CN111769600A - Power system source load storage coordination rolling scheduling method based on flexibility margin - Google Patents

Power system source load storage coordination rolling scheduling method based on flexibility margin Download PDF

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CN111769600A
CN111769600A CN202010581689.7A CN202010581689A CN111769600A CN 111769600 A CN111769600 A CN 111769600A CN 202010581689 A CN202010581689 A CN 202010581689A CN 111769600 A CN111769600 A CN 111769600A
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power
flexibility
rolling
load
output
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CN111769600B (en
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郭琦
张强
杭晨辉
齐军
呼斯乐
王小海
黄鹏翔
周云海
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Inner Mongolia Power Group Co ltd
China Three Gorges University CTGU
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Inner Mongolia Power Group Co ltd
China Three Gorges University CTGU
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    • 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
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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]
    • 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
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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Abstract

The invention provides a power system source load storage coordination rolling scheduling method based on flexibility margin, and relates to the field of power generation scheduling of power systems. Firstly, determining uncertain intervals of wind power output and load, and establishing a flexibility supply capacity model corresponding to each flexibility resource; on the basis, a flexibility demand capacity model and a flexibility supply capacity model of the power system are respectively established, and a flexibility margin index is determined; then establishing a source load storage day-ahead coordinated scheduling model and solving; and establishing a source load storage in-day rolling scheduling model of the rolling period and solving for each rolling period in the day to finally obtain a scheduling result of the rolling period in each day. The scheduling method takes the wind turbine generator, the thermal power generator, the gas turbine generator, the pumped storage generator, the interruptible load and other flexible resources into consideration, and the scheduling plan formulated by the method can effectively meet the actual engineering and optimize the operation economy and reliability of the power system.

Description

Power system source load storage coordination rolling scheduling method based on flexibility margin
Technical Field
The invention relates to the field of power generation scheduling of a power system, in particular to a power system source load storage coordination rolling scheduling method based on flexibility margin.
Background
The new energy power generation in China is rapidly developed and becomes the second largest power supply in China, and the installed capacity world is the first, and a new energy power system taking new energy as the leading factor is gradually formed. The new energy permeability improvement provides a severe challenge for the flexibility of the power system, and the volatility and uncertainty of the new energy permeability increase the difficulty of operation and scheduling of the power grid. If the flexibility of the power system is insufficient, the power generation is difficult to follow the change of the net load, and wind abandoning and load shedding operation are needed, so that huge waste of power resources is caused. How to ensure the flexibility of the power system is abundant has important significance for new energy consumption and new energy power system construction. In order to solve the problem of wind abandoning and electricity limiting caused by insufficient flexibility of the power system, the power system needs to be configured with flexible resources, such as a gas-oil engine set, a pumped storage power station, demand side response and the like. The flexible resource climbing speed is high, the adjusting range is wide, and the method is a feasible way for realizing new energy consumption and ensuring the flexibility of the electric power system.
At present, many researches on scheduling models of new energy and other power sources are carried out, and the methods mainly include 4 methods: (1) and (4) out-of-order scheduling. The disordered scheduling method does not consider the coupling relation among various power supplies, has no scheduling sequence, is difficult to ensure the safe and stable operation of a power grid, and has no practical application value; (2) and (4) robust scheduling. The robust scheduling method considers the uncertain factors as a set, and the obtained result can simultaneously meet the optimal target function and the fluctuation of the uncertain factors, but the result is over conservative, so that the economy is difficult to achieve the optimal result; (3) and (5) layered scheduling. The hierarchical scheduling method decomposes different power supplies into different scheduling layers, associates through flexibility indexes, and solves the output of each power supply according to the power supply characteristics and the power grid supply and demand relationship, the result obtained by the method can only realize local optimization, is greatly influenced by an initial value, and different solutions or no solution can be obtained when the scheduling sequence is changed; (4) and coordinating and scheduling. The coordinated scheduling method comprehensively considers the output characteristics of all power supplies, describes all power supplies jointly as a constrained scheduling problem, can realize global optimization of a solution result, and has good practicability.
However, in the existing coordinated dispatching method, the new energy output is mostly used as a fixed value, a deterministic method is adopted for modeling, a net load value is used for participating in optimized dispatching, and although wind power is fully consumed, more rigorous requirements on the operation economy and reliability of a power system are provided; secondly, the time characteristics of the flexible resources are not considered, analysis is simply carried out on one time scale, and the relation among different time scales is not considered; in addition, many researches are not comprehensive in consideration of flexibility resources, the potential of exploiting the flexibility of the power system is avoided, the flexibility is considered to exist only on the power supply side, and meanwhile, the influence of various flexibility resource characteristics on operation scheduling of the power grid is not considered.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a power system source load storage coordination rolling scheduling method based on flexibility margin. The method takes the wind turbine generator, the thermal power generator, the gas turbine generator, the pumped storage generator, the interruptible load and other flexible resources into consideration, establishes the multi-time-scale source load and storage flexible resource coordination scheduling model based on the flexibility margin index, can effectively meet the actual engineering through the scheduling plan formulated by the method, and has important practical significance.
