CN112381424A - Multi-time scale active power optimization decision method for uncertainty of new energy and load - Google Patents

Multi-time scale active power optimization decision method for uncertainty of new energy and load Download PDF

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CN112381424A
CN112381424A CN202011289397.2A CN202011289397A CN112381424A CN 112381424 A CN112381424 A CN 112381424A CN 202011289397 A CN202011289397 A CN 202011289397A CN 112381424 A CN112381424 A CN 112381424A
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李文博
张世栋
张林利
刘洋
王峰
黄敏
刘合金
苏国强
李帅
张鹏平
由新红
艾芊
孙子茹
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Shandong Electric Power Co Ltd
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Abstract

A multi-time scale active optimization decision method for new energy and load uncertainty is characterized in that various adjustable and controllable resources including a new energy unit, an adjustable and controllable load, an energy storage system and the like are used for participating in active optimization regulation and control of a power grid, the problem of insufficient flexibility of a conventional active scheduling method is solved, and the utilization efficiency of the adjustable and controllable resources is improved. The uncertainty of active power optimization scheduling is responded to in a multi-time scale by using the differences of the response speed, the response cost and the like of the adjustable load, so that the economical efficiency and the flexibility of the operation of the power grid are improved.

Description

Multi-time scale active power optimization decision method for uncertainty of new energy and load
Technical Field
The invention relates to the technical field of active power optimization scheduling of a power system, in particular to a multi-time scale active power optimization decision method for uncertainty of new energy and load.
Background
In a traditional active power optimization scheduling method, most common is to use traditional units such as a thermal power plant to perform regulation and control. However, with the grid-connected penetration of various controllable resources such as a new energy generator set, an energy storage device, a controllable load and the like, the operation and control mode of a power grid becomes more complex, the traditional active optimization scheduling method cannot meet the operation requirements of the power grid such as the flexibility requirement of scheduling, and after the new energy such as wind, light and the like is connected to the grid, the randomness and the intermittent characteristic of the method increase the volatility and the uncertainty of the power system, the safe and economic operation of the power grid is influenced, and the conventional scheduling cannot better cope with the uncertainty of the new energy grid-connected.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides a method for participating in the active power optimization regulation and control of a power grid under multiple time scales by utilizing multiple adjustable and controllable resources, which can effectively deal with the uncertainty of new energy and load and improve the economy and flexibility of the power grid.
The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a multi-time scale active power optimization decision method for new energy and load uncertainty is characterized by comprising the following steps:
a) establishing a day-ahead scheduling model of the power system comprising the wind turbine generator, the conventional unit, the CAES and the flexible load according to active balance constraint, rotation reserve capacity constraint, conventional unit operation constraint, pumped storage power station constraint and transferable load constraint, and obtaining the start-stop state u of the conventional unit by taking the lowest day-ahead operation cost as an objective function and the lowest day-in operation cost as an objective functionGi,tRotational standby plan
Figure BDA0002781362010000011
Transferable load scheduling plan
Figure BDA0002781362010000012
b) Starting and stopping states u of conventional unitGi,tRotational standby plan
Figure BDA0002781362010000013
Transferable load scheduling plan
Figure BDA0002781362010000014
Performing rolling optimization in the day to obtain the output plan of the conventional unit
Figure BDA0002781362010000015
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA0002781362010000021
c) In the real-time phase, the output plan of the conventional unit is utilized
Figure BDA0002781362010000022
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA0002781362010000023
And utilizing a model prediction control method to adjust and correct the active scheduling of each adjustable resource in real time by taking the minimum power grid adjustment cost as a target to obtain an AGC unit output plan
Figure BDA0002781362010000024
Generating power of pumped storage power station
Figure BDA0002781362010000025
Pumping power of pumping storage power station
Figure BDA0002781362010000026
Electrochemical energy storage dispatch plan
Figure BDA0002781362010000027
DLC load scheduling plan
Figure BDA0002781362010000028
And wind abandon plan
Figure BDA0002781362010000029
Further, step a) is based on the formula
Figure BDA00027813620100000210
Calculating an objective function of the formula
Figure BDA00027813620100000211
For the ith conventional unit output in the day-ahead scheduling,
Figure BDA00027813620100000212
for the xth transferable load transfer in the day-ahead schedule,
Figure BDA00027813620100000213
for the xth transferable load transfer-out amount in the day-ahead schedule,
Figure BDA00027813620100000214
for scheduling the air volume to be abandoned in the day, bGi,cGiIs the power generation cost coefficient of the ith conventional unit, SGiFor the i-th conventional unit start-up cost, SpuGkFor the power generation of the kth pumped-storage power station, SpuPkStarting cost for pumped-storage state of kth pumped-storage power station, uGi,tStarting and stopping the ith conventional unit at the moment t, uGi,t-1The ith conventional unit is in a start-stop state at the moment of t-1, upuPk,tBinary variable u for pumping state of kth pumped storage power station at time tpuPk,t-1A binary variable, u, for the pumped-storage state of the kth pumped-storage power station at time t-1puGk,tA binary variable u of the generating state of the kth pumped storage power station at the time tpuGk,t-1Is a binary variable of the power generation state of the kth pumped-storage power station at the moment t-1,
Figure BDA0002781362010000031
to shift the load into a cost factor,
Figure BDA0002781362010000032
to convert cost coefficients, xiWIn order to obtain the cost coefficient of the waste wind,
Figure BDA0002781362010000033
providing a positive rotation reserve capacity cost coefficient for the ith conventional unit,
Figure BDA0002781362010000034
