CN108009672A - Water light complementation power station daily trading planning preparation method based on bi-level optimal model - Google Patents
Water light complementation power station daily trading planning preparation method based on bi-level optimal model Download PDFInfo
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Abstract
The present invention provides a kind of water light complementation power station daily trading planning preparation method based on bi-level optimal model, it is characterised in that including：Step 1：Predict photoelectricity time daily output process, generate a variety of photoelectricity output scenes based on photoelectricity output uncertainty models and calculate corresponding probability；Step 2：Predict reservoir next day two Phase flow process, long-term operation plan determines day water volume that can be utilized or electricity in；Step 3：Bi-level optimal model is established, outer layer model considers the distribution of power system requirements optimization water volume that can be utilized or electricity in time, and optimization aim is water light complementation power station gross capability process and typical day load curve correlation maximum；Interior layer model considers distribution of the operational efficiency optimization water in power station between unit, and optimization aim is hydroelectric station operation efficiency highest under Scenario；Step 4：Solving model, outer layer determine that the generating flow of power station day part and unit start number of units, internal layer determine load shifting rate strategy using dynamic programming using intelligent algorithm.
Description
Technical Field
The invention belongs to the cross field of renewable energy utilization and reservoir scheduling, and particularly relates to a daily power generation planning method for a water-light complementary power station based on a double-layer optimization model.
Technical Field
The development of solar photovoltaic power generation is an important measure for relieving the future energy crisis and improving the energy structure. However, the photoelectricity is a new energy source which is not schedulable, and is easily influenced by various meteorological factors, and the output presents strong intermittence, fluctuation and randomness. The direct photovoltaic grid connection can cause impact on the safe and stable operation of the power grid. The hydropower has the characteristics of flexible starting, high adjusting speed, low running cost and the like, and is an ideal adjusting power supply. The complementary power generation of water and light is implemented, the light and the light which fluctuate violently are accessed into a hydropower station, the rapid adjustment capability of the hydropower station is utilized for compensation, and the superposed stable output is sent into a power grid, so that the adverse effect on the operation of the power grid can be greatly reduced. The compilation of daily power generation plans of hydropower stations is a basic subject of the economic operation of the hydropower stations.
The traditional hydropower station power generation plan is made by formulating a reasonable starting and stopping sequence and a load distribution strategy among units under the condition of giving total water quantity per day or total electric quantity per day, so that the generating capacity or the water consumption of the hydropower station is the maximum. With random photoelectric access to the hydropower station, hydropower scheduling decisions become uncertain, so that a power generation plan made by the traditional method cannot effectively guide the actual operation of the water-photovoltaic complementary power station. How to make a daily power generation plan under the condition that the photoelectric output prediction is uncertain so that a water-light complementary power station can run safely and economically is an urgent problem to be solved in implementation of water-light complementary scheduling.
Disclosure of Invention
The present invention has been made to solve the above problems, and an object of the present invention is to provide a method for planning daily power generation of a water-photovoltaic hybrid power plant based on a two-layer optimization model.
In order to achieve the purpose, the invention adopts the following scheme:
the invention provides a water-light complementary power station daily power generation planning method based on a double-layer optimization model, which is characterized by comprising the following steps of: the method comprises the following steps: predicting a photoelectric next-day output process, generating a plurality of photoelectric scenes based on a photoelectric output uncertainty model, and calculating the probability corresponding to each scene; step two: predicting the runoff process of the storage of the next day of the reservoir, and determining the daily available water consumption or daily available electric quantity according to the medium-long term scheduling plan; step three: establishing a double-layer optimization model, wherein the outer layer model considers the requirement of the power system on optimizing the distribution of available water quantity or daily available electric quantity in time; the inner layer model optimizes the distribution of water between units by considering the operation efficiency of the hydropower station; the outer layer optimization target is that the correlation between the total output process of the water-light complementary power station and a typical daily load curve is maximum, and the calculation formula is as follows:the inner layer optimization target is that hydropower station operating efficiency is the highest under the many-situation scenes, and the calculation formula is:in the formula: r is a correlation coefficient of a total output process of the water-light complementary power station and a typical daily load curve; t is the total scheduling time period number; t is the scheduling time interval number;the total output of the water-light complementary power station in the time period t is obtained;the average output of the water-light complementary power station is obtained; d t Is a per unit value of a typical daily load curve t time period;the average value of the per unit values of the typical daily load curve; eta is the average generating efficiency coefficient of the hydropower station; m is the number of photoelectric scenes; m is a photoelectric scene number; rho m Probability corresponding to the mth photoelectric scene;the output of the hydropower station at the t moment under the mth scene is obtained; r m,t Generating flow of the hydropower station at the t moment under the mth scene; eta is the average generating efficiency coefficient of the hydropower station; step four: and solving a double-layer optimization model, determining the power generation flow and the number of the units in each period of the power station by adopting an intelligent algorithm at the outer layer, and determining the optimal load distribution strategy by adopting a dynamic programming method at the inner layer.
