CN117252425A - Power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risk - Google Patents
Power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risk Download PDFInfo
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Abstract
The invention relates to a power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risk, which comprises the following steps: acquiring a year time sequence load prediction curve, a month typical daily wind power and a photovoltaic output prediction curve of a power supply system; generating an uncertainty scene of wind power and photovoltaic output fluctuation; establishing a carbon emission risk assessment index and a power balance risk assessment index; establishing a three-layer power supply planning and thermal power transformation decision-making target; and establishing a double-layer objective function, establishing a rolling decision model, and carrying out decision solving by using a solver. According to the invention, the power supply regulation characteristic and the new energy fluctuation characteristic are considered, and the carbon emission and power balance risk assessment indexes considering the power supply regulation characteristic and the new energy fluctuation characteristic are respectively provided for aiming at the problems of excessive carbon emission, insufficient new energy consumption, load shedding and other risks in the operation process of the power system, so that the carbon emission risk and the power balance risk caused by the uncertainty of the new energy output are quantized.
Description
Technical Field
The invention relates to the technical field of low-carbon power supply planning considering thermal power carbon capture transformation, in particular to a power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks.
Background
The study of low-carbon power supply planning can be mainly divided into 2 directions: the environment protection policy and the carbon transaction system are introduced to indirectly realize the low-carbon power supply planning, the emission reduction technology is introduced, and the clean energy is introduced to directly realize the low-carbon power supply planning. Among low-carbon power generation technologies, carbon dioxide capturing and storing are a method for relieving the influence of a power plant on the environment, and carbon capturing and storing (carbon capture and storage, CCS) technologies have remarkable influence on the carbon emission reduction effect of a thermal power generating unit. The carbon capture equipment recovers and discharges CO in the flue gas by consuming a certain proportion of unit output 2 Thereby achieving the purpose of carbon emission reduction of the thermal power generating unit. However, the planning basis of carbon capture transformation and the quantitative evaluation of the emission reduction effect after transformation are fuzzy, and meanwhile, a large amount of carbon capture transformation reduces the new energy consumption capacity of the thermal power system, and causes the power balance risk under the condition of large-scale new energy access.
Disclosure of Invention
The invention aims to provide a power supply planning and thermal power transformation decision modeling method which can quantitatively evaluate the emission reduction effect and the operation risk of a system after thermal power carbon capture transformation planning, effectively reduce the number of days and the risk value of carbon emission risks in the operation process of a power system and reduce the consequent power balance risk and consider the carbon emission and the power balance risk.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a power supply planning and thermal power reconstruction decision modeling method taking carbon emissions and power balance risk into account, the method comprising the sequential steps of:
(1) Acquiring a year time sequence load prediction curve, a month typical daily wind power and photovoltaic output prediction curve of a power supply system, and various power supply types and parameters of the power supply system:
(2) Generating uncertainty scenes of wind power and photovoltaic output fluctuation by utilizing the month typical solar wind power and photovoltaic output prediction curve obtained in the step (1) through normal distribution, wherein the uncertainty scenes consist of scene output and scene probability;
(3) Establishing a carbon emission risk assessment index and a power balance risk assessment index which take the uncertainty of the output of new energy and the regulation characteristic of a power supply into consideration by utilizing the uncertainty scene of the fluctuation of the wind power and the photovoltaic output obtained in the step (2);
(4) Establishing three-layer power supply planning and thermal power transformation decision targets, wherein the three-layer power supply planning and thermal power transformation decision targets comprise an upper investment decision layer decision target, a middle operation simulation layer decision target and a lower risk assessment decision target, and adding the carbon emission risk assessment index and the power balance risk assessment index obtained in the step (3) into the lower risk assessment layer decision target;
(5) According to the three-layer power supply planning and thermal power transformation decision targets obtained in the step (4), a double-layer objective function is established, the double-layer objective function comprises an upper-layer investment decision and a lower-layer optimization evaluation, a rolling decision model is established according to the double-layer objective function and the constraint of the corresponding objective function, a month typical daily load prediction curve obtained in the step (1), various power supply types and parameters of a power supply system and an uncertainty scene obtained in the step (2) are imported, and a solver is utilized for decision solving.
The step (1) specifically comprises the following steps:
(1a) Acquiring a year time sequence load prediction curve of a power supply system: the annual time series load curve in recent years is multiplied by the load growth rate to obtain an annual time series load growth predicted value, and the annual time series load growth predicted value is added with the annual time series load curve in recent years to obtain a power supply system annual time series load predicted curve;
(1b) Obtaining a month typical daily load prediction curve:
in the annual time sequence load prediction curve of the power supply system obtained in the step (1 a), calculating the typical daily load of the month in a certain month, selecting the month r, calculating the average value according to the moment to obtain the typical daily load of the month r, and obtaining a month typical daily load prediction curve of each month of 12 months;
(1c) Obtaining a month typical solar power and photovoltaic output prediction curve:
(1c1) Acquiring a curve of wind power and photovoltaic time sequence output in the last 3 years;
(1c2) Calculating the predicted values of typical daily wind power and photovoltaic output of the month, such as the m month, for 3 groups of curves obtained in the step (1 c 1), and calculating the average output value of the same day and time of 3 years;
(1c3) Calculating an average value of the output average value of the mth month according to a time sequence corresponding to one day to obtain a typical daily wind power and photovoltaic output prediction curve of the mth month, thereby obtaining a month typical daily wind power and photovoltaic output prediction curve of each month of 12 months;
(1d) Acquiring various power supply types and parameters of a power supply system, including parameters of a thermal power unit, parameters of a wind power unit and a photovoltaic unit and energy storage parameters; the thermal power unit parameters comprise capacity, maximum and minimum technical output, minimum start-stop time, start-stop cost, construction cost, running cost, the number of original and newly-built upper limit units, up-and-down climbing rate and carbon emission coefficient; the parameters of the wind power and photovoltaic units comprise capacity, maximum and minimum technical output, original construction cost and newly built upper limit unit quantity; the energy storage parameters comprise capacity, construction cost, operation cost, charge and discharge efficiency, upper and lower limits of charge and discharge power and upper and lower limits of capacity.
The step (2) specifically comprises the following steps:
(2a) Obtaining a fluctuation range of typical daily predicted output according to a month typical daily wind power and photovoltaic output prediction curve, wherein the upper limit of fluctuation is the day with the maximum average value of wind power and photovoltaic output corresponding to the current moment of the month, and the lower limit is the day with the minimum average value, so that normal distribution parameters of wind power and photovoltaic output fluctuation are obtained;
(2b) Calculating scene probability f (x) of the uncertainty scene by using the normal distribution parameter and a normal distribution probability formula (1):
wherein x is wind power and photovoltaic scene output; sigma (sigma) 2 Representing the variance of the wind power and photovoltaic scene output x; mu is the mean value of x;
in view of seasonal and daily characteristics of wind speed and illumination intensity, wind power and photovoltaic output at the same moment every month every day in each year are represented by the same probability distribution; obtaining wind power and photovoltaic output normal distribution curves at 24 times per day of all typical days according to the month typical daily wind power and photovoltaic output prediction curves and the normal distribution parameters obtained in the step (2 a); dividing a normal distribution curve into s equal-width intervals, taking the midpoint of the interval as the scene output force x of wind power and photovoltaic, and calculating the scene probability f (x) corresponding to each x by the formula (1); each group of wind power and photovoltaic scene output force x and scene probability f (x) are combined to obtain an uncertainty scene, and total 12 x 24 x s scenes are obtained.
