CN115392526A - Multi-time scale coordinated scheduling method for wind-solar-water power generation system containing step hydropower - Google Patents

Multi-time scale coordinated scheduling method for wind-solar-water power generation system containing step hydropower Download PDF

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CN115392526A
CN115392526A CN202210560569.8A CN202210560569A CN115392526A CN 115392526 A CN115392526 A CN 115392526A CN 202210560569 A CN202210560569 A CN 202210560569A CN 115392526 A CN115392526 A CN 115392526A
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谢俊
赵心怡
葛远裕
段佳南
邢单玺
徐志诚
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Abstract

The invention discloses a multi-time scale coordinated scheduling method of a wind-solar-water power generation system containing step hydropower, which comprises the following steps: (1) Pre-establishing a day-ahead scheduling model of the wind-solar-water power generation system containing the cascade hydropower; (2) Pre-establishing a solar scheduling model of the wind-solar-water power generation system containing the cascade hydropower; (3) A real-time scheduling model of the wind-solar-water power generation system containing the cascade hydropower is established in advance. According to the invention, by pre-establishing a day-ahead, day-inside and real-time scheduling model of the wind-light-water power generation system containing the cascade hydropower, when the wind, light and load prediction accuracy is gradually improved along with the reduction of the time scale, the multi-time scale day-ahead, day-inside and real-time model achieves the purposes of good load tracking of wind-light-water output and improvement of the new energy consumption level.

Description

Multi-time scale coordinated scheduling method for wind-solar-water power generation system containing cascade hydropower
Technical Field
The invention relates to power dispatching, in particular to a multi-time scale coordinated dispatching method for a wind-solar-water power generation system with cascade hydropower.
Background
The energy is the basis of human survival and development, and along with the continuous improvement of energy demand and the gradual deterioration of environmental problems, the development of a clean, low-carbon, safe and efficient energy system must make full use of the abundant endowment conditions of national water, wind and light resources, optimize and configure clean energy, and construct a clean and low-carbon novel energy system.
Wind power generation and photovoltaic power generation depend on changeable meteorological characteristics such as wind speed and solar radiation to a great extent, output power of the wind power generation and photovoltaic power generation has the characteristics of randomness, volatility, intermittence and uncontrollable, and large-scale wind power and photovoltaic grid connection can bring severe examination to safe and stable operation of a power system. The hydroelectric generating set has the advantages of rapid start and stop, strong load tracking capability, nearly zero pollution, reproducibility and the like, and the water storage capacity of the reservoir can stabilize the short-term fluctuation of the natural incoming water, so that the hydroelectric generating set has good regulation characteristic. The combined operation of the cascade hydropower stations can comprehensively consider the water energy resources on the upper and lower reaches of the cascade of the drainage basin, and the regulating capacity of the cascade reservoir is further utilized. Therefore, the advantages of wind, light and water resources in China are fully combined, a wind-light-water complementary power generation system is built, a power supply structure can be optimized, and the consumption level of a power grid on new energy is improved.
The power system dispatching plan is mainly divided into a medium-long term plan, a day-ahead plan, a day-in plan, a real-time plan and the like on the time scale, and reasonable coordination and overall planning are implemented among different time scales, so that the dispatching plans can be effectively linked and smoothly executed, and the effect of global optimization is achieved. The traditional domestic scheduling mode mainly adopts a scheduling mode combining two time scales, namely a manual day-ahead scheduling plan and Automatic Generation Control (AGC), and the like, and the two time scales have large span and a relatively extensive scheduling mode and cannot adapt to the scheduling of a power grid after large-scale wind and light access. The wind and light absorption capacity of the power grid can be improved through the coordination and coordination of the multi-time scale scheduling plan. Part of documents directly absorb the predicted power reported by the wind power plant under different time scales, adjust the existing scheduling plan, and do not carefully consider the uncertainty of the wind power and load predicted values under different time scales. Some documents do not consider photovoltaic and do not take advantage of the good regulation capability of cascade hydroelectric power.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a multi-time scale coordinated scheduling method for a wind-solar-water power generation system with cascade hydropower, so as to cope with the uncertain influence brought by high-proportion new energy grid connection under the background of a novel power system and improve the new energy consumption level.
The technical scheme is as follows: the invention relates to a multi-time scale coordinated scheduling method of a wind-solar-water power generation system containing step hydropower, which comprises the following steps:
(1) Pre-establishing a day-ahead scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the day-ahead scheduling model comprises a first objective function and a corresponding first constraint condition, wherein the first objective function aims at maximizing the total energy storage added at the end of the step-level reservoir in the whole scheduling period and minimizing the wind curtailment and the light curtailment quantity;
(2) Pre-establishing a solar scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the in-day scheduling model comprises a second objective function and a corresponding second constraint condition, wherein the maximum energy storage added by the scheduling end reservoir is the target;
(3) Pre-establishing a wind-solar-water power generation system real-time scheduling model containing cascade hydropower; the real-time scheduling model comprises a third objective function and a corresponding third constraint condition, wherein the third objective function is used for aiming at the minimum real-time adjustment cost of the output of the hydroelectric generating set.
When the wind, light and load prediction accuracy is improved step by step along with the shortening of the time scale, the day-ahead scheduling model, the day-in scheduling model and the real-time scheduling model achieve the aims of effectively stabilizing the fluctuation of wind and light output and improving the new energy consumption level.