The invention provides a power system source load storage coordination rolling scheduling method based on flexibility margin, which is characterized by comprising the following steps of:
1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system to obtain uncertain intervals of wind power output and load; modeling the output characteristics of each source charge-storage flexible resource to respectively obtain a flexible supply capacity model corresponding to each flexible resource; the source loading and storage flexibility resources include: wind power generation units, thermal power generation units, gas power generation units, pumped storage power stations and interruptible loads; the method comprises the following specific steps:
1-1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system, and respectively establishing uncertainty models of wind power output and load to obtain uncertainty intervals of the wind power output and the load;
the uncertainty model expression of the wind power output is as follows:
Figure BDA0002553427530000021
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000022
actual wind power output at the moment t;
Figure BDA0002553427530000023
the predicted wind power output at the moment t is obtained;
Figure BDA0002553427530000024
and
Figure BDA0002553427530000025
respectively setting the upper limit and the lower limit of the wind power prediction error at the time t; x is the number ofwThe value range of the wind power prediction error fluctuation factor is-1 to 1, and when | x |, the wind power prediction error fluctuation factor iswWhen | ═ 1, the uncertainty of wind power output reaches the maximum;
the uncertainty model expression of the load is as follows:
Figure BDA0002553427530000026
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000027
is the actual value of the load at time t;
Figure BDA0002553427530000028
is the predicted value of the load at the time t;
Figure BDA0002553427530000029
and
Figure BDA00025534275300000210
the upper limit and the lower limit of the load prediction error at the moment t are respectively set; x is the number ofdThe value range of the load prediction error fluctuation factor is-1 to 1, when | xdWhen 1, the load uncertainty reaches the maximum;
1-2) modeling the output characteristics of each source charge-storage flexible resource to respectively obtain a flexible supply capacity model corresponding to each flexible resource; the method comprises the following specific steps:
the corresponding flexibility supply capacity model expression of the wind turbine generator is as follows:
Figure BDA00025534275300000211
in the formula, Pw,tWind power consumption at the moment t;
Figure BDA00025534275300000212
the capacity of the down-regulation flexibility provided by the wind power at the moment t;
the corresponding flexible supply capacity model expression of the thermal power generating unit is as follows:
Figure BDA0002553427530000031
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000032
and
Figure BDA0002553427530000033
the method comprises the steps that an up-regulation flexibility capacity and a down-regulation flexibility capacity are provided by a thermal power generating unit i at a time t respectively;
Figure BDA0002553427530000034
and
Figure BDA0002553427530000035
the ramp-up speed and the ramp-down speed of the thermal power generating unit i are respectively; pth,t,iThe active power output of the thermal power generating unit i at the moment t is obtained;
Figure BDA0002553427530000036
and
Figure BDA0002553427530000037
respectively representing the maximum technical output and the minimum technical output of the thermal power generating unit i; t is0Is a scheduling time;
the corresponding flexibility supply capacity model expression of the gas-electric machine set is as follows:
Figure BDA0002553427530000038
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000039
and
Figure BDA00025534275300000310
respectively providing an up-regulation flexible capacity and a down-regulation flexible capacity at the moment t by the gas-electric machine set j;
Figure BDA00025534275300000311
and
Figure BDA00025534275300000312
the upward climbing speed and the downward sliding speed of the gas-electric generator set j are respectively; pga,t,jThe active power output of the gas-electric machine set j at the moment t;
Figure BDA00025534275300000313
and
Figure BDA00025534275300000314
respectively the maximum technical output and the minimum technical output of the gas-electric machine set j;
the corresponding flexible supply capacity model expression of the pumped storage power station is as follows:
Figure BDA00025534275300000315
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300000316
and
Figure BDA00025534275300000317
the up-regulation flexible capacity and the down-regulation flexible capacity are respectively provided by the pumped storage power station at the moment t;
Figure BDA00025534275300000318
and
Figure BDA00025534275300000319
respectively the power generation rate and the pumping rate of the pumped storage power station;
Figure BDA00025534275300000320
and
Figure BDA00025534275300000321
the maximum storage capacity and the minimum storage capacity of the pumped storage power station are respectively; wpu,tThe capacity is the capacity of the pumped storage power station at the moment t;
the flexible supply capacity model for interruptible loads is expressed by:
Figure BDA00025534275300000322
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300000323
is the flexible capacity provided by the interruptible load at time t;
Figure BDA00025534275300000324
is the maximum interruptible load at time t;
2) respectively establishing a power system flexibility demand capacity model and a power system flexibility supply capacity model by using the result of the step 1); the method comprises the following specific steps:
2-1) establishing a power system flexibility demand capacity model expression according to the uncertainty model of the wind power output and the load obtained in the step 1-1):
Figure BDA00025534275300000325
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300000326
and
Figure BDA00025534275300000327
ξ, the up-regulation flexibility requirement and the down-regulation flexibility requirement of the power system at the moment t respectivelyw,tAnd ξd,tThe method comprises the following steps of respectively predicting wind power output errors and load errors at the time t, wherein the expressions are as follows:
Figure BDA0002553427530000041
2-2) establishing a flexible supply capacity model expression of the power system according to the flexible supply capacity model corresponding to each flexible resource obtained in the step 1-2):
Figure BDA0002553427530000042
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000043
and
Figure BDA0002553427530000044
respectively supplying capacity for adjusting up flexibility and supplying capacity for adjusting down flexibility of the power system at the time t;
3) establishing a flexibility margin index, wherein the flexibility margin is divided into an up-regulation flexibility margin and a down-regulation flexibility margin, and the expression is as follows:
Figure BDA0002553427530000045
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000046
and
Figure BDA0002553427530000047
respectively obtaining an up-regulation flexibility margin and a down-regulation flexibility margin of the power system at the time t; when in use
Figure BDA0002553427530000048
Or
Figure BDA0002553427530000049
When the ratio is less than or equal to 0, the shortage of flexibility of up regulation or the shortage of flexibility of down regulation occurs, namely:
Figure BDA00025534275300000410
in the formula, Piufc,tAnd Pidfc,tThe method comprises the steps that the flexibility shortage is adjusted up and down at t time respectively;
4) establishing a source load storage day-ahead coordination scheduling model, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
4-1) determining an objective function of a source load storage day-ahead coordinated scheduling model, wherein the expression is as follows:
Figure BDA00025534275300000411
in the formula, Cth,t,iThe output cost of the thermal power generating unit i at the moment t is shown; cga,t,jThe output cost of the gas-electric machine set j at the moment t is shown;
Figure BDA00025534275300000412
and
Figure BDA00025534275300000413
respectively starting cost and shutdown cost of the gas-electric machine set j at the moment t; lambda [ alpha ]ilIs the unit cost of the interruptible load; pil,tIs the interruptible load transfer amount at time t; lambda [ alpha ]wIs the unit wind abandon punishment cost; pw,tIs the wind power allowance at time t; λ is the flexibility deficit penalty factor; cpuThe unit peak regulation benefit of the pumped storage power station is obtained;
Figure BDA00025534275300000414
and
Figure BDA00025534275300000415
the pumping power and the generating power of the pumped storage power station at the moment t are respectively;
wherein, Cth,t,iAnd Cga,t,jIs obtained by the following formula:
Figure BDA00025534275300000416
in the formula, ath,i、bth,i、cth,iRespectively a quadratic coefficient, a primary coefficient and a constant coefficient of the operating cost of the thermal power generating unit i; a isga,j、bga,j、cga,jRespectively is a quadratic term coefficient, a primary term coefficient and a constant coefficient of the operation of the gas-electric machine set j;