The step (3) specifically comprises the following steps:
(3a) Due to fluctuation of wind power and photovoltaic output, the carbon emission of the thermal power unit is expected to exceed the economic and environmental safety risks caused by the limit of carbon emission on the same day, wherein the relation between the output and the carbon emission of the thermal power unit before and after carbon capture transformation is as follows:
wherein E is nc Carbon emission of the thermal power generating unit without carbon capture transformation; e (E) yc Carbon emission of the thermal power generating unit after carbon capture transformation is carried out;the method is that the output of the ith thermal power generating unit in the ith scene at the moment t; alpha i The carbon emission coefficient of the ith thermal power unit; beta is the trapping rate of the carbon trapping device;
(3b) Calculating a carbon emission risk assessment index R of a typical day from formula (4) E ：
Wherein R is E According to the number, the method is divided into the following four grades, 0 is no risk, 0-5 is low risk, 5-10 is medium risk,>10 is a high risk;a threshold for the safe carbon schedule for the d-th typical day is obtained by multiplying the annual carbon emission allowance by the daily load and dividing by the annual load; e (E) d The current typical daily carbon emission expected value is represented by formula (5):
in the formula Θ yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; f (f) s (x) The scene probability of the uncertainty scene corresponding to the s-th scene output x is the current typical day and t time; t is typical day length, t=24;
(3c) Calculating an expected value P of the waste wind and the waste light under uncertain scenes by using the steps (6) and (7) B Cut load expected value L B ：
In the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the predicted value of the output in the s-th scene at the moment of the photovoltaic t is +.> For the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; l (L) t The system load is t time;
(3d) Combining an analytic hierarchy process and an entropy weight process to obtain an expected value P of the abandoned wind and abandoned light B Expected value of cut load L B Giving weight and summing to obtain power balance risk assessment index R B ：
In the method, in the process of the invention,expected value P for discarding wind and light B Weight of->To cut the load expected value L B Weights of (2); r is R B According to the number, the number is divided into the following four grades, 0 is no risk, 0-1 is low risk, 1-3 is medium risk,>3 is a high risk; the weight of the kth expected value is obtained by comprehensively utilizing a hierarchical analysis method and an entropy weight method according to the formula (9):
in the method, in the process of the invention,for the weight of the kth expected value obtained by means of analytic hierarchy process,/o >The weight of the kth expected value obtained by using the entropy weight method.
The step (4) specifically comprises the following steps:
(4a) On the basis that an upper investment decision-making layer decision-making target of a traditional double-layer unit planning decision-making target minimizes investment and a middle operation simulation layer decision-making target minimizes operation cost, a new lower risk assessment layer decision-making target is added, so that the time sequence scene operation and the target value of a risk assessment index are minimized; the objective function of establishing a three-layer power supply planning and thermal power reconstruction decision target is represented by the formula (10):
wherein C is n For the upper layer decision goal: annual investment costs; c (C) op Is a middle layer decision goal: the wind power and the photovoltaic predict the running cost under the condition of the output force; c (C) r Is the lower layer decision target: sum of running cost and risk assessment index value under uncertain scene:the target coefficient is decided for the upper layer; />The middle layer decision target coefficient; />The target coefficient is decided for the lower layer;
(4b) Each layer decision target component is represented by equations (11), (12) and (13):
C n ＝min(C NT +C NW +C NPV +C NS +C ETC +C NTC ) (11)
wherein C is NT Investment cost for newly-built thermal power generating units; c (C) NW Investment cost for newly-built wind turbine generators; c (C) NPV Investment cost for newly-built photovoltaic units; c (C) NS Investment cost for newly-built energy storage equipment; c (C) ETC Investment cost is improved for original thermal power carbon capture; c (C) NTC Investment cost is improved for newly-built thermal power carbon capture; c (C) YC Predicting the running cost of the thermal power for carbon capture transformation; c (C) NC Predicting the running cost for unmodified thermal power; c (C) DU The starting and stopping cost of the thermal power is set; c (C) s Predicting an operating cost for the stored energy;running cost for the time sequence scene; r is R op For the comprehensive risk index value, the carbon emission risk assessment index R E Risk assessment index R for electric power balance B And adding to obtain:
R op ＝R E +R B (14)。
the step (5) specifically comprises the following steps:
(5a) Simplifying a three-layer power supply planning and thermal power transformation decision target into a double-layer objective function, and laminating a middle layer and a lower layer of the three-layer power supply planning and thermal power transformation decision target into a single layer to obtain the double-layer objective function, wherein the double-layer objective function comprises an upper investment decision and a lower optimization evaluation:
wherein C is l Objective function for the lower layer optimization evaluation: a sum of random production simulation cost and risk assessment index;optimizing the coefficients of the evaluated objective function for the lower layer; c (C) n Objective function for upper layer investment decisions: annual investment costs; />Coefficients of an objective function that is an upper level investment decision;
(5b) Constructing each cost component in an upper-layer objective function (16) of the rolling decision model according to a corresponding investment cost formula and a full life cycle cost theory:
C n ＝min(C NT +C NW +C NPV +C NS +C ETC +C NTC ) (16)
Establishing the investment cost of the newly-built thermal power generating unit by using the formula (17):
wherein C is NT Investment cost for newly-built thermal power generating units; gamma is the discount rate; theta (theta) NT The method is a thermal power generating unit set to be built; epsilon T The annual growth rate of the building cost of the thermal power generating unit is improved; τ i The investment cost of the new ith thermal power generating unit is set; v NT (i) For the newly built i-th thermal power generating unit, 1 is built, and 0 is not built;
establishing the investment cost of the newly-built wind turbine generator by using the formula (18):
wherein C is NW Investment cost for newly-built wind turbine generators; theta (theta) NW The method comprises the steps of setting a set of wind turbines to be built; epsilon W The annual growth rate of the building cost of the wind turbine generator is increased; upsilon (v) w The investment cost of the w-th wind turbine generator is newly built; v NW (w) a newly built w-th typhoon motor group building state, wherein 1 is built, and 0 is not built;
establishing the investment cost of the newly-built photovoltaic unit by using the method (19):
wherein C is NPV Investment cost for newly-built photovoltaic units; theta (theta) NPV The method comprises the steps of collecting photovoltaic units to be built; epsilon PV Annual growth rate for photovoltaic unit construction costs; pi p Investment cost for newly building a p-th photovoltaic unit; v NPV (p) a newly built p-th wind turbine generator system is built, 1 is built, and 0 is not built;
establishing investment cost of new energy storage equipment by using a formula (20):
wherein C is NS Investment cost for newly-built energy storage equipment; epsilon S Annual growth rate for energy storage device construction costs;investment cost for newly built energy storage unit capacity; e (E) NS To newly build energy storage capacity;
the original thermal power carbon capture transformation investment cost is established by utilizing the formula (21):
in the formula Θ ET Is the original thermal power collection;the method is characterized in that the method is the carbon capture reconstruction cost of the original ith thermal power generating unit; v ETC (i 0 ) The method is characterized in that the method is in an original i-th thermal power initial reforming state, wherein 1 is reformed, and 0 is not reformed; v ETC (i) The method is in an annual transformation state of the original ith thermal power, 1 is transformation, and O is not transformation;
establishing newly built thermal power carbon capture reconstruction investment cost by using a formula (22):
in the method, in the process of the invention,the method is used for reconstructing carbon capture of a newly-built thermal power generating unit; v NTC (j) The new year transformation state of the ith thermal power is established, 1 is transformation, and 0 is no transformation;
(5c) Constructing a constraint function corresponding to an upper investment decision of the rolling decision model:
establishing a capacity constraint function using equation (23):
wherein L is a time-series average load; i i The installed capacities of the thermal power respectively; i w The installed capacity of wind power; i p Is the installed capacity of the photovoltaic;
establishing a carbon emission constraint function using equation (24):
wherein d is the number of days; t is typical day length, t=24; e (E) pl Predicting carbon emissions for the year; e is an emission reduction index of the year; v TC (i) The modification state of the thermal power unit i is that modification is carried out 1, and modification is not carried out 0; e (E) nc Carbon emission of the thermal power generating unit without carbon capture transformation;E yc carbon emission of the thermal power generating unit after carbon capture transformation is carried out;
