Medium-and-long-term scheduling method of water-wind-light complementary system considering extreme climate conditions

Abstract

A medium-and-long-term scheduling method for a water-wind-light complementary system considering extreme climate conditions comprises the following steps that S1, a medium-and-long-term scheduling model for the water-wind-light complementary system considering the extreme climate conditions is established, and target functions of the scheduling model comprise a system total on-grid electric quantity function and a system operation performance function under the extreme low output situation; s2, target function merging is carried out by using a penalty function method, after a multi-target problem is converted into a single-target problem, the deterministic optimization model is regarded as a multi-stage decision problem, a discrete differential dynamic programming method is adopted for solving, and galleries are continuously divided to realize dimension reduction; and S3, constructing a scheduling function by adopting a linear fitting mode, and solving to realize medium-and-long-term scheduling of the water-wind-light complementary system. The method is used for solving the problems that extreme climate conditions are difficult to consider in long-term scheduling of an existing water-wind-light complementary system, and scheduling rules cannot adapt to the extreme climate conditions.

Description

Technical Field

[0001] The present invention relates to the field of clean energy utilization, and in particular to a long-term scheduling method for a water-wind-solar complementary system taking into account extreme climatic conditions. Background Art

[0002] In order to reasonably describe the uncertain input to guide the actual scheduling and operation of multi-energy complementary systems, existing research mainly has the following methods: (1) Explicit stochastic optimization. The long-sequence scheduling process is regarded as a Markov process accompanied by decision benefits. The probability statistical method is used to characterize the uncertainty of the input, which is explicitly added to the scheduling model, and then the deterministic optimization of the long-sequence process is solved. (2) Implicit stochastic optimization. The deterministic optimization results of a large number of historical input scenarios are statistically analyzed to extract the specific functional relationship between the decision variables and the preferred independent variables, and the uncertainty properties of the input are implicitly expressed in the scheduling model. (3) Parameter simulation optimization. The scheduling rules of parameter type are given in advance, and the algorithm is used to simulate the scheduling and optimization of different parameters, and finally the scheduling rules of the preferred parameters are obtained.

[0003] The disadvantages of the existing technology are that it is difficult to consider extreme climate conditions in the medium- and long-term scheduling of the water-wind-solar complementary system, and the traditional scheduling rules cannot adapt to extreme climate conditions. The existing medium- and long-term scheduling methods of the water-wind-solar complementary system do not take extreme climate conditions into account, and the objective function usually only considers the power generation and guarantee rate, ignoring the loss of complementary system benefits under extreme climate conditions. Summary of the invention

[0004] The purpose of the present invention is to provide a medium- and long-term scheduling method for a water-wind-solar complementary system taking into account extreme climatic conditions, so as to solve the problem that it is difficult to take extreme climatic conditions into account in the medium- and long-term scheduling of the existing water-wind-solar complementary system and the scheduling rules cannot adapt to extreme climatic conditions.

[0005] In order to solve the above problems, the technical solution of the present invention is:

[0006] A method for medium- and long-term scheduling of a water-wind-solar hybrid system considering extreme climatic conditions comprises the following steps:

[0007] S1: Establish a long-term dispatch model for the water-wind-solar hybrid system considering extreme climatic conditions. The dispatch model objective function includes the total grid-connected power function of the system and the system operation performance function under extreme low output scenarios;

[0008] S2: After merging the objective functions using the penalty function method and converting the multi-objective problem into a single-objective problem, the deterministic optimization model is regarded as a multi-stage decision-making problem and solved using the discrete differential dynamic programming method, continuously dividing the corridor to achieve dimensionality reduction;

[0009] S3: Use linear fitting to construct the dispatching function and solve the medium- and long-term dispatching model of the water-wind-solar complementary system; according to the optimal operation trajectory of the system obtained by deterministic optimization dispatching, first determine the correlation between various variables, and then determine the optimal decision variables and independent variables through linear fitting. The correlation between various variables is characterized by the Pearson correlation coefficient.

[0010] The beneficial effects of the present invention are:

[0011] Under the background of energy crisis and climate change, the water-wind-solar complementary system faces the problem of new energy consumption and energy supply: on the one hand, the problems of water abandonment, wind abandonment and solar abandonment are still serious; on the other hand, the problem of energy supply under extreme hydrological and meteorological conditions is severe. The present invention first establishes a medium- and long-term scheduling model for the water-wind-solar complementary system considering extreme climatic conditions, comprehensively considering the online power and the operation performance of the complementary system under extremely low output conditions; then, a penalty function is used to merge multiple objectives, and discrete differential dynamic programming is used to perform deterministic optimization scheduling of long-sequence water-wind-solar processes; finally, correlation analysis and simulation optimization are performed based on the obtained medium- and long-term scheduling results, the optimal type and parameters of the system scheduling rules are identified, and the medium- and long-term scheduling rules considering extreme climatic conditions are extracted, which solves the problem that the existing medium- and long-term scheduling methods of water-wind-solar complementary systems do not consider extreme climatic conditions and ignore the loss of benefits of complementary systems under extreme climatic conditions. The medium- and long-term scheduling rules for water-wind-solar complementary systems considering extreme climatic conditions are deduced, which can effectively improve the climate resilience of the complementary system and provide technical support for the scheduling and operation of the complementary system under extreme climate conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The present invention will be further described below in conjunction with the accompanying drawings:

