CN113890026A - Multi-scale random production simulation method considering ultra-high voltage direct current (UHVDC) delivery optimization - Google Patents
Multi-scale random production simulation method considering ultra-high voltage direct current (UHVDC) delivery optimization Download PDFInfo
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Abstract
The invention discloses a multi-scale random production simulation method considering ultra-high voltage direct current (UHVDC) delivery optimization, which comprises the steps of obtaining a monthly representative daily expectation scene and a monthly representative daily expectation scene; adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data; adopting a short-period distribution robust optimization objective function, and respectively carrying out day-ahead optimization and day-ahead rolling optimization by combining a given day-ahead prediction scene and a given day-ahead prediction scene; the direct current outgoing correction electric quantity is returned, the monthly production simulation and the daily production simulation are restarted to optimize the electric quantity of the rest months until the annual plan is completed.
Description
Technical Field
The invention relates to a multi-scale random production simulation method considering ultra-high voltage direct current outgoing optimization, and belongs to the technical field of power systems.
Background
The Chinese 'double carbon' target promotes the high-speed development of new energy, and the total installed capacity of wind power generation and solar power generation is estimated to reach more than 12 hundred million kilowatts by 2030. Due to the reasons of reverse distribution of loads and new energy installation, flexible adjustment of low power supply proportion, limited power grid interconnection level and the like, new energy consumption contradiction in local areas is increasingly prominent. The surplus power can be sent out in a trans-regional mode to achieve consumption of new energy power generation, wherein the extra-high voltage direct current transmission plays an important role in wind and light resource trans-regional consumption. Usually, a day-fixed step power transmission mode is set for extra-high voltage direct current power transmission according to interval long-term contract transaction electric quantity, the power regulation potential of direct current power transmission is difficult to be fully exerted, the randomness and the fluctuation of the power generation output of new energy such as wind power, photovoltaic and the like of a power grid at a transmitting end cannot be adapted, the electricity abandonment of new energy of the power grid at the transmitting end is easy to cause, and the peak regulation pressure of a traditional unit of the power grid at the transmitting end is increased. Therefore, how to fully consider the uncertainty of the output of the new energy such as wind power, photovoltaic and the like of the power grid at the sending end, reasonably distribute the long-term contract transaction electric quantity of the interval on the scale of year/month/day, and formulate the output plan of each unit of the power grid at the sending end has important significance for promoting the consumption of the new energy of the power grid at the sending end and ensuring the safe and stable operation of the interconnected power grid.
At present, the research of the power system through the direct current tie line across-provincial and cross-regional dispatching mostly focuses on provincial level safety constraint economic dispatching optimization. Existing research related to dc transmission plans has many times focused on optimally allocating known dc off-day power quantities before or during the day. However, for the direct current outgoing contract electric quantity with known long time scale such as year and month, how to obtain daily transaction electric quantity from the direct current outgoing contract electric quantity is little researched. In addition, due to the uncertainty of the actual operation scenario, the subsequent residual contract electric quantity and the distribution plan thereof need to be updated according to the electric quantity actually delivered every day.
For random power supplies with gradually increased permeability in a power grid, in recent years, scholars at home and abroad carry out a great deal of research on the problem of uncertain unit combination, and successively provide methods such as a scene method, opportunity constraint planning, robust optimization and the like. The uncertainty of the random new energy output is described by the scene method and the opportunity constraint planning according to the statistics or prediction of probability distribution, and the probability of the random new energy output distribution is difficult to accurately obtain, 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 opportunity constraint planning and robust optimization to establish a distributed robust optimization method, and a corresponding distributed robust scheduling decision model is established according to an introduced probability distribution set (fuzzy set) describing random variables.
Conventional units involved in power grid economic dispatching research are mostly thermal power units, and less units are involved in water, electricity, pumping storage and the like. In northwest China, more hydraulic and wind-solar power generation resources exist, the area load is generally small, a large amount of clean energy power generation is sent out through an inter-provincial direct current power transmission line, and the production simulation is particularly necessary by considering seasonal characteristics and reservoir capacity constraints of the hydraulic power generation.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a multi-scale random production simulation method considering ultra-high voltage direct current (UHVDC) delivery optimization, overcomes the defects of a random power supply with gradually increased permeability in the current power grid, fully considers the uncertainty of the output of new energy such as wind power, photovoltaic and the like of a delivery-end power grid and combines the seasonal characteristics of hydropower, reasonably distributes interval long-term contract transaction electric quantity on the annual/monthly/daily scale for the direct current delivery contract electric quantity of known years, months and the like, and formulates the output plan of each unit of the delivery-end power grid.
