CN113890026B - Multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization - Google Patents
Multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization Download PDFInfo
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Abstract
The invention discloses a multiscale random production simulation method considering extra-high voltage direct current outgoing optimization, which comprises the steps of obtaining a month-representative daily expected scene and a date hope scene; adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation; adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and day-to-day rolling optimization by combining a given day-ahead prediction scene and a given day-to-day prediction scene; the direct current output correction electric quantity is returned, the inter-month production simulation and the inter-day production simulation are restarted to optimize the electric quantity of the rest month until the whole year plan is completed, the accuracy of different time scale information is fully considered, certain safety is ensured, meanwhile, the economical efficiency of system operation is effectively improved, and the consumption of new energy is promoted.
Description
Technical Field
The invention relates to a multiscale random production simulation method considering extra-high voltage direct current outgoing optimization, and belongs to the technical field of power systems.
Background
The Chinese 'double carbon' target will promote the high-speed development of new energy, and the total installed capacity of wind power and solar power generation is estimated to reach more than 12 hundred million kilowatts in 2030. Due to the reverse distribution of the load and the new energy installation, the flexible adjustment of the power supply has low specific gravity, the limited interconnection and intercommunication level of the power grid and the like, the new energy consumption contradiction in local areas is increasingly prominent. The extra-high voltage direct current power transmission device can transmit surplus power outside a cross-region to realize the power generation of new energy, wherein the extra-high voltage direct current power transmission plays an important role in the power generation of wind and light resources in the cross-region. In general, a power transmission mode of a fixed step in the daytime is formulated for extra-high voltage direct current transmission according to interval long-term contract transaction electric quantity, so that the power regulation potential of direct current transmission is difficult to fully develop, randomness and fluctuation of power generation output of new energy sources such as wind power, photovoltaic and the like of a power transmission end power grid cannot be adapted, the power rejection of the new energy sources of the power transmission end power grid is easy to be caused, and the peak regulation pressure of a traditional unit of the power transmission end power grid is increased. Therefore, the uncertainty of the output of new energy sources such as wind power, photovoltaic and the like of the power transmission network is fully considered, the long-term contract transaction electric quantity is reasonably distributed in intervals on the annual/monthly/daily scale, and the output plan of each unit of the power transmission network is formulated, so that the method has important significance in promoting the new energy source of the power transmission network to be consumed and ensuring the safe and stable operation of the interconnected power network.
At present, research on cross-provincial and cross-regional scheduling of a power system through a direct current tie line is concentrated on provincial-level safety constraint economic scheduling optimization. Existing studies relating to dc transmission planning have focused on optimizing distribution of known dc off-day power before or during the day. However, there is little research on how to obtain daily transaction electricity from dc outgoing contract electricity on a longer time scale such as the known year, month, etc. In addition, due to uncertainty of actual operation scene, the subsequent remaining contract electric quantity and the distribution plan thereof are required to be updated according to the electric quantity actually delivered every day.
For a random power supply with gradually increased permeability in a power grid, in recent years, students at home and abroad conduct a great deal of research on the combination problem of an uncertainty unit, and methods such as a scene method, opportunistic constraint planning, robust optimization and the like are sequentially put forward. The scene method and the opportunity constraint programming describe uncertainty of the random new energy output according to statistics or prediction of probability distribution, and the probability of the random new energy output distribution is difficult to accurately acquire, so that the optimization result has safety risk. And the robust optimization decision focuses on the boundary condition of uncertain parameters, and the optimization result is conservative. In order to solve the contradiction, partial research combines opportunistic constraint planning and robust optimization, a distributed robust optimization method is established, and a corresponding distributed robust scheduling decision model is established according to an introduced probability distribution set (fuzzy set) describing random variables.