The method for compiling the daily power generation plan of the water-light complementary power station based on the double-layer optimization model can also have the following characteristics: in the second step: firstly, a mathematical statistical model or a physical model is adopted to predict the photoelectric output process (P) of the future day t T =1, \ 8230;, T); second, assume that the photoelectric prediction error e follows a normal distribution N (0, σ) 2 ) (ii) a Considering three prediction errors, i.e. the prediction is smaller (e) 1 = -sigma), prediction is accurate (e) 1 = 0) and prediction bias (e) 3 = + sigma), and different photoelectric output scenes can be obtained by subtracting different prediction error values from the prediction value; finally, discrete probability distribution is used for replacing continuous probability distribution to calculate probability rho corresponding to each photoelectric scene 1 ,ρ 2 ,ρ 3 ：
The method for compiling the daily power generation plan of the water-light complementary power station based on the double-layer optimization model can also have the following characteristics: in the second step, firstly, a mathematical statistic model or a hydrological model is adopted to predict the warehousing flow process of the reservoir in the future day (I) t T =1, \ 8230;, T); and secondly, evenly distributing the water to be drained in the medium-long term scheduling plan to each day, so as to determine the daily available water consumption.
The method for compiling the daily power generation plan of the water-light complementary power station based on the double-layer optimization model can also have the following characteristics: in the third step: in the formula: n is the number of hydroelectric generating sets; n is the unit number; u. u n,t The on-off state of the hydroelectric generating set is set (1 is on, and 0 is off);the output of the nth hydroelectric generating set in the t time period under the mth scene;the output of the photovoltaic power station in the mth scene and the tth time period; f. of rph The relation among the unit flow, output and water head in the power characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is formed; f. of qz The relationship between the downward discharge flow and the tail water level is adopted; r is m,n,t The method comprises the steps of obtaining a power generation reference flow of an nth hydroelectric generating set in an mth scene in a tth time period; h is m,t ，The net head, the dam front water level, the tail water level and the head loss in the mth scene at the t time period are respectively; v. of m,t And v m,t+1 The reservoir storage capacity is at the beginning of the t-th time period and at the end of the t-th time period in the mth scene.
The method for compiling the daily power generation plan of the water-light complementary power station based on the double-layer optimization model can also have the following characteristics: in the fourth step, the intelligent algorithm is adopted to determine the generating flow [ q ] of each time interval of the power station 1 ,…,q T ]And the number of units started [ y ] 1 ,…,y T ]Then, the encoding mode of the intelligent algorithm solution is represented by the following formula: solution = [ q ] 1 ,…,q T ,y 1 ,…,y T ]Two-stage recursion method when dynamic planning is adopted to carry out optimal load distributionThe process is as follows:in the formula:as a total load ofOptimal water consumption distributed among d units, f rph (p d,t ,h t ) As a load of p d,t Head of water h t The water consumption of the unit d is increased,as a total load ofOptimal water consumption is allocated among d-1 units.
Action and Effect of the invention
The invention fully considers the randomness characteristic of the photoelectric output, and can still provide a steady and efficient power generation plan for guiding the actual operation of the water-light complementary power station under the condition of inaccurate photoelectric prediction.
Drawings
Fig. 1 is a flowchart of a method for compiling a daily power generation plan of a water-light complementary power station based on a double-layer optimization model in an embodiment of the present invention.
Detailed Description
The following describes in detail a specific embodiment of a method for planning daily power generation of a water-photovoltaic hybrid power plant based on a two-layer optimization model according to the present invention with reference to the drawings.
< example >
As shown in fig. 1, the method for planning daily power generation of a water-light complementary power station based on a double-layer optimization model according to this embodiment includes the following steps:
1. and predicting the photoelectric next-day output process, generating a plurality of photoelectric output scenes based on the photoelectric output uncertainty model, and calculating the probability corresponding to each scene.