4-2) determining the constraint conditions of the source load storage day-ahead coordinated scheduling model, which are as follows:
4-2-1) power balance constraints;
Figure BDA0002553427530000051
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000052
and
Figure BDA0002553427530000053
of pumped storage power stations at time tGenerating capacity and pumping electric quantity;
4-2-2) system flexibility supply and demand constraints;
Figure BDA0002553427530000054
4-2-3) wind power output constraint;
Figure BDA0002553427530000055
4-2-4) output constraint of the thermal power generating unit;
Figure BDA0002553427530000056
4-2-5) output constraint of the gas-electric machine set;
Figure BDA0002553427530000057
4-2-6) limiting the start-stop time of the gas-electric machine set;
Figure BDA0002553427530000058
in the formula of Uga,t,jIs the starting and stopping state of the gas-electric machine set j at the moment t;
Figure BDA0002553427530000059
representing the minimum running time allowed by the unit j;
Figure BDA00025534275300000510
represents the minimum downtime allowed for unit j;
4-2-7) pumped storage power station constraint;
Figure BDA00025534275300000511
in the formula, mupThe pumping efficiency of the unit is improved;
Figure BDA00025534275300000512
and
Figure BDA00025534275300000513
the water pumping power and the power generation power at the moment t are respectively; u shapep,tAnd Ug,tThe two working states of the pumped storage power station are 0-1 variable; u shapep,tTaking 1 to represent a water pumping state, and taking 0 to represent a shutdown state; u shapeg,tTaking 1 to represent a power generation state, and taking 0 to represent a shutdown state;
Figure BDA00025534275300000514
and
Figure BDA00025534275300000515
respectively the power generation rate and the pumping rate of the pumped storage power station;
4-2-8) interruptible load constraints;
Figure BDA0002553427530000061
in the formula, Pil,tIs the interruptible load transfer amount at time t;
5) solving the model established in the step 4) to respectively obtain Pw,t、Pth,t,i、Uga,t,j、Pga,t,j、Up,t、
Figure BDA0002553427530000062
Ug,t、
Figure BDA0002553427530000063
Pil,t、Piufc,tAnd Pidfc,tAnd using the optimal solution as a day-ahead scheduling plan;
4-2-8) interruptible load constraints;
Figure BDA0002553427530000064
in the formula, Pil,tIs interruptible load invocation at time tAn amount;
Figure BDA0002553427530000065
is the maximum interruptible load at time t;
5) solving the model established in the step 4) to obtain Pw,t、Pth,t,i、Uga,t,j、Pga,t,j、Up,t、
Figure BDA0002553427530000066
Ug,t、
Figure BDA0002553427530000067
Pil,t、Piufc,tAnd Pidfc,tThe optimal solution of (a) is used as a day-ahead scheduling plan;
6) establishing a source load storage intraday rolling scheduling model for each rolling period in the day and solving to obtain an intraday rolling scheduling result; the method comprises the following specific steps:
6-1) setting initial data of rolling schedule, comprising: the operation data, the day-ahead scheduling plan, the initial state and the output of each flexible resource;
setting a rolling period;
6-2) automatically acquiring wind power output prediction data and load prediction data of each rolling period before each rolling period comes in the day, and taking the rolling period as the current rolling period;
6-3) establishing a source load storage day rolling scheduling model of the current rolling period, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
6-3-1) determining an objective function of the rolling scheduling model in the day of the current rolling period, wherein the expression is as follows:
Figure BDA0002553427530000068
in the formula, st is the starting time of the rolling scheduling of the current rolling period;
Figure BDA0002553427530000069
is a motor fired during a rolling period tThe cost of outing for group i;
Figure BDA00025534275300000610
is the output cost of the gas motor group j in the rolling time period t;
Figure BDA00025534275300000611
is the interruptible load transfer amount within the rolling time period t;
Figure BDA00025534275300000612
and
Figure BDA00025534275300000613
respectively predicting wind power output data and wind power acceptance in a rolling time period t;
Figure BDA00025534275300000614
and
Figure BDA00025534275300000615
respectively an up-regulation flexibility deficit and a down-regulation flexibility deficit in a rolling time period t;
Figure BDA00025534275300000616
and
Figure BDA00025534275300000617
respectively pumping power and generating power of the pumped storage power station in the rolling time period t;
Figure BDA00025534275300000618
and
Figure BDA00025534275300000619
respectively is the deviation punishment of thermal power unit i and gas generator j with the plan before the day in rolling period t, and the expression is as follows:
Figure BDA00025534275300000620
in the formula, ζthAnd ζgaRespectively thermal power generating unit and gas-electricityUnit power deviation punishment cost;
Figure BDA0002553427530000071
and
Figure BDA0002553427530000072
respectively the active power output of the thermal power generating unit i and the gas power generating unit j in the rolling time period t,
Figure BDA0002553427530000073
and
Figure BDA0002553427530000074
respectively planning output of the thermal power generating unit i and the gas power generating unit j at the moment t in the day ahead;
6-3-2) determining the constraint conditions of the rolling scheduling model in the source load storage day of the current rolling period; the method comprises the following specific steps:
6-3-2-1) power balance constraints;
Figure BDA0002553427530000075
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000076
is the load of the scroll period t;
Figure BDA0002553427530000077
is the interruptible load transfer amount within the rolling time period t;
6-3-2-2) system flexibility supply and demand constraints;
Figure BDA0002553427530000078
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000079
and
Figure BDA00025534275300000710
respectively, the power system during the rolling period tA required up-regulation flexibility requirement and a down-regulation flexibility requirement;
Figure BDA00025534275300000711
and
Figure BDA00025534275300000712
respectively supplying capacity for up-regulation flexibility and capacity for down-regulation flexibility of the power system in the rolling time period t;
Figure BDA00025534275300000713
and
Figure BDA00025534275300000714
respectively an up-regulation flexibility deficit and a down-regulation flexibility deficit in a rolling time period t;
6-3-2-3) wind power output constraint;
Figure BDA00025534275300000715
6-3-2-4) output constraint of the thermal power generating unit;
Figure BDA00025534275300000716
6-3-2-5) output constraint of the gas-electric machine set;
Figure BDA00025534275300000717
6-3-2-6) pumped storage power station constraints;
Figure BDA00025534275300000718
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300000719
the storage capacity of the pumped storage power station at the rolling time t moment is shown;
6-3-2-7) interruptible load constraints;
Figure BDA00025534275300000720
6-4) solving the model established in the step 6-3) to respectively obtain the rolling period in the current day
Figure BDA00025534275300000721
Figure BDA00025534275300000722
And
Figure BDA00025534275300000723
as a scheduling result of the current rolling cycle;
6-5) when the next rolling cycle comes, repeating the steps 6-2) to 6-4) until all the rolling cycles in the days are finished, and finally obtaining the scheduling result of the rolling cycles in each day.
The invention has the characteristics and beneficial effects that:
1. the flexibility margin index of the power system provided by the invention fully considers the uncertainties of wind power and load, comprehensively considers the operating characteristics and the flexibility supply capacity of the flexibility resources, can effectively consume large-scale wind power, and realizes the coordinated scheduling of various flexibility resources;
2. according to the invention, the pumped storage power station, the gas-electric unit and the interruptable load are utilized, the peak-valley difference is reduced, the load peak clipping and valley filling are realized, the wind power waste is reduced, the peak regulation pressure of the thermal power unit is effectively relieved, and the flexibility of the power system is improved;
3. the multiple flexible resource coordination rolling scheduling model provided by the invention solves the problems that a layered scheduling model cannot obtain a global optimal solution and the result of robust scheduling is too conservative, and the obtained scheduling result is obviously superior to the traditional scheduling result;
4. the day-in rolling scheduling model utilizes the ultra-short-term wind power prediction data to roll and correct the day-ahead scheduling plan, optimizes the output strategy of each flexible resource in the day, and further optimizes the operation economy and reliability of the power system;
5. the method is mainly applied to the field of power generation dispatching of the power system, and can provide reference and reference for dispatching and running of the power grid after large-scale wind power is accessed.
Detailed Description
The invention provides a power system source load storage coordination rolling scheduling method based on flexibility margin, and the invention is further described in detail below by combining specific embodiments.