In the step (1), the first objective function is:
Figure RE-GDA0003868465180000021
the first constraint includes:
and power balance constraint:
Figure RE-GDA0003868465180000022
wind power output constraint: PW of 0 or less t ≤PW max
PW t dn ≤PW t ≤PW t up
PW t dn ＝μ w,t -1.96σ w,t
PW t up ＝μ w,t +1.96σ w,t
AW t ＝min{PW t up ,PW max }-PW t
Photovoltaic power generation output restraint: PV 0 ≤ t ≤PV max
PV t dn ≤PV t ≤PV t up
PV t dn ＝μ v,t -1.96σ v,t
PV t up ＝μ v,t +1.96σ v,t
AV t ＝min{PV t up ,PV max }-PV t
Output linearization constraint of the hydroelectric generating set:
Figure RE-GDA0003868465180000031
Figure RE-GDA0003868465180000032
Figure RE-GDA0003868465180000033
Figure RE-GDA0003868465180000034
Figure RE-GDA0003868465180000035
and (3) water and electricity output restraint:
Figure RE-GDA0003868465180000036
and (3) power generation flow restriction: i is i,t ·Q min,i ≤Q i,t ≤I i,t ·Q max,i
And (3) water balance constraint:
Figure RE-GDA0003868465180000037
and (3) water storage amount restriction: i is i,t ·V min,i ≤V i,t ≤I i,t ·V max,i
Upward rotation reserve capacity constraint provided by step hydropower:
Figure RE-GDA0003868465180000038
Figure RE-GDA0003868465180000039
Ru t ≥(PW t -PW t l )+(PV t -PV t l )
downward rotation reserve capacity constraint provided by step hydropower:
Figure RE-GDA0003868465180000041
Figure RE-GDA0003868465180000042
Rd t ≥(PW t -PW t l )+(PV t -PV t l )
wherein, V i,T Representing the water storage capacity of the hydropower station i at the end of the dispatching cycle; v i ini Representing the initial water storage capacity of the hydropower station i; r is the hydropower station index downstream of the hydropower station i; e i A downstream hydropower station set which is a hydropower station i; theta r Converting the water storage capacity of the reservoir i into an energy storage coefficient; AW t day The abandoned wind power quantity of the wind power plant at the time t period before the day; AV (audio video) t day The photovoltaic power station is the light abandoning electric quantity of the photovoltaic power station in the period t in the day ahead; PW (pseudo wire) t day Scheduling output of the wind power plant in the t time period is planned in the day ahead; PV (photovoltaic) t day Planning the dispatching output of the photovoltaic power station in the t time period in the day ahead;
Figure RE-GDA0003868465180000043
dispatching output of a water motor set i in a time period t in a day-ahead plan;
Figure RE-GDA0003868465180000044
the predicted load of the time period t under the day-ahead scale is obtained; PW (pseudo wire) max The installed capacity of the wind power plant; PW (pseudo wire) t up And PW t dn Respectively the predicted upper and lower output limits of the wind power plant at the time period t; mu.s w,t And σ w,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the wind power plant at the time t; the economy of scheduling of the wind-light-water complementary power generation system can be reduced even the scheduling problem is solved if the wind power interval is too large, so that the wind power output interval is set to [ mu ] w -1.96σ w ,μ w +1.96σ w ]So as to ensure that at least 95 percent of wind power output is utilized; PV max The installed capacity of the photovoltaic power station; PV (photovoltaic) t up And PV t dn Respectively representing the predicted upper and lower output limits of the photovoltaic power station at the time interval t; mu.s v,t And σ v,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the photovoltaic power station at the t time period; similarly, the output interval of the photovoltaic power station is set to [ mu ] v -1.96σ v ,μ v +1.96σ v ]So as to ensure that at least 95 percent of photovoltaic power generation output is utilized; v i,t The water storage capacity of the hydropower station i in the time period t; q i,t The water consumption for generating power of the hydropower station i in the time period t; a is 1,i To a 6,i All are hydroelectric generation coefficients; dividing Q and V into the form [ Q ] k ,Q k+1 ]And [ V ] l ,V l+1 ]Where k =1.. M-1, l =1.. N-1, the original function is divided into a (m-1) · (n-1) mesh, each mesh is divided into two triangles, the upper left corner and the lower right corner,
Figure RE-GDA0003868465180000045
and ζ k,l Is an index representing the position of the triangle; PH value max,i And pH min,i Respectively the maximum and minimum technical output of the hydroelectric generating set i; I.C. A i,t Is a binary variable representing the state of a hydropower unit I in the time period t, I i,t =1 denotes the operating state, I i,t =0 for shutdown status; q max,i And Q min,i The upper limit value and the lower limit value of the generating water consumption of the hydropower station i are respectively set; nq (n q) i,t The natural water inflow of the cascade hydropower station i in the t period; qs i,t The water abandoning amount of the hydropower station i in the time period t is shown; lambda h The time required for the water flow to flow from the upstream hydropower station to the downstream hydropower station, namely the water flow delay; j is an upstream hydropower station index of the hydropower station i, and in the model, the hydropower stations are numbered in sequence from upstream to downstream; u shape h An upstream set of hydroelectric power stations i; v max,i And V min,i The maximum and minimum water storage capacity of the reservoir i; ru t The upward rotation reserve capacity is provided for the step hydropower in the time period t; ru max The maximum upward rotation reserve capacity can be provided for the step hydropower station in the t period; beta is a beta 1 Is an upward rotation standby factor; rd t Downward rotation reserve capacity provided for the step hydropower at the time t; rd max Maximum upward rotation reserve provided for step hydroelectric power in t periodCapacity; beta is a 2 The spare factor is rotated downward.
