i ( ⁇ ) represents the functional relationship between the output, head and flow of the i- power station
  • Z i, t-1 is the reservoir water level of the t-1 period of the i-power station
  • Q i, t is the power station of the i The inflow traffic of the t period
  • q i, t is the power generation flow of the t period of the i power station.
  • the trough load balancing reduction strategy is proposed, and the output of the load trough period is prioritized.
  • the purpose is to use the hydropower adjustment capability to achieve the depth adjustment of the load.
  • the specific adjustment strategy is as follows:
  • Step 1 Calculate by electric water, traverse all time periods to judge whether the given power output can be satisfied, and if so, the correction is completed; otherwise, the last time period when the mark cannot satisfy the given output is t′′.
  • Step 2 Mark the time period set in the interval [0, t"] as ⁇ , and remove the invalid period whose output reaches the lower limit value from the set. If the set is empty at this time, the calculation ends, otherwise go to step 3.
  • Step 3 According to the minimum duration number of station output extreme value T i min , select the continuous period range with the smallest load value of T i min from ⁇ , denoted as [t 3 , t 4 ].
  • Step 4 Adjust the output of each period according to formula (12) and return to step 1.
  • p i,t is the lower limit of the output of the power station t period of i; Correct the step size for a given output.
  • the second type of small and medium watershed cascade hydropower stations usually predetermine the total planned output process, and the load distribution between the cascade hydropower stations needs to be optimized. For this reason, the minimum water consumption goal of the cascade is adopted, and the hydropower dispatching constraints are considered.
  • the optimal scheduling model is constructed, and the optimal solution strategy is implemented for each period of time.
  • the objective function of load distribution of cascade hydropower stations is established with the minimum total water consumption as the optimization criterion, specifically:
  • N is the total number of hydropower stations.
  • p t is the total output for a given period of time t.
  • V i,t+1 V i,t +3600 ⁇ (Q i,t -q i,t -S i,t ) ⁇ t (16)
  • R i,t is the inbound flow of the i-stage power station during the time period t;
  • V i,t is the storage capacity of the i-th power station t;
  • S i,t is the i-power station The flow rate of abandoned water during the t period.
  • q i,t They are the lower and upper limits of the power generation flow rate of the i-stage power station.
  • Z i,t , Z i,t are the reservoir water level and its lower and upper limits for the t period of the i power station.
  • Output climbing slope constraint Limit the output increase and decrease range of the power station between adjacent time periods, which is applicable to unbalanced power plants (without automatic power generation control unit installed), as follows:
  • Unit stability and operation constraints avoid the cavitation and vibration areas of the power-on group under certain output or head, and ensure the safe production of the power station.
  • Ps i,t,k is the upper and lower limits of the kth restricted operating interval of the t period of the i power station.
  • ⁇ is the collection of power stations involved in a transmission section, It is the upper limit of the conveying capacity of the section ⁇ .
  • variable scale optimization method proposed by our previous research results is used as the main optimization algorithm.
  • the focus is on the formula (14).
  • the load balance constraint is processed by the external penalty function method, and the objective function is reconstructed.
  • the iterative optimization is used to obtain the step load optimization allocation scheme.
  • r t is the penalty coefficient
  • ⁇ is the maximum allowable load balance error
  • a is the penalty constant
  • the initial water level is calculated on a time-by-cycle basis, and the fixed water level adjustment calculation is carried out, and the total output of the power station p′ t is determined .
  • step (5) If p' t -p t > ⁇ , sort the stepped power stations in order of water consumption from large to small, and go to step (4); if p t -p' t > ⁇ , follow water consumption Rate the stepped power stations from small to large, and go to step (4); if
  • p d is the output adjustment step size, and it is necessary to comprehensively consider the upper limit of the climbing slope in the adjacent time period.
  • Large-scale watershed cascade hydropower stations are usually very important regulating power sources in the power grid. They are generally responsible for peak tasks such as peak shaving and load balancing, which are essential for grid dispatching, especially for short-term operations. Aiming at the hydropower station, combined with the actual demand of the power grid, a peaking scheduling model was constructed to optimize the power generation plan of the power station. For a few balanced power plants, the load distribution method based on equal load rate was proposed based on the system power demand. Realize grid-to-time load balancing.
  • R t is the residual load of the grid after t peaking after hydropower station peaking; It is the average value of the grid residual; L t is the grid load demand of the t period.
  • the above-mentioned peak-shaving model also needs to consider various constraints of short-term operation of hydropower. For detailed description, refer to the above-mentioned small- and medium-flow cascade hydropower station scheduling constraints, which will not be repeated here.
  • it is necessary to determine the minimum technical output of the power station operation according to the startup mode of the balanced power station, and deduct it from the load process, and use the remaining load ⁇ R 1 , R 2 ,..., R T ⁇
  • the above-mentioned variable scale method is used to optimize the power generation scheduling process of the hydropower station group.
  • the above-mentioned power plant optimization scheduling needs to consider complex constraints, especially the coupling-type constraints such as the output climbing limit, the minimum duration of opening and closing, and the output fluctuation control, it is difficult to directly achieve the power supply and demand balance in all time periods during the scheduling period.
  • the resulting residual load process usually has more “burrs” and the load fluctuations in adjacent periods are very frequent.
  • the common practice in actual dispatching is to select the power station with AGC unit as the balance power plant to balance the residual load demand.
  • the power dispatching can automatically adjust the output according to the AGC command. Therefore, the complex constraints such as the output fluctuation control and the climbing limit need not be considered in the calculation, and the load process estimation is generally combined in the actual dispatching of the power grid.
  • the daily electricity consumption of each balanced power plant is optimized as a control condition to determine a reasonable 96-point plan output. From the perspective of the fairness of the adjustment task, the balanced power station adopts the principle of equal power generation load rate optimization.
  • the present invention combines it as the control target and the load shedding, and proposes a balanced power plant scheduling method based on the equal load rate.
  • the method first needs to consider the unbalanced power of the power grid and balance the available capacity of the power station, and use equation (28) to estimate the target power generation of each power station.