In order to achieve the 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 ultra-high voltage direct current outgoing optimization, which comprises the following steps:
extracting information of the scales of the month and the day reflecting the load characteristics and the new energy output characteristics according to the historical operation data of the system, and acquiring a month representing date viewing scene and a date viewing scene;
adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and exerting output of a rolling optimization unit to execute a downstream direct-current delivery plan;
and returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
Furthermore, the expected scene of the month representing day is obtained by respectively averaging the data of the historical years of new energy output in each day of the month, and the sampling interval is 1 hour.
Furthermore, the prospect scene of the date is obtained by the average value of the new energy output of years in the history of each hour of the day, and the sampling interval is 1 hour.
Further, the inter-month production simulation optimizes the power distribution of the power supply of the power grid of the sending end and the direct current outgoing power in each month under the constraint condition of the contract outgoing power in the whole year and each month according to the expected scene of each month representative day, and sends the calculated direct current outgoing power in each month to the inter-month production simulation.
Further, the inter-day production simulation performs day-by-day optimized distribution on the target month direct-current outgoing power according to the expected scene 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 outgoing power distributed to the month is adjusted to be the month outgoing power constraint, the direct-current outgoing power of each day is optimized, the sum of the direct-current outgoing power of each day is used as the month direct-current outgoing modified power to be returned to the inter-day production simulation, and the inter-day production simulation is restarted to perform power optimization for each unexecuted month until the upper-layer iteration is completed.
Further, the day-ahead optimization comprises: the adjustment capacity of the direct current connecting line is utilized to carry out paid adjustment on the daily direct current outgoing electric quantity sent by 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 day-ahead direct current outgoing plan of the sending end power grid is sent to a daily prediction scene for rolling optimization;
further, the rolling optimization of the daytime prediction scene comprises: and combining a more accurate daytime prediction scene, and performing direct current delivery plan of downward delivery by rolling and optimizing the output of the unit.
In a second aspect, the present invention provides a multi-scale random production simulation apparatus for considering the ultra-high voltage dc delivery optimization, including:
the month representing date scene and date scene acquiring unit is used for extracting information of the month scale and the day scale reflecting the load characteristic and the new energy output characteristic according to the historical operation data of the system and acquiring the month representing date scene and the date scene;
the monthly production simulation and daily production simulation operation unit is used for respectively carrying out monthly production simulation and daily production simulation by adopting a long-period random production simulation objective function and combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and outputting power by the rolling optimization unit to execute a downward direct-current delivery plan;
and the iterative optimization unit is used for returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
In a third aspect, the invention provides a multi-scale random production simulation device considering ultra-high voltage direct current outgoing optimization, which is characterized in that: comprising 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 of the above.
In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that: which when executed by a processor performs the steps of any of the methods described above.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, load and new energy output characteristics are extracted according to the multi-year operation data of the system, seasonal characteristics of abundant water, electricity and dry season are considered, the power output and the direct current output power of a power grid at a sending end to be optimized in a target year or month are solved on a monthly and daily time scale, a distribution robustness optimization method is adopted, an uncertain problem is converted into a deterministic secondary constraint secondary planning problem according to the form of an uncertain distribution set, the direct current output power and the unit output power are optimized according to the daily direct current output power obtained by optimization and in combination with a day-ahead prediction scene, and the unit output power is optimized in a rolling mode according to a more accurate daytime prediction scene.
