CN113193547A - Day-ahead-day cooperative scheduling method and system for power system considering uncertainty of new energy and load interval - Google Patents

Day-ahead-day cooperative scheduling method and system for power system considering uncertainty of new energy and load interval Download PDF

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CN113193547A
CN113193547A CN202110293668.XA CN202110293668A CN113193547A CN 113193547 A CN113193547 A CN 113193547A CN 202110293668 A CN202110293668 A CN 202110293668A CN 113193547 A CN113193547 A CN 113193547A
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day
power
ahead
scheduling
load
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CN113193547B (en
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张振华
周海强
梁文腾
鞠平
周航
秦川
江叶峰
熊浩
付伟
罗建裕
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State Grid Jiangsu Electric Power Co Ltd
Hohai University HHU
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State Grid Jiangsu Electric Power Co Ltd
Hohai University HHU
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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
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load 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
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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
    • 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
    • H02J2203/20—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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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
    • H02J2300/28—The renewable source being wind 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

Abstract

The invention provides a day-ahead and day-in cooperative scheduling method and system for an electric power system, which take new energy and load interval uncertainty into account, wherein the day-ahead and day-in cooperative scheduling method comprises the steps of acquiring electric power system data and new energy and load day-ahead prediction data; constructing a mathematical model of a scheduling interval optimization problem in the day ahead; determining a day-ahead scheduling scheme and boundary conditions of scheduling in a day; acquiring new energy and load rolling prediction data in the day; and constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary condition of the in-day scheduling scheme and the model of the number of new energy and load in-day intervals. According to the day-ahead and day-in cooperative scheduling method of the power system, a mathematical model of the interval problem of day-ahead and day-in cooperative scheduling of the power system is comprehensively constructed; and by applying an interval optimization theory, the uncertain objective function and the constraint function are converted into a deterministic problem to be solved, and compared with an opportunity constraint planning method, the method has the advantages of low requirement on input data information, good decision flexibility and high calculation speed.

Description

Day-ahead-day cooperative scheduling method and system for power system considering uncertainty of new energy and load interval
Technical Field
The invention relates to the technical field of smart power grids, in particular to a power system scheduling technology, and specifically relates to a day-ahead-day cooperative scheduling method for a power system, which takes new energy and load interval uncertainty into account.
Background
In recent years, the occupation ratio of new energy represented by wind power and photovoltaic in a power system is continuously increased, the rapid development of the new energy is a necessary requirement for energy transformation and carbon emission goal realization in China, and the prediction of the new energy has certain errors inevitably because the new energy has the characteristics of randomness, volatility and intermittence. On the load side, the electricity demand is influenced by multiple factors such as weather, time, electricity price, economic development stage and consumption psychology, and large prediction errors exist. Therefore, the operation scenario of modern power systems has strong uncertainty. How to carry out scientific scheduling aiming at an uncertain power system and improve the economy of a scheduling scheme on the basis of controlling the risk of the scheduling scheme is a problem to be solved urgently by the power system.
At present, in the prior art of scheduling an uncertain power system, a scenario method and a probability method are commonly adopted. The scene method needs sampling to generate a scene set, and a large amount of calculation is carried out on the basis of the scene set. The probability method is also called as an opportunity constraint planning method, converts a constraint inequality into a deterministic inequality to solve at a certain confidence level according to a probability distribution function of input uncertainty variables such as new energy or load power, and the method is small in calculation amount.
Both of the above methods require knowing the exact probability distribution function of the input variables, but this often presents some difficulty for practical systems. In practice, it is often difficult to know the probability distribution exactly due to lack of sufficient historical data or weak regularity of the variables themselves.
Disclosure of Invention
The invention aims to provide a day-ahead and day-in cooperative scheduling method and system for a power system, which are used for calculating uncertainty of new energy and load intervals, aiming at the technical defects that in the prior art, the probability distribution function and the calculated amount of uncertainty variables of the new energy and the load are large and a day-ahead scheduling scheme is not fine enough.
According to a first aspect of the present invention, a method for day-ahead-day coordinated scheduling of an electric power system with consideration of uncertainty of new energy and load intervals is provided, which includes the following steps:
step 1, acquiring power system data and new energy and load day-ahead prediction data;
step 2, constructing a mathematical model of a scheduling interval optimization problem in the day ahead;
step 3, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling;
step 4, acquiring new energy and load rolling prediction data in the day; and
step 5, constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary conditions of the in-day scheduling scheme determined in the step 3 and the model of the new energy and load in-day intervals acquired in the step 4;
the power system data comprises maximum and minimum output power of a conventional generator set and a quick start-stop unit, start-stop cost of the generator set, an operation cost coefficient, climbing power, minimum start-up and stop time, and A, B, C three types of flexible loads Pila、Pilb、PilcThe grade number and the elastic coefficient of each grade capable of reducing the maximum capacity, the cost coefficient, the demand response and the maximum accumulated interruption time of the load, wherein the class A flexible load needs to be informed to the user 24h in advance, the class B flexible load needs to be informed to the user 15min-2h in advance, and the class C flexible load needs to be informed to the user 5-15min in advance;
the new energy and load day-ahead prediction data comprise the output power P of a wind power plant and a photovoltaic power plant 24 hours in the futurewt、PpvThe predicted value per hour and the fluctuation interval of the prediction error before the day, and the system load P of 24 hours in the futurelThe predicted value of each hour and the fluctuation interval of the prediction error before the hour.
According to a second aspect of the present invention, there is provided a day-ahead and day-in cooperative scheduling system for an electric power system, which includes:
one or more processors;
a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing the aforementioned processes of co-scheduling processing.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. according to the day-ahead-day cooperative scheduling method for the electric power system considering the uncertainty of the new energy and the load, the characteristic that the prediction error of the new energy and the load is reduced along with the reduction of the time scale is utilized, the flexibility of various units and the multi-time scale characteristic of the flexible load are considered, the operation cost of a generator, the penalty cost of wind curtailment or light curtailment and various expenses required by the flexible load participating in the scheduling of the electric power system are comprehensively considered, and the day-ahead-day cooperative scheduling of the electric power system is realized;
2. under the condition of uncertain input data probability distribution, applying an interval optimization theory, converting an uncertain objective function into a deterministic function, and converting an interval inequality into a deterministic inequality under a certain interval probability, so that an uncertain problem is converted into a deterministic problem to be solved, and compared with an opportunity constraint planning method, the method has the advantages of low requirement on input data information, good decision flexibility, high calculation speed and the like;
3. finally, simulation check is carried out on the day-ahead and day-in cooperative scheduling scheme, and the technical defect that the day-ahead scheduling scheme is not fine enough is verified, so that the safety of the system is improved under the condition of reducing the day operation cost, and the method has better practicability.
It should be understood that all combinations of the foregoing concepts and additional concepts described in greater detail below can be considered as part of the inventive subject matter of this disclosure unless such concepts are mutually inconsistent. In addition, all combinations of claimed subject matter are considered a part of the presently disclosed subject matter.
The foregoing and other aspects, embodiments and features of the present teachings can be more fully understood from the following description taken in conjunction with the accompanying drawings. Additional aspects of the present invention, such as features and/or advantages of exemplary embodiments, will be apparent from the description which follows, or may be learned by practice of specific embodiments in accordance with the teachings of the present invention.
Drawings
The drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in various figures may be represented by a like numeral. For purposes of clarity, not every component may be labeled in every drawing. Embodiments of various aspects of the present invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is a flowchart of an embodiment of a day-ahead-day coordinated scheduling method for a power system, which accounts for uncertainty of new energy and load intervals according to the present invention.
Fig. 2 is a diagram of an IEEE10 computer 39 node arithmetic system structure according to an embodiment of the present invention, wherein "WT" denotes a wind farm and "FG" denotes a fast start-stop unit.
Fig. 3 is a schematic diagram of a daily load curve according to an embodiment of the invention, wherein "°" denotes a pre-day predictor, "-" denotes an intra-day predictor, "Δ" denotes a load predictor interval lower bound, "# denotes a load predictor interval upper bound.
FIG. 4 is a schematic representation of a diurnal wind power curve showing "°" a pre-solar predictor, "-" an intra-day predictor, "Δ" a lower wind predictor interval boundary, ". v" an upper wind predictor interval boundary, in accordance with an embodiment of the present invention.
Fig. 5 is a graph comparing the daily total power generation of a conventional genset with a day-ahead-in-day coordinated schedule in accordance with an embodiment of the present invention.
