CN103795088A - Load curve quantization-based pumped storage power station optimized dispatching method - Google Patents
Load curve quantization-based pumped storage power station optimized dispatching method Download PDFInfo
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Abstract
The invention discloses a load curve quantization-based pumped storage power station optimized dispatching method. The method comprises the following steps: arranging a set repairing plan; collecting load predetermination data of a power system; establishing a state transition equation of a pumped storage power station and a constraint condition thereof; establishing a load curve quantization index of a thermal power generating set; and using load curve quantization index minimization of the thermal power generating set as an objective function and carrying out optimized dispatching on the operation of the pumped storage power station by using a dynamic planning method. According to the invention, the fluctuation degree of a load curve carried by a thermal power generating set can be reduced; the power generation load rate can be improved; and the power supply coal consumption of the thermal power generating set can be reduced.
Description
Technical field
The present invention relates to hydroenergy storage station technology, be specifically related to a kind of hydroenergy storage station Optimization Scheduling quantizing based on load curve.
Background technology
Hydroenergy storage station is to utilize unnecessary electric energy in electric power system, water in reservoir low elevation (common name " lower storage reservoir ") is extracted in the reservoir that elevation is high (common name " upper storage reservoir "), is stored in the mode of potential energy, when system needs electric power, then from the upper storage reservoir hydroelectric station of generating electricity to lower storage reservoir that discharges water.No matter hydroenergy storage station is for frequency modulation, emergency duty or Steam Generator in Load Follow, can in the time that electrical network needs, provide active power support with the speed that meets stability of a system requirement, with the safety and stability of maintenance system operation.Since the nearly more than ten years, hydroenergy storage station is very fast in China's development, and it increases day by day in the effect in electric power system.
Power system operation cost relates generally to the consumption coal fuel cost of fired power generating unit, and it is subject to payload that fired power generating unit bears and the impact of pace of change.The assessment of fired power generating unit fuel cost is according to embodying a concentrated reflection of on its load curve of bearing.
The load centrifugal pump straight line that fired power generating unit load curve is born by it is formed by connecting.Load curve quantizes to be divided into two parts, and a part is to be made up of each scheduling interval load and the angle (representing with θ) of trunnion axis, and another part is to be made up of the angle of each scheduling interval (representing with ψ), as shown in Figure 1.These two parts have all reflected that load curve is exerted oneself to fired power generating unit and the requirement of creep speed, and angle is larger, requires fired power generating unit adjustment larger, and corresponding coa consumption rate is higher, and fuel cost is larger.
From dispatching of power netwoks angle, how hydroenergy storage station is moved to optimization, its performance fired power generating unit is not had or uneconomic function, be a problem of needing solution badly.The starting point or the target function that at present hydroenergy storage station are optimized to scheduling are all the economic indexs such as system operation is most economical, generator operation network minimal, carry out Economic Evaluation by fuel cost, the outage cost etc. set, its set point difference can affect the result of optimization to a certain extent, needs to be further improved.
Summary of the invention
Goal of the invention: for existing methodical deficiency, the object of this invention is to provide a kind of hydroenergy storage station Optimization Scheduling quantizing based on load curve.