(5d) Applying artificial weight according to an entropy weight method, and constructing a lower-layer optimization evaluation objective function of a rolling decision model considering carbon emission and power balance risk:
establishing a lower objective function component by using the formula (25), and starting and stopping the cost of the thermal power generating unit:
wherein C is DU The starting and stopping cost of the thermal power is set; theta (theta) yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; u (U) i The starting cost of the thermal power unit i is; d (D) i The shutdown cost of the thermal power unit i is; v i，t The method is characterized in that the method is in a starting and stopping state at t moment of an ith thermal power generating unit, wherein 1 is started, and 0 is stopped;
establishing a lower objective function component by using the formula (26), and capturing and reforming the operation cost of the thermal power generating unit:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after carbon capture transformation is used; epsilon op The annual growth rate of the running cost of thermal power and energy storage is achieved; a, a i 、b i 、c i The running cost coefficient of the ith thermal power is the running cost coefficient of the ith thermal power; />The output loss is improved for thermal power carbon capture; />The method is that the output of the ith thermal power generating unit in the ith scene at the moment t;
Establishing a lower objective function component by using the formula (27), wherein the operation cost of the thermal power generating unit is not improved by carbon capture:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after the carbon capture transformation is not carried out;
establishing the lower layer objective function component using equation (28), energy storage device operating cost:
in the method, in the process of the invention,the operation cost of the energy storage equipment is; g s The energy storage running cost; p (P) t dis，s The energy storage discharge power in the s-th scene at the t moment; p (P) t ch，s The energy storage charging power in the s-th scene at the t moment; η (eta) dis The energy storage and discharge efficiency is achieved; η (eta) ch The energy storage and charging efficiency is improved;
using equation (29), the cost component obtained above, and equation (14), a lower-layer optimization evaluation objective function is established taking into account carbon emissions and power balance risk, as shown in equation (30):
in the method, in the process of the invention,running cost for the time sequence scene; />To apply the entropy weight method to the scene operation cost average valueAdding manual weight; />The artificial weight applied to the comprehensive risk value by the entropy weight method meets the following conditions: />R op Is a comprehensive risk index value;
(5e) Constructing a constraint function corresponding to lower-layer optimization evaluation of a rolling decision model according to unit combination production simulation:
establishing a power balance constraint function under the uncertain scene of the lower-layer production simulation moment by using the formula (31):
In the method, in the process of the invention,for the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; thermal power generating unit carbon capture output loss>Output force of thermal power generating unit i under s scene at t moment +.>The relationship is as follows:
wherein alpha is i The carbon emission coefficient of the thermal power unit i; beta is the trapping of carbon trapping equipmentA rate; lambda (lambda) GE Is the capture unit CO 2 The consumed thermal power is a constant value;
establishing a network power constraint function of an actual time sequence scene of the new energy of the lower layer by using the steps (33) and (34):
in the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the output predicted value is the output predicted value of the photovoltaic t moment under the s-th scene;
establishing a lower layer load actual uncertainty scene internet power constraint by using a formula (35):
in the method, in the process of the invention,supplying actual load under the s-th scene of the system t moment; l (L) t The system load is t time;
(5f) Leading in a month typical daily load prediction curve and an uncertainty scene wind-light power output curve, calling a yalminip and a gurobi solver to solve a double-layer objective function and constraint of a rolling decision model to obtain the number of new thermal power generation units, wind power generation units, photovoltaic unit and energy storage equipment capacity, obtaining the carbon capture transformation number of original and new thermal power generation units, obtaining the carbon emission risk and electric balance risk assessment index result of the year, and obtaining the annual decision cost;
(5g) Judging whether all decisions of the planning period N are completed, if so, outputting the decisions and the assessment results of each risk index, and ending the cycle; otherwise, n=n+1, part of unit cost increases, and the process returns to the step (5 a), the basic parameters are updated according to the annual growth rate of each cost, the annual growth and reduction rate of each output load and the carbon emission requirement, the original unit quantity and capacity parameters are updated according to the newly-built unit data obtained by calculation, and the decision of the next year is made sequentially.
According to the technical scheme, the beneficial effects of the invention are as follows: firstly, considering the power supply regulation characteristic and the new energy fluctuation characteristic, respectively providing carbon emission and power balance risk assessment indexes considering the power supply regulation characteristic and the new energy fluctuation characteristic for solving the problems of excessive carbon emission, insufficient new energy consumption, load shedding and other risks in the operation process of the power system, and quantifying carbon emission risk and power balance risk caused by the uncertainty of the new energy output; secondly, the invention establishes a power supply planning and thermal power transformation rolling decision model considering carbon emission and power balance risks based on a traditional power supply planning objective function, and comprises the following steps: compared with the traditional model, the three levels of investment decision-making, operation simulation, carbon emission and power balance risk assessment are more comprehensive in consideration of power supply types, and quantitative assessment is carried out on the emission reduction effect of the decision-making and the safety stability of a power supply system while the decision-making is carried out; thirdly, the invention provides an objective function simplification method and a simplified objective model based on the coupling relation between three layers of targets, thereby effectively reducing the calculation difficulty and improving the calculation efficiency; fourth, the invention is based on the traditional power supply planning decision method, proposes a power supply planning and thermal power transformation rolling decision method taking carbon emission and power balance risk into consideration, and comprehensively utilizes an entropy weight method and a analytic hierarchy process to construct an objective function taking economic efficiency and carbon emission and power balance comprehensive risk indexes, thereby having very obvious effect and very important significance for reducing the carbon emission risk and the power balance risk of the power supply planning decision result.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1, a power supply planning and thermal power transformation decision modeling method taking carbon emission and power balance risk into consideration includes the following sequential steps:
(1) Acquiring a year time sequence load prediction curve, a month typical daily wind power and photovoltaic output prediction curve of a power supply system, and various power supply types and parameters of the power supply system:
(2) Generating uncertainty scenes of wind power and photovoltaic output fluctuation by utilizing the month typical solar wind power and photovoltaic output prediction curve obtained in the step (1) through normal distribution, wherein the uncertainty scenes consist of scene output and scene probability;
(3) Establishing a carbon emission risk assessment index and a power balance risk assessment index which take the uncertainty of the output of new energy and the regulation characteristic of a power supply into consideration by utilizing the uncertainty scene of the fluctuation of the wind power and the photovoltaic output obtained in the step (2);
(4) Establishing three-layer power supply planning and thermal power transformation decision targets, wherein the three-layer power supply planning and thermal power transformation decision targets comprise an upper investment decision layer decision target, a middle operation simulation layer decision target and a lower risk assessment decision target, and adding the carbon emission risk assessment index and the power balance risk assessment index obtained in the step (3) into the lower risk assessment layer decision target;
(5) According to the three-layer power supply planning and thermal power transformation decision targets obtained in the step (4), a double-layer objective function is established, the double-layer objective function comprises an upper-layer investment decision and a lower-layer optimization evaluation, a rolling decision model is established according to the double-layer objective function and the constraint of the corresponding objective function, a month typical daily load prediction curve obtained in the step (1), various power supply types and parameters of a power supply system and an uncertainty scene obtained in the step (2) are imported, and a solver is utilized for decision solving.