Fig. 6 is a graph comparing air curtailment for day-ahead scheduling and day-in-day coordinated scheduling of an embodiment of the present invention. .
Fig. 7 is a schematic diagram of system power balancing for day-ahead-day cooperative scheduling according to an embodiment of the present invention.
Fig. 8 is a schematic diagram of the system power balance and the possibility of the positive and negative standby power constraints for day-ahead-day cooperative scheduling according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical content of the present invention, specific embodiments are described below with reference to the accompanying drawings.
In this disclosure, aspects of the present invention are described with reference to the accompanying drawings, in which a number of illustrative embodiments are shown. Embodiments of the present disclosure are not necessarily intended to include all aspects of the invention. It should be appreciated that the various concepts and embodiments described above, as well as those described in greater detail below, may be implemented in any of numerous ways, as the disclosed concepts and embodiments are not limited to any one implementation. In addition, some aspects of the present disclosure may be used alone, or in any suitable combination with other aspects of the present disclosure.
In combination with the drawings, a method for day-ahead-day cooperative scheduling of an electric power system considering uncertainty of a new energy and load interval is disclosed according to an exemplary embodiment of the present invention, and fig. 1 is a flowchart of the method for day-ahead-day cooperative scheduling of an electric power system considering uncertainty of a new energy and load interval, including the following steps: step 1, acquiring power system data and new energy and load day-ahead prediction data; step 2, constructing a mathematical model of a scheduling interval optimization problem in the day ahead; step 3, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling; step 4, acquiring new energy and load rolling prediction data in the day; and step 5, constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary conditions of the in-day scheduling scheme determined in the step 3 and the new energy and load in-day interval number model obtained in the step 4.
Specific implementations of the above steps are described in more detail below with reference to the accompanying drawings.
Step one, acquiring data of an electric power system and day-ahead prediction data of new energy and load
The power system data comprises maximum and minimum output power of a conventional generator set and a quick start-stop unit, start-stop cost of the generator set, an operation cost coefficient, climbing power, minimum start-up and stop time, and A, B, C three types of flexible loads Pila、Pilb、PilcAnd a load maximum capacity, a cost factor, a demand response flexibility factor, and a maximum cumulative interrupt time per gear, wherein class AThe flexible load needs to be informed to the user 24 hours in advance, the time for informing the user by the B-type flexible load is 15min-2 hours in advance, and the time for informing the user by the C-type flexible load is 5-15min in advance;
the new energy and load day-ahead prediction data comprise the output power P of a wind power plant and a photovoltaic power plant 24 hours in the futurewt、PpvThe predicted value per hour and the fluctuation interval of the prediction error before the day, and the system load P of 24 hours in the futurelThe predicted value of each hour and the fluctuation interval of the prediction error before the hour.
Step two, establishing a mathematical model of a day-ahead scheduling interval optimization problem
The predicted value before the new energy power generation day is assumed to be
Figure BDA0002983456120000041
Its prediction error in the day ahead
Figure BDA0002983456120000042
Located in a section
Figure BDA0002983456120000043
The upper mark "+" represents the upper bound of the interval number, and the upper mark "-" represents the lower bound of the interval number, so that the new energy power generation P can be deducedN1Has an upper bound of
Figure BDA0002983456120000044
Lower boundary is
Figure BDA0002983456120000045
PN1The model of the number of the day-ahead intervals is
Figure BDA0002983456120000046
Therefore, a day-ahead interval number model of wind power and photovoltaic power generation can be established
Figure BDA0002983456120000047
Assume a predicted value of the load before the day is
Figure BDA0002983456120000048
Its prediction error in the day ahead
Figure BDA0002983456120000049
Located in a section
Figure BDA00029834561200000410
In this way, the load power P can be derivedl1Has an upper bound of
Figure BDA0002983456120000051
Lower boundary is
Figure BDA0002983456120000052
The number of the load power in the day-ahead interval model is
Figure BDA0002983456120000053
In order to better connect the day-ahead scheduling, the invention takes the day-ahead scheduling time scale as 15 minutes, carries out linear interpolation on the predicted value of the new energy and the load every hour as the day-ahead predicted value every 15 minutes, and carries out the day-ahead scheduling on the basis.
Therefore, the objective function of the power system day-ahead scheduling optimization problem is described as follows:
min f1＝f11+f12+f13+f14 (1)
Figure BDA0002983456120000054
the method is characterized in that the method is the operation cost of a conventional generator set, the item 1 on the right side is the start-stop cost of the conventional generator set, and the item 2 is the power generation cost.
Figure BDA0002983456120000055
Is the number of conventional generators, T1For scheduling period, T is taken as 15 minutes for day-ahead scheduling 196, i.e. 96 scheduling periods.
Figure BDA0002983456120000056
Respectively a starting control 0-1 variable and a cold starting control 0-1 variable of the ith conventional generator at the moment j,
Figure BDA0002983456120000057
a "1" indicates that the ith conventional generator receives a start or cold start command at time j,
Figure BDA0002983456120000058
a "0" indicates no start or cold start command at time j.
Chot,i、Ccold,iThe hot start cost and the cold start cost of the ith conventional generator respectively,
Figure BDA0002983456120000059
the variable is 0-1, the 1 indicates that the ith conventional generator is in the on state at the j moment, and the 0 indicates that the ith conventional generator is in the off state.
Figure BDA00029834561200000510
For the power generation cost coefficient of the ith conventional generator,
Figure BDA00029834561200000511
the power of the ith conventional generator at time j.
Figure BDA00029834561200000512
The running cost of the unit is quickly started and stopped.
Wherein the content of the first and second substances,
Figure BDA00029834561200000513
the number of the generators is rapidly started;
Figure BDA00029834561200000514
operating cost system for fast start-stop unit of ith stationThe number of the first and second groups is,
Figure BDA00029834561200000515
and the power of the ith station for rapidly starting and stopping the unit at the moment j.
Figure BDA00029834561200000516
The variable 0-1 is controlled for starting the quick start-stop unit,
Figure BDA00029834561200000517
the number of the start-up and stop units is 1, the ith quick start-up and stop unit receives the start instruction at the moment j,
Figure BDA00029834561200000518
the starting cost of the ith unit for quickly starting and stopping the unit is saved.
Figure BDA00029834561200000519
Penalizing costs for wind curtailment, wherein
Figure BDA00029834561200000520
Wind power cut down for moment j, CwA penalty factor for wind curtailment.
Figure BDA00029834561200000521
The control expense for A, B, C three types of flexible loads, ns is the number of grades of the flexible loads, wherein
Figure BDA00029834561200000522
The regulation power of s gear of three flexible loads at the moment of j A, B, C respectively, and the response of the flexible loads has elasticity, so
Figure BDA00029834561200000523
The number of the intervals is the number of the intervals,
Figure BDA00029834561200000524
when three types of flexible loads, namely A, B, C, participate in regulation and control in s gearThe cost factor of (2).