providing a negative spinning reserve capacity cost factor for the ith conventional unit,
Figure BDA0002781362010000035
the positive rotation reserve capacity provided for the ith conventional unit,
Figure BDA0002781362010000036
negative rotation reserve capacity provided for ith conventional unit, T is total scheduling duration, NGNumber of conventional units, NpuNumber of energy stored for pumping, NLThe number of transferable loads is delta t, and the unit scheduling time length is delta t;
by the formula
Figure BDA0002781362010000037
Establishing an active balance constraint in which
Figure BDA0002781362010000038
Pumping power at the moment t before the kth pumped storage power station,
Figure BDA0002781362010000039
generating power for the kth pumped storage power station at the moment t in the day ahead,
Figure BDA00027813620100000310
is the day-ahead predicted value of the wind power,
Figure BDA00027813620100000311
the predicted value of the load before the day;
by the formula
Figure BDA00027813620100000313
Establishing a rotational reserve capacity constraint, wherein fCr{. is a representation of a fuzzy function,
Figure BDA00027813620100000314
a positive error is predicted for the wind power day ahead,
Figure BDA00027813620100000315
the negative error is predicted for the wind power in the day ahead,
Figure BDA00027813620100000316
a positive error is predicted for the load demand ahead of day,
Figure BDA00027813620100000317
predicting a negative error for a load demand ahead of date;
by the formula
PGi,min≤PGi,t≤PGi,max
Figure BDA0002781362010000041
Figure BDA0002781362010000042
Figure BDA0002781362010000043
Figure BDA0002781362010000044
Figure BDA0002781362010000045
Figure BDA0002781362010000046
Figure BDA0002781362010000047
Figure BDA0002781362010000048
Establishing conventional unit operation constraints of formula, PGi,minIs the minimum output, P, of the conventional unit iGi,maxIs the maximum output, P, of the conventional unit iGi,tFor the ith conventional unit in the scheduling to output power at the time t, PGi,t-1In order to output power for the ith conventional unit at the moment t-1 in the dispatching process,
Figure BDA0002781362010000049
is the climbing rate of the conventional unit,
Figure BDA00027813620100000410
the slip ratio of the conventional unit is the slip ratio,
Figure BDA00027813620100000411
for the duration of the operation of the conventional unit before time t,
Figure BDA00027813620100000412
for the duration of the shutdown of the conventional unit before time t,
Figure BDA00027813620100000413
for the minimum run time of a conventional unit,
Figure BDA00027813620100000414
is the minimum down time, Δ t, of a conventional unitRFor adjusting the response time in rotating reserve capacity, NAGCIs the total number of the AGC units,
Figure BDA00027813620100000415
in order to start and stop the AGC unit at the time t,
Figure BDA00027813620100000416
is the maximum output of the AGC machine set,
Figure BDA00027813620100000417
is the minimum output of the AGC unit,
Figure BDA00027813620100000418
for the AGC unit to output power at the time t,
Figure BDA00027813620100000419
the positive rotation reserve capacity provided for the AGC units,
Figure BDA00027813620100000420
negative for AGC setReserve capacity of rotation, Δ tAAdjusting the response duration, V, of the spinning reserve capacity for an AGC plantAGC,tCapacity requirements are adjusted for AGC expected during the scheduling phase of the day ahead,
Figure BDA00027813620100000421
is the slope climbing rate of the AGC unit,
Figure BDA00027813620100000422
the slip rate of the AGC unit is obtained;
by the formula
upuP,tPpuP,min≤PpuP,t≤upuP,tPpuP,max
upuG,tPpuG,min≤PpuG,t≤upuG,tPpuG,max
upuP,t+upuG,t≤1
Figure BDA0002781362010000051
Figure BDA0002781362010000052
Figure BDA0002781362010000053
TpuG,t≥TpuG,min
TpuP,t≥TpuP,min
TpuSTOP,t≥TpuSTOP,min
Figure BDA0002781362010000054
Figure BDA0002781362010000055
Establishing pumped storage power station constraints of the formulapuP,tBinary variable, u, for pumped storage power station pumped state at time tpuG,tFor binary variables, P, of the power generation state of pumped-storage power stations at time tpuP,tFor pumped storage power stations, pumping power at time t, PpuG,tGenerating power for pumped storage power station at time t, PpuP,minIs the lower limit of pumping power, PpuP,maxUpper limit of pumping power, PpuG,minTo lower limit of generated power, PpuG,maxUpper limit of generated power, Vu,tAt time t, upper reservoir capacity, Vu,t+1Upper reservoir capacity at time t +1, Vd,tAt t moment, reservoir capacity, Vd,t+1At t +1 moment, PpuGk,tPumping power for the kth pumped-storage power station at time t, PpuPk,tGenerating power eta for the kth pumped storage power station at time tpuGWater/electricity conversion ratio in the power generation state, etapuPWater quantity/electric quantity conversion ratio V in the water pumping stateu,minIs the minimum storage capacity, V, of the upper reservoiru,maxIs the maximum storage capacity of the upper reservoir, Vd,minIs the minimum storage capacity of the lower reservoir, Vd,maxIs the maximum storage capacity, eta, of the lower reservoirpuFor pumped storage efficiency, TpuG,tFor the duration of power generation, TpuP,tFor a long duration of pumping water, TpuSTOP,tFor a continuous period of outage, TpuG,minFor a minimum duration of power generation, TpuP,minFor minimum duration of pumping, TpuSTOP,minTo a minimum duration of continuous outage, Vpu,tAdjusting capacity demand, P, for AGC predicted for a scheduling phase in the day aheadpuPk,tPumping power for the kth pumped-storage power station at time t, PpuGk,tGenerating power for the kth pumped storage power station at the moment t;
by the formula
Figure BDA0002781362010000061
Figure BDA0002781362010000062
Figure BDA0002781362010000063
Establishing transferable load constraints, wherein
Figure BDA0002781362010000071
A transfer-in state of the transferable load for the non-transfer period,
Figure BDA0002781362010000072
in the transition-out state of the transferable load for the non-transfer period,
Figure BDA0002781362010000073
in order for the load to be in the transition state,
Figure BDA0002781362010000074
in order that the load is in the roll-out state,
Figure BDA0002781362010000075
for the upper limit constraint of the load transfer amount of the transferable load,
Figure BDA0002781362010000076
for the lower limit constraint of the load transfer amount of the transferable load,
Figure BDA0002781362010000077
for the upper limit constraint of the amount of load transfer that can be transferred,
Figure BDA0002781362010000078
for the lower limit constraint of the amount of load transfer that can be transferred,
Figure BDA0002781362010000079
for the transfer amount of the transferable load at time t,
Figure BDA00027813620100000710
is rotatable at time tAmount of transfer of load, PTL,x,maxConstrained by the upper limit of the total transfer amount, TdA set of transferable periods allowed for the system.