The step (1) specifically comprises the following steps:
(1a) Acquiring a year time sequence load prediction curve of a power supply system: the annual time series load curve in recent years is multiplied by the load growth rate to obtain an annual time series load growth predicted value, and the annual time series load growth predicted value is added with the annual time series load curve in recent years to obtain a power supply system annual time series load predicted curve;
(1b) Obtaining a month typical daily load prediction curve:
in the annual time sequence load prediction curve of the power supply system obtained in the step (1 a), calculating the typical daily load of the month in a certain month, selecting the month r, calculating the average value according to the moment to obtain the typical daily load of the month r, and obtaining a month typical daily load prediction curve of each month of 12 months;
(1c) Obtaining a month typical solar power and photovoltaic output prediction curve:
(1c1) Acquiring a curve of wind power and photovoltaic time sequence output in the last 3 years;
(1c2) Calculating the predicted values of typical daily wind power and photovoltaic output of the month, such as the m month, for 3 groups of curves obtained in the step (1 c 1), and calculating the average output value of the same day and time of 3 years;
(1c3) Calculating an average value of the output average value of the mth month according to a time sequence corresponding to one day to obtain a typical daily wind power and photovoltaic output prediction curve of the mth month, thereby obtaining a month typical daily wind power and photovoltaic output prediction curve of each month of 12 months;
(1d) Acquiring various power supply types and parameters of a power supply system, including parameters of a thermal power unit, parameters of a wind power unit and a photovoltaic unit and energy storage parameters; the thermal power unit parameters comprise capacity, maximum and minimum technical output, minimum start-stop time, start-stop cost, construction cost, running cost, the number of original and newly-built upper limit units, up-and-down climbing rate and carbon emission coefficient; the parameters of the wind power and photovoltaic units comprise capacity, maximum and minimum technical output, original construction cost and newly built upper limit unit quantity; the energy storage parameters comprise capacity, construction cost, operation cost, charge and discharge efficiency, upper and lower limits of charge and discharge power and upper and lower limits of capacity.
The step (2) specifically comprises the following steps:
(2a) Obtaining a fluctuation range of typical daily predicted output according to a month typical daily wind power and photovoltaic output prediction curve, wherein the upper limit of fluctuation is the day with the maximum average value of wind power and photovoltaic output corresponding to the current moment of the month, and the lower limit is the day with the minimum average value, so that normal distribution parameters of wind power and photovoltaic output fluctuation are obtained;
(2b) Calculating scene probability f (x) of the uncertainty scene by using the normal distribution parameter and a normal distribution probability formula (1):
wherein x is wind power and photovoltaic scene output; sigma (sigma) 2 Representing the variance of the wind power and photovoltaic scene output x; mu is the mean value of x;
in view of seasonal and daily characteristics of wind speed and illumination intensity, wind power and photovoltaic output at the same moment every month every day in each year are represented by the same probability distribution; obtaining wind power and photovoltaic output normal distribution curves at 24 times per day of all typical days according to the month typical daily wind power and photovoltaic output prediction curves and the normal distribution parameters obtained in the step (2 a); dividing a normal distribution curve into s equal-width intervals, taking the midpoint of the interval as the scene output force x of wind power and photovoltaic, and calculating the scene probability f (x) corresponding to each x by the formula (1); each group of wind power and photovoltaic scene output force x and scene probability f (x) are combined to obtain an uncertainty scene, and total 12 x 24 x s scenes are obtained.
The step (3) specifically comprises the following steps:
(3a) Due to fluctuation of wind power and photovoltaic output, the carbon emission of the thermal power unit is expected to exceed the economic and environmental safety risks caused by the limit of carbon emission on the same day, wherein the relation between the output and the carbon emission of the thermal power unit before and after carbon capture transformation is as follows:
wherein E is nc Carbon emission of the thermal power generating unit without carbon capture transformation; e (E) yc Carbon emission of the thermal power generating unit after carbon capture transformation is carried out;the method is that the output of the ith thermal power generating unit in the ith scene at the moment t; alpha i The carbon emission coefficient of the ith thermal power unit; beta is the trapping rate of the carbon trapping device;
(3b) Calculating a carbon emission risk assessment index R of a typical day from formula (4) E ：
Wherein R is E According to the number, the method is divided into the following four grades, 0 is no risk, 0-5 is low risk, 5-10 is medium risk,>10 is a high risk;a threshold for the safe carbon schedule for the d-th typical day is obtained by multiplying the annual carbon emission allowance by the daily load and dividing by the annual load; e (E) d The current typical daily carbon emission expected value is represented by formula (5):
in the formula Θ yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; f (f) s (x) The scene probability of the uncertainty scene corresponding to the s-th scene output x is the current typical day and t time; t is typical day length, t=24;
(3c) Calculating an expected value P of the waste wind and the waste light under uncertain scenes by using the steps (6) and (7) B Cut load expected value L B ：
In the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the predicted value of the output in the s-th scene at the moment of the photovoltaic t is +.> For the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; l (L) t The system load is t time;
(3d) Combining an analytic hierarchy process and an entropy weight process to obtain an expected value P of the abandoned wind and abandoned light B Expected value of cut load L B Giving weight and summing to obtain power balance risk assessment index R B ：
In the method, in the process of the invention,expected value P for discarding wind and light B Weight of->To cut the load expected value L B Weights of (2); r is R B According to the number, the number is divided into the following four grades, 0 is no risk, 0-1 is low risk, 1-3 is medium risk,>3 is a high risk; the weight of the kth expected value is obtained by comprehensively utilizing a hierarchical analysis method and an entropy weight method according to the formula (9):
in the method, in the process of the invention,for the weight of the kth expected value obtained by means of analytic hierarchy process,/o >The weight of the kth expected value obtained by using the entropy weight method.