Firstly, a mathematical statistical model or a physical model is adopted to predict the photoelectric output process (P) of the future day t ，t＝1，…，T)；
Second, assume that the photoelectric prediction error e follows a normal distribution N (0, σ) 2 ) (ii) a Considering three prediction errors, i.e. the prediction is smaller (e) 1 = -sigma), prediction is accurate (e) 1 = 0) and prediction bias (e) 3 = + σ), different photoelectric output scenes can be obtained by subtracting different prediction error values from the predicted values;
finally, the discrete probability distribution is used to replace the continuous probability distribution to calculate the probability (rho) corresponding to each photoelectric scene 1 ,ρ 2 ,ρ 3 ) The following were used:
2. and predicting the runoff process of the next day of storage of the reservoir, and determining the daily available water consumption according to the medium-term and long-term scheduling plan.
Firstly, a mathematical statistic model or a hydrological model is adopted to predict the warehousing flow process of the reservoir in the next day (I) t ，t＝1，…，T)；
Secondly, the water to be drained in the medium-long term (month or ten days) scheduling plan is evenly distributed to each day, so that the daily available water consumption is determined.
3. Establishing a double-layer optimization model, wherein the outer layer model considers the requirement of a power system to optimize the distribution of available water quantity on time; the inner layer model optimizes the distribution of water among units by considering the running efficiency of the hydropower station.
The outer layer optimization target is that the correlation between the total output process of the water-light complementary power station and a typical daily load curve is maximum, and the calculation formula is as follows:
in the formula: r is a correlation coefficient of a total output process of the water-light complementary power station and a typical daily load curve; t is the total scheduling time period number; t is the scheduling time interval number;the total output of the water-light complementary power station in the time period t is obtained;the average output of the water-light complementary power station is obtained; d t Is a per unit value of a typical daily load curve t time period;the mean value of the per unit value of the typical daily load curve; n is the number of hydroelectric generating sets; n is the unit number; u. of n,t The on-off state of the hydroelectric generating set is set (1 is on, and 0 is off); m is a photoelectric scene number;the output of the nth unit in the mth scene of the hydroelectric generating set in the tth time period;the output is the output of the photovoltaic power station in the mth scene and the tth time period. f. of rph The relation among the unit excess flow, output and water head in the dynamic characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is formed; f. of qz The relationship of the lower discharge flow and the tail water level is adopted; r is m,n,t The method comprises the steps of obtaining a power generation reference flow of an nth hydroelectric generating set in an mth scene in a tth time period; h is m,t ，The net head, the dam front water level, the tail water level and the head loss in the mth scene at the t time period are respectively; v. of m,t And v m,t+1 The initial and final reservoir capacities at the t-th and t-th time periods in the m-th scenario.
The inner layer optimization target is that hydropower station operating efficiency is the highest under the many-situation scenes, and the calculation formula is:
in the formula: eta is the average generating efficiency coefficient of the hydropower station; m is the total photoelectric scene number; m is a photoelectric scene number; ρ is a unit of a gradient m Probability corresponding to the mth photoelectric scene;the output of the hydropower station at the t moment under the m scene is obtained; r m,t For hydropower station in mth scene at the t momentThe generated power flow rate of (1).
The constraint conditions considered by the established double-layer optimization model are as follows: the method comprises the following steps of water balance constraint, daily water consumption constraint (or daily generated energy constraint), reservoir capacity constraint, unit machine flow passing constraint, unit output constraint, load standby constraint, output lifting constraint, minimum start-stop constraint and vibration area constraint.
v m,t+1 ＝v m,t +(I t -R m,t -L m,t )Δt (11)
In the formula: v. of m,t And v m,t+1 The storage capacities of the reservoir at the beginning and the end of the t time period under the mth scene are respectively; I.C. A t The reservoir warehousing flow is the t-th time period; l is m,t The water discharge of the reservoir is determined; Δ W is the daily available water amount (if a power generation plan is made based on the daily available electric quantity, here, the daily available electric quantity of hydropower is changed);andrespectively the lower limit and the upper limit of the storage capacity;andrespectively is a lower limit value and an upper limit value of the generating flow of the nth unit in the t period;andrespectively the lower limit and the upper limit of the unit output; LR t A load reserve value at the t-th time period of the hydropower station; p is a radical of m,n,t And p m,n,t-1 Respectively are the force output values of the nth unit in the t-th time period and the t-1 time period under the mth scene; Δ p of d And Δ p u Respectively the upper limit values of the output descending speed and the output ascending speed of the hydropower station; SU n And SD n The minimum duration of the starting and stopping states of the hydroelectric generating set; su n,t Indicating the state for the starting process of the unit (1 is starting, and 0 is non-starting); sd n,t Indicating the state (1 is closed and 0 is not closed) for the shutdown process of the unit;andrespectively the lower limit and the upper limit of the vibration area of the unit.