The method for determining the constraint conditions of the day-ahead scheduling optimization problem of the power system comprises the following steps:
active power balance equation
Figure BDA0002983456120000061
In the formula (2), the left side is the sum of the conventional generator set, the quick start-stop unit and the wind power generation, the right side is the reduction of the total load of the system minus the 3 types of flexible loads, and the active power balance equation is an interval equation;
② restraining the output and climbing power of the conventional generator
Figure BDA0002983456120000062
Figure BDA0002983456120000063
In the formula (3)
Figure BDA0002983456120000064
The minimum and maximum power of the ith conventional generator respectively,
Figure BDA0002983456120000065
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith conventional generator;
third, the minimum on-off time constraint of the conventional generator
Figure BDA0002983456120000066
Figure BDA0002983456120000067
In formulas (5) and (6)
Figure BDA0002983456120000068
Respectively the minimum starting time and the minimum shutdown time of the ith conventional generator;
fourthly, restraint of cold and hot start of conventional generator set
Figure BDA0002983456120000069
Figure BDA00029834561200000610
Equations (7) and (8) are the constraint of the cold start and the hot start of the ith conventional generator at the moment j,
Figure BDA00029834561200000611
the cold start time of the ith conventional generator is set;
fast start-stop unit maximum and minimum power and climbing power constraint
Figure BDA00029834561200000612
Figure BDA00029834561200000613
Equation (9) is the minimum, maximum power constraint for a fast start generator,
Figure BDA00029834561200000614
respectively the minimum power and the maximum power of the ith quick start-stop unit,
Figure BDA00029834561200000615
the variable is 0-1, the 1 indicates that the ith quick unit is in a starting state at the moment j, and the 0 indicates that the ith quick unit is in a stopping state; the formula (10) is the power constraint of the rapid unit for climbing downwards and upwards,
Figure BDA0002983456120000071
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith quick start-stop unit;
quick set minimum start-up time and minimum stop time constraint
Figure BDA0002983456120000072
Figure BDA0002983456120000073
The formula (11) and the formula (12) are respectively the minimum starting time of the ith quick unit at the moment j
Figure BDA0002983456120000074
Minimum down time
Figure BDA0002983456120000075
The constraint of (a) to (b),
Figure BDA0002983456120000076
to stop the continuous running time of the ith fast unit at time j,
Figure BDA0002983456120000077
stopping the continuous shutdown time of the ith quick unit at the moment j;
seventh, the wind is abandoned to restrain
Figure BDA0002983456120000078
In the formula (13)
Figure BDA0002983456120000079
Respectively reducing the wind power at the moment j in the day-ahead scheduling and the lower bound of the wind power fluctuation interval;
flexible load constraint
Figure BDA00029834561200000710
Figure BDA00029834561200000711
Figure BDA00029834561200000712
Wherein, Pila,s,j、Pilb,s,j、Pilc,s,jPower is regulated for the s-gear of three types of flexible loads at time j A, B, C,
Figure BDA00029834561200000713
the maximum value of the regulated power of the s gear of A, B, C three types of flexible loads,
Figure BDA00029834561200000714
and
Figure BDA00029834561200000715
elastic fluctuation intervals of response coefficients of s-gear of A, B, C three types of flexible loads respectively;
ninthly positive and negative standby power constraints
Figure BDA00029834561200000716
Figure BDA00029834561200000717
Figure BDA00029834561200000718
Figure BDA00029834561200000719
Wherein
Figure BDA00029834561200000720
The positive standby power which can be provided by the conventional unit and the quick unit at the moment j, r is a standby coefficient,
Figure BDA00029834561200000721
Figure BDA00029834561200000722
the negative standby power which can be provided by the conventional unit and the quick unit at the moment j,
Figure BDA00029834561200000723
is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,
Figure BDA00029834561200000724
the fluctuation interval is when the load is downward and the wind speed is upward.
Thus, the foregoing equations (1) - (20) collectively form a mathematical model of the day-ahead scheduling interval optimization problem.
Step three, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling
Electric power system day-ahead scheduling interval optimization problem objective function f described by formula (1)1For the interval function, the upper and lower bounds are set to
Figure BDA0002983456120000081
Wherein:
Figure BDA0002983456120000082
Figure BDA0002983456120000083
setting the mean value of the interval objective function as
Figure BDA0002983456120000084
Radius of the objective function of
Figure BDA0002983456120000085
Converting an interval objective function to
Figure BDA0002983456120000086
β1Is a weighting coefficient;
then, the interval inequality constraints described by formulas (2), (17) and (19) in the power system day-ahead scheduling interval optimization problem are converted into deterministic inequalities under preset interval possibility degrees, and the interval possibility degrees with work power balance and establishment of a standby constraint equation are zeta11、ζ12And ζ13Then, according to the interval probability theory, the formulas (2), (17) and (19) can be transformed into:
Figure BDA0002983456120000087
Figure BDA0002983456120000088
Figure BDA0002983456120000089
thus, the scheduling interval optimization problem in the day ahead can be converted into the deterministic problem as follows:
min F1 (24)
the constraint conditions include formulas (3) to (16), formulas (18) and (20), and formulas (21) to (23);
and then, solving the deterministic problem by using a mixed integer linear programming method to obtain a day-ahead scheduling interval optimization scheme, so that the boundary conditions of the day-ahead scheduling problem can be determined, namely: the starting state of the conventional unit is kept unchanged, the regulation and control quantity of the A-type flexible load is kept unchanged, and the starting and stopping state of the quick start-stop unit and the flexible loads of the B, C-type flexible loads need to be adjusted in daily scheduling.
Step four, acquiring new energy and load rolling prediction data in day
And carrying out 1-time ultra-short-term prediction on wind power, photovoltaic power and load power for 2 hours in the future every 15 minutes, wherein the time scale is 15 minutes, and obtaining prediction data from the first scheduling period k which is 1. As mentioned above, the scheduling period is 96. The predicted value in the new energy power generation day is assumed to be
Figure BDA0002983456120000091
Error of prediction in day
Figure BDA0002983456120000092
Located in a section
Figure BDA0002983456120000093
In addition, the new energy power generation P can be deducedN2Has an upper bound of
Figure BDA0002983456120000094
Lower boundary is
Figure BDA0002983456120000095
PN2The model of the number of intervals in the day is
Figure BDA0002983456120000096
The model of the day-to-day interval digital model of wind power and photovoltaic power generation can be established respectively
Figure BDA0002983456120000097
Assume a predicted value of load in the day of
Figure BDA0002983456120000098
Error of prediction in day
Figure BDA0002983456120000099
Located in a section
Figure BDA00029834561200000910
Therein, therebyThe load power P can be deducedl2Has an upper bound of
Figure BDA00029834561200000911
Lower boundary is
Figure BDA00029834561200000912
The number of intervals per day model of the load power is
Figure BDA00029834561200000913
And step five, establishing a daily scheduling problem mathematical model and solving a daily scheduling scheme.
Establishing a mathematical model of the intraday optimal scheduling problem based on the boundary conditions of the intraday scheduling scheme determined in the step three and the model of the intraday intervals of the new energy and the load obtained in the step four; having an objective function of
f2＝f21+f22+f23+f24 (25)
Figure BDA00029834561200000914
For the conventional generator set generation cost, T for scheduling in the day2＝8；
Figure BDA00029834561200000915
The running cost of the unit is quickly started and stopped;
Figure BDA00029834561200000916
cost for wind abandon;
Figure BDA00029834561200000917
wherein, the constraint condition for determining the optimization scheduling problem in the day comprises:
active power balance equation
Figure BDA00029834561200000918
Wind abandon restriction
Figure BDA00029834561200000919
Positive and negative standby power constraints
Figure BDA00029834561200000920
Figure BDA00029834561200000921
Figure BDA00029834561200000922
Figure BDA0002983456120000101
Wherein the content of the first and second substances,
Figure BDA0002983456120000102
is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,
Figure BDA0002983456120000103
the fluctuation interval is when the load fluctuates downwards and the wind speed fluctuates upwards;
in addition, the constraint conditions for optimizing the scheduling problem in the day further comprise: constraint inequalities (3) - (4) of the output and climbing power of a conventional generator, constraint inequalities (9) - (12) of a quick start-stop unit and constraint equations (15) - (16) of a flexible load;
wherein, the electric power system scheduling interval optimization problem objective function f in the day described in the formula (25)2For the interval function, the upper and lower bounds are set to
Figure BDA0002983456120000104
Wherein:
Figure BDA0002983456120000105
Figure BDA0002983456120000106
setting the mean value of the interval objective function as
Figure BDA0002983456120000107
Radius of the objective function of
Figure BDA0002983456120000108
Converting an interval objective function to
Figure BDA0002983456120000109
β2Is a weighting coefficient;
the interval possibility degree of establishing an active power balance equation (26), a standby frequency constraint equation (28) and an equation (30) of the scheduling interval optimization problem in the day of the power system is zeta21、ζ22And ζ23According to the interval probability theory, equations (26), (28) and (30) can be transformed into:
Figure BDA00029834561200001010
Figure BDA00029834561200001011
Figure BDA00029834561200001012
thus, the scheduling interval optimization problem in the day ahead can be converted into the deterministic problem as follows:
min F2(35) the constraints thereof include formulae (3) to (4), formulae (9) to (12), formulae (15) to (16), formulae (27), (29), (31), and formulae (32) to (34);
substituting the starting state maintenance of the conventional unit and the regulating and controlling quantity of the A-type flexible load obtained in the fourth step into the calculation as boundary conditions, and solving the deterministic problem by using a mixed integer linear programming method to obtain an intra-day scheduling interval optimization scheme;
and generating a day-ahead-day cooperative scheduling scheme at the time k, outputting the cooperative scheduling scheme if k is 96, and if k is less than 96, changing k to k +1, and continuing the processing in the fourth step until k reaches 96.
In a further preferred scheme, after k reaches 96 to determine the cooperative scheduling scheme, the economy and the safety of the day-ahead-day cooperative scheduling scheme are checked.
