CN117879063B - Water-wind-light resource distribution robust optimal configuration method, device, equipment and medium - Google Patents
Water-wind-light resource distribution robust optimal configuration method, device, equipment and medium Download PDFInfo
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Abstract
The invention relates to the technical field of multi-energy complementary power generation, in particular to a method, a device, equipment and a medium for robust optimization configuration of water-wind-light resource distribution, which comprise the following steps: establishing capacity optimization configuration constraint conditions and system operation constraint conditions which consider hydraulic coupling by taking the equivalent daily investment cost and the daily operation cost as the minimum objective function; performing uncertainty set modeling on wind power and photovoltaic output, and clustering historical scene data to obtain a typical scene; constructing a distributed robust planning model of scene probability driven extraction and storage transformation cascade small hydropower collaborative configuration wind and light resources by using an objective function and constraint conditions, and synthesizing 1-norm and 1-norm-A set of norm constraint uncertainty probability distribution confidence values; linearizing the distributed robust planning model by using a McCormick linearization method to obtain a mixed integer linear planning model; and solving the mixed integer linear programming model by adopting a column and constraint generation algorithm to obtain the optimal capacity optimization configuration method.
Description
Technical Field
The invention relates to the technical field of multi-energy complementary power generation, in particular to a method, a device, equipment and a medium for robust optimization configuration of water-wind-light resource distribution.
Background
Along with the continuous progress of scientific technology, the development cost of clean energy sources such as wind power, photovoltaic, water power and the like is continuously reduced, the development of the clean energy sources such as wind power, photovoltaic, water power and the like is greatly carried out in all countries of the world, and the development of the clean energy sources is an important measure for leading the economic sustainable development of all countries, is also a key for changing the energy supply structure and realizes green development and low-carbon development. The traditional energy consumption mode with coal as the main source is difficult to succeed. Clean energy sources such as wind power, photovoltaic, hydropower and the like are getting more and more attention.
When the specific gravity occupied by new energy in an electric power system is gradually increased, the reduction of the new energy consumption power rejection rate is particularly important. The micro-grid technology can realize on-site development and on-site digestion of new energy, and reduce the pressure of large-scale grid connection of the new energy. Therefore, in the process of the transformation and upgrading of the national energy structure, the micro-grid plays an important role, and particularly, the micro-grid containing small hydropower stations in partial areas of China is not only an important support of the grid terminal, but also an important power source and an economic source of small villages and small villages. In the process of the transformation and upgrading of the national energy structure, the reconstruction and construction of the micro-grid are indispensable.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a robust optimization configuration method, device, equipment and medium for water-wind-solar resource distribution, thereby effectively solving the problems in the background technology.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a robust optimization configuration method for water-wind-solar resource distribution comprises the following steps:
Taking the minimum equivalent daily investment cost and daily operation cost as objective functions, and establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint of hydraulic coupling as a main factor;
performing uncertainty set modeling on wind power and photovoltaic output, and clustering historical scene data to obtain a typical scene;
constructing a distributed robust planning model for cooperatively configuring wind and light resources by using off-grid type pumping and accumulating reconstruction cascade small hydropower driven by scene probability according to the objective function and constraint conditions, and synthesizing 1-norm sum -A set of norm constraint uncertainty probability distribution confidence values;
Linearizing the distributed robust programming model by using a McCormick linearization method to obtain a mixed integer linear programming model;
And solving the mixed integer linear programming model by adopting a column and constraint generation algorithm to obtain the optimal capacity optimization configuration method.
Further, the minimizing the equivalent daily investment cost and daily operation cost as the objective function includes:
the method aims at minimizing the sum of the investment operation and maintenance recovery cost of the equidaily value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit and the abandoned wind and abandoned light punishment cost generated during the system operation:
；
；
；
；
；
wherein, The daily comprehensive cost of the system; /(I)The investment operation and maintenance recovery cost of the equal-daily-value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit of the system is realized; /(I)The cost is punished for wind and light abandoning generated during system operation; /(I)The operation and maintenance recovery cost is equal daily value investment of the pumping and storage unit; /(I)The operation and maintenance recovery cost is invested for the equal daily value of the wind power and photovoltaic unit; /(I)The investment cost of the pumping and accumulating unit is used; /(I)The operation and maintenance cost of the pumping and accumulating unit is realized; /(I)Is the fund discount rate; /(I)The fund recovery period of the pumping and accumulating unit; And/> Investment costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)AndThe operation and maintenance costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)The fund recovery period of the wind turbine generator and the photovoltaic turbine generator is set; /(I)Punishment cost for the abandoned wind generated during the system operation; /(I)Punishment cost for discarding light generated during system operation; /(I)AndWind power and light Fu Di/>, respectivelyPredicting output at moment; /(I)AndWind power and light Fu Di/>, respectivelyThe actual force at the moment.
Further, the establishing the capacity optimization configuration constraint condition taking the cascade hydropower constraint of the hydraulic coupling as the main constraint condition and the system operation constraint condition comprises the following steps:
Capacity optimization configuration constraint conditions: the method comprises the steps of installing capacity constraint of wind power and photovoltaic power stations, configuration capacity constraint of a pumping and accumulating unit and system power balance constraint;
System operation constraints: the system power supply reliability constraint, the wind power and photovoltaic power station output constraint, the cascade hydropower station water quantity balance constraint, the flow constraint, the reservoir capacity control constraint, the hydropower unit water discharge limit constraint, the hydropower unit output characteristic constraint, the hydropower unit start-stop state constraint, the pumping storage unit flow constraint, the pumping storage unit power upper and lower limit constraint and the running state constraint of the hydropower unit and the pumping storage unit.
Further, the capacity optimization configuration constraint condition includes:
and (3) the installed capacity constraint of wind power and photovoltaic power stations:
；
wherein, The minimum installed capacity of the wind farm is set; /(I)Maximum installed capacity of the wind farm; /(I)The minimum installed capacity of the photovoltaic power station is set; /(I)The maximum installed capacity of the photovoltaic power station is set;
Configuration capacity constraint of pumping and storage unit:
Due to 、、、Generally related to installed capacity, and 0-1 variables such as、The multiplication forms a nonlinear constraint. Converting the nonlinear constraint into a linear constraint based on the mccomick envelope;
；
；
Wherein, AndIs a converted linear variable; /(I)、、The device comprises a speed-changing pumped storage unit, a speed-changing pump storage unit and a speed-changing pump storage unit, wherein the speed-changing pump storage unit is respectively a speed-changing pump storage unit; /(I)、The lower limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity;、 the lower limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity;
System power balance constraint:
；
wherein, ForStage cascade hydropower stationActual output at moment; /(I)For pumping and storing unitGenerating power at moment; /(I)Pumping and accumulating unit (S /)Pumping power at moment; /(I)For the system atPower shortage generated at the moment; /(I)For the system atLoad at time.