4. And solving a double-layer optimization model, wherein an intelligent algorithm (such as a genetic algorithm and a cuckoo algorithm) is adopted in the outer layer to determine the power generation flow of each time period of the power station and the number of the units during starting.
The intelligent algorithm is adopted to determine the generating flow ([ q ] of the power station in each time interval 1 ,…,q T ]) And the number of units started ([ y ] 1 ,…,y T ]) Then, the encoding method of the intelligent algorithm solution (individual) can be represented by the following formula:
solution＝[q 1 ,…,q T ,y 1 ,…,y T ] (21)
when the dynamic planning method is adopted in the inner layer to optimize the load distribution strategy, the calculation of the dynamic planning can be completed in advance due to the fact that the calculation time consumption of the dynamic planning is large. Namely, calculating the optimal distribution strategy (the load born by each unit and the generated flow) of all the loads under all possible water heads under different starting units. When double-layer optimization is carried out, related calculation results are directly called. When the dynamic planning is adopted to carry out the optimal load distribution, the two-stage recursion equation is as follows:
in the formula:as a total loadOptimal water consumption distributed among the d units; f. of rph (p d,t ,h t ) As a load of p d,t Head of water h t Water consumption of the time unit d;as a total load ofOptimal water consumption is allocated among d-1 units.
The above embodiments are merely illustrative of the technical solutions of the present invention. The method for planning daily power generation of a water-photovoltaic complementary power station based on a double-layer optimization model is not limited to the contents described in the above embodiments, but is subject to the scope defined by the claims. Any modification or supplement or equivalent replacement made by a person skilled in the art on the basis of this embodiment is within the scope of the invention as claimed in the claims.
Claims (5)
1. A water-light complementary power station daily power generation planning method based on a double-layer optimization model is characterized by comprising the following steps:
the method comprises the following steps: predicting the photoelectric next-day output process, generating a plurality of photoelectric output scenes based on the photoelectric output uncertainty model, and calculating the probability corresponding to each scene;
step two: forecasting the runoff process of the next day of storage of the reservoir, and determining the daily available water consumption or daily available electric quantity according to the medium-long term scheduling plan;
step three: establishing a double-layer optimization model, wherein the outer layer model considers the requirement of the power system on optimizing the distribution of available water quantity or daily available electric quantity in time; the inner layer model optimizes the distribution of water between units by considering the operation efficiency of the hydropower station;
the outer layer optimization target is that the correlation between the total output process of the water-light complementary power station and a typical daily load curve is maximum, and the calculation formula is as follows:
the inner layer optimization target is that hydropower station operating efficiency is the highest under the many-situation scenes, and the calculation formula is:
in the formula: r is a correlation coefficient between the total output process of the water-light complementary power station and a typical daily load curve; t is the total scheduling time period number; t is the scheduling time interval number; p t sg The total output of the water-light complementary power station in the time period t is obtained;the average output of the water-light complementary power station is obtained; d t Is a per unit value of a typical daily load curve t time period;the average value of the per unit values of the typical daily load curve; eta is the average generating efficiency coefficient of the hydropower station; m is the number of photoelectric scenes; m is a photoelectric scene number; rho m The probability corresponding to the mth photoelectric scene;the output of the hydropower station at the t moment under the mth scene is obtained; r m,t Generating flow of the hydropower station at the t moment under the mth scene; eta is the average generating efficiency coefficient of the hydropower station;
step four: and solving a double-layer optimization model, determining the generation flow and the number of the units in each period of the power station by adopting an intelligent algorithm on an outer layer, and determining an optimal load distribution strategy by adopting a dynamic programming method on an inner layer.