And (4) taking the day-ahead optimized scheduling scheme obtained in the third step as a scheme A, taking the day-ahead-day cooperative scheduling scheme obtained in the fifth step as a scheme B, and comparing the operating cost and the default probability of the two schemes.
Assuming that new energy and load prediction errors are uniformly distributed in respective intervals, all variables are independent from each other, and the correlation among scenes at different moments is not considered, a Monte Carlo method is applied to sample the daily prediction errors of the wind power and the load power in a fluctuation interval, and N is generated at each moments30000 different scenes form a test sample set; n at time jsIn each scene, the number of scenes in which the violation of the positive standby power constraint inequality (28) occurs is
Figure BDA0002983456120000111
The number of scenes in which a violation of the negative standby power constraint inequality (30) occurs is
Figure BDA0002983456120000112
The probability Prob of occurrence of a positive backup shortage within one scheduling dayuComprises the following steps:
Figure BDA0002983456120000113
and probability Prob of occurrence of negative standby deficiencydComprises the following steps:
Figure BDA0002983456120000114
and (8) carrying out statistics on the default probability and daily operating cost of the two schemes A, B, and comparing and verifying the economy and the safety of the schemes.
Fig. 2 is a structural diagram of an IEEE10 computer 39 node example system according to an embodiment of the present invention, which applies the method of the present invention to an IEEE10 computer 39 node example system including a new energy resource and a fast start-stop unit, performs day-ahead-day cooperative optimal scheduling on the system, and analyzes the comprehensive performance of the day-ahead-day optimal scheduling scheme proposed by the present invention.
The process shown in FIG. 1 is implemented as follows:
step 1, acquiring data of an electric power system and day-ahead prediction data of new energy and load
The schematic system structure of the IEEE10 machine 39 node with new energy and fast start-stop unit of this embodiment is shown in fig. 2. On the basis of an IEEE10 machine 39 node system standard example, a wind power station and a quick start-stop unit are additionally arranged, and the power of a generator is adjusted, so that the total generated power of the system is kept unchanged before and after modification.
Maximum and minimum output power of each generator of conventional generator set
Figure BDA0002983456120000115
And
Figure BDA0002983456120000116
operating cost parameter
Figure BDA0002983456120000117
And
Figure BDA0002983456120000118
hot start cost of generator Chot,iAnd coolStarting charge Ccold,iMinimum starting time of generator
Figure BDA0002983456120000119
Minimum down time
Figure BDA00029834561200001110
Cold start time Tcold,iPower of climbing a slope
Figure BDA00029834561200001111
The parameters are shown in table 1.
Maximum and minimum output power of quick start-stop unit
Figure BDA00029834561200001112
And
Figure BDA00029834561200001113
coefficient of operating cost
Figure BDA00029834561200001114
Cost of start-up
Figure BDA00029834561200001115
Minimum generator run time
Figure BDA0002983456120000121
Minimum down time
Figure BDA0002983456120000122
Climbing power
Figure BDA0002983456120000123
The parameters are shown in table 2.
TABLE 1 conventional Generator parameters
Figure BDA0002983456120000124
TABLE 2 fast Start stop Generator parameters
Figure BDA0002983456120000125
The system day-ahead predicted load curve of 24 hours in the future is shown in fig. 3, and the predicted value is shown in table 3; the wind power curve of the future 24 hours is shown in FIG. 4, the predicted value is shown in Table 4, and the wind curtailment penalty factor CwTaking 100 $/MW; A. b, C the number of steps of the three types of flexible loads and the maximum capacity, cost coefficient and elasticity coefficient of the demand response of each step are shown in Table 5.
TABLE 3 predicted value of system load power day ahead 24 hours in the future
Figure BDA0002983456120000126
TABLE 4. predicted value of wind farm power day-ahead 24 hours in the future
Figure BDA0002983456120000127
TABLE 5, A, B, C type three Flexible load parameters
Figure BDA0002983456120000131
Step 2, establishing a mathematical model of a day-ahead scheduling interval optimization problem
The system forecasts the load power P in the future 24 hours per hour dayl1As shown in Table 3, assume a prediction error of day ahead
Figure BDA0002983456120000132
Namely, it is
Figure BDA0002983456120000133
This makes it possible to derive the load power Pl1Has an upper bound of
Figure BDA0002983456120000134
Lower boundary is
Figure BDA0002983456120000135
The number of the load power in the day-ahead interval model is
Figure BDA0002983456120000136
Predicted value P of wind farm power 24 hours before day in futurewt1As shown in Table 5, assume prediction error
Figure BDA0002983456120000137
The model of the number of the day-ahead intervals of the output power of the wind power plant can be deduced as
Figure BDA0002983456120000138
Describing an objective function of the power system day-ahead scheduling optimization problem as follows:
min f1＝f11+f12+f13+f14 (1)
wherein the content of the first and second substances,
Figure BDA0002983456120000139
the operation cost of the conventional generating set is shown in item 1 on the right, the starting and stopping cost of the conventional generating set is shown in item 2,
Figure BDA00029834561200001310
is the number of conventional generators, T1For scheduling period, T is taken as 15 minutes for day-ahead scheduling1＝96，
Figure BDA00029834561200001311
Respectively a starting control 0-1 variable and a cold starting control 0-1 variable of the ith conventional generator at the moment j,
Figure BDA00029834561200001312
a "1" indicates that the ith conventional generator receives a start or cold start command at time j,
Figure BDA00029834561200001313
a "0" indicates no start or cold start command at time j, Chot,i、Ccold,iThe hot start cost and the cold start cost of the ith conventional generator respectively,
Figure BDA00029834561200001314
the variable is 0-1, the 1 indicates that the ith conventional generator is in a starting state at the j moment, the 0 indicates that the ith conventional generator is in a stopping state,
Figure BDA00029834561200001315
for the power generation cost coefficient of the ith conventional generator,
Figure BDA00029834561200001316
the power of the ith conventional generator at the moment j;
Figure BDA00029834561200001317
in order to quickly start and stop the running cost of the unit,
Figure BDA00029834561200001318
the number of the generators is rapidly started;
Figure BDA00029834561200001319
for the operation cost coefficient of the ith quick start-stop unit,
Figure BDA00029834561200001320
the power of the ith station for quickly starting and stopping the unit at the moment j,
Figure BDA00029834561200001321
the variable 0-1 is controlled for starting the quick start-stop unit,
Figure BDA0002983456120000141
the number of the start-up and stop units is 1, the ith quick start-up and stop unit receives the start instruction at the moment j,
Figure BDA0002983456120000142
starting cost of the ith quick start-stop unit is saved;
Figure BDA0002983456120000143
penalizing costs for wind curtailment, wherein
Figure BDA0002983456120000144
Wind power cut down for moment j, CwA penalty factor for wind abandon;
Figure BDA0002983456120000145
the control expense for A, B, C three types of flexible loads, ns is the number of grades of the flexible loads, wherein
Figure BDA0002983456120000146
The regulation power of s gear of three flexible loads at the moment of j A, B, C respectively, and the response of the flexible loads has elasticity, so
Figure BDA0002983456120000147
The number of the intervals is the number of the intervals,
Figure BDA0002983456120000148
a, B, C cost factors when three types of flexible loads participate in regulation and control in s gear respectively;
the constraint conditions of the day-ahead scheduling optimization problem of the power system comprise:
active power balance equation
Figure BDA0002983456120000149
In the formula (2), the left side is the sum of the conventional generator set, the quick start-stop unit and the wind power generation, the right side is the reduction of the total load of the system minus the 3 types of flexible loads, and the active power balance equation is an interval equation;
② restraining the output and climbing power of the conventional generator
Figure BDA00029834561200001410
Figure BDA00029834561200001411
In the formula (3)
Figure BDA00029834561200001412
The minimum and maximum power of the ith conventional generator respectively,
Figure BDA00029834561200001413
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith conventional generator;
third, the minimum on-off time constraint of the conventional generator
Figure BDA00029834561200001414
Figure BDA00029834561200001415
In formulas (5) and (6)
Figure BDA00029834561200001416
Respectively the minimum starting time and the minimum shutdown time of the ith conventional generator;
fourthly, restraint of cold and hot start of conventional generator set
Figure BDA00029834561200001417
Figure BDA00029834561200001418
Equations (7) and (8) are the constraint of the cold start and the hot start of the ith conventional generator at the moment j,
Figure BDA00029834561200001419
the cold start time of the ith conventional generator is set;