[0013] Figure 1 is a flow chart of the present invention,

[0014] Figure 2 Schematic diagram of the optimal running trajectory of the present invention. DETAILED DESCRIPTION

[0015] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] like Figure 1 and 2 As shown, a medium- and long-term scheduling method for a water-wind-solar hybrid system considering extreme climatic conditions includes the following steps:

[0017] S1: Scheduling model construction:

[0018] Establish a long-term dispatch model for the hydro-wind-solar hybrid system considering extreme climate conditions. The dispatch model objective function includes the total grid-connected power function of the system and the system operation performance function under extreme low output scenarios.

[0019] S2: Scheduling model solution:

[0020] In order to solve the long-term scheduling model of the water-wind-solar complementary system, the penalty function method is used to merge the objective functions. After converting the multi-objective problem into a single-objective problem, the deterministic optimization model can be regarded as a multi-stage decision-making problem. The discrete differential dynamic programming method is used to solve it, and the corridors are continuously divided to achieve dimensionality reduction.

[0021] S3: Scheduling rule extraction: The scheduling function is constructed by linear fitting. According to the optimal operation trajectory of the system obtained by deterministic optimization scheduling, the correlation between various variables is first determined, and then the optimal decision variables and independent variables are determined by linear fitting. The correlation between various variables is characterized by the Pearson correlation coefficient.

[0022] In step S1, the total online power function of the system is:

[0023]

[0024] N(t)=N HP (t)+N WD (t)+N PV (t);

[0025] Where: E T is the total grid-connected power of the system during the dispatch period; P(t) and N(t) are the grid-connected power output and power generation output of the system in the tth period, respectively; N c (t) is the power output of the system in the t period; N HP (t), N WD (t), N PV (t) are the power generation output of hydropower, wind power and photovoltaic power in the system in the tth period; T is the number of time periods in the dispatch period; Δt is the length of the time period.

[0026] In step S1, the system operation performance function under the extremely low output scenario is:

[0027]

[0028] Where: FR is the complementary system guarantee rate; P f To ensure output for complementary systems; P min The minimum on-grid output of the complementary system is lower than the guaranteed output; D b is the set of time periods when the system's online output is lower than the guaranteed output, i.e., Db ={t|P(t)<P f}={n1,n2,…,n b};P avn The average value of the on-grid output of the complementary system is lower than the guaranteed output; T b is the number of time periods when the system's online output is lower than the guaranteed output, i.e. D b Length.

[0029] In step S1, the constraints of the scheduling model include: water balance constraints, reservoir capacity constraints, power generation head constraints, hydropower station output constraints, transmission capacity constraints, discharge capacity constraints, hydropower station maximum flow capacity constraints, ecological flow constraints and boundary condition constraints.

[0030] The penalty function used in step S2 includes:

[0031]

[0032] Where: E T * is the total on-grid power of the system considering the penalty item; P p,i (t) corresponds to the penalty terms of the three objective functions under the extremely low output scenario, i = 1, 2, 3; α i , β i are the penalty coefficients and penalty powers of the three penalty items; P min (t), P avn (t) is the P calculated according to the current process during the calculation process min , P avn .

[0033] In step S2, the recursive equation of a dynamic programming includes:

[0034]

[0035] Where: V (i) (t) is the i-th discrete value of the initial reservoir storage capacity in period t, i = 1~N;

[0036] G t [V (i) (t),V (j) (t+1)] is the initial and final states of the time period t, respectively V (i) (t), V (j) At (t+1), the available power generation in the stage; The initial state of the period t is V (i) (t), the maximum power generation that can be obtained during the period t to T.

[0037] The scheduling function described in step S3 is:

[0038]

[0039] Where: m is the serial number of the medium- to long-term period, such as the number of months, ten days, or days; a m , b m The parameters that characterize the quantitative relationship between the decision variable and the independent variable determined for the linear fit; is a decision variable, which can be the discharge flow during the period, the system output during the period, the reservoir storage at the end of the period, or the reservoir water level at the end of the period, etc.; It is an independent variable, which can be the available water volume or available energy in a period of time.

[0040] The available energy of the water-wind-solar hybrid system during the period is calculated as follows:

[0041] AE(t)=I(t)+S(t)

[0042] I(t)=[KQ in (t)H(t)+N WD (t)+N PV (t)]×Δt

[0043] S(t)=K[V(t)-V min ]H′(t);

[0044] Where: AE(t) is the available energy of the system in period t; I(t) is the input energy of the system in period t; S(t) is the initial system storage energy in period t; K is the comprehensive output coefficient of the hydropower station; Q in (t) is the inflow flow in period t; H(t) is the net head of the reservoir in period t; H′(t) is the average head of the period when all the water in the reservoir is discharged at the beginning of period t; V min It is the lower limit of reservoir water demand.