The invention provides a power system source load storage coordination rolling scheduling method based on flexibility margin, which comprises the following steps:
1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system to obtain uncertain intervals of wind power output and load; acquiring information of each source charge-storage flexible resource, modeling the output characteristics of each source charge-storage flexible resource, and respectively acquiring a flexible supply capacity model corresponding to each flexible resource; the source loading and storage flexibility resources include: wind power generation units, thermal power generation units, gas power generation units, pumped storage power stations and interruptible loads; the method comprises the following specific steps:
1-1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system, and respectively establishing uncertainty models of wind power output and load by using a robust optimization thought to obtain uncertainty intervals of the wind power output and the load;
the uncertainty model expression of the wind power output is as follows:
Figure BDA0002553427530000081
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000082
actual wind power output at the moment t;
Figure BDA0002553427530000083
the predicted wind power output at the moment t is obtained;
Figure BDA0002553427530000084
and
Figure BDA0002553427530000085
respectively setting the upper limit and the lower limit of the wind power prediction error at the time t; x is the number ofwPredicting an error fluctuation factor (the value range is-1 to 1) for wind power, and when | xwWhen | ═ 1, the uncertainty of wind power output reaches the maximum.
The uncertainty model expression of the load is as follows:
Figure BDA0002553427530000091
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000092
is the actual value of the load at time t;
Figure BDA0002553427530000093
is the predicted value of the load at the time t;
Figure BDA0002553427530000094
and
Figure BDA0002553427530000095
the upper limit and the lower limit of the load prediction error at the moment t are respectively set; x is the number ofdThe load prediction error fluctuation factor (the value range is-1 to 1) is obtained when | xdThe load uncertainty is maximized at 1.
1-2) modeling the output characteristics of each source charge-storage flexible resource to respectively obtain a flexible supply capacity model corresponding to each flexible resource; the method comprises the following specific steps:
wind power is used as one of power supply side flexibility resources, down-regulation flexibility supply capacity can be provided for a power system through wind abandoning operation, and a flexibility supply capacity model expression corresponding to a wind turbine generator set is as follows:
Figure BDA0002553427530000096
in the formula, Pw,tWind power consumption at the moment t;
Figure BDA0002553427530000097
the capacity of the down-regulation flexibility provided by the wind power at the time t. In the above formula, the first formula represents the wind power consumption range, and the second formula represents the down-regulation flexibility capacity that the wind power can provide.
The thermal power generating unit provides up-regulation flexibility supply capacity and down-regulation flexibility supply capacity through climbing and landslide, and the corresponding flexibility supply capacity model expression of the thermal power generating unit is as follows:
Figure BDA0002553427530000098
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000099
and
Figure BDA00025534275300000910
the method comprises the steps that an up-regulation flexibility capacity and a down-regulation flexibility capacity are provided by a thermal power generating unit i at a time t respectively;
Figure BDA00025534275300000911
and
Figure BDA00025534275300000912
the ramp-up speed and the ramp-down speed of the thermal power generating unit i are respectively; pth,t,iThe active power output of the thermal power generating unit i at the moment t is obtained;
Figure BDA00025534275300000913
and
Figure BDA00025534275300000914
respectively representing the maximum technical output and the minimum technical output of the thermal power generating unit i; t is0The scheduling time is changed along with the change of the scheduling time scale.
The gas-electric machine set has the advantages of high climbing speed, large adjusting range, low minimum output and good flexibility, and the corresponding flexibility supply capacity model expression of the gas-electric machine set is as follows:
Figure BDA00025534275300000915
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300000916
and
Figure BDA00025534275300000917
respectively providing an up-regulation flexible capacity and a down-regulation flexible capacity at the moment t by the gas-electric machine set j;
Figure BDA00025534275300000918
and
Figure BDA00025534275300000919
the upward climbing speed and the downward sliding speed of the gas-electric generator set j are respectively; pga,t,jThe active power output of the gas-electric machine set j at the moment t;
Figure BDA00025534275300000920
and
Figure BDA00025534275300000921
respectively the maximum technical output and the minimum technical output of the gas-electric machine set j; t is0Is a scheduled time.
The pumped storage power station is used as a flexible resource of an energy storage side, has good flexibility adjusting capability, and has the following corresponding flexible supply capacity model expression:
Figure BDA0002553427530000101
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000102
and
Figure BDA0002553427530000103
respectively provided by pumped storage power stations at time tFlexible capacity up-regulation and flexible capacity down-regulation;
Figure BDA0002553427530000104
and
Figure BDA0002553427530000105
respectively the power generation rate and the pumping rate of the pumped storage power station;
Figure BDA0002553427530000106
and
Figure BDA0002553427530000107
the maximum storage capacity and the minimum storage capacity of the pumped storage power station are respectively; wpu,tThe capacity is the capacity of the pumped storage power station at the moment t; t is0Is a scheduled time. It should be noted that Wpu,t、
Figure BDA0002553427530000108
And
Figure BDA0002553427530000109
the water quantity is converted into corresponding electric quantity.
The response rate of the interruptible load is high, the load shedding operation is used for providing the up-regulation flexible supply capacity, and the flexible supply capacity model corresponding to the interruptible load is expressed by the following formula:
Figure BDA00025534275300001010
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001011
is the flexible capacity provided by the interruptible load at time t;
Figure BDA00025534275300001012
is the maximum interruptible load at time t.
2) Respectively establishing a power system flexibility demand capacity model and a power system flexibility supply capacity model by using the result of the step 1); the method comprises the following specific steps:
2-1) carrying out quantitative analysis on flexibility requirements according to the uncertainty model of the wind power output and the load obtained in the step 1-1), wherein the flexibility requirements are divided into 2 directions of up-regulation flexibility requirements and down-regulation flexibility requirements. The power system flexibility demand capacity model expression is as follows:
Figure BDA00025534275300001013
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001014
and
Figure BDA00025534275300001015
ξ, the up-regulation flexibility requirement and the down-regulation flexibility requirement of the power system at the moment t respectivelyw,tAnd ξd,tThe method comprises the following steps of respectively predicting wind power output errors and load errors at the time t, wherein the expressions are as follows:
Figure BDA00025534275300001016
2-2) carrying out quantitative analysis on the flexibility supply according to the flexibility supply capacity model corresponding to each flexibility resource obtained in the step 1-2), wherein the flexibility supply is also divided into up-regulation flexibility supply and down-regulation flexibility supply in 2 directions. The power system flexibility supply capacity model expression is as follows:
Figure BDA00025534275300001017
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001018
and
Figure BDA00025534275300001019
the power system up-regulation flexibility supply capacity and the down-regulation flexibility supply capacity are respectively at the time t.
3) And establishing a flexibility margin index. The flexibility margin of the power system is defined as the difference value between the flexibility supply and the flexibility demand in the same direction in each time period, so the flexibility margin is divided into an up-regulation flexibility margin and a down-regulation flexibility margin, and the expression is as follows:
Figure BDA0002553427530000111
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000112
and
Figure BDA0002553427530000113
the up-regulation flexibility margin and the down-regulation flexibility margin of the power system at the time t are respectively. When in use
Figure BDA0002553427530000114
Or
Figure BDA0002553427530000115
When the difference is less than or equal to 0, the shortage of flexibility in up-regulation or the shortage of flexibility in down-regulation occurs. Namely:
Figure BDA0002553427530000116
in the formula, Piufc,tAnd Pidfc,tThere is a t-time up-regulation flexibility deficit and a t-time down-regulation flexibility deficit, respectively.