fast start-stop unit maximum and minimum power and climbing power constraint
Figure BDA0002983456120000151
Figure BDA0002983456120000152
Equation (9) is the minimum, maximum power constraint for a fast start generator,
Figure BDA0002983456120000153
respectively the minimum power and the maximum power of the ith quick start-stop unit,
Figure BDA0002983456120000154
the variable is 0-1, the 1 indicates that the ith quick unit is in a starting state at the moment j, and the 0 indicates that the ith quick unit is in a stopping state; the formula (10) is the power constraint of the rapid unit for climbing downwards and upwards,
Figure BDA0002983456120000155
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith quick start-stop unit;
quick set minimum start-up time and minimum stop time constraint
Figure BDA0002983456120000156
Figure BDA0002983456120000157
The formula (11) and the formula (12) are respectively the minimum starting time of the ith quick unit at the moment j
Figure BDA0002983456120000158
Minimum down time
Figure BDA0002983456120000159
The constraint of (a) to (b),
Figure BDA00029834561200001510
to stop the continuous running time of the ith fast unit at time j,
Figure BDA00029834561200001511
stopping the continuous shutdown time of the ith quick unit at the moment j;
seventh, the wind is abandoned to restrain
Figure BDA00029834561200001512
In the formula (13)
Figure BDA00029834561200001513
Respectively reducing the wind power at the moment j in the day-ahead scheduling and the lower bound of the wind power fluctuation interval;
flexible load constraint
Figure BDA00029834561200001514
Figure BDA00029834561200001515
Figure BDA00029834561200001516
Wherein, Pila,s,j、Pilb,s,j、Pilc,s,jPower is regulated for the s-gear of three types of flexible loads at time j A, B, C,
Figure BDA00029834561200001517
the maximum value of the regulated power of the s gear of A, B, C three types of flexible loads,
Figure BDA00029834561200001518
and
Figure BDA00029834561200001519
elastic fluctuation intervals of response coefficients of s-gear of A, B, C three types of flexible loads respectively;
ninthly positive and negative standby power constraints
Figure BDA00029834561200001520
Figure BDA00029834561200001521
Figure BDA0002983456120000161
Figure BDA0002983456120000162
Wherein
Figure BDA0002983456120000163
The positive standby power which can be provided by the conventional unit and the quick unit at the moment j, r is a standby coefficient,
Figure BDA0002983456120000164
Figure BDA0002983456120000165
the negative standby power which can be provided by the conventional unit and the quick unit at the moment j,
Figure BDA0002983456120000166
for upward loading and downward wave of wind speedThe fluctuation interval of the time of the motion,
Figure BDA0002983456120000167
the fluctuation interval is when the load fluctuates downwards and the wind speed fluctuates upwards; equations (1) - (20) together form a mathematical model of the day-ahead scheduling interval optimization problem.
Step 3, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling
Electric power system day-ahead scheduling interval optimization problem objective function f described by formula (1)1For the interval function, the upper and lower bounds are set to
Figure BDA0002983456120000168
Wherein:
Figure BDA0002983456120000169
Figure BDA00029834561200001610
setting the mean value of the interval objective function as
Figure BDA00029834561200001611
Radius of the objective function of
Figure BDA00029834561200001612
Converting an interval objective function to
Figure BDA00029834561200001613
β1For the weighting coefficients, β is here taken1＝0.1；
Secondly, interval inequality constraints described by formulas (2), (17) and (19) in the power system day-ahead scheduling interval optimization problem are converted into deterministic inequalities under certain interval possibility degrees, and the interval possibility degrees with work power balance and establishment of a standby constraint equation are zeta11＝0.85、ζ120.85 and ζ130.85, according to the interval probability theoryFormulae (2), (17) and (19) can be converted to:
Figure BDA00029834561200001614
Figure BDA00029834561200001615
Figure BDA00029834561200001616
thus, the scheduling interval optimization problem in the day ahead can be converted into the deterministic problem as follows:
min F1 (24)
the constraints include expressions (3) to (16), expressions (18) and (20), and expressions (21) to (23); solving the deterministic problem by using a mixed integer linear programming method to obtain a day-ahead scheduling interval optimization scheme serving as a scheme A; from this, the boundary conditions for the scheduling problem within the day can be determined, namely: the starting state of the conventional unit is kept unchanged, the regulation and control quantity of the A-type flexible load is kept unchanged, and the starting and stopping state of the quick start-stop unit and the flexible loads of the B, C-type flexible loads need to be adjusted in daily scheduling.
Step 4, acquiring new energy and load rolling prediction data in day
Carrying out 1-time ultra-short-term prediction on wind power and load power for 2 hours in the future every 15 minutes, wherein the time scale is 15 minutes, the ultra-short-term prediction load curve in a system day is shown in a figure 3, and the prediction value is shown in a table 6; the ultra-short-term predicted wind power curve in the day is shown in fig. 4, and the predicted value is shown in table 7; in ultra-short-term prediction, the prediction precision is more accurate, and the prediction error is smaller than that of the prediction error in the day ahead; suppose that
Figure BDA0002983456120000171
Therefore, the new energy power P can be derivedN2Has an upper bound of
Figure BDA0002983456120000172
Lower boundary is
Figure BDA0002983456120000173
PN2The model of the number of intervals in the day is
Figure BDA0002983456120000174
Suppose that
Figure BDA0002983456120000175
The model of the number of intervals in the day from which the load power can be derived is
Figure BDA0002983456120000176
TABLE 6 predicted value of system load power in day
Figure BDA0002983456120000177
TABLE 7 predicted values of wind farm power in days
Figure BDA0002983456120000181
Step 5, establishing a mathematical model of the scheduling problem in the day and solving the scheduling scheme in the day
Establishing a mathematical model of the intraday optimal scheduling problem based on the boundary conditions of the intraday scheduling scheme determined in the step 3 and the intraday interval number model of the new energy and the load power obtained in the step 4; having an objective function of
f2＝f21+f22+f23+f24 (25)
Wherein the content of the first and second substances,
Figure BDA0002983456120000182
for the conventional generator set generation cost, T for scheduling in the day2＝8；
Figure BDA0002983456120000183
The running cost of the unit is quickly started and stopped;
Figure BDA0002983456120000184
cost for wind abandon;
Figure BDA0002983456120000185
the constraint conditions of the intra-day optimization scheduling problem comprise:
active power balance equation
Figure BDA0002983456120000191
Wind abandon restriction
Figure BDA0002983456120000192
Positive and negative standby power constraints
Figure BDA0002983456120000193
Figure BDA0002983456120000194
Figure BDA0002983456120000195
Figure BDA0002983456120000196
Wherein the content of the first and second substances,
Figure BDA0002983456120000197
is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,
Figure BDA0002983456120000198
the fluctuation interval is when the load fluctuates downwards and the wind speed fluctuates upwards;
in addition, the constraint conditions for optimizing the scheduling problem in the day further comprise: constraint inequalities (3) - (4) of the output and climbing power of a conventional generator, constraint inequalities (9) - (12) of a quick start-stop unit and constraint equations (15) - (16) of a flexible load;
the intra-day scheduling interval optimization problem objective function f of the power system described by the formula (25)2For the interval function, the upper and lower bounds are set to
Figure BDA0002983456120000199
Wherein:
Figure BDA00029834561200001910
setting the mean value of the interval objective function as
Figure BDA00029834561200001911
Radius of the objective function of
Figure BDA00029834561200001912
Converting an interval objective function to
Figure BDA00029834561200001913
β2For the weighting factor, β is taken here2＝0.1；
The interval possibility degrees of establishment of an active power balance equation (26), a standby constraint equation (28) and an equation (30) of the scheduling interval optimization problem in the day of the power system are respectively zeta21＝0.95、ζ220.99 and ζ230.99, according to the interval probability theory, formula (26), formula (28), and (30) can be converted to:
Figure BDA00029834561200001914
Figure BDA0002983456120000201
Figure BDA0002983456120000202
thus, the scheduling interval optimization problem in the day ahead can be converted into the deterministic problem as follows:
min F2 (35)
the constraints include formulae (3) to (4), formulae (9) to (12), formulae (15) to (16), formulae (27), (29), (31), and formulae (32) to (34); and (4) substituting the startup state maintenance of the conventional unit and the regulation and control quantity of the A-type flexible load obtained in the step (4) as boundary conditions for calculation, and solving the deterministic problem by using a mixed integer linear programming method to obtain an intra-day scheduling interval optimization scheme.
