A long-term scheduling method for water-wind-solar complementary system considering extreme climate conditions

Abstract

A kind of long-term scheduling method of water, wind and light complementary system considering extreme climate conditions, comprising the following steps: S1: long-term scheduling model of water, wind and light complementary system considering extreme climate conditions is established, and scheduling model objective function includes system total on-grid power function and system operation performance function under extreme low power output scenario;S2: using penalty function method is carried out function merging, after multiple objective problem is converted into single objective problem, this deterministic optimization model is regarded as multi-stage decision problem, and discrete differential dynamic programming method is used to solve, and corridor is constantly divided to realize dimension reduction;S3: linear fitting is used to construct scheduling function, and long-term scheduling of water, wind and light complementary system is solved.This application is used to solve the problem that long-term scheduling of existing water, wind and light complementary system is difficult to consider extreme climate conditions, and scheduling rule cannot adapt to extreme climate situation.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of clean energy utilization, in particular to a long-term scheduling method of a water-wind-solar complementary system considering extreme climate conditions. BACKGROUND

[0002] In order to reasonably describe uncertain inputs to guide the actual multi-energy complementary system scheduling operation, the 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 input uncertainty is characterized by using probability and statistics method, and the long sequence process is determined by adding the scheduling model to the scheduling model. Optimization solution. (2) Implicit stochastic optimization. Statistical analysis is performed on the results of a large number of historical input optimization, and the specific function relationship between the decision variables and the preferred independent variables is extracted, and the input uncertainty property is implicitly expressed in the scheduling model. (3) Parameter simulation optimization. The scheduling rules of the parameter type are given in advance, and the algorithm is used for simulation scheduling and optimization of different parameters, and finally the optimal parameter scheduling rule is obtained.

[0003] The disadvantages of the prior art are that the long-term scheduling of the water-wind-solar complementary system is difficult to consider the extreme climate conditions, and the traditional scheduling rules cannot adapt to the extreme climate conditions. The existing long-term scheduling method of the water-wind-solar complementary system does not consider the extreme climate conditions, and the objective function usually only considers the power generation and the guarantee rate, ignoring the loss of complementary system benefits under extreme climate conditions. SUMMARY

[0004] The purpose of the present application is to provide a long-term scheduling method of a water-wind-solar complementary system considering extreme climate conditions, which solves the problem that the existing long-term scheduling of the water-wind-solar complementary system is difficult to consider the extreme climate conditions, and the scheduling rules cannot adapt to the extreme climate conditions.

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

[0006] A long-term scheduling method of a water-wind-solar complementary system considering extreme climate conditions, comprising the following steps:

[0007] S1: Establish a long-term scheduling model of a water-wind-solar complementary system considering extreme climate conditions, and the objective function of the scheduling model includes a system total on-grid power function and a system operation performance function under extreme low output scenario;

[0008] S2: Use the penalty function method to merge the objective function, convert the multi-objective problem into a single-objective problem, and regard the deterministic optimization model as a multi-stage decision problem, and solve it by using the discrete differential dynamic programming method, and constantly divide the corridor to realize dimension reduction;

[0009] S3: a linear fitting method is used to construct a scheduling function, and a long-term scheduling model of the water-wind-solar complementary system is solved; according to the optimal operation trajectory of the system obtained by the deterministic optimization scheduling, the correlation of each variable is first judged, and then the optimal decision variable and independent variable are determined through linear fitting, and the correlation between each variable is represented by the Pearson correlation coefficient.

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

[0011] Under the background of energy crisis and climate change, the water-wind-solar complementary system faces the problems of new energy consumption and energy supply: on the one hand, the problems of abandoned water, wind and light are still serious; on the other hand, the problem of energy supply under extreme hydro-meteorological conditions is serious. The present application first establishes a long-term scheduling model of the water-wind-solar complementary system considering extreme climate conditions, comprehensively considers the online power and the operation performance of the complementary system under the condition of extremely low output, then uses a penalty function to combine the multiple objectives, uses discrete differential dynamic programming to perform deterministic optimization scheduling on the long sequence water-wind-solar process, and finally performs correlation analysis and simulation optimization according to the obtained long-term scheduling results to identify the optimal type and parameters of the system scheduling rules, extracts the long-term scheduling rules considering extreme climate conditions, and solves the problem that the existing long-term scheduling method of the water-wind-solar complementary system does not consider extreme climate conditions and ignores the loss of the complementary system under extreme climate conditions. The long-term scheduling rules of the water-wind-solar complementary system considering extreme climate conditions are derived, 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 DRAWINGS