Technical scheme: for achieving the above object, the technical solution used in the present invention is as follows:
The hydroenergy storage station Optimization Scheduling quantizing based on load curve, is characterized in that comprising the following steps:
(1) arrange unit maintenance plan: determine available fired power generating unit quantity and pump storage plant generator unit number of units; And typing can be used fired power generating unit and pump storage plant generator unit parameter; Described parameter comprises the specified generated output of each fired power generating unit, the specified generated output of each pump storage plant generator unit, the draw water-powergenerationcycleefficiency of hydroenergy storage station, given reservoir storage and maximum storage capacity;
(2) gather Load Prediction In Power Systems data: described Load Prediction In Power Systems data time interval is fixed as Δ
t, unit is h, total N point; Time adjacent described Load Prediction In Power Systems data 2 are connected with straight line under m-Load Prediction In Power Systems value coordinate system, form power system load curve; Power system load curve is divided into N scheduling interval by described Load Prediction In Power Systems data;
(3) set up the state transition equation of hydroenergy storage station:
In described (1) formula
x t be t scheduling interval hydroenergy storage station finish time upper storage reservoir reservoir storage, unit is MWh,
, its state set X is:
In described (2) formula,
p h for hydroenergy storage station
hthe rated capacity of platform unit, unit is MW,
h=1,2,3
h,
hfor pump storage plant generator unit number of units,
pfor the greatest common divisor of the each generating set capacity of hydroenergy storage station, unit is MW,
x maxfor the maximum storage capacity of hydroenergy storage station, unit is MWh; Described state set element number M is determined by following formula:
In described (1) formula
d t be t scheduling interval decision variable, unit is MW,
;
ηwhen ignoring the affecting of nature water, the draw water-powergenerationcycleefficiency of hydroenergy storage station; Described t scheduling interval decision variable
d t for the energy output in t scheduling interval of hydroenergy storage station or the power consumption that draws water,
d t be greater than at 1 o'clock in generating state;
d t be less than at 1 o'clock in the state of drawing water, its state set D is:
The constraints of state transition equation is:
In described (5) formula
x 0be the 1st scheduling interval hydroenergy storage station zero hour upper storage reservoir reservoir storage, unit is MWh, and C is the given reservoir storage of hydroenergy storage station, and unit is MWh;
(4) set up fired power generating unit load curve quantizating index Q:
； （6）
In described (6) formula,
for
tthe angle of individual scheduling interval fired power generating unit load curve and horizontal axis, unit is degree
, 
for
tscheduling interval with
t-angle between 1 scheduling interval fired power generating unit load curve, unit is degree; Described fired power generating unit load curve deducts hydroenergy storage station favour by power system load curve and holds optimization force curve and obtain;
(5) set up target function
, use dynamic programming to be optimized scheduling to hydroenergy storage station, from the 1st scheduling interval to N scheduling interval, carry out successively following sub-step:
1) set up t scheduling interval target function
g t (
x t ,
d t ),
:
； （7）
2) process of establishing target function
recurrence formula:
Wherein
be the in-process metrics function of t scheduling interval, unit is degree;
3) take described (8) formula as target function, solve described t scheduling interval decision variable
d t optimal value
d t * , carry it in state transition equation (1) formula of described hydroenergy storage station, try to achieve described t scheduling interval state variable
x t optimal value
x t * ;
4) described in inciting somebody to action
d t * with
x t * in-process metrics function recurrence formula (8) formula described in substitution together, obtains the in-process metrics function of described t scheduling interval
optimal solution
, be t+1 scheduling interval use dynamic programming be optimized scheduling ready.
Described
tthe angle of scheduling interval fired power generating unit load curve and horizontal axis
θ t computational methods be:
Wherein
,
for
tmoment fired power generating unit load curve value, unit is MW,
t=1,2,3
n,and
;
Described
tscheduling interval and
t-angle between 1 scheduling interval fired power generating unit load curve
ψ t computational methods be:
Wherein
.
The beneficial effect producing by technique scheme is:
1, allow as far as possible hydroenergy storage station bear the variation part of sequential load curve when Optimized Operation of the present invention, the degree of fluctuation of the load curve that effectively reduction fired power generating unit is born, the load that fired power generating unit is born is more steady, avoids occurring frequently, first and second frequency modulation action of large degree.
2, adopt the present invention to be optimized scheduling to hydroenergy storage station and optimize thermal power generation unit load curve, realize fired power generating unit and adjust minimum, reduced coa consumption rate, saved fuel cost.
Accompanying drawing explanation
Fig. 1 is Optimized Operation flow chart of the present invention;
Fig. 2 is fired power generating unit load curve quantizating index schematic diagram of the present invention;
Fig. 3 is power system load curve of the present invention and hydroenergy storage station optimization force curve figure;
Fig. 4 is fired power generating unit load curve after hydroenergy storage station Optimized Operation of the present invention.
Embodiment
Below in conjunction with specific embodiment, the present invention is described further.