In this case, the economic and safety checks are carried out in the manner of the preceding exemplary embodiment.
Table 8 shows a comparison of the comprehensive performance of A, B in the two schemes, and as can be seen from table 8, the daily operating cost of the system in scheme B is significantly reduced, and the average value and the fluctuation range of the fluctuation interval are small, i.e., the economy of scheme B is better; meanwhile, the probability of the occurrence of the positive and negative standby shortages in the scheme B is lower than that in the scheme A, which shows that the day-ahead-day cooperative scheduling can fully utilize the multi-time scale characteristics of the generator set and the flexible load, effectively stabilize the amount of power unbalance caused by uncertainty of the new energy power and the load power, and can meet the economic and safety requirements.
TABLE 8 comparison of the comprehensive Performance of the day ahead scheduling and day ahead-in-day Co-scheduling schemes
Figure BDA0002983456120000203
A- - -a day-ahead scheduling scheme; b- -a day-ahead-day cooperative scheduling scheme;
the comparison graph of the total power generation amount of the conventional generator of the scheme A and the comparison graph of the air abandoning amount of the conventional generator of the scheme B is shown in figure 5, the comparison graph of the air abandoning amount of the conventional generator of the scheme B is shown in figure 6, and as can be seen from figures 5 and 6, the total power generation amount and the air abandoning amount of the conventional generator of the scheme B are both smaller than the total power generation amount and the air abandoning amount of the scheme A, which shows that the scheme B can consume more new energy, reduce energy waste and operation cost, relieve peak regulation pressure of the conventional generator set, reduce power generation cost of the conventional generator set, and improve economical efficiency of system operation.
A power balance schematic diagram of the scheme B system is shown in fig. 7, and as can be seen from fig. 7, the total generated power and the total load power of the scheduled system fluctuate within a certain interval, and the possibility that the generated power is greater than the load power is greater than a preset value in the whole scheduling period; under the scheduling scheme B, the interval probability that the system satisfies the power balance and the positive and negative standby constraints at each time is as shown in fig. 8, as can be seen from fig. 8, 0: 00-5: 00, because the wind power is sufficient in the time period, the main problem of the system is that the downward-adjusted standby power is insufficient, namely the system is easy to have insufficient negative standby; in the daytime 13: 00-17: 00, because the load power in the time period is higher, and the output of the wind power in the daytime is smaller, the problem of insufficient positive standby power of the system is more prominent.
According to an embodiment of another aspect of the present invention, in combination with the example shown in fig. 1, there is also provided a day-ahead-day cooperative scheduling system of an electric power system, for example, implemented in a manner of a server or a server array, including:
one or more processors;
a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing an implementation of the power system day-ahead-day co-scheduling method of any of the foregoing embodiments, and in particular the implementation of the embodiment of fig. 1.
In conclusion, the electric power system day-ahead-day internal cooperative scheduling method considering the uncertainty of the new energy and the load interval overcomes the technical defects that the probability distribution and the calculated amount of new energy and load uncertainty variables are required to be confirmed and the day-ahead scheduling scheme is not fine enough in the prior art, and on the basis of the prediction data of the new energy and the load day-ahead and day-internal, the operation cost of a generator, the wind curtailment of the new energy, the light curtailment penalty cost of the new energy and the flexible load and the cost required by the flexible load participating in the electric power system scheduling are comprehensively considered by utilizing the characteristic that the prediction error of the new energy and the load is reduced along with the reduction of the time scale on the basis of the flexibility of various units and the multi-time scale characteristic of the flexible load, so that an interval mathematical problem model of the day-ahead-day internal cooperative scheduling of the electric power system is constructed; by applying an interval optimization theory, an uncertain objective function and a constraint function are converted into a deterministic problem to be solved, and compared with an opportunity constraint planning method, the method has the advantages of low requirement on input data information, good decision flexibility, high calculation speed and the like; finally, simulation check is carried out on the day-ahead and day-in cooperative scheduling scheme, and it is verified that the day-ahead and day-in cooperative scheduling scheme provided by the invention can better absorb new energy, reduce resource waste, and give consideration to the economy and safety of system operation under the uncertain scene.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims (9)

1. A day-ahead and day-in cooperative scheduling method for a power system considering uncertainty of new energy and load intervals is characterized by comprising the following steps:
step 1, acquiring power system data and new energy and load day-ahead prediction data;
step 2, constructing a mathematical model of a scheduling interval optimization problem in the day ahead;
step 3, determining a day-ahead scheduling scheme and boundary conditions of day-in scheduling;
step 4, acquiring new energy and load rolling prediction data in the day; and
step 5, constructing a mathematical model of the in-day scheduling problem and solving the in-day scheduling scheme based on the boundary conditions of the in-day scheduling scheme determined in the step 3 and the model of the new energy and load in-day intervals acquired in the step 4;
the power system data comprises maximum and minimum output power of a conventional generator set and a quick start-stop unit, start-stop cost of the generator set, an operation cost coefficient, climbing power, minimum start-up and stop time, and A, B, C three types of flexible loads Pila、Pilb、PilcThe grade number and the elastic coefficient of each grade capable of reducing the maximum capacity, the cost coefficient, the demand response and the maximum accumulated interruption time of the load, wherein the class A flexible load needs to be informed to the user 24h in advance, the class B flexible load needs to be informed to the user 15min-2h in advance, and the class C flexible load needs to be informed to the user 5-15min in advance;
the new energy and load day-ahead prediction data comprise the output power P of a wind power plant and a photovoltaic power plant 24 hours in the futurewt、PpvThe predicted value per hour and the fluctuation interval of the prediction error before the day, and the system load P of 24 hours in the futurelThe predicted value of each hour and the fluctuation interval of the prediction error before the hour.
2. The method for day-ahead and day-inside collaborative scheduling of an electric power system considering uncertainty of a new energy and a load interval according to claim 1, wherein in the step 2, the process of constructing a mathematical model of a day-ahead scheduling interval optimization problem comprises:
the predicted value before the new energy power generation day is assumed to be
Figure FDA0002983456110000011
Its prediction error in the day ahead
Figure FDA0002983456110000012
Located in a section
Figure FDA0002983456110000013
The upper mark "+" represents the upper boundary of the interval number, and the upper mark "-" represents the lower boundary of the interval number, so that the new energy power generation power P is obtainedN1Has an upper bound of
Figure FDA0002983456110000014
Lower boundary is
Figure FDA0002983456110000015
PN1The model of the number of the day-ahead intervals is
Figure FDA0002983456110000016
So as to respectively establish a model of the number of the day-ahead intervals of wind power generation and photovoltaic power generation
Figure FDA0002983456110000017
Assume a predicted value of the load before the day is
Figure FDA0002983456110000018
Its prediction error in the day ahead
Figure FDA0002983456110000019
Located in a section
Figure FDA00029834561100000110
From which the load power P is derivedl1Has an upper bound of
Figure FDA00029834561100000111
Lower boundary is
Figure FDA00029834561100000112
The number of the load power in the day-ahead interval model is
Figure FDA00029834561100000113
Taking the time scale of day-ahead scheduling as 15 minutes and the time scale of day-in-day scheduling as 15 minutes, carrying out linear interpolation on the predicted values of new energy and load every hour, taking the linear interpolation as the day-ahead predicted value every 15 minutes, and carrying out day-ahead scheduling on the basis;
thus, the objective function of the power system day-ahead scheduling optimization problem is described as:
min f1＝f11+f12+f13+f14 (1)
wherein f is11Representing the operating costs of a conventional generator set, f12Representing the operating cost of the unit, f13Represents a wind curtailment penalty charge, f14The control cost is represented as A, B, C three types of flexible load;
meanwhile, determining the constraint conditions of the day-ahead scheduling optimization problem of the power system comprises the following steps:
active power balance;
restraining the output of a conventional generator and the climbing power;
a conventional generator minimum on-off time constraint;
the cold and hot start of the conventional generator set is restricted;
restraining the maximum and minimum power and the climbing power of the quick start-stop unit;
the minimum starting time and the minimum stopping time of the quick start-stop unit are restricted;
a flexible load constraint;
wind abandon restriction; and
positive and negative standby power constraints;
and (3) forming a mathematical model of the day-ahead scheduling interval optimization problem by the formula (1) and the nine constraint conditions.