4) Establishing a source load storage day-ahead coordination scheduling model, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
4-1) determining an objective function of a source load storage day-ahead coordinated scheduling model, wherein the expression is as follows:
Figure BDA0002553427530000117
the objective function is to minimize the total cost;
in the formula, Cth,t,iThe output cost of the thermal power generating unit i at the moment t is shown; cga,t,jThe output cost of the gas-electric machine set j at the moment t is shown;
Figure BDA0002553427530000118
and
Figure BDA0002553427530000119
respectively starting cost and shutdown cost of the gas-electric machine set j at the moment t; lambda [ alpha ]ilIs the unit cost of the interruptible load (based on the price value provided by the load aggregator); pil,tIs the interruptible load transfer amount at time t; lambda [ alpha ]wThe unit wind abandon punishment cost (according to the strength of punishment strength, the multiple of unit wind power price is taken); pw,tIs the wind power allowance at time t; lambda is a flexibility shortage penalty factor (according to the strength of penalty strength, the multiple of unit electricity price is taken); cpuIs the unit peak regulation benefit of the pumped storage (namely the pumped storage power station),
Figure BDA00025534275300001110
and
Figure BDA00025534275300001111
respectively pumping water pumping power and generating power at the moment t; piufc,tAnd Pidfc,tThere is a t-time up-regulation flexibility deficit and a t-time down-regulation flexibility deficit, respectively.
Further, Cth,t,iAnd Cga,t,jCan be obtained by the following formula:
Figure BDA00025534275300001112
in the formula, ath,i、bth,i、cth,iRespectively a quadratic coefficient, a primary coefficient and a constant coefficient of the operating cost of the thermal power generating unit i; a isga,j、bga,j、cga,jRespectively is a quadratic term coefficient, a primary term coefficient and a constant coefficient of the operation of the gas-electric machine set j; pth,t,iRepresenting the active power output, P, of the thermal power generating unit i at the moment tga,t,jThe active power output of the gas-electric machine set j at the moment t.
4-2) determining the constraint conditions of the source load storage day-ahead coordinated scheduling model, which are as follows:
4-2-1) power balance constraints;
Figure BDA00025534275300001113
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001114
and
Figure BDA00025534275300001115
the generated energy and pumped electricity of the pumped storage power station at the moment t are respectively.
4-2-2) system flexibility supply and demand constraints;
Figure BDA0002553427530000121
4-2-3) wind power output constraint;
Figure BDA0002553427530000122
4-2-4) output constraint of the thermal power generating unit;
Figure BDA0002553427530000123
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000124
and
Figure BDA0002553427530000125
respectively representing the maximum technical output and the minimum technical output of the thermal power generating unit i;
Figure BDA0002553427530000126
and
Figure BDA0002553427530000127
the ramp rate and the landslide rate of the thermal power generating unit i are respectively.
4-2-5) output constraint of the gas-electric machine set;
Figure BDA0002553427530000128
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000129
and
Figure BDA00025534275300001210
respectively the maximum technical output and the minimum technical output of the gas-electric machine set j;
Figure BDA00025534275300001211
and
Figure BDA00025534275300001212
the upward climbing speed and the downward sliding speed of the gas-electric generator set j are respectively; pga,t,jThe active power output of the gas-electric machine set j at the moment t.
4-2-6) limiting the start-stop time of the gas-electric machine set;
Figure BDA00025534275300001213
in the formula of Uga,t,jIs the starting and stopping state of the gas-electric machine set j at the moment t;
Figure BDA00025534275300001214
representing the minimum running time allowed by the unit j;
Figure BDA00025534275300001215
representing the minimum downtime allowed for unit j.
4-2-7) pumped storage power station constraint;
Figure BDA00025534275300001216
in the formula (I), the compound is shown in the specification,Wpu,tthe storage capacity of the pumped storage power station at the moment t; mu.spThe pumping efficiency of the unit is improved;
Figure BDA00025534275300001217
and
Figure BDA00025534275300001218
the water pumping power and the power generation power at the moment t are respectively; u shapep,tAnd Ug,tIs two working states of a pumped storage power station, which are 0-1 variable, Up,t1 is taken to represent the water pumping state, 0 is taken to represent the shutdown state, Ug,tTaking 1 to represent a power generation state, and taking 0 to represent a shutdown state;
Figure BDA00025534275300001219
and
Figure BDA00025534275300001220
respectively the power generation rate and the pumping rate of the pumped storage power station;
Figure BDA00025534275300001221
and
Figure BDA00025534275300001222
the minimum storage capacity and the maximum storage capacity of the pumped storage power station are respectively.
4-2-8) interruptible load constraints;
Figure BDA0002553427530000131
in the formula, Pil,tIs the interruptible load transfer amount at time t;
Figure BDA0002553427530000132
is the maximum interruptible load at time t.
5) Based on the MATLAB platform, calling CPLEX software by means of a Yalmip tool box, solving the model established in the step 4), and solving to obtain the output plan of each flexible resource in the day ahead. Through solving, the wind power admission P can be respectively obtainedw,tEach thermal power machinePlanned output power P before group dayth,t,iDay-ahead start-stop state U of each gas-electric machine setga,t,jAnd power Pga,t,jDay-ahead water pumping state U of water pumping energy storage power stationp,tWater pumping power
Figure BDA0002553427530000133
And a power generation state Ug,tGenerated power
Figure BDA0002553427530000134
Interruptible load day-ahead call capacity Pil,tUp-regulation of flexibility deficit P per houriufc,tAnd adjusting downward flexibility deficit Pidfc,tAnd taking the optimal solution as a day-ahead scheduling plan.
6) And establishing a source load storage intra-day rolling scheduling model for each rolling period in a day, and solving to obtain an intra-day rolling scheduling result. The method comprises the following specific steps:
6-1) setting initial data of rolling schedule, comprising: the operational data, the day-ahead scheduling plan, and the initial operational state and output of each flexible resource.