  • C is the number of balanced power stations;
  • the minimum technical output of the balanced power station needs to be deducted from the load to meet the power-on output requirements of the power station, and the remaining power of each power station can be obtained:
  • a power-cut-based load-cut strategy is proposed.
  • the method of the present invention is verified by taking the preparation of the hydropower system of Yunnan Power Grid as an example.
  • Yunnan Power Grid is one of the two largest provincial power grids in China.
  • 162 hydropower stations have been adjusted, and the installed capacity of hydropower is over 60 million kW, accounting for more than 70% of the total installed capacity of the whole network.
  • it also needs to undertake complex task requirements such as peak shaving, frequency modulation, and power transmission from west to east. It faces very prominent dispatching problems, especially the efficient solution of large-scale systems, which is directly related to the daily grid preparation.
  • the efficiency of the power generation plan and the practicability of the plan are difficult to meet the actual requirements by relying solely on mathematical optimization methods.
  • the actual engineering characteristics and scheduling requirements need to be incorporated into the modeling and solving process to effectively improve efficiency and result availability.
  • the power generation plan is compiled. According to the idea of the method of the present invention, it is first necessary to classify and group all hydropower stations in Yunnan province. There are about 79 hydropower stations in the first category. The power generation plan or scheduling mode of each power station is pre-determined based on actual needs. The water quality check analysis of some complete data stations is focused on, and the plan is revised in combination with the check results.
  • Manwan and Dachaoshan have AGC units, which are used as balanced power plants. Balance system load fluctuations.
  • the output plan of 11 coal power stations in Yunnan Power Grid has been pre-determined, and the new energy output such as photovoltaics and wind power adopts the principle of full consumption, so the load demand faced by the hydropower system is the equivalent load after deducting the rest of the power supply.
  • the hydropower dispatching results must meet the peaking requirements of the power grid, and at the same time, it is necessary to ensure the power balance constraint of the period of 96 points throughout the day.
  • Figure 1 shows the balance calculation results of the whole grid.
  • Figure 2 shows the analysis results of some fixed-station mode power stations.
  • Figure 3 shows the load distribution results of Xiqiao River cascade power stations.
  • Figure 4 and Figure 5 show the total output of hydropower stations participating in the optimization calculation. The output process of two balanced power plants.
  • the hydropower system does have a major power supply role in Yunnan Power Grid.
  • the daily power generation is about 556.8 million kWh, accounting for 85.3% of the total network. It is also the system peaking and wind power and photovoltaic power fluctuation.
  • the main regulation power supply, the peaking depth reaches 12242MW, which is 96.4% of the maximum load peak-to-valley difference of the whole network, giving full play to the high-quality regulation of hydropower.
  • Figure 2 shows the results of the output check of the four hydropower stations in Nuozhadu, Liyuan, Gongguoqiao and Songshanhekou.
  • the Luzhadu and Liyuan power stations use the plan of the Southern Power Grid to adjust the output.
  • the Gongguo Bridge and Songshan Estuary adopt the recommended Power generation plan. It can be seen from the analysis of the water regime that the Zhazadu and Liyuan are all operating in strict accordance with the planned plan, and no water or venting occurs.
  • the reservoir water level and power generation flow meet the given upper and lower limits, which is related to the power station. There is a direct correlation between the regulation performance, and the regulation of the water supply is small. The fluctuation of the water level in the day is small.
  • the pear garden is adjusted weekly, and the initial water level is relatively low.
  • the water level in the day rises by about 4m, but the overall control Within the reasonable operating range; the Gongguo Bridge did not generate electricity according to the proposed output plan, mainly due to the large amount of incoming water, and the reservoir generated abandonment of water.
  • the water disposal correction strategy increases the power generation output until full operation, but there is still water. Occurred, the result is reasonable; the power generation output of the Songshan Hekou Hydropower Station did not meet the requirements of the proposed plan. The main reason is that the reservoir has dropped to the dead water level and cannot meet the given output value.
  • the equilibrium reduction strategy is preferentially reduced.
  • the output of the trough period as can be seen from the figure, the power output of the power station is appropriately reduced from 01:30 to 04:30, and the rest of the time is basically operated according to the given output.
  • the actual net adjustment needs are basically the same.
  • Figure 4 shows the load distribution results of the Xiqiao River cascade. Under the condition of a total output of 93MW in each period, the minimum output of the dispatching period is used to optimize the output process of the cascade hydropower station.
  • Figure 3(a) can be used to visually see The total output of the power station fully meets the total output requirements of the cascade.
  • Figure 3(b) shows that the reservoir water level of the power station is also operating within a reasonable range.
  • the total water consumption of the cascade corresponding to the scheme is 2.17 million m 3 , compared with the conventional uniform power generation method. The daily water consumption was reduced by about 110,000 m 3 , and the savings reached 5%, indicating that the cascade power compensation actually reduced the overall power generation rate of the power station and improved the water use efficiency.
  • the total output process of the participating power plants in Figure 4 is basically consistent with the system load demand. It shows that the compensation and adjustment capability of the cascade hydropower stations in large-scale watersheds is indeed a good hydropower peak-shaving effect, and the peak-shaving depth reaches 4619 MW. Among them, Manwan and Dachaoshan are used as balanced power stations, which have strong load tracking ability. The output process fluctuates frequently. In actual operation, it can be automatically adjusted by AGC function. The side reflects the frequency and amplitude of the two power stations participating in load regulation. They are relatively large, effectively suppressing load fluctuations and meeting the power balance requirements of each period.