Embodiment:
The hydroenergy storage station Optimization Scheduling quantizing based on load curve, comprises the following steps, as shown in Figure 1:
(1) arrange unit maintenance plan: determine available fired power generating unit quantity and pump storage plant generator unit number of units; And typing can be used fired power generating unit and pump storage plant generator unit parameter; Described parameter comprises the specified generated output of each fired power generating unit, the specified generated output of each pump storage plant generator unit, the draw water-powergenerationcycleefficiency of hydroenergy storage station, given reservoir storage and maximum storage capacity;
(2) gather Load Prediction In Power Systems data: described Load Prediction In Power Systems data time interval is fixed as Δ
t, unit is h, total N point; Time adjacent described Load Prediction In Power Systems data 2 are connected with straight line under m-Load Prediction In Power Systems value coordinate system, form power system load curve; Power system load curve is divided into N scheduling interval by described Load Prediction In Power Systems data;
(3) set up the state transition equation of hydroenergy storage station:
In described (1) formula
x t be t scheduling interval hydroenergy storage station finish time upper storage reservoir reservoir storage, unit is MWh,
, its state set X is:
In described (2) formula,
p h for hydroenergy storage station
hthe rated capacity of platform unit, unit is MW,
h=1,2,3
h,
hfor pump storage plant generator unit number of units,
pfor the greatest common divisor of the each generating set capacity of hydroenergy storage station, unit is MW,
x maxfor the maximum storage capacity of hydroenergy storage station, unit is MWh; Described state set element number M is determined by following formula:
； （3）
In described (1) formula
d t be t scheduling interval decision variable, unit is MW,
;
ηwhen ignoring the affecting of nature water, the draw water-powergenerationcycleefficiency of hydroenergy storage station; Described t scheduling interval decision variable
d t for the energy output in t scheduling interval of hydroenergy storage station or the power consumption that draws water,
d t be greater than at 1 o'clock in generating state;
d t be less than at 1 o'clock in the state of drawing water, its state set D is:
The constraints of state transition equation is:
In described (5) formula
x 0be the 1st scheduling interval hydroenergy storage station zero hour upper storage reservoir reservoir storage, unit is MWh, and C is the given reservoir storage of hydroenergy storage station, and unit is MWh;
(4) set up fired power generating unit load curve quantizating index Q:
As shown in Figure 2, in described (6) formula,
for
tthe angle of individual scheduling interval fired power generating unit load curve and horizontal axis, unit is degree
, 
for
tscheduling interval with
t-angle between 1 scheduling interval fired power generating unit load curve, unit is degree; Described fired power generating unit load curve deducts hydroenergy storage station favour by power system load curve and holds optimization force curve and obtain;
(5) set up target function
, use dynamic programming to be optimized scheduling to hydroenergy storage station, from the 1st scheduling interval to N scheduling interval, carry out successively following sub-step:
1) set up t scheduling interval target function
g t (
x t ,
d t ),
:
2) process of establishing target function
recurrence formula:
Wherein
be the in-process metrics function of t scheduling interval, unit is degree;
3) take described (8) formula as target function, solve described t scheduling interval decision variable
d t optimal value
d t * , carry it in state transition equation (1) formula of described hydroenergy storage station, try to achieve described t scheduling interval state variable
x t optimal value
x t * ;
4) described in inciting somebody to action
d t * with
x t * in-process metrics function recurrence formula (8) formula described in substitution together, obtains the in-process metrics function of described t scheduling interval
optimal solution
, be t+1 scheduling interval use dynamic programming be optimized scheduling ready.
Described
tthe angle theta of scheduling interval fired power generating unit load curve and horizontal axis
t computational methods be:
Wherein
,
for
tmoment fired power generating unit load curve value, unit is MW,
t=1,2,3
n,and
;
Described
tscheduling interval and
t-angle ψ between 1 scheduling interval fired power generating unit load curve
t computational methods be:
Wherein
.
In the present embodiment, electric power system comprises 1 of equivalent fired power generating unit, specified generated output 60000MW; 1 of hydroenergy storage station, draw water-powergenerationcycleefficiency is 79.90%, and the given reservoir storage of hydroenergy storage station is 1000MWh, and maximum storage capacity is 12000MWh, 8 of pump storage plant generator units, specified generated output is 300MW, total installation of generating capacity 2400MW.
In the present embodiment, Load Prediction In Power Systems curve was by Guangdong tracking on January 3 short-term load forecasting data formation in 2012.This Load Prediction In Power Systems time interval is
be fixed as 0.25h, 15 minutes, within one day, have 96 Load Prediction In Power Systems data, as shown in table 1.Time adjacent described Load Prediction In Power Systems data 2 are connected with straight line under m-Load Prediction In Power Systems value coordinate system, form Load Prediction In Power Systems curve.Power system load curve is divided into N scheduling interval by these 96 Load Prediction In Power Systems data.Hydroenergy storage station is optimized to scheduling by method of the present invention, obtains a series of optimal values
d t *, be the scheduling scheme of hydroenergy storage station at each scheduling interval end.The optimum results of the present embodiment is shown in Figure 3.For Load Prediction In Power Systems curve and hydroenergy storage station optimization force curve are drawn in a figure, in figure, Load Prediction In Power Systems value is dwindled 10 times and is illustrated.