The conventional units involved in the power grid economic dispatching research are mostly thermal power units, and the units such as hydropower and pumping and storage are less. The northwest region of China has more hydraulic and wind-solar power generation resources, the regional load is generally smaller, a large amount of clean energy power generation is sent out through an inter-provincial direct current transmission line, and the production simulation is particularly necessary by considering the seasonal characteristics and the reservoir capacity constraint of the hydraulic power generation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provide a multiscale random production simulation method for extra-high voltage direct current outgoing optimization, overcome the problem of random power supply with gradually increased permeability in the current power grid, fully consider the uncertainty of new energy output of wind power, photovoltaic and the like of a power transmission end power grid, combine the seasonal characteristics of hydropower, and for the direct current outgoing contract electric quantity of a known long time scale such as year, month and the like, reasonably distribute the long-term contract transaction electric quantity of an interval on the year/month/day scale, and formulate the output plan of each unit of the power transmission end power grid.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides a multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization, comprising the following steps:
extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data, and acquiring a month representation date view scene and a date view scene;
adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation;
adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and daytime rolling optimization by combining a given day-ahead prediction scene and a given daytime prediction scene, wherein the rolling optimization unit outputs to execute a downward direct current delivery plan;
and returning the direct current output corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
Further, the month representing daily expected scene is obtained by respectively averaging the data of each hour of each day of the month of the new energy output of the historic years, and the sampling interval is 1 hour.
Further, the date view scene is obtained from an average value of the output of new energy sources in a plurality of years of history of each hour of the same day, and the sampling interval is 1 hour.
Furthermore, the inter-month production simulation optimizes the power distribution of the power supply of the power grid at the power supply end and the power distribution of the direct current power transmission at each month under the constraint conditions of the contract power transmission quantity of each month and the whole year according to the expected daily scene represented by each month, and transmits the calculated direct current power transmission quantity of each month to the inter-month production simulation.
Further, the daily production simulation performs daily optimization distribution on the target month direct current external power transmission quantity according to expected scenes of each day in a target month to be optimized, when the distribution result cannot meet the operation constraint of a certain month of the system, the fixed external power transmission quantity distributed to the month is adjusted to be the month external power transmission quantity constraint, the direct current external power transmission quantity of each day is optimized, the sum of the direct current external power transmission quantities of each day is used as month direct current external power transmission correction electric quantity to be returned to the monthly production simulation, and the monthly production simulation is restarted to perform electric quantity optimization for each month which is not executed until the upper layer iteration is completed.
Further, the day-ahead optimization includes: the adjustment capacity of the direct current tie line is utilized to carry out paid adjustment on the daily direct current external power transmission quantity sent under the daily production simulation, the adjustment quantity is used as daily direct current correction electric quantity to be returned to the daily production simulation, and the obtained daily direct current external power transmission plan of the power grid at the transmitting end is sent to the daily prediction scene for rolling optimization;
further, the rolling optimization of the daytime prediction scene comprises: and combining a more accurate daytime prediction scene, rolling and optimizing the output of the unit to execute a downward direct current delivery plan.