  • Table 1 list of virtual power stations

Abstract

一种超大规模水电站群短期实用化调度方法，属于水电调度运行领域。该方法将所有水电站按照系统层级、空间属性、任务要求、计划特殊性等调度特征分为三类，提出基于调峰响应的弃水修正策略和低谷时段负荷均衡削减策略，实现固定调度方式电站的校核分析与出力调整；中小流域梯级水电站，以总出力过程为控制条件，以总耗水最小为目标，优化梯级电站间负荷分配；大型流域梯级水电站，构建调峰调度模型，提出平衡电站等负荷率调度方法，实现系统调峰响应和全时段负荷平衡。该方法能够快速给出合理的水电调度结果，满足电网实际运行的时效性和实用性要求，为超大规模水电系统调度高效求解提供了新的技术途径。

Description

一种超大规模水电站群短期实用化调度方法 技术领域
本发明涉及水电调度运行领域，特别涉及一种超大规模水电站群短期实用化调度方法。
背景技术
中国水电的规模化发展已经实现了质的飞越，在澜沧江、金沙江、雅砻江、红水河等特大流域形成了一批超千万千瓦级的巨型梯级水电站群，并通过特高压交直流互联构成了超大规模水电系统，出现了装机规模超八千万千瓦的高比例水电省级电网和亿千瓦级区域电网，如此超大规模水电系统的调度难度系数和以往中小规模或者单一流域梯级水电站不可同日而语。特大流域梯级水电站求解规模庞大，维数灾问题凸显，并且大多都是高水头、巨型发电机组，不仅面临着短期出力爬坡限制、关联时段出力精细化控制、开停机最小持续时间等传统的运行约束条件，而且还存在复杂并网结构导致的多级嵌套断面限制和差异化送电要求，电力与水力联系相互耦合，构成了日益精细化控制条件，加大了调度难度。如何突破复杂水电系统超大规模、高维、非线性时空高度耦合等障碍，获取水电优化运行调度方式，以期对实际工程起到指导作用，满足实用化要求，是目前实际生产中亟需解决的关键问题。
现有针对大规模水电站群调度的研究多是从数学角度出发，集中在目标函数与约束建模以及优化算法方面，取得了突出的研究成果。Barros和Zambon等对巴西百余座水电站进行了优化调度建模，构建了线性规划和非线性规划模型，但该研究主要以长期规划为主，且进行了许多简化以采用MINOS、CPLEX等商业软件求解，时效性和结果精度等都难以满足更为复杂的短期实用化调度运行需要；还有很多公开报道是从算法方面开展水电站群优化调度研究，并结合不同思路提出了有效的降维方法和策略，在一些大型流域梯级水电系统进行了模拟分析。总体来说，已有成果作为具有前瞻性的基础研究，虽扩大了计算规模，提高了计算速度和精度，但在面对超大规模的水电系统求解问题时，“维数灾”仍不可避免，尤其单纯的数学优化研究成果在实际生产中多归类于策略导向性方案，缺乏实用性，目前针对调度计划实际生产业务相结合的实用化研究也较为鲜见。因此寻找一种兼顾优化调度且实用的超大规模水电系统短期调度方法，切实考虑水电系统调度特征和实际运行中的专家经验，突破水电调度技术瓶颈是一项具有重要理论和实践价值的研究。
针对以上问题，本发明提出一种超大规模水电站群短期实用化调度方法，并以云南电网162座水电站日前计划编制为工程背景对其进行应用测试，结果显示本发明成果可快速给出合理的水电调度结果，满足电网实际运行的时效性和实用性要求。
发明内容
本发明要解决的技术问题是超大规模水电站群优化调度“维数灾”和结果实用性问题，其成果可以依据问题特征和电站特性进行水电站分类调度，实现大系统分组迭代求解，显著缓解“维数灾”问题，同时可利用电站和电网需求与实际经验有效简化电站发电调度方式，减少优化水电站数量，降低建模与求解难度，提高结果的可用和实用性。
本发明的技术方案：
一种超大规模水电站群短期实用化调度方法，包括如下步骤：
(1)基于调度特征进行水电站分类：根据问题的层级、自然空间属性、任务要求、尺度 特点、计划特殊性的调度特征对水电站进行分类和分组，并选择不同的建模方法和求解策略，以实现可求解、快速求解和得到满足工程实际要求计算结果目的；水电站分为三类：固定调度方式电站、中小流域梯级水电站、大型流域梯级水电站；
(2)固定调度方式电站，由于相邻日系统负荷需求、电站来水、控制要求变化不大，没有必要大幅调整前一天电站计划或者调度安排，这无论对计划实施还是保障电网、电站和机组安全运行都是有利的，这样可以确定电站的调度方式，包括定出力和定水位，减少参与优化的水电站数目，只需要对日前计划进行校核，对有弃水和其他特殊需求不能满足的电站进行微调；具体的调整方法包括如下两种典型情况：
(a)当库水位高于上限产生弃水时，采用基于调峰响应的弃水修正策略，从最后一个弃水时段之前的所有时段内选择多个负荷值最大的T i min连续时段，记为[t 1,t 2]，并采用式(1)增加电站计划出力，避免或者减少弃水；并进行迭代修正，直至所有时段出力或发电流量，以及库水位均达到上限边界；
Figure PCTCN2018079307-appb-000001
Figure PCTCN2018079307-appb-000002
式中，p d为出力调整步长；pr i为i号电站在相邻时段间允许的最大出力增减值；p′ i,t为i号电站t时段的出力，p i,t，
Figure PCTCN2018079307-appb-000003
分别为i号电站t时段的平均出力及出力上限；El i为弃水电量；Δt为t时段的小时数；t，T分别为调度时段编号和总数；[t 1,t 2]为T i min个负荷值最大的连续时段；T i min为出力极值的最小持续时段数；
(b)当水位低于下限出现库空无法满足给定的调度方式或者计划出力时，提出一种低谷负荷均衡削减策略，选择负荷值最小的T i min个连续时段，记为[t 3,t 4]，并采用式(3)减少电站出力，保证出力的可行性；并进行迭代修正，直至所有时段出力均达到下限边界；
Figure PCTCN2018079307-appb-000004
式中， p i，t为i号电站t时段的出力下限；
Figure PCTCN2018079307-appb-000005