Further, the starting and stopping state u of the conventional unit is utilized in the step b)Gi,tRotational standby plan
Figure BDA00027813620100000711
Transferable load scheduling plan
Figure BDA00027813620100000712
Aiming at the lowest day operation cost, respectively optimizing the active output of each unit in the next hour by taking the active balance constraint, the rotation reserve capacity constraint, the conventional unit operation constraint, the power storage station constraint and the interruptible load constraint as constraint conditions according to the updated new energy and the load day prediction result to obtain the conventional unit output plan
Figure BDA00027813620100000713
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA00027813620100000714
Wherein the objective function of the intraday roll optimization is
Figure BDA00027813620100000715
In the formula cILIn order to be able to interrupt the load scheduling cost,
Figure BDA00027813620100000716
in order to reduce the amount of interruptible load reduction,
Figure BDA00027813620100000717
for the air volume abandoning in the day, the rotating reserve capacity constraint in the rolling optimization in the day is
Figure BDA0002781362010000082
In the formula
Figure BDA0002781362010000083
The positive rotation reserve capacity already used for this phase,
Figure BDA0002781362010000084
the negative spinning reserve capacity already used for this stage,
Figure BDA0002781362010000085
for predicting the forward error in the wind energy day,
Figure BDA0002781362010000086
for predicting negative errors in the wind energy day,
Figure BDA0002781362010000087
a forward error is predicted for the load demand,
Figure BDA0002781362010000088
predicting negative error for load demand, interruptible load constraint in rolling optimization within a day as
Figure BDA00027813620100000810
In the formula
Figure BDA00027813620100000811
For binary variables with interruptible loads in a curtailed state,
Figure BDA00027813620100000812
for the upper limit constraint on the amount of interruptible load reduction,
Figure BDA00027813620100000813
for the lower limit constraint of the interruptible load reduction amount,
Figure BDA00027813620100000814
the maximum reduction amount of the interruptible load in the scheduling period.
Further, step c) comprises the steps of:
c-1) selecting the controllable resources with quick response according to the regulation characteristics and evaluation indexes of the controllable resources in the power transmission network, wherein the controllable resources comprise a new energy source unit, an AGC unit, a pumped storage power station, electrochemical energy storage and DLC load for real-time scheduling;
c-2) according to the historical active information of the controllable resources { Pt-τ,Δut-τL tau is more than or equal to 1 and input future active power regulation and control information (delta u)t+τ-1Predicting future active response of power grid (P)t+τ}，
Figure BDA00027813620100000815
Figure BDA00027813620100000816
Control disturbance for issued commands;
c-3) passing through a formula according to the priority H and Q of the controllable resources
Figure BDA0002781362010000091
The optimal performance index that the future prediction output and the reference output track have the minimum deviation and the minimum power grid regulation cost is obtained,
Figure BDA0002781362010000092
for decision variable reference values, Deltau, derived from a given scheduling reference linet+τFor the input future active regulation and control information, the vector thereof comprises the output adjustment quantity of the AGC unit
Figure BDA0002781362010000093
Generated power adjustment amount of pumped storage power station
Figure BDA0002781362010000094
Pumping power regulation for pumping power station
Figure BDA0002781362010000095
Electrochemical energy storage discharge power adjustment
Figure BDA0002781362010000096
Electrochemical energy storage chargingAmount of power adjustment
Figure BDA0002781362010000097
Amount of waste air adjustment
Figure BDA0002781362010000098
DLC load scheduling increase adjustment
Figure BDA0002781362010000099
DLC load scheduling reduction adjustment
Figure BDA00027813620100000910
Xt+τRepresenting the scheduling command by a decision variable vector, wherein the vector representation comprises AGC unit output
Figure BDA00027813620100000911
Generating power of pumped storage power station
Figure BDA00027813620100000912
Pumping power of pumping storage power station
Figure BDA00027813620100000913
Electrochemical energy storage discharge power
Figure BDA00027813620100000914
Electrochemical energy storage charging power
Figure BDA00027813620100000915
DLC load scheduling increment
Figure BDA00027813620100000916
DLC load reduction
Figure BDA00027813620100000917
And abandon the amount of wind
Figure BDA00027813620100000918
c-4) observing and updating the running state of the system, and sampling the measured value
Figure BDA00027813620100000919
Combining disturbance errors sigma in the sampling processpPrediction model P as a feedback pair0tThe correction is carried out so that the correction is carried out,
Figure BDA00027813620100000920
c-5) returning to the step c-1) until the prediction is finished.
The invention has the beneficial effects that: the method has the advantages that various adjustable resources including a new energy unit, an adjustable load, an energy storage system and the like are used for participating in the active optimization regulation of the power grid, the problem of insufficient flexibility of a conventional active scheduling method is solved, and the utilization efficiency of the adjustable resources is improved. The uncertainty of active power optimization scheduling is responded to in a multi-time scale by using the differences of the response speed, the response cost and the like of the adjustable load, so that the economical efficiency and the flexibility of the operation of the power grid are improved.
Detailed Description
The present invention is further explained below.
A multi-time scale active power optimization decision method for uncertainty of new energy and load comprises the following steps:
a) establishing a day-ahead scheduling model of the power system comprising the wind turbine generator, the conventional unit, the CAES and the flexible load according to active balance constraint, rotation reserve capacity constraint, conventional unit operation constraint, pumped storage power station constraint and transferable load constraint, and obtaining the start-stop state u of the conventional unit by taking the lowest day-ahead operation cost as an objective function and the lowest day-in operation cost as an objective functionGi,tRotational standby plan
Figure BDA0002781362010000101
Transferable load scheduling plan
Figure BDA0002781362010000102
b) Starting and stopping states u of conventional unitGi,tRotational standby plan
Figure BDA0002781362010000103
Transferable load scheduling plan
Figure BDA0002781362010000104
Performing rolling optimization in the day to obtain the output plan of the conventional unit
Figure BDA0002781362010000105
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA0002781362010000106
c) In the real-time phase, the output plan of the conventional unit is utilized
Figure BDA0002781362010000107
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA0002781362010000108
And utilizing a model prediction control method to adjust and correct the active scheduling of each adjustable resource in real time by taking the minimum power grid adjustment cost as a target to obtain an AGC unit output plan
Figure BDA0002781362010000109
Generating power of pumped storage power station
Figure BDA00027813620100001010
Pumping power of pumping storage power station
Figure BDA00027813620100001011
Electrochemical energy storage dispatch plan
Figure BDA00027813620100001012
DLC load scheduling plan
Figure BDA00027813620100001013
And wind abandon plan
Figure BDA00027813620100001014
The method has the advantages that various adjustable resources including a new energy unit, an adjustable load, an energy storage system and the like are used for participating in the active optimization regulation of the power grid, the problem of insufficient flexibility of a conventional active scheduling method is solved, and the utilization efficiency of the adjustable resources is improved. The uncertainty of active power optimization scheduling is responded to in a multi-time scale by using the differences of the response speed, the response cost and the like of the adjustable load, so that the economical efficiency and the flexibility of the operation of the power grid are improved.