The step (4) specifically comprises the following steps:
(4a) On the basis that an upper investment decision-making layer decision-making target of a traditional double-layer unit planning decision-making target minimizes investment and a middle operation simulation layer decision-making target minimizes operation cost, a new lower risk assessment layer decision-making target is added, so that the time sequence scene operation and the target value of a risk assessment index are minimized; the objective function of establishing a three-layer power supply planning and thermal power reconstruction decision target is represented by the formula (10):
wherein C is n For the upper layer decision goal: annual investment costs; c (C) op Is a middle layer decision goal: the wind power and the photovoltaic predict the running cost under the condition of the output force; c (C) r Is the lower layer decision target: sum of running cost and risk assessment index value under uncertain scene:the target coefficient is decided for the upper layer; />The middle layer decision target coefficient; />The target coefficient is decided for the lower layer;
(4b) Each layer decision target component is represented by equations (11), (12) and (13):
wherein C is NT Investment cost for newly-built thermal power generating units; c (C) NW Investment cost for newly-built wind turbine generators; c (C) NPV Investment cost for newly-built photovoltaic units; c (C) NS Investment cost for newly-built energy storage equipment; c (C) ETC Investment cost is improved for original thermal power carbon capture; c (C) VTC Investment cost is improved for newly-built thermal power carbon capture; c (C) YC Predicting the running cost of the thermal power for carbon capture transformation; c (C) NC Predicting the running cost for unmodified thermal power; c (C) DU The starting and stopping cost of the thermal power is set; c (C) s Predicting an operating cost for the stored energy;running cost for the time sequence scene; r is R op For the comprehensive risk index value, the carbon emission risk assessment index R E Risk assessment index R for electric power balance B And adding to obtain:
R op ＝R E +R B (14)。
the step (5) specifically comprises the following steps:
(5a) Simplifying a three-layer power supply planning and thermal power transformation decision target into a double-layer objective function, and laminating a middle layer and a lower layer of the three-layer power supply planning and thermal power transformation decision target into a single layer to obtain the double-layer objective function, wherein the double-layer objective function comprises an upper investment decision and a lower optimization evaluation:
wherein C is l Objective function for the lower layer optimization evaluation: a sum of random production simulation cost and risk assessment index;optimizing the coefficients of the evaluated objective function for the lower layer; c (C) n Objective function for upper layer investment decisions: annual investment costs; />Coefficients of an objective function that is an upper level investment decision;
(5b) Constructing each cost component in an upper-layer objective function (16) of the rolling decision model according to a corresponding investment cost formula and a full life cycle cost theory:
C n ＝min(C NT +C NW +C NPV +C NS +C ETC +C NTC ) (16)
Establishing the investment cost of the newly-built thermal power generating unit by using the formula (17):
wherein C is NT Investment cost for newly-built thermal power generating units; gamma is the discount rate; theta (theta) NT The method is a thermal power generating unit set to be built; epsilon T The annual growth rate of the building cost of the thermal power generating unit is improved; τ i The investment cost of the new ith thermal power generating unit is set; v NT (i) For the newly built i-th thermal power generating unit, 1 is built, and 0 is not built;
establishing the investment cost of the newly-built wind turbine generator by using the formula (18):
wherein C is NW Investment cost for newly-built wind turbine generators; theta (theta) NW The method comprises the steps of setting a set of wind turbines to be built; epsilon W The annual growth rate of the building cost of the wind turbine generator is increased; upsilon (v) w The investment cost of the w-th wind turbine generator is newly built; v NW (w) a newly built w-th typhoon motor group building state, wherein 1 is built, and 0 is not built;
establishing the investment cost of the newly-built photovoltaic unit by using the method (19):
wherein C is NPV Investment cost for newly-built photovoltaic units; theta (theta) NPV The method comprises the steps of collecting photovoltaic units to be built; epsilon PV Annual growth rate for photovoltaic unit construction costs; pi p Investment cost for newly building a p-th photovoltaic unit; v NPV (p) a newly built p-th wind turbine generator system is built, 1 is built, and 0 is not built;
establishing investment cost of new energy storage equipment by using a formula (20):
wherein C is NS Investment cost for newly-built energy storage equipment; epsilon S Annual growth rate for energy storage device construction costs;investment cost for newly built energy storage unit capacity; e (E) NS To newly build energy storage capacity;
the original thermal power carbon capture transformation investment cost is established by utilizing the formula (21):
/>
in the formula Θ ET Is the original thermal power collection;the method is characterized in that the method is the carbon capture reconstruction cost of the original ith thermal power generating unit; v ETC (i 0 ) The method is characterized in that the method is in an original i-th thermal power initial reforming state, wherein 1 is reformed, and 0 is not reformed; v ETC (i) The method is in an annual transformation state of the original ith thermal power, wherein 1 is transformation, and 0 is non-transformation;
establishing newly built thermal power carbon capture reconstruction investment cost by using a formula (22):
in the method, in the process of the invention,the method is used for reconstructing carbon capture of a newly-built thermal power generating unit; v NTC (j) The new year transformation state of the ith thermal power is established, 1 is transformation, and 0 is no transformation;
(5c) Constructing a constraint function corresponding to an upper investment decision of the rolling decision model:
establishing a capacity constraint function using equation (23):
wherein L is a time-series average load; i i The installed capacities of the thermal power respectively; i w The installed capacity of wind power; i p Is the installed capacity of the photovoltaic;
establishing a carbon emission constraint function using equation (24):
wherein d is the number of days; t is typical day length, t=24; e (E) pl Predicting carbon emissions for the year; e is an emission reduction index of the year; v TC (i) The modification state of the thermal power unit i is that modification is carried out 1, and modification is not carried out 0; e (E) nc Carbon emission of the thermal power generating unit without carbon capture transformation; e (E) yc Carbon emission of the thermal power generating unit after carbon capture transformation is carried out;
(5d) Applying artificial weight according to an entropy weight method, and constructing a lower-layer optimization evaluation objective function of a rolling decision model considering carbon emission and power balance risk:
establishing a lower objective function component by using the formula (25), and starting and stopping the cost of the thermal power generating unit:
wherein C is DU The starting and stopping cost of the thermal power is set; theta (theta) yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; u (U) i The starting cost of the thermal power unit i is; d (D) i The shutdown cost of the thermal power unit i is; v i，t The method is characterized in that the method is in a starting and stopping state at t moment of an ith thermal power generating unit, wherein 1 is started, and 0 is stopped;
establishing a lower objective function component by using the formula (26), and capturing and reforming the operation cost of the thermal power generating unit:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after carbon capture transformation is used; epsilon op The annual growth rate of the running cost of thermal power and energy storage is achieved; a, a i 、b i 、c i The running cost coefficient of the ith thermal power is the running cost coefficient of the ith thermal power; />The output loss is improved for thermal power carbon capture; />The method is that the output of the ith thermal power generating unit in the ith scene at the moment t;
Establishing a lower objective function component by using the formula (27), wherein the operation cost of the thermal power generating unit is not improved by carbon capture:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after the carbon capture transformation is not carried out; />
Establishing the lower layer objective function component using equation (28), energy storage device operating cost:
in the method, in the process of the invention,the operation cost of the energy storage equipment is; g s The energy storage running cost; p (P) t dis，s The energy storage discharge power in the s-th scene at the t moment; p (P) t ch，s The energy storage charging power in the s-th scene at the t moment; η (eta) dis The energy storage and discharge efficiency is achieved; η (eta) ch The energy storage and charging efficiency is improved;