Setting a rolling period (3 hours in this example);
6-2) before each rolling period comes in a day, automatically acquiring wind power output prediction data and load prediction data of the rolling period (adopting a mature wind power ultra-short-term prediction method, changing an original wind speed sequence into a stable random sequence through differential processing, ordering an autoregressive moving average model (ARMA), determining an ARMA model of the sequence, and then performing ultra-short-term prediction of wind power), and taking the rolling period as the current rolling period;
6-3) establishing a source load storage day rolling scheduling model of the current rolling period, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
6-3-1) utilizing the result obtained in the step 5), the rolling scheduling plan is made on the basis of the day-ahead scheduling plan, and in order to better link the rolling scheduling plan with the day-ahead scheduling plan, the deviation between the rolling scheduling plan and the day-ahead scheduling plan is not too large, so that the target function of the day-ahead rolling scheduling model adds the deviation punishment of the unit output and the day-ahead scheduling plan into the target function of the day-ahead coordinated scheduling model. The objective function of the intra-day rolling scheduling model of the current rolling cycle is shown as follows:
Figure BDA0002553427530000135
in the formula, st is the starting time of the rolling scheduling of the current rolling period;
Figure BDA0002553427530000136
the output cost of the thermal generator set i in the rolling time period t is shown;
Figure BDA0002553427530000137
is the output cost of the gas motor group j in the rolling time period t;
Figure BDA0002553427530000138
is the interruptible load transfer amount within the rolling time period t;
Figure BDA0002553427530000139
and
Figure BDA00025534275300001310
respectively wind power output prediction data (obtained in the step 6-2) and wind power receiving capacity in the rolling time period t;
Figure BDA00025534275300001311
and
Figure BDA00025534275300001312
respectively an up-regulation flexibility deficit and a down-regulation flexibility deficit in a rolling time period t;
Figure BDA00025534275300001313
and
Figure BDA00025534275300001314
respectively pumping power and generating power of the pumped storage power station in the rolling time period t；
Figure BDA0002553427530000141
And
Figure BDA0002553427530000142
respectively is the deviation punishment of thermal power unit i and gas generator j with the plan before the day in rolling period t, and the expression is as follows:
Figure BDA0002553427530000143
in the formula, ζthAnd ζgaRespectively punishing cost of unit power deviation of the thermal power generating unit and the gas generating unit;
Figure BDA0002553427530000144
and
Figure BDA0002553427530000145
respectively the active power output P of the thermal power generating unit i and the gas power generating unit j in the rolling time period tth,t,iAnd Pga,t,jRespectively, planned output (obtained by step 5) of the thermal power generating unit i and the gas power generating unit j before the day at the time t).
6-3-2) determining the constraint conditions of the rolling scheduling model in the source load storage day of the current rolling period;
constraint conditions of the rolling scheduling model in the day are the same as those of the coordinated scheduling model in the day, and the constraint conditions comprise power balance constraint, system flexibility supply and demand constraint, wind power output constraint, thermal power unit output constraint, gas and electric power unit output constraint, pumped storage power station constraint and interruptible load constraint; the method comprises the following specific steps:
6-3-2-1) power balance constraints;
Figure BDA0002553427530000146
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000147
and
Figure BDA0002553427530000148
respectively the active power output of the thermal power generating unit i and the gas power generating unit j in the rolling time period t;
Figure BDA0002553427530000149
is the wind power acceptance within the rolling time period t;
Figure BDA00025534275300001410
and
Figure BDA00025534275300001411
respectively pumping power and generating power of the pumped storage power station in the rolling time period t;
Figure BDA00025534275300001412
is the load at time t of the rolling period;
Figure BDA00025534275300001413
is the amount of interruptible load calls during the scrolling period t.
6-3-2-2) system flexibility supply and demand constraints;
Figure BDA00025534275300001414
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001415
and
Figure BDA00025534275300001416
respectively meeting the up-regulation flexibility requirement and the down-regulation flexibility requirement required by the power system in the rolling time period t;
Figure BDA00025534275300001417
and
Figure BDA00025534275300001418
respectively supplying capacity for up-regulation flexibility and capacity for down-regulation flexibility of the power system in the rolling time period t;
Figure BDA00025534275300001419
and
Figure BDA00025534275300001420
respectively, a flexibility up deficit and a flexibility down deficit over the rolling period t.
6-3-2-3) wind power output constraint;
Figure BDA00025534275300001421
6-3-2-4) output constraint of the thermal power generating unit;
Figure BDA00025534275300001422
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001423
and
Figure BDA00025534275300001424
respectively representing the maximum technical output and the minimum technical output of the thermal power generating unit i;
Figure BDA00025534275300001425
and
Figure BDA00025534275300001426
the ramp rate and the landslide rate of the thermal power generating unit i are respectively.
6-3-2-5) output constraint of the gas-electric machine set;
Figure BDA00025534275300001427
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000151
and
Figure BDA0002553427530000152
are respectively a gas-electric machine set jMaximum and minimum technical output of (c);
Figure BDA0002553427530000153
and
Figure BDA0002553427530000154
respectively the upward climbing speed and the downward sliding speed of the gas-electric machine set j.
6-3-2-6) pumped storage power station constraints;
Figure BDA0002553427530000155
in the formula (I), the compound is shown in the specification,
Figure BDA0002553427530000156
and
Figure BDA0002553427530000157
respectively pumping power and generating power of the pumped storage power station in the rolling time period t;
Figure BDA0002553427530000158
the storage capacity of the pumped storage power station at the rolling time t moment is shown; mu.spThe pumping efficiency of the unit is improved;
Figure BDA0002553427530000159
and
Figure BDA00025534275300001510
respectively the power generation rate and the pumping rate of the pumped storage power station;
Figure BDA00025534275300001511
and
Figure BDA00025534275300001512
the minimum storage capacity and the maximum storage capacity of the pumped storage power station are respectively.
6-3-2-7) interruptible load constraints;
Figure BDA00025534275300001513
in the formula (I), the compound is shown in the specification,
Figure BDA00025534275300001514
is the amount of interruptible load calls during the scrolling period t.
6-4) calling CPLEX software based on an MATLAB platform by means of a Yalmip toolbox, solving the model established in the step 6-3), and solving to obtain the output plan of each flexible resource in the current rolling period. Through solving, the wind power admission in the rolling period in the day can be respectively obtained
Figure BDA00025534275300001515
Intraday rolling planned output of each thermal power generating unit
Figure BDA00025534275300001516
Intraday rolling planned output of each gas-electric machine set
Figure BDA00025534275300001517
Intraday rolling pumping power of pumped storage power station
Figure BDA00025534275300001518
And generated power
Figure BDA00025534275300001519
Interruptible load rolling call capacity in day
Figure BDA00025534275300001520
Flexibility deficit on hourly roll-in-day
Figure BDA00025534275300001521
And adjust downward flexibility deficit
Figure BDA00025534275300001522
As a result of the scheduling of the current roll period.
6-5) when the next rolling cycle comes, repeating the steps 6-2) to 6-4) until the scheduling in the day is finished, and finally obtaining the scheduling result of the rolling cycle in each day.