Fig. 4 is the fired power generating unit load curve after hydroenergy storage station Optimized Operation; Can find out, with the present invention to hydroenergy storage station Optimized Operation after, fired power generating unit load curve compared with Load Prediction In Power Systems curve, degree of fluctuation reduce.
Table 2 is the result comparison that whether with the present invention, the operation of pumped-storage power generation station is optimized scheduling." hold without favour " and refer to that hydroenergy storage station does not participate in network load and distributes; " favour is held after experience operation " refers to experience operational mode, hydroenergy storage station be dispatched; " favour is held and optimized after operation " refers to, with the present invention, hydroenergy storage station is optimized to scheduling.Can find out, hydroenergy storage station is optimized to tune with the present invention, load pulsation quantizating index reduces, and power generation load rate increases.The optimization of these two indexs means that the operating cost of fired power generating unit reduces, and has great importance in actual production.
The impact of fired power generating unit load curve on net coal consumption rate, not only relevant with its power generation load rate, also relevant with the degree of fluctuation of load curve.Even under identical average load rate, stable operation of unit is more conducive to reduce net coal consumption rate than load fluctuation.Below carry out the quantitative calculating on net coal consumption rate impact of power system load curve and fired power generating unit load curve degree of fluctuation, verify practicality of the present invention.
For the ease of calculating, fired power generating unit load curve value is dwindled after 100 times, born its net coal consumption rate by a 600MW supercritical thermal power unit
u(
x) in table 3.In his-and-hers watches 3, data are carried out 2 order polynomial matchings, and fitting result is:
u(
x)=0.0003
x 2-0.3701
x+422.4429
If Load Prediction In Power Systems curve is
l'
1(
t), fired power generating unit load curve is
l'
2(
t).The net coal consumption rate computing formula of one day is:
Wherein,
h 1for the Optimized Operation net coal consumption rate of the previous day,
h 2for Optimized Operation net coal consumption rate one day after.
Obtain thus
h 1=3.5362 × 10
9gram,
h 2=3.4978 × 10
9gram, use after Optimized Operation of the present invention, within one day, can save 38.4 tons of coals.
Table 1 power system load data
| Load point | Load value (MW) | Load point | Load value (MW) |
| 1 | 38000 | 49 | 49000 |
| 2 | 37400 | 50 | 46300 |
| 3 | 36900 | 51 | 45700 |
| 4 | 36500 | 52 | 45600 |
| 5 | 36100 | 53 | 45500 |
| 6 | 35700 | 54 | 47000 |
| 7 | 35300 | 55 | 48600 |
| 8 | 34900 | 56 | 51200 |
| 9 | 34600 | 57 | 51800 |
| 10 | 34300 | 58 | 52400 |
| 11 | 34100 | 59 | 52600 |
| 12 | 33800 | 60 | 52700 |
| 13 | 33600 | 61 | 52800 |
| 14 | 33400 | 62 | 53000 |
| 15 | 33200 | 63 | 53200 |
| 16 | 33100 | 64 | 53400 |
| 17 | 33000 | 65 | 53900 |
| 18 | 33000 | 66 | 54400 |
| 19 | 33000 | 67 | 54900 |
| 20 | 33000 | 68 | 55400 |
| 21 | 33100 | 69 | 55600 |
| 22 | 33200 | 70 | 55700 |
| 23 | 33400 | 71 | 54400 |
| 24 | 33700 | 72 | 54000 |
| 25 | 34000 | 73 | 54700 |
| 26 | 34700 | 74 | 55900 |
| 27 | 35400 | 75 | 56700 |
| 28 | 36400 | 76 | 57000 |
| 29 | 37400 | 77 | 56700 |
| 30 | 38900 | 78 | 56400 |
| 31 | 40100 | 79 | 56000 |
| 32 | 41900 | 80 | 55500 |
| 33 | 44900 | 81 | 55000 |
| 34 | 49200 | 82 | 54700 |
| 35 | 51100 | 83 | 54400 |
| 36 | 52400 | 84 | 54100 |
| 37 | 53000 | 85 | 53700 |
| 38 | 53600 | 86 | 53100 |
| 39 | 54000 | 87 | 52300 |
| 40 | 54400 | 88 | 51500 |
| 41 | 54800 | 89 | 50700 |
| 42 | 55200 | 90 | 49800 |
| 43 | 55600 | 91 | 48700 |
| 44 | 56000 | 92 | 47500 |
| 45 | 56300 | 93 | 45900 |
| 46 | 56500 | 94 | 44300 |
| 47 | 55500 | 95 | 42700 |
| 48 | 53500 | 96 | 41300 |
The comparison of table 2 optimum results
| Index | Hold without favour | Favour is held after experience operation | Favour is held after optimization |
| Load pulsation quantizating index | 550.96 | 538.74 | 524.64 |
| Power generation load rate | 81.79% | 84.13% | 85.96% |
Table 3 600MW fired power generating unit net coal consumption rate