为给定的出力修正步长；[t 3,t 4]为T i min个负荷值最小的连续时段；
(3)中小流域梯级水电站，以总出力过程为控制条件，以总耗水最小为目标，构建梯级负荷分配优化模型，并采用文献(申建建,程春田,李卫东,等.复杂时段耦合约束水电站群短期变尺度优化方法.中国电机工程学报,2014,34(1):87-95.)中变尺度方法进行模型求解；在求解过程中，重点针对负荷平衡约束式(4)采用外点罚函数方法进行处理，并引入目标惩罚项，具体见式(5)和(6)；
Figure PCTCN2018079307-appb-000006
Figure PCTCN2018079307-appb-000007
Figure PCTCN2018079307-appb-000008
式中，p t为给定的t时段的总出力；N为水电站的总数；F pen为目标惩罚函数；r t为惩罚系数；ε为允许的最大负荷平衡误差；a为惩罚常数；
(4)大型流域梯级水电站，构建调峰调度模型，同时提出平衡电站等负荷率调度方法，实现系统调峰响应和全时段负荷平衡；
步骤1：优化非平衡电站出力：利用式(7)构建调峰优化模型，并采用变尺度方法进行模型求解；
Figure PCTCN2018079307-appb-000009
式中，minF 1为最小电站负荷分配量；R t为经过水电站调峰后t时段电网余留负荷；
Figure PCTCN2018079307-appb-000010
为电网余荷平均值；L t为t时段电网负荷需求；
步骤2：优化平衡电站出力：将每个电站的余留电量
Figure PCTCN2018079307-appb-000011
等分为Y份，采用
Figure PCTCN2018079307-appb-000012
表示，且
Figure PCTCN2018079307-appb-000013
将各电站初始电量设置为
Figure PCTCN2018079307-appb-000014
c＝1,2,...,C，C为平衡电站个数，按照上下游顺序，采用逐次切负荷方法确定各电站出力过程；重复步骤2，将电站电量依次更新为下一分块电量，确定对应的出力过程，直至完成所有分块电量的计算，并累加得到最终的电站出力。
本发明的有益效果：本发明结合系统层级、空间属性、任务要求、计划特殊性等调度特征进行水电站分类分组降维求解，提出基于调峰响应的弃水修正策略和低谷时段负荷均衡削减策略，实现固定调度方式电站的校核分析与出力调整；针对中小流域梯级水电站，以总出力过程为控制条件，以总耗水最小为目标，优化梯级电站间负荷分配；针对大型流域梯级和主要水电站，构建调峰调度模型，提出平衡电站等负荷率调度方法，实现系统调峰响应和全时段负荷平衡。相比以往仅依靠数学算法进行降维的方法，本发明能够快速给出合理的水电调度结果，满足电网实际运行的时效性和实用性要求，为我国西南地区超大规模水电系统调度高效求解提供了新的技术途径。
附图说明
图1是电网整体的平衡计算结果示意图；
图2(a)是固定调度方式电站-糯扎渡出力计算结果示意图；
图2(b)是固定调度方式电站-糯扎渡水位计算结果示意图；
图2(c)是固定调度方式电站-梨园出力计算结果示意图；
图2(d)是固定调度方式电站-梨园水位计算结果示意图；
图2(e)是固定调度方式电站-功果桥出力计算结果示意图；
图2(f)是固定调度方式电站-功果桥水位计算结果示意图；
图2(g)是固定调度方式电站-松山河口出力计算结果示意图；
图2(h)是固定调度方式电站-松山河口水位计算结果示意图；
图3是西洱河梯级电站的负荷分配结果示意图；
图4是参与优化计算的水电站总出力过程示意图；
图5(a)是平衡电站-漫湾出力过程示意图；
图5(b)是平衡电站-大朝山出力过程示意图。
具体实施方式
下面结合附图和技术方案，进一步描述本发明的具体实施方式。
对于百座级超大规模水电系统调度问题，为了实现有效降维，根本途径是降低参与计算的水电站数、减少优化迭代过程中的决策变量和状态数目，这可以结合工程问题特点总结相应的减少规则，具体来说可以根据问题的层级、自然空间属性、任务要求、尺度特点、计划特殊性等调度特征对水电站进行分类和分组，并选择不同的建模方法和求解策略，从而达到问题可求解、快速求解和得到满足工程实际要求计算结果目的。
(1)大规模水电系统调度特征降维
按照上述水电调度特征降维思路，以省级电网大规模水电系统为工程背景，针对日前计划编制问题，兼顾效率、可行性、实用性等原则，提出了水电富集电网大规模水电站群短期调度方法，将电网统调水电站按调度特征进行分类分组迭代求解，降低优化计算规模。
第一类为固定调度方式电站，在实际制定日前计划时，当第2天系统负荷需求、电站来水、控制要求等变化不大时，没有必要大幅调整前一天电站计划或者调度安排，这无论对计划实施还是保障电网、电站和机组安全运行都是有利的，这样可以基本确定电站的调度方式，如以电定水、以水定电等，大大减少参与优化的水电站数目，只需要对日前计划进行校核，对有弃水和其他特殊需求不能满足的电站进行微调；
第二类为中小流域梯级水电站，水电富集电网通常包括很多中小流域梯级，呈现级数少和规模小特点，发电调度对电网整体影响相对较小，所以在制定这类电站发电计划时，电网往往将其视为一个整体即虚拟电站，并给定梯级总的发电出力过程，再由梯级电站进行负荷优化分配，以进一步减少计算规模；
第三类为大型梯级水电站，这类电站具有比较好的调节性能，且装机规模大，电网一般根据中长期调度控制要求，可以预先确定各电站的日发电量或者水位控制条件，并考虑系统负荷、调峰等需求，进行建模和优化计算，当电站规模仍然较大时，可以采用分组迭代优化，减少单次优化的电站数量。
(2)固定调度方式电站求解方法
第一类为固定调度方式电站，如固定计划出力过程或者按照特定的调度指令发电，这类电站重点需要进行水情校核分析，并根据水位越限、弃水、库空等情况，采用适合的策略修 正初始调度方式或者发电出力计划，以保证结果的可行性。为此，针对两种典型的校核越限情形：当库水位高于上限产生弃水时，提出一种基于调峰响应的弃水修正策略，适当增加计划电量，避免或者减少弃水；当水位低于下限出现库空无法满足给定的调度方式或者计划出力时，提出一种低谷负荷均衡削减策略，适当减少计划电量，保证出力的可行性。