In the step (2), the second objective function is:
Figure RE-GDA0003868465180000051
the second constraint includes:
and power balance constraint:
Figure RE-GDA0003868465180000052
relative to the planned hydroelectric output adjustment value in the day ahead:
Figure RE-GDA0003868465180000053
wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model;
wherein, PW t hour Dispatching output for the wind power in the day of the time period t; PV (photovoltaic) t hour Scheduling output for the photovoltaic in the day of the t time period;
Figure RE-GDA0003868465180000054
dispatching output for hydropower in the day of the time period t,
Figure RE-GDA0003868465180000055
predicting the output of the load before the hour of the t time period;
Figure RE-GDA0003868465180000056
an output adjustment value of the hydroelectric generating set i relative to the day-ahead hydropower dispatching in a time period t;
Figure RE-GDA0003868465180000057
and (4) optimizing the output of the hydroelectric generating set i in the time period t for the day-ahead scheduling model.
In the step (3), the third objective function is:
Figure RE-GDA0003868465180000058
the third constraint includes:
and (3) power balance constraint:
Figure RE-GDA0003868465180000061
relative to the planned hydroelectric power adjustment value before the day:
Figure RE-GDA0003868465180000062
wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model.
Wherein, gamma is i Adjusting cost for unit output of the hydroelectric generating set i when output is frequently adjusted;
Figure RE-GDA0003868465180000063
a real-time output adjustment value of a hydropower unit scheduling plan 1 hour before a time t is compared; PW (pseudo wire) t rtl Output is scheduled for the wind power in real time at the time interval t; PV (photovoltaic) t rtl Output is dispatched for the photovoltaic in real time at the time interval t;
Figure RE-GDA0003868465180000064
dispatching output for hydropower in real time at a time interval t;
Figure RE-GDA0003868465180000065
predicting force output for the load 15 minutes before t period;
Figure RE-GDA0003868465180000066
and (4) optimizing the output of the hydroelectric generating set i in the time period t for the intraday scheduling model.
A computer storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the above-mentioned multi-time scale coordinated scheduling method for a wind, photovoltaic and water power generation system with cascaded hydropower.
A computer device comprises a storage, a processor and a computer program stored on the storage and capable of running on the processor, wherein the processor executes the computer program to realize the multi-time scale coordination scheduling method of the wind-solar-water power generation system with the cascade hydropower.
Aiming at the characteristic that the wind, light and load prediction accuracy is improved step by step along with the shortening of the time scale, the wind, light and water output is ensured to track the load well and the new energy consumption level is improved through the coordination and coordination of a 24-hour day-ahead power generation plan, a 1-hour day power generation plan and a real-time 15-minute power generation plan. The wind and light absorption capacity of the system is greatly influenced by the incoming water of the cascade hydropower, the provided multi-time scale coordinated dispatching model can effectively stabilize the wind and light output fluctuation, the wind and light abandonment is reduced, and the optimal configuration and the full utilization of wind, light and water clean energy are realized.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. compared with single time scale scheduling, the multi-time scale coordinated scheduling strategy can effectively stabilize wind and light output fluctuation, realize good tracking of load and improve the consumption level of the power grid on uncertain energy sources such as wind and light.
2. The dispatching model can fully utilize the characteristic that the precision of the wind and light load forecasting in the day-ahead, in the day and in real time is improved step by step.
3. Compared with a day-ahead scheduling model, the scheduling model realizes less wind and light abandonment, and improves the utilization rate of clean energy; meanwhile, less rotary spare capacity is needed, the adjustment load of the hydroelectric generating set is reduced, and the safe and economic operation of the system is facilitated.
Drawings
Fig. 1 is a schematic diagram of a day-ahead-day-inside-real-time coordinated scheduling relationship.
FIG. 2 is a dispatch plan flow chart.
Fig. 3 is a schematic diagram of a wind power and photovoltaic predicted output interval.
FIG. 4 is a schematic diagram of the predicted load contribution and the actual load contribution.
FIG. 5 is a diagram of model one and model two 24h scheduling results.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, a multi-time scale coordinated scheduling method for a wind, light and water power generation system with cascade hydropower includes the following steps:
(1) Pre-establishing a day-ahead scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the day-ahead scheduling model comprises a first objective function and corresponding first constraint conditions, wherein the first objective function takes the maximum sum of energy storage increased at the end of the whole scheduling period of the stepped reservoir and the minimum amount of abandoned wind and abandoned light as targets.
(2) Pre-establishing a daily scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the day scheduling model comprises a second objective function and a corresponding second constraint condition, wherein the maximum energy storage added by the scheduling end-of-term reservoir is the target.
(3) Pre-establishing a wind-solar-water power generation system real-time scheduling model containing cascade hydropower; the real-time scheduling model comprises a third objective function and a corresponding third constraint condition, wherein the third objective function is used for aiming at the minimum real-time adjustment cost of the output of the hydroelectric generating set.
When the wind, light and load prediction accuracy is improved step by step along with the shortening of the time scale, the day-ahead scheduling model, the day-in scheduling model and the real-time scheduling model achieve the aims of effectively stabilizing the fluctuation of wind and light output and improving the new energy consumption level.