The above description is only a preferred and non-limiting invention, and it is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (1)

1. A power system source load-storage coordinated rolling scheduling method based on flexibility margin is characterized by comprising the following steps:
1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system to obtain uncertain intervals of wind power output and load; modeling the output characteristics of each source charge-storage flexible resource to respectively obtain a flexible supply capacity model corresponding to each flexible resource; the source loading and storage flexibility resources include: wind power generation units, thermal power generation units, gas power generation units, pumped storage power stations and interruptible loads; the method comprises the following specific steps:
1-1) acquiring day-ahead wind power output prediction data and day-ahead load prediction data of a power system, and respectively establishing uncertainty models of wind power output and load to obtain uncertainty intervals of the wind power output and the load;
the uncertainty model expression of the wind power output is as follows:
Figure FDA0002553427520000011
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000012
actual wind power output at the moment t;
Figure FDA0002553427520000013
the predicted wind power output at the moment t is obtained;
Figure FDA0002553427520000014
and
Figure FDA0002553427520000015
respectively setting the upper limit and the lower limit of the wind power prediction error at the time t; x is the number ofwThe value range of the wind power prediction error fluctuation factor is-1 to 1, and when | x |, the wind power prediction error fluctuation factor iswWhen | ═ 1, the uncertainty of wind power output reaches the maximum;
the uncertainty model expression of the load is as follows:
Figure FDA0002553427520000016
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000017
is the actual value of the load at time t;
Figure FDA0002553427520000018
is the predicted value of the load at the time t;
Figure FDA0002553427520000019
and
Figure FDA00025534275200000110
the upper limit and the lower limit of the load prediction error at the moment t are respectively set; x is the number ofdThe value range of the load prediction error fluctuation factor is-1 to 1, when | xdWhen 1, the load uncertainty reaches the maximum;
1-2) modeling the output characteristics of each source charge-storage flexible resource to respectively obtain a flexible supply capacity model corresponding to each flexible resource; the method comprises the following specific steps:
the corresponding flexibility supply capacity model expression of the wind turbine generator is as follows:
Figure FDA00025534275200000111
in the formula, Pw,tWind power consumption at the moment t;
Figure FDA00025534275200000112
the capacity of the down-regulation flexibility provided by the wind power at the moment t;
the corresponding flexible supply capacity model expression of the thermal power generating unit is as follows:
Figure FDA00025534275200000113
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000021
and
Figure FDA0002553427520000022
the method comprises the steps that an up-regulation flexibility capacity and a down-regulation flexibility capacity are provided by a thermal power generating unit i at a time t respectively;
Figure FDA0002553427520000023
and
Figure FDA0002553427520000024
the ramp-up speed and the ramp-down speed of the thermal power generating unit i are respectively; pth,t,iThe active power output of the thermal power generating unit i at the moment t is obtained;
Figure FDA0002553427520000025
and
Figure FDA0002553427520000026
respectively representing the maximum technical output and the minimum technical output of the thermal power generating unit i; t is0Is a scheduling time;
the corresponding flexibility supply capacity model expression of the gas-electric machine set is as follows:
Figure FDA0002553427520000027
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000028
and
Figure FDA0002553427520000029
respectively providing an up-regulation flexible capacity and a down-regulation flexible capacity at the moment t by the gas-electric machine set j;
Figure FDA00025534275200000210
and
Figure FDA00025534275200000211
the upward climbing speed and the downward sliding speed of the gas-electric generator set j are respectively; pga,t,jThe active power output of the gas-electric machine set j at the moment t;
Figure FDA00025534275200000212
and
Figure FDA00025534275200000213
respectively the maximum technical output and the minimum technical output of the gas-electric machine set j;
the corresponding flexible supply capacity model expression of the pumped storage power station is as follows:
Figure FDA00025534275200000214
in the formula (I), the compound is shown in the specification,
Figure FDA00025534275200000215
and
Figure FDA00025534275200000216
the up-regulation flexible capacity and the down-regulation flexible capacity are respectively provided by the pumped storage power station at the moment t;
Figure FDA00025534275200000217
and
Figure FDA00025534275200000218
respectively the power generation rate and the pumping rate of the pumped storage power station;
Figure FDA00025534275200000219
and
Figure FDA00025534275200000220
the maximum storage capacity and the minimum storage capacity of the pumped storage power station are respectively; wpu,tThe capacity is the capacity of the pumped storage power station at the moment t;
the flexible supply capacity model for interruptible loads is expressed by:
Figure FDA00025534275200000221
in the formula (I), the compound is shown in the specification,
Figure FDA00025534275200000222
is the flexible capacity provided by the interruptible load at time t;
Figure FDA00025534275200000223
is the maximum interruptible load at time t;
2) respectively establishing a power system flexibility demand capacity model and a power system flexibility supply capacity model by using the result of the step 1); the method comprises the following specific steps:
2-1) establishing a power system flexibility demand capacity model expression according to the uncertainty model of the wind power output and the load obtained in the step 1-1):
Figure FDA00025534275200000224
in the formula (I), the compound is shown in the specification,
Figure FDA00025534275200000225
and
Figure FDA00025534275200000226
ξ, the up-regulation flexibility requirement and the down-regulation flexibility requirement of the power system at the moment t respectivelyw,tAnd ξd,tThe method comprises the following steps of respectively predicting wind power output errors and load errors at the time t, wherein the expressions are as follows:
Figure FDA00025534275200000227
2-2) establishing a flexible supply capacity model expression of the power system according to the flexible supply capacity model corresponding to each flexible resource obtained in the step 1-2):
Figure FDA0002553427520000031
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000032
and
Figure FDA0002553427520000033
respectively supplying capacity for adjusting up flexibility and supplying capacity for adjusting down flexibility of the power system at the time t;
3) establishing a flexibility margin index, wherein the flexibility margin is divided into an up-regulation flexibility margin and a down-regulation flexibility margin, and the expression is as follows:
Figure FDA0002553427520000034
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000035
and
Figure FDA0002553427520000036
up-regulation flexibility margin and down-regulation flexibility of the power system at time tMargin; when in use
Figure FDA0002553427520000037
Or
Figure FDA0002553427520000038
When the ratio is less than or equal to 0, the shortage of flexibility of up regulation or the shortage of flexibility of down regulation occurs, namely:
Figure FDA0002553427520000039
in the formula (I), the compound is shown in the specification,
Figure FDA00025534275200000310
and
Figure FDA00025534275200000311
the method comprises the steps that the flexibility shortage is adjusted up and down at t time respectively;
4) establishing a source load storage day-ahead coordination scheduling model, wherein the model consists of a target function and constraint conditions; the method comprises the following specific steps:
4-1) determining an objective function of a source load storage day-ahead coordinated scheduling model, wherein the expression is as follows:
Figure FDA00025534275200000312
in the formula, Cth,t,iThe output cost of the thermal power generating unit i at the moment t is shown; cga,t,jThe output cost of the gas-electric machine set j at the moment t is shown;
Figure FDA00025534275200000313
and
Figure FDA00025534275200000314