Further, step a) is based on the formula
Figure BDA0002781362010000112
Calculating an objective function of the formula
Figure BDA0002781362010000113
For the ith conventional unit output in the day-ahead scheduling,
Figure BDA0002781362010000114
for the xth transferable load transfer in the day-ahead schedule,
Figure BDA0002781362010000115
for the xth transferable load transfer-out amount in the day-ahead schedule,
Figure BDA0002781362010000116
for scheduling the air volume to be abandoned in the day, bGi,cGiIs the power generation cost coefficient of the ith conventional unit, SGiFor the i-th conventional unit start-up cost, SpuGkFor the power generation of the kth pumped-storage power station, SpuPkStarting cost for pumped-storage state of kth pumped-storage power station, uGi,tStarting and stopping the ith conventional unit at the moment t, uGi,t-1The ith conventional unit is in a start-stop state at the moment of t-1, upuPk,tBinary variable u for pumping state of kth pumped storage power station at time tpuPk,t-1Binary system for pumping state of kth pumped storage power station at t-1 momentVariable upuGk,tA binary variable u of the generating state of the kth pumped storage power station at the time tpuGk,t-1Is a binary variable of the power generation state of the kth pumped-storage power station at the moment t-1,
Figure BDA0002781362010000117
to shift the load into a cost factor,
Figure BDA0002781362010000118
to convert cost coefficients, xiWIn order to obtain the cost coefficient of the waste wind,
Figure BDA0002781362010000119
providing a positive rotation reserve capacity cost coefficient for the ith conventional unit,
Figure BDA00027813620100001110
providing a negative spinning reserve capacity cost factor for the ith conventional unit,
Figure BDA00027813620100001111
the positive rotation reserve capacity provided for the ith conventional unit,
Figure BDA00027813620100001112
negative rotation reserve capacity provided for ith conventional unit, T is total scheduling duration, NGNumber of conventional units, NpuNumber of energy stored for pumping, NLThe number of transferable loads is delta t, and the unit scheduling time length is delta t;
by the formula
Figure BDA0002781362010000121
Establishing an active balance constraint in which
Figure BDA0002781362010000122
Pumping power at the moment t before the kth pumped storage power station,
Figure BDA0002781362010000123
generating power for the kth pumped storage power station at the moment t in the day ahead,
Figure BDA0002781362010000124
is the day-ahead predicted value of the wind power,
Figure BDA0002781362010000125
the predicted value of the load before the day;
by the formula
Figure BDA0002781362010000127
Establishing a rotational reserve capacity constraint, wherein fCr{. is a representation of a fuzzy function,
Figure BDA0002781362010000128
a positive error is predicted for the wind power day ahead,
Figure BDA0002781362010000129
the negative error is predicted for the wind power in the day ahead,
Figure BDA00027813620100001210
a positive error is predicted for the load demand ahead of day,
Figure BDA00027813620100001211
predicting a negative error for a load demand ahead of date;
by the formula
PGi,min≤PGi,t≤PGi,max
Figure BDA0002781362010000131
Figure BDA0002781362010000132
Figure BDA0002781362010000133
Figure BDA0002781362010000134
Figure BDA0002781362010000135
Figure BDA0002781362010000136
Figure BDA0002781362010000137
Figure BDA0002781362010000138
Establishing conventional unit operation constraints of formula, PGi,minIs the minimum output, P, of the conventional unit iGi,maxIs the maximum output, P, of the conventional unit iGi,tFor the ith conventional unit in the scheduling to output power at the time t, PGi,t-1In order to output power for the ith conventional unit at the moment t-1 in the dispatching process,
Figure BDA0002781362010000139
is the climbing rate of the conventional unit,
Figure BDA00027813620100001310
the slip ratio of the conventional unit is the slip ratio,
Figure BDA00027813620100001311
for the duration of the operation of the conventional unit before time t,
Figure BDA00027813620100001312
for the duration of the shutdown of the conventional unit before time t,
Figure BDA00027813620100001313
for the minimum run time of a conventional unit,
Figure BDA00027813620100001314
is the minimum down time, Δ t, of a conventional unitRFor adjusting the response time in rotating reserve capacity, NAGCIs the total number of the AGC units,
Figure BDA00027813620100001315
in order to start and stop the AGC unit at the time t,
Figure BDA00027813620100001316
is the maximum output of the AGC machine set,
Figure BDA00027813620100001317
is the minimum output of the AGC unit,
Figure BDA00027813620100001318
for the AGC unit to output power at the time t,
Figure BDA00027813620100001319
the positive rotation reserve capacity provided for the AGC units,
Figure BDA00027813620100001320
negative spinning reserve capacity, Δ t, for AGC unitsAAdjusting the response duration, V, of the spinning reserve capacity for an AGC plantAGC,tCapacity requirements are adjusted for AGC expected during the scheduling phase of the day ahead,
Figure BDA00027813620100001321
is the slope climbing rate of the AGC unit,
Figure BDA00027813620100001322
the slip rate of the AGC unit is obtained;
by the formula
upuP,tPpuP,min≤PpuP,t≤upuP,tPpuP,max
upuG,tPpuG,min≤PpuG,t≤upuG,tPpuG,max
upuP,t+upuG,t≤1
Figure BDA0002781362010000141
Figure BDA0002781362010000142
Figure BDA0002781362010000143
TpuG,t≥TpuG,min
TpuP,t≥TpuP,min