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

[0013] Figure 1 The flowchart of the present application is shown in the figure,

[0014] Figure 2 The optimal operation trajectory of the present application is shown in the figure. DETAILED DESCRIPTION

[0015] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0016] As shown in Figure 1 and 2 , a long-term scheduling method of a water-wind-solar complementary system considering extreme climate conditions comprises the following steps:

[0017] S1: scheduling model construction:

[0018] A long-term scheduling model of the water-wind-solar complementary system considering extreme climate conditions is established. The objective function of the scheduling model includes a total on-grid power function of the system and a system operation performance function under an extreme low output scenario;

[0019] S2: solving the scheduling model: To solve the long-term scheduling model of the water-wind-solar complementary system, the objective function is combined using the penalty function method, and after converting the multi-objective problem into a single-objective problem, the deterministic optimization model can be regarded as a multi-stage decision problem, which is solved by using the discrete differential dynamic programming method, and the dimension is reduced by continuously dividing the corridor.

[0021] S3: extraction of scheduling rules: a scheduling function is constructed by linear fitting; according to the optimal operation trajectory of the system obtained by deterministic optimization scheduling, the correlation of each variable is first judged, and then the optimal decision variable and independent variable are determined by linear fitting. The correlation between variables is represented by the Pearson correlation coefficient.

[0022] In step S1, the total on-grid power function of the system is:

[0023] ;

[0024] In the formula: is the total on-grid power of the system in the scheduling period; , are the on-grid output and power output of the system in the time period, respectively; is the curtailment output of the system in the time period; , , are the power outputs of the water, wind and solar power of the system in the time period, respectively; is the number of periods in the scheduling period; is the period length.

[0025] In step S1, the system operation performance function under the extreme low output scenario is:

[0026] ;

[0027] In the formula: is the complementary system guarantee rate; is the complementary system guaranteed output; is the minimum on-grid output of the complementary system below the guaranteed output; is the set of periods in which the on-grid output of the system is lower than the guaranteed output, i.e. ; The complementary system's output is lower than the average grid-connected output guaranteed by the grid. The number of time periods during which the system's internet access output is lower than the guaranteed output, i.e. The length.

[0028] 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.

[0029] The penalty functions used in step S2 include:

[0030] ;

[0031] In the formula: The total system power consumption is considered in relation to penalties. These correspond to the penalty terms of the three objective functions under the extreme low output scenario. ; , These are the penalty coefficients and penalty powers of the three penalty terms; , This is the result of calculations performed based on the existing process. , .

[0032] The recurrence equations for a linear dynamic programming problem in step S2 include:

[0033] ;

[0034] In the formula: for The first period of reservoir water storage A discrete value, ; for The initial and final states of the time period are respectively , The amount of electricity that can be generated at a given time; for The initial state of the time period is At that time, what can be obtained Maximum power generation during the phase.

[0035] The scheduling function mentioned in step S3 is:

[0036]

[0037] In the formula: For medium- to long-term time periods, such as months, ten-day periods, and days; , parameters representing quantitative relationship between decision variable and independent variable determined for linear fitting; The decision variable can be periodical outflow, periodical system output, periodical reservoir storage or periodical reservoir water level, etc. The independent variable can be periodical available water or periodical available energy, etc.

[0038] Periodical available energy of the water, wind and solar complementary system is calculated as follows:

[0039] ;

[0040] In the formula: is periodical system available energy; is periodical system input energy; is periodical system storage energy at the beginning of the period; is comprehensive output coefficient of the hydropower station; is periodical inflow; is periodical net water head of the reservoir; is periodical average water head of the reservoir when all the water storage is discharged at the beginning of the period; is lower limit of water demand of the reservoir.