Further, the system operating constraints include:
System power supply reliability constraints:
；
wherein, ForThe maximum load shedding rate of the system at the moment is 95%;
wind power and photovoltaic power station output constraint:
；
wherein, For wind farmA time period maximum predicted output; /(I)For photovoltaic power stationA time period maximum predicted output;
Step hydropower station water balance constraint:
Primary power station:
；
wherein, ForReservoir capacity of the time period primary power station; /(I)The initial reservoir capacity of the primary power station reservoir; /(I)ForWater coming upstream of the time period; /(I)ForThe period primary power station discharging flow;
And (3) a secondary power station:
；
wherein, ForReservoir capacity of the time period secondary power station; /(I)The method is characterized by comprising the steps of (1) setting an initial reservoir capacity for a secondary power station reservoir; /(I)ForThe secondary power station discharge flow in the period;
Three-stage power station:
；
wherein, ForThe period three-level power station discharging flow;
Flow constraint:
；
wherein, ForStage cascade hydropower stationA period of time drain; /(I)ForStage cascade hydropower stationGenerating flow in a period of time; /(I)ForFlow under the power generation working condition of the time period pumping and accumulating unit; /(I)ForFlow under the pumping working condition of the time period pumping and accumulating unit; /(I)ForStage cascade hydropower stationWater flow is abandoned in a period;
reservoir capacity control constraints:
；
wherein, ForMinimum storage capacity of the cascade hydropower station; /(I)ForMaximum storage capacity of the cascade hydropower station; For/> The initial time storage capacity of the cascade hydropower station; /(I)ForFinal time storage capacity of the cascade hydropower station;
Water discharge limit constraint of hydroelectric generating set:
；
wherein, ForMaximum water flow rate of the cascade hydropower station;
The output characteristic constraint of the hydroelectric generating set:
；
wherein, ForMinimum output of a water turbine of the cascade hydropower station; /(I)ForMaximum output of a water turbine of the cascade hydropower station; /(I)ForThe operation state variable of the water turbine of the cascade hydropower station;
And (3) restraining a startup and shutdown state body of the hydroelectric generating set:
；
wherein, ForA water turbine starting state variable of the cascade hydropower station; /(I)ForA water turbine shutdown state variable of the cascade hydropower station;
flow constraint of pumping and storage unit:
；
wherein, The power generation flow is the power generation flow under the power generation working condition of the pumping and accumulating unit; /(I)Pumping flow under the pumping working condition of the pumping and accumulating unit;
and the upper and lower limits of the power of the pumping and storage unit are constrained:
；
wherein, For pumping and storing unitOperating state variables of the time period power generation working condition; /(I)For pumping and storing unitRunning state variables of the period pumping working condition; /(I)，The minimum output and the maximum output of the pumping and accumulating unit under the power generation working condition are obtained;， minimum output and maximum output under the pumping working condition of the pumping and accumulating unit;
and the running states of the water turbine unit and the pumping and storage unit are constrained:
。
Further, the distribution robust planning model includes:
The first stage is an investment stage, and the pumping and storage capacity and the configuration capacity of wind power photovoltaics are determined;
and the second stage is an operation stage, and the pumping and accumulating and wind-light configuration capacity obtained in the first stage is transmitted to the second stage for simulation operation, and the aim of minimum wind-discarding and light-discarding punishment generated in the operation process is fulfilled, and the method comprises the following steps:
；
；
；
；
；
wherein, The decision variables of the first stage comprise the configuration capacity of the pumping and accumulating and wind power photovoltaics; /(I)The method is an investment decision variable set for pumping and accumulating and wind power photovoltaics; /(I)The method is that the/>, which is obtained by clustering the load and wind power photovoltaic historical scene dataA typical output scenario; /(I)When the first-stage investment decision variables are given, the system operation decision variables under the probability distribution of wind power and photovoltaic output scenes are collected, wherein the system operation decision variables comprise step hydroelectric real-time output, wind power and photovoltaic real-time output, extraction and storage real-time output and the like; /(I)The operation cost is invested for the equal daily value of the pumping and accumulating and wind power photovoltaic; /(I)When the first-stage investment decision variables are given, the probability distribution is uncertain, and the operation cost expected value corresponding to the worst probability distribution in the set comprises punishment cost of wind and light discarding; /(I)、、、、、、、、、Is a corresponding coefficient matrix;
；
wherein, Initial probability distribution of an uncertainty typical scene of wind power and photovoltaic output; /(I)ForInitial probabilities of individual typical scenes; /(I)A mixed probability distribution uncertainty set for a typical output scenario; /(I)、1-Norm and/>, respectively-A probability tolerance limit for a wind power photovoltaic output scenario under a norm constraint;
；
wherein, Probability of establishing an inequality in parentheses; /(I)、The confidence with which the two probability distribution inequalities are established, respectively.
Further, the solving the mixed integer linear programming model by adopting a column and constraint generation algorithm comprises the following steps:
Splitting the original min-max-min three-layer problem into a mixed integer programming main problem and a sub-problem which can be solved in parallel, wherein the method comprises the following steps:
；
；
；
；
；
；
wherein, The iteration times;
When the first stage invests in decision variables Given, the sub-problem is solved in parallel, including:
；
the sub-problem is solved in two steps, wherein the first step is to solve the minimum problem of the target inner layer in the sub-problem in parallel, and the second step is to solve the probability updating problem of the outer layer typical scene in the sub-problem.