using equation (29), the cost component obtained above, and equation (14), a lower-layer optimization evaluation objective function is established taking into account carbon emissions and power balance risk, as shown in equation (30):
in the method, in the process of the invention,running cost for the time sequence scene; />The artificial weight is applied to the scene running cost average value through an entropy weight method; />The artificial weight applied to the comprehensive risk value by the entropy weight method meets the following conditions: />R op Is a comprehensive risk index value;
(5e) Constructing a constraint function corresponding to lower-layer optimization evaluation of a rolling decision model according to unit combination production simulation:
establishing a power balance constraint function under the uncertain scene of the lower-layer production simulation moment by using the formula (31):
In the method, in the process of the invention,for the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; thermal power generating unit carbon capture output loss>Output force of thermal power generating unit i under s scene at t moment +.>The relationship is as follows:
wherein alpha is i The carbon emission coefficient of the thermal power unit i; beta is the trapping rate of the carbon trapping device; lambda (lambda) GE Is the capture unit CO 2 The consumed thermal power is a constant value;
establishing a network power constraint function of an actual time sequence scene of the new energy of the lower layer by using the steps (33) and (34):
in the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the output predicted value is the output predicted value of the photovoltaic t moment under the s-th scene;
establishing a lower layer load actual uncertainty scene internet power constraint by using a formula (35):
in the method, in the process of the invention,supplying actual load under the s-th scene of the system t moment; l (L) t The system load is t time;
(5f) Leading in a month typical daily load prediction curve and an uncertainty scene wind-light power output curve, calling a yalminip and a gurobi solver to solve a double-layer objective function and constraint of a rolling decision model to obtain the number of new thermal power generation units, wind power generation units, photovoltaic unit and energy storage equipment capacity, obtaining the carbon capture transformation number of original and new thermal power generation units, obtaining the carbon emission risk and electric balance risk assessment index result of the year, and obtaining the annual decision cost;
(5g) Judging whether all decisions of the planning period N are completed, if so, outputting the decisions and the assessment results of each risk index, and ending the cycle; otherwise, n=n+1, part of unit cost increases, and the process returns to the step (5 a), the basic parameters are updated according to the annual growth rate of each cost, the annual growth and reduction rate of each output load and the carbon emission requirement, the original unit quantity and capacity parameters are updated according to the newly-built unit data obtained by calculation, and the decision of the next year is made sequentially.
In summary, the invention considers the power supply regulation characteristic and the new energy fluctuation characteristic, respectively provides the carbon emission and power balance risk assessment indexes considering the power supply regulation characteristic and the new energy fluctuation characteristic for the problems of excessive carbon emission, insufficient new energy consumption, load shedding and other risks in the operation process of the power system, and quantifies the carbon emission risk and the power balance risk caused by the uncertainty of the new energy output; the invention establishes a power supply planning and thermal power transformation rolling decision model considering carbon emission and power balance risks based on a traditional power supply planning objective function, and comprises the following steps: compared with the traditional model, the three levels of investment decision, operation simulation and carbon emission and power balance risk assessment are more comprehensive in consideration of power supply types, and quantitative assessment is carried out on the emission reduction effect of the decision and the safety stability of a power supply system while the decision is made.
Claims (6)
1. A power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risk is characterized in that: the method comprises the following steps in sequence:
(1) Acquiring a year time sequence load prediction curve, a month typical daily wind power and photovoltaic output prediction curve of a power supply system, and various power supply types and parameters of the power supply system:
(2) Generating uncertainty scenes of wind power and photovoltaic output fluctuation by utilizing the month typical solar wind power and photovoltaic output prediction curve obtained in the step (1) through normal distribution, wherein the uncertainty scenes consist of scene output and scene probability;
(3) Establishing a carbon emission risk assessment index and a power balance risk assessment index which take the uncertainty of the output of new energy and the regulation characteristic of a power supply into consideration by utilizing the uncertainty scene of the fluctuation of the wind power and the photovoltaic output obtained in the step (2);
(4) Establishing three-layer power supply planning and thermal power transformation decision targets, wherein the three-layer power supply planning and thermal power transformation decision targets comprise an upper investment decision layer decision target, a middle operation simulation layer decision target and a lower risk assessment decision target, and adding the carbon emission risk assessment index and the power balance risk assessment index obtained in the step (3) into the lower risk assessment layer decision target;
(5) According to the three-layer power supply planning and thermal power transformation decision targets obtained in the step (4), a double-layer objective function is established, the double-layer objective function comprises an upper-layer investment decision and a lower-layer optimization evaluation, a rolling decision model is established according to the double-layer objective function and the constraint of the corresponding objective function, a month typical daily load prediction curve obtained in the step (1), various power supply types and parameters of a power supply system and an uncertainty scene obtained in the step (2) are imported, and a solver is utilized for decision solving.
2. The power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks according to claim 1, characterized in that: the step (1) specifically comprises the following steps:
(1a) Acquiring a year time sequence load prediction curve of a power supply system: the annual time series load curve in recent years is multiplied by the load growth rate to obtain an annual time series load growth predicted value, and the annual time series load growth predicted value is added with the annual time series load curve in recent years to obtain a power supply system annual time series load predicted curve;
(1b) Obtaining a month typical daily load prediction curve:
in the annual time sequence load prediction curve of the power supply system obtained in the step (1 a), calculating the typical daily load of the month in a certain month, selecting the month r, calculating the average value according to the moment to obtain the typical daily load of the month r, and obtaining a month typical daily load prediction curve of each month of 12 months;
(1c) Obtaining a month typical solar power and photovoltaic output prediction curve:
(1c1) Acquiring a curve of wind power and photovoltaic time sequence output in the last 3 years;
(1c2) Calculating the predicted values of typical daily wind power and photovoltaic output of the month, such as the m month, for 3 groups of curves obtained in the step (1 c 1), and calculating the average output value of the same day and time of 3 years;
(1c3) Calculating an average value of the output average value of the mth month according to a time sequence corresponding to one day to obtain a typical daily wind power and photovoltaic output prediction curve of the mth month, thereby obtaining a month typical daily wind power and photovoltaic output prediction curve of each month of 12 months;
(1d) Acquiring various power supply types and parameters of a power supply system, including parameters of a thermal power unit, parameters of a wind power unit and a photovoltaic unit and energy storage parameters; the thermal power unit parameters comprise capacity, maximum and minimum technical output, minimum start-stop time, start-stop cost, construction cost, running cost, the number of original and newly-built upper limit units, up-and-down climbing rate and carbon emission coefficient; the parameters of the wind power and photovoltaic units comprise capacity, maximum and minimum technical output, original construction cost and newly built upper limit unit quantity; the energy storage parameters comprise capacity, construction cost, operation cost, charge and discharge efficiency, upper and lower limits of charge and discharge power and upper and lower limits of capacity.