In the wind, photovoltaic and water power generation system day-ahead scheduling model with the cascade hydropower, the first objective function is as follows:
Figure RE-GDA0003868465180000071
the first constraint condition comprises:
1) And (3) power balance constraint:
Figure RE-GDA0003868465180000081
2) Wind power output restraint:
0≤PW t ≤PW max (3)
PW t dn ≤PW t ≤PW t up (4)
PW t dn ＝μ w,t -1.96σ w,t (5)
PW t up ＝μ w,t +1.96σ w,t (6)
AW t ＝min{PW t up ,PW max }-PW t (7)
3) Photovoltaic power generation output restraint:
0≤PV t ≤PV max (8)
PV t dn ≤PV t ≤PV t up (9)
PV t dn ＝μ v,t -1.96σ v,t (10)
PV t up ＝μ v,t +1.96σ v,t (11)
AV t ＝min{PV t up ,PV max }-PV t (12)
4) Output linearization constraint of the hydroelectric generating set:
Figure RE-GDA0003868465180000082
Figure RE-GDA0003868465180000083
5) And (3) water and electricity output restraint:
Figure RE-GDA0003868465180000084
6) And (3) power generation flow restriction:
I i,t ·Q min,i ≤Q i,t ≤I i,t ·Q max,i (16)
7) And (3) water balance constraint:
Figure RE-GDA0003868465180000091
8) And (3) water storage capacity constraint:
I i,t ·V min,i ≤V i,t ≤I i,t ·V max,i (18)
9) Upward rotation reserve capacity constraint provided by step hydropower:
Figure RE-GDA0003868465180000092
Figure RE-GDA0003868465180000093
Ru t ≥(PW t -PW t l )+(PV t -PV t l ) (21)
10 Step hydropower provided downward rotation reserve capacity constraint:
Figure RE-GDA0003868465180000094
Figure RE-GDA0003868465180000095
Rd t ≥(PW t -PW t l )+(PV t -PV t l ) (24)
wherein, V i,T To representDispatching the water storage capacity of the hydropower station i at the end of the period; v i ini Representing the initial water storage capacity of the hydropower station i; r is the downstream hydropower station index of the hydropower station i; e i A downstream hydropower station set which is a hydropower station i; theta r Converting the water storage capacity of the reservoir i into an energy storage coefficient; AW t day The method comprises the following steps of (1) obtaining the abandoned wind electric quantity of a wind power plant at a time t period in the day ahead; AV (Audio video) t day The photovoltaic power station is the light abandoning electric quantity of the photovoltaic power station in the period t in the day ahead; PW (pseudo wire) t day Scheduling output of a planned wind power plant at a time t in the day ahead; PV (photovoltaic) t day Planning the dispatching output of the photovoltaic power station in the t time period in the day ahead;
Figure RE-GDA0003868465180000096
dispatching output of a water motor set i in a time period t in a day-ahead plan;
Figure RE-GDA0003868465180000097
the predicted load of the time interval t under the scale of the day ahead; PW (pseudo wire) max Is the installed capacity of the wind farm; PW (pseudo wire) t up And PW t dn Respectively representing predicted upper and lower output limits of the wind power plant at t time period; mu.s w,t And σ w,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the wind power plant at the time t; the economy of scheduling of the wind-light-water complementary power generation system can be reduced even the scheduling problem is solved if the wind power interval is too large, so that the wind power output interval is set to [ mu ] w -1.96σ w ,μ w +1.96σ w ]So as to ensure that at least 95 percent of wind power output is utilized; PV (photovoltaic) max The installed capacity of the photovoltaic power station; PV (photovoltaic) t up And PV t dn Respectively representing the predicted upper and lower output limits of the photovoltaic power station at the time interval t; mu.s v,t And σ v,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the photovoltaic power station in the t period; similarly, the output interval of the photovoltaic power station is set to [ mu ] v -1.96σ v ,μ v +1.96σ v ]So as to ensure that at least 95 percent of photovoltaic power generation output is utilized; v i,t The water storage capacity of the hydropower station i in the time period t; q i,t The water consumption for generating power of the hydropower station i in the t time period; a is 1,i To a 6,i All are hydroelectric generation coefficients; dividing Q and V into forms [ Q ] k ,Q k+1 ]And [ V ] l ,V l+1 ]Where k =1.. M-1, l =1.. N-1, the original function is divided into a (m-1) · (n-1) mesh, each mesh is divided into two triangles, the upper left corner and the lower right corner,
Figure RE-GDA0003868465180000101
and ζ k,l Is an index representing the position of the triangle; PH value max,i And pH min,i Respectively the maximum and minimum technical output of the hydroelectric generating set i; I.C. A i,t Is a binary variable representing the state of a hydropower unit I in a time period t, I i,t =1 denotes operating state, I i,t =0 for shutdown status; q max,i And Q min,i The upper limit value and the lower limit value of the generating water consumption of the hydropower station i are respectively set; nq (n/q) i,t The natural water inflow amount of the cascade hydropower station i in the t period; qs (quaternary ammonium salt) i,t The water abandoning amount of the hydropower station i in the time period t is determined; lambda h The time required for the water flow to flow from the upstream hydropower station to the downstream hydropower station, namely the water flow delay; j is an upstream hydropower station index of the hydropower station i, and in the model, the hydropower stations are numbered in sequence from upstream to downstream; u shape h An upstream set of hydroelectric power stations i; v max,i And V min,i The maximum and minimum water storage capacity of the reservoir i; ru t The upward rotating reserve capacity is provided for the step hydropower in the t period; ru max The maximum upward rotation reserve capacity can be provided for the step hydropower station in the t period; beta is a beta 1 Is an upward rotation standby coefficient; rd t Downward rotation reserve capacity is provided for the step hydropower in a time period t; rd max The maximum upward rotation reserve capacity can be provided for the step hydropower station in the t period; beta is a 2 The spare factor is rotated downward.