3. The method of claim 2, wherein the expression of the objective function is a running cost f of a conventional generator set11Quick start-stop unit operation cost f12And represents a wind curtailment penalty cost f13And A, B, C control cost f of flexible load14The obtaining comprises the following steps:
1) operating costs f of conventional generator sets11：
Figure FDA0002983456110000021
Figure FDA0002983456110000022
Representing the starting and stopping cost of a conventional unit;
Figure FDA0002983456110000023
expressed as the cost of electricity generation;
wherein the content of the first and second substances,
Figure FDA0002983456110000024
is the number of conventional generators, T1For scheduling periods, T1＝96，
Figure FDA0002983456110000025
Respectively setting a starting control 0-1 variable and a cold starting control 0-1 variable of the ith conventional generator at the moment j;
Figure FDA0002983456110000026
a "1" indicates that the ith conventional generator receives a start or cold start command at time j,
Figure FDA0002983456110000027
if the value is 0, no starting or cold starting instruction is given at the moment j;
Chot,i、Ccold,ithe hot start cost and the cold start cost of the ith conventional generator respectively,
Figure FDA0002983456110000028
the variable is 0-1, the 1 indicates that the ith conventional generator is in a starting state at the moment j, and the 0 indicates that the ith conventional generator is in a stopping state;
Figure FDA0002983456110000031
for the power generation cost coefficient of the ith conventional generator,
Figure FDA0002983456110000032
the power of the ith conventional generator at the moment j;
Figure FDA0002983456110000033
in order to quickly start and stop the running cost of the unit,
Figure FDA0002983456110000034
the number of the generators is rapidly started;
Figure FDA0002983456110000035
for the operation cost coefficient of the ith quick start-stop unit,
Figure FDA0002983456110000036
the power of the ith station for quickly starting and stopping the unit at the moment j,
Figure FDA0002983456110000037
the variable 0-1 is controlled for starting the quick start-stop unit,
Figure FDA0002983456110000038
the number of the start-up and stop units is 1, the ith quick start-up and stop unit receives the start instruction at the moment j,
Figure FDA0002983456110000039
starting cost of the ith quick start-stop unit is saved;
Figure FDA00029834561100000310
penalizing costs for wind curtailment, wherein
Figure FDA00029834561100000311
Wind power cut down for moment j, CwA penalty factor for wind abandon;
Figure FDA00029834561100000312
the control expense for A, B, C three types of flexible loads, ns is the number of grades of the flexible loads, wherein
Figure FDA00029834561100000313
The regulation power of s gear of three flexible loads at the moment of j A, B, C respectively, and the response of the flexible loads has elasticity, so
Figure FDA00029834561100000314
The number of the intervals is the number of the intervals,
Figure FDA00029834561100000315
a, B, C, respectively, are cost factors when the flexible loads participate in regulation and control in the s-gear.
4. The electric power system day-ahead-day cooperative scheduling method considering uncertainty of new energy and load intervals according to claim 3, wherein the electric power system day-ahead scheduling optimization problem constraint condition comprises:
active power balance equation
Figure FDA00029834561100000316
In the formula (2), the left side is the sum of the conventional generator set, the quick start-stop unit and the wind power generation, the right side is the reduction of the total load of the system minus the 3 types of flexible loads, and the active power balance equation is an interval equation;
② restraining the output and climbing power of the conventional generator
Figure FDA00029834561100000317
Figure FDA00029834561100000318
In the formula (3)
Figure FDA00029834561100000319
The minimum and maximum power of the ith conventional generator respectively,
Figure FDA00029834561100000320
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith conventional generator;
third, the minimum on-off time constraint of the conventional generator
Figure FDA00029834561100000321
Figure FDA0002983456110000041
In formulas (5) and (6)
Figure FDA0002983456110000042
Respectively the minimum starting time and the minimum shutdown time of the ith conventional generator;
fourthly, restraint of cold and hot start of conventional generator set
Figure FDA0002983456110000043
Figure FDA0002983456110000044
Equations (7) and (8) are the constraint of the cold start and the hot start of the ith conventional generator at the moment j,
Figure FDA0002983456110000045
the cold start time of the ith conventional generator is set;
fast start-stop unit maximum and minimum power and climbing power constraint
Figure FDA0002983456110000046
Figure FDA0002983456110000047
Equation (9) is the minimum, maximum power constraint for a fast start generator,
Figure FDA0002983456110000048
respectively the minimum power and the maximum power of the ith quick start-stop unit,
Figure FDA0002983456110000049
the variable is 0-1, the 1 indicates that the ith quick unit is in a starting state at the moment j, and the 0 indicates that the ith quick unit is in a stopping state; the formula (10) is the power constraint of the rapid unit for climbing downwards and upwards,
Figure FDA00029834561100000410
respectively representing the limit values of the downward climbing power and the upward climbing power of the ith quick start-stop unit;
sixthly, quickly starting and stopping unit minimum starting time and minimum stopping time constraint
Figure FDA00029834561100000411
Figure FDA00029834561100000412
The formula (11) and the formula (12) are respectively the minimum starting time of the ith quick unit at the moment j
Figure FDA00029834561100000413
Minimum down time
Figure FDA00029834561100000414
The constraint of (a) to (b),
Figure FDA00029834561100000415
to stop the continuous running time of the ith fast unit at time j,
Figure FDA00029834561100000416
stopping the continuous shutdown time of the ith quick unit at the moment j;
seventh, the wind is abandoned to restrain
Figure FDA00029834561100000417
In the formula (13)
Figure FDA00029834561100000418
Respectively reducing the wind power at the moment j in the day-ahead scheduling and the lower bound of the wind power fluctuation interval;
flexible load constraint
Figure FDA00029834561100000419
Figure FDA00029834561100000420
Figure FDA0002983456110000051
Wherein, Pila,s,j、Pilb,s,j、Pilc,s,jIs j time AB, C the power is regulated and controlled by three kinds of flexible loads in s gear,
Figure FDA0002983456110000052
the maximum value of the regulated power of the s gear of A, B, C three types of flexible loads,
Figure FDA0002983456110000053
and
Figure FDA0002983456110000054
elastic fluctuation intervals of response coefficients of s-gear of A, B, C three types of flexible loads respectively;
ninthly positive and negative standby power constraints
Figure FDA0002983456110000055
Figure FDA0002983456110000056
Figure FDA0002983456110000057
Figure FDA0002983456110000058
Wherein the content of the first and second substances,
Figure FDA0002983456110000059
the positive standby power which can be provided by the conventional unit and the quick unit at the moment j, r is a standby coefficient,
Figure FDA00029834561100000510
Figure FDA00029834561100000511
the negative standby power which can be provided by the conventional unit and the quick unit at the moment j,
Figure FDA00029834561100000512
is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,
Figure FDA00029834561100000513
the fluctuation interval is when the load is downward and the wind speed is upward.
5. The method of claim 4, wherein the determining the day-ahead scheduling scheme and the boundary conditions of day-ahead scheduling comprises:
first, the power system day-ahead scheduling interval optimization problem objective function f described by equation (1)1For interval functions, the upper and lower bounds are set to f1 +、f1 -Wherein:
Figure FDA00029834561100000514
Figure FDA00029834561100000515
setting the mean value of the interval objective function as
Figure FDA00029834561100000516
Radius of the objective function of
Figure FDA00029834561100000517
Converting an interval objective function to F1＝(1-β1)f1 m+β1f1 w，β1Is a weighting coefficient;
then, the interval inequality constraints determined by the formula (2), the formula (17) and the formula (19) in the power system day-ahead scheduling interval optimization problem are converted into deterministic inequalities under the preset interval possibility, and the interval possibility with work power balance and the establishment of a standby constraint equation is zeta11、ζ12And ζ13Then, according to the interval probability principle, the equations (2), (17) and (19) are respectively converted into:
Figure FDA0002983456110000061
Figure FDA0002983456110000062
Figure FDA0002983456110000063
therefore, the day-ahead scheduling interval optimization problem is converted into the following deterministic problem:
min F1 (24)
the constraints of formula (24) include formulas (3) - (16), formulas (18), (20), and formulas (21) - (23);
finally, solving the deterministic problem by using a mixed integer linear programming method to obtain a day-ahead scheduling interval optimization scheme, thereby determining boundary conditions of the scheduling problem in the day, namely: the starting state of the conventional unit is kept unchanged, the regulation and control quantity of the A-type flexible load is kept unchanged, and the starting and stopping state of the quick start-stop unit and the flexible loads of the B, C-type flexible loads need to be adjusted in daily scheduling.