3. The power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks according to claim 1, characterized in that: the step (2) specifically comprises the following steps:
(2a) Obtaining a fluctuation range of typical daily predicted output according to a month typical daily wind power and photovoltaic output prediction curve, wherein the upper limit of fluctuation is the day with the maximum average value of wind power and photovoltaic output corresponding to the current moment of the month, and the lower limit is the day with the minimum average value, so that normal distribution parameters of wind power and photovoltaic output fluctuation are obtained;
(2b) Calculating scene probability f (x) of the uncertainty scene by using the normal distribution parameter and a normal distribution probability formula (1):
wherein x is wind power and photovoltaic scene output; sigma (sigma) 2 Representing the variance of the wind power and photovoltaic scene output x; mu is the mean value of x;
in view of seasonal and daily characteristics of wind speed and illumination intensity, wind power and photovoltaic output at the same moment every month every day in each year are represented by the same probability distribution; obtaining wind power and photovoltaic output normal distribution curves at 24 times per day of all typical days according to the month typical daily wind power and photovoltaic output prediction curves and the normal distribution parameters obtained in the step (2 a); dividing a normal distribution curve into s equal-width intervals, taking the midpoint of the interval as the scene output force x of wind power and photovoltaic, and calculating the scene probability f (x) corresponding to each x by the formula (1); each group of wind power and photovoltaic scene output force x and scene probability f (x) are combined to obtain an uncertainty scene, and total 12 x 24 x s scenes are obtained.
4. The power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks according to claim 1, characterized in that: the step (3) specifically comprises the following steps:
(3a) Due to fluctuation of wind power and photovoltaic output, the carbon emission of the thermal power unit is expected to exceed the economic and environmental safety risks caused by the limit of carbon emission on the same day, wherein the relation between the output and the carbon emission of the thermal power unit before and after carbon capture transformation is as follows:
wherein E is nc Carbon emission of the thermal power generating unit without carbon capture transformation; e (E) yc Carbon emission of the thermal power generating unit after carbon capture transformation is carried out;the method is that the output of the ith thermal power generating unit in the ith scene at the moment t; alpha i The carbon emission coefficient of the ith thermal power unit; beta is the trapping rate of the carbon trapping device;
(3b) Calculating a carbon emission risk assessment index R of a typical day from formula (4) E ：
Wherein R is E According to the numerical valueThe size is divided into the following four grades, 0 is no risk, 0-5 is low risk, 5-10 is medium risk,>10 is a high risk;a threshold for the safe carbon schedule for the d-th typical day is obtained by multiplying the annual carbon emission allowance by the daily load and dividing by the annual load; e (E) d The current typical daily carbon emission expected value is represented by formula (5):
In the formula Θ yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; f (f) s (x) The scene probability of the uncertainty scene corresponding to the s-th scene output x is the current typical day and t time; t is typical day length, t=24;
(3c) Calculating an expected value P of the waste wind and the waste light under uncertain scenes by using the steps (6) and (7) B Cut load expected value L B ：
In the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the predicted value of the output in the s-th scene at the moment of the photovoltaic t is +.> For the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; lt is the system load at time t;
(3d) Combining an analytic hierarchy process and an entropy weight process to obtain an expected value P of the abandoned wind and abandoned light B Expected value of cut load L B Giving weight and summing to obtain power balance risk assessment index R B ：
In the method, in the process of the invention,expected value P for discarding wind and light B Weight of->To cut the load expected value L B Weights of (2); r is R B According to the number, the number is divided into the following four grades, 0 is no risk, 0-1 is low risk, 1-3 is medium risk,>3 is a high risk; the weight of the kth expected value is obtained by comprehensively utilizing a hierarchical analysis method and an entropy weight method according to the formula (9):
In the method, in the process of the invention,for the weight of the kth expected value obtained by means of analytic hierarchy process,/o>The weight of the kth expected value obtained by using the entropy weight method.
5. The power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks according to claim 1, characterized in that: the step (4) specifically comprises the following steps:
(4a) On the basis that an upper investment decision-making layer decision-making target of a traditional double-layer unit planning decision-making target minimizes investment and a middle operation simulation layer decision-making target minimizes operation cost, a new lower risk assessment layer decision-making target is added, so that the time sequence scene operation and the target value of a risk assessment index are minimized; the objective function of establishing a three-layer power supply planning and thermal power reconstruction decision target is represented by the formula (10):
wherein C is n For the upper layer decision goal: annual investment costs; c (C) op Is a middle layer decision goal: the wind power and the photovoltaic predict the running cost under the condition of the output force; c (C) r Is the lower layer decision target: sum of running cost and risk assessment index value under uncertain scene:the target coefficient is decided for the upper layer; />The middle layer decision target coefficient; />The target coefficient is decided for the lower layer;
(4b) Each layer decision target component is represented by equations (11), (12) and (13):
C n ＝min(C NT +C NW +C NPV +C NS +C ETC +C NTC ) (11)
Wherein C is NT Investment cost for newly-built thermal power generating units; c (C) NW Investment cost for newly-built wind turbine generators; c (C) NPV Investment cost for newly-built photovoltaic units; c (C) NS Investment cost for newly-built energy storage equipment; c (C) ETC Investment cost is improved for original thermal power carbon capture; c (C) VTC Investment cost is improved for newly-built thermal power carbon capture; c (C) YC Predicting the running cost of the thermal power for carbon capture transformation; c (C) NC Predicting the running cost for unmodified thermal power; c (C) DU The starting and stopping cost of the thermal power is set; c (C) s Predicting an operating cost for the stored energy;running cost for the time sequence scene; r is R op For the comprehensive risk index value, the carbon emission risk assessment index R E Risk assessment index R for electric power balance B And adding to obtain:
R op ＝R E +R B (14)。
6. the power supply planning and thermal power transformation decision modeling method considering carbon emission and power balance risks according to claim 1, characterized in that: the step (5) specifically comprises the following steps:
(5a) Simplifying a three-layer power supply planning and thermal power transformation decision target into a double-layer objective function, and laminating a middle layer and a lower layer of the three-layer power supply planning and thermal power transformation decision target into a single layer to obtain the double-layer objective function, wherein the double-layer objective function comprises an upper investment decision and a lower optimization evaluation:
wherein C is l Objective function for the lower layer optimization evaluation: a sum of random production simulation cost and risk assessment index; Optimizing the coefficients of the evaluated objective function for the lower layer; c (C) n Objective function for upper layer investment decisions: annual investment costs; />Coefficients of an objective function that is an upper level investment decision;
(5b) Constructing each cost component in an upper-layer objective function (16) of the rolling decision model according to a corresponding investment cost formula and a full life cycle cost theory:
C n ＝min(C NT +C NW +C NPV +C NS +C ETC +C NTC )(16)
establishing the investment cost of the newly-built thermal power generating unit by using the formula (17):
wherein C is NT Investment cost for newly-built thermal power generating units; gamma is the discount rate; theta (theta) NT The method is a thermal power generating unit set to be built; epsilon T The annual growth rate of the building cost of the thermal power generating unit is improved; τ i The investment cost of the new ith thermal power generating unit is set; v NT (i) For the newly built i-th thermal power generating unit, 1 is built, and 0 is not built;
establishing the investment cost of the newly-built wind turbine generator by using the formula (18):