| Load x（MW） | Net coal consumption rate u( x)（g/kw·h） | Load x（MW） | Net coal consumption rate u( x)（g/kw·h） |
| 350 | 330 | 500 | 313 |
| 400 | 322 | 550 | 310 |
| 450 | 316 | 600 | 308 |
Claims (2)
1. the hydroenergy storage station Optimization Scheduling quantizing based on load curve, is characterized in that comprising the following steps:
(1) arrange unit maintenance plan: determine available fired power generating unit quantity and pump storage plant generator unit number of units; And typing can be used fired power generating unit and pump storage plant generator unit parameter; Described parameter comprises the specified generated output of each fired power generating unit, the specified generated output of each pump storage plant generator unit, the draw water-powergenerationcycleefficiency of hydroenergy storage station, given reservoir storage and maximum storage capacity;
(2) gather Load Prediction In Power Systems data: described Load Prediction In Power Systems data time interval is fixed as Δ
t, unit is h, total N point; Time adjacent described Load Prediction In Power Systems data 2 are connected with straight line under m-Load Prediction In Power Systems value coordinate system, form power system load curve; Power system load curve is divided into N scheduling interval by described Load Prediction In Power Systems data;
(3) set up the state transition equation of hydroenergy storage station:
In described (1) formula
x t be t scheduling interval hydroenergy storage station finish time upper storage reservoir reservoir storage, unit is MWh,
, its state set X is:
In described (2) formula,
p h for hydroenergy storage station
hthe rated capacity of platform unit, unit is MW,
h=1,2,3
h,
hfor pump storage plant generator unit number of units,
pfor the greatest common divisor of the each generating set capacity of hydroenergy storage station, unit is MW,
x maxfor the maximum storage capacity of hydroenergy storage station, unit is MWh; Described state set element number M is determined by following formula:
In described (1) formula
d t be t scheduling interval decision variable, unit is MW,
; η is while ignoring the affecting of nature water, the draw water-powergenerationcycleefficiency of hydroenergy storage station; Described t scheduling interval decision variable
d t for the energy output in t scheduling interval of hydroenergy storage station or the power consumption that draws water,
d t be greater than at 1 o'clock in generating state;
d t be less than at 1 o'clock in the state of drawing water, its state set D is:
； (4)
The constraints of state transition equation is:
In described (5) formula
x 0be the 1st scheduling interval hydroenergy storage station zero hour upper storage reservoir reservoir storage, unit is MWh, and C is the given reservoir storage of hydroenergy storage station, and unit is MWh;
(4) set up fired power generating unit load curve quantizating index Q:
In described (6) formula,
for
tthe angle of individual scheduling interval fired power generating unit load curve and horizontal axis, unit is degree
, 
for
tscheduling interval with
t-angle between 1 scheduling interval fired power generating unit load curve, unit is degree; Described fired power generating unit load curve deducts hydroenergy storage station favour by power system load curve and holds optimization force curve and obtain;
(5) set up target function
, use dynamic programming to be optimized scheduling to hydroenergy storage station, from the 1st scheduling interval to N scheduling interval, carry out successively following sub-step:
1) set up t scheduling interval target function
g t (
x t ,
d t ),
:
2) process of establishing target function
recurrence formula:
Wherein
be the in-process metrics function of t scheduling interval, unit is degree;
3) take described (8) formula as target function, solve described t scheduling interval decision variable
d t optimal value
d t * , carry it in state transition equation (1) formula of described hydroenergy storage station, try to achieve described t scheduling interval state variable
x t optimal value
x t * ;
4) described in inciting somebody to action
d t * with
x t * in-process metrics function recurrence formula (8) formula described in substitution together, obtains the in-process metrics function of described t scheduling interval
optimal solution
, be t+1 scheduling interval use dynamic programming be optimized scheduling ready.