Drawings
FIG. 1 is a block diagram of a strategy flow of a multi-scale stochastic production simulation method involving an ultra-high voltage DC delivery optimization according to an embodiment of the present invention;
FIG. 2 is a flow chart of a long-period stochastic production simulation provided by an embodiment of the present invention;
fig. 3 is a flowchart of short-period optimization according to 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 illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
As shown in fig. 1 to fig. 3, this embodiment introduces a multi-scale random production simulation method considering the ultra-high voltage dc delivery optimization, including:
extracting information of the scales of the month and the day reflecting the load characteristics and the new energy output characteristics according to the historical operation data of the system, and acquiring a month representing date viewing scene and a date viewing scene;
adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and exerting output of a rolling optimization unit to execute a downstream direct-current delivery plan;
and returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
The modeling steps of the upper layer model of the multi-time scale random production simulation framework are as follows:
(1) long period random production simulation objective function
The goal of long-cycle stochastic production simulation is to minimize the total operating cost in the desired scenario, which can be expressed as:
in the formula, t is a time period sequence number in a calculation cycle; t is a time period set in the calculation cycle; setting the time length of each time interval as delta t; i is the serial number of the thermal power generating unit; g is a thermal power generating unit set; pi,tThe active power output of the thermal power generating unit i in the time period t is obtained; c (P)i,t) Generating cost of the thermal power generating unit i in a time period t;
the emission cost of the system in time period t; delta is the price of the water and electricity on-line electricity; h is the serial number of the hydropower station; h is a hydropower station set; a. thehThe power generation output coefficient of the hydropower station h;
the water flow is the water discharge flow of the hydropower station h in the time period t, and the water flow cannot enter a hydroelectric generating set to generate electricity, so certain loss is generated; t is the direct current outgoing electricity price in a time period t; pdc,tDirect current outgoing power for a time period t; alpha is alphai，1、αi，2、αi，3The parameters are power generation cost parameters of the thermal power generating unit i;
and the emission cost coefficient is the emission cost coefficient of the thermal power generating unit i.
In the process of forming the framework upper layer model, the following constraint conditions need to be met:
constraint 1: system operational constraints
1) Power balance constraint
In the formula, j is the serial number of the hydroelectric generating set; j is a hydroelectric generating set; ph,j,tThe active power output of a hydroelectric generating set j corresponding to the hydropower station h in the time period t; n is the serial number of the new energy machine set; n is a new energy machine set; pn,tThe active power output of the new energy machine set n in the time period t is obtained; pL,tThe load demand for time period t.
2) Branch current flow restraint
In the formula, B is a system branch set;
respectively injecting transfer distribution factor vectors into the branch b by the unit node and the load node;
the injection transfer distribution factor of the direct current outgoing node to the branch b; (.)TIs a matrix transposition operation. PtThe output vectors of all the units in the time period t are obtained; pL,tLoad vectors of all nodes in the time period t;
the upper limit for branch b power transmission.
3) System backup constraints
In the formula (I), the compound is shown in the specification,
P irespectively obtaining the maximum and minimum technical output of the thermal power generating unit i;
P h,jrespectively the maximum and minimum technical output of the hydroelectric generating set j; spoAnd SnegRespectively as positive and negative system for standby.
Constraint 2: thermal power generating unit constraint
2) Thermal power generating unit climbing restraint
In the formula (I), the compound is shown in the specification,
the maximum climbing rate and the minimum climbing rate of the thermal power generating unit i are respectively.
Constraint 3: hydro-power generating unit restraint
1) Restraint of output
2) Output expression of hydroelectric generating set
In the formula (I), the compound is shown in the specification,
the flow rate for hydropower station h for generating electricity during time period t.
3) Reservoir capacity constraint
In the formula (I), the compound is shown in the specification,
V hthe upper limit and the lower limit of the reservoir capacity of the hydropower station h are respectively set; vh,tThe reservoir capacity of the hydropower station h at the initial moment of the time period t is shown.
4) Power generation flow restriction
In the formula (I), the compound is shown in the specification,
the maximum and minimum generating flow of the hydropower station h are respectively.
5) Waste water flow restriction
In the formula (I), the compound is shown in the specification,
the upper limit of the water discharge of the hydropower station h.
6) Water balance equation constraints
In the formula, Wh,tThe natural water inflow for the hydropower station h during the time period t.