The invention also comprises a water-wind-light resource distribution robust optimization configuration device, which comprises the following steps:
the target constraint unit is used for establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint taking hydraulic coupling into account as a main function with the minimum equivalent daily investment cost and daily operation cost;
The modeling unit is used for performing uncertainty set modeling on wind power and photovoltaic output and obtaining a typical scene through historical scene data clustering;
the planning model building unit is used for building a distributed robust planning model for cooperatively configuring wind and light resources by using off-grid type extraction and storage reconstruction cascade small hydropower driven by scene probability according to the objective function and constraint conditions, and synthesizing 1-norm and constraint condition -A set of norm constraint uncertainty probability distribution confidence values;
the linearization unit is used for linearizing the distribution robust planning model through a McCormick linearization method to obtain a mixed integer linear planning model;
And the solving unit is used for solving the mixed integer linear programming model by adopting a column and constraint generating algorithm to obtain an optimal capacity optimizing configuration method.
The invention also includes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the method as described above when executing the computer program.
The invention also includes a storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
The beneficial effects of the invention are as follows: the invention can fully consider the probability uncertainty of a typical scene, the uncertainty of wind power photovoltaic output and the adjustment capability of pumped storage in the system, and takes the equivalent daily investment cost of the whole system and the abandoned wind and abandoned light quantity under the daily operation condition into consideration, thereby realizing the complementary operation of off-grid hybrid pumping storage reconstruction step hydropower and wind power photovoltaic resources.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a flow chart of the method of example 1;
FIG. 2 is a schematic view of the structure of the device in example 1;
FIG. 3 is a graph of wind power predicted force per unit value of twelve typical scenarios obtained after clustering historical data in example 2;
FIG. 4 is a graph of the photovoltaic predicted force per unit value of twelve typical scenarios obtained after clustering of historical data in example 2;
FIG. 5 is a graph of load per unit value of twelve typical scenarios obtained after clustering of historical data in example 2;
FIG. 6 is a graph of the system output results for exemplary scenario 1 of example 2;
FIG. 7 is a graph of the system output results for exemplary scenario 2 of example 2;
FIG. 8 is a graph of the system output results for exemplary scenario 3 of example 2;
FIG. 9 is a graph of the system output results for exemplary scenario 4 of example 2;
FIG. 10 is a solution flow chart in example 2;
Fig. 11 is a schematic structural diagram of a computer device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
As shown in fig. 1: a robust optimization configuration method for water-wind-solar resource distribution comprises the following steps:
Taking the minimum equivalent daily investment cost and daily operation cost as objective functions, and establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint of hydraulic coupling as a main factor;
performing uncertainty set modeling on wind power and photovoltaic output, and clustering historical scene data to obtain a typical scene;
Constructing a distributed robust planning model of scene probability driven off-grid type pumping and accumulating reconstruction cascade small hydropower collaborative configuration wind and light resources by using an objective function and constraint conditions, and synthesizing 1-norm sum -A set of norm constraint uncertainty probability distribution confidence values;
Linearizing the distributed robust planning model by using a McCormick linearization method to obtain a mixed integer linear planning model;
And solving the mixed integer linear programming model by adopting a column and constraint generation algorithm to obtain the optimal capacity optimization configuration method.
As a preference of the above embodiment, taking the equivalent daily investment cost and daily running cost as the minimum objective functions includes:
the method aims at minimizing the sum of the investment operation and maintenance recovery cost of the equidaily value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit and the abandoned wind and abandoned light punishment cost generated during the system operation:
；
；
；
；
；
wherein, The daily comprehensive cost of the system; /(I)The investment operation and maintenance recovery cost of the equal-daily-value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit of the system is realized; /(I)The cost is punished for wind and light abandoning generated during system operation; /(I)The operation and maintenance recovery cost is equal daily value investment of the pumping and storage unit; /(I)The operation and maintenance recovery cost is invested for the equal daily value of the wind power and photovoltaic unit; /(I)The investment cost of the pumping and accumulating unit is used; /(I)The operation and maintenance cost of the pumping and accumulating unit is realized; /(I)Is the fund discount rate; /(I)The fund recovery period of the pumping and accumulating unit; And/> Investment costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)AndThe operation and maintenance costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)The fund recovery period of the wind turbine generator and the photovoltaic turbine generator is set; /(I)Punishment cost for the abandoned wind generated during the system operation; /(I)Punishment cost for discarding light generated during system operation; /(I)AndWind power and light Fu Di/>, respectivelyPredicting output at moment; /(I)AndWind power and light Fu Di/>, respectivelyThe actual force at the moment.
As a preference of the above embodiment, establishing a capacity optimization configuration constraint condition taking into account a cascade hydropower constraint of a hydraulic coupling and a system operation constraint condition includes:
Capacity optimization configuration constraint conditions: the method comprises the steps of installing capacity constraint of wind power and photovoltaic power stations, configuration capacity constraint of a pumping and accumulating unit and system power balance constraint;
System operation constraints: the system power supply reliability constraint, the wind power and photovoltaic power station output constraint, the cascade hydropower station water quantity balance constraint, the flow constraint, the reservoir capacity control constraint, the hydropower unit water discharge limit constraint, the hydropower unit output characteristic constraint, the hydropower unit start-stop state constraint, the pumping storage unit flow constraint, the pumping storage unit power upper and lower limit constraint and the running state constraint of the hydropower unit and the pumping storage unit.
As preferable of the above embodiment, the capacity-optimized configuration constraint conditions include:
and (3) the installed capacity constraint of wind power and photovoltaic power stations:
；
wherein, The minimum installed capacity of the wind farm is set; /(I)Maximum installed capacity of the wind farm; /(I)The minimum installed capacity of the photovoltaic power station is set; /(I)The maximum installed capacity of the photovoltaic power station is set;
Configuration capacity constraint of pumping and storage unit:
Due to 、、、Generally related to installed capacity, and 0-1 variables such as、The multiplication forms a nonlinear constraint. Converting the nonlinear constraint into a linear constraint based on the mccomick envelope;
；
；
Wherein, AndIs a converted linear variable; /(I)、、The device comprises a speed-changing pumped storage unit, a speed-changing pump storage unit and a speed-changing pump storage unit, wherein the speed-changing pump storage unit is respectively a speed-changing pump storage unit; /(I)、The lower limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity; /(I)、The lower limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity;
System power balance constraint:
；
wherein, ForStage cascade hydropower stationActual output at moment; /(I)For pumping and storing unitGenerating power at moment; /(I)Pumping and accumulating unit (S /)Pumping power at moment; /(I)For the system atPower shortage generated at the moment; /(I)For the system atLoad at time.