In a second aspect, the invention provides a multi-scale random production simulation device considering extra-high voltage direct current delivery optimization, comprising:
the system comprises a month representation date view scene and date view scene acquisition unit, a data processing unit and a data processing unit, wherein the month representation date view scene and date view scene acquisition unit is used for extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data to acquire month representation date view scenes and date view scenes;
the inter-month production simulation and inter-day production simulation running unit is used for carrying out inter-month production simulation and inter-day production simulation respectively by adopting a long-period random production simulation objective function and combining with acquisition of date and expected scene data of month representation;
the rolling optimization unit is used for adopting a short-period distribution robust optimization objective function, combining a given day-ahead prediction scene and a given day-ahead prediction scene to respectively perform day-ahead optimization and day-to-day rolling optimization, and rolling the output of the optimizing unit to execute a downward direct current delivery plan;
and the iterative optimization unit is used for returning the direct current output correction electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
In a third aspect, the invention provides a multiscale random production simulation device considering extra-high voltage direct current outgoing optimization, which is characterized in that: comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative according to the instructions to perform the steps of the method according to any one of the preceding claims.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program characterized in that: which when executed by a processor performs the steps of the method of any of the preceding claims.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, load and new energy output characteristics are extracted according to the system years of operation data, the seasonal characteristics of hydropower augmentation and withered water are considered, the power output and direct current output of a power grid at a power supply end in a target year or month to be optimized are solved on a month-day time scale, a distributed robust optimization method is adopted, an uncertain problem is converted into a deterministic secondary constraint quadratic programming problem according to an uncertain distribution set form, direct current output power and unit output are optimized according to the daily direct current output power obtained through optimization, a daily prediction scene is combined, rolling optimization of unit output is carried out according to a more accurate daily prediction scene, accuracy of different time scale information can be fully considered by the frame and the method, economical efficiency of system operation is effectively improved while certain safety is guaranteed, and new energy consumption is promoted.
Drawings
FIG. 1 is a strategy flow diagram of a multi-scale random production simulation method taking into account extra-high voltage direct current outgoing optimization provided by an embodiment of the invention;
FIG. 2 is a flow chart of a long-period random production simulation provided by an embodiment of the present invention;
fig. 3 is a short period optimization flow chart provided by an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
As shown in fig. 1 to 3, this embodiment describes a multi-scale random production simulation method considering extra-high voltage dc output optimization, including:
extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data, and acquiring a month representation date view scene and a date view scene;
adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation;
adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and daytime rolling optimization by combining a given day-ahead prediction scene and a given daytime prediction scene, wherein the rolling optimization unit outputs to execute a downward direct current delivery plan;
and returning the direct current output corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
The modeling steps of the upper model of the multi-time scale random production simulation framework are as follows:
(1) Long period random production simulation objective function
The goal of the long-period random production simulation is to minimize the total running cost in the desired scenario, which can be expressed as:
wherein t is a time period sequence number in a calculation period; t is a time period set in a calculation period; the duration of each period is set as delta t; i is the serial number of the thermal power generating unit; g is a thermal power unit set; p (P) i,t Active output of the thermal power generating unit i in a period t; c (P) i,t ) The power generation cost of the thermal power generating unit i in the period t is set;the emission cost of the system in the period t; delta is the electricity price of the hydroelectric power on-line; h is the serial number of the hydropower station; h is a hydropower station set; a is that h Generating a power generation output coefficient for the hydropower station h; />The water flow rate of the hydropower station h in the period t is the water flow rate which cannot enter the hydropower unit to generate electricity, so that certain loss can be generated; t is the direct current electricity price of the direct current electricity supply in the period t; p (P) dc,t The direct current output power is the period t; alpha i，1 、α i，2 、α i，3 The power generation cost parameter is the power generation cost parameter of the thermal power generating unit i; />Is the discharge cost coefficient of the thermal power unit i.
In forming the upper layer model of the frame, the following constraints need to be met:
constraint 1: system operation constraints
1) Power balance constraint
Wherein j is the serial number of the hydroelectric generating set; j is a hydroelectric generating set; p (P) h,j,t The active output of the hydropower unit j corresponding to the hydropower station h in the period t is obtained; n is the serial number of the new energy unit; n is a new energy unit set; p (P) n,t Active output of the new energy unit n in a period t is obtained; p (P) L,t Is the load demand for period t.
2) Branch tide constraint
Wherein B is a system branch set;the injection transfer distribution factor vectors of the unit node and the load node to the branch b are respectively; />The distribution factor is transferred for the injection of the direct current delivery node to the branch b; (. Cndot. T And (5) performing transposition operation on the matrix. P (P) t All the unit output vectors are in a period t; p (P) L,t Load vectors for all nodes of period t; />The upper power transfer limit for leg b.