Constraint 4: new energy unit constraints
1) Restraint of output
2) New energy electricity abandoning constraint
Constraint 5: direct current delivery restraint
1) DC delivery power constraints
2) Ramp restraint of DC (direct current) delivery power
3) Single day DC regulation times constraint
In the formula (I), the compound is shown in the specification,
P dc,tthe upper limit and the lower limit of the outgoing power of the direct current tie line are respectively; y istIs an 0/1 integer variable;
respectively adjusting the upper limit and the lower limit of the amplitude for single direct current;
adjusting the frequency limit for the single-day direct current outgoing power; in addition, in order to maintain the stability of the dc link, the output power should be kept constant for a while after the adjustment is completed.
4) Annual transaction power constraint
In the formula (I), the compound is shown in the specification,
Ethe upper limit and the lower limit of the annual contract delivery electric quantity are respectively set; m is a set of executed months;
the sum of the delivered electricity quantity of the executed month;
direct current outgoing power in the mth month t period; delta EmCorrecting the electric quantity for the month of the mth month; eresThe dividable power is left for the year;
maximum days in the mth month; t is the set of time periods within the day.
5) Monthly transaction power constraint
In the formula (I), the compound is shown in the specification,
E mrespectively the upper limit and the lower limit of the delivered power of the contract in the mth month; d is the set of executed days in the month;
the total of the delivered electricity quantity of the executed days in the month;
d, direct current outgoing power of t time period on day d; delta EdCorrecting the electric quantity for the day of the day d;
the dividable electricity amount remains for this month.
The modeling steps of the lower model of the multi-time scale random production simulation framework are as follows:
(2) short period distributed robust optimization
The objective of the short-period distribution robust optimization is to minimize the running cost in the prediction scenario, which can be expressed as:
in the formula (I), the compound is shown in the specification,
the method comprises the following steps of (1) providing a running base point of a thermal power generating unit i in a time period t; tau is the direct current outgoing electric quantity correction price; delta Pdc,tThe direct current is sent for correcting power before the day in the time period t, and the part only exists in optimization before the day; rhoPenalty factor for abandoning new energy;
the electric power is abandoned for the new energy source unit n in the time period t. And after the optimization before the day is finished, returning the direct current outgoing corrected electric quantity to the daily production simulation.
In the process of forming the framework lower layer model, the following constraint conditions need to be met:
constraint 1: power balance constraint
In the formula (I), the compound is shown in the specification,
the operation base point of the hydroelectric generating set j in the time period t is shown.
Constraint 2: the method establishes a random variable vector omegatRepresenting the output prediction error of each new energy unit, and introducing an adjusting factor to represent the prediction error power shared by each adjustable unit, wherein the active output of each adjustable unit is as follows:
in the formula, e is a column vector with all elements being 1; sigmai,tAn adjustment factor of the thermal power generating unit i in a time period t is obtained; sigmaj,tAnd (4) an adjustment factor of the hydroelectric generating set j in the time period t.
Constraint 3: robust opportunity constraint of unit output
Wherein 1-epsilon is the confidence level; d is characteristic omegatThe set of distributions of uncertainty, see equation (27).
Constraint 4: robust opportunity constraint for maximum adjustable capacity of unit
In the formula, ri up、ri dnThe upper limit and the lower limit of the maximum adjustable capacity of the thermal power generating unit i are respectively set;
of hydroelectric generating sets j respectivelyThe upper and lower limits of the maximum adjustable capacity.
Constraint 5: branch flow robust opportunity constraints
In the formula, Pb,tThe transmission power for branch b at time period t;
respectively injecting transfer distribution factor vectors into the branch b by thermal power unit nodes, hydroelectric power unit nodes and new energy unit nodes; pG,tThe output vectors of all the thermal power generating units in the time period t are obtained; pJ,tThe output vectors of all the hydroelectric generating sets in the time period t are shown; pN,tAll new energy bank output vectors are provided for the time period t.
Constraint 6: other constraints
The remaining constraints are the same as equations (7), (9) - (13) and (16) - (18).
1) And (3) constructing a distribution set: the method uses the selected uncertain set to describe omegatThe set does not limit the probability distribution of the random vector, retains the fluctuation of moment information, and is a more complete and universal fuzzy set expression form.