As preferred for the above embodiment, the system operation constraints include:
System power supply reliability constraints:
；
wherein, ForThe maximum load shedding rate of the system at the moment is 95%;
wind power and photovoltaic power station output constraint:
；
wherein, For wind farmA time period maximum predicted output; /(I)For photovoltaic power stationA time period maximum predicted output;
Step hydropower station water balance constraint:
Primary power station:
；
wherein, ForReservoir capacity of the time period primary power station; /(I)The initial reservoir capacity of the primary power station reservoir; /(I)ForWater coming upstream of the time period; /(I)ForThe period primary power station discharging flow;
And (3) a secondary power station:
；
wherein, ForReservoir capacity of the time period secondary power station; /(I)The method is characterized by comprising the steps of (1) setting an initial reservoir capacity for a secondary power station reservoir; /(I)ForThe secondary power station discharge flow in the period;
Three-stage power station:
；
wherein, ForThe period three-level power station discharging flow;
Flow constraint:
；
wherein, ForStage cascade hydropower stationA period of time drain; /(I)ForStage cascade hydropower stationGenerating flow in a period of time; /(I)ForFlow under the power generation working condition of the time period pumping and accumulating unit; /(I)ForFlow under the pumping working condition of the time period pumping and accumulating unit; /(I)ForStage cascade hydropower stationWater flow is abandoned in a period;
reservoir capacity control constraints:
；
wherein, ForMinimum storage capacity of the cascade hydropower station; /(I)ForMaximum storage capacity of the cascade hydropower station; For/> The initial time storage capacity of the cascade hydropower station; /(I)ForFinal time storage capacity of the cascade hydropower station;
Water discharge limit constraint of hydroelectric generating set:
；
wherein, ForMaximum water flow rate of the cascade hydropower station;
The output characteristic constraint of the hydroelectric generating set:
；
wherein, ForMinimum output of a water turbine of the cascade hydropower station; /(I)ForMaximum output of a water turbine of the cascade hydropower station; /(I)ForThe operation state variable of the water turbine of the cascade hydropower station;
And (3) restraining a startup and shutdown state body of the hydroelectric generating set:
；
wherein, ForA water turbine starting state variable of the cascade hydropower station; /(I)FirstA water turbine shutdown state variable of the cascade hydropower station;
flow constraint of pumping and storage unit:
；
wherein, The power generation flow is the power generation flow under the power generation working condition of the pumping and accumulating unit; /(I)Pumping flow under the pumping working condition of the pumping and accumulating unit;
and the upper and lower limits of the power of the pumping and storage unit are constrained:
；
wherein, For pumping and storing unitOperating state variables of the time period power generation working condition; /(I)For pumping and storing unitRunning state variables of the period pumping working condition; /(I)，The minimum output and the maximum output of the pumping and accumulating unit under the power generation working condition are obtained;， minimum output and maximum output under the pumping working condition of the pumping and accumulating unit;
and the running states of the water turbine unit and the pumping and storage unit are constrained:
。
as a preference to the above embodiment, the distributed robust planning model includes:
the first stage is an investment stage, and the pumping and storage capacity and the configuration capacity of wind power photovoltaics are determined;
the second stage is an operation stage, and the pumping and accumulating and wind-light configuration capacity obtained in the first stage is transferred to the second stage for simulation operation, and the aim of minimum wind-discarding and light-discarding punishment generated in the operation process is achieved, and the method comprises the following steps:
；
；
；
；
；
wherein, The decision variables of the first stage comprise the configuration capacity of the pumping and accumulating and wind power photovoltaics; /(I)The method is an investment decision variable set for pumping and accumulating and wind power photovoltaics; /(I)The method is that the/>, which is obtained by clustering the load and wind power photovoltaic historical scene dataA typical output scenario; /(I)When the first-stage investment decision variables are given, the system operation decision variables under the probability distribution of wind power and photovoltaic output scenes are collected, wherein the system operation decision variables comprise step hydroelectric real-time output, wind power and photovoltaic real-time output, extraction and storage real-time output and the like; /(I)The operation cost is invested for the equal daily value of the pumping and accumulating and wind power photovoltaic; /(I)When the first-stage investment decision variables are given, the probability distribution is uncertain, and the operation cost expected value corresponding to the worst probability distribution in the set comprises punishment cost of wind and light discarding; /(I)、、、、、、、、、Is a corresponding coefficient matrix;
；
wherein, Initial probability distribution of an uncertainty typical scene of wind power and photovoltaic output; /(I)ForInitial probabilities of individual typical scenes; /(I)A mixed probability distribution uncertainty set for a typical output scenario; /(I)、1-Norm and/>, respectively-A probability tolerance limit for a wind power photovoltaic output scenario under a norm constraint;
；
wherein, Probability of establishing an inequality in parentheses; /(I)、The confidence with which the two probability distribution inequalities are established, respectively.
As a preference of the above embodiment, solving the mixed integer linear programming model using a column and constraint generation algorithm includes:
Splitting the original min-max-min three-layer problem into a mixed integer programming main problem and a sub-problem which can be solved in parallel, wherein the method comprises the following steps:
；/>
；
；
；
；
；
wherein, The iteration times;
When the first stage invests in decision variables Given, the sub-problem is solved in parallel, including:
；
the sub-problem is solved in two steps, wherein the first step is to solve the minimum problem of the target inner layer in the sub-problem in parallel, and the second step is to solve the probability updating problem of the outer layer typical scene in the sub-problem.
The method and the device can fully consider probability uncertainty of a typical scene, uncertainty of wind power photovoltaic output and adjustment capability of pumped storage in the system, consider equivalent daily investment cost of the whole system and abandoned wind and abandoned light quantity under the daily operation condition, and realize complementary operation of off-grid hybrid pumping storage reconstruction step hydropower and wind power photovoltaic resources.
As shown in fig. 2, the embodiment further includes a robust optimization configuration device for water-wind-light resource distribution, and the method includes:
The target constraint unit is used for establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint taking hydraulic coupling into account as a main function with the equivalent daily investment cost and the daily operation cost as minimum;
The modeling unit is used for performing uncertainty set modeling on wind power and photovoltaic output and obtaining a typical scene through historical scene data clustering;
the planning model building unit is used for building a distributed robust planning model for cooperatively configuring wind and light resources by using off-grid type pumping and accumulating reconstruction cascade small hydropower driven by scene probability according to an objective function and constraint conditions, and integrating 1-norm and constraint conditions -A set of norm constraint uncertainty probability distribution confidence values;
the linearization unit is used for linearizing the distributed robust programming model through a McCormick linearization method to obtain a mixed integer linear programming model;
And the solving unit is used for solving the mixed integer linear programming model by adopting a column and constraint generating algorithm to obtain the optimal capacity optimizing configuration method.