(2.1)基于调峰响应的弃水修正策略
对于任一电站i，进行逐时段弃水校验，若存在任一时段Ql i,t>0，则需要进行弃水修正。以响应电网峰值负荷需求为启发信息，提出一种迭代的弃水修正策略，具体步骤如下：
步骤1：确定弃水时段t′。逐时段遍历判断电站m是否存在弃水，若Ql i,t≤0,1≤t≤T，则弃水修正结束；否则标记最后一个弃水时段为t′。
步骤2：标记[0,t′]内时段集合为Ψ，并将[0,t′]内无效时段(即电站出力或发电流量，以及库水位达到上限边界的时段)从Ψ中剔除，判断Ψ集合是否为空，若是，则弃水修正结束，否则转至下一步。
步骤3：估算弃水电量。采用下式计算电站i的总弃水电量。
Figure PCTCN2018079307-appb-000015
式中：Ql i,t为t时段i号电站的弃水流量；r i,t为t时段i号电站的发电耗水率。
步骤4：标记高峰负荷时段[t 1,t 2]。根据电站出力极值最小持续时段数T i min要求，从时段集合Ψ中选择T i min个负荷值最大的连续时段范围，记为[t 1,t 2]。
步骤5：确定出力调整步长。采用公式(9)确定出力调整步长。
Figure PCTCN2018079307-appb-000016
步骤6：修正电站出力。采用式(10)修正t 1～t 2时段的电站出力，并按照下文方法对t 1～T时段进行定出力计算，确定对应的发电流量、库水位等结果，返回步骤1。
Figure PCTCN2018079307-appb-000017
定出力计算方法：对于任一时段t，以电站出力为控制目标，以最大和最小发电流量为边界条件，采用二分法进行迭代搜索，直至计算出力与给定出力满足收敛精度；在搜索过程中采用式(11)确定计算出力。
p i,t＝f i(Z i,t-1,Q i,t,q i,t,Ql i,t,Δt)       (11)
式中：f i(·)表示i号电站的出力、水头、流量之间的函数关系；Z i,t-1为i号电站t-1时段的库水位；Q i,t为i号电站t时段的入库流量；q i,t为i号电站t时段的发电流量。
(2.2)低谷负荷均衡削减策略
当库水位达到水位下限，无法满足给定的调度方式时，需要适当减小电站出力，以保证计划的可行性。为此，提出低谷负荷均衡削减策略，优先调整负荷低谷时段的出力，目的是尽可能利用水电的调节能力实现负荷的深度调节。具体的调整策略如下：
步骤1：以电定水计算，遍历所有时段判断给定的电站出力是否可以满足，若是，则修正完毕；否则标记无法满足给定出力的最后一个时段为t″。
步骤2：标记[0,t″]区间内时段集合为Γ，并将出力达到下限值的无效时段从集合中剔除，若此时集合为空，则计算结束，否则转至步骤3。
步骤3：根据电站出力极值最小持续时段数T i min要求，从Γ中选择T i min个负荷值最小的连续时段范围，记为[t 3,t 4]。
步骤4：按照公式(12)调整各时段出力，并返回步骤1。
Figure PCTCN2018079307-appb-000018
式中： p i，t为i号电站t时段的出力下限；
Figure PCTCN2018079307-appb-000019
为给定的出力修正步长。
(3)中小流域梯级水电站求解方法
按上文所述，第二类中小流域梯级水电站通常预先确定了总的计划出力过程，需要进行梯级电站间的负荷优化分配。为此，采用梯级耗水最小目标，考虑水电调度约束条件，构建了优化调度模型，并针对各时段梯级总出力约束，采用适合的求解策略实现高效求解。
(3.1)梯级耗水最小目标
在流域梯级水电站总负荷给定的情况下，为节省水能资源，提高梯级电站期末蓄能，以总耗水量最小为优化准则，建立了梯级水电站负荷分配的目标函数，具体为：
Figure PCTCN2018079307-appb-000020
式中：N为水电站的总数。
(3.2)约束条件
1)负荷平衡约束
Figure PCTCN2018079307-appb-000021
式中：p t为给定的t时段总出力。
2)水量平衡约束
Figure PCTCN2018079307-appb-000022
V i,t+1＝V i,t+3600×(Q i,t-q i,t-S i,t)Δt        (16)
式中：R i,t为i号电站在时段t的区间入库流量；
Figure PCTCN2018079307-appb-000023
为考虑水流滞时后k号电站流入i号电站t时段的总流量，为发电流量和弃水流量之和；V i,t为i号电站t时段的库容；S i,t为i号电站t时段的弃水流量。
3)发电流量约束
Figure PCTCN2018079307-appb-000024
式中： q i，t ，
Figure PCTCN2018079307-appb-000025
分别为i号电站t时段发电流量的下限与上限。
4)电站出力约束
Figure PCTCN2018079307-appb-000026
5)库水位约束
Figure PCTCN2018079307-appb-000027
式中：Z i,t, Z i,t ,
Figure PCTCN2018079307-appb-000028
分别为i号电站t时段的库水位及其下限、上限。
6)出力爬坡约束：限制电站在相邻时段间的出力增减幅度，适用于非平衡电厂(未安装自动发电控制机组)，具体如下：
Figure PCTCN2018079307-appb-000029
7)机组安稳运行约束：避开机组在某些出力或水头下的气蚀和振动区域，保证电站的安全生产
Figure PCTCN2018079307-appb-000030
式中：
Figure PCTCN2018079307-appb-000031
ps i,t,k为i号电站t时段第k个限制运行区间的上限与下限。
8)机组开停机时间限制约束
Figure PCTCN2018079307-appb-000032
式中：
Figure PCTCN2018079307-appb-000033