Wherein f (Ω)t) Is omegatA joint probability density function of (a); mu.s0、Σ0Are respectively omegatThe first moment and the second moment of the vector; gamma ray1Defining parameters for the uncertainty set radius for the desired ellipsoid; gamma ray2Defining parameters of a semi-definite cone uncertainty set range of the covariance matrix; e (-) is the expected operator symbol(ii) a And DEG is a semi-negative fixed sign.
2) The uncertainty model is converted into a deterministic QCQP optimization problem: the key to the solution of the short-period distribution robust optimization model is the equivalent transformation of equations (26) - (28), and the specific theorem is as follows.
The general form of the robust opportunity constrained inequality:
in the formula: a is the coefficient vector and b is the limit.
If omegatSubject to the set (30), there is the following equivalence transformation theorem:
1) when gamma is1/γ2When < ε, formula (31) can be equivalently expressed as:
2) when gamma is1/γ2When ε, formula (31) is equivalently represented as:
hereinafter only for γ1/γ2The case < ε derives the equivalent transformation of equations (27) - (29), γ1/γ2The same is true for > ε.
First, when gamma is1/γ2When < epsilon, equations (27) and (28) can be directly converted into the following constraint form by the theorem:
in the formula:
deterministic equivalence of a bilateral robust opportunity constraint in the form of equation (29) is complex, so first the bilateral opportunity constraint is equivalent to two unilateral opportunity constraints [26,28] as follows:
the robust opportunity constraint (36) is expressed in the form of equation (31)
Since the decision variable σ exists in a, the direct conversion increases the difficulty of model solution, so the equation (32) is considered to be equivalently converted:
substituting a and b for equations (37) into constraints containing quadratic inequalities as follows:
in the formula:
adjusting factor vectors of all thermal power generating units at the moment t;
the adjustment factor vectors of all hydroelectric generating sets at the moment t are obtained;
in summary, the robust opportunity constraints (26) - (28) have all completed the equivalent transformation, forming a deterministic QCQP optimization problem. However, the QCQP optimization problem still belongs to an NP-hard problem, and a certain difficulty still exists in direct solution, so that RLT is considered herein to be used for solving the QCQP optimization problem by relaxation processing into an LP problem.
3) Relaxation was performed using the RLT method:
now to facilitate RLT relaxation, the QCQP optimization problem is further transformed into the general form:
in the formula (I), the compound is shown in the specification,
all decision variables are included (the hydroelectric generating set is combined with the formula (9) for conversion);
xupper and lower limits for x, respectively; omega is a high-dimensional symmetrical constant coefficient matrix; ecIs an equality constraint set; i iscIs an inequality constraint set; a is0，aiHigh-dimensional constant coefficient column vectors which are respectively an objective function and a constraint condition; b0，biConstant coefficients corresponding to the objective function and constraint condition, respectively.
Comparing equations (40) - (43) with the QCQP optimization problem above, it can be seen that ω is0ω of the second constraint in equations (22), (24), (32) - (33), and (36) - (37) is a diagonal matrixiAll are 0 arrays. ω for the first constraint in equations (38) - (39)iSolving for referable [30]And will not be described herein.
According to the characteristics of RLT, the original QCQP optimization problem is expressed as:
wherein X is xxT；
The constraint of X is further determined, whereby the QCQP optimization problem has been relaxed to be a LP problem.
The establishment of the multi-time scale annual random production simulation two-layer framework specifically comprises the following steps:
(1) the generation of the desired scene.
In the stage, according to the historical operating data of the system for many years, information of the scales of the months and days reflecting the load characteristics and the new energy output characteristics is extracted, and a month representing prospect scene and a prospect scene are generated, wherein the specific contents are as follows:
step 1: acquiring a month representative date viewing scene: the month representing day expectation scene is obtained by respectively averaging the historical data of new energy output in years in each hour of each day in the month, and the sampling interval is 1 hour.
Step 2: acquiring a prestige scene: the prospect scene of the date is obtained by the average value of the new energy output for years in each hour of the day, and the sampling interval is 1 hour.
(2) And (5) a frame upper layer.