Example 2:
As shown in fig. 3, to verify the superiority of the embodiment of the present invention and the prior art, a specific case is used for verification in the embodiment, where the specific case includes three cascade hydropower stations, one wind power station and one photovoltaic power station, the total capacity of the cascade hydropower stations is 141MW, the maximum capacity of the photovoltaic power station is 500MW, the maximum capacity of the wind power station is 500MW, the cascade hydropower stations have three stages, only one stage has a smaller reservoir capacity, too much water cannot be stored, and the third stage is radial flow type. And each stage of hydropower station is provided with a water turbine unit, and the hydropower conversion efficiency of the three water turbine units is the result of multiplying the average conversion efficiency by the average water head. The pumping and storage unit is arranged at the first-stage hydropower station, the average power generation efficiency of the pumping and storage unit is 0.9, and the average pumping efficiency is 1.2. The capital discount rate is set to be 8%, the investment recovery period of the pumping and accumulating unit is 50 years, and the investment recovery period of the wind power station and the photovoltaic power station is 20 years. The punishment cost of unit waste wind and waste light is 4500 yuan/(MW.h).
The method is characterized in that 24 hours are used as a scheduling period, a K-means clustering method is adopted based on load, wind power, photovoltaic and water supply data of one year, load, wind power, photovoltaic and water supply data of 4 typical days are obtained in a reduction mode, and the load, wind power, photovoltaic and water supply data are analyzed as 4 typical scenes.
The water supply of the small-step hydropower station considers the dead water period, the flat water period and the rich water period, the water supply flow of 24 hours a day in the dead water period is uniformly set to 10MW/h, the water supply flow of 24 hours a day in the flat water period is uniformly set to 15MW/h, and the water supply flow of 24 hours a day in the rich water period is uniformly set to 20MW/h. In the south China, the water leveling period is basically 5 months and 11 months each year, the water withering period is 1 month-4 months and 12 months, and the water plumping period is 6 months-10 months. The water data, the load data and the wind and light data of one year are combined, a matrix of 4 rows and 96 columns is obtained through K-means clustering, the rows represent 4 typical scenes, and the 96 columns are formed by combining 24-hour data of each day of the load, wind power, photovoltaic and water.
FIG. 3 is a graph of wind power predicted force per unit values of twelve typical scenes obtained after historical data clustering; FIG. 4 is a graph of the photovoltaic predicted force per unit value of twelve typical scenarios obtained after historical data clustering; FIG. 5 is a graph of load per unit values of twelve typical scenarios obtained after clustering of historical data; FIG. 6 is a graph of system output results for exemplary scenario 1; FIG. 7 is a graph of system output results for exemplary scenario 2; FIG. 8 is a graph of system output results for exemplary scenario 3; fig. 9 is a graph of the system output results for exemplary scenario 4.
Firstly, establishing an objective function of capacity optimization configuration of wind-light resource of cooperative configuration of small gradient hydropower station of reconstruction of an off-grid pumping and accumulating unit, wherein the objective function is as follows:
and the aim is to minimize the sum of the investment operation and maintenance recovery cost of the equal-daily-value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit and the abandoned wind and abandoned light punishment cost generated during the system operation.
；
；
；
；
；
Wherein,The daily comprehensive cost of the system; /(I)The investment operation and maintenance recovery cost of the equal-daily-value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit of the system is realized; /(I)The cost is punished for wind and light abandoning generated during system operation; /(I)The operation and maintenance recovery cost is equal daily value investment of the pumping and storage unit; /(I)The operation and maintenance recovery cost is invested for the equal daily value of the wind power and photovoltaic unit; /(I)The investment cost of the pumping and accumulating unit is used; /(I)The operation and maintenance cost of the pumping and accumulating unit is realized; /(I)Is the fund discount rate; /(I)The fund recovery period of the pumping and accumulating unit; And/> Investment costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)AndThe operation and maintenance costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)The fund recovery period of the wind turbine generator and the photovoltaic turbine generator is set; /(I)Punishment cost for the abandoned wind generated during the system operation; /(I)Punishment cost for discarding light generated during system operation; /(I)AndWind power and light Fu Di/>, respectivelyPredicting output at moment; /(I)AndWind power and light Fu Di/>, respectivelyThe actual force at the moment.
Investment cost and operation and maintenance cost of the variable-speed pumping and storage unit, the photovoltaic unit and the wind turbine unit are shown in table 1:
Table 1 investment cost and operation and maintenance cost of variable speed pumping and accumulating unit, photovoltaic unit and wind turbine unit
Then, establishing a capacity optimization configuration constraint condition taking hydraulic coupling into account and mainly taking the cascade hydropower constraint and a constraint condition when the system operates as follows:
and (3) the installed capacity constraint of wind power and photovoltaic power stations:
；
wherein, The minimum installed capacity of the wind farm is set; /(I)Maximum installed capacity of the wind farm; /(I)The minimum installed capacity of the photovoltaic power station is set; /(I)The maximum installed capacity of the photovoltaic power station.
Configuration capacity constraint of pumping and storage unit
Due to、、、Generally related to installed capacity, and 0-1 variables such as、The multiplication forms a nonlinear constraint. The nonlinear constraint is converted to a linear constraint based on the mccomick envelope.
；
；
Wherein,AndIs a converted linear variable; /(I)、、The device comprises a speed-changing pumped storage unit, a speed-changing pump storage unit and a speed-changing pump storage unit, wherein the speed-changing pump storage unit is respectively a speed-changing pump storage unit; /(I)、The lower limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity; /(I)、The lower limit of the power generation power of the variable-speed pumping and accumulating unit and the conversion coefficient of the installed capacity are respectively the upper limit of the power generation power of the variable-speed pumping and accumulating unit and the conversion coefficient of the installed capacity.
System power balance constraint
；/>
Wherein,ForStage cascade hydropower stationActual output at moment; /(I)For pumping and storing unitGenerating power at moment; /(I)Pumping and accumulating unit (S /)Pumping power at moment; /(I)For the system atPower shortage generated at the moment; /(I)For the system atLoad at time.