2. The double-layer optimization model-based daily power generation planning method for the water-light complementary power station according to claim 1, characterized in that:
wherein, in the step two: firstly, a mathematical statistical model or a physical model is adopted to predict the photoelectric output process (P) of the future day t T =1, \8230;, T); second, assume that the photoelectric prediction error e follows a normal distribution N (0, σ) 2 ) (ii) a Considering three prediction errors, i.e. the prediction is smaller (e) 1 = -sigma), prediction is accurate (e) 1 = 0) and prediction bias (e) 3 = + σ), different photoelectric output scenes can be obtained by subtracting different prediction error values from the predicted values; finally, discrete probability distribution is used to replace continuous probability distribution to calculate the probability (rho) corresponding to each photoelectric scene 1 ,ρ 2 ,ρ 3 )：
3. The double-layer optimization model-based daily power generation planning method for the water-light complementary power station according to claim 1, characterized in that:
in the second step, firstly, a mathematical statistic model or a hydrological model is adopted to predict the warehousing flow process of the reservoir in the next day (I) t T =1, \8230;, T); and secondly, evenly distributing the water to be drained in the medium-long term scheduling plan to each day, so as to determine the daily available water consumption.
4. The double-layer optimization model-based daily power generation planning method for the water-light complementary power station according to claim 1, characterized in that:
wherein, in the third step:
in the formula: n is the number of hydroelectric generating sets; n is the unit number; u. of n,t The on-off state of the hydroelectric generating set is set (1 is on, and 0 is off);the output of the nth hydroelectric generating set in the t time period under the mth scene;the output of the photovoltaic power station in the mth scene and the tth time period; f. of rph The relation among the unit flow, output and water head in the power characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is obtained; f. of qz The relationship between the downward discharge flow and the tail water level is adopted; r is m,n,t The method comprises the steps of obtaining a power generation reference flow of an nth hydroelectric generating set in an mth scene in a tth time period; h is m,t ， The water purification head, the dam front water level, the tail water level and the head loss in the mth scene at the t time period are respectively; v. of m,t And v m,t+1 The reservoir storage capacity is at the beginning of the t-th time period and at the end of the t-th time period in the mth scene.
5. The double-layer optimization model-based daily power generation planning method for the water-light complementary power station according to claim 1, characterized in that:
wherein, in the fourth step, the intelligent algorithm is adopted to determine the generating flow [ q ] of the power station in each time interval 1 ,…,q T ]And the number of units started [ y 1 ,…,y T ]Then, the encoding mode of the intelligent algorithm solution is represented by the following formula:
solution＝[q 1 ,…,q T ,y 1 ,…,y T ]，
when the dynamic planning is adopted to carry out the optimal load distribution, the two-stage recursion equation is as follows:
in the formula:as a total load ofOptimal water consumption, f, allocated among d units rph (p d,t ,h t ) Is a load of p d,t Water head of h t The water consumption of the d-th unit is calculated,as a total load ofOptimal water consumption for distribution among d-1 units.
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| CN112165123A (en) * | 2020-08-10 | 2021-01-01 | 郑州大学 | Photovoltaic capacity calculation method for small and medium-sized water-light complementary system |
| CN113205250A (en) * | 2021-04-28 | 2021-08-03 | 河海大学 | Wind-solar-water complementary power generation plan compilation method considering reservoir scheduling risk |
| CN113946783A (en) * | 2021-09-26 | 2022-01-18 | 武汉大学 | Water-light complementary optimization scheduling method and system |
| CN115860282A (en) * | 2023-02-28 | 2023-03-28 | 长江水利委员会水文局 | Method and device for controllably forecasting total power of water and wind power system |
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| CN113205250B (en) * | 2021-04-28 | 2022-08-19 | 河海大学 | Wind-solar-water complementary power generation plan compilation method considering reservoir scheduling risk |
| CN113946783A (en) * | 2021-09-26 | 2022-01-18 | 武汉大学 | Water-light complementary optimization scheduling method and system |
| CN113946783B (en) * | 2021-09-26 | 2024-07-02 | 武汉大学 | Water-light complementary optimal scheduling method and system |
| CN115860282A (en) * | 2023-02-28 | 2023-03-28 | 长江水利委员会水文局 | Method and device for controllably forecasting total power of water and wind power system |
| CN115860282B (en) * | 2023-02-28 | 2023-06-06 | 长江水利委员会水文局 | Method and device for forecasting total power of water-wind-solar system in controllable manner |
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