In the stage, load and new energy output characteristics are extracted according to the multi-year operation data of the system, seasonal characteristics of water, electricity and dry season are considered, and the year or month power output and direct current output electric quantity of a power supply end power grid to be optimized are solved on the time scale of month and day, wherein the specific contents are as follows:
and step 3: and (3) adopting a long-period random production simulation objective function, and combining the data obtained in the step (1) and the step (2) to respectively carry out monthly production simulation and daily production simulation.
And 4, step 4: and (3) optimizing the power distribution of the power supply of the power grid of the sending end and the direct current outgoing power in each month under the constraint condition of contract outgoing power of the whole year and each month according to the expected scene of each month representative day in the interpupillary production simulation, and sending the calculated direct current outgoing power in each month to the interpupillary production simulation.
And 5: and performing day-by-day optimized distribution on the target month direct current outgoing power according to the expected scene of each day in the target month to be optimized by the interplay production simulation, adjusting the fixed outgoing power distributed to the month to be the interplay power constraint when the distribution result cannot meet the operation constraint of the system for a certain month, optimizing the direct current outgoing power of each day, returning to the interplay production simulation by using the sum of the direct current outgoing power of each day as the interplay direct current outgoing modified power, and restarting the interplay production simulation to perform power optimization for each unexecuted month until the upper-layer iteration is completed.
(3) A frame lower layer.
In the stage, a distributed robust optimization method is adopted, the direct current outgoing power and the unit output are optimized according to the daily direct current outgoing power obtained by upper layer optimization, the day-ahead prediction scene is combined, the unit output is optimized in a rolling mode according to the more accurate day-ahead prediction scene, and the specific content is as follows:
step 6: and respectively carrying out day-ahead optimization and day-time rolling optimization by adopting a short-period distribution robust optimization objective function and combining a given day-ahead prediction scene and a given day-time prediction scene.
And 7: day-ahead optimization fully utilizes the adjusting capacity of the direct current connecting line according to a day-ahead prediction scene, paid adjustment is carried out on daily direct current outgoing electric quantity sent under the day-ahead production simulation, and the adjusting quantity is used as daily direct current correction electric quantity and is returned to the day-ahead production simulation. And simultaneously, sending the obtained day-ahead direct current delivery plan of the power grid of the sending end to the daytime for rolling optimization.
And 8: and the daytime rolling optimization is combined with a more accurate daytime prediction scene, and the output of the rolling optimization unit is used for executing a downward direct current delivery plan.
(4) And (5) carrying out upper and lower layer iteration optimization.
In the stage, the upper-layer and lower-layer iterative optimization contents are displayed, and in step 7, a day-ahead direct current delivery plan is determined, so that the direct current delivery capacity of the part is compensated and adjustable to ensure the economy of day-ahead optimization.
And step 9: and (7) if the returned day direct current correction electric quantity exists in the calculation result obtained in the step (7), uploading and restarting the day production simulation, and redistributing the electric quantity for the remaining day of the month.
Step 10: and after the iteration of the month is finished, returning the direct current corrected electric quantity of the month in the year, and restarting the production simulation of the month to optimize the electric quantity of the rest month until the full-year plan is finished.