System power supply reliability constraints
；
Wherein,ForAnd the maximum load shedding rate of the system at the moment is 95%.
Wind power and photovoltaic power station output constraint
；
Wherein,For wind farmA time period maximum predicted output; /(I)For photovoltaic power stationThe time period is the maximum predicted output.
Cascade hydropower station water balance constraint
1) Primary power station
；
Wherein,ForReservoir capacity of the time period primary power station; /(I)The initial reservoir capacity of the primary power station reservoir; /(I)ForWater coming upstream of the time period; /(I)ForAnd the period of time is the discharging flow of the primary power station.
2) Two-stage power station
；
Wherein,ForReservoir capacity of the time period secondary power station; /(I)The method is characterized by comprising the steps of (1) setting an initial reservoir capacity for a secondary power station reservoir; /(I)ForAnd the secondary power station discharges flow in a period of time.
3) Three-stage power station
；
Wherein,ForAnd 3, discharging flow of the time period three-stage power station.
Basic parameters of the step hydroelectric generating set are shown in table 2:
table 2 basic parameters of step hydroelectric generating set
Flow restriction
；
Wherein,ForStage cascade hydropower stationA period of time drain; /(I)ForStage cascade hydropower stationGenerating flow in a period of time; /(I)ForFlow under the power generation working condition of the time period pumping and accumulating unit; /(I)ForFlow under the pumping working condition of the time period pumping and accumulating unit; /(I)ForStage cascade hydropower stationWater flow is discarded in time periods.
Reservoir capacity control constraints
；
Wherein,ForMinimum storage capacity of the cascade hydropower station; /(I)ForMaximum storage capacity of the cascade hydropower station; For/> The initial time storage capacity of the cascade hydropower station; /(I)ForFinal time storage capacity of the cascade hydropower station.
Water discharge limiting constraint for hydroelectric generating set
；
Wherein,ForMaximum water flow rate of the cascade hydropower station.
Constraint of output characteristics of hydroelectric generating set
；
Wherein,ForMinimum output of a water turbine of the cascade hydropower station; /(I)ForMaximum output of a water turbine of the cascade hydropower station; /(I)ForAnd the operation state variable of the water turbine of the cascade hydropower station.
Start-stop state body constraint of hydroelectric generating set
；
Wherein,ForA water turbine starting state variable of the cascade hydropower station; /(I)FirstAnd the shutdown state variable of the water turbine of the cascade hydropower station.
Flow restriction of pumping and accumulating unit
；
Wherein,The power generation flow is the power generation flow under the power generation working condition of the pumping and accumulating unit; /(I)And pumping flow under the pumping working condition of the pumping and accumulating unit.
Power upper and lower limit constraint of pumping and storage unit
；
Wherein,For pumping and storing unitOperating state variables of the time period power generation working condition; /(I)For pumping and storing unitRunning state variables of the period pumping working condition; /(I)，The minimum output and the maximum output of the pumping and accumulating unit under the power generation working condition are obtained;， and the minimum output and the maximum output of the pumping and accumulating unit under the pumping working condition are obtained.
Running state constraint of water turbine unit and pumping and accumulating unit
。
Next, the first stage is an investment stage, and the pumping and storage capacity and the configuration capacity of wind power photovoltaics are determined; the second stage is an operation stage, the pumping storage and wind-light configuration capacity obtained in the first stage is transferred to the second stage for simulation operation, and the aim of minimum wind-discarding and light-discarding punishment generated in the operation process is achieved, and the method comprises the following steps:
；
；
；
；
；
wherein, The decision variables of the first stage comprise the configuration capacity of the pumping and accumulating and wind power photovoltaics; /(I)The method is an investment decision variable set for pumping and accumulating and wind power photovoltaics; /(I)The method is that the/>, which is obtained by clustering the load and wind power photovoltaic historical scene dataA typical output scenario; /(I)When the first-stage investment decision variables are given, the system operation decision variables under the probability distribution of wind power and photovoltaic output scenes are collected, wherein the system operation decision variables comprise step hydroelectric real-time output, wind power and photovoltaic real-time output, extraction and storage real-time output and the like; /(I)The operation cost is invested for the equal daily value of the pumping and accumulating and wind power photovoltaic; /(I)When the first-stage investment decision variables are given, the probability distribution is uncertain, and the operation cost expected value corresponding to the worst probability distribution in the set comprises punishment cost of wind and light discarding; /(I)、、、、、、、、、Is a corresponding coefficient matrix.
；
Wherein,Initial probability distribution of an uncertainty typical scene of wind power and photovoltaic output; /(I)ForInitial probabilities of individual typical scenes; /(I)A mixed probability distribution uncertainty set for a typical output scenario; /(I)、1-Norm and/>, respectively-A probability allowable deviation limit for a wind power photovoltaic output scenario under a norm constraint.
；/>
Wherein,Probability of establishing an inequality in parentheses; /(I)、The confidence with which the two probability distribution inequalities are established, respectively.
As shown in fig. 10, finally, the mixed integer linear programming model is solved by adopting a column and constraint generation (C & CG) algorithm, and the original min-max-min three-layer problem is split into a mixed integer programming main problem and a sub-problem which can be solved in parallel, so as to obtain an optimal capacity optimization configuration method, which comprises the following steps:
；
；
；
；
；
；
wherein, Is the number of iterations.
When the first stage invests in decision variablesGiven, the sub-problem is solved in parallel, including:
；
Because the second-stage operation variable in the sub-problem and the probability value of the uncertainty of the typical scene are mutually independent, the sub-problem can be solved in two steps, the first step is to solve the problem of the minimum value of the target inner layer in the sub-problem in parallel, and the second step is to solve the problem of the probability update of the outer typical scene in the sub-problem.