TpuSTOP,t≥TpuSTOP,min
Figure BDA0002781362010000144
Figure BDA0002781362010000145
Establishing pumped storage power station constraints of the formulapuP,tBinary variable, u, for pumped storage power station pumped state at time tpuG,tFor binary variables, P, of the power generation state of pumped-storage power stations at time tpuP,tFor pumped storage power stations, pumping power at time t, PpuG,tGenerating power for pumped storage power station at time t, PpuP,minIs the lower limit of pumping power, PpuP,maxUpper limit of pumping power, PpuG,minTo lower limit of generated power, PpuG,maxUpper limit of generated power, Vu,tAt time t, upper reservoir capacity, Vu,t+1Upper reservoir capacity at time t +1, Vd,tAt t moment, reservoir capacity, Vd,t+1At t +1 moment, PpuGk,tPumping power for the kth pumped-storage power station at time t, PpuPk,tGenerating power eta for the kth pumped storage power station at time tpuGWater/electricity conversion ratio in the power generation state, etapuPWater quantity/electric quantity conversion ratio V in the water pumping stateu,minIs the minimum storage capacity, V, of the upper reservoiru,maxIs the maximum storage capacity of the upper reservoir, Vd,minIs the minimum storage capacity of the lower reservoir, Vd,maxIs the maximum storage capacity, eta, of the lower reservoirpuFor pumped storage efficiency, TpuG,tFor the duration of power generation, TpuP,tFor a long duration of pumping water, TpuSTOP,tFor a continuous period of outage, TpuG,minFor a minimum duration of power generation, TpuP,minFor minimum duration of pumping, TpuSTOP,minTo a minimum duration of continuous outage, Vpu,tAdjusting capacity demand, P, for AGC predicted for a scheduling phase in the day aheadpuPk,tPumping power for the kth pumped-storage power station at time t, PpuGk,tGenerating power for the kth pumped storage power station at the moment t;
by the formula
Figure BDA0002781362010000151
Figure BDA0002781362010000152
Figure BDA0002781362010000153
Establishing transferable load constraints, wherein
Figure BDA0002781362010000161
A transfer-in state of the transferable load for the non-transfer period,
Figure BDA0002781362010000162
roll-out of transferable loads for non-transfer periodsThe state of the optical disk is changed into a state,
Figure BDA0002781362010000163
in order for the load to be in the transition state,
Figure BDA0002781362010000164
in order that the load is in the roll-out state,
Figure BDA0002781362010000165
for the upper limit constraint of the load transfer amount of the transferable load,
Figure BDA0002781362010000166
for the lower limit constraint of the load transfer amount of the transferable load,
Figure BDA0002781362010000167
for the upper limit constraint of the amount of load transfer that can be transferred,
Figure BDA0002781362010000168
for the lower limit constraint of the amount of load transfer that can be transferred,
Figure BDA0002781362010000169
for the transfer amount of the transferable load at time t,
Figure BDA00027813620100001610
for transferable load transfer at time t, PTL,x,maxConstrained by the upper limit of the total transfer amount, TdA set of transferable periods allowed for the system.
1. Further, the starting and stopping state u of the conventional unit is utilized in the step b)Gi,tRotational standby plan
Figure BDA00027813620100001611
Transferable load scheduling plan
Figure BDA00027813620100001612
Aiming at the lowest daily operation cost, respectively constraining and rotating the active power balance according to the updated new energy and the load daily prediction resultOptimizing the active output of each unit in the next hour by taking the standby capacity constraint, the conventional unit operation constraint, the power storage station constraint and the interruptible load constraint as constraint conditions to obtain the output plan of the conventional unit
Figure BDA00027813620100001613
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure BDA00027813620100001614
Wherein the objective function of the intraday roll optimization is
Figure BDA00027813620100001615
In the formula cILIn order to be able to interrupt the load scheduling cost,
Figure BDA00027813620100001616
in order to reduce the amount of interruptible load reduction,
Figure BDA00027813620100001617
for the air volume abandoning in the day, the rotating reserve capacity constraint in the rolling optimization in the day is
Figure BDA0002781362010000172
In the formula
Figure BDA00027813620100001716
The positive rotation reserve capacity already used for this phase,
Figure BDA0002781362010000173
the negative spinning reserve capacity already used for this stage,
Figure BDA0002781362010000174
for predicting the forward error in the wind energy day,
Figure BDA0002781362010000175
for predicting negative errors in the wind energy day,
Figure BDA0002781362010000176
a forward error is predicted for the load demand,
Figure BDA0002781362010000177
predicting negative error for load demand, interruptible load constraint in rolling optimization within a day as
Figure BDA0002781362010000179
In the formula
Figure BDA00027813620100001710
For binary variables with interruptible loads in a curtailed state,
Figure BDA00027813620100001711
for the upper limit constraint on the amount of interruptible load reduction,
Figure BDA00027813620100001712
for the lower limit constraint of the interruptible load reduction amount,
Figure BDA00027813620100001713
the maximum reduction amount of the interruptible load in the scheduling period.