3) System standby constraints
In the method, in the process of the invention, P i the maximum and minimum technical output of the thermal power unit i are respectively; /> P h,j The maximum and minimum technical output of the hydroelectric generating set j are respectively; s is S po And S is neg Respectively for positive and negative standby of the system.
Constraint 2: thermal power generating unit constraint
2) Climbing constraint of thermal power generating unit
In the method, in the process of the invention,the maximum ramp rate and the minimum ramp rate of the thermal power generating unit i are respectively.
Constraint 3: hydropower unit constraint
1) Force constraint
2) Hydroelectric generating set output expression
In the method, in the process of the invention,is the flow rate of the hydropower station h for generating electricity in the period t.
3) Reservoir capacity constraint
In the method, in the process of the invention, V h the upper limit and the lower limit of reservoir capacity of the hydropower station h are respectively; v (V) h,t The reservoir capacity of the hydropower station h at the starting moment of the period t is obtained.
4) Power generation flow constraints
In the method, in the process of the invention,the maximum and minimum power generation flow of the hydropower station h are respectively.
5) Reject flow restriction
In the method, in the process of the invention,the upper limit of the waste water flow of the hydropower station h.
6) Constraint of water balance equation
In which W is h,t Is the natural water inflow of the hydropower station h in the period t.
Constraint 4: new energy unit constraint
1) Force constraint
2) New energy power-discarding constraint
Constraint 5: direct current delivery constraints
1) DC delivery power constraint
2) Direct current power delivery climbing constraint
3) Single day DC adjustment frequency constraint
In the method, in the process of the invention, P dc,t the upper limit and the lower limit of the direct current tie line outgoing power are respectively set; y is t Is an integer variable of 0/1;the upper limit and the lower limit of the single direct current adjustment amplitude are respectively adopted; />The frequency of adjustment of the single-day direct current output power is limited; in addition, in order to maintain the stability of the dc link, the outgoing power should be kept constant for a period of time after the adjustment is completed.
4) Annual transaction electricity constraints
In the method, in the process of the invention, Ethe upper limit and the lower limit of the annual contract outward-sending electric quantity are respectively; m is a set of executed months;the total of the external power transmission amount for the executed month; />The direct current output power is the mth month t period; ΔE m Correcting the electric quantity for the month of the mth month; e (E) res The electricity quantity can be distributed for the rest of the year; />Maximum number of days for month m; t is the set of time periods within the day.
5) Month trade electric quantity constraint
In the method, in the process of the invention, E m the upper limit and the lower limit of the contract outgoing electricity quantity of the mth month are respectively; d is a set of executed days of the month;the total of the external power transmission amount of the executed day of the month; />Dc transmit power for the d-th day t period; ΔE d Correcting the electric quantity for the day of the d day; />And (5) distributing the electricity quantity for the rest of the month.
The modeling steps of the multi-time scale random production simulation framework lower model are as follows:
(2) Short period distribution robust optimization
The goal of short-period distributed robust optimization is to minimize the running cost in the prediction scenario, which can be expressed as:
in the method, in the process of the invention,the operation base point of the thermal power generating unit i in the period t is adopted; τ is the direct current external power supply correction price; ΔP dc,t The correction power is sent to the direct current outside of the day in the period t, and the correction power is only optimized before the day; ρ is a new energy abandon penalty factor; />And (5) discarding electric power of the new energy unit n in a period t. And after the optimization before the day is finished, returning the direct current output correction electric quantity to the day-to-day production simulation.
In forming the underlying model of the frame, the following constraints need to be met:
constraint 1: power balance constraint
In the method, in the process of the invention,is the operating base point of the hydroelectric generating set j in the period t.
Constraint 2: the method establishes a random variable vector omega by unit adjustment factor constraint t Representing the output prediction error of each new energy unit, and introducing an adjustment factor to represent the prediction error power allocated by each adjustable unit, wherein the active output of each adjustable unit is as follows:
wherein e is a column vector with all elements being 1; sigma (sigma) i,t The adjustment factor of the thermal power generating unit i in the period t is as follows; sigma (sigma) j,t Is the adjustment factor of the hydroelectric generating set j in the period t.