6. The method of claim 5, wherein obtaining new energy and load intra-day rolling prediction data comprises obtaining new energy and load intra-day rolling prediction data
Carrying out 1-time ultra-short-term prediction on wind power, photovoltaic power and load power for 2 hours in the future every 15 minutes, wherein the time scale is 15 minutes;
the predicted value in the new energy power generation day is assumed to be
Figure FDA0002983456110000064
Error of prediction in day
Figure FDA0002983456110000065
Located in a section
Figure FDA0002983456110000066
In the method, the new energy power generation power P is obtainedN2Has an upper bound of
Figure FDA0002983456110000067
Lower boundary is
Figure FDA0002983456110000068
PN2The model of the number of intervals in the day is
Figure FDA0002983456110000069
The model of the day-to-day interval digital model of wind power and photovoltaic power generation is established
Figure FDA00029834561100000610
Assume a predicted value of load in the day of
Figure FDA00029834561100000611
Error of prediction in day
Figure FDA00029834561100000612
Located in a section
Figure FDA00029834561100000613
From which the load power P is derivedl2Has an upper bound of
Figure FDA00029834561100000614
Lower boundary is
Figure FDA00029834561100000615
The number of intervals per day model of the load power is
Figure FDA00029834561100000616
7. The electric power system day-ahead-day cooperative scheduling method considering uncertainty of new energy and load intervals as claimed in claim 5, wherein the constructing a mathematical model of a day-ahead scheduling problem and solving a day-ahead scheduling scheme comprises:
establishing a mathematical model of the intraday optimization scheduling problem, wherein an objective function is as follows:
f2＝f21+f22+f23+f24 (25)
Figure FDA0002983456110000071
for the cost of power generation of conventional generator sets, T2＝8；
Figure FDA0002983456110000072
The running cost of the unit is quickly started and stopped;
Figure FDA0002983456110000073
cost for wind abandon;
Figure FDA0002983456110000074
determining the constraint condition of the optimization scheduling problem in the day, comprising the following steps:
active power balance;
wind abandon restriction;
positive and negative standby power constraints;
restraining the output of a conventional generator and the climbing power;
restraining the quick start-stop unit; and
flexible load restraint.
8. The electric power system day-ahead-day cooperative scheduling method taking into account uncertainty of new energy and load interval according to claim 7, wherein the constraint condition of the day-ahead optimization scheduling problem comprises:
active power balance equation
Figure FDA0002983456110000075
Wind abandon restriction
Figure FDA0002983456110000076
Positive and negative standby power constraints
Figure FDA0002983456110000077
Figure FDA0002983456110000078
Figure FDA0002983456110000079
Figure FDA00029834561100000710
Wherein the content of the first and second substances,
Figure FDA00029834561100000711
is a fluctuation interval when the load fluctuates upwards and the wind speed fluctuates downwards,
Figure FDA0002983456110000081
the fluctuation interval is when the load fluctuates downwards and the wind speed fluctuates upwards;
conventional generator contribution and hill climb power constraints, i.e. constraints determined by said equations (3) - (4);
a fast start-stop train constraint, i.e. a constraint determined by said equations (9) - (12); and
a flexible load constraint equation, i.e., a constraint determined by the equations (15) - (16);
thus, the power system intra-day scheduling interval optimization problem objective function f described by equation (25)2For the interval function, the upper and lower bounds are set to
Figure FDA0002983456110000082
Wherein:
Figure FDA0002983456110000083
Figure FDA0002983456110000084
setting the mean value of the interval objective function as
Figure FDA0002983456110000085
Radius of the objective function of
Figure FDA0002983456110000086
Converting an interval objective function to
Figure FDA0002983456110000087
β2Is a weighting coefficient;
the interval possibility degrees of establishment of an active power balance equation (26), a standby power constraint equation (28) and a standby power constraint equation (30) of the scheduling interval optimization problem in the day of the power system are respectively zeta21、ζ22And ζ23Equations (26), (28) and (30) are converted to:
Figure FDA0002983456110000088
Figure FDA0002983456110000089
Figure FDA00029834561100000810
then, according to the interval probability principle, converting the day-ahead scheduling interval optimization problem into the following deterministic problem:
min F2 (35)
the constraints include formulae (3) to (4), formulae (9) to (12), formulae (15) to (16), formulae (27), (29), (31), and formulae (32) to (34);
substituting the startup state maintenance of the conventional unit and the regulation and control quantity of the A-type flexible load obtained in the step 4 into a boundary condition for calculation, and solving the deterministic problem by using a mixed integer linear programming method to obtain an intra-day scheduling interval optimization scheme;
and generating a day-ahead and day-inside cooperative scheduling scheme at the time k, further judging whether k reaches 96, if so, outputting the day-ahead and day-inside cooperative scheduling scheme, otherwise, making k equal to k +1, and returning to the step 4 for processing until k reaches 96.
9. A day-ahead-day cooperative scheduling system for a power system, which takes new energy and load interval uncertainty into account, is characterized by comprising:
one or more processors;
a memory storing instructions that are operable, when executed by the one or more processors, to cause the one or more processors to perform operations comprising performing a process of any one of claims 1-8.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114050609A (en) * 2021-11-05 2022-02-15 国网江苏省电力有限公司盐城供电分公司 Adaptive robust day-ahead optimal scheduling method for high-proportion new energy power system
CN114050570A (en) * 2021-11-17 2022-02-15 许继集团有限公司 Source-grid load-storage system cooperative regulation and control method and device
CN114156883A (en) * 2021-12-09 2022-03-08 国网(苏州)城市能源研究院有限责任公司 Power and standby cooperative optimization modeling method considering source-load double randomness
CN114336783A (en) * 2022-01-07 2022-04-12 国网能源研究院有限公司 City and county new energy consumption optimization method and system
CN115828497A (en) * 2022-09-21 2023-03-21 中国电力科学研究院有限公司 Monte Carlo and interval number fused power grid network congestion analysis method and system
CN116454924A (en) * 2023-06-16 2023-07-18 华东交通大学 Flexible load power regulation and control method based on correlation coefficient
CN114156883B (en) * 2021-12-09 2024-04-26 国网(苏州)城市能源研究院有限责任公司 Modeling method for power and standby collaborative optimization considering source-load dual randomness

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013128953A1 (en) * 2012-02-27 2013-09-06 株式会社 東芝 Optimization apparatus, optimization method, and optimization program for storing electricity and heat.
CN111641233A (en) * 2020-05-25 2020-09-08 国网江苏省电力有限公司 Electric power system day-based flexible peak regulation method considering new energy and load uncertainty

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2013128953A1 (en) * 2012-02-27 2013-09-06 株式会社 東芝 Optimization apparatus, optimization method, and optimization program for storing electricity and heat.
CN111641233A (en) * 2020-05-25 2020-09-08 国网江苏省电力有限公司 Electric power system day-based flexible peak regulation method considering new energy and load uncertainty

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN114050609B (en) * 2021-11-05 2023-11-24 国网江苏省电力有限公司盐城供电分公司 Adaptive robust day-ahead optimization scheduling method for high-proportion new energy power system
CN114050570A (en) * 2021-11-17 2022-02-15 许继集团有限公司 Source-grid load-storage system cooperative regulation and control method and device
CN114050570B (en) * 2021-11-17 2024-03-01 许继集团有限公司 Collaborative regulation and control method and device for source network charge storage system
CN114156883A (en) * 2021-12-09 2022-03-08 国网(苏州)城市能源研究院有限责任公司 Power and standby cooperative optimization modeling method considering source-load double randomness
CN114156883B (en) * 2021-12-09 2024-04-26 国网(苏州)城市能源研究院有限责任公司 Modeling method for power and standby collaborative optimization considering source-load dual randomness
CN114336783A (en) * 2022-01-07 2022-04-12 国网能源研究院有限公司 City and county new energy consumption optimization method and system
CN114336783B (en) * 2022-01-07 2022-09-27 国网能源研究院有限公司 City and county new energy consumption optimization method and system
CN115828497A (en) * 2022-09-21 2023-03-21 中国电力科学研究院有限公司 Monte Carlo and interval number fused power grid network congestion analysis method and system
CN115828497B (en) * 2022-09-21 2023-11-28 中国电力科学研究院有限公司 Power grid network blocking analysis method and system integrating Monte Carlo and interval number
CN116454924A (en) * 2023-06-16 2023-07-18 华东交通大学 Flexible load power regulation and control method based on correlation coefficient
CN116454924B (en) * 2023-06-16 2023-09-29 华东交通大学 Flexible load power regulation and control method based on correlation coefficient

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