2. Further, step c) comprises the steps of:
c-1) selecting the controllable resources with quick response according to the regulation characteristics and evaluation indexes of the controllable resources in the power transmission network, wherein the controllable resources comprise a new energy source unit, an AGC unit, a pumped storage power station, electrochemical energy storage and DLC load for real-time scheduling;
c-2) according to the historical active information of the controllable resources { Pt-τ,Δut-τL tau is more than or equal to 1 and input future active power regulation and control information (delta u)t+τ-1Predicting future active response of power grid (P)t+τ}，
Figure BDA00027813620100001714
Figure BDA00027813620100001715
Control disturbance for issued commands;
c-3) passing through a formula according to the priority H and Q of the controllable resources
Figure BDA0002781362010000181
The optimal performance index that the future prediction output and the reference output track have the minimum deviation and the minimum power grid regulation cost is obtained,
Figure BDA0002781362010000182
for decision variable reference values, Deltau, derived from a given scheduling reference linet+τFor the input future active regulation and control information, the vector thereof comprises the output adjustment quantity of the AGC unit
Figure BDA0002781362010000183
Generated power adjustment amount of pumped storage power station
Figure BDA0002781362010000184
Pumping power regulation for pumping power station
Figure BDA0002781362010000185
Electrochemical energy storage discharge power adjustment
Figure BDA0002781362010000186
Electrochemical energy storage charging power adjustment
Figure BDA0002781362010000187
Amount of waste air adjustment
Figure BDA0002781362010000188
DLC load scheduling increase adjustment
Figure BDA0002781362010000189
DLC load scheduling reduction adjustment
Figure BDA00027813620100001810
Xt+τDeciding variable vectors for scheduling instructionsRepresentation of the vector including AGC plant output
Figure BDA00027813620100001811
Generating power of pumped storage power station
Figure BDA00027813620100001812
Pumping power of pumping storage power station
Figure BDA00027813620100001813
Electrochemical energy storage discharge power
Figure BDA00027813620100001814
Electrochemical energy storage charging power
Figure BDA00027813620100001815
DLC load scheduling increment
Figure BDA00027813620100001816
DLC load reduction
Figure BDA00027813620100001820
And abandon the amount of wind
Figure BDA00027813620100001817
c-4) observing and updating the running state of the system, and sampling the measured value
Figure BDA00027813620100001818
Combining disturbance errors sigma in the sampling processpPrediction model P as a feedback pair0tThe correction is carried out so that the correction is carried out,
Figure BDA00027813620100001819
c-5) returning to the step c-1) until the prediction is finished.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. A multi-time scale active power optimization decision method for new energy and load uncertainty is characterized by comprising the following steps:
a) establishing a day-ahead scheduling model of the power system comprising the wind turbine generator, the conventional unit, the CAES and the flexible load according to active balance constraint, rotation reserve capacity constraint, conventional unit operation constraint, pumped storage power station constraint and transferable load constraint, and obtaining the start-stop state u of the conventional unit by taking the lowest day-ahead operation cost as an objective function and the lowest day-in operation cost as an objective functionGi,tRotational standby plan
Figure FDA0002781361000000011
Transferable load scheduling plan
Figure FDA0002781361000000012
b) Starting and stopping states u of conventional unitGi,tRotational standby plan
Figure FDA0002781361000000013
Transferable load scheduling plan
Figure FDA0002781361000000014
Performing rolling optimization in the day to obtain the output plan of the conventional unit
Figure FDA0002781361000000015
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure FDA0002781361000000016
c) In the real-time phase, the output plan of the conventional unit is utilized
Figure FDA0002781361000000017
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure FDA0002781361000000018
And utilizing a model prediction control method to adjust and correct the active scheduling of each adjustable resource in real time by taking the minimum power grid adjustment cost as a target to obtain an AGC unit output plan
Figure FDA0002781361000000019
Generating power of pumped storage power station
Figure FDA00027813610000000110
Pumping power of pumping storage power station
Figure FDA00027813610000000111
Electrochemical energy storage dispatch plan
Figure FDA00027813610000000112
DLC load scheduling plan
Figure FDA00027813610000000113
And wind abandon plan
Figure FDA00027813610000000114
2. The new energy and load uncertainty multi-time scale active optimization decision method according to claim 1, characterized by: in step a) according to the formula
Figure FDA0002781361000000021
Calculating an objective function of the formula
Figure FDA0002781361000000022
For the ith conventional unit output in the day-ahead scheduling,
Figure FDA0002781361000000023
for the xth transferable load transfer in the day-ahead schedule,
Figure FDA0002781361000000024
for the xth transferable load transfer-out amount in the day-ahead schedule,
Figure FDA0002781361000000025
for scheduling the air volume to be abandoned in the day, bGi,cGiIs the power generation cost coefficient of the ith conventional unit, SGiFor the i-th conventional unit start-up cost, SpuGkFor the power generation of the kth pumped-storage power station, SpuPkStarting cost for pumped-storage state of kth pumped-storage power station, uGi,tStarting and stopping the ith conventional unit at the moment t, uGi,t-1The ith conventional unit is in a start-stop state at the moment of t-1, upuPk,tBinary variable u for pumping state of kth pumped storage power station at time tpuPk,t-1A binary variable, u, for the pumped-storage state of the kth pumped-storage power station at time t-1puGk,tA binary variable u of the generating state of the kth pumped storage power station at the time tpuGk,t-1Is a binary variable of the power generation state of the kth pumped-storage power station at the moment t-1,
Figure FDA0002781361000000026
to shift the load into a cost factor,
Figure FDA0002781361000000027
to convert cost coefficients, xiWIn order to obtain the cost coefficient of the waste wind,
Figure FDA0002781361000000028
is the ith oneThe gauge set provides a positive rotation reserve capacity cost factor,
Figure FDA0002781361000000029
providing a negative spinning reserve capacity cost factor for the ith conventional unit,
Figure FDA00027813610000000210
the positive rotation reserve capacity provided for the ith conventional unit,
Figure FDA00027813610000000211
negative rotation reserve capacity provided for ith conventional unit, T is total scheduling duration, NGNumber of conventional units, NpuNumber of energy stored for pumping, NLThe number of transferable loads is delta t, and the unit scheduling time length is delta t;
by the formula
Figure FDA0002781361000000031
Establishing an active balance constraint in which
Figure FDA0002781361000000032
Pumping power at the moment t before the kth pumped storage power station,
Figure FDA0002781361000000033
generating power for the kth pumped storage power station at the moment t in the day ahead,
Figure FDA0002781361000000034
is the day-ahead predicted value of the wind power,
Figure FDA0002781361000000035
the predicted value of the load before the day;
by the formula
Figure FDA0002781361000000036
Establishing a rotational reserve capacity constraint, wherein fCr{. is a representation of a fuzzy function,
Figure FDA0002781361000000037
a positive error is predicted for the wind power day ahead,
Figure FDA0002781361000000038
the negative error is predicted for the wind power in the day ahead,
Figure FDA0002781361000000039