Constraint 3: robust opportunity constraint of unit output
Wherein 1- ε is a confidence level; d is the characterization omega t The distribution set of uncertainties is shown in equation (27).
Constraint 4: robust opportunity constraint of maximum adjustable capacity of unit
Wherein r is i up 、r i dn The upper limit and the lower limit of the maximum adjustable capacity of the thermal power unit i are respectively;the upper and lower limits of the maximum adjustable capacity of the hydroelectric generating set j are respectively.
Constraint 5: robust opportunity constraint for branch tidal current
Wherein P is b,t The transmission power of the branch b in the period t;the injection transfer distribution factor vectors of the thermal power unit node, the hydroelectric unit node and the new energy unit node to the branch b are respectively; p (P) G,t The output vector of all thermal power generating units in the period t; p (P) J,t The output vectors of all hydroelectric generating sets are the period t; p (P) N,t And (5) outputting vectors of all new energy units in the period t.
Constraint 6: remaining constraints
The remaining constraints are as in formulas (7), (9) - (13) and (16) - (18).
1) Construction of a distribution set: the uncertainty set selected by the method describes omega t The set has no limitation on probability distribution of random vectors, retains fluctuation of moment information, and is a more complete and universal fuzzy set expression form.
Wherein f (Ω t ) Is omega t Is a joint probability density function of (1); mu (mu) 0 、Σ 0 Respectively omega t Statistics vector of first and second moments; gamma ray 1 Uncertainty of a defined parameter of the radius of the collection for the desired ellipsoid; gamma ray 2 Defining parameters of a semi-fixed cone uncertainty set range of a covariance matrix; e (·) is a desired operation symbol; the degree is a half negative definite sign.
2) Converting the uncertainty model into a deterministic qqp optimization problem: the key to the solution of the short-period distributed robust optimization model described above is the equivalent transformation of equations (26) - (28), the specific theorem being shown below.
The robust opportunity constrains the general form of the inequality:
wherein: a is a coefficient vector and b is a limit value.
If omega t Obeying the set (30), there is the following equivalent transformation theorem:
1) When gamma is 1 /γ 2 When < ε, formula (31) can be equivalently expressed as:
2) When gamma is 1 /γ 2 At > ε, formula (31) can be equivalently represented as:
hereafter only for gamma 1 /γ 2 The case < ε derives the equivalent transformations of formulas (27) - (29), γ 1 /γ 2 The case of > ε is similarly available.
First, when gamma 1 /γ 2 When < ε, formulas (27) and (28) can be directly converted into the following constraint form by theorem:
wherein:
the deterministic equivalence of the bilateral robust opportunity constraint of the form (29) is more complex, so the bilateral opportunity constraint is first equivalent to the following two unilateral opportunity constraints [26,28]:
the robust opportunity constraint (36) is expressed as the form of the formula (31) to obtain
Since the decision variable sigma exists in a, direct conversion increases the difficulty of model solving, so the equivalent conversion of the equation (32) is considered to be firstly:
substituting a and b into equation (37) translates into the following constraint containing a quadratic inequality:
wherein: the adjustment factor vector of all thermal power generating units at the moment t;the adjustment factor vector of all the hydroelectric generating sets at the moment t; /> In summary, the robust opportunity constraint equations (26) - (28) have all completed the equivalent transformation, creating a deterministic qqp optimization problem. However, the qqp optimization problem still belongs to the NP-hard problem, and there is still a certain difficulty in directly solving, so the qqp optimization problem is considered to be relaxed into the LP problem for solving by RLT.