The influence of the cooperative configuration of wind and light resources with or without the pumping and storage unit transformation step and small hydropower stations in the system on the capacity configuration result is shown in Table 3:
TABLE 3 influence of the presence or absence of the pumping and accumulating units on the planning result
The difference of the capacity optimization configuration results of the wind and light resources cooperatively configured by the small water and electricity in the variable speed pumping and storage unit transformation cascade small water and electricity in the system is shown in table 4:
table 4 differentiation of the variable speed pumping unit and the constant speed pumping unit on the planning results
The difference between the random optimization method and the distributed robust method for the planning result is shown in table 5:
table 5 differentiation of random optimization method and distributed robust method for planning results
Comparison of the optimized results using mixed norms versus single norms is shown in Table 6:
TABLE 6 comparison of mixed norms to optimized results for single norms
Comparison of the optimized results using mixed norms versus single norms is shown in Table 7:
TABLE 7 comparison of Mixed norms to optimized results for Single norms
The impact of historical data scale on planning results is shown in table 8:
TABLE 8 influence of historical data Scale on planning results
The effect of the different confidence values on the computational efficiency of the C & CG algorithm is shown in table 9:
Table 9 influence of different confidence values on the calculation efficiency of the C & CG algorithm
Comparison of optimized results using variable or constant speed pumped-storage units is shown in Table 10:
table 10 comparison of variable or constant speed pumped storage units to optimized results
Please refer to fig. 11, which illustrates a schematic structure of a computer device according to an embodiment of the present application. The computer device 400 provided in the embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing a computer program executable by the processor 410, which when executed by the processor 410 performs the method as described above.
The embodiment of the present application also provides a storage medium 430, on which storage medium 430 a computer program is stored which, when executed by the processor 410, performs a method as above.
The storage medium 430 may be implemented by any type or combination of volatile or nonvolatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (ELECTRICALLY ERASABLE PROGRAMMABLE READ-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (7)
1. The robust optimal configuration method for the water-wind-solar resource distribution is characterized by comprising the following steps of:
Taking the minimum equivalent daily investment cost and daily operation cost as objective functions, and establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint of hydraulic coupling as a main factor;
performing uncertainty set modeling on wind power and photovoltaic output, and clustering historical scene data to obtain a typical scene;
constructing a distributed robust planning model of scene probability driven off-grid type pumping and accumulating reconstruction cascade small hydropower collaborative configuration wind and light resources according to the objective function and constraint conditions, and synthesizing a 1-norm and ++norm constraint uncertainty probability distribution confidence set;
Linearizing the distributed robust programming model by using a McCormick linearization method to obtain a mixed integer linear programming model;
Solving the mixed integer linear programming model by adopting a column and constraint generation algorithm to obtain an optimal capacity optimization configuration method;
The method takes the equivalent daily investment cost and daily operation cost as the minimum objective function and comprises the following steps:
the method aims at minimizing the sum of the investment operation and maintenance recovery cost of the equidaily value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit and the abandoned wind and abandoned light punishment cost generated during the system operation:
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；
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wherein, C is the daily comprehensive cost of the system; The investment operation and maintenance recovery cost of the equal-daily-value pumping and accumulating unit, the wind turbine unit and the photovoltaic unit of the system is realized; /(I) The cost is punished for wind and light abandoning generated during system operation; /(I)The operation and maintenance recovery cost is equal daily value investment of the pumping and storage unit; /(I)The operation and maintenance recovery cost is invested for the equal daily value of the wind power and photovoltaic unit; /(I)The investment cost of the pumping and accumulating unit is used; /(I)The operation and maintenance cost of the pumping and accumulating unit is realized; /(I)Is the fund discount rate; /(I)The fund recovery period of the pumping and accumulating unit; /(I)AndInvestment costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)AndThe operation and maintenance costs of the photovoltaic unit and the wind turbine unit are respectively; /(I)The fund recovery period of the wind turbine generator and the photovoltaic turbine generator is set; /(I)Punishment cost for the abandoned wind generated during the system operation; /(I)Punishment cost for discarding light generated during system operation; /(I)AndWind power and light Fu Di/>, respectivelyPredicting output at moment; /(I)AndWind power and light Fu Di/>, respectivelyActual output at moment;
The distribution robust planning model comprises:
The first stage is an investment stage, and the pumping and storage capacity and the configuration capacity of wind power photovoltaics are determined;
and the second stage is an operation stage, and the pumping and accumulating and wind-light configuration capacity obtained in the first stage is transmitted to the second stage for simulation operation, and the aim of minimum wind-discarding and light-discarding punishment generated in the operation process is fulfilled, and the method comprises the following steps:
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wherein x is a decision variable of the first stage, and comprises configuration capacity of pumping and accumulating and wind power photovoltaic; x is an investment decision variable set of pumping and accumulating and wind power photovoltaic; the method is that the/>, which is obtained by clustering the load and wind power photovoltaic historical scene data A typical output scenario; /(I)When the first-stage investment decision variables are given, the system operation decision variables under the probability distribution of wind power and photovoltaic output scenes are collected, wherein the system operation decision variables comprise cascade hydroelectric real-time output, wind power and photovoltaic real-time output and extraction and storage real-time output; /(I)The operation cost is invested for the equal daily value of the pumping and accumulating and wind power photovoltaic; /(I)When the first-stage investment decision variables are given, the probability distribution is uncertain, and the operation cost expected value corresponding to the worst probability distribution in the set comprises punishment cost of wind and light discarding; /(I)、、、、、、、、、Is a corresponding coefficient matrix;
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Wherein, ForInitial probabilities of individual typical scenes; /(I)A mixed probability distribution uncertainty set for a typical output scenario; /(I)、The probability allowable deviation limit values of wind power photovoltaic output scenes under the constraint of-norm and ++norm respectively;
；
wherein, Probability of establishing an inequality in parentheses; /(I)、Confidence that two probability distribution inequalities are established respectively;
The solving the mixed integer linear programming model by adopting a column and constraint generation algorithm comprises the following steps:
Splitting the original min-max-min three-layer problem into a mixed integer programming main problem and a sub-problem which can be solved in parallel, wherein the method comprises the following steps:
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Wherein n is the iteration number;
When the first stage invests in decision variables Given, the sub-problem is solved in parallel, including:
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the sub-problem is solved in two steps, wherein the first step is to solve the minimum problem of the target inner layer in the sub-problem in parallel, and the second step is to solve the probability updating problem of the outer layer typical scene in the sub-problem.