a positive error is predicted for the load demand ahead of day,
Figure FDA00027813610000000310
predicting a negative error for a load demand ahead of date;
by the formula
PGi,min≤PGi,t≤PGi,max
Figure FDA0002781361000000041
Figure FDA0002781361000000042
Figure FDA0002781361000000043
Figure FDA0002781361000000044
Figure FDA0002781361000000045
Figure FDA0002781361000000046
Figure FDA0002781361000000047
Figure FDA0002781361000000048
Establishing conventional unit operation constraints of formula, PGi,minIs the minimum output, P, of the conventional unit iGi,maxIs the maximum output, P, of the conventional unit iGi,tFor the ith conventional unit in the scheduling to output power at the time t, PGi,t-1In order to output power for the ith conventional unit at the moment t-1 in the dispatching process,
Figure FDA0002781361000000049
is the climbing rate of the conventional unit,
Figure FDA00027813610000000410
the slip ratio of the conventional unit is the slip ratio,
Figure FDA00027813610000000411
for the duration of the operation of the conventional unit before time t,
Figure FDA00027813610000000412
for the duration of the shutdown of the conventional unit before time t,
Figure FDA00027813610000000413
for the minimum run time of a conventional unit,
Figure FDA00027813610000000414
is the minimum down time, Δ t, of a conventional unitRFor adjusting the rotationResponse duration in converting spare capacity, NAGCIs the total number of the AGC units,
Figure FDA00027813610000000415
in order to start and stop the AGC unit at the time t,
Figure FDA00027813610000000416
is the maximum output of the AGC machine set,
Figure FDA00027813610000000417
is the minimum output of the AGC unit,
Figure FDA00027813610000000418
for the AGC unit to output power at the time t,
Figure FDA00027813610000000419
the positive rotation reserve capacity provided for the AGC units,
Figure FDA00027813610000000420
negative spinning reserve capacity, Δ t, for AGC unitsAAdjusting the response duration, V, of the spinning reserve capacity for an AGC plantAGC,tCapacity requirements are adjusted for AGC expected during the scheduling phase of the day ahead,
Figure FDA00027813610000000421
is the slope climbing rate of the AGC unit,
Figure FDA00027813610000000422
the slip rate of the AGC unit is obtained;
by the formula
upuP,tPpuP,min≤PpuP,t≤upuP,tPpuP,max
upuG,tPpuG,min≤PpuG,t≤upuG,tPpuG,max
upuP,t+upuG,t≤1
Figure FDA0002781361000000051
Figure FDA0002781361000000052
Figure FDA0002781361000000053
TpuG,t≥TpuG,min
TpuP,t≥TpuP,min
TpuSTOP,t≥TpuSTOP,min
Figure FDA0002781361000000054
Figure FDA0002781361000000055
Establishing pumped storage power station constraints of the formulapuP,tBinary variable, u, for pumped storage power station pumped state at time tpuG,tFor binary variables, P, of the power generation state of pumped-storage power stations at time tpuP,tFor pumped storage power stations, pumping power at time t, PpuG,tGenerating power for pumped storage power station at time t, PpuP,minIs the lower limit of pumping power, PpuP,maxUpper limit of pumping power, PpuG,minTo lower limit of generated power, PpuG,maxUpper limit of generated power, Vu,tAt time t, upper reservoir capacity, Vu,t+1Upper reservoir capacity at time t +1, Vd,tAt t moment, reservoir capacity, Vd,t+1At t +1 moment, PpuGk,tPumping power for the kth pumped-storage power station at time t, PpuPk,tIs the k-thThe power generation power eta of the pumped storage power station at the moment tpuGWater/electricity conversion ratio in the power generation state, etapuPWater quantity/electric quantity conversion ratio V in the water pumping stateu,minIs the minimum storage capacity, V, of the upper reservoiru,maxIs the maximum storage capacity of the upper reservoir, Vd,minIs the minimum storage capacity of the lower reservoir, Vd,maxIs the maximum storage capacity, eta, of the lower reservoirpuFor pumped storage efficiency, TpuG,tFor the duration of power generation, TpuP,tFor a long duration of pumping water, TpuSTOP,tFor a continuous period of outage, TpuG,minFor a minimum duration of power generation, TpuP,minFor minimum duration of pumping, TpuSTOP,minTo a minimum duration of continuous outage, Vpu,tAdjusting capacity demand, P, for AGC predicted for a scheduling phase in the day aheadpuPk,tPumping power for the kth pumped-storage power station at time t, PpuGk,tGenerating power for the kth pumped storage power station at the moment t;
by the formula
Figure FDA0002781361000000061
Figure FDA0002781361000000062
Figure FDA0002781361000000063
Establishing transferable load constraints, wherein
Figure FDA0002781361000000071
A transfer-in state of the transferable load for the non-transfer period,
Figure FDA0002781361000000072
in the transition-out state of the transferable load for the non-transfer period,
Figure FDA0002781361000000073
in order for the load to be in the transition state,
Figure FDA0002781361000000074
in order that the load is in the roll-out state,
Figure FDA0002781361000000075
for the upper limit constraint of the load transfer amount of the transferable load,
Figure FDA0002781361000000076
for the lower limit constraint of the load transfer amount of the transferable load,
Figure FDA0002781361000000077
for the upper limit constraint of the amount of load transfer that can be transferred,
Figure FDA0002781361000000078
for the lower limit constraint of the amount of load transfer that can be transferred,
Figure FDA0002781361000000079
for the transfer amount of the transferable load at time t,
Figure FDA00027813610000000710
for transferable load transfer at time t, PTL,x,maxConstrained by the upper limit of the total transfer amount, TdA set of transferable periods allowed for the system.
3. The new energy and load uncertainty multi-time scale active optimization decision method according to claim 2, characterized by: utilizing the starting and stopping state u of the conventional unit in the step b)Gi,tRotational standby plan
Figure FDA00027813610000000711
Transferable load scheduling plan
Figure FDA00027813610000000712
Aiming at the lowest day operation cost, respectively optimizing the active output of each unit in the next hour by taking the active balance constraint, the rotation reserve capacity constraint, the conventional unit operation constraint, the power storage station constraint and the interruptible load constraint as constraint conditions according to the updated new energy and the load day prediction result to obtain the conventional unit output plan
Figure FDA00027813610000000713
Operation condition u of pumping power stationpuP,t,upuG,tInterruptible load scheduling plan
Figure FDA00027813610000000714
Wherein the objective function of the intraday roll optimization is
Figure FDA00027813610000000715
In the formula cILIn order to be able to interrupt the load scheduling cost,
Figure FDA00027813610000000716
in order to reduce the amount of interruptible load reduction,
Figure FDA00027813610000000717
for the air volume abandoning in the day, the rotating reserve capacity constraint in the rolling optimization in the day is
Figure FDA0002781361000000081
In the formula
Figure FDA0002781361000000082
The positive rotation reserve capacity already used for this phase,
Figure FDA0002781361000000083
has been used for this stageThe negative rotational reserve capacity of (a) is,
Figure FDA0002781361000000084
for predicting the forward error in the wind energy day,
Figure FDA0002781361000000085
for predicting negative errors in the wind energy day,
Figure FDA0002781361000000086
a forward error is predicted for the load demand,
Figure FDA0002781361000000087
predicting negative error for load demand, interruptible load constraint in rolling optimization within a day as
Figure FDA0002781361000000088
In the formula
Figure FDA0002781361000000089
For binary variables with interruptible loads in a curtailed state,
Figure FDA00027813610000000810
for the upper limit constraint on the amount of interruptible load reduction,
Figure FDA00027813610000000811
for the lower limit constraint of the interruptible load reduction amount,
Figure FDA00027813610000000812
the maximum reduction amount of the interruptible load in the scheduling period.