[0045] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The protection scope of the present invention should not be regarded as limited to the specific forms described in the embodiments. The protection scope of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A medium- and long-term scheduling method for a water-wind-solar complementary system considering extreme climatic conditions, characterized in that: The steps include: S1: Establish a long-term dispatch model for the water-wind-solar hybrid system considering extreme climatic conditions. The dispatch model objective function includes the total grid-connected power function of the system and the system operation performance function under extreme low output scenarios; S2: Use the penalty function method to merge the objective functions and convert the multi-objective problem into a single-objective problem to solve the long-term scheduling model of the water-wind-solar complementary system. The deterministic optimization model is regarded as a multi-stage decision-making problem and solved by the discrete differential dynamic programming method. The corridors are continuously divided to achieve dimensionality reduction. S3: Construct the scheduling function by linear fitting. According to the optimal operation trajectory of the system obtained by deterministic optimization scheduling, first determine the correlation between the variables, then determine the optimal decision variables and independent variables through linear fitting, and construct a linear scheduling function. The correlation between the variables is characterized by the Pearson correlation coefficient.

2. According to claim 1, a medium- and long-term scheduling method for a water-wind-solar complementary system considering extreme climate conditions is characterized in that: In step S1, the total online power function of the system is: N(t)=N HP (t)+N WD (t)+N PV (t); Where: E T is the total grid-connected power of the system during the dispatch period; P(t) and N(t) are the grid-connected power output and power generation output of the system in the tth period, respectively; N c (t) is the power output of the system in the t period; N HP (t), N WD (t), N PV (t) are the power generation output of hydropower, wind power and photovoltaic power in the system in the tth period; T is the number of time periods in the dispatch period; Δt is the length of the time period.

3. According to claim 1, a medium- and long-term scheduling method for a water-wind-solar complementary system considering extreme climate conditions is characterized in that: In step S1, the system operation performance function under the extremely low output scenario is: Where: FR is the complementary system guarantee rate; P f To ensure output for complementary systems; P min The minimum on-grid output of the complementary system is lower than the guaranteed output; D b is the set of time periods when the system's online output is lower than the guaranteed output, i.e., D b ={t|P(t)<P f }={n1,n2,…,n b };P avn The average value of the on-grid output of the complementary system is lower than the guaranteed output; T b is the number of time periods when the system's online output is lower than the guaranteed output, i.e. D b Length.

4. According to claim 1, a medium- and long-term scheduling method for a water-wind-solar complementary system considering extreme climate conditions is characterized in that: In step S1, the constraints of the scheduling model include: water balance constraints, reservoir capacity constraints, power generation head constraints, hydropower station output constraints, transmission capacity constraints, discharge capacity constraints, hydropower station maximum flow capacity constraints, ecological flow constraints and boundary condition constraints.

5. According to claim 1, a medium- and long-term scheduling method for a water-wind-solar complementary system considering extreme climate conditions is characterized in that: The penalty function used in step S2 includes: Where: is the total on-grid power of the system considering the penalty item; P p,i (t) corresponds to the penalty terms of the three objective functions under the extremely low output scenario, i = 1, 2, 3; α i , β i are the penalty coefficients and penalty powers of the three penalty items; P min (t), P avn (t) is the P calculated according to the current process during the calculation process min , P avn .

6. The method for medium- and long-term scheduling of a water-wind-solar complementary system considering extreme climate conditions according to claim 1, characterized in that: In step S2, the recursive equation of a dynamic programming includes: Where: V (i) (t) is the i-th discrete value of the initial reservoir storage capacity in period t, i = 1~N; G t [V (i) (t),V (j) (t+1)] is the initial and final states of the time period t, respectively V (i) (t), V (j) At (t+1), the power generation of the stage is obtained; The initial state of the period t is V (i) At (t), the maximum power generation from t to T is obtained.

7. The method for medium- and long-term scheduling of a water-wind-solar hybrid system considering extreme climate conditions according to claim 1, characterized in that: The scheduling function described in step S3 is: Where: m is the sequence number of the medium and long term period; a m 、b m The parameters that characterize the quantitative relationship between the decision variable and the independent variable determined for the linear fit; As decision variables, the present invention sets the reservoir water level at the end of the period, the outflow flow during the period, and the system output during the period as alternatives for correlation analysis; As independent variables, the present invention sets the time period inflow flow and time period available energy as alternatives for correlation analysis.

8. The method for medium- and long-term scheduling of a water-wind-solar hybrid system considering extreme climate conditions according to claim 7 is characterized in that: The available energy of the water-wind-solar hybrid system during the period is calculated as follows: AE(t)=I(t)+S(t) I(t)=[KQ in (t)H(t)+N WD (t)+N PV (t)]×Δt S(t)=K[V(t)-V min ]H′(t); Where: AE(t) is the available energy of the system in period t; I(t) is the input energy of the system in period t; S(t) is the initial system storage energy in period t; K is the comprehensive output coefficient of the hydropower station; Q in (t) is the inflow flow in period t; H(t) is the net head of the reservoir in period t; H′(t) is the average head of the period when all the water in the reservoir is discharged at the beginning of period t; V min It is the lower limit of reservoir water demand.

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