Example 2
The embodiment provides a take into account extra-high voltage direct current outgoing optimization's multiscale random production analogue means, includes:
the month representing date scene and date scene acquiring unit is used for extracting information of the month scale and the day scale reflecting the load characteristic and the new energy output characteristic according to the historical operation data of the system and acquiring the month representing date scene and the date scene;
the monthly production simulation and daily production simulation operation unit is used for respectively carrying out monthly production simulation and daily production simulation by adopting a long-period random production simulation objective function and combining the acquired monthly representative date scene and date scene data;
the 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 carry out day-ahead optimization and day-ahead rolling optimization, and the rolling optimization unit outputs power to execute a downward direct current delivery plan;
and the iterative optimization unit is used for returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
Example 3
The embodiment provides a take into account extra-high voltage direct current and send out multiscale random production analogue means of optimizing, its characterized in that: comprising 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:
extracting information of the scales of the month and the day reflecting the load characteristics and the new energy output characteristics according to the historical operation data of the system, and acquiring a month representing date viewing scene and a date viewing scene;
adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and exerting output of a rolling optimization unit to execute a downstream direct-current delivery plan;
and returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
Example 4
The present embodiment provides a computer-readable storage medium having a computer program stored thereon, characterized in that: the program when executed by a processor implementing the steps of any one of the following methods:
extracting information of the scales of the month and the day reflecting the load characteristics and the new energy output characteristics according to the historical operation data of the system, and acquiring a month representing date viewing scene and a date viewing scene;
adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and exerting output of a rolling optimization unit to execute a downstream direct-current delivery plan;
and returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A multi-scale random production simulation method considering ultra-high voltage direct current (UHVDC) delivery optimization is characterized by comprising the following steps:
extracting information of the scales of the month and the day reflecting the load characteristics and the new energy output characteristics according to the historical operation data of the system, and acquiring a month representing date viewing scene and a date viewing scene;
adopting a long-period random production simulation objective function, and respectively carrying out monthly production simulation and daily production simulation by combining the acquired monthly representative date scene and date scene data;
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 carry out day-ahead optimization and day-ahead rolling optimization, and exerting output of a rolling optimization unit to execute a downstream direct-current delivery plan;
and returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
2. The method of claim 1, wherein the method comprises the following steps: the month representing day expectation scene is obtained by respectively averaging the data of the new energy output of the historical years in each hour of each day in the month, and the sampling interval is 1 hour.
3. The method of claim 1, wherein the method comprises the following steps: the prospect scene of the date is obtained by the average value of the new energy output for years in each hour of the day, and the sampling interval is 1 hour.
4. The method of claim 1, wherein the method comprises the following steps: and optimizing the power distribution of the power supply of the power grid at the transmitting end and the direct current outgoing power in each month under the constraint condition of contract outgoing power in all the year and each month according to the monthly expected scene of each month, and transmitting the calculated direct current outgoing power in each month to the daily production simulation.
5. The method of claim 1, wherein the method comprises the following steps: and the inter-day production simulation carries out day-by-day optimized distribution on the direct current outgoing power of the target month according to the expected scene of each day in the target month to be optimized, when the distribution result cannot meet the operation constraint of the system in a certain month, the fixed outgoing power distributed to the month is adjusted to be the month outgoing power constraint, the direct current outgoing power of each day is optimized, the sum of the direct current outgoing power of each day is used as the month direct current outgoing modified power to be returned to the inter-day production simulation, and the inter-day production simulation is restarted to carry out power optimization for each unexecuted month until the upper-layer iteration is completed.
6. The method of claim 1, wherein the method comprises the following steps: the day-ahead optimization comprises: and (4) carrying out paid adjustment on the daily direct current outgoing electric quantity sent by the daily production simulation by utilizing the adjustment capacity of the direct current connecting line, returning the adjustment quantity serving as daily direct current correction electric quantity to the daily production simulation, and sending the obtained day-ahead direct current outgoing plan of the sending-end power grid to a daily prediction scene for rolling optimization.
7. The method of claim 1, wherein the method comprises the following steps: the rolling optimization of the daytime prediction scene comprises the following steps: and combining a more accurate daytime prediction scene, and performing direct current delivery plan of downward delivery by rolling and optimizing the output of the unit.
8. The utility model provides a take into account super high voltage direct current outgoing optimization's multiscale random production analogue means which characterized in that: the method comprises the following steps:
the month representing date scene and date scene acquiring unit is used for extracting information of the month scale and the day scale reflecting the load characteristic and the new energy output characteristic according to the historical operation data of the system and acquiring the month representing date scene and the date scene;
the monthly production simulation and daily production simulation operation unit is used for respectively carrying out monthly production simulation and daily production simulation by adopting a long-period random production simulation objective function and combining the acquired monthly representative date scene and date scene data;
the 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 carry out day-ahead optimization and day-ahead rolling optimization, and the rolling optimization unit outputs power to execute a downward direct-current outward delivery plan;
and the iterative optimization unit is used for returning the direct current outgoing corrected electric quantity, restarting the monthly production simulation and the daily production simulation, and optimizing the electric quantity of the rest month until the annual plan is finished.
9. The utility model provides a take into account super high voltage direct current outgoing optimization's multiscale random production analogue means which characterized in that: comprising 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 claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the program when executed by a processor implements the steps of the method of any one of claims 1 to 7.
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