In the solar-photovoltaic-water power generation system day scheduling model containing the cascade hydropower,
the second objective function is:
Figure RE-GDA0003868465180000111
the second constraint includes:
1) And (3) power balance constraint:
Figure RE-GDA0003868465180000112
2) Hydroelectric power output adjustment value relative to day-ahead plan
Figure RE-GDA0003868465180000113
Wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model;
wherein, PW t hour Dispatching output for the wind power in the day of the t time period; PV t hour Scheduling output for the photovoltaic in the day of the t time period;
Figure RE-GDA0003868465180000114
dispatching output for hydropower in the day of the time period t,
Figure RE-GDA0003868465180000115
predicting the output of the load before the hour of the t period;
Figure RE-GDA0003868465180000116
an output adjustment value of the hydroelectric generating set i relative to the day-ahead hydropower dispatching in the t period;
Figure RE-GDA0003868465180000117
and (4) optimizing the output of the hydroelectric generating set i in the t time period for the day-ahead scheduling model.
In a wind-solar-water power generation system real-time scheduling model containing cascade hydropower,
the third objective function is:
Figure RE-GDA0003868465180000118
the third constraint includes:
1) And power balance constraint:
Figure RE-GDA0003868465180000119
2) Relative planned hydroelectric power output adjustment value in the day
Figure RE-GDA00038684651800001110
Wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model.
Wherein, γ i Adjusting the cost for the unit output of the hydroelectric generating set i when the output is frequently adjusted;
Figure RE-GDA00038684651800001111
a real-time output adjustment value of a hydropower dispatching plan 1 hour before a hydropower set i is compared at a time t; PW (pseudo wire) t rtl Output is scheduled for the wind power in real time at the time interval t; PV (photovoltaic) t rtl Output is scheduled for the photovoltaic in real time at the time period t;
Figure RE-GDA0003868465180000121
dispatching output for hydropower in real time at a time interval t;
Figure RE-GDA0003868465180000122
predicting force for the load 15 minutes before t period;
Figure RE-GDA0003868465180000123
and (4) optimizing the output of the hydroelectric generating set i in the time period t for the scheduling model in the day.
In order to reasonably schedule various power generation resources at different time scales, a coordination scheduling model of day-ahead scheduling, in-day 1-hour scheduling and real-time 15-minute scheduling is established in the embodiment. The resources determined by the previous timescale are treated as known quantities in the optimization of the subsequent timescale.
(1) Day-ahead scheduling model: every 24h, 1 time a day, with a resolution of 1h. Forecasting data of wind, light and load 24 hours in advance is adopted, and a hydroelectric generating set start-stop plan and various resource planned output in the future 24 hours are obtained in an optimized mode;
(2) 1 hour scheduling model in day: every 1h, 24 times a day, 15min resolution. Optimizing to obtain planned output and unit output adjustment of water, wind and light for 1 hour in the future by using wind, light and load prediction data 1 hour in advance and a hydroelectric generating set start-stop plan determined by a day-ahead scheduling plan;
(3) Real-time 15-minute scheduling model: every 15min, 96 times a day, with a resolution of 15min. And (3) optimizing to obtain the planned output of the hydroelectric generating set in the future 15 minutes and the output adjustment quantity of the hydroelectric generating set by using the forecast data of wind, light and load in advance for 15 minutes and the planned output of the hydroelectric generating set determined by scheduling in the day.
In order to more clearly explain the present embodiment, the following description is made with reference to fig. 2 to 5.
The system solution flow chart is shown in fig. 2. According to the characteristic that wind power output, photovoltaic power generation output and load prediction accuracy are improved step by step along with reduction of time scales, a multi-time scale coordinated scheduling model including day-ahead scheduling, in-day 1-hour scheduling and real-time 15-minute scheduling is established by using continuously updated prediction information, the flexible adjustment capacity of cascade hydropower is fully exerted, the uncertainty caused by new energy power generation is effectively relieved, the influence of the cascade hydropower station in rich, flat and dry periods on wind, light and light consumption is analyzed, and efficient utilization of clean wind, light and water energy is realized.
The clean energy base design example containing two-stage cascade hydropower, a wind power plant and a photovoltaic power station is adopted for verification, relevant data, power generation coefficients and incoming water of a hydroelectric generating set are shown in tables 1-3, the natural incoming water amount in the rich period is 8 times of that in the flat period, the natural incoming water amount in the flat period is 3 times of that in the dry period, the total installed capacity of the wind power plant is 140MW, and the total installed capacity of the photovoltaic power station is 80MW. Fig. 3 shows the predicted output intervals of the wind power and the photovoltaic power at different time scales. FIG. 4 is a multi-time scale load prediction curve for typical days of the rich season, the normal season and the dry season.
TABLE 1 hydroelectric generating set data sheet
Figure RE-GDA0003868465180000124
Figure RE-GDA0003868465180000131
TABLE 2 hydroelectric power coefficient table
Unit number a 1,i a 2,i a 3,i a 4,i a 5,i a 6,i
1 -0.0040 -0.30 0.030 0.90 10.0 -65
2 -0.0042 -0.30 0.015 1.14 9.5 -75
TABLE 3 Natural water meter for hydroelectric generating set in horizontal period
Time/h nq 1 nq 2 Time/h nq 1 nq 2
1 6.8256 4.0122 13 6.3126 3.8232
2 5.4162 3.9258 14 6.399 3.753
3 6.0912 3.8988 15 6.318 3.7098
4 5.7456 3.8718 16 6.2856 3.294
5 5.6862 3.7584 17 6.3558 3.3534
6 5.9346 3.5154 18 6.4476 3.3156
7 6.0102 3.2994 19 6.5718 3.3966
8 6.2316 3.2886 20 6.669 3.3372
9 6.3072 3.5802 21 6.5124 3.7044
10 6.1128 3.7314 22 6.426 3.3048
11 6.1938 3.7206 23 6.5556 3.4182
12 6.2316 3.5478 24 6.7176 3.483
And (II) the step hydroelectric power is used for responding to wind and light output fluctuation by providing a rotary spare capacity. Table 4 shows the ratio of the up and down rotation reserve capacity provided by the ladder hydropower station to the available rotation reserve capacity in the day-ahead, day-in and real-time dispatching model under different water periods. The available up-rotating and down-rotating reserve capacity provided by the hydroelectric power is determined as the installed capacity of the hydropower station multiplied by the corresponding reserve demand coefficient according to the equations (20) and (23).