3) Relaxation was performed using RLT method:
to now facilitate RLT relaxation, the qqp optimization problem is further converted into a general form:
in the method, in the process of the invention,all decision variables are included (the hydroelectric generating set is transformed in combination with formula (9)); /> xThe upper and lower limits of x are respectively; omega is a high-dimensional symmetric constant coefficient matrix; e (E) c A set of constraints for the equation; i c Constraint sets for inequality; a, a 0 ，a i Gao Weichang coefficient column vectors for the objective function and constraint, respectively; b 0 ，b i And the constant coefficients are respectively corresponding to the objective function and the constraint condition.
As can be seen by comparing formulas (40) - (43) with the QQP optimization problem above, ω 0 Omega for the second constraint in formulas (22), (24), formulas (32) - (33) and formulas (36) - (37) for the diagonal arrays i All are 0 arrays. Omega for the first constraint in formulas (38) - (39) i Solution ref [30 ]]And will not be described in detail herein.
According to the characteristics of RLT, the original QQP optimization problem is expressed as:
wherein x=xx T ；
The constraint of X is further determined, whereby the qqp optimization problem has been relaxed to the LP problem.
The establishment of the multi-time scale annual random production simulation two-layer framework specifically comprises the following steps:
(1) Generation of a desired scene.
The stage extracts month and day scale information reflecting load characteristics and new energy output characteristics according to historical operation data of a system for a plurality of years and generates month representative date looking scenes and date looking scenes, and the specific contents are as follows:
step 1: acquiring a month representative date view scene: the month representative daily expected scene is obtained by respectively averaging the data of each hour of each day of the month of the new energy output of the historic years, and the sampling interval is 1 hour.
Step 2: acquiring a date view scene: the daily expected scene is obtained by the average value of the output of new energy sources in each hour of the day, and the sampling interval is 1 hour.
(2) And a frame upper layer.
The method comprises the steps of extracting load and new energy output characteristics according to system operation data of years, and solving power output and direct current external power supply quantity of a power supply network to be optimized in a target year or month on a month-day time scale by considering seasonal characteristics of water and electricity in a rich and dry period, wherein the specific contents are as follows:
step 3: and (3) adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining the data obtained in the step (1) and the step (2).
Step 4: and the inter-month production simulation optimizes the power distribution of the power supply of the power grid at the transmitting end and the power distribution of direct current transmission in each month under the constraint conditions of contract transmission power in each month and the whole year according to the expected daily scene represented by each month, and transmits the calculated direct current transmission power in each month to the inter-month production simulation.
Step 5: and (3) carrying out day-by-day optimization distribution on the direct current external power transmission quantity of the target month according to expected scenes of each day in the target month to be optimized, adjusting the fixed external power transmission quantity distributed to the month to be the external power transmission quantity constraint when the distribution result cannot meet the operation constraint of a certain month of the system, optimizing the direct current external power transmission quantity of each day, using the sum of the direct current external power transmission quantities of each day as the direct current external power transmission correction quantity of each month to be returned to the inter-month production simulation, restarting the inter-month production simulation to carry out power optimization for each month which is not executed until the upper layer iteration is completed.
(3) And a lower layer of the frame.
The method comprises the following steps of adopting a distributed robust optimization method, optimizing direct current output power and unit output according to daily direct current output power obtained by upper layer optimization and combining a daily prediction scene, and performing rolling optimization of unit output according to a more accurate daily prediction scene, wherein the specific contents are as follows:
step 6: and adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and day-to-day rolling optimization by combining a given day-ahead prediction scene and a given day-to-day prediction scene.
Step 7: the day-ahead optimization makes full use of the adjustment capability of the direct current tie line according to the day-ahead prediction scene, carries out paid adjustment on the day direct current external power delivered by the day production simulation, and returns the adjustment quantity as day direct current correction power to the day production simulation. And simultaneously, sending the obtained forward-day direct current delivery plan of the power grid at the delivery end to daytime rolling optimization.