分别为i号电站k号机组允许的最短开停机时间；u i,k,t为机组t时段的运行状态，1表示开机，0表示停机。
9)输电断面限制
Figure PCTCN2018079307-appb-000034
式中Ω为某输电断面涉及的电站集合，
Figure PCTCN2018079307-appb-000035
为断面Ω的输送能力上限。
(3.3)求解方法
梯级水电站优化调度问题已有大量相关研究，也形成了许多可行的求解方法，本节以我们前期研究成果提出的变尺度优化方法作为主体寻优算法，在此基础上，重点针对式(14)负荷平衡约束，采用外点罚函数方法进行处理，并重构目标函数，通过迭代寻优获取梯级负荷优化分配方案。
对于负荷平衡约束构建罚函数为：
Figure PCTCN2018079307-appb-000036
Figure PCTCN2018079307-appb-000037
式中r t为惩罚系数；ε为允许的最大负荷平衡误差；a为惩罚常数。
为了保证初始解满足梯级电站的负荷平衡约束，同时尽可能响应耗水最小的目标需求，提出基于耗水率排序的迭代搜索策略，以确定高效的初始可行解。具体步骤如下：
(1)从上游至下游逐时段逐个电站以初始水位为基准，进行定水位调节计算，并确定各时段电站总出力p′ t；
(2)初始化t＝1；
(3)若p′ t-p t>ε，则按照耗水率从大到小顺序对梯级电站进行排序，转至步骤(4)；若p t-p′ t>ε，则按照耗水率从小到大顺序对梯级电站进行排序，转至步骤(4)；若|p′ t-p t|≤ε，转至步骤(5)；
(4)按照一定步长，采用公式(26)调整排序最靠前的i号电站t时段出力，并判断当前电站调整前后出力值是否变化，若是，重新计算p′ t，转至步骤(3)；否则按照相同方法调整处于序位号第2位的电站，以此类推。
Figure PCTCN2018079307-appb-000038
式中p d为出力调整步长，需要综合考虑相邻时段的爬坡上限确定。
(5)令t＝t+1，若t≤T，转至步骤(3)，否则输出初始解，计算结束。
以初始可行解为基础，按照前述变尺度方法进行迭代寻优，获取优化的梯级负荷分配方案，具体步骤请见前述文献。
(4)大型流域梯级水电站求解方法
大型流域梯级水电站群通常是电网中非常重要的调节电源，一般担负着电网的调峰、负荷平衡等关键任务，对于电网调度特别是短期运行至关重要。针对这类水电站，结合电网实际需求，构建了调峰调度模型，以优化电站的日前发电出力计划，并针对少数平衡电厂，以系统电量需求为控制条件，提出基于等负荷率的负荷分配方法，实现电网逐时段负荷平衡。
(4.1)水电调峰优化模型及其求解方法
对于调节性能较好的水电站，应充分利用其调峰容量和快速的启停与爬坡能力尽可能跟踪响应系统负荷变化，使经水电调节后的电网余留负荷过程尽可能平滑，以减少调节性能较差的火电机组的启停次数和出力的频繁波动，提高电站整体运行效率，保障电网的安稳高效运行。为此，采用电网余留负荷方差最小构建如下优化目标：
Figure PCTCN2018079307-appb-000039
式中R t为经过水电站调峰后t时段电网余留负荷；
Figure PCTCN2018079307-appb-000040
为电网余荷平均值；L t为t时段电网负荷需求。
上述调峰模型还需要考虑水电短期运行的各种约束条件，其详细描述可参考上文中小流域梯级水电站调度约束，此处不再赘述。在模型求解时，需要先根据平衡电站的开机方式，确定电站运行的最小技术出力，将其从面临负荷过程中扣除，并以余留负荷{R 1,R 2,...,R T}作为调峰需求条件，采用上文提及的变尺度方法优化水电站群的发电调度过程。
(4.2)平衡电站等负荷率调度方法
由于上述电站优化调度需要考虑复杂的约束条件，特别是出力爬坡限制、开停机最小持续时间、出力波动控制等时段耦合型约束，使得优化结果很难直接实现调度期所有时段的电力供需平衡，得到的余留负荷过程通常“毛刺”较多，相邻时段的负荷波动非常频繁，实际调度中常用的做法是选择具有AGC机组的电站作为平衡电厂，以平衡余留负荷需求。
对于平衡电厂，由于安装了AGC机组，发电调度时可以根据AGC指令自动进行出力调节，所以计算中无需考虑出力波动控制、爬坡限制等复杂约束，在电网实际调度中一般结合面临的负荷过程估算各平衡电厂的日电量，将其作为控制条件优化确定合理的96点计划出力。从承担调节任务的公平性角度出发，平衡电站采用了等发电负荷率优化原则，本发明将其作为控制目标与切负荷相结合，提出基于等负荷率的平衡电站调度方法。
该方法首先需要考虑电网未平衡电量大小和平衡电站的可用容量，采用式(28)估算各电站目标发电量。
Figure PCTCN2018079307-appb-000041
式中：C为平衡电站个数；
Figure PCTCN2018079307-appb-000042
为电站i c的最大可用容量。
在优化前，需要从面临负荷中扣除平衡电站的最小技术出力，以满足电站的开机出力要求，同时可得到各电站的余留电量：
Figure PCTCN2018079307-appb-000043
式中
Figure PCTCN2018079307-appb-000044
为t时段电站i c的最小技术出力。
为实现发电负荷率尽量相等，提出基于电量分块的切负荷策略，具体思路是将上述每个电站的余留电量
Figure PCTCN2018079307-appb-000045
等分为Y份，采用
Figure PCTCN2018079307-appb-000046
表示，且
Figure PCTCN2018079307-appb-000047
将各电站初始电量设置为
Figure PCTCN2018079307-appb-000048
c＝1,2,...,C，按照上下游顺序，采用逐次切负荷方法确定各电站出力过程；重复前述步骤，将电站电量依次更新为下一分块电量，确定对应的出力过程，并累加得到最终的电站出力。在按照上述思路求解时，需要重点注意两点：
1)每次更新下一分块电量后，应结合电站各时段最小技术出力和已得到的累计出力，计算余留的可用容量，作为下一步计算时的出力上限，具体如下：