TABLE 4 rotating reserve capacity ratio table provided by hydropower in multi-time scale scheduling model under different water periods
Figure RE-GDA0003868465180000141
In the water abundance period, the natural incoming water of the reservoir is abundant, the output of the hydroelectric generating set is close to full, the space for providing upward rotation reserve for the step hydroelectric is small, and the upward rotation reserve capacity provided by the hydroelectric generating set in different time scale models in the water abundance period in the table 4 is only 30.3% of the upper limit of the reserve capacity at most; the hydropower station can discard water to provide downward rotating reserve capacity, and the proportion of the downward rotating reserve capacity provided by hydropower in the water-rich period in the table 4 is 100 percent, namely the available downward rotating reserve capacity.
The natural water inflow amount in the horizontal period is moderate, the water storage regulating capacity of the reservoir is strong, and the maximum up-and-down rotating reserve capacity can be provided to cope with wind and light output fluctuation, so that the up-and-down rotating reserve capacity in the horizontal period in the table 4 accounts for 100 percent.
The reservoir in the dry season is limited by incoming water, the generating capacity of the hydroelectric generating set is less, and the available lower rotary reserve capacity is limited. In addition, when the reserve capacity provided by the cascade hydropower station does not reach the maximum reserve capacity (see the reserve ratio in the rich water period and the reserve ratio in the dry water period in table 4), the time scale is more and more close to real time along with the time sequence from the day-ahead scheduling and the intra-day scheduling to the real-time scheduling, the precision of wind and light prediction is gradually improved, the requirement of regulating wind and light fluctuation by the cascade hydropower station is reduced, and the requirement of rotating the reserve capacity is also gradually reduced. Through analysis of the rotating reserve capacities in different water periods, the wind-solar energy consumption level is summarized as the highest level period, the second lowest level period and the lowest full period according to different water periods.
And thirdly, the 24-hour wind and light abandoning conditions of the day-ahead, day-inside and real-time scheduling model in different water periods are shown in the table 5. Table 5 shows that, under different time scale scheduling, large wind abandon and light abandon are generated in the rich water period, and the water and electricity can not absorb wind and light; in the leveling period, the amount of abandoned wind and abandoned light is zero, and at the moment, wind power and photovoltaic output are output according to the upper limit of a prediction interval, and hydropower can completely stabilize wind-light fluctuation; the wind and light abandoning amount is less than that in the water-rich period in the dry period, although the wind and light fluctuation can not be completely stabilized, the wind and light absorption level is obviously improved compared with that in the water-rich period. From day-ahead scheduling, in-day scheduling to real-time scheduling, the wind abandoning amount and the light abandoning amount show descending trends along with the reduction of time scale, and the wind abandoning amount and the light abandoning amount are not generated even under a real-time scheduling model in a dry season, so that the maximum utilization of clean energy is realized.
TABLE 5 wind and light abandoning situation table for multi-time scale scheduling in different water periods
Figure RE-GDA0003868465180000151
In conclusion, the level cascade hydropower in the flat water period can completely absorb wind and light output according to the difference of the natural water inflow of the reservoir under different time scales, the absorption capacity in the rich and dry water periods is limited, but the level of the wind and light output fluctuation absorbed in the dry water period is superior to that in the rich water period; along with the gradual reduction of the time scale, the wind and light abandoning amount in the rich and dry season is gradually reduced, and the wind and light are completely absorbed in the flat season.
And (IV) in order to analyze the effectiveness of the multi-time scale scheduling model provided by the method, the multi-time scale scheduling model is compared with other scheduling models for research. Model one: and only adopting the day-ahead 24-hour scheduling model to optimally schedule the water, wind and light resources. Model II: the multi-time scale coordinated scheduling model herein is employed. Taking a typical day of the water-rich period as an example, table 6 shows the wind-solar utilization ratio and the up-and-down rotation reserve capacity comparison conditions of the model one and the model two in one day, and fig. 5 shows the 24-hour scheduling results of the model one and the model two.
TABLE 6 comparison table of wind-light utilization ratio and rotation reserve capacity of model I and model II
Figure RE-GDA0003868465180000152
Figure RE-GDA0003868465180000161
In table 6, the wind power utilization rate and the photovoltaic utilization rate of the model two are 92.3% and 96.2%, which are respectively higher than the wind utilization rate and the light utilization rate of the model one, which indicates that the multi-time scale scheduling of the model two is beneficial to improving the system's consumption level of uncertain energy such as wind, light and the like. From the analysis of the table 4, the ratio of the lower reserve capacity provided by hydropower in the water-rich period is 100%, so that the downward rotating reserve capacity of the model I or the model II is the maximum value 1056MW; the prediction accuracy of wind, light and load in the intra-day scheduling and real-time scheduling stages of the scheduling model is greatly improved compared with the prediction accuracy in the previous scheduling stage because the upward rotating reserve capacity provided by the model II is lower than that of the model I, so that the uncertainty of the wind, light and load can be compensated by the hydropower by only providing less rotating reserve capacity than the model I, and the adjustment burden of the hydroelectric generating set is relieved. Compared with the graph in FIG. 5, the multi-time scale coordinated scheduling model can effectively stabilize the fluctuation of wind-solar power output, and the complementary effect of wind-solar water resources is ensured by realizing good tracking of loads.