Step 8: the daytime rolling optimization is combined with a more accurate daytime prediction scene, and the output of the unit is optimized in a rolling mode to execute a downward direct current delivery plan.
(4) And (5) performing iterative optimization on the upper layer and the lower layer.
The upper layer and the lower layer of iterative optimization contents are displayed at the stage, a day-ahead direct current delivery plan is determined in step 7, and the direct current delivery electric quantity of the part is paid and adjustable in order to ensure the economy of day-ahead optimization.
Step 9: and (3) if the returned daily direct current corrected electric quantity exists in the calculation result obtained in the step (7), uploading and restarting the daily production simulation, and redistributing the electric quantity on the remaining day of the month.
Step 10: and after the month iteration is finished, returning the month direct current corrected electric quantity in the year, restarting the inter-month production simulation to optimize the electric quantity of the rest month until the whole year plan is completed.
Example 2
The embodiment provides a multiscale random production simulation device considering extra-high voltage direct current outgoing optimization, which comprises:
the system comprises a month representation date view scene and date view scene acquisition unit, a data processing unit and a data processing unit, wherein the month representation date view scene and date view scene acquisition unit is used for extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data to acquire month representation date view scenes and date view scenes;
the inter-month production simulation and inter-day production simulation running unit is used for carrying out inter-month production simulation and inter-day production simulation respectively by adopting a long-period random production simulation objective function and combining with acquisition of date and expected scene data of month representation;
the optimizing unit is used for adopting a short-period distribution robust optimizing objective function, combining a given day-ahead prediction scene and a given day-ahead prediction scene to respectively perform day-ahead optimization and day-to-day rolling optimization, and rolling the output of the optimizing unit to execute a downward direct current delivery plan;
and the iterative optimization unit is used for returning the direct current output correction electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
Example 3
The embodiment provides a multiscale random production simulation device considering extra-high voltage direct current outgoing optimization, which is characterized in that: comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the following:
extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data, and acquiring a month representation date view scene and a date view scene;
adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation;
adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and daytime rolling optimization by combining a given day-ahead prediction scene and a given daytime prediction scene, wherein the rolling optimization unit outputs to execute a downward direct current delivery plan;
and returning the direct current output corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
Example 4
The present embodiment provides a computer-readable storage medium having stored thereon a computer program characterized in that: the program, when executed by a processor, performs the steps of the method of any one of the following:
extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data, and acquiring a month representation date view scene and a date view scene;
adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation;
adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and daytime rolling optimization by combining a given day-ahead prediction scene and a given daytime prediction scene, wherein the rolling optimization unit outputs to execute a downward direct current delivery plan;
and returning the direct current output corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (7)
1. A multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization is characterized by comprising the following steps:
extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data, and acquiring a month representation date view scene and a date view scene;
adopting a long-period random production simulation objective function, and respectively carrying out inter-month production simulation and inter-day production simulation by combining with acquisition of date view scene and date view scene data of month representation; the inter-month production simulation is used for optimizing the power distribution of a power supply of a power grid at a transmitting end and direct current external transmission in each month under the constraint conditions of the contract external transmission power of each month according to the expected daily scene represented by each month, and transmitting the calculated direct current external transmission power of each month to the inter-month production simulation; the solar production simulation performs day-by-day optimization distribution on the direct current external power transmission quantity of a target month according to expected scenes of each day in the target month to be optimized, when the distribution result cannot meet the operation constraint of a certain month of the system, the fixed external power transmission quantity distributed to the month is adjusted to be the month external power transmission quantity constraint, the direct current external power transmission quantity of each day is optimized, the sum of the direct current external power transmission quantity of each day is used as the month direct current external power transmission correction quantity to be returned to the solar production simulation, the solar production simulation is restarted to perform power optimization for each month which is not executed until the upper layer iteration is completed;
adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and daytime rolling optimization by combining a given day-ahead prediction scene and a given daytime prediction scene, wherein the rolling optimization unit outputs to execute a downward direct current delivery plan; the day-ahead optimization includes: the adjustment capacity of the direct current tie line is utilized to carry out paid adjustment on the daily direct current external power transmission quantity sent under the daily production simulation, the adjustment quantity is used as daily direct current correction electric quantity to be returned to the daily production simulation, and the obtained daily direct current external power transmission plan of the power grid at the transmitting end is sent to the daily prediction scene for rolling optimization;
and returning direct current to send corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
2. The multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization as claimed in claim 1, wherein the simulation method is characterized in that: the month representing daily expected scene is obtained by respectively averaging the data of each hour of each day of the month of the new energy output of the historic years, and the sampling interval is 1 hour.