Figure PCTCN2018079307-appb-000049
式中
Figure PCTCN2018079307-appb-000050
为t时段电站i c的余留的最大可用容量；Y′为已计算完成的电量块数；
Figure PCTCN2018079307-appb-000051
为t时段电站i c第y份电量对应的出力。
2)全部分块电量计算完成后，若所有时段均实现电力平衡，则计算结束；否则计算系统未平衡电量，并采用公式(30)确定需要重新计算的分块电量数目Y″，同时按发电负荷率从高至低对各电站进行排序，依次采用切负荷方法确定其出力过程。重复前述步骤，直至达到负荷平衡。需要提及，在每次迭代过程中，均需要采用以电定水方法计算各电站的库水位、发电流量、弃水流量等结果，确保发电计划满足各种调度约束条件。
Figure PCTCN2018079307-appb-000052
现以云南电网水电系统日前计划编制为例，对本发明方法进行验证。云南电网是我国水电规模最大的两个省级电网之一，截止2017年底省调平衡水电站162座，水电装机容量超6000万kW，占全网总装机比重超过70％，水电系统除满足基本的电力供应要求外，还需要承担系统的调峰、调频、西电东送等复杂任务要求，面临非常突出的调度运行问题，特别是如此庞大规模系统的高效求解，这直接关系到电网每日编制发电计划的效率和计划的可行实用性，单纯依靠数学优化方法很难满足实际要求，需要将实际工程特点和调度需求纳入建模以及求解过程，以切实提高效率和结果可用性。
采用2017年某日实际数据模拟编制发电计划，按照本发明方法的思路，首先需要对云南电网省调平衡所有水电站进行分类分组。第一类水电站约79座，主要结合实际需求预先确定了各电站的日前发电计划或者调度方式，重点进行部分资料完整电站的水情校核分析，并结合校核结果进行计划修正。第二类虚拟电站20座，包括实际水电站61座，具体情况见表1；虚拟电站计划出力过程一般由电网公司和流域梯级协调确定，梯级各电站间的负荷分配由第二类电站的求解方法完成，但由于部分梯级缺少计算所需的基础资料，所以本例主要针对以礼河、苏帕河、西洱河梯级进行了计算研究。第三类电站22座，主要分布在澜沧江下游、金沙江中游、以及李仙江、大盈江等大型流域或者重点关注的流域，其中漫湾和大朝山具有AGC机组，将其作为平衡电厂，用于平衡系统负荷波动。另外，需要补充说明云南电网11座煤电站的出力计划已预先确定，光伏和风电等新能源出力采用全额消纳原则，所以水电系统面临的负荷需求为扣除其余电源出力后的等效负荷，水电调度结果既要满足电网的调峰要求，同时需要保证全天96点的时段电力平衡约束。
在IBM System X3750 M4服务器(CPU 2.20GH，内存64G)上计算，不考虑约束条件输入与修改耗时，总的计算时间可控制在2分钟左右，可以满足电网实际计划编制的时效性要求。图1为电网整体的平衡计算结果，图2为部分固定调度方式电站的分析计算结果，图3为西洱河梯级电站的负荷分配结果，图4、图5为参与优化计算的水电站总出力和两座平衡 电厂的出力过程。
从电网整体的平衡结果来看，水电系统在云南电网确实承担主要的电力供应作用，日发电量约5.568亿kWh，占全网比重为85.3％，同时也是系统调峰和平抑风电与光伏功率波动的主要调节电源，调峰深度达到12242MW，是全网最大负荷峰谷差的96.4％，充分发挥了水电的优质调节作用。
图2为糯扎渡、梨园、功果桥、松山河口四座水电站的出力校核分析结果，其中糯扎渡和梨园电站采用南方电网总调下达的计划出力，功果桥、松山河口采用建议的发电计划。从水情分析结果可以看出，糯扎渡、梨园均严格按照下达的计划出力运行，未发生弃水或放空的情况，库水位和发电流量均满足了给定的上下限要求，这与电站调节性能有直接关联，糯扎渡为多年调节，日内库水位波动很小，梨园为周调节，初始水位相对较低，在来水较大的情况下，日内水位上升了4m左右，但总体控制在合理运行范围内；功果桥未按建议的出力计划发电，主要由于来水较大，水库产生了弃水，按照本发明弃水修正策略增加发电出力直至满出力运行，但仍有弃水发生，结果是合理的；松山河口电站的发电出力未达到给定的建议计划要求，主要原因是水库已降至死水位，无法满足给定的出力值，按照本发明均衡削减策略，优先减小低谷时段出力，从图中可以看出，电站在01:30～04:30的出力适当减小，其余时段基本按给定出力运行，这与电网实际调节需求基本一致。
图4为西洱河梯级的负荷分配结果，在给定各时段总出力93MW的条件下，采用调度期耗水最小目标优化了梯级电站的出力过程，从图3(a)可以直观看出各电站出力总和完全满足了梯级总出力要求，图3(b)显示电站的库水位也运行在合理范围内，该方案对应的梯级总耗水量为217万m 3，与常规的均匀发电方式相比，日减少耗水约11万m 3，节省幅度达到5％，表明通过梯级补偿确实降低了电站的整体发电耗水率，提高了水能利用效率。
图4给出的参与优化电站的总出力过程与系统负荷需求基本一致，说明利用大型流域梯级水电站群的补偿调节能力，确实发挥了很好的水电调峰作用，调峰深度达到4619MW。其中漫湾、大朝山作为平衡电站，具有较强的负荷跟踪能力，其出力过程波动较为频繁，在实际运行中可以通过AGC功能自动调节实现，侧面反映这两个电站参与负荷调节的频率和幅度都比较大，有效平抑了负荷波动，满足了各时段电力平衡要求。
表1虚拟电站列表
Figure PCTCN2018079307-appb-000053
Figure PCTCN2018079307-appb-000054

Claims (2)

  1. 一种超大规模水电站群短期实用化调度方法，其特征在于，包括如下步骤：
    (1)基于调度特征进行水电站分类：根据问题的层级、自然空间属性、任务要求、尺度特点、计划特殊性的调度特征对水电站进行分类和分组，并选择不同的建模方法和求解策略，以实现可求解、快速求解和得到满足工程实际要求计算结果目的；水电站分为三类：固定调度方式电站、中小流域梯级水电站、大型流域梯级水电站；