wherein C is NW Investment cost for newly-built wind turbine generators; theta (theta) NW The method comprises the steps of setting a set of wind turbines to be built; epsilon W The annual growth rate of the building cost of the wind turbine generator is increased; v w The investment cost of the w-th wind turbine generator is newly built; v NW (w) a newly built w-th typhoon motor group building state, wherein 1 is built, and 0 is not built;
establishing the investment cost of the newly-built photovoltaic unit by using the method (19):
wherein C is NPV Investment cost for newly-built photovoltaic units; theta (theta) NPV The method comprises the steps of collecting photovoltaic units to be built; epsilon PV Annual growth rate for photovoltaic unit construction costs; pi p Investment cost for newly building a p-th photovoltaic unit; v NPV (p) a newly built p-th wind turbine generator system is built, 1 is built, and 0 is not built;
establishing investment cost of new energy storage equipment by using a formula (20):
wherein C is NS Investment cost for newly-built energy storage equipment; epsilon S Annual growth rate for energy storage device construction costs;investment cost for newly built energy storage unit capacity; e (E) NS To newly build energy storage capacity;
the original thermal power carbon capture transformation investment cost is established by utilizing the formula (21):
in the formula Θ ET Is the original thermal power collection;the method is characterized in that the method is the carbon capture reconstruction cost of the original ith thermal power generating unit; v ETC (i 0 ) The method is characterized in that the method is in an original i-th thermal power initial reforming state, wherein 1 is reformed, and 0 is not reformed; v ETC (i) The method is in an annual transformation state of the original ith thermal power, wherein 1 is transformation, and 0 is non-transformation;
establishing newly built thermal power carbon capture reconstruction investment cost by using a formula (22):
in the method, in the process of the invention,the method is used for reconstructing carbon capture of a newly-built thermal power generating unit; v NTC (j) The new year transformation state of the ith thermal power is established, 1 is transformation, and 0 is no transformation;
(5c) Constructing a constraint function corresponding to an upper investment decision of the rolling decision model:
Establishing a capacity constraint function using equation (23):
wherein L is a time-series average load; i i The installed capacities of the thermal power respectively; i w The installed capacity of wind power; i p Is the installed capacity of the photovoltaic;
establishing a carbon emission constraint function using equation (24):
wherein d is the number of days; t is typical day length, t=24; e (E) pl Predicting carbon emissions for the year; e is an emission reduction index of the year; v TC (i) The modification state of the thermal power unit i is that modification is carried out 1, and modification is not carried out 0; e (E) nc Carbon emission of the thermal power generating unit without carbon capture transformation; e (E) yc Carbon emission of the thermal power generating unit after carbon capture transformation is carried out;
(5d) Applying artificial weight according to an entropy weight method, and constructing a lower-layer optimization evaluation objective function of a rolling decision model considering carbon emission and power balance risk:
establishing a lower objective function component by using the formula (25), and starting and stopping the cost of the thermal power generating unit:
wherein C is DU The starting and stopping cost of the thermal power is set; theta (theta) yc The method comprises the steps of collecting thermal power units subjected to carbon capture transformation; theta (theta) nc The method is a thermal power generating unit set which is not subjected to carbon capture transformation; l (L) j The starting cost of the thermal power unit i is; d (D) i The shutdown cost of the thermal power unit i is; v i，t The method is characterized in that the method is in a starting and stopping state at t moment of an ith thermal power generating unit, wherein 1 is started, and 0 is stopped;
Establishing a lower objective function component by using the formula (26), and capturing and reforming the operation cost of the thermal power generating unit:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after carbon capture transformation is used; epsilon op Is thermal powerAnd annual growth rate of energy storage operation cost; a, a i 、b i 、c i The running cost coefficient of the ith thermal power is the running cost coefficient of the ith thermal power; />The output loss is improved for thermal power carbon capture; />The method is that the output of the ith thermal power generating unit in the ith scene at the moment t;
establishing a lower objective function component by using the formula (27), wherein the operation cost of the thermal power generating unit is not improved by carbon capture:
in the method, in the process of the invention,the operation cost of the thermal power generating unit after the carbon capture transformation is not carried out;
establishing the lower layer objective function component using equation (28), energy storage device operating cost:
in the method, in the process of the invention,the operation cost of the energy storage equipment is; g S The energy storage running cost; p (P) t dis，s The energy storage discharge power in the s-th scene at the t moment; p (P) t ch，s The energy storage charging power in the s-th scene at the t moment; η (eta) dis The energy storage and discharge efficiency is achieved; η (eta) ch The energy storage and charging efficiency is improved;
using equation (29), the cost component obtained above, and equation (14), a lower-layer optimization evaluation objective function is established taking into account carbon emissions and power balance risk, as shown in equation (30):
in the method, in the process of the invention,running cost for the time sequence scene; />The artificial weight is applied to the scene running cost average value through an entropy weight method; / >The artificial weight applied to the comprehensive risk value by the entropy weight method meets the following conditions: />R op Is a comprehensive risk index value;
(5e) Constructing a constraint function corresponding to lower-layer optimization evaluation of a rolling decision model according to unit combination production simulation:
establishing a power balance constraint function under the uncertain scene of the lower-layer production simulation moment by using the formula (31):
in the method, in the process of the invention,for the actual output of the w-th wind turbine generator in the s-th scene at the t moment,/the wind turbine generator is in the S-th scene at the t moment>The actual output of the p-th photovoltaic unit in the s-th scene at the t moment is obtained; />Supplying actual load under the s-th scene of the system t moment; thermal power generating unit carbon capture output loss>Output force of thermal power generating unit i under s scene at t moment +.>The relationship is as follows:
wherein alpha is i The carbon emission coefficient of the thermal power unit i; beta is the trapping rate of the carbon trapping device; lambda (lambda) GE Is the capture unit CO 2 The consumed thermal power is a constant value;
establishing a network power constraint function of an actual time sequence scene of the new energy of the lower layer by using the steps (33) and (34):
in the method, in the process of the invention,for the predicted value of the output under the s-th scene of the moment t of wind power,>the output predicted value is the output predicted value of the photovoltaic t moment under the s-th scene;
establishing a lower layer load actual uncertainty scene internet power constraint by using a formula (35):
In the method, in the process of the invention,supplying actual load under the s-th scene of the system t moment; l (L) t The system load is t time;
(5f) Leading in a month typical daily load prediction curve and an uncertainty scene wind-light power output curve, calling a yalminip and a gurobi solver to solve a double-layer objective function and constraint of a rolling decision model to obtain the number of new thermal power generation units, wind power generation units, photovoltaic unit and energy storage equipment capacity, obtaining the carbon capture transformation number of original and new thermal power generation units, obtaining the carbon emission risk and electric balance risk assessment index result of the year, and obtaining the annual decision cost;
(5g) Judging whether all decisions of the planning period N are completed, if so, outputting the decisions and the assessment results of each risk index, and ending the cycle; otherwise, n=n+1, part of unit cost increases, and the process returns to the step (5 a), the basic parameters are updated according to the annual growth rate of each cost, the annual growth and reduction rate of each output load and the carbon emission requirement, the original unit quantity and capacity parameters are updated according to the newly-built unit data obtained by calculation, and the decision of the next year is made sequentially.
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