4. The active optimization decision method for multiple time scales of new energy and load uncertainty according to claim 1, wherein the step c) comprises the following steps:
c-1) selecting the controllable resources with quick response according to the regulation characteristics and evaluation indexes of the controllable resources in the power transmission network, wherein the controllable resources comprise a new energy source unit, an AGC unit, a pumped storage power station, electrochemical energy storage and DLC load for real-time scheduling;
c-2) according to the historical active information of the controllable resources { Pt-τ,Δut-τL tau is more than or equal to 1 and input future active power regulation and control information (delta u)t+τ-1Predicting future active response of power grid (P)t+τ}，
Figure FDA00027813610000000813
Figure FDA00027813610000000814
Control disturbance for issued commands;
c-3) passing through a formula according to the priority H and Q of the controllable resources
Figure FDA0002781361000000091
The optimal performance index that the future prediction output and the reference output track have the minimum deviation and the minimum power grid regulation cost is obtained,
Figure FDA0002781361000000092
for decision variable reference values, Deltau, derived from a given scheduling reference linet+τFor the input future active regulation and control information, the vector thereof comprises the output adjustment quantity of the AGC unit
Figure FDA0002781361000000093
Generated power adjustment amount of pumped storage power station
Figure FDA0002781361000000094
Pumping power regulation for pumping power station
Figure FDA0002781361000000095
Electrochemical energy storage discharge power adjustment
Figure FDA0002781361000000096
Electrochemical energy storage charging power adjustment
Figure FDA0002781361000000097
Amount of waste air adjustment
Figure FDA0002781361000000098
DLC load scheduling increase adjustment
Figure FDA0002781361000000099
DLC load scheduling reduction adjustment
Figure FDA00027813610000000910
Xt+τRepresenting the scheduling command by a decision variable vector, wherein the vector representation comprises AGC unit output
Figure FDA00027813610000000911
Generating power of pumped storage power station
Figure FDA00027813610000000912
Pumping power of pumping storage power station
Figure FDA00027813610000000913
Electrochemical energy storage discharge power
Figure FDA00027813610000000914
Electrochemical energy storage charging power
Figure FDA00027813610000000915
DLC load scheduling increment
Figure FDA00027813610000000916
DLC load reduction
Figure FDA00027813610000000917
And abandon the amount of wind
Figure FDA00027813610000000918
c-4) observing and updating the system running state, and sampling the measured value Pt realCombining disturbance errors sigma in the sampling processpPrediction model P as a feedback pair0tMake a correction of P0t＝Pt real+σp；
c-5) returning to the step c-1) until the prediction is finished.
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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017000853A1 (en) * 2015-06-30 2017-01-05 中国电力科学研究院 Active power distribution network multi-time scale coordinated optimization scheduling method and storage medium
CN109962499A (en) * 2019-03-25 2019-07-02 国家电网公司华中分部 A kind of power grid Multiple Time Scales dispatching method
CN111769600A (en) * 2020-06-23 2020-10-13 内蒙古电力(集团)有限责任公司 Power system source load storage coordination rolling scheduling method based on flexibility margin

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017000853A1 (en) * 2015-06-30 2017-01-05 中国电力科学研究院 Active power distribution network multi-time scale coordinated optimization scheduling method and storage medium
CN109962499A (en) * 2019-03-25 2019-07-02 国家电网公司华中分部 A kind of power grid Multiple Time Scales dispatching method
CN111769600A (en) * 2020-06-23 2020-10-13 内蒙古电力(集团)有限责任公司 Power system source load storage coordination rolling scheduling method based on flexibility margin

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
任佳依等: "基于模型预测控制的主动配电网多时间尺度有功无功协调调度", 《中国电机工程学报》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113381399A (en) * 2021-05-27 2021-09-10 浙江大学 Load scheduling method and device considering both power grid safety and new energy consumption
CN113471966A (en) * 2021-07-01 2021-10-01 南京工程学院 Large industrial load flexible regulation and control method considering interrupt priority
CN113937796A (en) * 2021-09-15 2022-01-14 东北电力大学 Multi-time scale optimization method for wind, light, storage and storage combined system
CN114050611A (en) * 2022-01-12 2022-02-15 清华四川能源互联网研究院 Operation scheduling linearization modeling method suitable for pumped storage power station with multiple units
CN114050611B (en) * 2022-01-12 2022-04-01 清华四川能源互联网研究院 Operation scheduling linearization modeling method suitable for pumped storage power station with multiple units
CN114884101A (en) * 2022-07-04 2022-08-09 华中科技大学 Pumped storage dispatching method based on self-adaptive model control prediction
CN114884101B (en) * 2022-07-04 2022-09-30 华中科技大学 Pumped storage dispatching method based on self-adaptive model control prediction
CN115811098A (en) * 2023-02-09 2023-03-17 南方电网数字电网研究院有限公司 Wind power station AGC instruction optimization method and system considering power margin
CN115811098B (en) * 2023-02-09 2023-10-20 南方电网数字电网研究院有限公司 Wind power storage station AGC instruction optimization method and system considering power margin
CN117408535A (en) * 2023-12-15 2024-01-16 国网山东省电力公司电力科学研究院 New energy climbing event rolling early warning method and system based on deep learning
CN117408535B (en) * 2023-12-15 2024-04-12 国网山东省电力公司电力科学研究院 New energy climbing event rolling early warning method and system based on deep learning

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