Through the analysis, the multi-time scale coordination scheduling model provided by the method can effectively improve the utilization rate of clean energy such as wind and light and improve the complementary effect of wind, light and water resources; compared with a single day-ahead scheduling model, the scheduling model needs less rotary spare capacity, so that safe and economic operation of the wind-light-water complementary power generation system is facilitated, and the effectiveness of the multi-time scale scheduling model provided by the text is verified.

Claims (6)

1. A multi-time scale coordinated scheduling method for a wind, photovoltaic and water power generation system with cascade hydropower is characterized by comprising the following steps:
(1) Pre-establishing a day-ahead scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the day-ahead scheduling model comprises a first objective function and corresponding first constraint conditions, wherein the first objective function takes the maximum sum of energy storage increased at the end of the whole scheduling period of the stepped reservoir and the minimum amount of abandoned wind and abandoned light as targets;
(2) Pre-establishing a daily scheduling model of the wind-solar-water power generation system containing the cascade hydropower; the in-day scheduling model comprises a second objective function and a corresponding second constraint condition, wherein the maximum energy storage added by the scheduling end reservoir is the target;
(3) Pre-establishing a wind-solar-water power generation system real-time scheduling model containing cascade hydropower; the real-time scheduling model comprises a third objective function and a corresponding third constraint condition, wherein the third objective function aims at minimizing the output real-time adjustment cost of the hydroelectric generating set.
2. The multi-time scale coordinated scheduling method of the wind, solar and water power generation system with cascaded hydropower according to claim 1, wherein in the step (1), the first objective function is:
Figure RE-FDA0003868465170000011
the first constraint condition comprises:
and (3) power balance constraint:
Figure RE-FDA0003868465170000012
wind power output restraint: PW of 0 or less t ≤PW max
Figure RE-FDA0003868465170000013
Figure RE-FDA0003868465170000014
Figure RE-FDA0003868465170000015
Figure RE-FDA0003868465170000016
Photovoltaic power generation output restraint: PV 0 ≤ t ≤PV max
Figure RE-FDA0003868465170000017
Figure RE-FDA0003868465170000018
Figure RE-FDA0003868465170000021
Figure RE-FDA0003868465170000022
Output linearization constraint of the hydroelectric generating set:
Figure RE-FDA0003868465170000023
Figure RE-FDA0003868465170000024
Figure RE-FDA0003868465170000025
Figure RE-FDA0003868465170000026
Figure RE-FDA00038684651700000214
and (3) restriction of water and electricity output:
Figure RE-FDA0003868465170000027
and (3) power generation flow restriction: I.C. A i,t ·Q min,i ≤Q i,t ≤I i,t ·Q max,i
And (3) water balance constraint:
Figure RE-FDA0003868465170000028
and (3) water storage capacity constraint: I.C. A i,t ·V min,i ≤V i,t ≤I i,t ·V max,i
Upward rotation reserve capacity constraint provided by step hydropower:
Figure RE-FDA0003868465170000029
Figure RE-FDA00038684651700000210
Figure RE-FDA00038684651700000211
downward rotation reserve capacity constraint provided by step hydropower:
Figure RE-FDA00038684651700000212
Figure RE-FDA00038684651700000213
Figure RE-FDA0003868465170000031
wherein, V i,T Representing the water storage capacity of the hydropower station i at the end of the dispatching cycle;
Figure RE-FDA0003868465170000032
representing the initial water storage capacity of the hydropower station i; r is the downstream hydropower station index of the hydropower station i; e i A downstream hydropower station set which is a hydropower station i; theta.theta. r Converting the water storage capacity of the reservoir i into an energy storage coefficient;
Figure RE-FDA0003868465170000033
the abandoned wind power quantity of the wind power plant at the time t period before the day;
Figure RE-FDA0003868465170000034
the photovoltaic power station is the light abandoning electric quantity of the photovoltaic power station in the period t in the day ahead;
Figure RE-FDA0003868465170000035
scheduling output of the wind power plant in the t time period is planned in the day ahead;
Figure RE-FDA0003868465170000036
planning the dispatching output of the photovoltaic power station in the t time period in the day ahead;
Figure RE-FDA0003868465170000037
reclaimed water electric machine set for day-ahead plani scheduling output at time t;
Figure RE-FDA0003868465170000038
the predicted load of the time period t under the day-ahead scale is obtained; PW (pseudo wire) max The installed capacity of the wind power plant;
Figure RE-FDA0003868465170000039
and
Figure RE-FDA00038684651700000310
respectively representing predicted upper and lower output limits of the wind power plant at t time period; mu.s w,t And σ w,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the wind power plant at the time t; the economy of scheduling of the wind-light-water complementary power generation system can be reduced even the scheduling problem is solved if the wind power interval is too large, so that the wind power output interval is set to [ mu ] w -1.96σ w ,μ w +1.96σ w ]So as to ensure that at least 95 percent of wind power output is utilized; PV (photovoltaic) max The installed capacity of the photovoltaic power station;
Figure RE-FDA00038684651700000311
and
Figure RE-FDA00038684651700000312
respectively representing the predicted upper and lower output limits of the photovoltaic power station at the time interval t; mu.s v,t And σ v,t Respectively predicting the mean value and the standard deviation of the output probability distribution of the photovoltaic power station in the t period; similarly, the output interval of the photovoltaic power station is set to [ mu ] v -1.96σ v ,μ v +1.96σ v ]So as to ensure that at least 95 percent of photovoltaic power generation output is utilized; v i,t The water storage capacity of the hydropower station i in the time period t; q i,t The water consumption for generating power of the hydropower station i in the t time period; a is 1,i To a 6,i All are hydroelectric generation coefficients; dividing Q and V into forms [ Q ] k ,Q k+1 ]And [ V ] l ,V l+1 ]Wherein k =1.. M-1, l =1.. N-1, the original function is divided into a grid of (m-1) · (n-1), each gridThe grid is divided into two triangles with the upper left corner and the lower right corner,