    (2)固定调度方式电站，相邻日系统负荷需求、电站来水、控制要求变化不大，确定电站的调度方式，包括定出力和定水位，减少参与优化的水电站数目，只需要对日前计划进行校核，对有弃水和其他特殊需求不能满足的电站进行微调；具体的调整方法包括如下两种典型情况：
    (a)当库水位高于上限产生弃水时，采用基于调峰响应的弃水修正策略，从最后一个弃水时段之前的所有时段内选择多个负荷值最大的T i min连续时段，记为[t 1,t 2]，并采用式(1)增加电站计划出力，避免或者减少弃水；并进行迭代修正，直至所有时段出力或发电流量，以及库水位均达到上限边界；
    Figure PCTCN2018079307-appb-100001
    Figure PCTCN2018079307-appb-100002
    式中，p d为出力调整步长；pr i为i号电站在相邻时段间允许的最大出力增减值；p′ i,t为i号电站t时段的出力，p i,t，
    Figure PCTCN2018079307-appb-100003
    分别为i号电站t时段的平均出力及出力上限；El i为弃水电量；Δt为t时段的小时数；t，T分别为调度时段编号和总数；[t 1,t 2]为T i min个负荷值最大的连续时段；T i min为出力极值的最小持续时段数；
    (b)当水位低于下限出现库空无法满足给定的调度方式或者计划出力时，提出一种低谷负荷均衡削减策略，选择负荷值最小的T i min个连续时段，记为[t 3,t 4]，并采用式(3)减少电站出力，保证出力的可行性；并进行迭代修正，直至所有时段出力均达到下限边界；
    Figure PCTCN2018079307-appb-100004
    式中， p i,t为i号电站t时段的出力下限；
    Figure PCTCN2018079307-appb-100005
    为给定的出力修正步长；[t 3,t 4]为T i min个负荷值最小的连续时段；
    (3)中小流域梯级水电站，以总出力过程为控制条件，以总耗水最小为目标，构建梯级负荷分配优化模型，并采用变尺度方法进行模型求解；在求解过程中，重点针对负荷平衡约束式(4)采用外点罚函数方法进行处理，并引入目标惩罚项，具体见式(5)和(6)；
    Figure PCTCN2018079307-appb-100006
    Figure PCTCN2018079307-appb-100007
    Figure PCTCN2018079307-appb-100008
    式中，p t为给定的t时段的总出力；N为水电站的总数；F pen为目标惩罚函数；r t为惩罚系数；ε为允许的最大负荷平衡误差；a为惩罚常数；
    (4)大型流域梯级水电站，构建调峰调度模型，同时提出平衡电站等负荷率调度方法，实现系统调峰响应和全时段负荷平衡；
    步骤1：优化非平衡电站出力；利用式(7)构建调峰优化模型，并采用变尺度方法进行模型求解；
    Figure PCTCN2018079307-appb-100009
    式中，min F 1为最小电站负荷分配量；R t为经过水电站调峰后t时段电网余留负荷；
    Figure PCTCN2018079307-appb-100010
    为电网余荷平均值；L t为t时段电网负荷需求；
    步骤2：优化平衡电站出力：将每个电站的余留电量
    Figure PCTCN2018079307-appb-100011
    等分为Y份，采用
    Figure PCTCN2018079307-appb-100012
    表示，且
    Figure PCTCN2018079307-appb-100013
    将各电站初始电量设置为
    Figure PCTCN2018079307-appb-100014
    C为平衡电站个数，按照上下游顺序，采用逐次切负荷方法确定各电站出力过程；重复步骤2，将电站电量依次更新为下一分块电量，确定对应的出力过程，直至完成所有分块电量的计算，并累加得到最终的电站出力。
  2. 根据权利要求1所述的一种超大规模水电站群短期实用化调度方法，其特征在于，大型流域梯级水电站的步骤2，具体如下：
    首先，考虑电网未平衡电量大小和平衡电站的可用容量，采用式(8)估算各电站目标发电量；
    Figure PCTCN2018079307-appb-100015
    式中：C为平衡电站个数；
    Figure PCTCN2018079307-appb-100016
    为电站i c的最大可用容量；
    其次，从面临负荷中扣除平衡电站的最小技术出力，以满足电站的开机出力要求，同时得到各电站的余留电量，见式(9)：
    Figure PCTCN2018079307-appb-100017
    式中：
    Figure PCTCN2018079307-appb-100018
    为t时段电站i c的最小技术出力；
    每次更新下一分块电量后，结合电站各时段最小技术出力和已得到的累计出力，计算余 留的可用容量，作为下一步计算时的出力上限，见式(10)：
    Figure PCTCN2018079307-appb-100019
    式中：
    Figure PCTCN2018079307-appb-100020
    为t时段电站i c的余留的最大可用容量；Y′为已计算完成的电量块数；
    Figure PCTCN2018079307-appb-100021
    为t时段电站i c第y份电量对应的出力；
    全部分块电量计算完成后，若所有时段均实现电力平衡，则计算结束；否则计算系统未平衡电量，并采用式(10)确定需要重新计算的分块电量数目Y″，同时按发电负荷率从高至低对各电站进行排序，依次采用切负荷方法确定其出力过程；重复前述步骤，直至达到负荷平衡。
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