2. the hydroenergy storage station Optimization Scheduling quantizing based on load curve as claimed in claim 1, is characterized in that: described the
tthe angle of scheduling interval fired power generating unit load curve and horizontal axis
θ t computational methods be:
Wherein
,
for
tmoment fired power generating unit load curve value, unit is MW,
t=1,2,3
n,and
;
Described
tscheduling interval and
t-angle between 1 scheduling interval fired power generating unit load curve
ψ t computational methods be:
Wherein
.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104216383A (en) * | 2014-09-22 | 2014-12-17 | 国家电网公司 | Operating efficiency optimizing method of small hydropower station unit |
| CN104483837A (en) * | 2014-11-25 | 2015-04-01 | 华中科技大学 | Adaptive control method for reversible machinery group |
| CN107910883A (en) * | 2017-12-01 | 2018-04-13 | 中国南方电网有限责任公司电网技术研究中心 | Random production simulation method based on pumped storage power station corrected time sequence load curve |
| CN108701332A (en) * | 2016-04-07 | 2018-10-23 | 株式会社日立制作所 | The maintenance plan support system of generating unit groups |
| CN109149571A (en) * | 2018-09-21 | 2019-01-04 | 国网福建省电力有限公司 | A kind of energy storage Optimal Configuration Method of the combustion gas of consideration system and fired power generating unit characteristic |
| CN115564154A (en) * | 2022-12-07 | 2023-01-03 | 南方电网调峰调频发电有限公司检修试验分公司 | Scheduling optimization method, device, equipment and medium for pumped storage power station |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102780235A (en) * | 2012-08-02 | 2012-11-14 | 南通大学 | Pumped storage power station dispatching method on basis of genetic algorithm |
| CN103268570A (en) * | 2013-04-28 | 2013-08-28 | 中国南方电网有限责任公司 | Power grid economic dispatching evaluating system and method |
-
2013
- 2013-10-16 CN CN201310482458.0A patent/CN103795088B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102780235A (en) * | 2012-08-02 | 2012-11-14 | 南通大学 | Pumped storage power station dispatching method on basis of genetic algorithm |
| CN103268570A (en) * | 2013-04-28 | 2013-08-28 | 中国南方电网有限责任公司 | Power grid economic dispatching evaluating system and method |
Non-Patent Citations (2)
| Title |
|---|
| 张鹏等: "基于风蓄协调的节能调度方法", 《电力系统保护与控制》 * |
| 李文武等: "混合式抽水蓄能电站水库中长期优化调度", 《电力自动化设备》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104216383A (en) * | 2014-09-22 | 2014-12-17 | 国家电网公司 | Operating efficiency optimizing method of small hydropower station unit |
| CN104483837A (en) * | 2014-11-25 | 2015-04-01 | 华中科技大学 | Adaptive control method for reversible machinery group |
| CN104483837B (en) * | 2014-11-25 | 2017-04-12 | 华中科技大学 | Adaptive control method for reversible machinery group |
| CN108701332A (en) * | 2016-04-07 | 2018-10-23 | 株式会社日立制作所 | The maintenance plan support system of generating unit groups |
| CN108701332B (en) * | 2016-04-07 | 2022-02-11 | 株式会社日立制作所 | Maintenance schedule support system for power generation unit group |
| CN107910883A (en) * | 2017-12-01 | 2018-04-13 | 中国南方电网有限责任公司电网技术研究中心 | Random production simulation method based on pumped storage power station corrected time sequence load curve |
| CN107910883B (en) * | 2017-12-01 | 2021-04-13 | 中国南方电网有限责任公司电网技术研究中心 | Random production simulation method based on pumped storage power station corrected time sequence load curve |
| CN109149571A (en) * | 2018-09-21 | 2019-01-04 | 国网福建省电力有限公司 | A kind of energy storage Optimal Configuration Method of the combustion gas of consideration system and fired power generating unit characteristic |
| CN109149571B (en) * | 2018-09-21 | 2022-04-01 | 国网福建省电力有限公司 | Energy storage optimal configuration method considering characteristics of system gas and thermal power generating unit |
| CN115564154A (en) * | 2022-12-07 | 2023-01-03 | 南方电网调峰调频发电有限公司检修试验分公司 | Scheduling optimization method, device, equipment and medium for pumped storage power station |
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