2. The method for robust optimization configuration of water, wind and solar resource distribution according to claim 1, wherein the establishing of capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint of hydraulic coupling into consideration as a main factor comprises:
Capacity optimization configuration constraint conditions: the method comprises the steps of installing capacity constraint of wind power and photovoltaic power stations, configuration capacity constraint of a pumping and accumulating unit and system power balance constraint;
System operation constraints: the system power supply reliability constraint, the wind power and photovoltaic power station output constraint, the cascade hydropower station water quantity balance constraint, the flow constraint, the reservoir capacity control constraint, the hydropower unit water discharge limit constraint, the hydropower unit output characteristic constraint, the hydropower unit start-stop state constraint, the pumping storage unit flow constraint, the pumping storage unit power upper and lower limit constraint and the running state constraint of the hydropower unit and the pumping storage unit.
3. The method for robust optimization configuration of water-wind-solar resource distribution according to claim 2, wherein the capacity optimization configuration constraint condition comprises:
and (3) the installed capacity constraint of wind power and photovoltaic power stations:
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wherein, The minimum installed capacity of the wind farm is set; /(I)Maximum installed capacity of the wind farm; /(I)The minimum installed capacity of the photovoltaic power station is set; /(I)The maximum installed capacity of the photovoltaic power station is set;
Configuration capacity constraint of pumping and storage unit:
、、、 And 0-1 variable/> 、Multiplying to form nonlinear constraint, and converting the nonlinear constraint into linear constraint based on the McCormick envelope;
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wherein, AndIs a converted linear variable; /(I)、、The device comprises a speed-changing pumped storage unit, a speed-changing pump storage unit and a speed-changing pump storage unit, wherein the speed-changing pump storage unit is respectively a speed-changing pump storage unit; /(I)、The lower limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the pumping power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity; /(I)、The lower limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity are respectively the upper limit of the power generation power of the variable-speed pumping storage unit and the conversion coefficient of the installed capacity;
System power balance constraint:
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wherein, ForStage cascade hydropower stationActual output at moment; /(I)For pumping and storing unitGenerating power at moment; /(I)Pumping and accumulating unit (S /)Pumping power at moment; /(I)For the system atPower shortage generated at the moment; /(I)Is in the systemLoad at time.
4. The method for robust optimization configuration of water-wind-solar resource distribution according to claim 2, wherein the system operation constraint conditions comprise:
System power supply reliability constraints:
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wherein, ForThe maximum load shedding rate of the system at the moment is 95%;
wind power and photovoltaic power station output constraint:
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wherein, For wind farmA time period maximum predicted output; /(I)For photovoltaic power stationA time period maximum predicted output;
Step hydropower station water balance constraint:
Primary power station:
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wherein, ForReservoir capacity of the time period primary power station; /(I)The initial reservoir capacity of the primary power station reservoir; /(I)ForWater coming upstream of the time period; /(I)ForThe period primary power station discharging flow;
And (3) a secondary power station:
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wherein, Reservoir capacity for the second-stage power station in the second period; /(I)The method is characterized by comprising the steps of (1) setting an initial reservoir capacity for a secondary power station reservoir; /(I)ForThe secondary power station discharge flow in the period;
Three-stage power station:
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wherein, ForThe period three-level power station discharging flow;
Flow constraint:
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wherein, The method comprises the steps that the discharge flow is discharged in a t period of an i-th cascade hydropower station; /(I)Generating flow for the ith period of the ith-stage cascade hydropower station; /(I)The flow under the power generation working condition of the pumping and accumulating unit in the first period is obtained; /(I)The flow under the pumping working condition of the pumping and accumulating unit in the t period is provided; /(I)Discarding water flow for the ith stage cascade hydropower station in the first period;
reservoir capacity control constraints:
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wherein, The minimum storage capacity of the step hydropower station of the ith level; /(I)The maximum storage capacity of the i-th cascade hydropower station; the storage capacity is the initial time of the i-th cascade hydropower station; /(I) The final time storage capacity of the i-th cascade hydropower station;
Water discharge limit constraint of hydroelectric generating set:
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wherein, The maximum water discharge flow of the hydropower station with the i-th step is obtained;
The output characteristic constraint of the hydroelectric generating set:
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wherein, The minimum output of the hydraulic turbine of the i-th cascade hydropower station is obtained; /(I)The maximum output of the hydraulic turbine of the i-th cascade hydropower station is obtained; /(I)The hydraulic turbine operation state variable is the hydraulic turbine operation state variable of the i-th cascade hydropower station;
And (3) restraining a startup and shutdown state body of the hydroelectric generating set:
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wherein, The method is characterized in that the method is a water turbine starting state variable of an ith-stage cascade hydropower station; /(I)The hydraulic turbine shutdown state variable is the hydraulic turbine shutdown state variable of the i-th cascade hydropower station;
flow constraint of pumping and storage unit:
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wherein, The power generation flow is the power generation flow under the power generation working condition of the pumping and accumulating unit; /(I)Pumping flow under the pumping working condition of the pumping and accumulating unit;
and the upper and lower limits of the power of the pumping and storage unit are constrained:
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wherein, The operation state variable is the power generation working condition of the pumping and accumulating unit in the t period; /(I)The operation state variable of the pumping working condition of the pumping and accumulating unit in the t period; /(I)，The minimum output and the maximum output of the pumping and accumulating unit under the power generation working condition are obtained;， minimum output and maximum output under the pumping working condition of the pumping and accumulating unit;
and the running states of the water turbine unit and the pumping and storage unit are constrained:
。
5. A water-wind-light resource distribution robust optimal configuration device, characterized in that the method according to any one of claims 1 to 4 is used, comprising:
the target constraint unit is used for establishing capacity optimization configuration constraint conditions and system operation constraint conditions taking the cascade hydropower constraint taking hydraulic coupling into account as a main function with the minimum equivalent daily investment cost and daily operation cost;
The modeling unit is used for performing uncertainty set modeling on wind power and photovoltaic output and obtaining a typical scene through historical scene data clustering;
The planning model building unit is used for building a distributed robust planning model of scene probability driven off-grid type extraction and storage reconstruction cascade small hydropower collaborative configuration wind and light resources according to the objective function and constraint conditions, and synthesizing a 1-norm and +_norm constraint uncertainty probability distribution confidence set;
the linearization unit is used for linearizing the distribution robust planning model through a McCormick linearization method to obtain a mixed integer linear planning model;
And the solving unit is used for solving the mixed integer linear programming model by adopting a column and constraint generating algorithm to obtain an optimal capacity optimizing configuration method.
6. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1-4 when executing the computer program.
7. A storage medium having stored thereon a computer program which, when executed by a processor, implements the method of any of claims 1-4.
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