3. The multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization as claimed in claim 1, wherein the simulation method is characterized in that: the date view scene is obtained from the average value of the output of new energy sources in the history of hours on the same day, and the sampling interval is 1 hour.
4. The multi-scale random production simulation method considering extra-high voltage direct current outgoing optimization as claimed in claim 1, wherein the simulation method is characterized in that: the rolling optimization of the daytime prediction scene comprises the following steps: and combining a more accurate daytime prediction scene, rolling and optimizing the output of the unit to execute a downward direct current delivery plan.
5. A multi-scale random production simulation device considering extra-high voltage direct current outgoing optimization is characterized in that: comprising the following steps:
the system comprises a month representation date view scene and date view scene acquisition unit, a data processing unit and a data processing unit, wherein the month representation date view scene and date view scene acquisition unit is used for extracting month and day scale information reflecting load characteristics and new energy output characteristics according to system historical operation data to acquire month representation date view scenes and date view scenes;
the inter-month production simulation and inter-day production simulation running unit is used for carrying out inter-month production simulation and inter-day production simulation respectively by adopting a long-period random production simulation objective function and combining with acquisition of date and expected scene data of month representation; the inter-month production simulation is used for optimizing the power distribution of a power supply of a power grid at a transmitting end and direct current external transmission in each month under the constraint conditions of the contract external transmission power of each month according to the expected daily scene represented by each month, and transmitting the calculated direct current external transmission power of each month to the inter-month production simulation; the solar production simulation performs day-by-day optimization distribution on the direct current external power transmission quantity of a target month according to expected scenes of each day in the target month to be optimized, when the distribution result cannot meet the operation constraint of a certain month of the system, the fixed external power transmission quantity distributed to the month is adjusted to be the month external power transmission quantity constraint, the direct current external power transmission quantity of each day is optimized, the sum of the direct current external power transmission quantity of each day is used as the month direct current external power transmission correction quantity to be returned to the solar production simulation, the solar production simulation is restarted to perform power optimization for each month which is not executed until the upper layer iteration is completed;
the optimizing unit is used for adopting a short-period distribution robust optimizing objective function, combining a given day-ahead predicted scene and a given day-ahead predicted scene to respectively perform day-ahead optimization and day-to-day rolling optimization, and rolling and optimizing the output of the unit to execute a downward direct current delivery plan; the day-ahead optimization includes: the adjustment capacity of the direct current tie line is utilized to carry out paid adjustment on the daily direct current external power transmission quantity sent under the daily production simulation, the adjustment quantity is used as daily direct current correction electric quantity to be returned to the daily production simulation, and the obtained daily direct current external power transmission plan of the power grid at the transmitting end is sent to the daily prediction scene for rolling optimization;
and the iterative optimization unit is used for returning direct current to send corrected electric quantity, restarting the inter-month production simulation and the inter-day production simulation to optimize the electric quantity of the rest month until the annual plan is completed.
6. A multi-scale random production simulation device considering extra-high voltage direct current outgoing optimization is characterized in that: comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1 to 4.
7. A computer-readable storage medium having stored thereon a computer program, characterized by: the program, when executed by a processor, implements the steps of the method of any of claims 1 to 4.
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