Figure RE-FDA00038684651700000313
and ζ k,l Is an index representing the position of the triangle; PH value max,i And pH min,i Respectively the maximum technical output and the minimum technical output of the hydroelectric generating set i; i is i,t Is a binary variable representing the state of a hydropower unit I in a time period t, I i,t =1 denotes operating state, I i,t =0 for shutdown status; q max,i And Q min,i Respectively the upper limit value and the lower limit value of the generating water consumption of the hydropower station i; nq (n/q) i,t The natural water inflow of the cascade hydropower station i in the t period; qs i,t The water abandoning amount of the hydropower station i in the time period t is determined; lambda h The time required for the water flow to flow from the upstream hydropower station to the downstream hydropower station, namely the water flow delay; j is an upstream hydropower station index of the hydropower station i, and in the model, the hydropower stations are numbered in sequence from upstream to downstream; u shape h An upstream set of hydroelectric stations, i; v max,i And V min,i The maximum and minimum water storage capacity of the reservoir i; ru t The upward rotating reserve capacity is provided for the step hydropower in the t period; ru max The maximum upward rotation reserve capacity can be provided for the step hydropower in the time period t; beta is a beta 1 Is an upward rotation standby factor; rd t Downward rotation reserve capacity is provided for the step hydropower in a time period t; rd max The maximum upward rotation reserve capacity can be provided for the step hydropower in the time period t; beta is a beta 2 The spare factor is rotated downward.
3. The coordinated scheduling method for multiple timescales of wind, photovoltaic and water power generation system with cascaded hydropower according to claim 1, wherein in the step (2), the second objective function is:
Figure RE-FDA0003868465170000041
the second constraint includes:
and power balance constraint:
Figure RE-FDA0003868465170000042
relative to the planned hydroelectric power adjustment value before the day:
Figure RE-FDA0003868465170000043
wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model;
wherein the content of the first and second substances,
Figure RE-FDA0003868465170000044
dispatching output for the wind power in the day of the time period t;
Figure RE-FDA0003868465170000045
scheduling output for the photovoltaic in the day of the t time period;
Figure RE-FDA0003868465170000046
dispatching output for hydropower in the day of the time period t,
Figure RE-FDA0003868465170000047
predicting the output of the load before the hour of the t time period;
Figure RE-FDA0003868465170000048
an output adjustment value of the hydroelectric generating set i relative to the day-ahead hydropower dispatching in the t period;
Figure RE-FDA0003868465170000049
and (4) optimizing the output of the hydroelectric generating set i in the t time period for the day-ahead scheduling model.
4. The coordinated scheduling method for multiple timescales of wind, photovoltaic and water power generation system with cascaded hydro-power generation as claimed in claim 1, wherein in the step (3), the third objective function is:
Figure RE-FDA00038684651700000410
the third constraint includes:
and (3) power balance constraint:
Figure RE-FDA00038684651700000411
relative to the planned hydroelectric power adjustment value before the day:
Figure RE-FDA0003868465170000051
wind and light output constraints, cascade hydropower constraints and rotary standby constraints are similar to a day-ahead scheduling model.
Wherein, γ i Adjusting cost for unit output of the hydroelectric generating set i when output is frequently adjusted;
Figure RE-FDA0003868465170000052
a real-time output adjustment value of a hydropower unit scheduling plan 1 hour before a time t is compared;
Figure RE-FDA0003868465170000053
outputting power for the real-time scheduling of the wind power in the t time period;
Figure RE-FDA0003868465170000054
output is dispatched for the photovoltaic in real time at the time interval t;
Figure RE-FDA0003868465170000055
the power is output for real-time scheduling of water and electricity in a time period t;
Figure RE-FDA0003868465170000056
predicting force for the load 15 minutes before t period;
Figure RE-FDA0003868465170000057
and (4) optimizing the output of the hydroelectric generating set i in the time period t for the intraday scheduling model.
5. A computer storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a multi-timescale coordinated scheduling method for a wind, photovoltaic and water power generation system with cascaded hydroelectricity according to any of claims 1 to 4.
6. Computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor when executing the computer program implements a multi-timescale coordinated scheduling method of a wind, photovoltaic and water power generation system comprising cascaded hydro-power according to any of the claims 1-4.
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CN116307632A (en) * 2023-05-11 2023-06-23 长江三峡集团实业发展(北京)有限公司 Hydropower station economic load distribution method and device, electronic equipment and storage medium
CN117649102A (en) * 2024-01-30 2024-03-05 大连理工大学 Optimal scheduling method of multi-energy flow system in steel industry based on maximum entropy reinforcement learning

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Publication number Priority date Publication date Assignee Title
CN116307632A (en) * 2023-05-11 2023-06-23 长江三峡集团实业发展(北京)有限公司 Hydropower station economic load distribution method and device, electronic equipment and storage medium
CN116307632B (en) * 2023-05-11 2023-08-18 长江三峡集团实业发展(北京)有限公司 Hydropower station economic load distribution method and device, electronic equipment and storage medium
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