[0041] The content described in the specification is only a list of implementation forms of the inventive concept, and the protection scope of the present application should not be regarded as limited to the specific forms stated in the embodiments, and the protection scope of the present application also extends to equivalent technical means that can be thought of by those skilled in the art according to the inventive concept.

Claims

1. A long-term scheduling method for a hydro-wind-solar hybrid system considering extreme weather conditions, characterized in that, Includes the following steps: S1: Establish a medium- and long-term scheduling model for a hydro-wind-solar hybrid system that considers extreme weather conditions. The objective function of the scheduling model includes the total grid-connected power generation function and the system operation performance function under extreme low power output scenarios. S2: The penalty function method is used to merge the objective functions, transforming the multi-objective problem into a single-objective problem, in order to solve the long-term scheduling model of the water-wind-solar hybrid system. This model is regarded as a multi-stage decision problem and is solved by discrete differential dynamic programming method, continuously dividing the corridor to achieve dimensionality reduction. S3: The scheduling function is constructed by linear fitting. Based on the optimal operating 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 to construct a linear scheduling function. The correlation between various variables is characterized by the Pearson correlation coefficient. The scheduling function mentioned in step S3 is: ； In the formula: m is the sequence number of the medium-to-long-term period; , The parameters determined for linear fitting that characterize the quantitative relationship between decision variables and independent variables; As decision variables, reservoir water level at the end of the time period, outflow from the reservoir during the time period, and system output during the time period are set as alternatives for relevant analysis; Using inbound flow and available energy during the time period as independent variables, relevant analysis was conducted with these variables as alternatives. The available energy for a hydro-wind-solar hybrid system during a given time period is calculated as follows: ； In the formula: for Available energy for the time period system; for The system inputs energy during specific time periods; for The system stores energy at the beginning of the period; This refers to the overall power output coefficient of the hydropower station. for Inbound flow during specific time periods; for The net water head of the reservoir during a given period; for The initial assumption for the time period is that the average head during the time period will be when all the water stored in the reservoir is released. This is the lower limit of the reservoir's water demand. The penalty functions used in step S2 include: ； In the formula: The total system power consumption is considered in relation to penalties. These correspond to the penalty terms of the three objective functions under the extreme low output scenario. ; , These are the penalty coefficients and penalty powers of the three penalty terms; , This is the result of calculations performed based on the existing process. , ; For the system in the first Internet usage during specific time periods; This refers to the number of time periods during the scheduling period; The time period is long.

2. The long-term scheduling method for a hydro-wind-solar hybrid system considering extreme weather conditions according to claim 1, characterized in that, In step S1, the total system power consumption function is: ； In the formula: This refers to the total on-grid electricity generated by the system during the scheduling period. For the system in the first Power generation output during a given period; For the system in the first Power curtailment during certain time periods; , , The system at the 1st The power output of hydropower, wind power and solar power during the period.

3. The long-term scheduling method for a hydro-wind-solar hybrid system considering extreme weather conditions according to claim 1, characterized in that, In step S1, the system performance function under the extreme low output scenario is: ； In the formula: For the guarantee rate of complementary systems; To ensure output for complementary systems; The complementary system's output is lower than the minimum guaranteed output for grid connection. The set of time periods during which the system's internet access output is lower than the guaranteed output, i.e. ; The complementary system's output is lower than the average grid-connected output guaranteed by the grid. The number of time periods during which the system's internet access output is lower than the guaranteed output, i.e. Length; For the system in the first Internet usage during specific time periods; This represents the number of time periods in the scheduling period.

4. The long-term scheduling method for a hydro-wind-solar hybrid system considering extreme weather conditions according to claim 1, 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. A long-term scheduling method for a hydro-wind-solar hybrid system considering extreme weather conditions, as described in claim 1, is characterized in that... The recurrence equations for a linear dynamic programming problem in step S2 include: ； In the formula: for The first period of reservoir water storage A discrete value, ; for The initial and final states of the time period are respectively , The amount of electricity generated during that period; for The initial state of the time period is At that time, what was obtained Maximum power generation during the phase.

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