respectively starting cost and shutdown cost of the gas-electric machine set j at the moment t; lambda [ alpha ]ilIs the unit cost of the interruptible load; pil,tIs the interruptible load transfer amount at time t; lambda [ alpha ]wIs a unit abandonA wind penalty cost; pw,tIs the wind power allowance at time t; λ is the flexibility deficit penalty factor; cpuThe unit peak regulation benefit of the pumped storage power station is obtained;
Figure FDA00025534275200000315
and
Figure FDA00025534275200000316
the pumping power and the generating power of the pumped storage power station at the moment t are respectively;
wherein, Cth,t,iAnd Cga,t,jIs obtained by the following formula:
Figure FDA00025534275200000317
in the formula, ath,i、bth,i、cth,iRespectively a quadratic coefficient, a primary coefficient and a constant coefficient of the operating cost of the thermal power generating unit i; a isga,j、bga,j、cga,jRespectively is a quadratic term coefficient, a primary term coefficient and a constant coefficient of the operation of the gas-electric machine set j;
4-2) determining the constraint conditions of the source load storage day-ahead coordinated scheduling model, which are as follows:
4-2-1) power balance constraints;
Figure FDA00025534275200000318
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000041
and
Figure FDA0002553427520000042
respectively generating capacity and pumped electricity quantity of the pumped storage power station at the moment t;
4-2-2) system flexibility supply and demand constraints;
Figure FDA0002553427520000043
4-2-3) wind power output constraint;
Figure FDA0002553427520000044
4-2-4) output constraint of the thermal power generating unit;
Figure FDA0002553427520000045
4-2-5) output constraint of the gas-electric machine set;
Figure FDA0002553427520000046
4-2-6) limiting the start-stop time of the gas-electric machine set;
Figure FDA0002553427520000047
in the formula of Uga,t,jIs the starting and stopping state of the gas-electric machine set j at the moment t;
Figure FDA0002553427520000048
representing the minimum running time allowed by the unit j;
Figure FDA0002553427520000049
represents the minimum downtime allowed for unit j;
4-2-7) pumped storage power station constraint;
Figure FDA00025534275200000410
in the formula, mupThe pumping efficiency of the unit is improved;
Figure FDA00025534275200000411
and
Figure FDA00025534275200000412
the water pumping power and the power generation power at the moment t are respectively; u shapep,tAnd Ug,tThe two working states of the pumped storage power station are 0-1 variable; u shapep,tTaking 1 to represent a water pumping state, and taking 0 to represent a shutdown state; u shapeg,tTaking 1 to represent a power generation state, and taking 0 to represent a shutdown state;
Figure FDA00025534275200000413
and
Figure FDA00025534275200000414
respectively the power generation rate and the pumping rate of the pumped storage power station;
4-2-8) interruptible load constraints;
Figure FDA00025534275200000415
in the formula, Pil,tIs the interruptible load transfer amount at time t;
5) solving the model established in the step 4) to respectively obtain Pw,t、Pth,t,i、Uga,t,j、Pga,t,j、Up,t、
Figure FDA00025534275200000416
Ug,t、
Figure FDA0002553427520000051
Pil,t、Piufc,tAnd Pidfc,tAnd using the optimal solution as a day-ahead scheduling plan;
4-2-8) interruptible load constraints;
Figure FDA0002553427520000052
in the formula, Pil,tIs the interruptible load transfer amount at time t;
Figure FDA0002553427520000053
is the maximum interruptible load at time t;
5) solving the model established in the step 4) to obtain Pw,t、Pth,t,i、Uga,t,j、Pga,t,j、Up,t、
Figure FDA0002553427520000054
Ug,t、
Figure FDA0002553427520000055
Pil,t、Piufc,tAnd Pidfc,tThe optimal solution of (a) is used as a day-ahead scheduling plan;
6) establishing a source load storage intraday rolling scheduling model for each rolling period in the day and solving to obtain an intraday rolling scheduling result; the method comprises the following specific steps:
6-1) setting initial data of rolling schedule, comprising: the operation data, the day-ahead scheduling plan, the initial state and the output of each flexible resource;
setting a rolling period;
6-2) automatically acquiring wind power output prediction data and load prediction data of each rolling period before each rolling period comes in the day, and taking the rolling period as the current rolling period;
6-3) establishing a source load storage day rolling scheduling model of the current rolling period, wherein the model consists of an objective function and constraint conditions; the method comprises the following specific steps:
6-3-1) determining an objective function of the rolling scheduling model in the day of the current rolling period, wherein the expression is as follows:
Figure FDA0002553427520000056
in the formula, st is the starting time of the rolling scheduling of the current rolling period;
Figure FDA0002553427520000057
of the group of live-fire-motors i during the rolling period tThe output cost;
Figure FDA0002553427520000058
is the output cost of the gas motor group j in the rolling time period t;
Figure FDA0002553427520000059
is the interruptible load transfer amount within the rolling time period t;
Figure FDA00025534275200000510
and
Figure FDA00025534275200000511
respectively predicting wind power output data and wind power acceptance in a rolling time period t;
Figure FDA00025534275200000512
and
Figure FDA00025534275200000513
respectively an up-regulation flexibility deficit and a down-regulation flexibility deficit in a rolling time period t;
Figure FDA00025534275200000514
and
Figure FDA00025534275200000515
respectively pumping power and generating power of the pumped storage power station in the rolling time period t;
Figure FDA00025534275200000516
and
Figure FDA00025534275200000517
respectively is the deviation punishment of thermal power unit i and gas generator j with the plan before the day in rolling period t, and the expression is as follows:
Figure FDA00025534275200000518
in the formula, ζthAnd ζgaRespectively punishing cost of unit power deviation of the thermal power generating unit and the gas generating unit;
Figure FDA00025534275200000519
and
Figure FDA00025534275200000520
respectively the active power output P of the thermal power generating unit i and the gas power generating unit j in the rolling time period tth,t,iAnd Pga,t,jRespectively planning output of the thermal power generating unit i and the gas power generating unit j at the moment t in the day ahead;
6-3-2) determining the constraint conditions of the rolling scheduling model in the source load storage day of the current rolling period; the method comprises the following specific steps:
6-3-2-1) power balance constraints;
Figure FDA0002553427520000061
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000062
is the load of the scroll period t;
Figure FDA0002553427520000063
is the interruptible load transfer amount within the rolling time period t;
6-3-2-2) system flexibility supply and demand constraints;
Figure FDA0002553427520000064
in the formula (I), the compound is shown in the specification,
Figure FDA0002553427520000065
and
Figure FDA0002553427520000066
respectively, power during the rolling period tThe up-regulation flexibility requirement and the down-regulation flexibility requirement required by the system;
Figure FDA0002553427520000067
and
Figure FDA0002553427520000068
respectively supplying capacity for up-regulation flexibility and capacity for down-regulation flexibility of the power system in the rolling time period t;
Figure FDA0002553427520000069
and
Figure FDA00025534275200000610
respectively an up-regulation flexibility deficit and a down-regulation flexibility deficit in a rolling time period t;
6-3-2-3) wind power output constraint;
Figure FDA00025534275200000611
6-3-2-4) output constraint of the thermal power generating unit;
Figure FDA00025534275200000612
6-3-2-5) output constraint of the gas-electric machine set;
Figure FDA00025534275200000613
6-3-2-6) pumped storage power station constraints;
Figure FDA00025534275200000614
in the formula (I), the compound is shown in the specification,
Figure FDA00025534275200000615
the storage capacity of the pumped storage power station at the rolling time t moment is shown;
6-3-2-7) interruptible load constraints;
Figure FDA00025534275200000616
6-4) solving the model established in the step 6-3) to respectively obtain the rolling period in the current day
Figure FDA00025534275200000617
Figure FDA00025534275200000618
And
Figure FDA00025534275200000619
as a scheduling result of the current rolling cycle;
6-5) repeating steps 6-2) to 6-4) when the next rolling period comes, until the rolling period is finished in all days,
and finally, obtaining the scheduling result of the rolling period in each day.
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